NATIONAL AND KAPODISTRIAN UNIVERSITY OF ATHENS

SCHOOL OF SCIENCES

POST GRADUATE COURSE IN OCEANOGRAPHY AND MARINE ENVIRONMENTAL MANAGEMENT

Ph. D. THESIS

Impact of a coastal ferronickel metallurgy on the marine ecosystem: The case of Larymna Bay

LEILA BORDBAR MARINE BIOLOGIST

ATHENS

FEBRUARY 2017

Ph. D. THESIS

Impact of a coastal ferronickel metallurgy on the marine ecosystem. The case of Larymna bay

LEILA BORDBAR

Α.Μ.: 052

SUPERVISOR:

Emmanuel Dassenakis, Professor, Department of Chemistry, National and Kapodistrian University of Athens

SUPERVISORY COMMITEE:

Emmanuel Dassenakis, Professor NKUA Persefoni Megalofonou, Associate Professor, Dept. of Biology, NKUA Vasiliki- Angeliki Catsiki, Research Director, HCMR

EXAMINING COMMITEE Emmanuel Dassenakis, Professor, Dept of Chemistry, NKUA Persefoni Megalofonou, Associate Professor, Dept. of Biology, NKUA Dr. Angeliki-Vasiliki Catsiki, Research Director, HCMR Serafeim Poulos, Professor, Dept of Geology and Geoenvironment, NKUA Evangelia Krasakopoulo, Associate Professor, Dept. of Marine Sciences, University of Aegean Dr. Eleni Kaberi, Researcher, HCMR Dr. Nomiki Simboura, Research Director, HCMR

DATE OF EXAMINATION 03/02/2017

ABSTRACT

Larymna Bay in N. Evoikos hosts a leading mining and metallurgical company of Greece which is among the biggest of this type in Europe. Apart from the raw materials produced from domestic ores, large quantities of ferronickel by- product are also produced. The slag has been dumped in N. Evoikos in a government authorized area since 1969.

This study was carried out accordingly to previous research and monitoring campaigns in this area to investigate the long term impact of the dumped slag to the marine environment including the deposition area and the coastline. For these purposes two suits of samples were collected in this area; one from the deposition area which is defined as offshore and one from the coastline, assigned as inshore (near shore area) where the smelting plant is located. Surface sediment, surface and bottom water (1 m above the sediment), along with three crustacean species (Munida rugosa, Liocarcinus depurator and Nephrops norvegicus) were collected from the slag dumping area and a reference station (Offshore area) for three years from 2009 to 2011. From the coastline (Inshore area), water samples were collected and two species of gastropods (Phorcus turbinatus and Patella caerulea) were handpicked from seven stations along the shore for four seasons representing a possibly contaminated area. In addition gastropods samples were also collected from two other stations far from the smelting plant possibly representing reference conditions. For biomarker analysis, crustacean samples (Munida rugosa) from the offshore area, gastropod samples (Ph.turbinatus) and one fish species (Sparus aurata) from the inshore area were taken. Ni, Cr, Fe, Mn, Al, Zn, Cu and Hg measured in the surface sediment samples. All the mentioned metals expect for Al and Hg were determined in the seawater samples, in the three different tissues of crustaceans (gill, muscle and exoskeleton) and in the soft tissue of gastropods. In terms of biomarkers; AChE (acetylcholinesterase), CAT (Catalase) and GST (glutathione-s-transferase) were analyzed in the muscle, hepatopancreas (M.rugosa), liver (fish), eye and gill of biota samples.

The results showed high concentrations of Ni, Fe, Cr and Mn in seawaters and sediment samples from both the contaminated and reference areas. The concentrations of Cu and Zn which are mostly attributed to other anthropogenic sources of pollution are also relatively high in seawater samples. The Enrichment factors (EF) and Geo- Index (Igeo) calculated from bibliographic data of a deep core sample from the area

1 and the average shale showed that area is heavily impacted from the metals related to the smelting plant and in particular Cr. The concentrations of two metals, Ni and Cr, in the sediment are higher than the US EPA criteria and could definitely cause adverse biological effects. However, the dissolved concentrations of these metals in the sea water were lower than the chronic and acute toxicity concentrations of USEPA standards.

Quite high concentrations of all metals were detected in the gill tissue of all the crustacean samples from both areas. Besides, the higher concentrations of metals found in female specimens are mostly attributed to the physiological needs of females during the reproduction cycle and their specific strategy of food uptake during ovigerous cycle. The concentration of all metals in the soft tissues of all the crustacean species from the off shore area except for Mn are lower than the international standards proposed by FAO, 1993, WHO,1998 and USDA, 2009.

High concentrations of all metals related to the smelting plant were also detected in the gastropod samples. The results show that Patella is a good indicator for Mn and Phorcus seems to better accumulate Fe, Ni and Zn.

The clear inhibition of AChE activities in the samples from the contaminated area in comparison to those from the reference area showed that this enzyme responds well to metal pollution. GST showed obvious increased activities in the liver of S.aurata samples from the contaminated area. However no significant differences were found in the activities of GST and CAT in M. rugosa and Ph. turbinatus.

Finally, the long term slag discharging in this area has highly impacted not only the sediments, but has also caused elevated concentrations of metals bioaccumulated by marine organisms. Due to the severe impact of the smelting plant to the marine organisms based on the findings from this study and the previous researches carried out in this area, it is suggested that regular monitoring of this area is implemented, focusing mostly to edible marine organisms. Furthermore, the assessment of possible impacts of the smelting plant on human health should be seriously taken into consideration, planned and executed.

SUBJECT AREA: BIOGEOCHEMICAL OCEANOGRAPHY

KEYWORDS: heavy metals, bioaccumulation, crustaceans, gastropods, biomarkers

2 Περίληψη

΢ηνλ Β. Δπβνηθό θόιπν, ζηε Λάξπκλα, βξίζθεηαη κηα από ηηο ζεκαληηθόηεξεο εμνξπθηηθέο θαη κεηαιινπξγηθέο βηνκεραλίεο ηεο Διιάδα (ΛΑΡΚΟ) θαη κηα απν ηηο κεγαιύηξεο ηνπ είδνπο ηεο ζηελ Δπξώπε. ΢ην εξγνζηάζην απηό νξπθηή πξώηε ύιε από νξπρεία ηεο Έπβνηαο θαη ηεο Β. Διιάδαο πθίζηαηαη επεμεξγαζία θαη παξάγεηαη ζηδεξνληθέιην πξνο εμαγσγή. Δπηπιένλ όκσο παξάγνληαη θαη πνιύ κεγάιεο πνζόηεηεο παξαπξντόληνο (ζθνπξηά). Η ζθνπξηά απνξξίπηεηαη ζηε ζάιαζζα από ην 1969 ζε θαζνξηζκέλε από ηελ πνιηηεία πεξηνρή.

Η παξνύζα κειέηε πξαγκαηνπνηήζεθε αθνινπζώληαο θνληηλή (παξόκνηα;) κεζνδνινγία κε παιηόηεξεο κειέηεο θαη πξνγξάκκαηα παξαθνινύζεζεο ηεο ξύπαλζεο ζηελ ζπγθεθξηκέλε πεξηνρή. ΢θνπόο ηεο εξγαζίαο ήηαλ λα δηεξεπλήζεη ηε καθξνρξόληα επίδξαζε ηεο απνξξηπηόκελε ζθνπξηάο ζην ζαιάζζην πεξηβάιινλ, ηόζν αθξηβώο ζηελ πεξηνρή απόζεζεο όζν θαη ζηελ αθηνγξακκή. Πξνθεηκέλνπ λα επηηεπρζεί ν ζθνπόο ηεο εξγαζίαο ζπιιέρζεζαλ δείγκαηα ηόζν από ηελ πεξηνρή απόζεζεο (αλνηρηά) όζν θαη από ηελ αθηνγξακκή (παξάθηηα) όπνπ βξίζθεηαη θαη ην εξγνζηάζην. Από ηελ αλνηρηή ζαιάζζηα πεξηνρή (ζεκεία απόζεζεο θαη από έλα ζηαζκό αλαθνξάο) ιήθζεζαλ δείγκαηα επηθαλεηαθνύ ηδήκαηνο, ζαιαζζηλνύ λεξνύ από ηελ επηθάλεηα θαη ηνλ ππζκέλα (1 κ πάλσ από ην ίδεκα) θαζώο θαη δείγκαηα νξγαληζκώλ από ηξία είδε θαξθηλνεηδώλ (Munida rugosa, Liocarcinus depurator and Nephrops norvegicus) γηα ηξία έηε (2009 έσο 2011). Από ηελ παξάθηηα πεξηνρή ζπιιέρζεθαλ δείγκαηα λεξνύ θαη νξγαληζκώλ (γαζηεξόπνδα Phorcus turbinatus θαη Patella caerulea) ζε ηέζζεξηο επνρηαθέο δεηγκαηνιεςίεο από 7 ζηαζκνύο θνληά ζην εξγνζηάζην πνπ αληηπξνζσπεύνπλ ζπλζήθεο πςειήο ξύπαλζεο θαη δύν ζηαζκνύο αξθεηά καθξηά πνπ αληηπξνζσπεύνπλ ζπλζήθεο αλαθνξάο. Γηα ηηο αλαιύζεηο βηνδεηθηώλ επηιέρζεθαλ δείγκαηα Munida rugosa από ηελ αλνηρηή ζαιάζζηα πεξηνρή θαη δείγκαηα Ph.turbinatus από ηελ παξάθηηα θαζώο θαη έλα είδνο ςαξηνύ από θνληηλή ηρζπνθαιιηέξγεηα (Sparus aurata). ΢ηα επηθαλεηαθά ηδήκαηα κεηξήζεθαλ ηα κέηαιια Ni, Cr, Fe, Mn, Al, Zn, Cu θαη Hg. Δπίζεο όια απηά ηα κέηαιια εθηόο ηνπ Al θαη Hg πξνζδηνξίζηεθαλ ζηα δείγκαηα ζαιαζζηλνύ λεξνύ, ζε ηξείο δηαθνξεηηθνύο ηζηνύο ησλ θαξθηλνεηδώλ (βξάγρηα, κπο θαη εμσζθειεηόο) θαη ζην καιαθό ηζηό ησλ γαζηεξόπνδσλ. Ωο πξνο ηνπο βηνδείθηεο, πξνζδηνξίζηεθαλ νη AChE (αθεηπινρνιηλεζηεξάζε), CAT (Καηαιάζε) and GST (κεηαθνξάζε ηεο γινπηαζεηόλεο) ζην κπ θαη ην επαηνπάγθξεαο ηνπ (M.rugosa) θαζώο θαη ζε δείγκαηα από ζπθώηη, κάηηα θαη βξάγρηα ησλ ςαξηώλ.

3 Σα απνηειέζκαηα έδεημαλ αξθεηά πςειέο ζπγθεληξώζεηο Ni, Fe, Cr θαη Mn ζην ζαιαζηζηλό λεξό θαη ηα ηδήκαηα ηόζν από ηε ξππαζκέλε πεξηνρή όζν θαη από ηηο πεξηνρέο αλαθνξάο. Οη ζπγθεληξώζεηο Cu θαη Zn, είλαη επίζεο ζρεηηθά πςειέο ζην ζαιαζζηλό λεξό θαη απνδίδνληαη ζε άιιεο αλζξσπνγελείο δξαζηεξηόηεηεο. Οη ζπληειεζηέο εκπινπηηζκνύ (EF) θαη Geo-Index (Igeo) ζηα ηδήκαηα πνπ ππνινγίζηεθαλ από βηβιηνγξαθηθά δεδνκέλα από παιηό δείγκα ππξήλα ηεο πεξηνρήο (ζε πάλσ από 50 cm βάζνο) θαη από ηηο πεξηεθηηθόηεηεο ηνπ κέζνπ θινηνύ έδεημαλ όηη ε πεξηνρή είλαη ηδηαίηεξα επηβαξπκέλε κε κέηαιια πνπ έρνπλ ζρέζε κε ηε κεηαιινπξγηθή δξαζηεξηόηεηα θαη ηδηαίηεξα κε Cr. Οη ζπγθεληξώζεηο ηνπ Ni θαη Cr ζην ίδεκα είλαη πςειόηεξεο από ηα αλώηεξα θξηηήξηα πνηόηεηαο ηεο US EPA θαη άξα κπνξνύλ κε βεβαηόηεηα λα πξνθαιέζνπλ επηβιαβείο βηνινγηθέο επηπηώζεηο ζε νξγαληζκνύο. Πάλησο νη ζπγθεληξώζεηο ησλ κεηάιισλ απηώλ ζην ζαιαζζηλό λεξό είλαη ρακειόηεξεο από ηα όξηα ρξόληαο θαη νμείαο ηνμηθόηεηαο ηεο USEPA.

Μεηξήζεθαλ αξθεηά πςειέο ζπγθεληξώζεηο όισλ ησλ κεηάιισλ ζηα βξάγρηα ησλ θαξθηλνεηδώλ θαη ζηηο δύν πεξηνρέο (απόζεζεο θαη αλαθνξάο). Δπίζεο κεηξήζεθαλ ζρεηηθά πςειόηεξεο ζπγθεληξώζεηο κεηάιισλ ζηα ζειπθά, θάηη πνπ απνδόζεθε ζηηο απμεκέλεο αλάγθεο ηνπο θαηά ηε δηάξθεηα ηνπ αλαπαξαγσγηθνύ θύθινπ θαη ηηο ζηξαηεγηθέο δηαηξνθήο ηνπο θαηά ηε δηάξθεηα ηεο σνηνθίαο. Οη ζπγθεηξληξώζεηο όισλ ησλ κεηάιισλ, εθηόο από ην Mn, ζηνπο καιαθνύο ηζηνύο ησλ θαξθηλνεηδώλ ήηαλ ρακειόηεξεο από ηα δηεζλή πξόηππα θαη όξηα (FAO, 1993, WHO,1998 and USDA, 2009).

΢ηα γαζηεξόπνδα αληρλεύηεθαλ πςειέο ζπγθεληξώζεηο ησλ κεηάιισλ πνπ ζρεηίδνληαη κε ηε κεηαιινπξγηθή δξαζηεξηόηεηα. Σα απνηειέζκαηα επίζεο έδεημαλ όηη πην θαιόο βηνδείθηεο γηα ην Mn είλαη ν Patella ελώ γηα ηα κέηαιια Fe, Ni and Zn. o Phorcus

Η μεθάζαξε αλαζηνιή ηεο δξαζηηθόηεηαο ηεο AChE ζηα δείγκαηα από ηε ξππαζκέλε πεξηνρή ζε ζρέζε κε ηελ πεξηνρή αλαθνξάο δείρνπλ όηη ν βηνδείθηεο απηόο απνθξίλεηαη πνιύ θαιά ζηελ ξύπαλζε από κέηαιια. Ο βηνδείθηεο GST έδεημε απμεκέλε δξαζηηθόηεηα ζε δείγκαηα από ζπθώηη ηνπ S.aurata από ηε ξππαζκέλε πεξηνρή. Πάλησο δελ βξέζεθαλ δηαθνξέο ζηηο δξαζηηθόηεηεο ηεο GST θαη CAT ζηα M. rugosa θαη Ph. turbinatus.

Σέινο, αλαθέξεηαη όηη ε καθξνρξόληα απόζεζε ηεο ζθνπξηάο ζηελ πεξηνρή απηή έρεη επηδξάζεη πνιύ αξλεηηθά ζηα ηδήκαηα αιιά έρεη πξνθαιέζεη θαη απμεκέλεο ζπγθεηλξώζεηο βηνζπζζσξεπκέλσλ κεηάιισλ ζε ζαιάζζηνπο νξγαληζκνύ.ο Λόγσ απηήο ηεο ζεκαληηθήο επηβάξπλζεο πνπ ηεθκεξηώλεηαη από ηελ παξνύζα θαη από

4 πξνεγνύκελεο κειέηεο ζηελ πεξηνρή ζα πξέπεη λα γίλεηαη ζπζηεκαηηθή παξαθνινύζεζε κε ηδηαίηεξα έκθαζε ζηα βξώζηκα ζαιάζζηα είδε. Δπηπιένλ ζα πξέπεη λα γίλεη θαη εθηίκεζε ηεο επίπησζεο ηεο κεηαιινπξγίαο ζηελ αλζξώπηλε πγεία θαη ελώ πξέπεη λα νξγαλσζνύλ θαη λα δηεθπεξαησζνύλ θαηάιιειεο επηδεκηνινγηθέο κειέηεο.

ΘΕΜΑΣΙΚΗ ΠΕΡΙΟΧΗ : ΒΙΟΓΔΩΧΗΜΙΚΗ ΩΚΔΑΝΟΓΡΑΦΙΑ

ΛΕΞΕΙ΢ ΚΛΕΙΔΙΑ: βαξέα κέηαιια, βηνζπζζώξεπζε, θαξθηλνεηδή, γαζηεξόπνδα, βηνδείθηεο.

5

Dedicated

to my parents

6 AKNOWLEDGMENTS

There are many people whom I would like to thank throughout the course of this PhD. First of all, I am eternally thankful to my supervisor Dr. dassenakis for all of his technical support and words of encouragement over the years and his patience throughout this PhD. I would also sincerely like to thank Dr. Catsiki for all of her assistance, guidance and patience and her words of wisdom all over the years. Moreover, for making the laboratory resources available to me. I would also like to thank my academic advisor Dr. Megalofounou for all of her help throughout this process.

Thanks especially to my friend Viky Paraskevopolou from the chemistry Lab that without her assistance and guidance from the beginning to the end, this works has not been done. I would like to thank Dr. Tsangaris for her help and her guidance in analysis of biomarkers and the availability of the laboratory resources and chemical materials. I also like to thank Nikos Kouerinis from the ecotoxicology lab in HCMR Anavissos for his help from samplings, preparing the samples and measurements to his good company.

I would like to thanks all my friends for their encouragement and positive words through all the process of this work.

Finally and most importantly I wish to thank my parents and my family for their support and encouragement over the years.

7 Table of Contents

1-Theoritical background ...... 1

2-Introduction ...... 1

2.1 Marine pollution ...... 1

2.2 Heavy metals ...... 1

2.2.1 Heavy metals in water ...... 3

2.2.2 Heavy meals in sediments ...... 6

2.2.3 Heavy metals in aquatic organisms ...... 8

2.2.4 Specific/Studied metals and their toxicity ...... 25

2.3 Protection of the marine environment ...... 34

2.3.1 Study of the marine environment and monitoring programs ...... 35

2.3.2 Framework and legislation ...... 36

2.3.3. International conventions ...... 36

2.3.4. Regional conventions ...... 38

2.3.5 Marine water quality criteria ...... 40

2.3.6. Sediment Quality Guidelines ...... 42

2.3.7 Seafood quality criteria ...... 45

3. Study area ...... 46

3.4 Information on the study area ...... 46

3.4.1 General information ...... 46

3.4.2 Pollution in Evoikos Gulf ...... 47

3.4.3 LARCO GMMSA; Mining and Metallurgical Plant ...... 48

3.4.4 Information on marine currents in N.Evoikos gulf ...... 50

3.5 Scope of the study ...... 50

4. MATERIALS AND METHODS ...... 52

4.1. Strategy of this study ...... 52

4.1.1 Study Area ...... 52

4.1.2 Studied Parameters ...... 52

4.1.3. Sampling stations ...... 53

4.1.4. Samples ...... 55

8 4.1.5 Biotic species information ...... 56

4.1.6 Samplings ...... 65

4.2 Analytical procedures ...... 67

4.2.1. Seawater ...... 67

4.2.2 Sediment ...... 70

4.2.3. Biota ...... 73

4.2.4 Metal Measurement ...... 74

4.2.5 Biomarkers (Preparation of samples and analysis) ...... 74

4.3 Statistical treatment of the results ...... 81

5. Results and Discussions ...... 82

5.1 OFFSHORE AREA ...... 82

5.1.1 Sediments ...... 82

5.1.2 Seawater ...... 107

5.1.3 Biota ...... 130

Crustaceans ...... 130

5.3.7 Co occurrence of metals in crustaceans and the environmental conditions ...... 150

5.2 INSHORE AREA (Coastal area) ...... 170

5.2.1 Inshore water ...... 170

5.2.2 Gastropod samples from the coastal zone ...... 189

5.3 Biomarkers ...... 203

5.3.1 The biochemical effect on marine organisms-Biomarkers of exposure ...... 203

5.3.2 Biomarkers‘ responses ...... 203

5.3.3 Investigation the relations of metal level with biomarker responses ...... 207

6. CONCLUSIONS ...... 216

7. Future study ...... 219

8. REFERENCES ...... 220

9 Annex ...... 247

9 LIST OF FIGURES

Figure 1 Metal ligand reactions of detoxification/storage (MeB), toxicity (MeT) and essentiality (MeE). Non essential metals do not form MeE complexes and cannot exert beneficial effects...... 13

Figure 2: Schematic representation of the sequential order of responses to pollutant stress within a biological system (modified by van der Oost et al 2003) ...... 14

Figure 3: Responses in organisms after pollutant exposure (modified from Van der Oost 2003) ...... 19

Figure 4: Glutathione as an antioxidant and xenobiotic (Bellas, 2014) ...... 23

Figure 5: the direction of currents from 20 and 50 (m) depth from slag dumping area ...... 50

Figure 6: Location of the inshore sampling stations ...... 53

Figure 7: Location of the offshore stations. A= trawl in the contaminated area. B= trawl in the reference area, points= environmental samples stations, dashed square=slag dumping area where the sampling took place ...... 55

Figure 8: The schematic table showing the procedure of obtaining dissolved metals in water . 68

Figure 9: Schematic table showing the procedure of obtaining dissolved metals in water ...... 69

Figure 10: Schematic table showing the procedure of obtaining particulate metals in water ... 70

Figure 11: Schematic table of the procedure of total metals in sediment ...... 71

Figure 12: Schematic table showing the procedure of obtaining labile metals in sediment...... 72

Figure 13: Photo of the sampled sediment in the contaminated area of Larymna ...... 82

Figure 14: Metal levels in sediment size fractions from the slag dumping stations during the monitoring time (mg/kg) ...... 85

Figure 15: Spatial distribution of CO3% and TOC% per station ...... 86

Figure 16: Spatial distribution of the total concentrations of metals related to the by-product of smelting plant in surface sediments. The values are expressed in mg/kg ...... 87

Figure 17: Clustering of stations based on total metal concentrations during three years of study ...... 89

10 Figure 18: Distribution trends of heavy metal from 1992 to 2011. Al and Fe expressed in %, other metals were expressed in mg/kg ...... 90

Figure 19: Cluster analysis of total concentration (fractions<1mm) and fine fractions of metals from the dumping area...... 93

Figure 20: Cluster analysis of labile metals (total and fine fractions) from the dumping area . 95

Figure 21: The Multidimensional scaling (MDS) distribution of total and labile concentrations. R presents as the reference area ...... 97

Figure 22: Enrichment factors based on A) a less contaminated sample, B) on Average Shale content in the slag dumping area ...... 101

Figure 23: Variation in concentrations of dissolved metals per station in 2009 from both surface and bottom areas ...... 114

Figure 24: Variation in concentrations of dissolved metals per station in 2010 from both surface and bottom areas ...... 115

Figure 25: Variation in concentrations of dissolved metals per station in 2011 from both surface and bottom areas ...... 116

Figure 26: Cluster analysis of dissolved metals (mg/l) from surface and bottom areas in the slag dumping site ...... 117

Figure 27: Cluster of concentration of particulate metals (mg/l) from surface and bottom area in the slag dumping site ...... 118

Figure 28: Logarithm of partition coefficient (log kd ) of surface and bottom areas of slag dumping and reference areas during the sampling period ...... 120

Figure 29: Log(metal+1) concentration of metals in M.rugosa per tissue, seasons and sampling areas. (S=Spring, W=Winter, exo=exoskeleton, soft=muscle). Lines show statistical differences ...... 131

Figure 30: Log(metal+1) concentration of metals in per tissue of Liocarcinus depurator seasons and sampling areas. (S=Spring, W=Winter, exo=exoskeleton, soft=muscle) ...... 134

Figure 31: Log(metal+1) concentration of metals in per tissue of Nephrops norvegicus seasons and sampling areas. (S=Spring, W=Winter, exo=exoskeleton, soft=muscle). Lines show statistical differences ...... 135

11 Figure 32: The variation in the concentrations of metals among different species per tissue in the reference area. Dep=Liocarcinus depurator, Mud=Munida rugosa, Neph=Nephrops norvigicus ...... 137

Figure 33: The variations in the concentrations of metals in different species from the contaminated area. Dep=Liocarcinus depurator, Mud=Munida rugosa ...... 138

Figure 34: Principal Component Analysis (PCA) from the three tissues in the contaminated area ...... 139

Figure 35: Principal Component Analysis (PCA) from the three tissues in the reference area ...... 139

Figure 36: Average concentration of metals in M.rugosa per sex, tissue and sampling area. (F=female, M=male, cont=contaminated slag area, ref= reference area, exo=exoskeleton, soft=muscle) ...... 142

Figure 37a: Differences among concentration of metals in M.rugosa per sex, tissue and sampling areas. (F=female, M=male, exo=exoskeleton, soft=muscle). Lines connecting the same tissue in males and females are added to reveal trends...... 143

Figure 38b: Differences among concentration of metals in M.rugosa per sex, tissue and sampling areas. (F=female, M=male, exo=exoskeleton, soft=muscle). Lines connecting the same tissue in males and females are added to reveal trends ...... 144

Figure 39: Differences in log concentration of metals in L.depurator between Female (F) and male (M) from the contaminated and the reference areas. The significant difference between each pair of tissues was shown by line ...... 146

Figure 40 : Differences in metal concentrations among different tissues, gender and areas of L.depurator (F=female, M=male, R=reference area, cont=contaminated area) ...... 147

Figure 41: Differences in log concentration of metals in N. norvigicus between Female (F) and male (M) from the reference area. exo=exoskeleton, soft=muscle ...... 149

Figure 42: Differences in metal concentrations among different tissues and gender of N. norvigicus (F-R=female samples from reference area, M-R=male samples from reference area ...... 150

Figure 43: Bioconcentration factor (BCF) of metals in the studied species. G=gill, S=muscle, Neph= Nephrops norvegicus, Dep= Liocarcinus depurator, Mud=Munida rugosa ...... 153

12 Figure 44: Average concentrations of dissolved metals per season and station. Ref presents the reference area ...... 172

Figure 45:.Graphs of particulate metal concentrations per season and station. Ref represents the reference area...... 175

Figure 46: Dendrogram classifies stations according to A) dissolved metals, B) particulate metals...... 180

Figure 47: Dendrogram classifies stations according to A) dissolved metals, B) particulate metals...... 180

Figure 48: The two dimension distribution (MDS) of dissolved and particulate metals from the coastal area...... 181

Figure 49: Logarithim of partition coefficient (log Kd ) of metals in four seasons from the coastal area...... 182

Figure 50A: Heavy metals distribution in Ph. turbinatus and P. caerulea (µg/g dry wt) in different sampling stations and seasons. REF1 and REF2 indicate the Reference areas. 190

Figure 51: Cluster analysis in Ph turbinate and P caerulea, based on the sampling stations. REF indicates the two reference stations ...... 194

Figure 52: Cluster analysis in Ph turbinate and P caerulea, based on the bioaccumulated metals.in the soft tissue ...... 195

Figure 53: Biomarkers activities in M.rugosa from the North Evoikos Gulf. [(CON)=contaminated/slag dumping area, R= reference area; (CAT)= catalase activity, (GST) =glutathione S-transferase activities (AChE)= acetylcholinesterase activity, M- G=male gill, F-G=female gill, M-E=male eye, F-E=female eye, M-L=male liver, F-L= female liver...... 204

Figure 54: Biomarkers activities of Ph.turbinatus from the coastal line of Larymna bay. [(CON)= contaminated/slag dumping area (R)= reference area; (GST)= glutathione S- transferase activity, (AChE)= acetylcholinesterase activity.CON-LA1=station LA1 in contaminated area.CON-LA4=station LA4 in contaminated area...... 205

Figure 55: Biomarkers activities of S. aurata. (CONT)= the contaminated/slag dumping area, (R)= the reference area; (GST)= glutathione S-transferase activity, (AChE)= acetylcholinesterase activity, CON-LA1 and CON-LA4=stations LA1 and LA4 in the contaminated/ slag dumping area...... 206

13 Figure 56: Multidimensional scaling (MDS) results from the concentration of metals (Fe, Cu, Mn, Zn, Cr, Ni) in the soft tissue of M.rugosa and biomarkers (CAT, GST, AChE gill, AChE eye)...... 209

Figure 57: Multidimensional scaling (MDS) results from the concentration of metals (Fe, Cu, Mn, Zn, Cr, Ni) in the soft tissue of S.aurara and biomarkers (CAT, GST, AChE gill, AChE eye)...... 210

Figure 58: Multidimensional scaling (MDS) results from the concentration of metals (Fe, Cu, Mn, Zn, Cr, Ni) in the soft tissue of Ph.turbinatus and biomarkers (CAT, GST, AChEgill, AChE eye)...... 210

14 LIST OF TABLES:

Table 1: The classification of metal ions ...... 2

Table 2 : Classes of metals and metalloids relatively to living organisms (Chiarelli & Roccheri, 2014) ...... 3

Table 3: US EPA water quality criteria for metals. The values for freshwater was considered at 100mg/l hardness ...... 41

Table 4: The environmental quality standards (EQS) of WFD. All concentrations are expressed in μg/l.(1) AA is the allowable annual average value. (2)Inland surface waters encompass rivers, lakes and related artificial water or heavily modified water bodies. (3) MAC is the maximum allowable concentration. (4)Other surface waters are boundary (estuaries, lagoons) and coastal waters. The classes are defined according to harness. Coastal water is categorized in class...... 41

Table 5: Threshold effect sediment quality guidelines for metals (mg/kg) ...... 44

Table 6: Midrange effect of sediment quality guidelines for metals (mg/kg) SQ ...... 44

Table 7: Extreme effect sediment quality guidelines for metals (mg/kg) ...... 45

Table 8: Weight percentage of slag Composition [(A) IOFR (1985), (B) Zaharaki et al (2009), (C) Balomenos et al., (2013)] ...... 49

Table 9: Information of the coordinates of inshore stations...... 54

Table 10: Coordinates and depth of the offshore sampling stations...... 55

Table 11: Inshore sampling dates and matrices...... 65

Table 12: Offshore sampling dates and matrices ...... 66

Table 13: Assigned and recoveries of reference materials in the biota samples...... 80

Table 14: Recoveries, reproducibility (%RSDR) and LOD of dissolve metals ...... 80

Table 15: Certified reference materials (PACS-2 mg/kg) and recoveries for sediment samples. 1=MESS-3 certified materials used for Hg.2ISE 962 certified materials used for organic Carbone. Precision was estimated with replicate analysis (N=3-5) ...... 81

Table 16: The percentage of grain size fraction in three years of samplings F1=(63μm

15 Table 17: Average concentration of total organic carbon (TOC%) and carbonate (CO3 %) in sediments during the three years of study ...... 83

Table 18: Average concentration of metals in sediments per size class (mg/Kg) for the three years of study (ND= no data). N=15 ...... 84

Table 19: Average percentage of labile and non-labile fraction of metals in sediment in different stations ...... 91

Table 20: Spearman correlation coefficient of total concentration (fraction<1 mm) of metals, Organic Carbone and Carbonate in the surface sediments from the depositing area ..... 92

Table 21: Spearman correlation coefficient of fine fraction of metals (fraction <63µm) in the surface sediments from the depositing area ...... 92

Table 22: Spearman correlation coefficient of fine fraction of labile metals (fraction <63µm) in the surface sediments from the depositing area ...... 95

Table 23: Spearman correlation coefficient of fine fraction of labile metals (fraction <63µm) in the surface sediments from the depositing area ...... 96

Table 24: comparison between the average total concentration of metals from slag dumping area and the values proposed by US EPA and Ontario ministry of Canada. The heavily polluted metals were shown in bold. Values for all metals expressed in mg/kg. Hg concentration expressed in (µg/kg) ...... 98

Table 25: ERL and ERM guidelines value for trace metals (ppm, dry wet). ERL= Effects Range-Low; ERM= Effects Range Median. Concentrations above ERM value were indicated in bold ...... 99

Table 26: Index of geoaccumulation (Igeo) in surface sediments compared to shale average content. The number in bold indicated the moderately to extremely contaminated area ...... 102

Table 27: Average, minimum and maximum concentration of dissolved metals in surface water samples from the slag dumping and reference areas (R) ...... 108

Table 28: Average, minimum and maximum concentration of particulate metals in surface water samples from the slag dumping and reference areas (R) ...... 110

Table 29: Suspended particulate matter (SPM) (mg/l) of surface and bottom water samples (2009-2011) ...... 112

16 Table 30: Percentage of dissolved and particulate metals in surface and bottom areas. Dis:= dissolved and Par = particulate. R=reference area. The total concentration of metals is expressed in µg/l ...... 113

Table 31: The logarithm of partition coefficient log (kd) results from the water and sediment area of the present and other studies...... 120

Table 32: Comparison of Average and the range of metals concentrations in dissolved form (μg/l) from Larymna Bay in N. Evoikos gulf and some other areas in Greece with US EPA and WFD criteria. ND =no data ...... 122

Table 33: Average and the range of metals concentrations in particulate form (μg/l) from Larymna Bay in N. Evoikos gulf and some other areas in Greece. ND =no data ...... 124

Table 34: Average concentration of metals in M.rugosa (in μg/g dw). Maximum concentrations are in bold (Cont=slag deposit area, Ref=reference area, Exo=exoskeleton, spring=June 2009, winter=March 2010)...... 130

Table 35: Average concentration of metals in L.depurator (in μg/g dw). Maximum concentrations are in bold (Cont=slag deposit area, Ref=reference area, Exo=exoskeleton, spring=June 2009, winter=March 2010) ...... 133

Table 36: Average concentration of metals in N.norvegicus (in μg/g dw). Maximum concentrations are in bold (Cont=slag deposit area, Ref=reference area, Exo=exoskeleton, spring=June 2009, winter=March 2010) ...... 135

Table 37: Average concentration of metals in M. rugosa (in μg/g dw). Maximum concentrations during each season are in bold (Cont=slag deposit area, Ref=reference area, F=female, M=male, ND=not determined sex, Exo=exoskeleton, spring=June 2009, winter=March 2010)...... 141

Table 38: Average concentration of metals in L.depurator (μg/g dw) from reference (Ref) and contaminated (Cont) areas. Maximum concentrations in each season are in bold. 145

Table 39: Average concentration of metals in N.norvigicus (μg/g dw) from reference (Ref) and contaminated (Cont) areas. Maximum concentrations in each season are in bold. 148

Table 40: Bioaccumulation factor (BCF) in different tissues of Munida rugosa. Cont= contaminated area. Ref=Reference area. F= Female. M= Male. The maximum concentrations are in bold...... 151

17 Table 41: Bioaccumulation factor (BCF) in different tissues of Liocarcinus depurator. Cont= contaminated area. Ref=Reference area. F= Female. M= Male ...... 152

Table 42: the Bioaccumulation factor (BCF) in different tissues of Nephrops norvegicus. Ref=Reference area. F= Female. M= Male ...... 152

Table 43: Concentration of metals (µg/g dry wet) in different species of crustaceans. 1=October sampling, 2=April sampling, 3=sampling from the Germiston lake, 4=sampling from the Potchefsroom dam, Exo= exoskeleton. P.semisulcatus=Penaeus semisulcatus, N. norvegicus=Nephrops norvegicus, P.warrenti =Potamonautes warrenti, P.elegans= Palaemon elegans,C.pagarus =Cancer pagurus, P.serratus=Palaemon serratus, P.adspersus=Palaemon adspersus, S.serata=Scylla serata, N. granulata =Neohelice granulate...... 156

Table 44: Concentration of heavy metals (µg/g dry wet) in muscle of different species collected from the contaminated and reference area of N.Evoikos Gulf. T.lastoviza=Trigloporus lastoviza, E. guranardus =Eutrigla guranardus, M.barbatus=Mullus barbatus, P.erythrinus=Pagellus erythrinus, S.vulgaris=Solea vulgaris, A.laterna= Arnoglossus laterna, C.linguatula=Citharus linguatula, T.capelanus=Trisopterous capelanu, M.merluccius= merluccius merluccius, L.depurator=Liocarcinus depurator, M.rugosa=Munida rugosa, N.norvegicus=Nephrops norvegicus ...... 158

Table 45: Variation in concentrations of metals (µg/g dry wet) in soft tissue of Liocarcinus depurator from the contaminated area (2003-2015) ...... 159

Table 46: Variation in concentrations of metals (µg/g dry wet) in soft tissue of Liocarcinus depurator from reference area (2003-2015) ...... 160

Table 47: Variation in concentrations of metals (µg/g dry wet) in soft tissue of Munida rugosa from reference area (2002-2015) ...... 160

Table 48: Variation in concentrations of metals (µg/g dry wet) in soft tissue of Munida rugosa from contaminated area (2002-2015) ...... 161

Table 49: Variation in concentrations of metals (µg/g dry wet) in soft tissue of Nephrops norvegicus from reference area (2002-2015) ...... 162

Table 50: Seasonal variations of dissolved metal concentrations in coastal water around the smelting plant. Values are expressed in µg/l. high concentrations are shown in bold. ND= no data ...... 171

18 Table 51: Basic statistics of particulate metals concentrations per season, expressed in µg/l high concentrations are in bold ...... 174

Table 52: Average percentage of dissolved and particulate metals in coastal area of Larymna Bay during four seasons. ND=no data. Dis = dissolved metals. Par = particulate metals. R = for reference area ...... 176

Table 53: Concentration of Suspended particulate matters (SPM) (mg/l) in different seasons and stations. ND= no data. R=reference station ...... 177

Table 54: Range of Suspended particulate matters (SPM) concentrations (mg/l) from offshore and inshore area ...... 177

Table 55: Spearman correlation coefficient A) dissolved metals B) particulate metals from the seawater sample of costal area ...... 178

Table 56: Comparison of average concentrations of dissolved metals (µg/l) from coastal areas in Greece. The high concentrations are in bold ...... 187

Table 57: Comparison of average concentrations of particulate metals (µg/l) from coastal areas in Greece. The high concentrations are in bold ...... 188

Table 58: Summary statistics of heavy metal concentrations in P. caerulea and Ph. turbinatus (in μg/g dry weight) in the contaminated and the reference areas ...... 189

Table 59: Spearman correlations among: a) the concentration of metals in seawater with those in gastropod tissues, b) among bioconcentrated metals (M= Ph. turbinatus, P= P. caerulea, W= Seawater). The significant values are in bold ...... 193

Table 60: Bioconcentration factor (BCF) calculated for the gastropods Ph. turbinatus and P. caerulea ...... 196

Table 61: Average concentration of bioaccumuated metals in Patella from Larymn bay. The values are expressed in µg/g d wt ...... 197

Table 62: Range and average concentration of bioaccumuated metals in Ph turbinatus in different areas. The values are expressed in µg/g d wt .* Ph.turbinatus was known before as Monodonta turbinate ...... 197

Table 63: Range and average concentration of bioaccumuated metals in P.caerulea and P. aspera in different areas. The values are expressed in µg/g dw ...... 198

19

1-Theoritical background 2-Introduction 2.1 Marine pollution

The oceans and seas have always been subject to human activities, which in most cases have adverse impacts on the state of the marine environment. Pollution is by far the more significant and has become a matter of widespread public concern only over the past years or so. Since the industrial revolution, the constituents of the marine environment (surface water, sediments and marine organisms) have been exposed to a wide variety of contaminants, such as, heavy metals. (Fukue et al., 2007) methyl mercury, polycyclic aromatic hydrocarbons (PAH), other organic compounds and sulphur (Walker et al., 2006). The early production of chemicals has reached more than 400 million tons in present time compared to 7 million tons in the 1950‘s. Marine environment in most cases are the final receiver of pollutants, thus excessive production, unconscious usage and reckless discharge of chemicals can pose serious hazards for marine ecosystems and ultimately to human health (Yarsan and Yipel, 2013).

The sources and types of chemical pollutants to which water bodies can be exposed are many and varied. The significant research concerning trace metal pollution in the marine environment, undertaken in the past 50 years followed a few relatively minor, but much publicized incidents of local pollution by Hg and Cd (Minamata and Itai-itai diseaces). Earlier work on the chemistry and biology of metals considered also the relevant topics of nature and availability of trace metals (Brayan, 1979).

2.2 Heavy metals

Metals comprise three quarters of the elements in the periodic table (Ballatori, 2002). Life has evolved in the presence of metals at a wide range of available concentrations and with a similarly broad array of chemical attributes. Another descriptive term used very loosely in the literature is the term "heavy metal"—a category usually undefined by authors. On one side the term heavy metal refers to any metallic chemical element that has a relatively high density and is toxic or poisonous at low concentrations. On the other side, as Nieboer and Richardson,(1980) have discussed, the groups of lanthanides and actinides should also be included in this category, however they are not usually considered as "heavy" by their chemical and hence biological properties. Therefore, they have proposed that the term "heavy metal" be abandoned in favour of a classification of metal ions based on their chemical properties, derived ultimately from the classification of elements into hard and soft acids and bases. Metal ions are therefore classified as class A, class B or borderline (Table 1).

1 Simplistically class A metal ions have an affinity for oxygen as a metal binding donor atom in ligands, class B for sulphur and borderline metal ions have a more catholic affinity (Masona and Simkiss, 1982 ; Rainbow, 1997).

Table 1: The classification of metal ions

Class A Borderline Class B Lithium (Li) Titanium (Ti) Beryllium (Be) Vanadium (V) Sodium (Na) Chromium (Cr) Magnesium (Mg) Manganese (Mn) Aluminium (Al) Iron (Fe) Potassium (K) Cobalt (Co) Calcium (Ca) Nickel (Ni) Scandium (Sc) Copper II (Cu2+) Copper I (Cu+) Rubidium (Rb) Zinc (Zn) Strontium (Sr) Gallium (Ga) Yttrium (Y) Arsenic (As) Rhodium (Rh) Caesium (Cs) Molybdenum (Mo) Palladium (Pd) Barium (Ba) Cadmium (Cd) Silver (Ag) Lanthanum (La) + Indium (In) Iridium (Ir) the Lanthanides Tin (Sn) Platinum (Pt) Francium (Fr) Antimony (Sb) Gold (Au) Radium (Ra) Tungsten (W) Mercury (Hg) Actinium (Ac) + Osmium (Os) Thallium (Tl) the Actinides Lead II (Pb2+) Lead IV (Pb4+) Bismuth (Bi)

Heavy metals are also considered as trace elements because of their presence in trace concentrations [from below ppb (µg/l)) to less than 10 ppm (mg/l)] in various environmental matrices. Their bioavailability is influenced by physical factors such as temperature, phase association, adsorption and sequestration (Tchounwou et al., 2012) and sometimes their low amounts are necessary to maintain normal health conditions in various organisms (Rainbow, 1997).

Metals and metalloids, relatively to living organisms, can be separated in three classes: essentials, non-essentials and borderline (Table 2 ) (Chiarelli and Roccheri, 2014). The lists of essential trace metals vary between authors but may include iron, manganese, copper, zinc, cobalt, molybdenum, chromium, vanadium, selenium, nickel and tin. It is believed that non-essential metals do not play any required role in metabolism, and usually include cadmium, mercury, lead, arsenic, vanadium silver and gold as well as more obscure metals of large atomic weight (e.g. gallium, tellutrium, uranium) (Alloway and Ayres 1997; Tchounwou et al., 2012). Furthermore, essential metals can become toxic above certain threshold levels. Also, even for some of the non-essential metals there are scarce and isolated evidence of biological roles in few organisms, for example Cd in marine diatoms and

2 vanadium in ascidians. Therefore the concept of trace metal essentiality vs toxicity in linked to both the existing levels of metals but also the varieties of biota (prokaryotes and eukaryotes or animals and plants). Non-essential metals particularly mercury, cadmium and silver are extremely toxic at relatively low concentrations, their toxic effect presumably being caused by their chemical similarity to more commonly available essential metals, which they substitute. Nonessential metals in critical molecules of metabolic pathways may prevent further reaction and so block that pathway (Rainbow, 1977; Michibata et al., 1990).

Table 2 : Classes of metals and metalloids relatively to living organisms (Chiarelli & Roccheri, 2014)

Types of heavy metal Heavy Metals Essentials Calcium (Ca), Magnesium (Mg), Manganese (Mn), Potassium (K), Sodium (Na), Strontium (Sr), Zinc (Zn), Iron (Fe), Copper (Cu)

Non-essentials Cadmium (Cd), Mercury (Hg), Silver (Ag) Tallium (Ti), Lead (Pb)

Borderline Chromium (Cr), Cobalt (Co) Nickel (Ni), Arsenic (As), Vanadium (V), Tin (Sn)

2.2.1 Heavy metals in water

Liquid water covers about 71% of the earth‘s surface, predominantly (99.99%) as salt water in the oceans, and the rest as fresh water in lakes, rivers and ponds. The total volume of water in the oceans is about 1,350 million cubic kilometres (Turekian, 1976). Virtually all of this water contains life, ranging in size from viruses to whales, making aquatic ecosystems important by volume alone. Furthermore, water is historically the cradle of earthly life, and even today, tiny marine phytoplankton control much of the earth‘s oxygen supply. Therefore, water is a unique solvent and vital for any living organisms. (Chester, 2003)

Seawater contains a number of different components that can be divided into the following phases:

1- Solids (materials that does not pass through a 0.45µm filters) a- Particulate organic materials (biotic detritus) b- Particulate inorganic materials (minerals) 2- Gases a- Conservative (N2,Ar,Xe) b- Non conservative (O2 and CO2) 3- Colloids (pass through a 0.45µm filters but are not dissolved) a- Organic (Complex sugars) b- Inorganic (Iron hydroxides) 4- Dissolved solutes a- Inorganic solutes

3 1. Major (>1 ppm – mg/L) 2. Minor (<1 ppm – mg/L) b- Organic solutes

Furthermore, 14 elements found in seawater (O, H, Cl, Na, Mg, Ca, K, Br, C, Sr, B, Si and F) have concentrations greater than 1 ppm – mg/L. Many of the remaining elements called minor, are involved in inorganic and biological reactions in the marine environments, Bruland (1983) divided the elements into three classes based on their concentrations. Minor and trace elements in the ocean have a wide range of concentrations because of their reactivity (Frank J. Millero, 2001).

1- Major elements: 0.05 to 750 mM 2- Minor elements: 0.05 to 50 µM 3- Trace elements: 0.05 to 50 nM

There are various sources through which metals enter aquatic systems:

1- Geologic weathering and diffusion from shelf sediments:

The major elements (O, Si, Al, Fe, Ca, Na, K, Mg, Ti, H, P and S) comprise 99% of the earth‘s crust, while trace metals account for approximately 1%. The weathering of primary and secondary minerals from the earth‘s surface is the major natural source of metals to the aquatic environments and the one responsible for the baseline or background metal levels in water (Alloway and Ayers, 1997). Another natural source of metals is the contribution of volcanic, hydrothermal and geothermal processes to groundwater, to the atmosphere and to marine waters (Pfeifer et al 2000). Metals are not fixed in the sediments and changes in geochemical parameters (e.g. pH), oxidation of anoxic sediments by bioturbation or by resuspension caused by flooding and concentration gradients within the sediments and porewaters may cause mobilisation and diffusion back to the water body. The origin of metals in sediments, however are both natural and anthropogenic (Zoumis et al., 2001), but usually when increased concentrations of metals are found in the marine environment they mainly come from anthropogenic polluting activities such as mining, various types of industries and their processes, industrial, domestic and agricultural effluents, fossil fuel combustion etc (Alloway and Ayres, 1997; Salomons and Förstner, 1984).

2- Industrial processing of ore and metals (Mining Effluents):

The serious effects of mine effluents on the water quality in rivers and lakes as well as on the biotopes, particularly on the aquatic population, have been known for many years. One of the very first descriptions of this problem is the fifth report of the 1868 River Pollution Commission in Britain. Mine drainage does not occur only from the mine itself but also from the rock dumps and tailing areas. These two sources often contain high concentration of

4 sulfides and /or sulfo salts which are associated with the most ore and coal bodies. The most commonly occurring sulfides are those of iron (Chester, 2003).

3- Industrial Effluents:

The disposal of industrial waste is still often conducted without any critical evaluation and no consideration is taken with regards to the deleterious environmental impact upon the receiving water body, especially when guides on maximum permissible pollutant levels are not available on not enforced. There are numerous sources of industrial effluents leading to heavy metal enrichment of the aquatic environment. A classic historical example is the discharge of the catalyst methylated mercury chloride into Minamata from a factory manufacturing plastics (Tsubaki and Irukayama, 1977). A variety of metals or one single metal can be emitted by various industries i.e. metallurgy –steel production, petroleum refining, as well as the production of fertilizers, electronics, paints, catalysts, medical alloys and pharmaceutical compounds etc.

4- Domestic Effluents and Urban Storm Water Runoff:

In urban environments, one of the most important point sources of pollution is the discharge from the wastewater collection system, of either untreated sewage from illegal pipes or where a treatment plant exists, of the effluent from the plant (Taebi and Droste, 2004). Therefore there is increased concern over the trace metal enrichment in the areas of wastewater discharge points especially because the solid wastewater particles may cause appreciable increase in metal levels compared to the natural suspended load in waters. (Campbell, 1983)

With regard to pollution resulting from urbanized areas, there is also an increasing awareness that urban runoff presents a serious problem of heavy metal contamination. Heavy rainfall in urban areas is no longer regarded as only a downpour of ―rainwater‖, since it has been reported on some occasions that a large storm event may cause shock to the receiving water body many times greater than an ordinary regular sanitary effluent load. The important feature is that potential contamination may occur during short periods of storm runoff, where trace elements resulting from long term atmospheric emissions and subsequent deposition on various surface materials may be transported very quickly to the nearby drained system (Taebi and Droste, 2004).

5- Atmospheric Sources:

Natural and anthropogenic processes have been shown to result in metal containing airborne particles. Depending on prevailing climatic conditions, these particulates are transported over great distances; nonetheless their residence time is short (between days and weeks) and ultimately they return to the earth‘s surface (lithosphere and hydrosphere) as dry

5 and wet precipitation (rain or snow fall). Atmospheric particles may occur both by natural - geogenic sources such as volcanic eruptions, weathering of soils, seasalt spray and wild forest fires, or anthropogenic sources (Nriagu, 1989). The major anthropogenic sources are fossil fuel combustion in industries, residential areas and transportation means, cement production, waste incineration, and last but not least, metallurgical processes i.e iron, steel and non ferrous metal production and manufacturing (Pacyna et al., 2006). A few more details will provided for the metallurgical sources because it is the main pollution source in the study area of the present thesis. ―Smelting‖ is a process where ore or ore concentrate is fused with suitable flux (i.e. material added to a furnace charge to combine with the gangue and form a fusible slag). Obviously, smelting operations cause the emission of particulates which often contain toxic constituents that enter into the environment. Despite the installation of high- intensity electrostatic precipitators and high refectory-linked stacks in accordance with modern technological advances, the problem of pollution and smelting operations has not yet been overcome. Smelting operations have long been known to contain toxic substances especially metals (Chester, 2003).

2.2.2 Heavy meals in sediments

The bottom of the seas and oceans i.e. the ocean floor is covered by a layer of consolidated and unconsolidated sediments of varying thickness.

Marine sediments are complex mixtures of particles and they can be categorized according to the origin of the solid materials, their composition and grain size. The two major categories based on the origin of the particulate matter are the terrigenous and biogenic sediments. The terrigenous or lithogenic sediments are composed from weathered materials from land (quartz, clay and carbonate minerals). The biogenic sediments are accumulations of shell particles containing calcium carbonate or silica and non degradable organic particles all deriving from the life cycle of aquatic biota. Three more categories of lesser importance and limited occurrence in specific environments are the hydrogenous, volcanic and cosmogenic (from interstellar space) sediments. The hydrogenous sediments consist of inorganic precipitate particles that form in situ in the aquatic environment in specific environments and conditions. Based on the grain size sediments are characterized as fine grained or coarse grained. Most commonly fine grained particles are smaller than 63κm and are further subdivided to silts (4-63κm) and clays (<4κm). The coarse grained particles are subdivided to sands (63κm-2mm) and gravel (>2mm) [Salomons and Förstner, 1984; Papathanasiou and Zenetos, 2005; Pickering, 1986; Bridge and Demico, 2008].

The transport and deposition of sediments are related to hydrological and geomorphological phenomena. The main processes that take place are: erosion of the sediment particles from land (river bank or earth surface structure), formation of biogenic or

6 hydrogenous particles in situ, vertical transport (sinking) or horizontal transport of particles, deposition on the bottom of an aquatic environment (river, lake, sea) and finally consolidation of the deposits. These phenomena interact and vary in space and time. Marine sediment deposition occurs in two very different regimes i.e near shore locations and deep sea areas (bottom depths above 500m). The near shore sediments are deposited on the shelf regions in estuaries, fjords, bays, lagoons, deltas, tidal flats, the continental terrace and the marginal basins, which are dynamic environments highly influenced by the adjacent coastal land areas (Salomons and Förstner, 1984; Chester, 1990).

The chemical composition of sediments depends on the composition of the parent weathered minerals and the biogenic and hydrogenous particles it consists of. Thus the sediment particles at the atomic level are made up of the following major and minor elements.

A) Major elements: There are 25 known rock forming minerals which are the most abundant in the earth‘s crust. The most abundant minerals in sediments (75%) are sillicates (quartz and various clays that contain silica and aluminium). The remaining minerals include oxides, carbonates, sulphides, sulphates, halides and native elements (Cu, S). Thus the major elements in terrigenous sediments are Si, O, Al, Fe, Ca, Na, K and Mg. The major elements in biogenic sediments are Si, O, Ca and C (in calcium carbonate and detrital organic matter). Mn and Fe are included in both the major and minor elements because of significant variations in their levels and because their phases act as scavengers for dissolved trace metals.

B) Trace elements and trace metals: Apart from the major constituents mentioned above the sediments contain a variety of minor elements (Ti, P, Mn, Ba, Sr, V, Cr, Ni, Zn, Cu, Co, Li, Pb, Sc, Be, U, Sn, Mo, As, Sb, Cd, Ag, Hg, Se and rare earths) in amounts ranging from slightly below 1% to 0.05κg/g. The most important trace metals are Cr, Cd, Cu, Mo, Ni, Co, Hg, As and Mn and they are found either in major mineral phases (on cation exchange sites of clay minerals, bound to Fe and Mn oxides, chelated on detrital organic matter, substituting major cations in the lattice of minerals) or as discrete compounds (sulphides, oxides, hydroxides and carbonates) (Salomons and Förstner, 1984; Manahan, 1994; Chester, 2009).

Sediments are being used intensively to monitor the aquatic environments because they are the major store of contaminants, including trace metals. The sediments act mostly as sinks of metals but in some cases they can be a secondary source to the interstitial waters and the water column due to the natural diagenetic processes, changes in redox conditions, re- suspension and bioturbation processes. Metal concentrations in sediments are orders of magnitude higher than in the overlying water column and thus easier to quantify, therefore

7 they have been used extensively in several studies as quality indicators. Furthermore the excess accumulation of toxic and potentially toxic metals (Cd, Pb, Hg, Cu, Zn etc.) in the sediments from polluting activities such as mining and metallurgic processes (smelting, plating etc) has to be monitored in order to assess the possible impacts to aquatic biota and to humans via biomagnification through the food chains. Sediments are very useful as a monitoring tool because they reflect not only the current water quality but can also provide a historical record of natural metal background levels, past short term pollution events that can no longer be found in the overlying waters and the evolution (increase and decrease) of metal levels in a given area as a result of the onset of polluting activities and improvements through counter pollution measures (Salomons and Forstner, 1984; Burton, 2002; Selvaraj et al., 2004; Gedik & Boran, 2013; Chester 2009).

2.2.3 Heavy metals in aquatic organisms

2.2.3.1 Introduction

In the last three to four decades the role of heavy metals in aquatic organisms and aquatic ecosystems has been increasingly investigated. Originally, research was focused on the toxicity of metals (lethal and sub-lethal doses) but very soon there was a shift and emphasis also to the ability of organisms to accumulate metals (exposure and bioaccumulation) and to their influence on metabolic processes and the subsequent effects at the individual and ecosystem level.

The essential and non essential metals transported to aquatic ecosystems are persistent pollutants, non biodegradable; those have the potential to be toxic to living organisms if present at availabilities above a threshold which varies between taxa, or even between species.

Basic ecotoxicological terms and aspects are bioaccumulation, bioavailability, biomonitoring, metal biomonitor organisms, biomarkers which will be described in detail in the following sections.

2.2.3.2 Bioavailability of metals, exposure and uptake mechanisms, bioaccumulation and elimination

When considering the interactions of metals with aquatic organisms there are three levels of concern that need to be addressed: 1) metal species in the aquatic habitat of the organisms, 2) metal interactions with the biological membranes separating the organism from the environment or with the organs that are in contact and exchange materials with it and 3) the fate of metals (reactions, partition, accumulation, excretion) inside the organism (Campanella et al., 1994).

8 It is established that the total concentrations of metals in environmental media do not determine their actual bioavailability. The main species of metals in waters that need to be taken into account for direct interaction with biota are the hydrated metal ions, dissolved inorganic and organic complexes, metals in dispersed colloidal species and finally metals adsorbed on colloids or suspended matter. The main inorganic ligands are chloride, carbonates, sulphate, hydroxy and oxo anions. The organic ligands include well-characterized compounds of low molecular weights such as amino acids and monosaccharides and complex compounds with high molecular weights such as fulvic acids, peptides, humic acids, polypeptides, polysaccharides, lipids and proteins (Mota and Correia dos Santos, 1995). The abiotic parameters that control metal speciation and the uptake of these species by biota in natural waters are ionic strength, salinity, pH, hardness, alkalinity, temperature, dissolved oxygen, illumination, total dissolved organic carbon and individual organic ligands, the presence and concentration of the various inorganic ligands, the presence and levels of other metals or major compounds that could act synergistically or antagonistically and finally the concentration and type of suspended particulate matter (Wang, 1987; Witters, 1998).

As for the routes of metal uptake by biota, aquatic plants receive metals mostly from the aqueous phase while most animals from both water and their food (including suspended or sediment particles). Most dissolved metal species are considered to passively enter the cells along a diffusion gradient which is maintained by sequestering of the metals once they pass through by various intracellular ligands. Uptake of dissolved metals in small soft bodied organisms can take place across their entire body surface, in some organisms that imbibe water dissolved forms pass through the digestive epithelium and finally the respiratory organs – gills are sites of known high permeability. Metals in solution are considered more bioavailable but the importance of living and feeding strategies cannot be ignored. For example in some cases animals in contact with interstitial water and sediments absorb and accumulate the higher available metal concentrations. Also metal concentrations in food, suspended and sediment particles are usually much higher and strategies such as filter and deposit feeding can lead to increased metal contents in some organisms. Therefore the species specificity (including the feeding and living strategy and tolerance characteristics), the size and life stage, the nutritional status, the metabolic and physiological status with respect to diurnal and seasonal changes and the food quality and availability are the biotic factors that influence metal accumulation by aquatic organisms (Wang 1987; Langston and Spence, 1995; Witters, 1998).

The specific routes of metal entry are: hydrophobic solution into the cell membrane, attachment to membrane proteins or carbohydrates, or the lipids, endocytosis of membrane components with attached metals on them, permeation through water channels, specific and non specific channels and permeation by general active processes (e.g. electrochemical potentials) or by specific active processes (ATPases). Biologically important elements (H+,

9 Na+, K+, Mg2+, Ca2+) cross the cell membrane by specific channels and carriers. Manganese is transported inside the cells through the calcium channel, while iron which is also a very important essential trace metal is transported by the endocytotic ferri-transferin receptor pathway. This specialized pathway seems to have evolved in vertebrates as a means to transport Fe3+ ion which forms very insoluble compounds under normal physiological conditions. In microorganisms iron is believed to be taken as ferri-siderophore complexes. The metals Cu and Zn, also biologically essential have been known to enter the cells either by passive diffusion or by specific membrane transport proteins. Organometallic compound of the toxic metals Hg, Sn and Pb penetrate the lipid membrane via direct interaction due to their lipid solulibility. Inorganic Pb forms and Cd enter by passive diffusion or through Mn pathways. Finally, anionic metals (i.e Cr in chromate ions) enter the cells through anion - - 2- 2- channels (Cl , HCO3 , SO4 , HPO4 ) (Simkiss and Taylor, 1995).

After entering the cells metals may either transfer to sites of increased binding strength and accumulate or follow various paths of elimination (excretion). In organisms with blood systems metals are transported via hemolymph compounds (pigments such as hemocyanin, hemoglobin and hemerythrin, or hemocytes or the hemolymph itself). The subsequent storage of metals occurs in tissues rich in or capable of synthesizing metal binding ligands. The nature and quantities of the ligands are species dependent and include various types of granules and metal binding proteins (i.e. metallotheionins, phytochelatin etc.). Despite the interspecific differences in storage mechanisms this takes place in similar tissues (digestive gland, hepatoncreas, liver and kidney). The elimination routes include: passive exit (desorption from external surfaces or through permeable membranes e.g. of the gills), in higher animals removal from the blood and elimination in solution (urine), metals in inert forms (granules, vesicles) from various tissues (digestive gland, hepatoncreas, cells of the alimentary tract, kidneys) are passed out in the feces or into the urine (Langston and Spence, 1995).

2.2.3.3 Bioaccumulation

Bioaccumulation is an important process through which chemicals can affect the living organisms by increasing the concentration of a chemical in a biological organism over time, compared to the chemical‘s concentration in the environment.

Accumulated metal concentrations are interpreted in terms of different trace metal accumulation patterns, dividing accumulated metals into two components; metabolically available metal and stored detoxified metal. Moreover, toxicity does not depend on the total accumulated metal concentration, but it is related to a threshold concentration of internal metabolically available metal (Rainbow, 2007) and is triggered when the total rate of uptake exceeds the combination rates of detoxification and excretion.

10 Metabolically available metal will be metal available to bind at the correct place to play an essential role, and presumably metal that is already bound ‗correctly‘ to play such a role, as in metallo-enzymes ( Rainbow and Luoma, 2011).

Most metal ions are usually present in tissues as divalent cations, which are free or complex to different classes of biological ligands. Trace metals typically have an affinity to sulphydryl, hydroxyl, carboxyl, imidazole and amino residues of proteins as well as to the NH and C=O groups of the protein chain backbone. Moreover, metals bind to the O and N electron donor of heterocyclic bases, the (de) oxyribose hydroxyl of nucleosides, and also to the phosphate groups of nucleotides and nucleic acids, etc (Ballatori, 2002).

For certain essential metals such as copper and iron, receptor mediated endocytotic and exocytotic mechanisms play a critical role in their homeostasis. The predominant mechanism of iron transport from blood plasma into hepatocystes and certain other cell types is believed to be the transferring receptors (Ballatori, 2002). Thus on entry, a trace metal is ‗metabolically available‘, at least until the physiology of the invertebrate interacts to excrete it or bind it to a particular molecular or site of high affinity from which the metal is unlikely to escape (Mason and jenkis, 1995).

2.2.3.4 Toxicity mechanisms of metals and regulation detoxification processes

Biologically important metals (Na+, K+, Mg2+, Ca2+,Ba2+, Li+, Mg2+) are class A elements (ionized in solution, resulting in cations with a closed cell configuration and inert gas structure).They are non polarized, highly stable and generally form weak complexes via electrostatic bonds. They are the main cations in bodily fluid electrolytes, participate in chemiosmotic and electrophysical processes and specifically Ca and Mg are incorporated in crystalline lattice structures that form protective endo and exoskeletal formations (bone, shells etc.). The ligand binding preference of these elements for anion elements follows the order O>P>N>S, but since the bonds are very weak they are very rarely found as constituents in biologically important macromolecules but rather act as cofactors of certain enzymes (Mason and Jenkins, 1995).

Class B metal cations are highly polarized and they tend to form covalent bonds with elements in anionic formations in the order S>N>P>O. Metals in this class are Ag+, Au+, Tl+, Hg2+, Bi3+, Tl3+ and also the marginal class B cations Cd2+ and Cu+. Apart from Cu they are nonessential, generally toxic even at low doses and interact with sulphydryl groups (-SH) and purine/pyrimidine (nitrogen rich) bases in proteins and nucleotides. (Mason and Jenkins 1995)

A third class of metals are called borderline and include Co2+, Cr2+, Cu2+, Fe2+, Mn2+, Ni2+, Pb2+ and Cd2+. Many of them are required in trace quantities in cells to be incorporated

11 in various macromolecules and metalloenzymes but are toxic in excess doses (Mason and Jenkins 1995). Two of them Cd and Pb have no known biological functions in animals, but some roles have been reported for Cd in marine diatoms (Lane and Morel 2000). The fact that borderline metals are required for the proper functioning of metalloenzymes is testimony of the increased strength of their binding to organic ligands in contrast to Class A metals, but there are no specific preferences for S, N or O donating groups. The roles of the metals in these molecules are stearic (stabilizing structures), catalytic in various types of reactions and mediating oxidation/reduction processes (Mason and Jenkins 1995).

The Class B elements are non essential and toxic for two major reasons. First they are relatively rare in their natural geological abundance; therefore possible uses in biological systems would be problematic in terms of acquisition and storage. The second and most important reason is their high reactivity and lack of binding specificity to organic ligands. It would be difficult if not impossible for the cells to transport a class B metal toward a target protein without the simultaneous increased risk of non specific binding to other sensitive molecules. Thus in general metals (Class B and borderline) exert toxicological activity through inappropriate non specific binding to physiologically important target molecules. The toxicity of a metal can be attributed to three general functions: a) blocking the essential functional biological groups of biomolecules, b) displacing essential metal ions in biomolecules and c) modifying the active conformation of biomolecules. In all these cases the activity or normal function of the biomolecules is either decreased or worst completely abolished and in turn important metabolic reactions are perturbed. Another important toxic effect of metals is the production of reactive radicals (Reactive Oxygen and Nitrogen species) when certain metals undergo redox cycling within the cells (Mason and Jenkins, 1995).

Metal detoxification represents a cellular or physiological process that prevents, ameliorates, reduces, reverses or eliminates a metal-induced toxic effect. The simple model describing metal requirement, toxicity and detoxification in biological systems is presented in Figure 1 (modified from Mason and Jenkins, 1995).

After entering a cell, metals are sequestered by intracellular ligands, categorized in three groups. Ligands named E induce a beneficial effect in binding metals (e.g. apometalloenzymes that require a specific essential metal for activation and functioning), ligands T cause an adverse physiological effect that leads to cellular toxicity (the target ligands are deactivated by the non specific metal binding). Finally the group B ligands exhibit little or no enzymatic or physiological activity and thus show no direct beneficial or toxic effects, however indirectly they can either sequester or donate metals altering the prevalence of MeE or MeT complexes. The term metal homeostasis is the biological control of binding metals to the three ligand groups in ways that cellular processes are protected by either deficiencies or excess of metals. In the cases of essential metals homeostasis would involve:

12 mechanisms that lead to optimal cellular levels of MeE, increased stability constants of MeE complexes relative to MeB and MeT and excess metal being sequestered in MeB complexes reversibly and according to physiological requirement. Through the storage in MeB complexes excess metals would not be available to T ligands and cause toxicity (detoxification) and at the same time be available for transformation to MeE and prevent cellular deficiency. A possible disadvantage of maintaining stores of excess metals would be the risk of inappropriate mobilization and onset of toxic effects. In the case of non essential metals the formation of both MeE and MeT complexes leads to toxicity and only MeB complexes are beign provided that the metal is bound irreversibly with no possibility of remobilization.

Figure 1 Metal ligand reactions of detoxification/storage (MeB), toxicity (MeT) and essentiality (MeE). Non essential metals do not form MeE complexes and cannot exert beneficial effects.

The procedures mentioned above define and describe detoxification from a strictly chemical perspective but in this process there is complex biological control with a number of molecular, cellular, physiological, genetic and behavioral mechanisms involved. The terms genetic adaptation, physiological acclimation, tolerance and resistance need to be addressed. Physiological acclimation is a strategy that an exposed animal activates and uses during its life span while genetic adaptation results from the natural selection for increased resistance to a metal and is passed on to the offspring. These two processes even though very different both lead to increased tolerance and resistance. Tolerance and resistance are different quantitatively. Resistance implies an ability to oppose and withstand toxic effects, being able to return to normal pre-exposure physiological functions in the presence of a stressor. Tolerance is the ability to endure the stress and sustain metabolic functions, which are however abnormal in some ways.

To achieve resistance or tolerance organisms employ two basic strategies: they regulate the intracellular levels of metals either by preventing entrance or by immediate expulsion or they synthesize ligands that bind the metal and remove it from reactions through

13 which it could cause deleterious effects. The organisms that employ the first strategy are called regulators and their intracellular metal body levels and relatively constant within a narrow range over a broad external concentration range while those who adopt a detoxification-sequestration system are called accumulators and they have elevated body contents of metals. The two strategies and their underlying mechanisms can both be employed at the same time but in extreme cases of increased metal influx they can be overwhelmed or damaged and then non specific metal binding causes toxicity (Mason and Jenkins, 1995). The sequential order of responses to pollutant stress within a biological system is visualized in Figure 2 (modified by Van der Oost et al., 2003).

Figure 2: Schematic representation of the sequential order of responses to pollutant stress within a biological system (modified by van der Oost et al 2003)

Pollutants, including metals, originally cause disturbance at the molecular level (destruction or inactivation of important biomolecules) which in turn affects metabolic pathways causing damage to the subcellular and cellular level. Metabolic and cellular disruptions cause perturbations to vital physiological functions and/or damage to tissues and organs and finally to the organism as a whole. Thus the adverse effects can range from serious biochemical disruption to mortality. If the pollutant exposure continues and the adverse effects to the organisms are not reversed later effects at the population, community and ecosystem level take place. Deleterious effects on populations are often difficult to detect since many of these effects tend to manifest only after longer periods of time. When the effect finally becomes clear, the destructive process may have gone beyond the point where it can be reversed by remedial actions or risk reduction. Therefore due to the growing awareness that chemical data alone are insufficient to reliably assess the potential risks of complex mixtures of contaminants in the aquatic environment research has increasingly focused on biomonitoring, i.e. the regular, systematic use of living organisms to evaluate changes in environmental or water quality. In this context bioaccumulation and pollution- induced biological and biochemical effects are studied and there are increasing efforts to

14 establish early-warning signals, through the study of biomarkers, reflecting the adverse biological responses before they become irreversible (Vemberg et al., 1982, Van der Oost et al., 2003). The most important aspects of biomonitoring (organisms, biomarkers and strategies) will be discussed in the following sections.

2.2.3.5 Biomonitoring

The levels of metals in aquatic (including marine) ecosystems are available through the knowledge of the exact concentrations in waters, sediments and biota. The measurement of dissolved metal concentrations presents difficulties because they are quite low (near the limits of analytical detection), therefore require preconcentration and are liable to contamination during collection and analysis. Moreover dissolved concentrations vary greatly (over season, time and conditions) therefore their monitoring programmes need to be intensive over extended periods and ultimately become costly in time and consumables. The analysis of sediments overcomes some of these disadvantages with high and easily measured concentrations, less susceptible to accidental contamination. Also the sediments offer a degree of time integration, overcoming the effects of temporal variability in a certain marine area. On the other hand metal accumulation by sediments is much affected by sediment characteristics that vary geographically (and possibly temporally), especially particle size, carbonates and organic carbon content. The most important drawback in using water and sediment measurements to estimate potential risks to ecosystems is the fact that generally total metal contents are measured while it is the actual bioavailable fraction that is of ecotoxicological relevance. Some advances have been made by employing speciation schemes, specific extraction protocols and analysis of interstitial waters but they all still provide estimations of the bioavailable metal fractions. On the other hand metals are accumulated by many aquatic organisms to very high body concentrations, which are easily measured, not extremely liable to contamination, and provide a time-integrated measure of metal supply over weeks, months, or even years, according to the species analysed. Most significantly, the metal accumulated is a time-integrated measure of the supply of bioavailable metal and the fraction of metal of direct ecotoxicological relevance is measured unambiguously. Such organisms are called biomonitors and are used widely to establish geographical and/or temporal variations in the bioavailable concentrations of heavy metals in coastal and estuarine waters. Bulking of biological samples over time or geographically can further reduce the costs of such an environmental biomonitoring programme.

Biomonitor organisms need to fulfil the following criteria in order to be used successfully to assess contamination and impact (Langston and Spence, 1995; Jiang et al., 2008).

15 -(1)- Organisms should be relatively sedentary in order to be representative of the environment under study and localized pollution,

-(2)- They should be of widespread geographical distribution, abundant and easily indentified and collected, providing sufficient material for repetition of samplings and analysis, but at the same time occupy an important position in the food chain.

-(3)- Populations should be relatively stable based on need to re-sample throughout the year to investigate possible temporal trends and their life cycle should also be appropriate if there is need to re-sample and quantify differences between the different stages.

-(4)- They must be reasonably tolerant of a range of metal concentrations and environmental conditions and be amenable to laboratory experimentation or transplantation (in order to investigate metal kinetics). In elaboration, the organisms should be easily reproduced and grown in the laboratory and the tissue metal concentrations should be high enough to be easily measured and less prone to analytical error but not so high as to result to extensive deaths and cancellation of experiments.

-(5)- There should be reasonable correlation and clear responses between metal concentrations in some compartments of the environment (sediment/water/food) and concentrations in the tissue(s) of the selected organisms.

-(6)- Concentration factors (the degree of enhancement of metal in the organism, relative to its environment) should be similar at all sites, though in practice and the differences may be used to identify underlying factors influencing metal availability and uptake.

From the above list it is clear that in any specific study site a preliminary investigation should take place and species should not be randomly selected. There may be more factors specific to certain locations that need to be taken into account (i.e. metal sources, specific metal contamination, time variation etc.) and perhaps the available species may not fulfil all the above criteria. To gain a complete picture of total heavy metal bioavailability in a marine habitat it is necessary, to use a suite of biomonitors, reflecting metal bioavailabilities in all available sources. For example, seaweeds to estimate dissolved sources only along with suspension feeders (mussels, oysters, barnacles) to include both water and suspended matter contributions. At the same time perhaps deposit feeding organisms should also be included but again distinction between organisms protected by the sediment and interstitial waters (bivalves) or sediment polychaetes in direct contact with their environment should be taken into account. Another key feature to the choice of biomonitors is the acknowledgment of their biology (extent of production of respiratory or irrigator currents, methods of feeding, life history and breeding season, length of life, age structure of population etc.). Comparative

16 approaches in specific study sites should be assessed carefully because for even closely related species from the same location accumulated concentrations of metals may differ greatly. If any attempt is to be made to compare absolute metal bioavailabilities over large geographical distances, or between published studies in different parts of the world, it is necessary to use biomonitoring species which are cosmopolitan.

Potential cosmopolitan biomonitoring species that have been widely studied are: the mussels of the genera Mytilus (M. edulis and M. galloprovincialis) and Perna (P. viridis), oysters of the genera Ostrea (O. edulis) and Crassostrea (C. gigas), barnacles (B. amphitrite, B. uliginosus, T. squamosa), and the talitrid amphipod Platorchestia platensis (Rainbow 1995).

Biomonitoring includes some or all of the following methods. Bioaccumulation monitoring (BAM): measuring contaminant levels in biota or determining the critical dose at a critical site to assess exposure. Biological effect monitoring (BEM): determination of early adverse alterations that are partly or fully reversible (biomarkers) to assess exposure and effects. Health monitoring (HM): occurrence of irreversible diseases or tissue damage in organisms to assess effects and Ecosystem monitoring (EM): assessment of the integrity of an ecosystem (species composition, density and diversity) (Van der Oost et al., 2003). In another classification some of the indices and parameters already mentioned are studied under the terms active and passive biomonitoring, which are defined as two general approaches to assess the pollutants and their toxic effects at different levels from species to community of any ecosystem. In passive monitoring at the level of individuals accumulation of toxic substances in specimens, in organs and tissues indicative of pollution in the environment are traced and at the population level the general degradation of the ecosystem through elimination of sensitive species and reduction of biodiversity is estimated. In active monitoring the behavioural patterns of specimens, specific function of organs (movement, feeding, respiration, reproduction and neural regulation) as well as cellular and sub cellular events are studied under the effect of toxic substances but this is done with sentinel organisms from a single ―non-contaminated‖ population deployed at sites under investigation in laboratory conditions with less stress exposure (Gupta and Singh, 2011; Tsangaris et al., 2011).

2.2.3.6 Biomarkers

In the previous sections the term ‗‘biomarkers‘‘ was mentioned briefly without elucidation. In this section some general information on biomarkers will be presented along with a few more specific details on the biomarkers studied in this thesis. Biomarkers are among emerging tools for the assessment of biological effects of contaminants in monitoring programs and reveal environmental stress caused by chemical contaminants and other

17 environmental variables and thus integration of biomarkers and chemical analysis could establish links between pollution and stress.

Several definitions have been given for the term ‗biomarker‘ but the most detailed and accurate is the following. A biomarker is any biological response to an environmental chemical at the sub-individual level, measured inside an organism or in its products (urine, faeces, hair, feathers, etc.), indicating a deviation from the normal status that cannot be detected in the intact organism. Biomarkers can be subdivided into three classes: biomarkers of exposure, of effects and of susceptibility. A) Biomarkers of exposure cover the detection and measurement of an exogenous substance or its metabolite or the product of an interaction between a xenobiotic agent and some target molecule or cell and are measured in a compartment within an organism. They can be used to confirm and assess the exposure of individuals or populations to a particular substance (group), providing a link between external exposure and internal dosimetry. Usually body burdens of the exogenous substance are not considered to be biomarkers since they do not provide information on deviations related to ‗health‘ and have been termed and distinguished as bioaccumulation markers. B) Biomarkers of effect include measurable biochemical, physiological, histological or morphological alteration within tissues or body fluids of an organism that can be used to document either preclinical alterations or adverse health impairements/diseases due to external exposure and absorption of a chemical. C) Biomarkers of susceptibility indicate the inherent or acquired ability of an organism to respond to a specific xenobiotic exposure, including genetic factors and changes in receptors which alter the susceptibility of the organism to that exposure and help to elucidate variations of responses between different individuals (Van der Oost et al., 2003; Viarengo et al., 2007; Tsangaris et al., 2011)

Biomarkers may be used after exposure to dietary, environmental or occupational sources, to elucidate cause/effect and dose/effect relationships in health risk assessment, in clinical diagnoses and for monitoring purposes. They are indices of both pollutant bioavailability and early warning signals of biological responses, and have been established as intermediate documents between pollutant exposure and higher-level effects to biota. Furthermore, they provide insight into the potential mechanisms of contaminant action and effects. A pollutant stress situation normally triggers a cascade of biological responses. Above a certain threshold (in pollutant dose or exposure time) the pollutant-responsive biomarker signals deviate from the normal range in an unstressed situation, finally leading to the manifestation of a multiple effect situation at higher hierarchical levels of biological organization (Figure 3, modified from Van der Oost, 2003).

Improper application or interpretation of biomarker responses, however, may lead to false conclusions as to pollutant stress or environmental quality. Certain responses established for one species are not necessarily valid for other species and ecotoxicological

18 data obtained in laboratory studies can be difficult to translate into accurate predictions of effects that may occur in the field therefore they must always be validated with field research (Van der Oost et al., 2003).

Figure 3: Responses in organisms after pollutant exposure (modified from Van der Oost 2003)

Some criteria for the selection of biomarkers have been proposed: i) the procedure used to quantify the biomarker should be reliable (with quality assurance (QA)), relatively cheap and easy to perform, ii) the biomarker response should be sensitive enough to pollutant exposure and/or effects in order to serve as an early warning parameter, iii) baseline data of the biomarker should be well defined in order to distinguish between natural variability (noise) and contaminant-induced stress (signal), iv) the impacts of confounding factors to the biomarker response should be well established, v) the underlying mechanism of the relationships between biomarker response and pollutant exposure (dosage and time) should be established, vi) the toxicological significance of the biomarker, e.g. the relationships between its response and the (longterm) impact to the organism, should be established. Another criterion suggested and applicable to environmental monitoring of protected or endangered species is that biomarkers should preferentially be non-invasive or non- destructive. With regard to the test organism, its basic biology and physiology should be known so that sources of uncontrolled variation (growth and development, reproduction, food sources) can be minimized. (Van der Oost et al., 2003)

The choice of biomarkers employed depends on the specific objectives of each study and the characteristics of the field site, while interpretation of the results has to take in account variations in abiotic and biotic factors that influence biomarker responses. In general, phenomena are more universal on a cellular level than at higher levels of biological

19 organization so biochemical responses may be similar in a large variety of organisms (Van der Oost et al., 2003).

In brief the following large categories of parameters are used as biomarkers: (Van der Oost, 2003)

1) Biotransformation enzymes: Generally, the most sensitive effect biomarkers are alterations in levels and activities of biotransformation enzymes. They are enzymes or co- factors that catalyze the conversion of xenobiotic compounds into more water-soluble and easily excreted forms than the original. During exposure and biotransformation of contaminants these enzyme levels and activities undergo changes that can be quantified either as induction (increase) of the levels and/or the activities or inhibition (decrease or blocking). They are categorized as Phase I, II and III enzymes but providing more detail on this classification is beyond the scope of this section (Van der Oost et al., 2003).

2) Oxidative stress parameters: Regulated production of free radicals (independently existing chemical species containing one or more unpaired electrons) in higher organisms and maintenance of ‗‗redox homeostasis‘‘ are essential for their physiological health. Useful roles of free radicals include cell signalling, defence against pathogens and others. Whether they are produced purposefully or by accident they are regulated by protective antioxidant mechanisms (enzymatic and non-enzymatic). A small percent of the free radicals in cells escape the protective mechanisms and cause oxidative damage (radicals react with critical macromolecules and possibly cause enzyme inactivation, lipid peroxidation (LPO), DNA damage and, ultimately, cell death). The two major categories are the ROS and RNS (Reactive Oxygen and Nitrogen Species). The three most important reactive molecules - derived from oxygen include superoxide (O2 ), hydrogen peroxide (H2O2) and the hydroxyl radical (OH). The imbalance between the generation and the neutralization of ROS by antioxidant mechanisms within an organism is called oxidative stress. Many environmental contaminants (or their metabolites) are redox-active compounds (aromatic diols and quinones, nitroaromatics, aromatic hydroxylamines, bipyridyls and certain transition metal chelates), they have been shown to produce or assist in the production of intracellular ROS and thus enhance oxidative stress and damage. Free radicals cannot be determined directly therefore possible suitable biomarkers indicating oxidative stress are adaptive responses [increased activities of antioxidant enzymes e.g. superoxide dismutase (SOD), catalase (CAT), glutathione-dependent peroxidase (GPOX) and glutathione reductase (GRED)], concentrations of non-enzymatic compounds [e.g. reduced and oxidized glutathione (GSH and GSSG), vitamins C and E], or manifestations of oxidant-mediated toxicity such as oxidations of proteins, lipids and nucleic acids, as well as perturbed tissue redox status (Van der Oost, 2003; Valavanidis et al., 2006; Halliwell and Gutteridge, 2015; Halliwell, 2015).

20 3) Biotransformation products: Another type of biomarker is the elevation in levels of biotransformation products, such as metabolite levels in body fluids or the amount of covalent adducts formed between metabolites of biodegradable chemicals and cellular macromolecules (proteins, RNA, DNA).

4) Stress proteins, metallothioneins and multixenobiotic resistance: The stress proteins (also called heat-shock proteins, HSP) comprise a set of abundant and inducible proteins involved in the protection and repair of the cell against stress and harmful conditions. Special groups of stress proteins are the metallothioneins (MTs), which are inducible by both essential and toxic heavy metals and the P-glycoproteins of the multixenobiotic resistance (MXR) mechanism, which may be induced or inhibited by a wide variety of chemicals.

5) Neuromuscular parameters: With respect to neuromuscular functions, recent studies indicated that the ‗old‘ biomarker acetylcholinesterase (AChE), which is sensitive to organophosphate (OP) and carbamate pesticides, may be responding to low levels of contaminants, including some metals in the environment.

A few more parameters especially used as fish biomarkers are the following:

6) Haematological parameters: Several haematological parameters, especially in fish are potential effect biomarkers. The leakage of specific enzymes (e.g. transaminases) into the blood may be indicative of the disruption of cellular membranes in certain organs. Although less specific, other haematological parameters, like hematocrit, hemoglobin, protein and glucose, may be sensitive to certain types of pollutants as well. In addition, the blood levels of specific steroid hormones or proteins normally induced by these hormones may be indicative for certain reproductive effects due to endocrine disruption.

7) Immunological parameters: A large number of environmental chemicals have the potential to impair components of the immune system. Both antibody- and cell-mediated immunity may be depressed by certain pollutants, and although most research on this system has been performed on mammalian species, it may be considered a promising field to search for new fish biomarkers

8) Reproductive and endocrine parameters: The impact of xenobiotic compounds on reproductive and endocrine effects has attracted growing interest in recent years, since a decreased reproductive capability in feral fish may in the long run threaten the survival of a large number of susceptible species; these parameters certainly deserve thorough examination. Hormone regulation may be impaired as a consequence of exposure to environmental pollutants.

9) Genotoxic parameters: The exposure of an organism to genotoxic chemicals may induce a cascade of events that include formation of structural alterations in DNA, procession

21 of DNA damage and subsequent expression in mutant gene products, and diseases (e.g. cancer) resulting from the genetic damage. The detection and quantification of various events in this sequence may be employed as biomarkers of exposure and effects.

10) Physiological and morphological parameters: The actual measurement of adverse effects or of the consequences of those effects may also be used as biomarkers. Determination of adverse effects can be performed histopathologically, by investigating lesions, alterations or tumour formation (neoplasms) in fish tissues. (Van der Oost, 2003)

In the present thesis three biomarkers were determined in selected samples to complement the metal bioaccumulation data. These biomarkers are Catalase (CAT), GST Glutathione S-transferases and Acetylcholinesterase (AChE). Some basic details about these biomarkers are given in the following sections.

2.2.3.6.1 Catalase (CAT)

Catalase is an enzyme of the antioxidant defense mechanisms. The various configurations and forms of catalase (CAT‘s) are hematin-containing enzymes that facilitate the removal of hydrogen peroxide (H2O2) by catalyzing its reduction to molecular oxygen

(O2) and water. The reaction is simply presented as:

CAT 2H2O2 O2 + 2H2O

It takes place in two steps, first an oxygen atom from one molecule of hydrogen peroxide is bound to catalase by the iron atom it contains (in one of 4 porphyrin heme groups) and is extracted and the first water molecule is released in the cell. Then a second hydrogen peroxide molecule is bound to the same catalase molecule in the same way, the two oxygen atoms bound to iron form molecular oxygen which is finally released in the cell along with the second water molecule. CAT concentrations are higher in the liver of organisms inside sub-cellular organelles called peroxisomes and also in erythrocytes. Erythrocyte catalase protects organs such as the heart, the brain and the muscles which usually contain less catalase than the liver. Catalase activity is commonly assayed by spectrophotometrically measuring the rate of disappearance of exogenous H2O2. The increase in activity of catalase can be used as a biomarker of oxidative stress caused by a wide range of contaminants including organic xenobiotics and heavy metals and the attempt of the organism to minimize the damage. Since CATs in the peroxisomes of most cells are also involved in fatty acid metabolism, changes in activities may often be difficult to interpret. Therefore, CAT activities in erythrocytes may be a more appropriate marker for oxidant exposures in vertebrates (Livingstone, 2001; Akcha et al., 2000; Roméo et al., 2003; Tsangaris et al.,

22 2011; Van der Oost 2003; Switaa and Loewen, 2002; Maehly and Chance, 1954; Cohen et al., 1970; Aebi, 1984).

2.2.3.6.2 Glutathione S-transferases (GSTs) Glutathione (GSH) is a tripeptide thiol compound found in all living cells in significant concentrations up to 12mM. Numerous important cellular functions are attributed to GSH, due to the presence and reactivity of the –SH group (sulfhydryl group) and the cycling between the reduced (GSH) and the oxidized form (GSSG), assisted by four groups of catalytic enzymes (GO‘s - glutathione oxidases, GPx‘s - glutathione peroxidases and GR - glutathione reductase and GST‘s - glutathione S transferases). The major GSH functions include the regulation of the cell thiol redox status and two types of detoxification mechanisms (xenobiotic binding and antioxidant activity). Other roles have also been reported pertaining to cell proliferation and death (cellular division and apoptosis) and synthesis and modification of leukotrienes and prostaglandins. GSH is synthesized inside the cells (except in epithelial) and is then distributed both in intracellular compartments (mitochondria, nucleus) and to the extracellular space (blood plasma and bile) for utilization by other cells and tissues. The highest concentrations of GSH are found in the liver, a fact which underlines its detoxification roles. The antioxidant and conjugating functions of GSH and its accompanying enzymes are summarized in Figure 4. The antioxidant action of glutathione (GSH) is manifested through the reactions with hydrogen peroxide (H2O2) and hydroperoxides (ROOH) assisted by GSH peroxidase (GPx) from which water and alcohols are produced, respectively. Catalase can only remove H2O2 but not ROOH under normal •− physiological conditions. GSH can also react non-enzymatically with superoxide (O2 ), nitric oxide (NO), hydroxyl radical (•OH), and peroxynitrite (ONOO−). GSH disulfide (GSSG), the oxidized form of GSH, is reduced back to GSH by the reaction of GSH reductase (GR) with NADPH.

Figure 4: Glutathione as an antioxidant and xenobiotic (Bellas, 2014)

23 GSH conjugates with (binds on) various electrophilic compounds (X), assisted by GSH-S-transferases (GSTs) and mediates their excretion from the cell or renders them harmless unable to damage other biomolecules. The conjugated molecules (X) can be endogenous compounds (i.e. Reactive Oxygen and Nitrogen species already mentioned and also bilirubin, steroids and thyroid hormones) and xenobiotic compounds or their metabolites produced after entrance in the cell (organic contaminants and metals).

GST‘s are a superfamily of dimeric, multifunctional and primarily soluble enzymes. They contain two active sites in each dimer and each active site consists of at least two binding regions, one highly specific for GSH and one less specific for the electrophilic compound. The electrophilic compounds or their metabolites should possess functional groups such as -COOH, -OH or -NH2. The most common electrophilic substrates for GST‘s are quinones, aldehydes, ketones, esters, epoxides derived from PAHs, aryl halids and nitroaroamatics. GSH has also been shown to form GS–Me complexes with various metals, through its thiolate sulfur atom; moreover, redox active metal ions such as Cu(II) and Fe(III) readily catalyze the oxidation of GSH giving rise to thyil and hydroxyl radicals. The GST enzymes are mainly located in the cytosolic fraction of the liver. Most studies determine the total GST activity using the artificial substrate 1-chloro-2,4-dinitrobenzene (CDNB), which is conjugated by three of the four known GST isoforms.

Thus the toxicity of many xenobiotic compounds (organic - PAHs, PCBs, pesticides, drugs and inorganic – metals) has been shown to be modulated by induction of GSTs. Some studies have shown increase of hepatic GST‘s after toxicant exposure but most fish studies have provided inconclusive or conflicting results and GST activity is not considered to be a very successful biomarker for fish. Some studies however have found conclusive results in mussels. Furthermore, there are increased indications that the induction responses of such enzymes (Phase II toxicant biotransformation) are generally less pronounced and may be masked by natural variability factors (such as sex, maturity, nutrition, season, temperature, etc.). Still, even slight alterations of the phase II activity may be harmful to an organism and in some cases inhibition (GSH: GSSG ratio and GST levels decreased) has also been observed which could indicate that toxic effects have advanced beyond repair (Van der Oost, 2003; Bainy et al., 2006; Bellas, 2014; Bockendahl and Ammon, 1965; Voigtmann and Uhlenbruck, 1971; Kumar and Elliot, 1973; Zhu et al., 2013).

1.2.3.6.3 Acetylocholinesterace (AChE)

With respect to adverse effects to neural functions, a group of enzymes called cholinesterases (ChE‘s) have been studied as biomarkers. Two types of ChE have been widely considered, AChE (acetylocholinesterace) with high affinity for acetylcholine and BChE (Butyrylcholinesterase) with affinity for butyrylcholin. AChE is involved in the

24 deactivation (hydrolyzation) of acetylcholin at nerve endings when a nerve impulse is transmitted from one nerve cell to another, preventing continuous nerve firings, which is vital for normal functioning of sensory and neuromuscular systems. ChE‘s have been detected in body tissues of vertebrates, in various tissues and the hemolymph of mussels and insects as well as in various tissues of serpents. Significant amounts of AChE have been measured in the hemolymph, the gills and the digestive tissues of Mytilus galloprovincialis and in the brain and muscle tissues of fish. Various trace pollutants (mostly organic) have been reported to inhibit AChE activity, affecting normal nervous system function which in turn may cause adverse effects on respiration, feeding and organism behavior. Many organophosphate (OP) and carbamate pesticides have been reported as effective AChE inhibitors and more recently, ChE inhibition has also been documented by some metals (cadmium, copper, zinc and mercury). On the other hand, it has also been proposed that metal bound to the AChE enzyme can stimulate its activity at low metal concentrations. The more recent findings refer to fish and bivalve molluscs. AChE, although known in biochemical studies since the 70‘s is increasingly used and shows promise as a pollution effects biomarker. Additional research is needed to better explain the species-specific differences in the relationship between AChE inhibition and mortality and to investigate other physiological perturbations associated with AChE inhibition (Van der Meer et al., 1988; Mazón et al., 1998; Bocquené et al., 1990; Diamantino et al., 2003; Guilhermino et al., 1998; Frasco et al., 2005; Sant‘Anna et al., 2011; Payne et al., 1996; Najimi et al., 1997; Hamza-Chaffai et al., 1998).

AChE activity is usually assayed at 412 nm by the method of Ellman et al, where sample supernatant with DTNB [5,5′-dithiobis(2-nitrobenzoic acid)] are added to acetylthiocholine. Hydrolysis of acetylthiocholine occurs and the produced thiocholine reacts with DTNB and produces the yellow coloured 5 thio- 2- nitro- benzoic acid. The rate of absorption increase of the yellow color is quantified (Ellman et al., 1961; Tsangaris et al., 2007).

2.2.4 Specific/Studied metals and their toxicity

In the following paragraphs some basic knowledge on the studied metals will be presented. Metals, as already mentioned, are the natural constituents of the earth crust. The main focuses will be general information on the studied metals, levels and forms / chemical species in the environment, anthropogenic uses and finally the role these metals in biological systems, i.e. biological functions and/ or toxicity effects.

2.2.4.1 Fe (Iron)

Iron is the forth most abundant element on Earth (30% of the total mass of Earth and 80% of the Earth‘s core). In the lithosphere iron is found in oxides, sulphide minerals and in significant quantities in alluminosilicate minerals mostly in the divalent state (Fe2+). In the

25 earth surface it is rapidly oxidized to the trivalent state (Fe3+). In surface oxygenated waters iron is found mostly in the trivalent state and in particulate form (oxyhydroxides) due to the insolulibility of the iron hydroxides that keep the concentrations of the dissolved species very low (1-10κg/L). However in cases of extreme pollution or special hypoxic/anoxic environments (groundwater, hydrothermal springs, pore waters etc.) dissolved iron concentrations can reach up and even surpass 400κg/L. The typical content of iron in the crust is 4.1% and it ranges from below 0.2% in sands and limestone to 6.5% in deep sea clays.

Iron oxides have been known and used by man since prehistoric times as colouring agents (cave and rock paintings). Between 4000 and 2000BC man discovered how to smelt iron oxides and produced weapons, utensils, tools and construction materials. Iron products and its magnetic properties and the use in compasses and navigation shaped the history of the world. The main modern applications are in the iron and steel industry, in pigments, as catalysts for industrial syntheses and as magnetic pigments in electronic devices. Minor uses are as adsorbents, in jewellery, in chemicals, in animal feeds, fertilizers and soil ameliorations, in nonferrous smelting, in battery and welding electrodes, in medical applications and others.

Iron is also the most abundant trace mineral in the human body and considered as an essential element in most biological systems. About 70% of the iron in mammals is found in hemoglobin, and 5% to 10% in myoglobin. There are several other very important iron containing enzymes (peroxidase, catalase, and cytochrome-c). Therefore iron is also an important nutrient for algae and other organisms. Iron deficiency can cause fatigue, headache, irritability and lowered work performance and iron overload by repeated blood transfusions, excessive dietary intake and rare metabolic disorders can cause hemosiderosis with original symptoms similar to the deficiency. Both the deficiency and overload of iron can lead in severe cases to further damage to important organs (liver), increase the risks of other diseases (diabetes, liver cancer, heart disease and arthritis) and in extreme untreated cases lead to death (Salomons and Forstner, 1984; Alloway and Ayers, 1997; Cox 1995; Drever, 1997, Cornell and Schwertmann, 2003; Xing and Liu, 2011).

2.2.4.2 Ni (Nickel)

Nickel (Ni) is the 7th most abundant element in earth (10% of the core) and the 24th in line in the Earth‘s crust. It is found mainly in oxides and sulphide minerals.

Typical content of Ni in the crust is 80mg/kg. In sands and limestone, Ni is found in levels below 50mg/kg and in deep sea clays it can reach 250mg/kg. However in some areas around the world, with mafic and ultramafic ores (laterites, ophiolites) and their weathered particles in soils and sediments, Ni levels range from above 250 up to 3000 mg/kg. In

26 seawater typical concentrations are in the range 0.1-0.5κg/L and in fresh waters are slightly elevated to approximately 1κg/L. In natural waters Ni is usually present in the dissolved form and mainly as the free hydrated cation Ni2+ (Cox, 1995; Williams, 2000; Salminen, 2005).

Major uses of Ni are the production of stainless steel and nickel alloys, in electroplating, in production of batteries, coins, jewelry and also catalysts, chemicals and pigments. Also particulates containing enriched contents of nickel are found in the atmosphere from the combustion of fossil fuels and waste incinerators (Williams, 2001; ATSDR, 2005; Clayton, 1994).

It is considered to be an essential element for biological systems but in mammals it is known only to exist in the enzyme urease (catalyst to the decomposition of urea to ammonia). Nickel is considered to be more important in anaerobic bacteria, it is found in the co-enzyme F-430, which catalyzes the formation of methane by hydrogen and carbon dioxide. In this co- enzyme nickel is found in a ring a position similar to the one of iron in haematite (component of hemoglobin). The relatively high concentration of nickel in oil is attributed to the co- enzyme F-430 of methanogenic bacteria. Nickel is fairly toxic and exposure to powders containing nickel has been associated with lung cancer in occupational exposure cases (nickel refining and processing plants). Another very common adverse effect of nickel is contact dermatitis from jewelry and coins with some people being more sensitive and others becoming increasingly sensitive with prolonged contact to nickel containing objects (Cox, 1995).

2.2.4.3 Mn (Manganese)

Mn is ranked between the 10th and 12th position of abundance in the lithosphere and is the 4th most abundant metal. Mn exists in two valency states (Mn2+ and Mn 4+) in the natural environment and is involved in many chemical processes due to its redox sensitivity. It is found in about 250 minerals of limited occurrence (oxides, sulphides and carbonates) as a major constituent but mostly in several thousand minerals (ferromagnesian and non ferrous silicates and various oxides) as a minor substituent for Fe and Mg in the crystal lattices. Typical levels of Mn in the lithosphere have been reported from 200-400 mg/kg in sandstone to 1500mg/kg in mafic rocks and up to 6700mg/kg in deep sea clays, with an average crust content of 950mg/kg. Another very important feature of Mn is the high absorption characteristics of the oxides MnO (OH)-manganite and MnO2 – pyrolusite which act as efficient scavengers of cations and more specifically transition metals in the aquatic environment. Mn in water is not significantly hydrolyzed but found mainly as Mn2+ and in seawater complexed (25% of the total dissolved) with chlorides and sulphates. Typical levels of dissolved Mn in seawater are below 0,5κg/L. However in bottom waters and sediment pore waters Mn 2+ concentrations as high as 24mg/L have been reported due to the diagenetic

27 processes after burial (oxygen consuming degradation of organic matter, followed by reduction of tetravalent manganese in oxides to the divalent state).

Manganese is used mainly in the steel industry (deoxidizing or desulphurizing additive or alloying constituent) and also in dry cell batteries. Other lesser uses include: chemicals, pigments (manganese violet - manganese ammonium pyrophosphate complex), matches and fireworks, as contrasting agent in medicine (MRI‘s), in fungicides, in an antiknock additive in fuel (MMT) and as a disinfectant in shrimp farms (potassium permanganate).

It is an essential metal and acts as cofactor or activator of enzymatic reactions (antioxidant defenses, electron transfer processes etc.). However toxic effects of manganese to humans and biota have been recorded. Manganese targets the brain and mine workers exposed to manganese rich dust exhibited a decease called manganism (manifesting originally with irritability, aggressiveness, hallucination, difficulty with concentration and memory problems and resulting in permanent neurological disorders such as speech disturbance, compulsive actions spasms, tremors and difficulty to walk). Toxic concentrations of Mn (10-20mg/L) have been proven to cause adverse effects to neuronal transmission signals in a variety of animals (mammals, crustaceans, invertebrates and fish) because Mn mimics Ca and passes through calcium channels (Glasby, 1984; Salomons and Forstner, 1984; Gilkes and McKenzie, 1988; Cox, 1995; Alloway and Ayers, 1997; Baden and Eriksson, 2006).

2.2.4.4 Cr (Chromium)

Chromium is the 7th most abundant element on earth but the major proportion is found in the mantle and the core. In the crust it is ranked as the 21st element at approximately 100mg/kg. Chromium is most abundant in basic and ultrabasic rocks where it substitutes magnesium and also in deposits of the ore chromite (FeCrO4). In these cases of ores and in the serpentinized soils, created by their weathering, chromium content has been reported enriched compared to the crustal level and between 200 and 3000mg/kg. A special characteristic of chromium, unlike other heavy metals, is the fact the there are two stable valency states in the environment, Cr (III) and Cr (VI) in chromate salts. The interconversion of chromium (III) and chromium (VI) is controlled by several factors, including the presence and concentrations of the chromium species and oxidizing or reducing agents, the electrochemical potentials of the oxidation and reduction reactions, ambient temperature, light, sorbents, acid-base reactions, complexing agents, and precipitation reactions. In most cases of natural waters very low concentrations of both forms (III and VI) are expected (below 1κg/L) but concentrations as high as 200κg/L have been reported in ground and river waters affected by chromium containing pollution sources or by geochemically enriched

28 soils. The main uses of chromium are in metallurgy, in tannery, in production of refractory materials and in the chemical industry. Therefore wastewaters from such activities and also leachates from illegal waste and sewage sludge disposal sites are enriched in dissolved chromium and can contaminate the receiving natural waters.

Chromium is among the biologically essential elements, and associated with glucose and lipid metabolism in animals and humans. At the same time, increased chromium levels have also been documented as potential environmental and health threats. For example increased phytotoxicity occurs in serpentinized soils and many marine organisms are susceptible mostly to Cr (VI). The hexavalent form (Cr VI) is also considered considerably toxic to humans; there have been documented adverse health effects to humans dating back almost 200 years. Firstly, there were confirmed cases of dermatitis to tannery workers, and damages to the upper respiratory system by inhalation of chromate containing fumes. Finally the most serious threat is the potential carcinogenicity of chromium. Hexavalent chromium is a recognized human carcinogen via inhalation, based on elevated rates of lung cancer associated with occupational exposure in certain industries with chromium (chemical industry, electroplating and tanneries). Furthermore, there have been some indications of potential carcinogenicity of chromium to other tissues (gastrointestinal and of the central nervous system) after prolonged ingestion of contaminated water. However several studies have produced conflicting results both proving and disproving this risk, with the actual total chromium level considered as safe for ingestion to be under dispute (Frumkin and Gerberding, 2008; Guertin, 2005).

2.2.4.5 Zn (Zinc)

Zinc is one of the most common elements in the Earth's crust and is found mainly in sulphide minerals and to a lesser degree in carbonates and sillicates Typical normal concentrations in the geosphere range from 20 to 200mg/kg and the average crustal content is 75mg/kg but in some cases of enriched rocks or soils or polluted sites contents up to a 1000mg/kg have been found. Natural dissolved concentrations of Zn are reported quite low, below 0.5κg/L in seawater and below 10κg/L in fresh waters. However, severe cases of zinc pollution have been associated with mining and smelting of Zn containing ores where sediment contents can range between 3000 and 20000 mg/kg and dissolved Zn in surface and porewaters has been reported between 18 and 4000mg/L.

The most extensive and ubiquitous zinc use is the coating of steel or iron materials (galvanization) to prevent rust and corrosion. Metallic zinc is also mixed with other metals to form alloys such as brass and bronze. Other uses include addition to paint coatings, ceramics, catalysts and precipitating agents in the chemical industry, wood preservatives, dyes and rubber production. Finally zinc is used extensively in the pharmaceutical industry (vitamin

29 supplements, sun blocks, diaper rash ointments, deodorants, various crèmes and lotions, and antidandruff shampoos). Therefore significant amounts of zinc are found in treated and untreated wastewaters as well as in sewage sludge. Zinc is an essential element but it is considered to cause increased phytotoxicity but low toxicity to animals and humans. In this context the maximum permissible zinc content in sewage sludge amended soil is 300mg/kg.

Zinc is extraordinarily useful in biological systems. It is involved in many biochemical processes that support life and are required for a host of physiological functions including normal immune function, sexual function and neurosensory function (cognition and vision). Zinc is an essential component of hundreds of proteins and metalloenzymes including alkaline phosphatase, lactate dehyrogenase, carbonic anhydrase, carboxypeptides, DNA and RNA polymerases found in most body tissues. Some important functions in humans where zinc biomolecules are known to participate are listed here: (a) cell proliferation, differentiation and apoptosis; (b) immune response onset and regulation, (c) protein synthesis; (d) DNA metabolism and repair; (e) energy metabolism; (f) vitamin A metabolism; (g) insulin storage and release; (h) spermatogenesis and steroidgenesis; (i) neurogenesis, synaptogenesis and neuronal growth; (j) sequestration of free radicals and protection against lipid peroxidation; (k) cellular division; (l) signal messenger and neuro- transmission; (m) stabilization of macromolecules. Finally, there are known cases of zinc deficiencies in crops worldwide, especially on sandstone soils, but also in extreme cases of zinc deficiency in humans may lead to delayed sexual maturity or problematic growth (very short stature) (Förstner and Wittmann, 1983; Salomons and Förstner, 1984; Byerley and Scharer, 1992; Cox, 1995, Alloway and Ayres, 1997; Tepavicharova et al., 2010; Gosar and Miller, 2011).

2.2.4.6 Cu (Copper)

Copper is an element closely related to iron therefore it is found mainly in the earth‘s core. In the crust it is considered of low to average abundance. It is mainly found in the form of sulphide minerals and to a lesser degree as carbonates, oxides and as metallic copper. The average crustal content is 50 mg/kg, in uncontaminated sediments and soils reported copper levels range from 1 to 250 mg/kg, while in contaminated areas, especially near mines, reported ranges are 69-10000 mg/kg. In uncontaminated freshwaters copper concentrations are below 10κg/L while in seawater a reported range is 0.03-0.69κg/L. However in miming affected areas concentrations as high as 74κg/L, 356 κg/L and 608 mg/L have been found in surface, waste and porewaters respectively. Most copper compounds occur in +1 Cu (I) and +2 Cu(II) valence states. The main inorganic complexing ligands in waters are hydroxyl(OH-) 2- and carbonate ions (CO3 ), but organic ligands are considered to play a larger role in copper chemistry and it is estimated that the percentages of organically complexed copper are 75- 99% of the total dissolved content.

30 The main sources of copper to the environment include mining, smelting and metallurgy, domestic sewage and agricultural diffuse pollution. Copper is used mostly in alloys (brass and bronze) to produce wires, pipes, sheets and other metallic products. At normal pH ranges (6.5-8.5) no increased copper levels are expected in drinking water circulating in copper pipes. A variety of copper compounds are used as fungicides in crop protection (vine, potatoes, etc.), as algicides in ponds and water supply reservoirs in antifouling paints for vessels.

Copper is an essential micronutrient for both plants and animals, incorporated into a number of metalloenzymes involved in hemoglobin formation, drug/xenobiotic metabolism, carbohydrate metabolism, catecholamine biosynthesis, the cross-linking of collagen, elastin, and hair keratin, and the antioxidant defence mechanism. Copper deficiency in soils leads to decreased yields in several crops but increased levels in the top soil, accumulated by excess chronic fungicide use, can be inadvertently toxic and destroy newly planted vines or the soil microbial biomass. Sheep are the animals most susceptible to both copper deficiency and toxicity. Decreased copper in sheep diet causes deficiency problems (myeline reduction leading to disorders of the nervous system) and increased levels cause toxicity. Copper deficiency in humans includes normocytic and hypochromic anemia, leukopenia, and osteoporosis. It is also associated with rheumatism and copper bracelets are worn as a practical way of treatment. However, exposure to excessive levels of copper can result in a number of adverse health effects including liver and kidney damage, anemia, immunotoxicity, and developmental toxicity. Excess copper absorbed into gastrointestinal mucosal cells induces the synthesis of the metal binding protein metallothionin. Copper that eludes binding to intestinal metallothionin –SH groups is transported to the liver. The best described human Cu toxicosis disorder is Wilson disease (WD) due to ATP7B gene mutation which leads to defective Cu transportation and excretion into the bile. The main pathogenesis of cellular injury in this disease is due to the presence of excess free Cu which results in liver cirrhosis, Kayser Fleischer ring, renal tubular dysfunction and brain damage.

Copper can be taken into the body upon ingestion of water/ food and soil that contains copper or by inhalation of copper-containing dust. Some copper in the environment is less tightly bound to soil or particles in water and may be soluble enough in water to be taken up by plants and animals. In the general population, soluble copper compounds (those that dissolve in water), which are most commonly used in agriculture, are more likely to threaten your health (Flemming and Trevors, 1989; Byerley and Scharer ,1992; Cox, 1995; Alloway and Ayres 1997; Schiff et al., 2004; Tepavicharova et al., 2010, Kalita et al., 2015; Luis et al., 2016; ASTDR, 2004).

31 2.2.4.7. Al (Aluminium)

Aluminium is the third most common element of the earth's crust and the most abundant metal (8.2% average content). The most common rocks in the crust are aluminosilicate schists, from which, through natural weathering processes on the earth surface, the common aluminosilicate minerals montorilonite and kalolinite are formed. Further weathering leads to the formation of aluminium oxides and hydroxides i.e. the bauxite ore which is the main raw material used in the production of aluminium. Bauxite is refined to produce alumina from which aluminium metal is recovered by electrolytic reduction but aluminium is also recycled from scrap. Aluminium hydroxides are very insoluble in water at neutral pH but in decreased pH environments (acid rain, acid mine drainage) elevated concentrations of dissolved aluminium can occur. Usual concentrations of dissolved aluminium in waters are below 1κg/L and normal levels in various types of sediments range from below 1-4.3 (limestone and sandstones) to 8.2%.

It is extensively used in many applications, i.e. in alloys substituting steel (transportation and construction), in electrical applications substituting copper, in packaging (cans), in pigments and paints, fuel additives, explosives and propellants, in abrasives, refractory materials, ceramics, in electrical insulators, catalysts, paper production, spark plugs, light bulbs, artificial gems, glass and heat resistant fibres. Aluminium is also used in the food pharmaceutical industries (preservatives, fillers, colouring agents, anticaking agents, emulsifiers and baking powder, antacids and buffered aspirin, antiperspirants, first aid antibiotic, antiseptics, diaper rash and prickly heat creams, insect sting and bite lotions, sunscreen and suntan lotions, and dry skin products). Finally, aluminium sulphate, or minerals such zeolite and bentonite are used in water purification to assist the coagulation and precipitation of suspended particulate material. An adverse effect of this use in natural waters is the simultaneous precipitation of aluminium phosphate which could lead to the disruption of the nutrient levels in a system and ultimately of its ecological balance.

Aluminium is not considered as essential to biota and there is increasing evidence of its toxicity. Exposure to aluminium occurs mainly through ingestion and inhalation. Increased amounts of aluminium may interfere with the metabolism of phosphorus and may lead to the substitution of iron in critical molecules. Occupational exposure studies and animal studies suggest that the lungs and nervous system may be the most sensitive targets of toxicity following inhalation exposure. There is a fair amount of human data on the toxicity of aluminum following oral exposure. Symptoms and disorders of aluminium toxicity include anaemia, bone diseases and brain dysfunction. Dialysis encephalopathy syndrome (a degenerative neurological syndrome also referred to as dialysis dementia) can result from this accumulation of aluminium in the brain. This syndrome characterized by the gradual loss of motor, speech, and cognitive functions. Another neurological disorder that has been proposed

32 to be associated with aluminium exposure is Alzheimer‘s disease (Cox, 1995; Alloway and Ayers, 1997; Krewereki, 2007).

2.2.4.8 Hg (Mercury)

Among the toxic trace metals, mercury (Hg) is one of the most hazardous environmental pollutants. Mercury can be found in the Earth‘s crust mostly in the form of cinnabar (HgS) which was traditionally mined for Hg production. This mineral is not very abundant but there are significant recorded deposits in several places around the world (USA, Mexico, Italy, the Balkan Peninsula and Turkey). Mining of cinnabar is largely abandoned and the demand for Hg is met by recovering from industrial sources and existing stock piles. The average crustal content is 0.05mg/kg and ranges between 0.08 and 0.29 mg/kg have been reported for various types of natural sediments and rocks. In uncontaminated riverine and coastal aquatic environments dissolved Hg has been reported between 0.2 and 2 ng/L and in contaminated sites between 0.6 and 4 ng/L. Particulate Hg ranges are 0.04-1.88 mg/kg and 0.1-30 mg/kg in reference and contaminated areas respectively.

Hg exists in various highly toxic species, belonging to three main categories: metallic mercury, inorganic and organic. Hg in natural waters can be found in 3 oxidative conditions 0 +1 +2 Hg , Hg , Hg and partitioned among the following major species Reactive Mercury (HgR), Ionic Mercury (Hg(II)), Elemental Mercury (Hg0), Dissolved Gaseous Mercury (DGM), Methylmercury (MHg) and Dimethylmercury (DMHg). These species participate in a very complex biogeochemical cycle by both natural and anthropogenic sources.

Mercury is used in various industrial processes, such as the manufacturing of cells used in chlor-alkali plants, paints, batteries, fluorescent and energy-saving lamps, switches, electrical and electronic devices, thermometers and blood pressure gauges, pesticides, fungicides, medicines, and cosmetics. Like all other metals mercury is released in the environment by natural (weathering, volcanoes and hyrdothermism) and anthropogenic sources.

The pathways and fate of mercury in the aquatic environment are important because in waters, sediments, and wetland soils inorganic mercury is converted into methylmercury through bacterial processes. Methylmercury is highly toxic and concentrates (bioaccumulates and biomagnifies) in animals. The majority of human exposure to mercury, and the health risk that comes with mercury exposure, is from consumption of marine food, where the accumulated levels of methylmercury are many times greater than levels in the surrounding water. It is considered that methylmercury is the form of mercury most easily absorbed through the gastrointestinal tract. Not all other forms of mercury easily enter the body, even if they come in contact with it. When metallic mercury is absorbed it can stay in one‘s body for weeks or months. Most of the metallic mercury will accumulate in the kidneys, and a fraction

33 also accumulates in the brain. When metallic mercury enters the brain, it is readily converted to an inorganic form and is ―trapped‖ there for a long time. Most of the metallic mercury absorbed into the body is eventually excreted in the urine and faeces, while smaller amounts leave the body in the exhaled breath. The toxicity of mercury was made painfully public by two severe poisoning incidents, the Minamata-Niigata disease and the 1971 Iraq poison grain disaster. In the first case wastes from chemical plants containing methyl mercury were released in rivers and the sea. The local populations consumed sea food with very high accumulated methylmercury levels which led to severe nervous system dysfunctions and to more than 1000 deaths. In the second case Iraqi rural populations mistakenly consumed imported grain, not meant for human consumption that was treated with a methylmercury fungicide. The official death toll was 650 people but a ten fold number has been suggested. Mercury mostly affects the brain and attacks the central nervous system but lesser and rare effects have been observed in the kidneys, the liver the digestive tract and the bone marrow. Metallic mercury vapors or organic mercury affects many different areas of the brain and their associated functions. Symptoms of mercury poisoning are personality changes (irritability, shyness, nervousness), tremors, changes in vision (constriction or narrowing of the visual field), deafness, muscle weakness, numbness in the hands and feet, loss of coordination, loss of sensation, and difficulties with memory, limb flexion and in the most extreme cases the victims suffered from insanity, paralysis, fell into coma and died.

The case of the mercury cycle, is of high significance in the Mediterranean region because of the combination of the known geochemical ‗‗anomaly‘‘ - large cinnabar deposits and high temperatures for many months of the year. An increased temperature due to climate change arise major concerns because with increased temperature the re-emission rate of Hg increases. Furthermore, warmer temperatures cause increased rates of organic productivity and bacterial activity that may lead to faster conversion of inorganic Hg to the neurotoxic methylmercury in fresh and marine water ecosystems and littoral sediments (Takeuchi et al., 1962; Bakir et al 1973; UNEP 2013; Verta et al., 2010; Horvat et al 1999).

2.3 Protection of the marine environment

Following the Second World War, the rapid development of the chemical industry gave rise to an enormous diversity of novel products and a concurrent increase in pollutant emissions. The general awareness of the risks (to the environment and to man) of large-scale contamination grew throughout the second half of the 20th century, as a number of incidents drew world-wide attention to the dangers of these developments. For example, in the late 1950s, the adverse environmental impacts of the pesticide DDT and its metabolites were first linked with decreased population sizes of brown pelicans, bald and white-tailed eagles and other wild birds in North America and in the Baltic. In 1961, a crippling and sometimes fatal,

34 disease was found to be related to industrial mercury discharges in Minamata (Japan). Since then, scientists have shown that, even in the open ocean, large fish sometimes contain high concentrations of mercury (Roose and Brinkman, 2005). Other well-known examples include the chemical accidents of Bhopal (1984) and of Seveso (1976) and the large oil spills (e.g. Torrey Canyon, 1967; Amoco Cadiz, 1978; and Exxon Valdez, 1989., Board-ESF, 2011) that gave rise to the extensive monitoring programs of dioxins and polycyclic aromatic hydrocarbons (PAHs) in the environment. The general awareness caused by these and other incidents has led to the development of policy measures to reduce or eliminate the release of contaminants into the environment.

Worldwide, the production of chemicals is increasing with a total production volume expected to double in comparison with 2000‘ levels by 2024. About 100,000 chemicals are available on the EU market. About 30,000 of these chemicals have a production volume higher than one ton per year and have been on the market for more than 20 years. Some of these substances end up in the marine environment and may result in harmful effects on aquatic species and wildlife and, ultimately, on human health, mainly through food web transfer. Contamination of the marine environment by chemical substances gives rise to considerable concern as it may result in serious adverse effects on the structure and functioning of ecosystems, the goods and services they provide, and on human health. Unwanted chemical substances may, for example, reduce biodiversity and productivity in marine ecosystems, resulting in a reduction and depletion of human marine food resources. Chemicals can cause not only direct intoxication and obvious effects such as death of marine biota, but they can also cause more subtle adverse effects such as impairment of the reproductive, hormone and immune systems. As stated by Paracelsus (16th century) it is ―the dose (concentration) that determines if a substance is a poison‖. This means that chemicals which are toxic at very low concentrations their release into the marine environment should be prevented at all costs. Other substances - which do not cause a direct effect – may cause indirect impacts through food-chain transfer. This awareness has resulted in the development of policies and measures to protect the marine environment from chemicals (Board-ESF, 2011).

2.3.1 Study of the marine environment and monitoring programs

Environmental study and monitoring generates the critical information to understand man-made changes against a background of natural variation that is essential for the decision makers and stakeholders to provide sound stewardship of the environment. The information is used to assess the current state of the environment, to predict the future environment, and to develop strategies for adapting to environmental changes. It also allows an assessment of whether measures taken to protect the environment are effective (www.oag-bvg.gc.ca/) chemical analysis of environmental matrixes such as water and sediment is the most direct

35 approach to reveal the pollution status in the environment, while it cannot provide powerful evidence on the integrated influence and possible toxicity of such pollution on the organisms and ecosystem (Krewski et al., 2009). Contrary to organics, the inorganic contaminants such as heavy metals do not undergo the process of disintegration that reduce their concentrations or toxicity and accumulate in the aquatic organisms with significant levels. This accumulation is important both in terms of potential effects on the aquatic organisms and on the human health. For this reason, ―biomonitoring‖ programs (biological monitoring) are required to determine the temporary and permanent effects of the contaminants on the coastal regions and their ecosystems (Yarsan and Yipel, 2013).

2.3.2 Framework and legislation

There are currently a number of international frameworks and regulatory measures in place to monitor, control and reduce pressures and impacts of chemical substances on the European marine environment. National marine monitoring programs have also been implemented for a long time in most European coastal countries and normally provided the primary source of information for international programs. At this time the central European regulatory devices, the EU Water Framework Directive (WFD) and the EU Marine Strategy Framework Directive (MSFD) are incorporating the national programs of individual member states and are connecting the EU environmental information to the international programs in which Europe participates.

2.3.3. International conventions

1. United Nations Environment Program

Monitoring activities on a global scale are linked through the United Nations Environment Program (UNEP). UNEP was established as a follow-up to the 1972 Stockholm Conference on the Human Environment, as the environmental component of the UN system (www.unep.gr)

UNEP was created for a comprehensive coordinated action within the UN on the problems of the human environment. It attempts to nurture partnerships with other UN bodies, the scientific communities and organizations such as OSPAR (Convention for the Protection of the Marine Environment of the North-East Atlantic). UNEP has several water- related programs such as the Regional Seas Program (RSP). It is also one of the implementing agencies for the Global Environment Facility (GEF). The Global International Waters Assessment (GIWA), led by UNEP and 50% funded by GEF, aims to produce a comprehensive and integrated global assessment of international waters, specifically the ecological status and the causes of environmental problems in sixty-six water bodies around

36 the world, and the key issues and problems facing the aquatic environment in transboundary waters (www.unep.gr; OSPAR, 2000)

2. The Stockholm Convention

The Stockholm Convention (2001) is a global treaty which has been signed by 152 governments and which is aimed at protecting human health and the environment from persistent organic pollutants (POPs). In implementing the Convention, governments have to take measures to eliminate or reduce the release of POPs into the environment (www.pops.int)

3. The EU Water Framework Directive:

The increasing demand by citizens of European countries and environmental organizations for cleaner rivers and lakes, groundwater and coastal beaches has been evident for a considerable time. Following this demand, on 23 October 2000, the "Directive 2000/60/EC of the European Parliament and of the Council establishing a framework for the Community action in the field of water policy" or, in short, the EU Water Framework Directive (or even shorter the WFD) was finally adopted. The EU Water Framework Directive is officially confirmed by the representative opinion of all the 25 EU countries, including Greece. The directive 2000/60/EC of the European Parliament establishing a framework for the Community action in the field of water policy, lays down a strategy against the pollution of water. That strategy involves the identification of priority substances amongst those that pose a significant risk to, or via, the aquatic environment at European level. Moreover, Directive 2008/105/EC of the European Parliament on 16 December 2008 established the Environmental Quality Standards (EQS) in the field of water policy for the 33 priority substances identified in Decision NO 2455/2001/EC and eight other pollutants that were already regulated at Union level (www.ec.europa.eu/). Since, 2000, Greece has adopted the WFD and is implementing monitoring programs for monitoring the inland fresh waters and coastal seawater. The sampling positions are available in http://geodata.gov.gr/. More than 600 surveillance and operational monitoring stations refer to surface waters (inland, transitional and coastal) and 800 stations refer to groundwater. The program monitors biological, general physicochemical, and specific chemical parameters, as well as priority pollutants and morphological and quantitative data. Additional site specific and action programs related monitoring programs provide further information of investigative nature. The data and information obtained are stored in electronic data bases, including the National Data Bank of Hydrological and Meteorological Information and the National Environmental Information Network and processed for reporting, and dissemination purposes. (http://www.ypeka.gr/Default.aspx?tabid=249&locale=en-US&language=el-GR).

37 4. The EU Marine Strategy Framework Directive

The aim of the European Union's ambitious Marine Strategy Framework Directive (MSFD) is to protect more effectively the marine environment across Europe. The Marine Directive was adopted on 17 June 2008, after several years of preparation and extensive consultation of all the relevant actors and the public. It was due to be transposed into Greek legislation in 2011. The Commission also produced in 2010 a set of detailed criteria and indicators to help Member States implement the Marine Directive. The Directive aims to achieve Good Environmental Status (GES) of the EU's marine waters by 2020 and to protect the resource base upon which marine-related economic and social activities depend. The Marine Strategy Framework Directive establishes European Marine Regions on the basis of geographical and environmental criteria. Each Member State - cooperating with other Member States and non-EU countries within a marine region - are required to develop strategies for their marine waters. The marine strategies to be developed by each Member State must contain a detailed assessment of the state of the environment, a definition of "Good Environmental Status" at regional level and the establishment of clear environmental targets and monitoring programs. The Greek authorities (Ministry of Environment and Energy) are currently planning the national monitoring program and are in the process of setting the necessary environmental targets.

http://ec.europa.eu/environment/marine/eu-coast-and-marine-policy/marine-strategy- framework-directive/index_en.htm, http://www.ypeka.gr/Default.aspx?tabid=254&language=en-US)

2.3.4. Regional conventions

1. AMAP:

The Arctic Monitoring and Assessment Program (AMAP), was established in 1991 and is aimed at implementing certain components of the Arctic Environmental Protection Strategy (AEPS). The Arctic Council, established in 1996 by the eight Arctic countries (CA, DK, FI, IS, NO, RU, SE, and the US), coordinates AMAP activities. It was conceived as a program which integrates both monitoring and assessment activities in relation to pollution issues and provides information and reports on the state of the arctic environment.

2. Barcelona Convention:

In 1976, 16 Mediterranean countries and the EU adopted the Barcelona Convention for the Protection of the Mediterranean Sea Against Pollution, overarching the Mediterranean Action Plan (MAP), approved one year earlier (www.unepmap.org/). The Barcelona Convention was amended in 1996, entered into force on 2005 and is now including all 21 Mediterranean countries (AL, DZ, BA, HR, CY, EG, ES, FR, GR, IL, IT, LB, LY, MT, MC,

38 MA, SI, SY, TN, TR) and the EU. Marine pollution monitoring has been implemented in the Mediterranean region under coordination of the Program for the Assessment and Control of Pollution in the Mediterranean region (MEDPOL) of UNEP, which was established on 1974. During the initial phases of the program, the main aim was the establishment of a network of institutions involved in marine pollution work and the collection of information concerning the levels of pollution in the Mediterranean Sea, through research and monitoring. In the 1990s, national monitoring programs were established in many Mediterranean countries and coordinated MEDPOL Phase III (1996-2005) and Phase IV (2006-2013). Also during MEDPOL Phase III and IV emphasis shifted from pollution assessment to pollution assessment and control, in the framework of the Strategic Action Program.

3. Bucharest Convention:

The Convention on the Protection of the Black Sea Against Pollution (the Bucharest Convention), was signed in Bucharest in April 1992, and ratified by the legislative assemblies of all six Black Sea countries (BG, GE, RO, RU, TR and UA) in early 1994 (www.blacksea- commission.org)

4. HELCOM:

The Baltic Marine Environment Protection Commission or the Helsinki Commission, is the governing body of the Convention on the Protection of the Marine environment of the Baltic Sea Area, signed in 1992 (www.helcom.fi). HELCOM‘s main goal is to protect the marine environment of the Baltic Sea from all sources of pollution, and to restore and safeguard its ecological balance. The present contracting parties to HELCOM are DE, DK, EE, EC, FI, LV, LT, PL, RU and SE.

5. OSPAR:

The Oslo Convention (1972), also called the Convention for the Prevention of Marine Pollution by Dumping from Ships and Airplanes, entered into force in 1974. The Convention regulated dumping operations involving industrial waste, dredged material and sewage sludge. The Paris Convention, or Convention for the Prevention of Marine Pollution from Land-Based Sources, was established in 1974 and came into force in 1978. Its principal aim was to prevent, reduce and, if necessary, eliminate pollution within the Convention area (marine environment of the North-East Atlantic) from land-based sources, which are discharges from rivers, pipelines, the coast, but also offshore installations and the atmosphere. The fifteen Governments are BE, DK, FI, FR, DE, Iceland, IE, LU, NL, NO, PT, ES, SE, CH and UK. (Board-ESF, 2011)

39 2.3.5 Marine water quality criteria

2.3.5.1 General information The mission of all the above mentioned international and national legislations is to protect human health and to safeguard the natural environment. The related establishments are responsible to improve and preserve the environment; work with partners to protect human health, ecosystems, and the beauty of the environment; promote innovative solutions to environmental problems; and protect and sustain the productivity of natural resources.

A number of US federal laws were enacted for the protection of water quality, going back to 1899. EPA issued the first water quality regulation in 1975, which was revised in 1983, and the 1983 regulation is currently in use. This regulation and supporting documents provide guidance to controlling pollutants by use of numerical criteria for specific pollutants and also by biotesting of effluents and receiving waters. The first national water quality criteria were published by EPA in 1976 (US EPA, 1976) Subsequent to the 1976 criteria, the United States system evolved to provide guidelines for development of individual national criteria, and specific criteria documents were prepared for a series of important chemical pollutants. The individual vs states were charged with developing water quality standards and monitoring compliance with these standards. The criteria are guidance; state or tribal standards are legally enforceable Guidelines were developed for ‗‗whole-effluent‘‘ discharges as well as for individual chemical pollutants. Under the Clean Water Act, EPA has established national water quality criteria for 157 pollutants for the protection of aquatic life (fish, shellfish, and wild- life; recreation, coral reef preservation; marinas) and the other usages (agriculture, groundwater recharge, industry, hydroelectric power) (Russo, 2002).

Based on the WFD the pollution which causes a threat to the aquatic environment, accumulated in the ecosystem and loss of habitats and biodiversity should be identified in the most economically and environmentally effective manner. The European parliament based on the decision No 2455/2001/EC established a list of priority substances in the field of water policy that were prioritized at Union level of for inclusion in Annex X to Directive 2000/60/EC ( www.ec.europa.eu/).

2.3.5.2 Water quality Criteria

The guidelines for deriving numerical national water quality criteria for the protection of aquatic organisms and their uses (Stephan et al., 1985) are based on four kinds of possible adverse effects: acute toxicity to animals, chronic toxicity to animals, toxicity to plants, and bioaccumulation. National water quality criteria are then calculated as:

• Criterion maximum concentration = one-half of the final acute value (CMC).

40 • Criterion continuous concentration = lowest of the final chronic value, final plant value, and final residue value (CCC).

The criterion maximum concentration is intended to protect against short-term exposure to pollutants, and the criterion continuous concentration is intended to protect against continuous exposure (Table 3).

Table 3: US EPA water quality criteria for metals. The values for freshwater was considered at 100mg/l hardness

Pollutant Freshwater Freshwater Saltwater Saltwater CMC CCC CMC CCC (acute)µg/l (chronic)µg/l (acute)µg/l (chronic)µg/l Cr(III) 570 74 - - Cr(VI) 16 11 1.100 30 Cu - - 4.8 3.1 Fe - 1000 - - Pb 65 2.5 210 8.1 (MeHg)+ 1.4 0.77 1.8 0.94 Ni 470 52 74 8.2 Se - 5.0 290 71 Ag 3.2 - 1.9 - Zn 120 120 90 81

Table 4: The environmental quality standards (EQS) of WFD. All concentrations are expressed in κg/l.(1) AA is the allowable annual average value. (2)Inland surface waters encompass rivers, lakes and related artificial water or heavily modified water bodies. (3) MAC is the maximum allowable concentration. (4)Other surface waters are boundary (estuaries, lagoons) and coastal waters. The classes are defined according to harness. Coastal water is categorized in class.

Pollutant AA-EQS(1) MAC-EQC(3) Inland surface water(2) Other surface water(4) Cadmium and its (Class1) ≤0.08 (Class1) ≤0.45 compounds (Depending on (Class 2) 0.087 (Class 2) 0.45 water hardness classes) (Class 3) 0.09 (Class 3) 0.6 (Class 4) 0.15 (Class 4) 0.9 (Class 5) 0.25 (Class 5) 1.5

Lead and its compounds 1.2 14 Mercury and its compounds 0.07 Nickel and its compounds 4 34

The environmental quality standards for priority substances and certain other pollutants based on the EU-WFD are given in Table 4.

2.3.5.3 Dumping Act The Marine Protection, Research and Sanctuaries Act of 1972, also known as the ‗‗Ocean Dumping Act‘‘, generally prohibited the ocean dumping, unless it is authorized by a

41 permit. EPA is responsible for developing ocean dumping criteria and designating sites for evaluating permit applications. Criteria are based on the need for dumping; the effects of dumping on human health and welfare, on fish, wildlife, shorelines, and marine ecosystems; the persistence and permanence of effects; the effect of dumping particular volumes and concentrations; and the effect on alternate uses of oceans (e.g., fishing, scientific study); designation of sites beyond the Continental Shelf wherever feasible (Russo, 2002).

2.3.6. Sediment Quality Guidelines

2.3.6.1 General information

Sediments accumulate contaminants and serve as sources of pollution to the ecosystems. They are connected with pathogens, nutrients, metals, and organic chemicals tend to absorb both inorganic and organic materials that eventually settle in depositional areas. If the loading of these contaminants into the waterways is large enough, the sediments may accumulate excessive quantities of contaminants that directly and indirectly disrupt the ecosystem, causing significant contamination and loss of desirable species through trophic transfer in the food webs. These repositories of contaminants were once ignored when the major problem of pollution was easily identified in the industrial discharges. As industrial discharges have improved in quality, diffuse sources of pollution, such as storm water runoff and sediments, are recognized as long-term, wide- spread sources of pollutants to aquatic systems. Furthermore, once contaminants are bound to a particle surface or absorb into its interior matrix, they become less likely to be bio-transformed and desorption is usually very slow; therefore, absorbed contaminants will reside for long periods in the sediment (Allen & Burton, 2002). Once the chemical contamination concentration reaches a point at which it causes adverse effects to biota, it is considered polluted. Sediment-associated contaminants are found in every type of aquatic environment from mountain streams to large rivers, from small lakes to the Great Lakes, and in estuaries and bays (UNEP, 2000).

2.3.6.2 Sediment quality criteria

Sediment criteria (i.e., guidelines) have been developed to deal with many environmental concerns and in response to regulatory programs. These have included determinations of whether sediments are contaminated in the context of restricted disposal of dredged materials, cleanup of industrial and municipal sites, effluent contamination, spatial extent of contamination in an area, ecological or human risk, fish tissue contamination, ranking of problem sites, and beneficial use impairments (Burton, 2002). Numerous approaches have been used in order to protect aquatic organisms living in or near the sediments from the toxic effects associated with sediment-bound contaminants (Christophoridis at al, 2009).

42 They include sediment quality criteria, sediment quality objectives and sediment quality standards. These guidelines are useful for the evaluation of spatial variations of sediment contamination, the classification of the contamination state of the sediments, the design of monitoring programs, interpretation of historical data, and for environmental assessments for future remedial actions etc. Sediment quality guidelines (SQGs) are developed using a variety of approaches, such as effect-range approach, effect-level approach and apparent effect- threshold approach. The selection of the most appropriate SQGs is not trivial; each derived numerical value may differ significantly based on the derivation procedure.

The most widely used SQGs for marine sediment samples, have been developed by the U.S. National Oceanic and Atmospheric Administration (NOAA) and they include sets of effect-range guidelines derived from a large series of chemical and biological data collected from North American coastal regions that incorporate field and laboratory data from many different methodologies, chemical and biological species. These are based on the co- occurrence of benthic macroinvertebrate effects and total sediment concentrations. These approaches include the effects range approach (Long and Morgan 1991; Ingersoll et al. 2000), effects level approach, apparent effects threshold approach (Cubbage et al. 1997) and screening level concentration approach (Persaud et al. 1993) (Table 5). These approaches generally threshold levels set as follows:

 One below which effects rarely occur [e.g., the lowest effect level (LEL)  Threshold effect level (TEL)  Effects range low (ERL)  Minimal effect threshold (MET)  Threshold effect concentration (TEC)  One above which effects are likely to occur [e.g., the severe effect level (SEL)  Probable effects level (PEL)  Effect range median (ERM)  Toxic effect threshold (TET)  Probable effect concentration (PEC)

The reliability of these thresholds is heavily dependent on the size of the database, because effect levels are a function of the contaminant-effects distribution. Chemical concentrations corresponding to the 10th and 50th percentiles of adverse biological effects were called the Effects- range-low (ERL) and Effects-range-median (ERM) respectively. (Allen & Burton, 2002; Christophoridis et al., 2009; Pekey, 2006).

Two sets of SQGs developed for saltwater (MacDonald et al., 1996), the effect range- low (ERL)/effect range-median (ERM) values and the threshold effect level (TEL)/probable

43 effect level (PEL) values are other sediment quality criteria to evaluate the toxicity significance of metal concentrations in sediments.

Table 5: Threshold effect sediment quality guidelines for metals (mg/kg)

SQG As Cd Cr Cu Pb Hg Ni Zn Ref

TEL 5.9 0.6 37.3 35.7 35 0.17 18 123 MacDonald et al., 2000

ERL 33 5 80 70 35 0.15 30 120 MacDonald et al., 2000

LEL 6 0.6 26 16 31 0.2 16 120 MacDonald et al., 2000

MET 7 0.9 55 28 42 0.2 35 150 MacDonald et al, 2000

CBTEC 9.79 0.99 43.4 31.6 35.8 0.18 22.7 121 MacDonald et al., 2001

EC-TEL 7.24 0.68 52.3 18.7 30.2 0.13 15.9 124 Smith et al.,1996

NOAA ERL 8.2 1.2 81 34 46.7 0.15 20.9 150 NOAA, 1999

ANZECC ERL 20 1.2 81 34 47 0.15 21 200 ANZECC, 1997

ANZECC ISQC-LOW 20 1.5 81 65 50 0.15 21 200 ANZECC, 1997

SQO-Netherland Target 2.9 0.8 36 85 0.3 140 Swartz, 1999

Hong Kong ISQV-LOW 8.2 1.5 80 6.5 75 0.15 40 200 ANZECC, 1997

SQG, Sediment quality guideline; TEL, threshold effect level; ERL, effects range low; LEL, lowest effect level; MET, minimal effect threshold; CB, Consensus Based; TEC, threshold effect concentration; EC, Environment Canada; NOAA, National Oceanic and Atmospheric Administration; ANZECC, Australian and New Zealand Environment and Conservation Council; ISQG: Interim Sediment Quality Guidelines Other sets of sediments quality guidelines are mentioned below in Tables 6 and 7.

Table 6: Midrange effect of sediment quality guidelines for metals (mg/kg) SQ

SQG As Cd Cr Cu Pb Hg Ni Zn Reference

PEL 17 3.53 90 197 91.3 0.47 36 315 MacDonald et al.,2000

ERM 85 9 145 390 110 1.3 50 270 MacDonald et al.,2000

EC-PE1 41.6 4.21 160 108 112 0.71 51.6 410 Smith et al.,1996

NOAA ERM 70 9.6 370 270 218 0.71 51.6 410 NOAA 1999

SQO-Netherland 55 2 36 530 0.5 480 Swartz 1999 Target

Hong Kong ISQV- 70 9.6 370 270 218 1 410 Chapman et al. 1999 HIGH

Norwegian Moderate 80 1 300 150 120 0.6 130 700 Helland et al.,1996

SQG, Sediment quality guideline; PEL, probable effects level; ERM, effect range median; EC, Environment Canada; NOAA, National Oceanic and Atmospheric Administration; SQAV, Sediment Quality Advisory Value; SQO, Sediment Quality Objective; ISQV, Interim Sediment Quality Value.

44 The sediment either rarely ( ERM)(Christophoridis et al., 2009).

Table 7: Extreme effect sediment quality guidelines for metals (mg/kg)

SQG As Cd Cr Cu Pb Hg Ni Zn Ref

TET 17 3 100 86 170 1 61 540 MacDonald et al.,2000

SEL 33 10 110 110 250 2 75 820 MacDonald et al.,2000

CB-PEC 33 4.98 111 149 128 1.06 48.6 459.9 ANZECC 1997

SQO-Netherland 55 12 - 190 530 10 - 720 MacDonald et al.,2000 Intervention

SQG, Sediment quality guideline; TET, toxic effect threshold; SEL, severe effect level; CB, Consensus Based; PEC, probable effect concentration; SQO, Sediment Quality Objective; SQAL, Sediment Quality Advisory Level.

2.3.7 Seafood quality criteria

There are around thirty chemical elements that play a vital role in various biochemical and physiological mechanisms in living organisms, and recognized as essential elements for life. In fact, for many food components, the intake of metal ions can be a double edged sword. Majority of the known metals and metalloids are very toxic to living organisms and even those considered as essential, can be toxic if present in excess. For the maintenance of health, a great deal of preventative measures is in place to avoid ingestion of potentially toxic metal ions. From monitoring endogenous levels of metal ions in foods and drinks to detecting contamination during food preparation, European countries spend significant resources to avoid metal intake by the general population (Sarkar, 2006 ; Mudgal et al., 2010).

In view of avoiding undesirable health hazards consequent of "excessive" intake of toxicants (including toxic metals), international and national scientific organisms such as FAO/WHO, FDA, European Union, etc have used the safety factor approach for establishing acceptable or tolerable intakes of substances that exhibit threshold toxicity. The acceptable daily intake (ADI) or tolerable daily intake (TDI) or provisional tolerable weekly intakes (PTWI) are used to describe "safe" levels of intake for several toxicants including toxic metals (Sarkar et al., 2006; Mudgal et al., 2010). For chemicals that give rise to such toxic effects, a tolerable daily intake (TDI), i.e. an estimate of the amount of a substance in food, expressed on a body weight basis (mg.kg-1 or mg.kg-1 of body weight) that can be ingested over a lifetime without appreciable health risk (Kroes and Kozianowski, 2002). An average human body weight of approximately 70kg was assumed.

The permissible dose of heavy metals consumption by humans according to (JECFA 2003., FAO ,1993; WHO, 1989) is as follows

45 Hg: 5 κg/kgb.w. = 350 κg/person/week = 0.35 mg/person/week Cd: 7 κg/kgb.w. = 490 κg/person/week / = 0.49 mg/person/week Pb: 25 κg/kgb.w. = 1750 κg/person/week = 1.75 mg/person/week Cu: 3500 κg/kgb.w. = 245000 κg/person/week = 245 mg/person/week Zn: 7000 κg/kgb.w. = 490000 κg/person/week = 490 mg/person/week Ni: 35 κg/kgb.w. = 2450 κg/person/week = 2.45 mg/person/week Fe: 5600 κg/kgb.w. = 392000 κg/person/week = 392 mg/person/week Mn: 980 κg/kgb.w. =68600 κg/person/week = 68.6 mg/person/week

In addition to the above calculation for obtaining the limit of heavy metals intake in seafood, there are some other standards which mark the maximum level of metals. Based on the European directive 629/2008 (European Union 2008a) the maximum concentration of Hg, Cd, Cu are 1, 1, 0.5 respectively, as for Cu and Zn the maximum level are 30 and 30 (mg/kg 100 ww), (FAO ,1993.,WHO, 1989) , respectively. According to the Food Standards Australia New Zealand (FSANZ, 2004) the Maximum (ML) and general Expected level (GEL) (mg/kg)) of Hg, Cd, Cu and Zn are 0.5, 5, 5 and 5, respectively in the soft tissue. Furthermore, the concentrations above; 10.2 (µg/g wet wt) of Fe (USDA, 2009), 0.15 (µg/g wet wt) of Mn (USDA, 2009) and 0.5 of Cr (µg/g wet wt) (European Union (2008b) and 1(µg/g wet wt) of Ni are announced toxic for human consumption (Kalantzi et al., 2013; Percin et al., 2011).

3. Study area

3.4 Information on the study area

3.4.1 General information

The North Evoikos Gulf is an elongated embayment in the central Aegean Sea (Eastern Mediterranean Sea), featuring a distinct NW-SE orientation and a total surface of approximately 390 km2. It is located between the Greek mainland, to the west, and the island of Evia to the east connected to the west Aegean Sea through the Oreos Channel and to the southern part of Evoikos Bay through a 40 m strait (Drinia and Anastasakis, 2012; Voutsinou-Taliadouri and Varnavas, 1993). In the southern part of the bay the seafloor dips gently, whereas towards the Evioa coasts it is very steep. A basin with a maximum depth of 421 m exists in the northern part of the bay. Measurements of water current velocities show that the north easterly surface water movements usually have a velocity of 5-15 cm/s, with a maximum value of 32 cm/s. Subsurface and bottom water currents have velocities less than 5 cm/s. Residual currents in the surface layer are of the order of 4-11 cm/s, whereas in the subsurface and bottom layers they are of lower velocities. In the surface layer only minor diurnal variations in the direction of the currents are observed, these having a more or less

46 constant north-easterly direction. In contrast, considerable diurnal variations are observed in the direction of the bottom currents (Balopoulos and Papageorgiou, 1989).

The North Evoikos is influenced by a relatively coldwater mass entering from the Oreos channel (Balopoulos and Papageorgiou, 1991). Although no significant tidal currents are known in the Mediterranean Sea, in Evoikos Gulf a peculiar strong tidal current, known since Aristotle's time (350 B.C.), occurs, which has been the subject of considerable study since the early 20th century (Livieratos, 1978). It propagates between the Oreos strait to the north and Petalia Island to the south in a NW-SE direction, being very strong at the Evripus Strait, where it reaches a maximum speed of 12 km/h. The tidal current reverses its course every 6 h. (Variagin, 1972; Livieratos, 1978). The geodynamic regime of central Greece which is characterized by active crustal extension during the Quaternary is responsible for the formation of the North Evoikos Gulf during the Pleistocene. The graben of North Evoikos Gulf is bound by a series of WNW-ESE to NW-SE fault zones which are active since early Miocene (Roberts and Jackson, 1991). The activity of these fault zones has strongly influenced the landscape evolution of the region as well and instrumentally recorded seismicity (Papaioannou et al., 1994; Papanastassiou et al., 2001).

The prevailing oceanographic conditions in northern Evoikos Bay, suggests that this sea is a favourable area for the formation of placer mineral deposits. Large volumes of magmatic and metamorphic rocks, including large ophiolitic masses, and a variety of ore deposits (e.g. Fe-Ni- ores, magnesites, chromites, manganese ores) are exposed on Euboea at a short distance from the coast (Voutsinou-Taliadouri and Varnavas, 1993).

3.4.2 Pollution in Evoikos Gulf

At the southern part of Evoikos near the Euripos strait is located the city of Chalkis with a resident population of about 51000 inhabitants. This area is significantly affected by anthropogenic activities as it receives large amounts of domestic and industrial wastes; several industries such as cement, textile, paint, food, metal-forming and ceramic factories, shipyards etc are located along the coastal zone. Transport (navigation and road traffic) is a significant source of pollution in the Evoikos gulf. Furthermore, some drainage canals from cultivated agricultural lands also contribute to the pollution through runoff. Fish farming has also been expanding in this area, introducing food remains, drug treatments and antifouling paints. However, the most important source of pollution located in the N. Evoikos Gulf, which is the main focus area of the present thesis, is the operation of one of the biggest smelting plant in Greece and Europe (LARCO). The smelting plant is located in Larymna Bay. LARCO is a leading mining and metallurgical company of Greece and among the biggest of this type in Europe (Kontos & Zevgolis, 2004). A significant amount of LARCO‘s by-products (slag), enriched in Fe, Ni, Cr & Mn are deposited daily into the Gulf in the form

47 of slag since 1969. Aside from the direct deposition of slag to the sea there is significant air- borne pollution due to various emissions of metal enriched dust and these two sources make this area one of the most important pollution hot spots in Greece.

The slag has been deposited in a permitted marine area of the N. Evoikos, 8km away from the smelting plant. The deposition area was marked out by the Greek authorities in 1969 and has been used ever since. The cooled and granulated slag is loaded from the cooling tanks of the industry to 500-tonnage transport barges, are then conveyed to the disposal area and released at a slow speed through the opening at the bottom of the cargo. Approximately 6000tn of slag are discharged daily in the designated area, but the specific co-ordinates of dumping and the exact daily loads are not publicly available by the company (Scoullos and Dassenakis, 1983; Dassenakis 2002, Michalopoulos et al., 2005; Balomenos et al., 2013).

The offshore deposition area has been regularly monitored since 1985. The regular monitoring was a requirement imposed by authorities to the company in order to continue dumping the slag. Metal levels in waters and sediments have been measured as part of official monitoring projects or isolated research campaigns.

The Hellenic Centre for Marine Research (HCMR) was responsible to assess the effects of slag dumping on the entire ecosystem since 1983 and monitor the bioaccumulation of the heavy metals in the marine organisms (IOFR, 1985; HCMR, 1992, 1998, 2000, 2001, 2002, 2005, 2007- 2008, 2010, 2011, 2012, 2013a, 2013b, 2014, 2015a, 2015b, 2015c). Metal kinetics and bioaccumulation have been studied in the coastal molluscs, algae angiosperms (Bordbar et al., 2015; Kozanglou and Catsiki, 1997; Nicolaidou and Nottt, 1998; Nott and Nicolaidou, 1990) and fish farm fish. Environmental effects of dumping include the enrichment of metals in the bottom sediment (Voutsinou-Taliadouri and Varnavas, 1987; Voutsinou-Taliadouri et al., 1993) and the flux of metal elements from the sediment to pore water and the overlying seawater (Kersten and Anagnostou, 1994, Scoullos and Dassenakis, 1983; Dassenakis, 2002; Michalopoulos et al., 2005). In addition, the effects of metallic dumping coarse material on the benthic communities of this area was investigated regularly (HCMR, 2011-HCMR, 2012;Simboura et al., 2007). However, there is not sufficient and updated information on the impact of pollution on the benthic communities in both the coastal zone and the dumping area. The most important issue is continues daily dumping of slag since 1969 which led to the formation of a deposit on the sea floor in the authorized are. The thickness of slag is varied and in some places it exeed of 2.5 m (Simboura., 2008).

3.4.3 LARCO GMMSA; Mining and Metallurgical Plant 3.4.3.1 General information LARCO in Larymna Bay in N. Evoikos Gulf is a leading mining and metallurgical company of Greece and among the biggest of this type in Europe. It is the only exploitation

48 vehicle of the nickeliferrous deposits in Greece and the only producer of ferronickel from domestic ores (Kontos and Zevgolis, 2004). LARCO was founded in 1963. The company‘s main activity is the production of nickel –in the form of ferronickel granules- with 17 - 26% Ni content. On a yearly basis approximately 2.000.000tn of ore are processed, 20.000tn of product are yielded and 280.000tn of by-product slag are also produced. A portion of the slag by-product is sold as heavy aggregate and the rest is deposited in N. Evoikos has already mentioned. (Kontos and Zevgolis, 2004; Balomenos et al., 2013; Larco.gr).

3.4.3.2 Mines and by-products

Nickel is extracted through both underground and surface mining. The mines are Evia mine: There are five operational surface mines at Evia. The Annual production is 1.2 to 1.5 million tons of ore. Average nickel concentration is 1%-1.03%. Agios Ioannis mine: The Neo Kokkino mine is 7km from the smelter at Larymna and the oldest mine. The annual production is 700,000 tons of ore (magnetic separation), from which about 60,000 tons of high nickel grade ore - 1.2 to 1.3% nickel - is extracted Kastoria mine: It was founded in the 1990s and is located near the Albanian border. Surface mining is employed. Annual production is 250,000- 300,000 tons of high nickel grade ore. Servia mine: The lignite mine was build at Servia in 1976. The lignite is surface mined and its annual production is approximately 250,000 tons of lignite, depending on the factory's needs from the Lava deposit.

Table 8: Weight percentage of slag Composition [(A) IOFR (1985), (B) Zaharaki et al (2009), (C) Balomenos et al., (2013)]

Chemical compositions Weight content%(A) Weight content% (B) Weight content% (C) FeO 2.7 - -

Fe2O3 41.1 43.74 39.78 Fetot 33.9 - - Ni 0.193 0.11 - Co 0.025 0.02 -

SiO2 33.6 32.74 38.27 CaO 3.3 3.73 3.47 MgO 3.4 2.76 5.13

Al2O3 8.9 8.32 9.69

Cr2O3 4.3 3.07 2.47 S 0.16 0.18 -

Mn3 O4 - 0.44 -

49 The by product of the smelting plant slag consists of loose and sand-size material, the average specific gravity of which is 3.6 g/cm. The mean diameter of the slag grains is 0.25 w and the sorting coefficient is 0.7 w (Voutsinou-Taliadouri and Varnavas, 1987). The chemical composition of the slag is given in Table 8. It has been proven that the slag undergoes some leaching by seawater leading to a selective fractionation between the metalliferous particles (Voutsinou-Taliadouri and Varnavas, 1987; Kersten and Anagnostou, 1994; Simboura et al., 2007).

3.4.4 Information on marine currents in N.Evoikos gulf

There is not enough information on the currents direction in this area. The only information was found from the Technical report of HCMR from 2005. Figure 5 illustrates a short time of currents direction in the slag dumping area from 20 and 50 meters depth.

Currents in 20m depth Currents in 50 m depth

Figure 5: the direction of currents from 20 and 50 (m) depth from slag dumping area.

3.5 Scope of the study

The Greek primary metallurgical production (LARCO) after more than 45 years of operation faces significant challenges in respect to the handling and disposal of their by- products. In line with other researches in this area, this interdisciplinary study was done to demonstrate and assess the updated pollution state of this area and the impact of several chemical and biological factors. An additional number of secondary scopes of this study include: 1. To investigate the variation of dissolved and particulate heavy metals concentration in seawater from both the slag dumping and reference area (offshore).

50 2. To investigate the variation of the dissolved and particulate heavy metals concentration in seawater from the area around the smelting plant (inshore). 3. To assess the bioaccumulation of metals in different organs of the benthic crustaceans collected from the slag dumping and reference area. 4. To evaluate the variation of bioaccumulated metals in two different native gastropods collected from the coastline near the smelting plant and the spatial distribution of metals through different seasons. 5. To determine the possible effects from the smelting plant operation on the function of antioxidant biomarkers in different organs of selected crustacean, cultivated fish and gastropods from the area.

51 4. MATERIALS AND METHODS

4.1. Strategy of this study

4.1.1 Study Area

In order to investigate the long term effects of the operation of the smelting plant LARCO on the marine environment of the N. Evoikos gulf, as well as the impact of its by- products dumped offshore for the last 50 years, a biogeochemical study was carried out between 2009 and 2011 and is presented in this thesis. The research activities undertaken were focused on both the coastline and the offshore area of slag deposition.

A) Inshore (coastal) area:

A long distance of coast line was selected, starting from east of the smelting plant in Larymna bay and continuing toward the northeast part of the Gulf. The status of the coastal environment was studied by the analysis of environmental (seawater and biota) samples collected from characteristic stations. Samplings were repeated for four seasons.

B) Offshore area:

Slag is deposited in marine area located about 8 km away from the coast line and the smelting plant after specific legal permission. This area was studied by the collection of different environmental samples (seawater, sediments and biota). Since the slag deposition area is quite wide (around 30 km2) the samples were collected from a network of stations for three years (2009-2010-2011). .

4.1.2 Studied Parameters

The available older research papers on this particular area and the technical reports of some monitoring projects have shown that the main impact to the marine environment is by metals from the operation of the smelting plant (raw materials and by products). Therefore in the present work two categories of heavy metals were chosen for measuring in abiotic and biotic samples A) the ones related to the smelting plant activities such as Fe, Ni, Mn and Cr and B) those related mostly to other anthropogenic activities such as Zn and Cu. Despite the fact that the toxic metals Pb and Cd are considered as priority contaminants for EU environmental legislation they were not included in this study because the past studies have shown that the measured levels were very low. In addition, in the sediment samples, Al and Hg along with the percentage of total organic carbon (TOC%) and the percentage of carbonate (CO3%) were selected to be assessed. There is not clear information about the concentration of Hg, TOC% and CO3% in the area from the past studies, therefore in this study these materials was measured in order to assess the area better.

52 In the biotic samples, three biomarkers Acetylcholinesterase (AChE), Catalse (CAT) and Glutathione-s -transferase were also analyzed to estimate the response of environmental pollution to the physiology of local species.

4.1.3. Sampling stations

A) Inshore stations Seven stations located at regular distances were chosen for biota and seawater collection ensuring sufficient coverage along the coastline of Larymna bay. Furthermore the two reference stations, about 17 and 19 Km southeast of Larymna, were chosen sufficiently far enough to avoid any influence from the smelting plant and provide background metal levels in a relatively unaffected area (Figure 6).

Figure 6: Location of the inshore sampling stations.

Generally, the coastline of the studied area is a rocky shore. Further details for the sampling stations are provided below and the co-ordinates are presented in Table 9.

LA1: This station was about 5 km away from the smelting plant and also far from any other anthropogenic activities.

LA2: This station is located about 2 km south of LA1 and near a fish farm (―Lagonisi‖) LA3: This is a coastal site mostly used for recreational activities such as swimming and boating. An ephemeral stream discharges into the Evoikos Gulf at this station.

53 LA4: This station is located in the village of Larymna, in front of the smelting plant.

Table 9: Information of the coordinates of inshore stations.

Stations latitude Longitude LA1 23.3238 38.5917 LA2 23.30648 38.58433 LA3 23.2978 38.5776 LA4 23.28785 38.5678 LA5 23.30253 38.5674 LA6 23.31235 38.5674 LA7 23.31575 38.56325 R1 23.49119 38.486 R2 23.42241 38.49731

LA5: It is a station east of the smelting plant, close to residences of the LARCO workers and near a fish farm.

LA6: About 600 m east of LA5 this stations is also close to fish culture activities. LA7: This last station is located on the coast line of Larymna Bay and near a fish farm. R1 & R2: These were chosen as reference stations since they are about 17-19 km far from the smelting plant. Both stations were located on rocky shores. Furthermore station R1was located near a residential place (some villas)

B) Offshore stations The slag dumping area was adequately covered by the collection of environmental samples at four stations (L8, L10, L12 and L14), that all are considered as contaminated. For biota samples the collection was performed by bottom trawl, and the path is designed by a line in Figure 6. The reference area (R) was chosen about 8 Km north of the slag dumping area boundaries. All stations are the same ones established by the HCMR for previous monitoring projects in order to ensure a level of comparability and are presented in Figure 7, while stations coordinates are given in Table 10. It is worth mentioning that the slag dumping area on the map is not symbolizing the licensed area defined by the authorities. The Licensed authorized area is much extensive and the dumping area on the map is only determining the area that the samples were taken.

54

Figure 7: Location of the offshore stations. A= trawl in the contaminated area. B= trawl in the reference area, points= environmental samples stations, dashed square=slag dumping area where the sampling took place.

Table 10: Coordinates and depth of the offshore sampling stations.

Station Longitude Latitude Depth (m) R 23. 1950 38. 3904 70 L8 23. 2530 38. 3700 85 L10 23. 2230 38. 3440 77 L12 23. 2220 38. 3550 73 L14 23. 2150 38. 3700 69

4.1.4. Samples

Seawater Determination of dissolved and particulate metals in seawater was done from 2 depths in offshore stations (surface and bottom) and surface water from the inshore stations Metals in water bodies, as a result of natural or anthropogenic pollution, are found in the dissolved or particulate form (particles larger than 0.45κm). The suspended solids in the water are a mixture of different materials, resuspended sediment, colloids, fecal material, organic decomposition materials, etc., which can easily adsorb metals from the water column and could constitute a source of contamination and toxicity for aquatic organisms, especially

55 filter feeders (Salomons and Forstner 1984; Scoullos et al., 2007; Riley and Taylor, 1968; Kingston et al., 1978).

Surface sediments: Total metal content and weakly bonded metals (labile)

Sediments accumulate contaminants and under certain conditions can serve as secondary sources of pollution to the ecosystems, causing significant environmental pressure. However, assessing the total concentration of metals in the sediments does not provide the biologically available fraction of heavy metals. In most sediments most of the metal content is held in the lattice of mineral particles and is not readily available, while the labile fraction of metals in more readily exchangeable forms can potentially be released from the sediments to become bioavailable to the organisms and ultimately a threat to the ecosystems (Salomons and Forstner 1984; Davidson, 1994; Pickering, 1986; Long, 1995, Nichols, 1991). In accordance to these, in the present study, both the total content and the labile fraction of the selected heavy metals were measured in the area.

Biota

Heavy metals were measured in three different species of crustaceans from the offshore area and two species of gastropods from the inshore stations.

Biomarkers

Biomarkers are considered useful tools and are increasingly incorporated into environmental monitoring programs. Three biomarkers: Acetylcholinesterase (AChE), Catalase (CAT) and Glutathione-s -transferase were analyzed in this study. AChE is a crucial enzyme in the nervous system of vertebrates and invertebrates, which is commonly associated with exposure to organophosphate or carbammate pesticides and metals. CAT is an efficient enzyme in the cells of plants, animals and aerobic (oxygen requiring) bacteria. It promotes the conversion of hydrogen peroxide to water and hydrogen. GST is a multi- isoform family of enzymes that detoxify endobiotic and xenobiotic compounds and play an important role complimenting the antioxidant defense system of the cell (Lam, 2009).

4.1.5 Biotic species information

Depending on availability in the studied area and their ecological or commercial importance six different aquatic species were chosen to conduct for the planned experiments. Two species of gastropods (Phorcus turbinatus and Patella caerulea) were collected from the inshore area.). As for the offshore area, 4 species were chosen for both heavy metal and biomarkers analysis. Three species of crustaceans including; Munida rugosa, Liocarcinus

56 depurator and Nephrops norvegicus were taken from both the contaminated and reference areas of the study. Finally, Sparus aurata individuals were obtained from a local fish farm for biomarker analysis. All the selected species are native and are of the most abundant in Mediterranean Sea. The selection criteria for bioindicator organisms already mentioned in introduction applied for both the gastropod species from the inshore area and the crustacean species from the offshore area.

4.1.5.1 Inshore species:

4.1.5.1.1 Phorcus turbinatus

Scientific name: Phorcus turbinatus (Born, 1778)

Phylum Mollusca Class Gastropod

Subclass Vetigastropod Superfamily Trochoidea

Family Trochidae Subfamily Cantharinae

Genus Phorcus Species turbinatus

Phorcus turbinatus is widespread in the upper layers of the littoral zone of the Mediterranean Sea Basin; i.e., the Aegean Sea, the Tyrrhenian Sea, the Ionian Sea, the Adriatic Sea, and the Sea of Crete. The high density and abundance of this species testifies its adaptation to different environmental conditions. Usually Ph.turbinarus inhabits places of great surf influence. It easily remains on the large coastal stones, in spite of its conical shell. At first sight, it seems to be unstable under powerful wave crashes, however due to its strong and elastic foot it remains stable on the substrate.

57 Similar to many other littoral mollusks inhabiting upper layers of the littoral zone, Ph.turbinata is well adapted to drying conditions. It is able to survive in air for a long time: for several hours during low tide and for many days in storm conditions. The life expectancy of Ph.turbinata without water is limited. The maximal survival rate of Ph.turbinata in air in clusters of seaweed at temperatures of 21–24ºС can be more than 14 days. However it has the ability to store water in the shell cavity. The volume of water stored in tissues and the shell cavity is significant and it is about one-tenth of the total body weight or half of the body weight without the shell.

Ph.turbinata is an herbivorous species. It gets food by scraping it from rocks and stones. The main food sources are diatoms, filamentous, unicellular alga, and detritus. The radula is the basis of the scraping mechanism. Its length comes to about ~70% of the shell height, and its weight is about 1/5 of the total weight of the buccal mass (Alyakrinskaya, 2010).

4.1.5.1.2 Patella caerulea

Scientific name: Patella caerulea (Linnaeus, 1758)

Taxonomy:

Phylum Mollusca Class Gastropod

Subclass patellogastropod Superfamily patellidea

Family Patelidae Subfamily

Genus Patella Species cearulea

Prosobranch limpets (Patellogastropoda) are some of the most familiar animals on rocky shores, with an almost worldwide distribution. They are believed to be one of the most primitive groups of gastropods, and are probably the first branch of the "Archaeogastropoda"

58 according to a recent classification. The majority of species of Patella occur in two geographic areas, the northeastern Atlantic Ocean, Mediterranean and southern Africa (Branch, 1981). P. caerulea is the most common Mediterranean species of the genus Patella, and an endemic Mediterranean species (Ayas, 2010).

Most limpets are generalist grazers feeding on any microflora or detritus available on the rock face but some species specialize on particular macroalgae (Branch, 1981). These patellid species live on different vertical zones of rocky shores. P. caerulea living in medio littoral zone also mostly feed on Cyanophyceae species (Ayas, 2010).

Temperature may have direct effects on killing limpets, and important sublethal effects. High-shore limpets are usually more tolerant to high temperatures (Branch, 1981). The effects of high temperature are often coupled with and augment those of desiccation so that comparable responses and adaptations can be expected, such as influencing metabolic rates and feeding rates (Bannister, 1975). Limpet tracks have a formality of pattern that catches the eye more easily than does the more erratic brushwork of the topshell (Crothers, 2001).

As for spawning both sexes of the studied gastropods (Ph.turbinatus, P.caerulea) liberate their gametes into the sea and fertilization occurs in the water. The males emit white clouds of spermatozoa which show little movement at first but become very active after 2 or 3 minutes. Females release oocytes separately, a few at each spasm (Crothers, 2001).

4.1.5.2 Offshore species:

4.1.5.2.1 Liocarcinus depurator

Scientific name: Liocarcinus depurator (Linnaeus, 1758)

Common name: Blue leg-swimming crab

Taxonomy:

Phylum Arthropoda Subphylum Crustacea

Superclass Multricrustacea Class Eumalacostraca

Subclass Eucarida Order Decapoda

Suborder Pleocyemata Family Portunidae

Genus Liocarcinus Species depurator

59

Liocarcinus depurator (Linnaeus, 1758) is a portunid crab common on the continental shelf and upper slope of the north- east Atlantic and Mediterranean Sea. Its distribution in coasts ranges from Western Sahara to Norway, including the Mediterranean, where it is one of the most abundant members of continental shelf communities (Abello et al., 2002; Ungardo et al., 1999). Around 80% of this species are living at 50-100 m. The percentage of age occurrence decreases sharply to depths deeper than 400m. Thus the distribution and abundance of this species appears to decline with depth. It prefers muddy sediments or coarser sediments normally associated with higher water current velocities (Minervini et al, 1982; Rufino et al., 2004; Rufino et., al, 2005). Liocarcinus depurator is typically a scavenger and a carnivore. Freire, (1996) suggests that the high diversity of food items in the diet of Liocarcinus depurator is due to the versatile functional structure of the chelipeds, thus it may exploit a wide range of dietary items. Females with eggs occur all year although a maximum proportion of ovigerous females have been observed indicating the existence of an annual reproductive cycle. In Plymouth, ovigerous females are reported from March to October (Anger, 2001). In the warmer waters of the northwestern Mediterranean peak numbers of ovigerous females have been observed in the winter months from November to February and males were found to be sexually mature throughout the year (Abelló, 1989). Sex classification of this species is based on the observation of abdominal shape (narrow for males and wide for females).

60 4.1.5.2.2 Munida rugosa

Scientific name: Munida rugosa (Fabricius, 1775)

Common name: Squat lobster or Plated lobster

Taxonomy

Phylum Arthropoda Subphylum Crustacea

Superclass Multricrustacea Class Eumalacostraca

Subclass Eucarida Order Decapoda

Suborder Pleocyemata Family Munidae

Genus Munida Species rugosa

Munida is the most diverse and cosmopolitan genus of the galatheid squat lobsters which has attracted much attention in recent years from both a systematic and evolutionary perspective. However, information regarding the biology, ecology and evolution of this genus is still limited. Munida rugosa is found in the Mediterranean Sea and in the North Eastern Atlantic Ocean. It inhabits rocky or soft mud substrata in shelf waters ranging from the shallow to the deeper continental slope. Similar to other Munida species, M. rugosa is gonochoric, but little is known about the mating behavior of this species. They are benthic as adults with a planktotrophic larval phase and a developmental period that lasts 3 to 4 months (Bailie et al., 2011; Gore 1979; Van Dover and Williams, 1991).

M rugosa genders have slightly different somatic characteristics; both sexes had symmetrical cheliped length and allometric cheliped growth over the size-range investigated, but males showed increased allometry of CL and cheliped and females had greater positive allometry in abdomen width than males (Claverie and Smith, 2007) (Claverie and Smith, 2009). Sex determination was done based on the position of gonopores and the presence of

61 the first and second pleopods: the first pleopods are absent in females while they are present and modified in males (Maiorano et al., 2013).

4.1.5.2.3 Nephrops norvegicus

Scientific name: Nephrops norvegicus (Linnaeus, 1758)

Common name: Norway lobster

Taxonomy

Phylum Arthropoda Subphylum Crustacea

Superclass Multricrustacea Class Malacostraca

Subclass Eumalacostraca Order Decapoda

Suborder Pleocyemata Family Nephropidae

Genus Nephrops Species norvegicus

The Norway lobster, Nephrops norvegicus, is widely distributed from Iceland and Norway in the north to Morocco and Greece in the south. It is found on muddy bottoms in marine waters to approximately 600 m depths (Farmer, 1975). Adult Nephrops live within the sediment, in complex burrow structures, foraging out on the sediment surface to feed, generally in the hours of darkness. Females spend longer amounts of time in the burrow and do not venture out to feed when they are carrying eggs (late autumn /winter). The species is restricted to certain types of sediment with specific percentage of silt-clay fraction (Bell et al., 2006). Nephrops fishing grounds are found throughout the Aegean Sea in deeper offshore waters and more rarely in shallow depths (less than 100m) with suitable muddy sediment and low temperature (Smith et al., 2001).

62 The fishery of Nephrops is highly valuable in Greece as well as in Europe. The highest proportion of Nephrops on the Hellenic continental shelf is found in the Ionian Sea and Saronikos Gulf. Adult females molt once a year in spring after hatching their eggs. Adult males may molt throughout the year, or at the end of spring with some having a second molting period coinciding with that of females. The seasonality of female Nephrops reproduction in the Mediterranean is well documented. The timing of the basic process of ovarian maturation and laying of embryos on the pleopods in very similar with the only minor differences recorded related to depth (shelf-slope) and latitude (north –south). In Greece the peak of females with mature green ovaries is in the summer (June-July), followed by egg bearing in the autumn (usually November) and hatching of embryos in winter (Papaconstantinou and Zenetos, 2007).

Male Nephrops have larger claws compared to the females showing additional sexual dimorphisms in the species. Furthermore, male lobsters have paired testes that lie dorsally in the body cavity and lead via vas deferentia to gonopores at the base of the fifth pair of walking legs. Female lobsters have paired ovaries, which also lie in the body cavity but lead via paired oviduct to the reproductive aperture at the base of the third walking legs (Katoh, 2011) In females the abdominal pleopods and the inner branch (endopods) have long developed setae, specialized to carry eggs during the incubation period (Farmer, 1972).

4.1.5.2.4 Sparus aurata

Scientific name: Sparus aurata (Linnaeus, 1758)

Common name: Gilt-head bream

Taxonomy

Phylum Chordata Subphylum Vertebrata

Superclass Gnathostoma Class Actinopteri

Subclass - Order Perciformes

Suborder Percoidei Family Sparidae

Genus Sparus Species aurata

63

Sparus aurata is common in the Mediterranean Sea, present along the Eastern Atlantic coasts from Great Britain to Senegal, and rare in the Black Sea. Due to its euryhaline and eurythermal habits, the species is found in both marine and brackishwater environments such as coastal lagoons and estuarine areas, in particular during the initial stages of its life cycle. Born in the open sea from October to December, juveniles typically migrate in early spring towards protected coastal waters, where they can find abundant trophic resources and milder temperatures. Very sensitive to low temperatures (lower lethal limit is 4°C) they return in late autumn to the open sea, where the adult fish breed. In the open sea gilthead seabream are usually found near rocky bottom and seagrass (Posidonia oceanica) meadows, but are also frequently caught on sandy grounds. Young fish remain in relatively shallow areas (up to 30m), whereas adults can reach deeper waters, but generally not more than 50 m. This species is a protandrous hermaphrodite. Sexual maturity develops in males at 2 years of age (20- 30cm) and in females at 2-3 years (33-40cm). Females are batch spawners that can lay 20.000-80.000 eggs every day for a period up to 4 months. In captivity, sex reversal is conditioned by social and hormonal factors (www.FAO.org).

Wild seabreams have been observed to feed by grazing for prey on rocky surfaces, consuming a variety of prey, with crustaceans, molluscs, polychaetes, teleosts and echinoderms being the major dietary groups (Wassef and Eisawy, 1985; Andrade, Erzini and Palma, 1996). The prey consumed also varies with fish size: juveniles (3–10cm in length) feed on zooplankton (soft-bodied animals such as polychaete larvae, calanid larvae and other small crustaceans) and even on zoobenthos; bigger fish (>10–25cm) also consume benthic plants and animals (barnacles, bivalves, polychaetes, amphipods and other finfish) (Wassef and Eisawy, 1985). The trophic level of the gilthead seabream is about 3.3–3.5. Some studies have also shown gilthead seabream to have a browsing and chewing feeding habit (Andrew et al., 2003).

There is a rapid development of production in sea cages of European sea bass in the Mediterranean region with Greece (49 %) being by far the largest producer in 2002. Turkey

64 (15%), Spain (14%) and Italy (6%) are also major Mediterranean producers. In addition, considerable production occurs in Croatia, Cyprus, Egypt, France, Malta, Morocco, Portugal and Tunisia. There is also gilthead Seabream production in the Red Sea, the Persian Gulf, and the Arabian Sea. The main producer is Israel (3% of total production in 2002); Kuwait and Oman are minor producers (www.FAO.org).

4.1.6 Samplings

A) Inshore area: Samplings in this area occurred between 2009 and 2010. Seawater and the two species of gastropods (Phorcus turbinatus and Patella caerulea) were collected from the 7 stations in Larymna Bay. Samples from the reference area were taken just once in March 2010. Ph.turbinata samples for the biomarkers analysis were collected only from two stations of the contaminated area (LA1 & LA4) and one station from Saronikos Gulf in Anavisos, about 50 Km south west of Athens toward Sounio which was considered as a reference / uncontaminated area. In addition to the gastropod samples, several individuals of sea bream (Sparus aurata) were obtained from a fish farm for biomarker analysis. Two sets of fish samples were obtained for the biomarker experiments; one from the inshore area of Larymna Bay (as contaminated area) and the other set was obtained from a fish culture in Anavisos. More information is given in Table 11.

Table 11: Inshore sampling dates and matrices.

Sampling dates Larymna Bay Reference area biomarkers 1.09.2009 Seawater+Gastropods 22.02.2010 Seawater+Gastropods 18.03.2010 Seawater+Gastropods 20.05.2010 Seawater+Gastropods 06.10.2010 Seawater+Gastropods Ph.turbinata(Cont. area) 26.03.2013 Ph.turbinata (Ref. area

B) Offshore area: three samplings were carried out in this area from June 2009 to March 2011 in both contaminated and reference areas by means of the Greek research vessel R/V Filia. Seawater samples were collected from the surface (1m depth from the surface water) and bottom area (1m above the sediment) as well as surface sediments.

65 Table 12: Offshore sampling dates and matrices

Sampling Abiotic Samples Crustaceans samples Biomarker samples dates 20.06.2009 Seawater+sediment (M.rugosa-L.depurator- N.norvegicus) 09.03.2010 Seawater+sediment (M.rugosa-L.depurator- N.norvegicus) 10.03.2011 Seawater+sediment 08.03.2012 (M.rugosa) 27.10.2012 (S. aurata) 13.12.2012 (S. aurata)

In addition to the abiotic samples, biotic specimens (Munida rugosa, Liocarcinus depurator, Nephrops norvegicus) were also collected at the offshore area by means of bottom trawl twice, in June 2009 and March 2010. For biomarkers analysis the crustacean species (M. rugosa) were collected in March 2012 from both the contaminated and reference areas. The exact samplings dates are shown in Table 12.

4.1.6.1 Water samples collection

All equipment and plastic wares were treated with dilute nitric acid (2N) for at least 24h, and then thoroughly rinsed with deionized water (Milliipore MilliQ production system – M-Q water). Water samples from the dumping area were collected using a Niskin bottle and transferred into acid-cleaned polyethylene containers.

The water samples from the coastal sites were received by hand directly in acid- cleaned polyethylene containers, not on the edge of the water but at about 4-5 m far from the coast. The containers were rinsed twice with the ambient water and then filled completely.

The water samples were then placed in mini portable refrigerators and were immediately transported to the laboratory. For selected samples in each campaign replicate analysis was performed by dividing the sample in 3 to 5 subsamples and analyzing them separately.

4.1.6.2 Sediment samples collection

Sediment samples (the top 1-5 cm layer) were taken by means of a Birge Ekman grab. From each grab, at each station, 3-5 sub-samples were received in polyethylene containers using a plastic spatula to prevent contamination, and were later homogenized to achieve a composite sample. After collection, samples were transported to the laboratory and kept in freezer.

66 4.1.6.3 Biota samples collection

A). Offshore biota: The crustacean samples from the offshore stations were collected by means of bottom trawl. According to their availability, about 60 individuals of each crustacean species were collected during each sampling occasion from each area. The specimens were put in clean polyethylene plastic bags, kept in thermal containers with constant temperature of 4ºC and transferred to the laboratory as soon as possible. At the laboratory, they were stored in the freezer at -20 ºC until analysis.

B). Inshore biota: About 120 individuals from each gastropod species (Patella caerulea and Phorcus turbinatus) were handpicked from the tidal zone of each sampling station. They were stored in polyethylene bags and brought to the laboratory in isotherm boxes where they were kept in the freezer (-20ºC) until analysis.

4.2 Analytical procedures

4.2.1. Seawater

4.2.1.1 Dissolved metals in water

Chelating resins have been widely used for the preconcentration of total trace metals from seawater, and in particular the commercially available Chelex-100 resin, which is considered as one of the most successful methods for this purpose. Chelex-100 has a strong affinity for heavy metals and alkaline earth metals but little affinity for alkaline metals such as sodium and potassium. Resin conditioning and preparation is critical. The Chelex-100 columns should be prepared free of metals by acid washing followed by rising with M-Q water and activation to the ammonium form with ammonia solution 2M. Then after the acidified water sample is brought back to neutral pH it comes into contact with the resin for a desired amount of time (sample flow 3mL min-1).

Finally after appropriate washings the absorbed metals can be quantitatively eluted using 10mL of dilute nitric acid 2N to volumetric flasks. The intermediate washings after sample flow through and before metal elution include Milli Q water and ammonium acetate 2M. The Milli-Q water is used to remove the remaining volume of seawater and ammonium acetate is used to elute cations (Ca, Mg, K, Na) that are bound to the resin and co-elute with the nitric acid but interfere with the measurement of trace metals. The final eluate samples are kept in pre-cleaned polyethylene vials in the refrigerator until measurement (Figure 8) (Paraskevopoulou et al., 2014; Scoullos et al., 2007; Riley and Taylor 1968; Kingston et al., 1978).

67

Figure 8: The schematic table showing the procedure of obtaining dissolved metals in water.

Total dissolved Cr in water

The general principle of total dissolved Cr determination is by co-precipitation with an alkaline Fe (II) suspension. The Fe (II) suspension is prepared by dissolving 0.6 g of

Ferrous ammonium sulphate [Fe(NH4)2(SO4)26H2O] in 50mL of deionized water and adding 1 drop of sulphuric acid (Pro Analysis). The blue coloured suspension is formed by addition of 3mL ammonia solution 10% v/v (ammonia PA grade). The suspension has to be added to the water samples within 10 minutes of its preparation. The water samples were placed in 50mL centrifuge tubes and the pH adjusted between 7.7-8.3.

After the addition of 0.5mL of suspension to the samples this solid phase turns yellow-brown and co-precipitates all dissolved metals, subsequently the tubes are sealed and shaken for 1 h on an orbital shaker and then and left over night for the precipitate to settle.. The samples were centrifuged for 15 min at 3000-3500 rpm and the supernatant water was separated and discarded. The precipitate was mixed with 0.5 ml of ultrapure HCl and diluted with M-Q water to 10 ml (preconcentration factor of 5) (Figure 9).

68

Figure 9: Schematic table showing the procedure of obtaining dissolved metals in water.

4.2.1.2 Metals in particulate fraction

In order to determine particulate metals approximately 2 L of water sample were filtered with the use of a vacuum pump, a polyethylene filtration unit and pre-cleaned 0.45µm filters (Millipore nitrocellulose filter). The filters were also pre-weighed. Millipore filters are wildly used for gravimetric and chemical determinations; they have well defined pore sizes that give them a relatively precise cutoff in the size of particles that they retain. The composition of the filters is relatively metal free and hydrophobic which makes them easy to reweigh after sample collection. The filters, then were collected and placed in Petri dishes, kept in desiccators and air-dried until constant weight. When the drying step was complete the filters were ready for the digestion procedure.

69

Figure 10: Schematic table showing the procedure of obtaining particulate metals in water

The filters were digested by using Teflon beakers with 5 ml of nitric acid (PA) on a hot plate over night and at a temperature above 80ºC. The next day the digests were diluted in 25ml volumetric flasks with M-Q water, were transported to acid rinsed plastic vials and stored in the refrigerator until analysis (Figure 10).

4.2.2 Sediment

Sediment samples were freeze dried at -45ºC before analysis. For grain size distribution, sediment samples were ground using an agate mortar and sieved by means of stainless steel sieves of 1000 µm and 63 µm mesh size.

Grain sizes were classified as silt-clay (<0.063 mm), sand (0.063–1mm) and gravel (>1 mm) according to Folk (1954). The gravel fraction was not analyzed for metals. Also fractions that comprised less than 10% of a sediment sample were also excluded from further analysis. The metal concentration in the total fraction (<1000 µm) was calculated using the grain size distributions and metal concentrations in the fine (<63 µm) and sandy (63-1000 µm) fractions.

The Concentrtaion of metal in total fraction= (%f<63 µm*Cf<63 µm/100)* (%f>63

µm*Cf>63 µm/100)*

70 Where:

%f<63 µm= Percentage of metal fraction <63µm

%f>63 µm= Percentage of metal fraction >63µm

Cf<63 µm= Concentration of metals in fraction <63µm

Cf>63 µm= Concentration of metals in fraction >63µm

Sediment sample (0.2-0.5g)

Digestion with Nitric acid 5ml, evaporation until 1 ml remain

2 times: 1ml HNO3+5ml HF+1ml HCLO4 1 time: 1ml HNO 3+5ml HF+0.5ml HCLO4

Redissolve the residue with 2M Nitric acid over night

Transfer the residue to the vials with 2M Nitric acid to 50ml

Measure GFAAS/FASS

Figure 11: Schematic table of the procedure of total metals in sediment.

Digestion for total metal content was carried out using a slightly modified ISO method (ISO 14869‐1:2000) in pre-cleaned Teflon beakers. Approximately 0.2-0.5g of sample were weighed into the beakers and digested with a mixture of concentrated acids [PA grade HNO3 (65%) +HF (38-40%) +HClO4 (65%)] on a hot plate (150-170ºC). The procedure started with the addition of 5 ml HNO3 and evaporation to approximately 1mL.

Then 5 ml HF and 1 ml HClO4 were added and the acid mixture was evaporated to almost dryness. Two more additions of this acid mixture (1 mL HNO3 -5mL HF -1mL HClO4 were done until evaporation to dryness.

The residue was rediluted with HNO3 (2N), left overnight on the hot plate (120ºC) and the next day the Teflon contents were transferred to volumetric vessels and diluted with nitric acid 2N to 50 ml. Blanks and reference samples were also run through the procedure. (Figure 11).

71 4.2.2.1 Labile fraction in sediments

The following method was used to obtain the labile fraction or weekly bounded metals.

50 mL of 0.5 M HCl was added to 1 g of sieved sediment in a centrifuge tube. The tube was placed on a shaker for about 16 h at room temperature. The shaker was used to keep the sediment sample in suspension during extraction. Blanks were also run through the procedure. Then the samples were centrifuged at 3000 rpm for 10 min and the supernatants were removed are fully and carefully transported to acid cleaned 50 ml bottles and refrigerated (4ºC) until measurement (Figure 12).

Figure 12: Schematic table showing the procedure of obtaining labile metals in sediment.

4.2.2.2 Total Organic Carbon (TOC)

For the determination Hg, as well as TOC% and CO3%, sediments were freeze dried, and ground to a fine powder using planetary mono mill (Pulverisette 6-Fritsch).

Total organic carbon (TOC) was carried out using a modified Walkley–Black titration method (detection limit 0.05 %) (Gaudette et al., 1974). For this analysis the sieved sediment with the fraction of <1000µm was used.

The organic carbon content was determined by K2CrO4 oxidation in sulphuric acid and back titration of excess K2CrO4 with iron (II). For this procedure 10 ml K2CrO4 (1 N) and

20 ml of sulphuric acid (H2SO4) were added to 0.5 g of sediment sample weighed in a conical flask, and left for about 1-2h to react with periodic agitation.

After the reaction, 10 mL of phosphoric acid, 0.2g of NaF and 60mL of deionized water were added to the flask and finally 15 drops of ferroin indicator. The titration was done

72 with Fe (II) solution and color the solution changed from green light blue green and finally to dark red at the end point. Blanks and reference materials were run through the procedure as well. Each sample was analyzed in duplicate.

4.2.2.3 Carbonate determination

The carbonate were determined by leaching the sediments (1 g) with 10ml HCl 6 M for 1 minute, the weight differences in sediments indicated the total carbonate content released through leaching (Loring & Rantala1992 with the slight modification).

CO3%=( differences in weight of sediments after 1 minute)*100/weight of sediments

For this analysis, sediment samples with fraction <1000µm were used.

4.2.3.4 Hg determination in sediment

Hg was determined according to Issaro, Abi-Ghanem & Bermond, (2009) method with a small modification: 0.25 g of samples were digested by 10 ml mixture of HNO3 and

H2SO4 (2:1) respectively in microwave and diluted to 25 ml. A solution of SnCl2 20% was used to convert Hg (II) to Hgº. The Hgº was then measured by the cold vapor atomic absorption method (Varian SpectrAA 200). Hg was measured in the faction < 1mm.

4.2.3. Biota

In the laboratory the biota samples were kept in the freezer -20 ºC until analysis. The preparation steps are the following:

A) Offshore biota samples: Total length, weight and sex of all crustacean species were recorded.

About 1-6 composite samples of pooled tissues (Muscle, gill and exoskeleton from each sex) containing about 10 individuals were prepared. In Munida rugosa species, 66 samples were collected (18 females, 42 males and 6 with indeterminable sex). In Liocarcinus depurator species 44 samples were obtained (9 females, 27 males and 6 with indeterminable sex). Finally, 31 samples of Nephrops norvegicus, were also found (16 females and 15 males).

B) Inshore biota samples:

At the laboratory, the weight and total length of gastropods were measured by means of Vernier Caliper, and then the soft tissues of gastropods were separated from the shells. For each sampling occasion 8 to 12 pooled samples were prepared for each species containing 10 individual tissues of P. Caerulea (range size of 22-35 mm) and 15 of Ph. turbinatus (range size of 25-37 mm) respectively. Overall, 181 samples of Ph. turbinatus

73 corresponding to 2715 individuals and 254 samples of P. caerulea corresponding to 2550 individuals were obtained for metals analysis.

Analysis consisted of:

The samples from different tissues were freeze dried, homogenized and about one gram of dried sample (and 0.2 g of gills were digested with 6ml of HNO3 (65%) and 2 ml of

H2O2 (30%) in microwave device (CEM -MDS 2100). Then, the samples were transferred to acid-cleaned volumetric vessels and diluted with M-Q water up to 20ml and were kept in the refrigerator until measurement. Blanks and reference materilas were run with every batch of digestion (Kozanglou and Catsiki, 1997; Tuzen, 2009).

All the tools used in laboratory of ecotoxicology were either made of plastic or glass. Before using each piece of equipment was carefully washed up with dilute nitric acid (2N) and rinsed with M-Q water.

4.2.4 Metal Measurement

Flame Atomic Absorption (FAAS) Varian SpectrAA-200 and Varian Spectra-A200 and Graphite Furnace Atomic Absorption Varian Spectra AA-640 Zeeman spectrometers were used for the measurements. The metals Cu, Zn, Fe, Cr, Mn and Al in sediments, Cu, Zn, Fe and Mn in biota and Zn and Fe in water and particulate samples measured using the flame technique. On the other hand Cu, Mn and Cr in water and particulate samples and , Cr in biota samples and Ni in all types of samples were measured using graphite furnace atomic absorption spectrometry.

4.2.5 Biomarkers (Preparation of samples and analysis)

A total number of 48 Munida rugosa individuals were collected from both sexes and areas. As for Phorcus turbinatus 75 individuals were taken from both the contaminated and the reference area. In terms of fish in total 30 seabreams were also dissected from both areas.

The samples were pooled; for Munida 3-4 pooled samples (3 animals/pool) for each sex from each area, For Phorcus 5 pooled samples (5 animals/pool) and finally for fish 5 pooled samples (3 animals/pool) were provided.

Three biomarkers [Acetylcholinesterase (AChE), Catalase (CAT) and Glutathione –s- transferas (GST)] were measured in different tissues with different homogenization ratios:

 Munida rugosa:

1:10 (w:v) eye stalk : AChE activity 1:3 (w:v) gill tissue : AChE activity 1:4 (w:v) liver tissue: CAT-GST activities

74  Phorcus turbinatus:

1:4 (w:v) soft body part: AChE activity 1:4 (w:v) soft body part: GST activity

 Sparus aurata:

1:4 (w:v) gill tissue: AChE activity 1:5(w:v) muscle: AChE activity 1:4 (w:v) liver tissue: CAT-GST activities

4.2.5.1 AChE determination

 Extraction of total AChE activity:

Around one gram of tissue (pooled of 5 individuals) was homogenized with 0.1M Tris-HCl buffer containing 0.1% TRITON X 100, pH 7 (the ratio of homogenization is different in different tissues, they were mentioned before) by means of Potter-Elvehjem homogenizer. Homogenates were centrifuged (Heraus Fresco 21 centrifuge, Thermo Scientific, Langenselbold, Germany) at10000g for 20 min. The supernatants were placed in 2 eppendorfs (one for AChE assay and one for the protein analysis) and stored at -80 ºC. All preparation procedures were carried out at 4 ºC.

 Measurement of total AChE activity:

AChE activity was assayed by the method of Ellman et al. (1961) adapted to microplate reading by Bocquene et al. (1993).

Reagents:

1- 0.1M Tris-HCl pH 7 + 0.1% TRITON X 100 2- 0.1M Tris-HCl pH 8 3- 0.01M DTNB ( prepared daily in 0.1 M Tris-HCl, pH 8) 4- ACTC

In each well of the microplate the following materials were added:

60µl of sample supernatant.

290µl of 0.1 M Tris-HCl buffer containing 0.1% TRITON X 100, pH 7.

20 µl of 0.01 M DTBN

75 The reaction initiated by adding 10 µl of 0.1M acetylenthiocholine substrate (ACTC was diluted by distilled water and should prepare daily)

The enzyme kinetic measures every 15 s for 2 min at 414 nm by using the microplate reader (Assys Digiscan reader, Assys Hitech GmbH, Eugendorf, Austria) based on the colorimetric method of Ellman (1961). Samples are measured in triplicate.

AChE activity was expressed as nmoles of acetylthiocholine hydrolysed per min per mg protein. One unit (U) of AChE activity is the variation of 0.0001 of OD and 1Γ of OD/min/mg Protein corresponds to the hydrolysis of 75 nanomoles of ACTC.

4.2.5.2 Catalase (CAT) determination

About one gram of tissue was homogenized with 100m M KH2PO4/K2HPO4, pH 7.4 (The ratio of homogenization was mentioned above) by means of Potter-Elvehjem homogenizer. The homogenate was then, centrifuged (Heraus Fresco 21 centrifuge, Thermo Scientific, Langenselbold, Germany) at 10000g for 30 min. The supernatants were placed in 4 eppendorfs (one for CAT assay, one for the protein analysis and the rest for backup) and stored at -80 ºC. All preparation procedures were carried out at 4 ºC. Catalase activity was measured by the method of Cohen, Kim, and Ogwu (1996) by the loss of H2O2 that was measured colorimetrically with ferrous ions and thiocyanate on a microplate reader.

H2O2+ sample (catalse) decrease in H2O2

 Determination of H2O2 concentration at 1 min and 4 min

 H2O2 concentration measured colorimetrically with added ferrous iron and SCN

+3  Ferrous iron (FeO) + H2O2 Ferric ion (Fe )

 Ferric ion+SCN Red color

 Enzyme Units ln(A1/A2)/(4-1)mg protein

 Samples preparation: Reagents:

The following materials should be kept on the Ice bath

1. 60 mM H2O2 (prepare daily, diluted from the 30% H2O2 stock and covered with aluminum foil) 2. 2.5 M KSCN

3. 10 mM FeSO4* 7 H2O

76 4. 10 mM KH2PO4/K2HPO4, pH 7

The following reagent can be kept in the room temperature:

1. 0.6 N H2SO4

2. 100mM KH2PO4/K2HPO4, pH 7.4

3. 10mM Sodium azide (NaN3)

 Measurement of total Catalase activity:

In order to assay the CAT activities, the supernatant of samples were diluted with phosphate buffer (100mM KH2PO4/K2HPO4, pH 7.4). The ratio of dilution for different samples is different, for fish the dilution ratio is normally 1:10 and for mussels this rate is 1:4. This ratio for the specific liver samples of M.rugosa was 1:20 and for the liver of S.aurata was 1:10.

The following procedure should be done on the Ice bath in 5ml tube:

850 µl buffers (10 mM KH2PO4/K2HPO4, pH 7) were added to 50µl of samples in 5 ml tubes and vortex in low speed. In order to initiate the reaction, 100 µl H2O2 was added to the solution. The solution was then placed in the vortex at low speed and H2O2 concentration was determined at 1 and 4 minutes after the initiation of reaction.

850 µl buffers (10 mM KH2PO4/K2HPO4, pH 7) + 50µl samples (low vortex speed) +

100 µl H2O2

Determination of H2O2 at 1 and 4 minutes after the initiation of reaction.

The following procedure should be done at the room temperature in 10 ml tube:

4 ml of 0.6N H2SO4 was added to 1ml of 10 mM FeSO4 in 10 ml of tubes and vortex in slow speed. The procedure was continued by adding the 100µl aliquots to the solution at 1 and 4 minutes, along with adding 400 µl of 2.5 M KSCN. Samples were continuously vortex after adding the new solution.

During all the process the samples should be covered with aluminum foil. The red color appeared due to the interaction of SCN with ferric ions is stable for several hours. For each single tube two replications were done. Samples were transferred to the microplate for reading. The enzyme reading was determined at 490 nm at room temperature.

4 ml 0.6N H2SO4 1ml of 10 mM FeSO4 (vortex in low speed) + 100µl aliquots at 1 and 4 min (vortex)

77 + 400 µl 2.5 M KSCN( vortex) Microplate reading at 490nm at room temperature

In order to distinguish the Catalase activity from other factors that may induce loss of

H2O2, the following preliminary test was done:

In a 5 ml tube 800µl of phosphate buffer 10mM pH7.0 was added to mixture of to

50µl diluted sample, 50 µl sodium azide (NaN3) 10mM and 100 µl H2O2 to initiate the interaction. The solution was vortexed. Determination of H2O2 concentration was measured at 1 and 4 minutes after initiation of interaction.

4.2.5.3 Glutathione s transferase (GST) determination

About one gram of tissue was homogenized with 100m M KH2PO4/K2HPO4, pH 7.4 (The ratio of homogenization varies with different tissues) by means of Potter-Elvehjem homogenizer. The homogenate was then, centrifuged (Heraus Fresco 21 centrifuge, Thermo Scientific, Langenselbold, Germany) at 10000g for 30 min. The supernatants were placed in 4 eppendorfs (one for CAT assay, one for the protein analysis and the rest for backup) and stored at -80ºC. All preparation procedures were carried out at 4ºC. GST activity was measured according to the Habig et al., 1974 procedure adapted to microplate reading by Mac Farland et al., 1999. GST activity is estimated by measuring photometrically, based on the rate of conjugated substrate CDNB-GS at 430 nm. GST activity is expressed as nmol CDNB conjugate formed /min.mg protein.

CDNB+GSH CDNB-GS+HCL

 Samples preparation:

Regents:

1. 0.2M phosphate buffer (KH2PO4/K2HPO4 pH 6.5) 2. GSH 42mM (prepared daily) should be kept on ice 3. CDNB 42mM (prepared daily) should be diluted in ethanol

Preparation of assay mixture in 30 ml vial:

20 ml 0.2M phosphate buffer pH 6.5 0.5 ml CDNB 42 mM + 0.5 ml GSH 42 mM

The final volume of mixture assay is 21 ml which should be vortexed to homogenize the solution before adding to the microplates.

78 Samples were diluted (1:40) with the phosphate buffer. In microplate three different concentrations of samples (5, 10, 15) were added to 10, 5, 0 µl concentration of buffer, respectively, finally 200 µl of mixture assay which was prepared before were added to the samples. The samples were run in triplicate. The read absorbance is done at 340nm for 4 min at 25 ºC at 30 second intervals. For each test the blank was prepared with the similar way as it was described, without sample.

4.2.5.4 Protein determination

The total protein was measured according to the Bradford (1976) procedure. The Bradford protein assay is a fast and simple procedure for determination of total protein concentrations in solutions that depends upon the change in absorbance based on the proportional binding of the dye Coomassie Blue G- 250 to proteins. The Coomassie blue G250 dye appears to bind most readily to arginyl and lysyl residues of proteins (not to the free amino acids). A set of standards is created from a stock of protein whose concentration is known. Bovine serum albumin (BSA) was used as a standard stock of protein. The Bradford values obtained for the standard are then used to construct a standard curve to which the unknown values obtained can be compared to determine their concentration.

Reagent:

1. BSA stock solution 0.1 mg/ml (Keep in refrigerator) 2. Bradford reagent (1:5 water diluted samples. Keep in refrigerator).

Samples should be diluted with phosphate buffer 100mM pH 7.4. In our study samples diluted as follows: (Ph.turbinatus 1:600, S. aurata 1:300-l:400, M. rugosa 1:200).

In the microplate 100 µl of diluted sample or standard were added to 280 µl of Bradford reagent.

BSA/diluted sample water or buffer 10 µl + 90 µl (1µl protein in 100µ) 10µ/ml 0.01mg/ml 20 µl + 80 µl (2 µl protein in 100µ) 20µ/ml 30 µl + 70 µl (3 µl protein in 100µ) 30µ/ml 40 µl + 60 µl (4 µl protein in 100µ) 40µ/ml 50 µl + 50 µl (5 µl protein in 100µ) 50µ/ml 60 µl + 40 µl (6 µl protein in 100µ) 60µ/ml

The samples were measured by the Assays Digiscan reader at 595 nm 1 min after color development. The color is stable for 1 hour).

79 4.2.5.5 Quality control

The accuracy of the analytical procedures used in this study was checked with certified reference materials. CRM NO 279 (Ulva) and IAEA 407 (for the biota samples multiple analyses (N=3-5) was used for the estimation of accuracy and precision (Table 13).

Table 13: Assigned and recoveries of reference materials in the biota samples.

Elements Certified value of CRM This study Recovery Analytical precision (µg/g) value(µg/g) (%) (%) Zn 51.3±1.2 52.04±3.8 101 7.3 Fe 2300±100 2413.2±111.8 104 4.63 Mn 2150±70 2353.6±202.8 109 5.7 Cu 13.14±0.37 13.46±0.47 102 3.49 Ni (9.7-11.6)±0.9 9.52±0.056 101 0.58 Cr 15.9±0.4 16.67±0.57 104 3.41

Table 14: Recoveries, reproducibility (%RSDR) and LOD of dissolve metals

Spike Limit of Analytical (µg/l) Detection Cass-4 (n= 2) Spiked seawater (n=8) precision LOD (%)

Recoveries (µg/l) Measured Assigned %RSDR %

Cu 8.97±0.47 9.32±0.87 0.2–1.0–2.0 99–96-93 5.4–3.2–1.9 0.008 5.5 Ni 5.86±0.17 5.35±0.51 0.2–1.0–5.0 99–97-99 6.6–4.1–4.7 0.006 8.5 Mn 2.79±0.12 2.78±0.19 0.2–1.0–5.0 98–98-101 4.8–3.5–2.7 0.09 11.7 Zn 6.25±0.46 5.83±0.87 1.0–2.0–5.0 105–101-100 5.8–7.5–5.2 0.1 6.4 Cr - 0.5–2.0–3.0 93–102-102 4.9–5.2–3.3 0.04 12

The recovery and reproducibility (%RSDR) of dissolved metals determination by the Chelex-100 resin has been tested in the laboratory using spiked seawater samples at three concentration levels, as well as by analyzing the CASS-4 certified reference material (Table 14). For the sediments accuracy and precision (% CV coefficient of variation) was checked using the PACS-2 and MESS-3 materials from the National Research Council of Canada. The quality of organic Carbone analyses was checked using two certified reference materials (ISE 962) (Table 15).

80 Table 15: Certified reference materials (PACS-2 mg/kg) and recoveries for sediment samples. 1=MESS-3 certified materials used for Hg.2ISE 962 certified materials used for organic Carbone. Precision was estimated with replicate analysis (N=3-5) Elements Certified value of CRM (mg/kg) Recovery (%) Analytical precision (%) Zn 364±23 85-115 1.5 Fe 4.09±0.06% 89-100 7.5 Mn 3.04±0.20 89-97 4.4 Cu 310±12 88-105 7.5 Ni 39.5±2.3 112 1.6 Cr 90.7±4.6 86-107 4.5 Hg1 0.091±0.009 96-101 9.9 TOC% 172±0.092 97 4.5

4.3 Statistical treatment of the results

In this study statistical analyses were performed using different statistical software package such as SPSS, Statgraphics and Primer. Data were checked for normality and homogeneity of variance prior to statistical testing. All the results from the heavy metal measurements were transformed to log (x+1). All data are means ± standard deviations (SD). Effect of different parameters such as season, sex and stations were evaluated using two-way ANOVA and one way ANOVA. A post-hoc mean comparison test (Tukey‘s test) was also performed to distinguish the significant differences between the two pairs of data.

The non-parametric tests of Kruskal–Wallis and Mann–Whitney (one-tail) were used to find the significant differences on the results from the biomarker analysis.

Pearson and Spearman correlation coefficients (P<0.05) were used to distinguish the relationships between dissolved metals in the seawater with those bioaccumulated in different tissues of biota samples as well as the dissolved metals with the suspended materials and biomarkers. In this study, hierarchical clustering methods based on a Euclidean distance measure and Ward‘s hierarchical agglomerative clustering technique were performed for both dissolved metals in the water and the sediment to visualize which metals are grouped in the same cluster.

Principal component analysis (PCA) and Multidimensional scaling (MDS) approach was run for the metals bioaccumulated in different tissues of biota samples and the biomarkers to visualize better the variables.

81 5. Results and Discussions

5.1 OFFSHORE AREA

5.1.1 Sediments

Due to the long term of (since the late 60s) deposition of the smelting plant‘s by- product offshore of Larymna (in the defined area in the N. Evoikos Gulf), the natural sediment of this area has been hidden under a thick layer of slag, thus what is called as ―sediment‖ in this area is not a natural material, but a mixture containing mostly the slag from the smelting plant with a little amount of natural sediment grains (Figure 13).

Figure 13: Photo of the sampled sediment in the contaminated area of Larymna

During the study 23 samples of sediments have been taken from the offshore sampling selected stations and analyzed for Fe, Ni, Cr, Mn, Zn, Cu and Hg.

5.1.1.1 Characteristics of sediment- Granulometry

Among the important characteristics of sediments are their granulometry and the average percentages of total organic carbon (TOC%) and carbonate (CO3%). Granulometry or the fraction size of sediment is affecting the metal content. Therefore the sediment samples were sieved to evaluate the three fraction sizes of <63κm, 63κm1mm.

The data on the sediment granulometry of the samples during the three years of study is shown in Table16, where F1 represents the fraction size between 63κm, 1mm (63κm

The deposited slag is a resistance, strict metal like ore material, containing high amounts of sand and granules. Therefore, after sieving very limited amount of fine materials (F< 63κm) was obtained from the samples in the dumping area. The usual practice is to exclude the fine fraction grain (F< 63µm) with less than 10% of the total weight of

82 sediments; in accordance to this point, 7 samples from the total 30 samples were excluded from the analysis.

Table 16: The percentage of grain size fraction in three years of samplings F1=(63μm

2009 2010 2011 Stations F1 F2 F1 F2 F1 F2 L12 100 0 100 0 95 5 L10 97 3 90 10 98 2 L14 79 21 92 8 95 5 L8 77 23 59 41 84 16 R 90 10 72 28 56 44

The average percentages of total organic carbon (TOC%) and carbonate (CO3 %) for the three years of study are presented in Table 17.

Table 17: Average concentration of total organic carbon (TOC%) and carbonate (CO3 %) in sediments during the three years of study .

R L8 L10 L12 L14

CO3% Mean±SD 21.4±6.2 6.7±1.3 2.6±2.8 0.45±0.36 3.4±3.5 TOC% Mean±SD 0.69±0.35 1.3±0.14 1.3±0.19 0.87±0.64 1.4±0.10

Concerning the CO3%, the highest concentration was found in the reference area, followed by L8 (this station has a measurable fine grained content and further to the north) which showed the highest concentration among the contaminated stations. As for TOC%, no remarkable differences were found between the stations in the slag dumping area. However the lower level of total organic carbon was detected in the reference area.

5.1.1.2 Metal levels of sediments -Spatial and temporal distribution

The average concentration of metals during the three years of study are presented in Table 18 per sediment fraction and station and expressed in mg/Kg. The total concentrations of metals coming from the sum of the two fractions according to their weights and the percentage are also included in the Table. The results for Hg are given as total value.

The comparison between the average values of metals concentrations based on the two fractions show that the higher concentrations of metals were measured in the coarse fractions especially for the metals such as Fe, Cr and Mn. Ni, however show slightly higher concentration is the fine fractions (Table 18, Figure 14).

83 Table 18: Average concentration of metals in sediments per size class (mg/Kg) for the three years of study (ND= no data). N=15

Metal Fractions R L8 L10 L12 L14 Mean>63µm±SD 184±124 634±109 581±75 550±15 540±89

Ni Mean<63µm±SD 276±9 619±181 1054±183 N.D 1,005±543 Total Mean±SD 202±84 638±121 603±38 550±14.7 572±46 Mean>63µm±SD 20,125±6,503 187,455±64,349 281,296±5,229 270,747±12,301 246,912±56,552 Fe Mean<63µm±SD 26,996±2,062 51,021±5,640 141,159±7,651 N.D 88,443±55,406 Total Mean±SD 21,640±4,957 146,956±31,668 273,320±9,012 270,747±12,301 229,704±65,938 Mean>63µm±SD 84±82 8,926±9,819 8,844±1,098 9,514±1,605 8,023±3,478 Cr Mean<63µm±SD 192±17 795±202 4,870±3,090 N.D 1,812±1,556 Total Mean±SD 108±69 5,948±5,449 8,544±3,572 9,514±1,605 7,417±3,572 Mean>63µm±SD 408±210 2,265±546 3,027±343 3,504±1421 2,724±807 Mn Mean<63µm±SD 609±91 850±89 1,672±374 N.D 1,168±317 Total Mean±SD 450±185 1,881±460 2,961±344 3,504±1,421 2,570±822 Mean>63µm±SD 19,415±14,421 39,086±3,289 42,197±1,071 42,847±6,869 46,600±7,882 Al Mean<63µm±SD 38,480±3,720 42,144±5,923 42,291±1,669 N.D 42,291±1,668 Total Mean±SD 23,762±1,197 40,083±4,489 41,777±1,384 42,847±6,869 45,846±8,389 Mean>63µm±SD 48±20 100±56 84±30 73±48 89±40 Zn Mean<63µm±SD 76±301 87±32 131±6 N.D 107±41 Total Mean±SD 54±24 103±5 84±31 73±46 89±43 Mean>63µm±SD 22±11 24±10 17±9 55±61 19±5 Cu Mean<63µm±SD 27±9 33±17 28±22 N.D 32±15 Total Mean±SD 22±12 27±13 45±45 57±61 20±7 Hg Total Mean±SD 76±8 33±7 29±4 27±2 25±6

84 In order to to clarify the role of the granulometry in the concentration of metals by sediments, Figure 14 was drawn.

Box-and-WhiskerN Ploti Fe Cr Box-and-Whisker Plot Box-and-Whisker Plot

1450 (X 100000) (X 1000) 3 24

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Figure 14: Metal levels in sediment size fractions from the slag dumping stations during the monitoring time (mg/kg).

Homogeneous distribution among grain size classes was also observed for Cu, Zn, and Al. On the opposite higher concentration of fine grain was recorded for Cr, Fe and Mn. Besides, the results from the Kruskal Wallis test showed significant differences (P<0.05) between the grain sizes in the latter 3 metals (Cr, Fe and Mn). Note that the results from the reference area were excluded from this statistical analysis to have a better understanding from the slag dumping area.

The spatial distribution of CO3%, TOC% and metals in superficial sediments during the three years of study is illustrated in Figure 15.

85 CO3% TOC%

Figure 15: Spatial distribution of CO3% and TOC% per stations.

It is clearly observed that the total concentrations of metals related to the operation of smelting plant (Ni, Cr, Fe, Mn) are higher in the slag dumping area, than those from the reference area (station R). Note that their maximum concentration in the contaminated area (638 mg/kg at station, 273320 mg/kg, 3416 mg/kg and 9352 mg/kg respectively) were at least three times higher than the levels of the reference site. Furthermore Kruskal Wallis non parametric comparison test confirmed the difference between the two areas (P<0.5).

Although Zn, Cu and Hg according to literature (IOFR, 1985) and other studies (Zaharaki et al., 2009; Balomenos et al., 2013) is not related to the by-products of the smelting plant but to the anthropogenic sources of contamination, higher concentrations of these metals were detected in the contaminated area rather than the reference site. In the case of Zn, no significant difference was found. The surfer distribution depicts the increased levels towards L14 and L8 (Figure 16).

86 Ni Fe

Cr Mn

Figure 16: Spatial distribution of the total concentrations of metals related to the by-product of smelting plant in surface sediments. The values are expressed in mg/kg.

87 Cu Zn

Hg Al

Figure 16 continued: Average total metal concentration of surface sediments in different stations. The values are expressed in mg/kg, for Hg in µg/kg.

Concerning the slag area, a slight variation was recorded among the stations. Therefore, station L8 presented lower concentrations of Fe, Cr and Mn and the only significant difference was detected for Fe in this station.

88 As for Ni the pattern of metal distribution was completely reverse, hence the highest concentration was found in L8 and the minimum in L12. Al followed the same pattern as Cr and Fe with significantly higher levels in the dumping area than in the reference site (P<0.05).

Concerning the second group of metals (Zn, Cu and Hg) at the slag dumping area, Zn and Cu did not show any apparent differences among stations. In contrast Hg, presented significantly higher concentrations in the reference station than the dumping area (P<0.05). It is worth mentioning that the spatial pattern of CO3 is similar with Hg (Figure 16).

The hierarchical clustering methods allow organizing the elements according to the distance matrix and similarity among the them (Tranchina et al., 2008). For this purpose the Euclidean distance was performed in this study between the sampling stations to illustrate the similarities among the stations according to the concentration of metals (Figure 17). Group average Transform: Log(X+1) Normalise Resemblance: D1 Euclidean distance 8

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Stations are clearly classified in two main clusters. The first one comprises of the reference station while the second contains the slag dumping stations divided into two sub groups; station L8 was grouped with L14 the furthest stations to the from north west sides of the deposition area. Furthermore, L10 was grouped with L12- the stations located in the center of dumping area- and the nearest ones to the smelting plant. Thus the cluster separated stations according to their enrichment level. It is interesting to compare the results of total metal concentration in sediments of the present study with that of previous studies in the area (Figure 18). Generally a peak in concentrations of both groups of metals (the ones related to the smelting plant operations and the ones related to the anthropogenic activities) was observed in 1993.

89

Figure 18: Distribution trends of heavy metal from 1992 to 2011. Al and Fe expressed in %, other metals were expressed in mg/kg.

In some extent similar patterns the temporal distribution were observed for the concentrations of Al % and Fe%, along with that of Cr and Ni. A sharp increase in concentration

90 of Mn and Cu was observed from 1999 to 2011. As for Ni, the most important metal of smelting plant, a constant concentration was found during the recent years.

5.1.1.3 Metal levels in labile fraction of sediments

Total metal concentrations are generally not sufficient to assess environmental impact and the estimation of the bioavailable fraction becomes necessary (Marmolejo-Rodríguez et al., 2007). The bioavailable fraction is defined as the proportion of the total metal concentration ―removed‖ by weal acid extraction and it is operationally known as the non-detrital (acid soluble or extractable labile) metal fraction of sediment. This fraction contains the proportion of metals precipitated or co- precipitated with carbonates and they may be potentially bio-available. From an environmental point of view metals in this fraction can be easily exchanged and are in equilibrium with the ionic content of water and ultimately became a threat to the environment (Krčmar et al., 2013; Martínez-sánchez et al.,2008).

The labile metal fraction varied among metals and areas. Concerning spatial distribution, the percentage of the labile faction of all metals except Mn was generally lower in the reference area. However, except for Al and Ni, no significant difference was found between the percentages of these fractions (labile and non-labile fractions) between the reference and contaminated areas. Apart from the metals related to the smelting plant by-product, the labile fraction of Zn and Cu is also slightly higher in the contaminated area (Table 19). Concerning the distribution of percentages between labile and non-labile fractions, it is observed that in the reference area the percentage of the non-labile fraction was greater to that of the labile except in the case of Mn. In fact in both areas Mn shows the highest amount of labile fraction than the non-labile. The lowest labile fraction, however in both areas was detected for Fe with 1.5% in the reference area and 0.22% in the contaminated area.

Table 19: Average percentage of labile and non-labile fraction of metals in sediment in different stations

METAL Reference area Slag dumping area Labile % Non-labile % Labile % Non-labile % Ni 36.3 63.7 59.3 40.7 Fe 1.5 98.4 0.22 99.7 Cr 46.6 53.4 58.2 41.7 Mn 78.3 21.7 69.0 31.0 Al 6.8 93.2 60.8 39.2 Cu 34.2 65.7 45.5 54.4 Zn 23.9 76.1 50.7 49.2

91 5.1.1.4 Investigation of metal level relations in sediments

Concerning the dumping site and regarding the high concentration of metals in the initial composition of the laterite ore, prior to be dumped, it is assumed that there should be a strong correlation among the laterite metals (Ni, Cr, Fe and Mn). But on the opposite the correlation coefficient of the metals concentrations in both fractions did not reveal such a finding, and poor correlation was detected among these metals (Table 20). However, strong positive correlation was found between Zn and Al (R=0.741, P<0.01) as well as between Cu and Cr (R=0.671, P<0.05).

Table 20: Spearman correlation coefficient of total concentration (fraction<1 mm) of metals, Organic Carbone and Carbonate in the surface sediments from the depositing area.

Ni Mn Fe Cu Zn Cr Al Hg CO3% TOC% Ni 1 Mn 0.168 1 Fe 0.091 0.531 1 Cu -0.105 0.126 0.168 1 Zn 0.056 -.685* 0.056 0.168 1 Cr 0.084 0.252 0.559 .671* 0.378 1 Al 0.077 -0.196 0.51 -0.014 .741** .594* 1 Hg 0.07 -0.266 -0.259 0.21 0.042 -0.161 -0.14 1

CO3% 0.07 -0.35 -0.545 -0.364 0.133 -0.517 -0.154 -0.14 1 TOC% 0.042 0.217 -0.392 -0.154 -0.455 -0.392 -.594* -0.503 0.524 1 *. Correlation is significant at the 0.05 level (2-tailed). **. Correlation is significant at the 0.01 level (2-tailed).

Table 21: Spearman correlation coefficient of fine fraction of metals (fraction <63µm) in the surface sediments from the depositing area. Correlations Ni<63 Fe<63 Cr<63 Mn<63 Cu<63 Zn<63 Al<63

Ni<63 1 Fe<63 .857* 1 Cr<63 0.536 0.607 1 Mn<63 0.643 0.714 .786* 1 Cu<63 0.321 0.036 -0.357 -0.25 1 Zn<63 1.000** .857* 0.536 0.643 0.321 1 Al<63 0.357 0 -0.321 -0.321 .964** 0.357 1 *. Correlation is significant at the 0.05 level (2-tailed). **. Correlation is significant at the 0.01 level (2-tailed).

92 In contract to the correlation results using the total concentrations of metals, in the fine fraction Mn and Cr showed significant relation (R=0.756, P<0.05). Besides, strong correlation was also found between Zn and Fe and Ni (metals related to the smelting plant) (Table 21). In this study the sediments did not have uniform granulometric composition or relatively uniform distribution of carbonate content, a fact which prevents us to use normalization techniques. Group averageTotal metals Transform: Log(X+1) Normalise Resemblance: D1 Euclidean distance 6

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Z F C M Figure 19: Cluster analysis of total concentrationSamples (fractions<1mm) and fine fractions of metals from the dumping area

93 To solve the problem, a cluster analysis was used in order to clarify the pattern of metal distributions in the sediments of depositing area. Classification was applied on long transformed and normalized data. The Euclidean distance was again used as similarity measure of variables. Figure 19 is illustrating metal clustering using all data of the three years of monitoring excluding however the data from reference area.

The clustering analysis of total metal from surface sediment revealed three main groups. The first main group included Cu, Cr, Fe and Mn (metals related to slag), the second main group contained Ni, Zn and Al and the last one comprised Hg, TOC% and CO3%.

The cluster analysis results of total metals from the fine fraction (fraction <63µm) were in some extent similar to that of the total metals. In this cluster (Figure 20.) three main groups were indicated, Ni and Zn were classified together with a very strong positive correlation (R=1.000, P<0.001). While Mn, Fe and Cr made up the second main cluster. Al was finally categorized with Cu. The above results from cluster analysis corresponded with the findings from correlation analysis.

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Similar to total metals, the cluster analysis of labile content was performed by means of the Primer 6 statistical software. The data were log transformed and normalized. The Euclidean distance was also used for this analysis. The results from the reference area have been excluded for both cluster analysis and correlation coefficient. Table 22 shows the cluster results for the labile metal contents in both the total sediment and the fine fraction.

Table 22: Spearman correlation coefficient of fine fraction of labile metals (fraction <63µm) in the surface sediments from the depositing area.

Correlations Ni Fe Cr Mn Cu Zn Al Ni 1.000 Fe .594* 1.000 Cr .615* .105 1.000 Mn .441 -.161 .811** 1.000 Cu .573 .993** .091 -.147 1.000 Zn .867** .573 .671* .336 .566 1.000 Al .776** .224 .846** .685* .217 .867** 1.000 *. Correlation is significant at the 0.05 level (2-tailed). **. Correlation is significant at the 0.01 level (2-tailed).

95

Table 23: Spearman correlation coefficient of fine fraction of labile metals (fraction <63µm) in the surface sediments from the depositing area

Correlations Zn Mn Ni Cr Al Cu Fe Zn 1.000 Mn .536 1.000 Ni .964** .500 1.000 Cr .893** .643 .857* 1.000 Al 1.000** .536 .964** .893** 1.000 Cu .286 .429 .321 .071 .286 1.000 Fe .929** .536 .964** .750 .929** .429 1.000 **. Correlation is significant at the 0.01 level (2-tailed). *. Correlation is significant at the 0.05 level (2-tailed).

The cluster analysis of total labile metals presents two main groups. The first group included Fe, Cu, Ni & Zn. The second group contained Mn, Cr and Al. The metals in fine fraction of labile were not classified similarly with that of the total labile. Thus, Cu and Mn were individually categorizing as independent classes. The third main group of the cluster contained Cr, Al, Zn and Fe. The positive correlation between the concentrations of Ni/Zn along with grouping in the same cluster was also indentified in the sediment total metal distribution. The very good correlation of Fe/Cu (R=0.993), Cr/Al (R=0.846) and Mn/Cr (R=0.811) in P<0.01 supported the demodulation from cluster analysis (Table 23).

Multidimensional scaling (MDS) is an exploratory data analysis technique that attains to aid reducing data into more manageable pieces of information by condensing large amount of data into a relatively simple spatial map. It also provides a visual representation of dissimilarities (or similarities) among objects, cases or, more broadly observations (Jaworska & Anastasova, 2009). Figure 21 is simulating a two- dimension distribution of total and labile concentration of heavy metals during three years of monitoring. All data have been log transformed; Euclidean distance was applied for this analysis.

96 Transform: Log(X+1) Resemblance: D1 Euclidean distance 2D Stress: 0.01 type Labile RR R Total

RRR CuFe

Zn

L12 Ni L14L8L8L10L12L14L10 Al L10L12L14L8 Mn Cr

L8 L14L14 L12L10L10L12L14L10

Figure 21: The Multidimensional scaling (MDS) distribution of total and labile concentrations. R presents as the reference area.

The application of the MDS created four distinguished groups which were visualized according to the distance differences. Not only the two reference area groups (total & labile) were clearly differentiated from the slag dumping area, but also the results from total and labile areas were sorted individually. The distances among the points illustrated the similarities, the closer the points are to each other the more similar they are; which is indicated by stress values. The stress function values lay between 0 and 1, the smallest the stress function, the better the model represent the input data (Jaworska and Anastasova, 2009). In this case the stress is 0.01, which suggested a perfect fitness of the model. Furthermore, all the vectors were toward the contaminated stations with the total concentrations of metals.

5.1.1.5 Assessment of sediments quality

Sediments act as both sources and sink for potential toxic compounds. In order to predict adverse biological effects in contaminated sediments numerous qualities guidelines (SQGs) have been developed during the past decades to protect animals, living in or near sediments, from the deleterious effects of sediment-bound contaminants, or to evaluate the spatial pattern of sediment contamination (Christophoridis et al., 2009; Mac Donald et al., 2000). The variety quality guidelines including US EPA limits can be used as screening tools to evaluate sediment chemistry data and to indentify the problem, not only from the chemical point

97 of view, but also in terms of the biological effects of the deposited slag on the marine organisms of the area. In this study the contamination of sediments has been assessed according to two guidelines: 1- Sediments quality guidelines used by US EPA 2- Application of Effect range low/Effect range median (ERL/ERM) guidelines which determine the percent incidence of adverse biological effects. Total concentration of metals are given in Table 24 which are compared with the US EPA limits classifying the sediments as: non-polluted, Moderately polluted and heavily polluted (Pekey, Karakaş, Ayberk, Tolun, & Bakoğlu, 2004).

According to these limits, the concentration of Ni and Cr in both studied sites ( slag dumping and reference) are around 100 fold greater than the value proposed for the polluted area, consequently both slag dumping and reference areas based on these two metals are categorized as heavily polluted area (Table 24).

Table 24: comparison between the average total concentration of metals from slag dumping area and the values proposed by US EPA and Ontario ministry of Canada. The heavily polluted metals were shown in bold. Values for all metals expressed in mg/kg. Hg concentration expressed in (µg/kg)

Ni Cr Zn Cu Hg US EPA Not polluted <20 <25 <90 <25 <300 US EPA Moderately polluted 20-50 25-75 90-200 25-50 300-1000 US EPA Heavily polluted >50 >75 >200 >50 >1000 Present study: Slag Dumping 588.4±69 7816±31.7 87.4±41.5 36.1±34.6 28.5±5.30 Present study: Reference area 201.6±6 107.9±69.4 54.4±24.2 22.5±11.6 76.2±8.12

Concerning the concentration of Zn and Cu the area is classified as moderately polluted. On the other hand Hg concentration is lower than the guideline standards (<300 ppb) - which grade the both areas as not polluted by this metal.

The two guideline values, ERL and ERM (Burton, 2002; Christophoridis et al., 2009; Long et al.,1995) delineate three concentration ranges for a particular chemical (Table 20). The concentrations below the ERL value represent a minimal effects range; in which effects would be hardly observed. Concentrations equal to and above the ERL, but below the ERM, represent a possible-effects range within which effects would occasionally occur. Finally, the concentrations equivalent to and above the ERM value represent a probable-effects range within which effects

98 would frequently occur (Long et al., 1995). According to Table 25, Ni and Cr from the slag dumping area show higher concentration than the ERM value which is in terms of thread to the marine organisms in this area. Furthermore, in the slag dumping area 6% of Cu values indicate possible effects. As for the reference area, except for Cr (more than 50%) and Ni (100%) indicate adverse range effects all the other metals are in the minimal effects range.

Table 25: ERL and ERM guidelines value for trace metals (ppm, dry wet). ERL= Effects Range- Low; ERM= Effects Range Median. Concentrations above ERM value were indicated in bold.

Sediment Quality % samples ranges of (SQGs) Slag % samples ranges of (SQGs) Reference Guidelines (µg/g dumping area area dw) ERL ERM

5.1.1.6 Estimation of pollution state in sediment

When studying contaminated samples, the determination of the extent or degree of pollution by a given heavy metal requires the pollutant concentration to be compared with an unpolluted reference material. Such reference material should be unpolluted or pristine sediment that is comparable with the studied samples. The reference material then would represent a bench- mark to which the pollutant concentrations in the given samples are compared. Pollution, in this case, will be measured as the amount (or ratio) of the sample metal enrichment above the concentrations present in the reference material (Abrahim & Parker, 2008). Various methods have been suggested for quantifying metal enrichment in surface sediments. A common method for comparing sediment metal concentrations with pre-civilization background levels was to compare the present day metal levels with their concentrations in standard earth materials such as average shale (Turekian & Wedepohl, 1961) or average crustal values (Abrahim & Parker, 2008; Christophoridis et al., 2009).

Another approach which might provide more meaningful results is to use the metal content found in deeper sediment samples of non-impacted sites as reference backgrounds (Christophoridis et al., 2009; Pekey, 2006; Violintzis, Arditsoglou, & Voutsa, 2009), however

99 the high enrichment factor of metals in certain areas might be due to the natural background source rather than anthropogenic sources (Reimann and de Caritat, 2005).

The calculation of the Enrichment factor (EF) is a useful technique in determining the degree of anthropogenic heavy metal pollution. Elemental concentrations can be compared with reported natural abundances of metals in soils and/or crustal rocks, by normalizing against geochemical markers (e.g., Al, Fe, Cs, Rb, Li, Si, total organic carbon, grain size) of the predominant natural mineralogical phases (Loring, 1995; Pekey, 2006; Tranchina et al., 2008). In this study Al was used as the reference element for geochemical normalization.

EF =M(s) ×Al(b)/ M(b) ×Al(s)

Where M(s) and M(b) are the concentrations of the examined metal in the sample and the background reference respectively; Al(s) and Al(b) are concentrations of Al in the sample and the background reference respectively. Since the elemental composition of local geological substrate may be different from shale average or crust value, thus the best approach is to use local data (Violintzis et al., 2009). Moreover Evoikos Gulf is naturally enriched with some metals such as Ni and Fe due to the existence of metal resources. In our case study as the background values, the data from a sediment core analyzed by Voutsinou-Taliadouri & Varnavas (1993) was used. EF values were interpreted as follows: where: EF < 1 indicates no enrichment; <3 is minor enrichment; 3–5 is moderate enrichment; 5–10 is moderately severe enrichment; 10– 25 is severe enrichment; 25–50 is very severe enrichment; and >50 is extremely severe enrichment (Krčmar et al., 2013).

The enrichment factors (EFs) of all studied metals were shown in Figure 22. The reference value in Figure 22 A) is based on less contaminated core samples from this area. However, the reference value in Figure 22 B) is based on the average shale content. The data from the reference station of the present study were not considered in box plots of Figure 22. In the reference area, the EF values of all metals were less than 1 which classified the area as non- polluted site expect for Fe with the EF value between 1&2, which indicate minor enrichment. In the slag dumping area significant enrichment factors were found in metals related to the dumped slag (Ni, Fe, Cr and Mn). The average values of EF for Ni, Fe and Mn were less than 3, which are classified as a minor enrichment; however Cr presented a high EF value and was categorized as very severe enrichment area. In contrast to the above results which based on the less contaminated sample from the area, Figure 9b presents high EF for almost all metals. The background concentration of Ni, Cr and Zn were at least two folds higher than the average shale content.

100 A) B)

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EF base on less contaminted sample contaminted less on base EF 0 0 Ni Fe Cr Mn Cu Zn Ni Fe Cr Mn Cu Zn Figure 22: Enrichment factors based on A) a less contaminated sample, B) on Average Shale content in the slag dumping area

Another common approach for estimating the enrichment of metal concentrations is to calculate the geoaccumulation index (Igeo). The method assesses the degree of metal pollution in terms of seven enrichment classes (Abrahim & Parker, 2008; Okay, Pekey, Morkoc, Basak, & Baykal, 2008). The index is calculated as follows:

Igeo=Log2 Cn/1.5*Bn

Cn is the concentration of elements in the sample area. Bn is the background or pristine value.

The average geoaccumulation index (Igeo ) was calculated for the surface sediments of the depositing area with the shale average as the background value. For this calculation the data from reference station were not considered (Table 26).

The average Igeo for Mn, Zn and Cu was lower than 1 which indicates uncontaminated to moderately contaminated area, while the relatively high value of Igeo for Ni & Cr classified the area as moderately to strongly contaminated and extremely contaminated area, respectively. The negative Igeo values found in the table showed low levels of contamination.

101 Table 26: Index of geoaccumulation (Igeo) in surface sediments compared to shale average content. The number in bold indicated the moderately to extremely contaminated area.

Index of geoaccumulation (Igeo) base on shale average content Igeo Igeo Sediments quality values Classe s Stations Ni Fe Cr Mn Cu Zn R 0.89 -0.9 -0.55 -1.67 1.75 1.48 >5 6 Extremely contaminated Strongly to extremely L8 2.28 0.47 5.01 0.44 1.41 0.61 4-5 5 contaminated L10 2.57 0.18 5.93 1.11 1.18 -0.81 3.-4 4 Strongly contaminated Moderately to strongly L12 2.4 1.35 6.05 1.26 1.14 1.16 2-3 3 contaminated Moderately contaminated L14 2.48 1.4 5.59 0.86 1.8 -0.79 1-2 2

Average value Uncontaminated to from the slag 2.43 0.85 5.65 0.92 1.38 -0.84 0-1 1 moderately contaminated dumping area 0 0 uncontaminated

102 5.1.1.7 Discussion

Metal polluted sites represent a potentially hazardous risk for human health and the environment. Metals, like other contaminants (organic chemicals, radionucleides) tend to be absorbed onto both inorganic and organic materials that eventually settle in sediments, and serve as secondary source of pollution to the marine environment and disrupt the ecosystem directly or indirectly, causing significant contamination and loss of desirable species (Beyer et al., 2014). Therefore, the slag dumping area in the Larymna Bay has been monitored for years to investigate the impacts of the huge amount of deposited by product in the marine environment. The results of the present study showed an increasing rate in metal concentrations in this area. Moreover, the influence of deposited slag is not only restricted to the authorized dumping area, but also to a much vaster area which is most probably due to the higher rate of depositing or to false depositing. The existence of small amounts of slag in the reference area could also be due to the possible transportation of these suspended particles from the slag area (Voutsinou, 1987) via physical sorting or by strong currents from the dumping area. Physical sorting of slag is occurring at the moment of dumping which may influence the spatial distribution of metals. In accordance, Voutsinou (1987) suggested that during the slag deposition the large particles fell to the seafloor at the site of discharge, while the smaller remained suspended for a longer time; therefore a selective fractionation of particles took place.

The granulometry is one of the important factors affecting the concentration of metals in the sediment. It is conditioned by several parameters such as the rate of the natural sedimentation in the area, the initial granulometry of the disposed slag (not continuously constant), as well as its age/freshness and consistency (Anagnostou, 1986). According to different studies (Loring and Rantala, 1992; Puig et al., 1999; Scoullos and Dassenakis, 1983), higher concentration of metals associated with the fine fractions, however except for Ni, the higher concentration of metals related to the smelting plant such as Fe, Cr and Mn were measured in the coarse fraction since the coarse materials is basically processed ore particles. In opposite, the higher concentration of Ni in the fine fraction may be due to the extraction process of this metal in the metallurgy.

Apart from the grain size, the spatial distribution of metals concentrations in the slag dumping area is probably related to the distance of the stations from the smelting plant. The L8 is the furthest station and L12 and L10 are the nearest ones to the smelting plant. Therefore it is suggested that the dumping cargo deposited the slag much frequently at the site closer to the smelting plant than to the furthest ones.

In this study the highest concentration of total Ni was found in L12 in southeast of the dumping area. In agreement with these findings, Voutsinou (1987) had suggested that Ni was

103 associated with the fraction of intermediate grain size. This element is released from the slag after being leached by seawater and possibility reincorporated with the suspended metalliferous material and absorbed to the newly formed Fe(OH)3 or to fine particles of slag which were transported southeast ward. Meanwhile, it should be considered that the process of resealing the metal ion from the deposited materials and its fate in the marine ecosystem is a complex processes which is influenced by many factors e.g the age of the deposited slag, the currents, the temperature etc.

Since Al is a constituent of natural sediment and the third most abundant element in the deposited slag, therefore high concentration of this metal is expected in the dumping area (Zaharaki and Komnitsas, 2009 ; IOFR 1985; Balomenos et al., 2013).

The high concentration of Zn and Cu found in both the smelting plant and the reference areas is most likely attributed to the anthropogenic activities such as municipal sewage from the residency area or the fish farming, since these two metals are not related to the slag composition. Hg is not a component material of laterite ore; therefore the concentration of this metal in the reference area might attribute to the background sources. However in the slag dumping area the composition of natural sediment has changed because of the long term depositing.

It is known that the carbonate content is acting as a dilutor of trace metals (Macõâas- zamora et al., 1999) and has negative correlation with metals (Tylmann et al., 2011) consequently the highest concentration of carbonate leads the lower levels of trace metals in the sediment. The elevated concentration of carbonate in the reference area may be attributed to the higher biogenic productivity which is related to high nutrient abundance and more stable environment conditions present in the reference area rather than the dumping area.

The total organic carbon in the slag dumping area is probably related to the continuous dumping of slag which implies hypoxic environmental (Dassenakis et al., 2003) and perhaps due to the direct releasing of heavy slag in this area which may cause harm to benthic population.

Since the labile metal fraction is often used as an indicator of anthropogenic contamination, the high labile concentration of Cu and Zn could be attributed to other such sources, and the high percentage of labile Al, Cr, Mn and Ni is directly related to the deposited slag. High concentration of labile Mn is also in agreement with the other studies (Dassenakis et al., 2003; Zabetoglou et al., 2002). Mn tends to be present in less thermodynamically stable sediments, which are easily reduced to Mn oxides from Mn+2, besides it is a non-lattice held element mostly associated with carbonate minerals (Angelidis & Aloupi, 2000) and therefore it is

104 able to leave the lattice easily. The low concentration of Fe (<20% in most cases) is probably due to the increased contribution of lattice-held fraction of the metal (Dassenakis et al., 2003).

The absence of statistically significant correlation of metals, especially the ones related to the smelting plant by-product in the dumping area is probably attributed to the continuous dissolution process in the short term desorption event during dumping. Based on an in-vitro experiment on fresh slag (the slag before depositing) from the same smelting plant Kersten and Anagnostou (1994) showed that more than 70% of Ni concentration was slightly washed out during the first 24 hours after introducing the slag to the sea water. On the other word, the fresher the dumped slag is, the higher amounts of metal ions are dissolved into the water.

The correlations of Fe and Mn with other metals imply the presence of Fe/Mn oxides. It is known that the Fe & Mn oxides/Hydroxides have a high affinity with most trace metals and Fe often correlate with other metals of the marine environment (Zabetoglou et al., 2002). Thus, grouping of Fe and Mn with Cr and Cu might be due to the role of these metals. Furthermore, the positive strong correlation between the two metals Ni/Zn along with clustering in the same group was in agreement with the other study (Voutsinou, 1993) from the same area. According to Voutsinou (1987), it is suggested that the association of these metals in the mettaliferous sediments might indicate same origin of the mentioned metals or another source nearby.

Association of CO3% and TOC% in the same cluster probably indicated hypoxia and low metals concentration in the environment. Besides the absence of correlation of carbonate and metals reflects the conflict of these two parameters in the sediment since carbonate is acting as a strong diluter in the environment. This result is in accordance with the Kiratli, 1996 findings from the Black Sea sediment which indicate that carbonates may be associated to the larger particles with lower concentrations of metals.

Cu and Zn are both non-lattice metals which might have an increased mobility in the environment (Scoullos & Dassenakis, 1983). Cu is a metal not related to the by-product of the smelting plant and mostly related to other types of anthropogenic contamination. Mn is also a non-lattice metal which is probably associated with carbonates (Dassenakis et al., 2003; Dassenakis et al., 1994) but in contrast with Cu, it is associated with the dumped slag. Hence, because of their different source these two non-lattice metals were not classified in the same group in the cluster of the labile phase.

Finally the results of this study were evaluated by applying different sediment quality guidelines in order to access the extent of contamination in the area.

105 The results from the core baseline, used in this study as a less contaminated sample to obtain the Enrichment Factor (EF) of area were taken from a previous study performed in 1993 by Voutsinou. The core was about 1 m deep which is still not considered as the natural base of crust in the area due to the high quantity of depositing but it perhaps represents the early operation of the smelting plant. Nevertheless, the minor enrichment for the metals related to the metallurgy operation such as Fe, Ni and Mn and the severe pollution for the Cr reflect how seriously this area is affected. In addition, the comparison of the result with the average shale value indicates a highly metal enriched area. Concerning the biological effects of metals, the concentrations of both Ni and Cr in the slag dumping and the reference areas were greatly above the US EPA standard which indicates adverse biological effects on the benthic organisms.

Consequently the evaluation of analytical data, along with the assessment of various factors; such as the Enrichment factors, Geo-acculumation Index suggests that the slag dumping area is highly enriched by the metals related to the smelting plant by-product and the possible harmful effects of the deposited slag is not restricted to the authorized area, but also obtained a more extensive area.

106 5.1.2 Seawater

Seawater samples were taken from two depths: surface (1 m beneath the surface line) and bottom (1m above the sediment) from the 4 stations in the slag dumping area and one station in the reference area. In these samples dissolved and particulate concentrations of the metals were determined. Totally 36 samples were analyzed during this study.

5.1.2.1 Levels of dissolved metals

The general statistics of Ni, Fe, Cr, Mn, Zn and Cu concentrations in surface and bottom seawater are presented per sampling area in Table 27 and 28.

It is observed that opposite to what was expected, the concentrations of dissolved metals from both the sampling depths did not show any considerable differences between the reference area and the slag dumping site. Moreover, in some cases greater concentrations of metals were detected in the reference area rather than the dumping site. For instance, remarkably high concentration of Fe was detected in both surface and bottom water samples. The average and the maximum concentrations of this metal in the surface water sample reached 166±269µg/l, and 477µg/l, respectively which are about 100 folds higher than the maximum level of this metal from the slag dumping area. Similar results were also found for the bottom water sample. Apart from Fe, the average concentration of dissolved Cr from the bottom area is (about 3-25 folds) higher than the slag dumping area. In the surface area, the concentration of this metals is around 1-5 folds higher than those from slag dumping area. Furthermore, the average concentration of Ni and Mn are slightly greater in this area than in the slag depositing area.

At the slag dumping area the concentrations of dissolved metals from the bottom were generally higher than those from the surface. The highest concentrations of almost all metals except Mn and Ni from the surface area were detected in station L12. In the bottom area, however, the highest level of metals was found in two stations L12 and L14. Hence, it presumed that L12 and L14 were the most contaminated stations among the other dumping stations.

Non-parametric statistical test (Kruskal Wallis) was performed to reveal significant differences in the concentration of metals among different stations or among different sampling depth (surface and bottom). The test did not showed any significant differences neither among the stations (all the stations of slag dumping area, including the reference area) nor between the surface and bottom sampling depths.

107 Table 27: Average, minimum and maximum concentration of dissolved metals in surface water samples from the slag dumping and reference areas (R).

Surface water Metals Dissolved L10 L12 L14 L8 R Ni(µg/l) Mean 2.3±0.28 3.0±0.52 2.7±0.29 2.8±0.30 3.1±0.79 Min 2.8 2.4 2.4 2.5 2.5 Max 3.3 3.3 3.0 3.1 4.0 Mean 4.4±0.42 19.0±23.7 15.9±12.4 6.5±3.1 166±269 Fe(µg/l) Min 3.64 5.3 4.7 4.2 3.1 Max 4.42 46.5 29.3 1.0 477 Mean 0.49±0.17 3.8±5.1 0.64±0.30 0.95±0.89 3.2±3.9 Cr(µg/l) Min 0.37 0.18 0.42 0.32 0.42 Max 0.61 7.4 0.85 1.58 5.9 Mean 0.78±0.41 1.1±0.6 2.05±1.9 0.78±0.45 2.1±1.5 Mn(µg/l) Min 0.64 0.4 0.68 0.45 1.2 Max 1.24 1.6 4.3 1.3 3.9 Mean 7.7±6.3 8.0±7.8 3.4±0.40 5.6±3.2 3.6±2.4 Zn(µg/l) Min 3.9 3.0 3.2 3.2 1.9 Max 15 17 3.9 9.2 6.4 Mean 0.67±0.22 0.91±0.93 0.36±0.20 0.42±0.15 0.37±0.16 Cu(µg/l) Min 0.48 0.10 0.15 0.25 0.20 Max 0.90 1.9 0.37 0.49 0.50

108 Table 28 Average, minimum and maximum concentration of dissolved metals in bottom water samples from the slag dumping and reference areas (R).

Bottom water Metals Dissolved L10 L12 L14 L8 R Ni(µg/l) Mean 4.7±2.4 3.9±1.62 2.8±0.7 3.3±1 2.8±0.7 Min 2.7 2.8 2.4 2.5 2.3 Max 7.4 5.8 3.7 4.5 3.7 Mean 4.3±2.6 30.9±34 50.7±79.7 11.6±12.5 80±123 Fe(µg/l) Min 1.3 8.7 3.7 1.3 6.7 Max 6.2 70.0 142.8 25.6 222.5 Mean 0.35±0.23 0.66±0.07 2.4±3.0 0.92±0.85 7.6±10.2 Cr(µg/l) Min 0.19 0.61 0.23 0.32 0.36 Max 0.51 0.71 4.5 1.5 14.8 Mean 1.4±1.8 1.8±1.7 1.4±0.76 1.2±1.1 1.6±1.1 Mn(µg/l) Min 0.48 0.69 0.60 0.32 0.56 Max 2.7 3.8 2.1 2.4 2.7 Mean 8.5±6.0 7.6±4.2 4.1±0.7 8.0±3.3 4.7±5.0 Zn(µg/l) Min 2.4 4.1 3.3 6.0 1.3 Max 14.9 12.3 4.7 11.8 10.4 Mean 0.55±0.22 0.57±0.19 0.33±0.06 0.40±0.15 0.34±0.11 Cu(µg/l) Min 0.24 0.43 0.27 0.25 0.30 Max 0.67 0.78 0.39 0.55 0.50

109 Table 28: Average, minimum and maximum concentration of particulate metals in surface water samples from the slag dumping and reference areas (R).

Surface water Metals Particulate L10 L12 L14 L8 R Ni(µg/l) Mean 0.31±0.27 0.41±0.65 0.57±0.31 0.14±0.18 0.13±0.15 Min 0.07 0.03 0.25 0.04 0.02 Max 0.60 1.16 0.88 0.35 0.30 Mean 16.0±11.2 24.1±30.1 52.4±21.5 16.2±8.9 14.1±13 Fe(µg/l) Min 6.0 6.3 30.91 9.1 5.1 Max 28.1 59 74.03 26.3 29.1 Mean 0.8±0.7 3.0±4.9 0.69±0.38 0.46±0.38 0.28±0.31 Cr(µg/l) Min 0.07 0.09 0.38 0.11 0.07 Max 1.5 8.7 1.1 0.86 0.64 Mean 0.3±0.2 0.54±0.43 1.3±1.4 0.37±0.21 0.48±0.45 Mn(µg/l) Min 0.20 0.17 0.43 0.22 0.14 Max 0.57 1.01 2.9 0.63 0.99 Mean 0.30±0.13 0.78±0.16 0.37±0.19 0.35±0.10 1.5±2.0 Zn(µg/l) Min 0.22 0.60 0.23 0.26 0.24 Max 0.46 0.88 0.59 0.46 3.9 Mean 0.07±0.02 0.11±0.09 0.08±0.03 0.06±0.01 0.06±0.03 Cu(µg/l) Min 0.04 0.04 0.06 0.06 0.04 Max 0.08 0.21 0.11 0.07 0.09

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Table 30: Average, minimum and maximum concentration of particulate metals in bottom water samples from the slag dumping and reference areas (R).

Bottom water Metals Particulate L10 L12 L14 L8 R Ni(µg/l) Mean 0.14±0.1 0.1±0.1 0.15±0.1 0.2±0.2 0.4±0.4 Min 0.03 0.03 0.04 0.04 0.02 Max 0.2 0.3 0.3 0.4 0.9 Mean 14.0±7.6 12.8±9.8 14.5±9.2 22.1±21.9 82.7±124.5 Fe(µg/l) Min 5.6 4.3 4.97 5.7 2.6 Max 20.7 23.6 23.4 47 226 Mean 0.6±0.9 0.4±0.5 0.5±0.6 1.0±1.5 2.2±3.6 Cr(µg/l) Min 0.03 0.05 0.03 0.06 0.3 Max 1.8 1 1.2 2.8 6.5 Mean 0.16±1 0.84±0.6 0.61±0.3 1.01±0.6 1.4±1.2 Mn(µg/l) Min 0.04 0.2 0.2 0.3 0.16 Max 2.06 1.5 1 1.6 2.5 Mean 0.3±0.1 0.2±0.06 0.3±0.1 0.5±0.3 0.2±0.05 Zn(µg/l) Min 0.2 0.2 0.2 0.3 0.2 Max 0.4 0.3 0.5 0.9 0.3 Mean 0.04±0.02 0.08±0.04 0.05±0.01 0.04±0.03 0.05±0.02 Cu(µg/l) Min 0.03 0.05 0.04 0.02 0.04 Max 0.06 0.12 0.06 0.08 0.08

111 5.1.2.2 Levels of particulate metals

The minimum, average and maximum concentration of particulate metals during the three years of studies is shown in Table 29 and 30.

Similarly to the results of the dissolved metals, the concentration of some particulate metals such as Fe, Cr and Mn was higher in the reference area than in the slag dumping one. This particular result was just detected in the surface water. In the bottom area, however the highest average concentration of metals related to the smelting plant by-product (Fe, Mn, Ni and Cr) was found in the L8, while for those not related to the deposited slag (Zn and Cu) measured in station L12. No significant differences were found neither between the concentrations of metals in the different sampling depths neither between the two studied areas nor among the sampling stations.

5.1.2.3 Levels of Suspended particulate matter

Suspended Particulate Matter (SPM) in seawater mostly originates from the fine sediment (mud) in the bottom and fluvial inflow and classified by the grain size. In particular, SPM distribution in the water column influences the plankton primary production by regulating the light penetration depth in seawater (Loring and Rantala, 1992) furthermore, SPM can absorb and thus transport some human- made contamination such as heavy metals (Haarich et al.,1993). Suspended particulate matter (SPM) of surface and bottom water samples are shown in Table 31. It is observed that the concentrations of SPM (mg/l) in different areas through the monitoring periods (2009-2011) were constant, except some exceptions detected in L12 in 2009 and L8 in 2011 from the surface area and L14 in 2011 from the bottom area. Meanwhile, in the reference area, the SPM level is as comparable as the dumping area.

Table 29: Suspended particulate matter (SPM) (mg/l) of surface and bottom water samples (2009-2011).

Suspended particulate matter (SPM) mg/l Surface Bottom 2009 2010 2011 2009 2010 2011 R 5.8 6.1 6.4 6.9 4.5 2.9 L8 5.7 6.7 2.8 6.2 6.4 6.6 L10 7.2 5.4 6.8 7.1 6.6 6.9 L12 10.1 6.0 7.0 6.0 4.6 6.8 L14 7.4 5.2 7.9 6.7 6.8 1.3

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The dissolved and particulate percentage of metals provides good information on the distribution of metals in the seawater (Table 32).

It is observed that in the surface water the percentage of dissolved metals such as Ni, Zn and Cu is considerably higher than the percentage of particulate metals in bottom water in both the slag dumping and the reference areas. In opposite, Fe showed greater concentration in particulate than in dissolved phase in all the stations (slag dumping and reference) in both areas. While, Cr and Mn behave differently. For these two metals slight differences were found between the dissolved and particulate percentages in the deposited area, however this variation is more obvious in the reference area. Apparently all the metals in the depositing site showed higher percentage of particulate metals in the surface and that of dissolved ones in the bottom (Table 32).

Table 30: Percentage of dissolved and particulate metals in surface and bottom areas. Dis:= dissolved and Par = particulate. R=reference area. The total concentration of metals is expressed in µg/l

Surface water Bottom water Stations L10 L12 L14 L8 R L10 L12 L14 L8 R Total metal 3.3 3.4 3.3 3.01 3.2 4.9 4.1 3.0 3.5 3.2 Ni %Dis 90 88 83 95 96 97 97 96 94 88 %Par 9 12 17 5 4 3 3 5 6 12 Total metal 20.2 43.2 68.3 22.7 180.2 18.3 43.7 65.1 33.7 162.7 Fe %Dis 20 44 23 29 92 23 71 78 34 49 %Par 80 56 77 72 8 77 29 22 66 51 Total metal 1.3 6.8 1.3 1.4 3.4 1.0 1.0 2.8 1.9 9.8 Cr %Dis 37 56 48 67 92 35 63 84 49 77 %Par 63 44 52 33 8 65 37 16 51 23 Total metal 1.1 1.7 3.9 1.2 2.6 1.6 2.6 2.0 2.2 3.0 Mn %Dis 69 68 53 67 82 90 68 70 54 53 %Par 31 32 33 33 18 10 32 30 46 47 Total metal 8.0 8.8 3.8 6.0 5.2 8.9 7.8 4.5 8.4 4.9 Zn %Dis 96 91 90 94 70 96 97 93 94 96 %Par 4 9 10 6 30 4 3 7 6 4 Total metal 0.7 1.7 0.4 0.5 0.4 0.6 0.7 0.4 0.4 0.4 Cu %Dis 91 54 82 88 86 93 88 87 91 79 %Par 9 46 18 13 14 7 12 13 9 12

In accordance to the variation of the total metal concentrations between slag dumping and reference area, it is observed that the total levels of all metals except for Cu were higher in the

113

reference area. The non-parametric Kruskal-Wallis test was run to reveal if there is any significant difference among total concentration of metals during the time. The results showed that in the slag dumping area significant differences (P<0.05) were detected in the concentrations of Ni, Mn, Cu and Cr of surface samples. In addition, the multiple comparison tests suggested that the significant differences for Mn occurred in 2010/2011 and for Ni in 2009/2010. In the bottom area of the slag area, as well as the surface area, the same metals (Ni, Mn and Cr) showed significant differences. Moreover, Significant references (P<0.05) were detected in the concentration of Fe in 2010/2011. Mn also showed significant difference in 2009/2011.

2009

Figure 23: Variation in concentrations of dissolved metals per station in 2009 from both surface and bottom areas

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2010

Figure 24: Variation in concentrations of dissolved metals per station in 2010 from both surface and bottom areas

2011

115

2011

Figure 25: Variation in concentrations of dissolved metals per station in 2011 from both surface and bottom areas

The variations in concentrations of dissolved metals from surface and bottom areas were illustrated in Figures 23-24 and 25. High concentration of Mn from 2009 and 2011and other metals related to the smelting plant such as Fe, Ni and Cr (2011 sampling) in the surface water of reference area is considerable.

5.1.2.4 Investigation of metal level relations in seawater and estimation of pollution state In order to establish a relationship between the different parameters of this study, cluster analysis was performed. This is a unique method which groups the cases that share the same trend in the variables (metals). In addition, correlation coefficient is analyzing the similarities between the variables (Gore, 2000). Usually the two methods are both used for confirming the results (Relić et al., 2005).

In this study, Ward‘s method and Euclidean distance were applied for clustering analysis of data. The data from the reference area have been excluded from this analyzing in order to have a better understanding of the metals and their relationship in the slag dumping area itself.

The cluster analysis of the concentrations of dissolved metals in the surface and bottom areas of slag dumping site is presented in Figure 26 A &B.

The analysis showed almost similar clusters for both the surface and bottom areas. In the surface area three clusters were found. The first group included Cr, Cu and Zn, the second

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contained Fe and Mn and third one compromised only Ni. The correlation coefficient analysis showed a significant correlation (P<0.05) between the grouped metals [Cu/Zn with R=0.578 and Cu /Ni with R=0.863]. The clusters plots in the bottom and surface area have almost the same pattern with the exception for Cr, which was grouped with the Fe and Mn and Ni which made an individual cluster. The results from the correlation coefficient is obviously in support with the cluster analysis, therefore a good relation was found between the two metals Zn/Ni (R=0.867 in P<0.05) from the first group and between Cr/Fe (R=0.886) and Mn/Cr (R=0.743) in P<0.01 from the second group. It is clearly observed that the relation between the grouped metals from the bottom area were much stronger than those from the surface.

The dendrograms for the slag dumping area show how stations are grouped together based on their similarities in the concentrations of dissolved metals.

A) B) Dendrogram Dendrogram Ward's Method,Euclidean Ward's Method,Euclidean 5 8

4 6 3 4

2

Distance Distance 2 1

0 0

Ni

Cr

Fe

Zn

Ni

Cu

Cr

Mn

Fe

Zn Cu Mn Dissolved metals from bottom area Dissolved metals from surface area C) D)

Dendrogram Dendrogram Ward's Method,Euclidean Ward's Method,Euclidean

8 8

6 6

4 4

Distance Distance 2 2

0 0

R

L8

R

L8

L10 L12 L14

L12 L14 L10 Dissolved metals-bottom area Dissolved metals-surface area

Figure 26: Cluster analysis of dissolved metals (mg/l) from surface and bottom areas in the slag dumping site. 117

The same results were obtained from the clustering of stations in both surface and bottom areas (Figure 26 C & D). Thus two main groups of stations were achieved for the both areas. The first group contained the reference station and L14 and the second one consisted of the remaining three stations (L10, L12 &L8). A stronger similarity between the reference station and station L14 was apparent when cluster analysis was based on bottom seawater than when based on surface seawater.

Similarly to the results from the dissolved metals in the slag dumping area, the cluster analysis of the particulate metals from the surface and the bottom areas are pretty much the same. (Figure 27).

A) B)

Dendrogram Ward's Method,Euclidean Dendrogram Ward's Method,Euclidean 4 6

3 5 4 2

3

Distance Distance 1 2 1

0 0

Ni

Cr

Fe

Zn

Ni

Cu

Cr

Fe

Mn

Zn

Cu Mn

Particulate metals from bottom area Particulate metals from surface area C) D) Dendrogram Dendrogram Ward's Method,Euclidean Ward's Method,Euclidean

8 8

6 6

4 4

Distance Distance 2 2

0 0

R

R

L8

L8

L10 L14 L12

L12 L14 L10 Particulate metals-bottom area Particulate metals-surface area

Figure 27: Cluster of concentration of particulate metals (mg/l) from surface and bottom area in the slag dumping site

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In both areas, the metals were divided into two main groups; the only difference is the location of Mn and Cr in the two groups: they are substituted by each other, since in the surface area Mn was grouped with Ni and Fe, while in the bottom area it was associated with Cu and Zn. The opposite occurred for Cr.

Furthermore, the results from the correlation coefficient of metals supported the cluster analysis. Hence strong significant correlation was found between Fe/Mn (R=0.755) with P<0.05 in the surface area and Fe/Cr (R=0.923, in p<0.01) in the bottom area. The dendrograms for stations of particulate metal concentrations in surface and bottom sampling depths were quite different (Figure 27). Although both diagrams consisted of two main groups, they presented conflict in the location of reference area. Therefore, in the bottom area the reference site grouped individually, but in the surface area it joined with the L8 and L10 stations from the slag dumping area.

The partition coefficient (kd) is defined as the ratio of the metal concentration in the particulate matters (Mp, in micrograms per kilogram) to that in the dissolved fractions (Md, in micrograms per liter) (Ambrose, 2005). Partition coefficients (Kd) and especially their logarithms

(log Kd), are convenient parameters to quantify and rank the relative strength of the association of individual contaminants, mainly trace metals, with the suspended particulate matter in natural water. They are calculated as follows:(García-Rico et al.,2011).

kd = Mp/Md

In which Mp is the concentration of metals in the particulate forms which express in

µg/kg and Md is the concentration of dissolved metals and express in µg/l (García-Rico et al., 2011)

The average ratio of Kd in the seawater of slag dumping and reference sites is presented in Figure 28. It can be clearly seen that the kd index in the reference area presents similar ratio of contamination as the dumping area. Although the statistical test did not reveal any significant difference neither among the stations in the slag dumping site in both surface and bottom areas nor between the reference area and the depositing site. Nevertheless, kd index showed similar results for the surface and bottom areas, therefore the order of Kd is; Cu>Fe>Cr>Mn>Zn>Ni.

Table 33 presents the comparison of partition coefficients in our study and other areas.

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Log Kd bottom water R L10 L12 L14 L8 7

6

5

4

3 Zn Cu Ni Mn Cr Fe

Log K surface water d R L10 L12 L14 L8 7

6

5

4

3 Zn Cu Ni Mn Cr Fe

Figure 28: Logarithm of partition coefficient (log kd ) of surface and bottom areas of slag dumping and reference areas during the sampling period.

Table 31: The logarithm of partition coefficient log (kd) results from the water and sediment area of the present and other studies.

areas Fe Ni Cr Mn Cu Zn Ref Galveston bay (water) 6.9±0.5 - - - - 5.3±0.2 Wen et al.,1999 Galveston bay (sediment) 4.3±0.5 - - 2.0±0.3 - 3.9±0.2 Warnken et al., 2001 Kalloni bay (sediment) 5.3±0.8 4.1±0.7 - 4.2±0.8 4.1±0.5 3.6±0.3 Gavriil et al., 2005 Kalloni bay (water) 6.8±0.6 5.1±0.3 - 5.7±0.3 4.8±0.2 4.8±0.2 Gavriil, 2002 Literature survey - 4.6 4.5 - 4.7 5.1 Ambrose, 2005 Maliakos Gulf - - - 5.7±0.5 4.9±0.2 5.2±0.5 Rousselaki, 2007 Larymna bay 5.6±0.2 4.1±0.2 5.3±0.1 4.8±0.1 6.4±0.1 4.4±0.2 Present study (slag/surface) Larymna bay 5.3±0.3 3.8±0.2 5.1±0.2 4.9±0.2 6.3±0.1 4.01±0.2 Present study (slag/bottom)

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In this respect, the Kd index of Mn, Cu and Cr from the slag dumping area in the Larymna Bay had higher levels than that from other studies, while the partition coefficient of Ni and Fe being slightly higher in Galveston bay (Wen et al. 1999) and Kalloni bay (Ambrose 2005; Gavriil and Angelidis, 2005; Gavriil, 2002).

5.1.2.5 Assessment of seawater quality Protection of water quality attempts to be achieved through criteria and standards and their implementation. The US Environment Protection Agency and EU water framework directive (WFD) have developed guidelines for deriving numerical national water quality criteria for the protection of aquatic organisms. These guidelines provide the methods for deriving water quality criteria.

A) CMC µg/l (Criterion maximum concentration) which is intended to protect against short-term exposure to pollutants or chronic toxicity. B) CCC µg/l (Criterion Continuous concentration) which is intended to protect against continuous concentration exposure or acute toxicity The US EPA chronic and acute and the WFD criteria were higher than the average concentration of all measured metals. The distribution range of heavy metal concentrations (Dissolved and Particulate) from this study were compared with other areas in Greece (Table 34).

According to SOHEIME reports (Papathanasoiu and Zenetos, 2005), dissolved Ni in Larymna stations was one of the highest of the Greek mainland sea water measurements in comparison with some other contaminated areas such as Maliakos Gulf (2000-2002), Astakos Gulf (1998-2000), Peiraias Port (1999) , Lavrio Port (2003), Elefisis Gulf (2004) and the west part of Saronikos (2004) with average Ni dissolved concentrations of 1.24κg/l, 0.54κg/l, 1.56κg/l and 2.5κg/l, 0.97κg/l, 0.6κg/l, respectively. Dissolved Mn was also among the highest concentrations determined along Greece, some of these contaminated places are Saronikos Gulf (1986-98), Elefsis Gulf (2004), Thermaikos Gulf (1997-98), Peiraias Port (1999) and Lavrio Port (2003) with average concentrations of 1.36κg/l, 0.79 κg/l, 1.57κg/l , 2.70κg/l and 1.46 κg/l. Peiraias Port (1999) 12.4 κg/l, Astakos Gulf (1998-2000) 11.2 κg/l and Lavrio Port (2003) 13.0 κg/l are considered much contaminated by Zn than the results of this study. The Dissolved Cu concentration is also lower than some other Greek areas such as Saronikos Gulf (1986-98), Elefsis Gulf (1986-98), Elefsis Gulf (2004), west part of Saronikos Gulf (2004) and Lavrio Port (2003) with average concentration of 1.19 κg/l, 1.56 κg/l , 0.47 κg/l, 0.18 κg/l , and 1.45 κg/l.

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Table 32: Comparison of Average and the range of metals concentrations in dissolved form (μg/l) from Larymna Bay in N. Evoikos gulf and some other areas in Greece with US EPA and WFD criteria. ND =no data

1-CCC (κg/l) is Criterion Continuous concentration 2- CMC (κg/l) is Criterion maximum concentration 3-MAC-EQC (κg/l) is maximum allowable concentration

Metals Ni Fe Mn Cr Zn Cu References Larymna Bay- Surface water 2.4-3.3 3.6-5.48 0.4-4.3 0.8-7.4 3-17. 0.1-1.9 This study offshore area 2.9 11.4 1.2 1.5 6.2 0.37 Larymna Bay-Bottom water 2.4-7.4 1.3-143 0.3-3.8 0.19-4.5 2.4-14.5 0.2-0.7 This study offshore are 3.7 24.4 1.4 1 7 0.44 North Aegean Sea (1986-98) 0.12-1.1 ND 0.004-1.4 ND ND 0.04-0.43 Zeri &Voutsinou, 2003 0.43 0.36 0.17 Saronikos Gulf(1986-98) 0.15-22.5 ND 0.10-2.2 ND 1-6.5 0.01-10.7 MED POL project-1986- 2 1.4 3.1 1.2 1999

Elefsis Gulf(1986-98) 0.4-41.9 ND 0.12-13 ND 2.6-9.4 0.30-9.5 MED POL project-1986- 2.8 3.8 6.5 1.6 1999 South Evoikos Gulf(1997-98) ND ND ND ND 0.3-38 0.13-2.0 Dassenakis et, al, 1999. 7.4 0.88 2003b

Elefsis Gulf (2004) 0.81-1.4 ND 0.43-1.2 ND 3.5-5.4 0.32-0.72 M. J. Scoullos et al. 2007 0.97 0.79 4.3 0.47 West part of Saronikos 0.3-1.5 ND 0.11-0.78 ND 0.87-5.4 0.07-0.26 M. J. Scoullos et al. 2007 Gulf(2004) 0.6 0.38 2.2 0.18 Peiraias Port(1999) 0.82-1.85 ND 1.8-3.2 ND 7.5-17.5 0.54-1.87 Sakellariadou,et al, 2001 1.6 2.7 12.4 0.96 Ionian Sea(2000) 0.28-1.7 ND 0.01-0.14 ND ND 0.07-0.13 HCMR(interring 0.51 0.06 0.09 project)unpublished data 122

Metals Ni Fe Mn Cr Zn Cu References Thermaikos Gulf(1997-98) 0.18-0.66 ND 0.06-6.1 ND 0.03-1.1 0.07-0.74 METRO MED project, 0.3 1.6 0.21 0.21 HCMR Maliakos Gulf(2000-2002) 0.55-2 ND 0.04-2.8 ND 0.25-59.8 0.12-3.4 Dassenakis et, al,2001 1.2 0.8 4.6 0.7 Astakos Gulf(1998-2000) 0.35-0.59 ND ND ND 7.08-16.1 0.92-1.21 Belias et al, 2003 0.54 11.2 1 Lavrio Port(2003) 1.4-6.4 ND 0.97-7.4 ND 7.5-30.7 0.45-3.9 Dassenakis et al, 2003a 2.5 1.5 13 1.4 CCC1 8.2 81 3.1 National recommended Water Criteria (US EPA 2006) CMC2 74 90 4.8 MAC-EQC3 34 www.ec.europa.eu/

Directive 2000/60/EC

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Table 33: Average and the range of metals concentrations in particulate form (μg/l) from Larymna Bay in N. Evoikos gulf and some other areas in Greece. ND =no data

Metals Ni Fe Mn Cr Zn Cu References Larymna Bay-surface 0.03-1.2 6.0-74.0 0.07-8.7 0.17-2.9 0.22-0.88 0.04-0.21 This study water offshore area 0.35 24.2 0.64 1.2 0.45 0.08 Larymna bay- bottom- 0.03-0.45 4.3-23.4 0.04-2.1 0.03-2.8 0.2-0.45 0.02-0.12 This study offshore area 0.15 15.8 0.61 0.84 0.33 0.05 North Agean sea (1986- 0.004-0.12 ND 0.010-3.2 ND 0.04-0.67 0.007-0.27 Zeri &Voutsinou, 98) 0.02 0.22 0.08 0.03 2003 Saronikos Gulf(1986-98) 0.04-7.1 ND 0.01-5.5 ND 0.43-7.1 0.02-2.1 MED POL project- 0.62 2.8 3.1 0.23 1986-1999 Elefsis Bay(1986-98) 0.03-18.1 ND 0.01-230 ND 0.85-9.3 0.04-8.5 MED POL project- 0.85 6.5 3.8 0.42 1986-1999 Elefsis Gulf (2004) 0.02-0.06 ND 0.14-0.5 ND 0.54-1 0.10-0.23 M. J. Scoullos et 0.04 0.32 0.67 0.16 al.2007 West part of Saronikos 0.01-0.06 ND 0.02-0.22 ND 0.07-0.60 0.03-0.17 M. J. Scoullos et Gulf (2004) 0.03 0.1 0.23 0.06 al.2007 South Evoikos ND ND ND ND 0.13-8.48 0.09-2.92 Dassenakis et, al, Bay(1997-98) 0.32 0.28 1999. 2003b Peiraias Port(1999) 0.24-0.58 ND 0.85-2.48 ND 1.6-5.6 0.67-2.27 Sakellariadou,et al, 0.14 1.5 2.9 1.4 2001

Ionian Sea(2000) 0.003-0.40 ND 0.01-0.65 ND 0.03-0.83 0.003-0.22 Hcmr (interring 0.03 0.08 0.12 0.02 project)unpublished data Thermaikos Gulf(1997- 0.004-0.7 ND 0.04-14.3 ND 0.004-0.7 0.007-0.30 METRO MED 98) 0.1 1.8 0.1 0.06 project, HCMR

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Metals Ni Fe Mn Cr Zn Cu References Maliakos Gulf(2000- 0.27-6 ND 1.6-7.6 ND 0.77-21.8 0.07-10.8 Dassenakis et, 2002) 1.8 4.5 3.8 0.65 al,2001 Astakos Gulf(1998- 0.11-0.35 ND ND ND 3.7-9.1 0.42-1.3 Belias et al, 2003 2000) 0.46 7.25 0.8 Lavrio Port(2003) 0.11-0.35 ND 0.46-7 ND 0.18-22.1 0.33-7.6 Dassenakis et al, 0.25 1.9 5.6 1.4 2003a

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The average particulate Ni concentration (Table 35) was higher than most of the studied areas such as Astakos Gulf (1998-2000), Peiraias Port (1999), Saronikos Gulf (1986-98), Elefsis Gulf (2004) and west part of Saronikos Gulf (2004) with concentration of 0.46κg/l, 0.14κg/l, 0.62κg/l, 0.04κg/l and 0.03κg/l, respectively. The average particulate concentration of Zn was similar to that of dissolved metals; therefore it was lower than that of other Greek contaminated water bodies such as Saronikos Gulf (1986-98), Elefisis Bay (1997-98), Astakos Gulf (1198- 2000), Lavrio Port (2003) and Peiraias Port (1999) with average concentration of 3.1κg/l, 3.80κg/l, 7.25κg/l, 5.56κg/l and 2.90κg/l, respectively. The particulate concentration of Cu was also lower than some hot spots such as Peiraias Port (1999), Maliakos Gulf (1998-2000) and Lavrio Port (2003).

5.1.2.6 Discussion

In Larymna Bay the metal levels and distribution are attributed both to point source such as anthropogenic inputs as well as non point source inputs from natural coastal erosion and diffuse anthropogenic activities. The point source inputs are attributed to the continuous slag dumping in the area for more than 45 years from the local ferronickel smelting plant. The smelting plant is located on coast of Larymna Bay and its by-products contain high amounts of some specific metals such as Ni, Cr, Fe and Mn, literally associated to the metallurgy operation. The non-point source inputs (Zn & Cu) are probably due to the untreated sewage from land activities, municipal effluent and mostly the aquaculture and antifouling paints which are used for painting the aquaculture nets.

The high concentrations of the metals related to the dumped slag in seawater are most probably because of dumped slag washed out in the first 24 hours after dumping (Karson and Anagnoustou, 1994). According to Voutsinou (1987) more than 50% of the total slag can be leached by the seawater. In accordance, the relatively high enrichment of dissolved metals in bottom water as compared to that of surface area could be related to the desorption of metals from particulates fractions (Sokolowski, Wolowicz, & Hummel, 2001). Furthermore, due to the continuous depositing, metals from the slag are released into pore water and created an unbalanced diffusion environment between the pore water and overlaying seawater. In such conditions a positive flux (from sediments to seawater) may transfer metals to the water column (Angelidis, 2005). Continuous dumping of slag implies the hypoxic environment that leads to increase the level of TOC% in the depositing area (Dassenakis, 2003) which provides a good condition for the reducing of iron and the high levels of dissolved Ni into the environment (Sokolowski et al., 2001). 126

Remarkably higher concentration of some metals related to the by-products of smelting plant such as Ni, Mn, Cr and particularly Fe in both dissolved and particulate forms in the reference than the slag dumping area is controversial and may be explained by different scenarios. The spatial distribution pattern of metals related to the slag was highly controlled by discharge of the slag from the ship to the sea surface and its subsequent accumulation on the seafloor. It is suggested that during the slag deposition, a selective fractionation of particles took place. Hence, the large and coarse particles fell to the seafloor more rapidly and deposited at the discharge area, while the small particles remained suspended for a longer period of time and were scattered away from the discharge area (Voutsinou et al., 1987), besides the unusual tidal phenomenon in Evoikos Gulf and the ions highly saturated environment above the slag, might diffuse dissolved metals toward the much unsaturated and uncontaminated water of the reference area. The last scenario could be due to the false slag deposition somewhere near the reference area and as it is described before the fine particles have the possibility to transport from the deposited area to other places nearby. Therefore one of these scenarios or synergism of some of them may contribute to the high concentration of metals in reference area.

The higher concentration of particulate metals in surface area than the bottom area is probably due to the higher amount of fine particles in surface including small sediment grains and the affinity of metals to absorb them, particularly Fe and Mn. The high levels of Zn and Cu (that are not related to the dumped slag), in both dissolved and particulate forms could be attributed to the great amount of suspended solid, feces and organic materials that are probably related to the aquaculture and agriculture activities in this area. High concentrations of Zn in the studied area is in accordance with the previous findings, which indicated high level of this metal in sediments of central and west parts of Evoikos Gulf- where dumping took place. Nevertheless, it cannot be excluded that other sources of this metal in the environment may exist (Voutsinou et.al., 1987).

The relatively high levels of Suspended particulate metals particulate metals in surface water in particular in the reference area could be due to variety of reasons such as the coincidence of sampling time and slag depositing, weather conditions (rainy and cloudy) and strong winds from the smelting plant toward Gulf, which could cause the particulate to be floated for longer time before settling and moreover transporting these fractions to the reference area.

A cluster analysis was carried out to identify behavior patterns among the different sites and metals (Carrasco, López-Ramírez, Benavente, López-Aguayo, & Sales, 2003). Based on the achieved results it is suggested that grouping of Cr with Fe in the bottom area for both dissolved 127

and particulate metals may be attributed to the ion releasing by slag as well the same origins of these metals. The clustering of Zn/ Ni and Mn is most probably due to the high concentration of these metals in the central part of Evoikos gulf (Voutsinou-Taliadouri and Varnavas, 1993; HCMR, 2005) or to the other sources of laterite ore in this area (Voutsinou et al.,1978). As discussed earlier, the reference area was so enriched with metals related to the dumped slag (Fe, Ni and Mn), thus strong similarity between this site and the most influenced station in dumping area was anticipated. In practice, the grouping of the reference station R with L14 & L8 for both dissolved and particulate metals was expected.

Partition coefficient (Kd) between dissolved and particulate form of metals plays a critical, or even dominating role in the distribution, transport, behavior and fate of metals in aquatic environments ranging from sediment pore waters and ground waters to rivers, lakes and oceans (Haven, 1999). The kd provides information about geochemical fate and mobility of metals within the particles column and the probability of contamination in the environment. High levels of kd indicate high metal adsorption onto sediments, therefore less free heavy metals available to aquatic organisms (García-Rico et al., 2011; Gavriil & Angelidis, 2005). In addition, the presence of colloidal particles would play a significant role in inducing such ‗‗particle concentration effect‘‘. With increasing concentrations of SPM, the concentration of fine-sized colloids (<0.45mm) is expected to increase. Such fine colloidal particles will be able to bind metals and retain them in solution, hence increasing the concentration of so-called dissolved metals, therefore, Kd is known for its dependence on environmental conditions.

In the present study the highest Kd levels were found in Cu, Fe, Cr and Mn. This phenomenon might be explained by mixing of suspended particles of different size and character, presence of colloidal particles in solution, and potential desorption of metals from particles metals in the ion exchange (Zhou, Liu, & Abrahams, 2003), but most probably the high concentration of partition coefficient in this particular region is due to the dumping and the fraction selection of smelting plant by-product. In other words the dumped particles, themselves are enriched with high concentrations of Ni, Cr, Fe and Mn; the coarse fractions with heavier weight were settling down quickly and the small particles regarding to the low gravity of these matters were floating in the water mass or adsorbed by colloidal particles, particularly in the surface area. It is important to mention that the percentage content of particulate metals was significantly lower than dissolved ones (Table 32). Therefore, high level of Kd is not due to the high concentration of particles in the water mass, but it is related to the particles‘ content. Other words, partition coefficient is mostly related to the qualities of particles not their quantities.

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High value of partition coefficient for Cu in comparison with other studies is more probably due to the low concentration of the dissolved phase when compared to the particulate one. Nevertheless, in the slag dumping area due to the hypoxic situation and enriched Mn II condition, the concentration of dissolved metals increased which subsequently decreased Kd. Fe just like Mn in the lower balance valence state had lower Kd (Ambrose, 2005). The Kd level of Mn, Cu and Cr from the dumping area is relatively higher than the other studies (Garcia, 2011 and Duc et al., 2013) and therefore the order level of Kd for different metals in this study did not follow the same order as the others.

The comparison of the results from this study with those from other Greek more and less contaminated water bodies showed that the average concentration of dissolved and particulate Ni, Fe and Mn were quite higher in the Larymna Bay. Peiraias Port (1999), Astakos Gulf (1998- 2000) and Lavrio Port (2003) were assuming to be much polluted by Zn and Cu than Larymna Bay.

Both chronic and acute US EPA criterions (CCC µg/l &CMC µg/l) and EU water framework directive (WFD) were higher than the average concentration of trace metals from Larymna Bay in N. Evoikos Gulf.

Finally, it should be considered that the Larymna bay is heavily impacted by receiving different sources of pollution. However, the most important one is the by- products of ferronickel smelting plant deposited for more than 45 years. The long-term dumping of slag and the contamination caused demands comprehensive research on their impact on the marine organism‘s populations and especially metals bioaccumulation and study of biological effects, as well as other biogeochemical studies and permanent monitoring.

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5.1.3 Biota

Crustaceans

Three different species of crustaceans (Munida rugosa, Liocarcinus depurator, Nephrops norvegicus) were collected from both the contaminated and the reference areas. Nephrops norvigicus is the only species that was captured at the reference area as it was not observed in the contaminated site during the samplings.

5.1.3.1 Metal levels in Munida rugosa

The average concentrations of metals in pooled samples of muscle (N=20), gill (N=20) and exoskeleton (N=20) of M. rugosa are presented in Table 36. Due to the high number of samples, Ni and Cr were not measured in the exoskeleton samples.

Table 34: Average concentration of metals in M.rugosa (in μg/g dw). Maximum concentrations during are in bold (Cont=slag deposit area, Ref=reference area, Exo=exoskeleton, spring=June 2009, winter=March 2010).

Zn Mn Fe Cu Ni Cr Area Tissue Season Mean±SD Mean±SD Mean±SD Mean±SD Mean±SD Mean±SD Cont Muscle Spring 42±1.8 3.3±2.5 69±47 21±4.3 3.6±2 4.4±5.7 Cont Exo Spring 12±5.8 65±11.7 315±105 14±3.8 Cont Gill Spring 132±34 129±97 4976±2455 190±57 64±28 249±102 Cont Exo Winter 5.7±1.3 80.7±20.2 648±431 21.3±4.3 Cont Gill Winter 72±11 145±193 3633±1762 179±25 145±55 109±49.8 Cont Muscle Winter 39±1.8 1.8±0.18 42.4±12.4 24±4.2 0.81±0.10 1.3±0.43 Ref Muscle Spring 35±4.7 0.85±0.7 38±22 21±3.9 Ref Exo Winter 7.3±1 46±24 409±117 29±3.1 Ref Gill Winter 67±25 30.6±41.3 1092±1398 205±21 59±11.6 20±12.1 Ref Muscle Winter 40±0.1 1.7±0.3 32±23 25±3.6 1.4±1.2 0.47±0.5

It is clearly seen that the highest concentration of all metals in both samplings concerning different seasons and areas was detected in gill with one exception. Mn is the only metal that showed the highest concentration in the exoskeleton during the winter sampling from the reference area. The results showed that the level of metals were considerably higher in the gill than in the muscle.

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ContaminatedMeans and 95.0 Percent Tukey HSDarea Intervals Means andReference 95.0 Percent Tukey HSD area Intervals Fe 9 7.8 8 6.8 7 5.8 6

log(Fe+1) 4.8 5 log(Fe+1)

4 3.8

3 2.8 Means and 95.0 Percent Tukey HSD Intervals Means and 95.0 Percent Tukey HSD Intervals exo-s exo-w gill-s gill-w soft-s soft-w exo-w gill-w soft-s soft-w Mn 6 5 5 4 4 3 3

log(Mn+1) 2 2 log(Mn+1)

1 1

0 0 exo-s Meansexo-w and 95.0gill-s Percent Tukeygill-w HSD Intervalssoft-s soft-w Means and 95.0 Percent Tukey HSD Intervals exo-w gill-w soft-s soft-w Ni 6 5 5 4 4 3 3

2 log(Ni+1) log(Ni+1) 2 1 1

0 0 gill-sMeans and 95.0gill-w Percent Tukeysoft-s HSD Intervalssoft-w Meansgill-w and 95.0 Percent Tukey HSDsoft-w Intervals Cr 6 4 5 3 4

3 2 log(Cr+1) 2 log(Cr+1) 1 1

0 0 gill-s Means andgill-w 95.0 Percent Tukeysoft-s HSD Intervalssoft-w Meansgill-w and 95.0 Percent Tukey HSDsoft-w Intervals Zn 5.6 4.8 4.3 4.6 3.8

3.6 3.3 log(Zn+1) log(Zn+1) 2.8 2.6 2.3

1.6 1.8 exo-s Meansexo-w and 95.0gill-s Percent Tukeygill-w HSD Intervalssoft-s soft-w exo-wMeans and gill-w95.0 Percent Tukeysoft-s HSD Intervalssoft-w Cu 6.4 5.9 5.4 5.4 4.9

4.4 4.4 log(Cu+1) log(Cu+1) 3.9 3.4 3.4

2.4 2.9 exo-s exo-w gill-s gill-w soft-s soft-w exo-w gill-w soft-s soft-w Figure 29: Log(metal+1) concentration of metals in M.rugosa per tissue, seasons and sampling areas. (S=Spring, W=Winter, exo=exoskeleton, soft=muscle). Lines show statistical differences.

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The highest levels of almost all metals in gills from the reference area were detected in winter. In the slag dumping area, however the highest concentrations in the gills during winter were found particularly in Fe, Ni and Cr. The results comparison between the concentrations of metals in different tissues (gill and muscle) show that the levels of Mn was about 40 to 80, that of Fe and Cr 55 to 85 and of Ni 20 to 180 folds in the gill tissues with those of muscles. A similar phenomenon was noticed between the gill and the exoskeleton tissues, however the differences were less substantial (Mn 2 folds, Fe 10 folds) than those between gill and muscle. Similar results were obtained for the reference area.

Fe and Mn in the exoskeleton collected from both areas (slag dumping and reference) presented the second highest concentration. Nevertheless, the concentrations of other metals such as Cu and Zn which are not related to the smelting plant were higher in the muscle than in the exoskeleton.

In order to investigate whether there is any significant difference among tissues or sampling seasons in each area separately, one way ANOVA test was applied. The results (Figure 29) clearly showed significant differences (P<0.05) between exoskeleton and gill in the contaminated area. The linear lines in Figure 29 emphasize the significant differences between the tissues during the two sampling seasons.

Therefore, based on the Tukey‘s confidential test Zn and Cu presented statistically significant differences (P<0.05) in exoskeleton between the two seasons. As for the gill samples the significant differences (P<0.05) were found in three metals Zn, Ni and Cr.

Regarding the spatial distribution of metals in the two areas, it is revealed that the contaminated area presented higher levels of metals than the reference area. However in some cases the opposite occurred; for example the concentrations of Cu in gill and muscle from the reference area were higher than those from the contaminated area. Furthermore, no statistical differences were found in the concentrations of metals related to the smelting plant by-product in the slag dumping and the reference area.

Concerning the concentration of metals in the two seasons of sampling, the ANOVA test revealed statistically significant differences (P<0.05) only in the muscle tissue in spring 2009, in the concentration of Zn, Mn and Fe from both areas. Furthermore, in winter 2010 sampling, significant results (P<0.05) were also found in the concentration of Fe in gill and Zn, Mn and Cu levels in exoskeleton.

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5.1.3.2 Metal levels in Liocarcinus depurator

The average concentrations of metals in pooled samples of muscle (N=12), gill (N=12) and exoskeleton (N=12) of L.depurator are presented in Table 37. Similarly to M. rugosa, in L. depurator the highest concentrations of almost all metals in the two areas were found in the gill samples. Moreover, significant differences were found between the concentration of Fe and Cu in the gill and those in the muscle and in the exoskeleton. Maximum concentrations are shown in bold.

Table 35: Average concentration of metals in L.depurator (in μg/g dw). Maximum concentrations are in bold (Cont=slag deposit area, Ref=reference area, Exo=exoskeleton, spring=June 2009, winter=March 2010)

Zn Mn Fe Cu Area Tissue Season Mean±SD Mean±SD Mean±SD Mean±SD Cont Muscle Spring 57.6±5.2 5.5±2.8 44.6±16.2 28.3±4.1 Cont Exo Winter 2.2±1.7 32.3±12.9 230.±162 9.1±6.2 Cont Gill Winter 54.6±5.0 77.8±58.1 4868±2607 195±50.7 Cont Muscle Winter 54±7.1 3.9±5.1 39.24±20.6 29±5.5 Ref Exo Spring 5.7±1.4 24.5±10 34.1±7.4 8.4±0.7 Ref Gill Spring 71.3±6.0 14.8±2 658±430 176±176 Ref Muscle Spring 46.4±26.4 13.4±20.1 18.4±2.6 23.9±1.6 Ref Exo Winter 5.1±0.9 24.7±9.1 34.4±3.6 9.56±3.1 Ref Gill Winter 64.4±16.9 13.6±4.7 485±246 135±57.3 Ref Muscle Winter 41.5±11.1 2.4±1 18.2±6.5 25.3±2

The higher concentrations of Mn were measured in the gill samples from the contaminated area. In spite, in the reference area; similar to M. rugosa the highest concentration of Mn was found in the exoskeleton instead of the gill. Furthermore, higher concentration of Zn was measured in the gill tissue from the reference area instead of the contaminated one.

The one way ANOVA was carried out to determine any significant differences in the concentration of metals among tissues based on the two sampling times (spring and winter) in each area. In accordance, no significant differences were found in the sampled tissues neither in the contaminated area, nor in the reference area (Figure 30). Despite the fact that the box plot show high differences between the variables but due to the high standard deviation (SD) no significant differences were presented). 133

The spatial distribution of metals in the two selected areas revealed higher metal concentrations in the contaminated area than the reference site.

ContaminatedMeans and 95.0 Percent Tukey HSDarea Intervals Means andReference 95.0 Percent Tukey HSD area Intervals Fe 8.9 7.4 7.9 6.4

6.9 5.4 5.9

4.4 log(Fe+1) log(Fe+1) 4.9 3.4 3.9

2.9 2.4 exo-wMeans and gill-w95.0 Percent Tukeysoft-s HSD Intervalssoft-w exo-s Meansexo-w and 95.0gill-s Percent Tukeygill-w HSD Intervalssoft-s soft-w Mn 5 5 4 4

3 3

2 2

log(Mn+1) log(Mn+1)

1 1

0 0 Means and 95.0 Percent Tukey HSD Intervals exo-w gill-w soft-s soft-w exo-s Meansexo-w and 95.0gill-s Percent Tukeygill-w HSD Intervalssoft-s soft-w Zn 5 5.4 4 4.4

3 3.4

2

log(Zn+1) log(Zn+1) 2.4 1

0 1.4 exo-wMeans andgill-w 95.0 Percent Tukeysoft-s HSD Intervalssoft-w exo-s Meansexo-w and 95.0gill-s Percent Tukeygill-w HSD Intervalssoft-s soft-w Cu 6.6 5.9 5.6 4.9

4.6 3.9

3.6 log(Cu+1) log(Cu+1) 2.9 2.6

1.6 1.9 exo-w gill-w soft-s soft-w exo-s exo-w gill-s gill-w soft-s soft-w Figure 30: Log(metal+1) concentration of metals in per tissue of Liocarcinus depurator seasons and sampling areas. (S=Spring, W=Winter, exo=exoskeleton, soft=muscle).

The statistical analysis between the two areas presented statistically significant differences (P<0.05) in the concentration of Fe in the muscle samples from spring. In spite of muscle, both gill and exoskeleton provided differences (P<0.05) in spring, along with Mn, Fe and Zn from winter sampling.

5.1.3.4 Metal levels in Nephrops norvegicus Nephrops norvegicus was only found in the reference area. This species was not observed at all in the contaminated area during the study period. The average concentrations of metals in each tissue (Muscle, N= 11, Gill, N=11, Exoskeleton, N=11) are presented in Table 33. 134

Table 36: Average concentration of metals in N.norvegicus (in μg/g dw). Maximum concentrations are in bold (Cont=slag deposit area, Ref=reference area, Exo=exoskeleton, spring=June 2009, winter=March 2010)

Zn Mn Fe Cu Area Tissue Season Mean±SD Mean±SD Mean±SD Mean±SD Ref Exo 2009 19±3.8 176±181 153±67 10.3±1.2 Ref Gill 2009 84±30 46±24 562±210 103±15.7 Ref Soft 2009 47±9.8 24±28 59±36 20±2.5 Ref Exo 2010 7.9±0.77 34±7.7 464±90 22±2.3 Ref Gill 2010 69±8.5 32±13.4 986±520 191±20 Ref Soft 2010 38±19 3.8±0.82 25±6.1 34±8

Means and 95.0Fe Percent Tukey HSD Intervals Means and 95.0Mn Percent Tukey HSD Intervals

7.7 6

5 6.7 4 5.7 3

4.7 log(Fe+1) log(Mn+1) 2

3.7 1 2.7 0 exo-s exo-w gill-s gill-w soft-s soft-w exo-s exo-w gill-s gill-w soft-s soft-w

Means and 95.0Zn Percent Tukey HSD Intervals Means and 95.0Cu Percent Tukey HSD Intervals

6.2 5.7 4.7 5.2

3.7 4.2

log(Cu+1) log(Zn+1) 2.7 3.2

1.7 2.2 exo-s exo-w gill-s gill-w soft-s soft-w exo-s exo-w gill-s gill-w soft-s soft-w

Figure 31: Log(metal+1) concentration of metals in per tissue of Nephrops norvegicus seasons and sampling areas. (S=Spring, W=Winter, exo=exoskeleton, soft=muscle). Lines show statistical differences

The highest concentration in each sampling time was shown in bold. Similar to the results from the other two species, the highest concentrations of all metals in the both sampling seasons were determined in the gill tissue except for Mn from the reference area. Thus, the highest

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concentration of this metal in this area was measured in the exoskeleton from the winter sampling.

The distribution of metals in different tissues was illustrated as box plot in Figure 31. The linear lines in this figure emphasize the significant differences between the tissues during the two sampling seasons. Therefore, regarding the two seasons of sampling, statistically significant differences (P<0.05) were found for all metals in exoskeleton. In addition to exoskeleton, gill also showed statically significantly difference (P<0.05) in the concentration of Cu.

5.1.3.5 Comparison of metal bioaccumulation among species

It is well known that the bioaccumulation of metals in different marine organisms is related to various factors such as habitat, trophy, metabolisms and etc (Gupta et al., 2011).

In order to have a better understanding for the absorption of metals among the studied species, the concentrations of bioaccumulated metals from different tissues were compared (Figure 32). It has been clearly seem that the concentrations of almost all metals from all the three sampling tissues were higher in Nephrops. However some exceptions were detected; for instance, the level of Fe and Cu in the gill and Cu in the exoskeleton from Liocarcinus were higher than those of Nephrops and Munida in the spring sampling. The results from the same area in winter 2010 were not similar to those of spring.

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Figure 32: The variation in the concentrations of metals among different species per tissue in the reference area. Dep=Liocarcinus depurator, Mud=Munida rugosa, Neph=Nephrops norvigicus

The high concentrations of Fe and Mn (metals related in the smelting plant) in all the studied tissues in the Neprhrops is considerable. As for the metals not related to the smelting plant, the highest concentration of Cu in both gill and exoskeleton were detected in Munida and the maximum concentration of Zn was found in soft tissue in liocarcinus from the winter sampling.

The results from one-way ANOVA, revealed statistically significant difference (P<0.05) in the concentrations of Cu and Mn from gill, Fe and Mn from muscle along with the Zn and Cu from exoskeleton. The Tukey‘s test detected the species with the significant differences of the above metals. Therefore, the significant differences in the levels of metals in the gill and muscle tissues were mostly occurred between Nephrops and Liocarcinus and in the exoskeleton was detected between Nephrops and both Munida and Liocarcinus (Figure 32).

In accordance to the contaminated area, the comparison was carried out between the two species of Munida rugosa and Liocarcinus depurator. The results showed no significant differences in the bioaccumulated metals in these species. Nevertheless higher concentrations of Zn, Mn and Cu were detected in Liocarcinus and Fe in Munida. Concerning the studied tissues, the highest concentrations of all metals were recorded in the exoskeleton in Munida (Figure 33).

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Figure 33: The variations in the concentrations of metals in different species from the contaminated area. Dep=Liocarcinus depurator, Mud=Munida rugosa.

5.1.3.6 The distribution of metals in different tissues

Principal component analysis (PCA) was employed to reduce the multidimensional data sets of several elements to fewer dimensions. Thus, it makes it much simple to present and interpret the data. PCA analysis, concerning all data from the three species and both studied areas was applied in Figure 34 and Figure 35. In the reference area the first component (PC1) covered 57.2% of the variation; meanwhile PC2 accounted for 31.9 % of all variations. Therefore, totally they explained 89.1% of variations. In the contaminated area, however both components explained about 92.7% of variations. It is clearly seem that in both graphs, the three different tissues were completely separated.

The smoothness of tissues separation in the reference area is much clearer than in the contaminated area. It is also apparent that in the both graphs the highest concentration of all metals was detected in the gill.

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1 tissue gillgill gill exo gill gill soft softsoft gill gill gillgill soft soft gill gill gill gill softsoftsoftsoftsoftsoft gill gill soft gill gill gill softsoft soft soft gill gill soft gill softsoft soft gill Cu Zn gill gill 0 soft soft soft exo Fe exo soft 2 exoexoexo C exoexoexo P exoexo exo exo exo exo exoexo exoexo exo exo exoexo -1

exo Mn

-2 -2 -1 0 1 2 PC1

Figure 34: Principal Component Analysis (PCA) from the three tissues in the contaminated area.

1 gill gill tissue gill softsoft exo gill gill gill soft soft gill softsoftsoftsoft gill gillgill gill soft softsoftsoftsoft gill softsoft soft gillgillgill Zn soft gill soft gill soft gill Cu 0

gill Fe exo

2 exo C P exo exoexoexo exo exo exo exo exo exo exoexo -1 exo Mn exo exo

exo

-2 -2 -1 0 1 2 PC1

Figure 35: Principal Component Analysis (PCA) from the three tissues in the reference area.

5.1.3.7 The factor of sex on metal bioaccumulation in crustaceans

It is believed that sex is an important ecological factor which affects on the bioaccumulation of metals. The sex of all crustacean samples was determined at the laboratory,

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therefore an adequate number of specimens from both sexes were not obtained for all the studied species. Regarding the number of individuals of different sex, it is interesting to note that the abundance of female specimens of Munida rugosa and Liocarcinus depurator was considerably low in comparison to that of males. In some cases this phenomenon resulted at the acquisition of only one composite sample of female tissues per sampling site. It is also worth noticing that no ovigerous females were observed during both samplings in spring 2009 and winter 2010.

5.1.3.7.1 Munida rugosa

The average concentrations of metals in muscle, gill and exoskeleton of M.rugosa samples (total male samples=42, total female samples=18) are presented in Table 39. Due to technical reasons Cr and Ni were not measured in exoskeleton samples.

The results of metal bioaccumulation in M. rugosa samples (Table 39) revealed that the concentrations of almost all metals were higher in females than in males. This phenomenon is more obvious in Figure 36 which presents the average metal values in biological tissues calculated for the ensemble of samplings. Exception was found for Cu which showed similar levels for both genders and Ni with higher concentrations in male than female in the reference area.

It is clearly observed that gills show the highest concentrations of metals in both sampling seasons and areas, followed by muscle and finally by exoskeleton. Especially the concentrations of Mn, Ni, Cr and Fe (metals abundant in the slag) were about 50 times higher in the gills than in the muscles, while for Cu and Zn this rate dropped to 2-10 folds higher.

The same phenomenon was also observed between muscle and exoskeleton; however, in this case the difference between the two tissues was less pronounced for Mn and Fe (rates about 3-6 folds higher in muscle than in exoskeleton) and much observed for Zn and Cu with rates of 7-8 folds higher in muscle than in exoskeleton.

Finally regarding the distribution of metals, the results showed higher levels of metals in the contaminated area than the reference site. However some exceptions were detected. Therefore, the concentration of Cu in all the studied samples as well as Fe level in the exoskeleton were higher in the reference area (Table 39, Figure 36).

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Table 37: Average concentration of metals in M. rugosa (in μg/g dw). Maximum concentrations during each season are in bold (Cont=slag deposit area, Ref=reference area, F=female, M=male, ND=not determined sex, Exo=exoskeleton, spring=June 2009, winter=March 2010).

Zn Mn Fe Cu Ni Cr Area Sex Tissue Season Mean±SD Mean±SD Mean±SD Mean±SD Mean±SD Mean±SD Cont F Gill Spring 118±17.7 58.6±14.7 5050±1268 193±63.6 55.8±36.1 243±119 Cont M Gill Spring 91.9±58 224±68 4877±3956 186±60.3 75.8±2.4 257±115 Cont F Gill Winter 90.8 515 4286 158.1 132 83.1 Cont M Gill Winter 68.9±7.8 71.6±73.7 3502±193 183±25.4 150 ±60.9 14.3±53.8 Ref F Gill Winter 77 122 2782 181 114 38.5 Ref M Gill Winter 65.6±27 15.2±8.5 810±522 209±20.5 50.1±11.6 17.4±12.1 Cont F Muscle Spring 43.0±1.4 3.9±3.1 119±47.6 23.2±4.5 4.0±2.4 5.3±7.3 Cont M Muscle Spring 41.8±2.4 2.6±1.7 64.1±26.1 18.4±2.3 2.9±1.1 3.3±3.7 Cont F Muscle Winter 40 1.9 29.6 31.2 0.7 0.82 Cont M Muscle Winter 39.2±2 1.8±0.2 45±12 22.7±2.5 0.8±0.1 1.4±0.4 Ref ND Muscle Spring 35.8 0.8 37.9 20.6 Ref M Muscle Winter 39.6±0.9 1.6±0.3 33.6±24.3 25.5±3.9 1.5±1.2 0.4±0.4 Ref F Muscle Winter 42.7 1.9 19.9 25.2 0.9 1.1 Cont F Exo Spring 10.5±3.4 66.7±9.3 310±74.6 13.0±4.3 Cont M Exo Spring 14.3±8.6 62.6±16.3 321±157 14.3±3.6 Cont F Exo Winter 7.07 111 27.7 26.1 Cont M Exo Winter 5.4±1.3 74.71±15.5 772±342 20.3±4.1 Ref F Exo Winter 8.8 92.2 565 22.9 Ref M Exo Winter 7.3±1.04 38.8±13.9 382±103 30.1±1.6

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Figure 36: Average concentration of metals in M.rugosa per sex, tissue and sampling area. (F=female, M=male, cont=contaminated slag area, ref= reference area, exo=exoskeleton, soft=muscle)

Concerning the seasonal variations, higher concentrations of metals in muscle and gill tissues were detected during spring in both contaminated and reference areas. On the opposite, in the exoskeleton the highest concentrations of metals were recorded in winter.

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Contaminated area by slag Reference area

10 10

8 8 Fe

6 6

log(Fe+1) log(Fe+1) 4 4

2 2 F-exo M-exo F-gill M-gill F-soft M-soft F-exo M-exo F-gill M-gill F-soft M-soft 6 6 5 5 Mn 4 4

3 3

log(Mn+1) 2

log(Mn+1) 2

1 1

0 0 F-exo M-exo F-gill M-gill F-soft M-soft F-exo M-exo F-gill M-gill F-soft M-soft 6 6 5 5 Ni 4 4

3 3 log(Ni+1) 2 log(Ni+1) 2

1 1

0 0 F-gill M-gill F-soft M-soft F-gill M-gill F-soft M-soft

Figure 37a: Differences among concentration of metals in M.rugosa per sex, tissue and sampling areas. (F=female, M=male, exo=exoskeleton, soft=muscle). Lines connecting the same tissue in males and females are added to reveal trends.

Although the ANOVA test (Figure. 37 a & b) showed that the observed differences between M. rugosa genders are in the majority of cases non-statistically significant, there is however an obvious trend of female specimens to accumulate metals at a higher degree than males. The Lines joining the same tissue in the two genders emphasize this finding.

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Contaminated area by slag Reference area 6 6 5 5 Cr 4 4 3 3

2 2

log(Cr+1) log(Cr+1) 1 1

0 0

-1 -1 F-gill M-gill F-soft M-soft F-gill M-gill F-soft M-soft 6 6

5 5

Zn 4 4

3 3

log(Zn+1) log(Zn+1)

2 2

1 1 F-exo M-exo F-gill M-gill F-soft M-soft F-exo M-exo F-gill M-gill F-soft M-soft 6 6

5 5 Cu

4 4

log(Cu+1) log(Cu+1) 3 3

2 2 F-exo M-exo F-gill M-gill F-soft M-soft F-exo M-exo F-gill M-gill F-soft M-soft

Figure 38b: Differences among concentration of metals in M.rugosa per sex, tissue and sampling areas. (F=female, M=male, exo=exoskeleton, soft=muscle). Lines connecting the same tissue in males and females are added to reveal trends.

Concerning the effect of season on the bioaccumulation of metal, one-way ANOVA showed that in the most cases the soft tissues (muscle) of both genders of M. rugosa with the exception of Cu exhibited statistically higher concentrations of metals during spring (P<0.05).This event was also found for Zn in gills, Mn in exoskeleton and Cr in gills.

5.1.3.7.2 Liocarcinus depurator

In this species the total pooled samples of 9 females and 27 males were dissected. In the case of Liocarcinus as well as Munida, the highest concentration of all metals during both sampling times (spring and winter), at both areas (contaminated and reference) and sexes were measured in the gills. The concentration of Zn, however, is similar in both gill and muscle samples. The concentration of Mn, Fe and Cu is at least 20, 70 and 5 folds respectively higher in 144

the gill than in the muscle. Besides, the comparison in the concentrations of metals between gills and exoskeleton showed higher level of Fe (15 folds) and Cu (21 folds) in gill and those in the exoskeleton (Table 39).

Different tissues bioaccumulated metals in different orders, thus the metals concentration decreased as follows: In the gill: Fe>Cu>Zn>Mn, in the muscle: Zn>Fe>Cu>Mn and in the exoskeleton: Fe>Mn>Cu>Zn.

Table 38: Average concentration of metals in L.depurator (μg/g dw) from reference (Ref) and contaminated (Cont) areas. Maximum concentrations in each season are in bold.

Zn Mn Fe Cu Area Sex Tissue Season Mean±SD Mean±SD Mean±SD Mean±SD Cont F Gill Winter 57.1 59.2 4971 131 Cont M Gill Winter 53.9±5.5 82.4±66 4842±30.9 211±41.5 Ref F Gill Spring 76.2±13.2 14.8±2.5 338±71.1 172±2.1

Ref M Gill Spring 63.8±12.4 13.9±4.1 647±340 145±54.2 Ref F Gill Winter 85.5 16.5 388 171 Ref M Gill Winter 57.3±11.4 12.7±5.2 517±291 124±64 Cont - Muscle Spring 57.6±5.2 5.5±208 44.6±16.2 28.3±4.1

Cont F Muscle Winter 66.9 3.5 73.8 40 Cont M Muscle Winter 51.3±3.4 3.8±5.9 32.3±13.1 26.7±1.3 Ref F Muscle Spring 55.6±12.9 2.7±1.01 21.9±5.3 23±3.3 Ref M Muscle Spring 38.2±19.2 1.9±0.5 16.6±2.7 24.4±1.1 Ref F Muscle Winter 46.45 3.4 25.7 27.7 Ref M Muscle Winter 39.06±14.4 2.0±0.7 14.5±0.6 24.2±0.03 Cont F Exo Winter 5.19 55.24 316.1 10.3 Cont M Exo Winter 1.5±0.24 26.6±1.6 209±179 8.9±7.1 Ref F Exo Spring 7.3 35.1 40.1 7.9 Ref M Exo Spring 4.9±0.3 19.2±5.8 31.1±7.5 8.6±0.8 Ref F Exo Winter 6.0 37.8 38.6 14.2

Ref M Exo Winter 4.8±0.8 20.3±3.2 33±2.8 8±0.4

The results based on the average concentration of metals (Figure 40) showed that in both areas, except some exceptions, females presented higher concentration of almost all metals than male specimens. The exception was found in the Mn concentration from the contaminated area

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and Fe level from the reference area which presented higher concentrations in males than in females.

ContaminatedMeans and 95.0 Percent Tukey area HSD Intervals MeansReference and 95.0 Percent Tukeyarea HSD Intervals Fe 12 7.4 10 6.4

8 5.4 6

4.4 log(Fe+1) log(Fe+1) 4 3.4 2

0 2.4 Means and 95.0 Percent Tukey HSD Intervals F-exo M-exo F-gill M-gill F-soft M-soft F-exo MeansM-exo and 95.0F-gill Percent TukeyM-gill HSD IntervalsF-soft M-soft Mn 7.7 5 4 5.7

3 3.7

2

log(Mn+1) log(Mn+1) 1.7 1

-0.3 0 Means and 95.0 Percent Tukey HSD Intervals F-exo M-exo F-gill M-gill F-soft M-soft F-exo MeansM-exo and 95.0F-gill Percent TukeyM-gill HSD IntervalsF-soft M-soft Zn 5 5.4 4 4.4 3 3.4

2

log(Zn+1) log(Zn+1)

1 2.4

0 1.4 F-exo MeansM-exo and 95.0F-gill Percent TukeyM-gill HSD F-softIntervals M-soft Means and 95.0 Percent Tukey HSD Intervals F-exo M-exo F-gill M-gill F-soft M-soft Cu 8 5.9

6 4.9

4 3.9

log(Cu+1) log(Cu+1)

2 2.9

0 1.9 F-exo M-exo F-gill M-gill F-soft M-soft F-exo M-exo F-gill M-gill F-soft M-soft

Figure 39: Differences in log concentration of metals in L.depurator between Female (F) and male (M) from the contaminated and the reference areas. The significant difference between each pair of tissues was shown by line.

One way ANOVA was performed in order to indicate the significant differences in the concentration of metals in different sex and seasons. The result showed that only for Zn significant difference (P<0.05) was found in exoskeleton of females from the contaminated area. The higher concentration of metals in female is more obvious in Figure 39.

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Concerning the impact of season (spring and winter), a statistically significant difference (P<0.05) in both areas was detected in the muscle and the exoskeleton. These differences were detected in the concentration of Zn and Cu in muscle along with the Zn and Mn in exoskeleton from the contaminated area and Cu from the reference area. Besides, the Tukey‘s interval confidence test revealed that the highest concentration in all metals was found in the females from the winter sampling (Figure 39).

Figure 40 : Differences in metal concentrations among different tissues, gender and areas of L.depurator (F=female, M=male, R=reference area, cont=contaminated area)

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5.1.3.7.3 Nephrops norvigicus

This species was only found in the reference area. Total pooled samples of 16 females and 15 males of Nephrops were analyzed for this study. The average concentration of metals in the studied tissues is shown in Table 40. Results were similar to the other crustacean samples, were taken from this area, with the highest concentration of metals found in the gill expect for Mn, with the maximum concentration in the exoskeleton.

In general, the concentration of Zn, Fe and Cu are about 2, 18 and 6 folds higher in gill than muscle, followed by 8, 2 and 9 folds higher than exoskeleton. In contrary to the other metals, as already found for the other studied crustacean samples (M. rugosa and L.depurator), the Mn concentration in exoskeleton is about 2-2.5 times higher than the one in gill.

Table 39: Average concentration of metals in N.norvigicus (μg/g dw) from reference (Ref) and contaminated (Cont) areas. Maximum concentrations in each season are in bold.

Zn Mn Fe Cu Area Sex Tissue Season Mean±SD Mean±SD Mean±SD Mean±SD Ref F Gill Spring 90.4±8.5 56.6±27.4 574±253 106±4.7 Ref M Gill Spring 76.1±49.9 32.1±7.1 545±186.8 98.9±25.6 Ref F Gill Winter 78.6 46.1 817 202.4

Ref M Gill Winter 63.9±0.1 24.9±7.7 1070±705 186±24.8 Ref F Muscle Spring 49.4±3.9 40.8±35.7 57.4±36.1 19.9±0.7

Ref M Muscle Spring 43.5±15.4 14.9±19.4 40.5±11.6 20±4.2 Ref F Muscle Winter 32.3±24.6 2.9±0.3 20.6±0.2 35.5±5.2 Ref M Muscle Winter 49.8±3.5 4.3±1.1 29.3±6.6 28.±1.1 Ref F Exo Spring 16.9±8.5 110±31.0 145±99.8 11.4±0.5 Ref M Exo Spring 17.7±5 92.2±31.3 161±32.1 9.2±0.4 Ref F Exo Winter 7.4±0.8 40.4±4.5 428±129 21.2±0.9

Ref M Exo Winter 8.3±0.5 27.8±0.9 500±47.5 23.5±3.1

Regarding the two seasons of sampling, Cu, showed the highest level in winter sampling and the highest concentration of Zn and Mn in all three tissues was detected in spring with some exceptions. Opposite to the other metals, the maximum concentration of Fe in both gill and exoskeleton was measured in winter and in muscle in spring.

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In Nephrops, as well as the other crustacean samples, the highest concentration of almost all metals was detected in females, however no significant differences (P<0.05) were discovered between sexes (Figure 42). Nevertheless, the statistical results based on two seasons, indicated statistically significant differences (P<0.05) in the concentration of Cu in both gill and muscle tissues along with Zn level in the exoskeleton. According to Figure 41, it is obvious that the highest concentration of almost all metals were measured in gill. The statistical test also proved significant differences (P<0.05) among the concentration of all metals in the gill and those in the other tissues.

Means and 95.0Fe Percent Tukey HSD Intervals Means and 95.0Mn Percent Tukey HSD Intervals

7.9 6

6.9 5

4 5.9 3

4.9 log(Fe+1) log(Mn+1) 2 3.9 1

2.9 0 F-exo M-exo F-gill M-gill F-soft M-soft F-exo M-exo F-gill M-gill F-soft M-soft Means and 95.0Zn Percent Tukey HSD Intervals Means and 95.0Cu Percent Tukey HSD Intervals

6 5.3

5 4.8

4 4.3

3 3.8 log(Zn+1) 2 log(Cu+1) 3.3

1 2.8

0 2.3 F-exo M-exo F-gill M-gill F-soft M-soft F-exo M-exo F-gill M-gill F-soft M-soft

Figure 41: Differences in log concentration of metals in N. norvigicus between Female (F) and male (M) from the reference area. exo=exoskeleton, soft=muscle

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Figure 42: Differences in metal concentrations among different tissues and gender of N. norvigicus (F-R=female samples from reference area, M-R=male samples from reference area

5.3.7 Co occurrence of metals in crustaceans and the environmental conditions

In this part several techniques will be discussed to understand the bioaccumulation of metals by marine organisms such as crustaceans. In particular the bioaccumulation and correlation coefficient will be implemented.

Bioaccumulation is an important process through which chemicals can affect living organisms. Thus it is interesting to compare their concentrations in biological organisms with that of the chemical‘s concentrations in the environment. The bioaccumulation factor (BCF) is defined as the ratio of the chemical concentration in an organism (CB) to the total chemical concentration in the water (CWT), which expressed as follows: (Yarsan & Yipel, 2013).

BCF= CB / CWT

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The calculation of the BCF is presented in Tables 41 to 43 in different crustacean species, tissues, seasons and sexes. Generally it seems that the highest level of BCF was detected in gills in the three species and both sexes.

Based on season, in Munida, the maximum bioaccumulation of metals was measured in winter in both tissues except for Fe which presented high BCF in spring (Table 41).

Concerning areas (Contaminated and Reference) a very high BCF in both gill and muscle was found in the reference area which is in some cases as Zn, the level of BCF is four folds higher than the one in the contaminated area.

Table 40: Bioaccumulation factor (BCF) in different tissues of Munida rugosa. Cont= contaminated area. Ref=Reference area. F= Female. M= Male. The maximum concentrations are in bold.

Area Sex Tissue Season Zn Mn Fe Cu Ni Cr Cont F Gill Spring 10.9 22.5 731 379 45.5 Cont M Gill Spring 8.5 86.2 706 365 14.2 Cont F Gill Winter 14.3 736 211 405 43.2 198 Cont M Gill Winter 10.9 102 17.3 470 49.2 34 Ref F Gill Winter 60.6 219 253 697 45.8 107 Ref M Gill Winter 51.7 27.1 73.7 804 20.1 48.3 Cont F Muscle Spring 4.0 1.5 17.2 45.5 0.76 Cont M Muscle Spring 3.9 1.0 9.3 36.1 0.55 Cont F Muscle Winter 6.3 2.7 1.5 80.0 0.22 2.0 Cont M Muscle Winter 6.2 2.57 2.2 58.2 0.27 3.4 Ref - Muscle Spring 3.4 0.53 5.7 41.2 Ref M Muscle Winter 31.2 2.9 3.1 96.1 0.61 1.03 Ref F Muscle Winter 33.6 3.4 1.8 96.9 0.37 3.0

Moreover, based on sex it seems that female Munida has better ability to bio-accumulate metals than the males. In particular, the bioaccumulation of these metals in the gill tissue in females collected in winter was about 3-12 times more than those found in the male Munida in both areas (Table 42). This phenomenon is sustained for both areas and seasons particularly in the reference area. The BCF level of Fe and Mn presented considerable differences in female and male samples.

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Table 41: Bioaccumulation factor (BCF) in different tissues of Liocarcinus depurator. Cont= contaminated area. Ref=Reference area. F= Female. M= Male

Area Sex Tissue Season Zn Mn Fe Cu Cont F Gill Winter 9.0 84.6 245 337 Cont M Gill Winter 8.5 74.9 238 541 Ref F Gill Spring 7.3 9.9 50.4 344 Ref M Gill Spring 6.1 9.3 96.6 290 Ref F Gill Winter 67.3 29.5 35.3 656 Ref M Gill Winter 45.1 22.7 47.0 476 Cont - Muscle Spring 5.3 2.1 6.4 55.5 Cont F Muscle Winter 10.6 5.0 1.6 68.5 Cont M Muscle Winter 8.1 5.4 2.2 58.2 Ref F Muscle Spring 5.3 1.8 3.3 46.8 Ref M Muscle Spring 3.7 1.3 2.5 48.8 Ref M Muscle Winter 30.8 3.6 1.3 93.1 Ref F Muscle Winter 36.6 6.1 2.3 106

As for L.depurator, results regarding the areas (contaminated and reference) showed that in the contaminated area, the highest concentration of BCF was determined for Fe and Mn (metals related to the smelting plant). In contrary, in the reference area the maximum level of BCF was detected in Zn and Cu (metals not-related to the smelting plant). This incident was detected in both seasons. Furthermore, the biocmmuluated level of these two metals was considerably increased in the winter sampling in this area.

Table 42: the Bioaccumulation factor (BCF) in different tissues of Nephrops norvegicus. Ref=Reference area. F= Female. M= Male

Area Sex Tissue Season Zn Mn Fe Cu Ref F Gill Spring 8.7 37.7 85.7 213 Ref M Gill Spring 7.3 21.4 81.3 198 Ref F Gill Winter 62 82.3 74.3 778 Ref M Gill Winter 50.3 44.5 9.7 715 Ref F Muscle Spring 4.75 27.2 8.57 39.8 Ref M Muscle Spring 4.18 9.93 6.04 40.0 Ref M Muscle Winter 25.4 5.18 1.87 136 Ref F Muscle Winter 39.2 7.68 2.67 108

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The elevated levels for Zn BCF was around 8 and 7 folds, in gill and muscle tissues, respectively and about 2 folds higher for Cu. Concerning sex, it seems that the BCF level was higher in females than males, particularly in the reference area (Table 43).

In Nephrops similar to the other crustacean‘s species, the highest concentration of BCF based on sex and tissues were detected in the gill of female samples. However an exception was discovered in the BCF level of Cu in muscle in both spring and winter which showed higher concentrations in males. Regarding the seasons, the highest levels of BCF was also found in winter.

In the above part, the bioconcentration level of each species was discussed individually. However, it is worth knowing which species bioaccumulate more and what specific metals by considering the fact that all the studied crustacean species inhabited the same area and exposed to the same environmental conditions.

Figure 43 clearly show the differences in the bioaccumulation factor (BCF) of metals in the studied species per sampling area and tissue.

The results of the BCF calculations showed that in both areas the observed differences in gills among species were no substantial. On the contrary, considerable differences were found in the bioconcentration of Zn and Fe in the muscle tissue of Munida.

Reference area Contaminated area

Figure 43: Bioconcentration factor (BCF) of metals in the studied species. G=gill, S=muscle, Neph= Nephrops norvegicus, Dep= Liocarcinus depurator, Mud=Munida rugosa

Liocarcinus seems to be accumulating Cu slightly higher than Munida, while Nephrops had higher BCF for Cu, Mn and Fe in muscle and therefore it is obvious this species may be a better bioaccmululator of metals than the other two.

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The correlation coefficient is a measure of similarity between two variables since it reveals whether one variable changes simultaneously with another one by similar amount.

Therefore to find out whether there is any similarity between the dissolved concentration of metals in the seawater and the bioaccumulated levels in the crustaceans, the Pearson correlation coefficient was examined. The correlation concerned all the studied tissues of the studied crustaceans sampled from both areas (Contaminated and reference) and both seasons.

Regarding the contaminated area, relatively poor significant correlation found between the metals accumulated by the studied tissues and those of dissolved ones in the seawater in the both sampling seasons. Nevertheless a strong negative correlation (P<0.05, R2= -0.912) was detected between the concentration of Mn in the gill tissue in winter time and its level in the seawater. In L. depurator a strong correlation (P<0.05, R2=0.948) was detected between the concentration of Zn in exoskeleton and the same dissolved metal in the seawater in winter sampling.

Although few significant correlations were found in the reference area, they were all strong and the calculated correlation coefficients (R2) were above 0.998 (P<0.05) in all cases.

Concerning the relations of the bioaccumulated metals by crustaceans and the contaminated sediment in the environment, only few significant correlations were found between the total content of metals in the sediments and those accumulated in tissues of M. rugosa and L. depurator from the contaminated area. Nevertheless, the only strong positive significant correlation (P<0.05) detected in the concentration of Cu in exoskeleton in both sexes in Munida as well as gill tissue of Liocarcinus with the total concentration of Cu in sediment; (Female Munida, R2=0.921. Male Munida, R2=0.721. Male Liocarcinus, R2=0.995 in P<0.001). In the contrary, powerful negative correlations (P<0.05) were found between concentrations of metals related to the slag and those in both exoskeleton and muscle tissues in both collected species.

At the reference area, among the three species of crustaceans, Nephrops showed slightly better correlation in both sexes. Strong positive correlation was found in the bioaccmulated Cu in exoskeleton in both females (P<0.05, R2=1.000) and males (P<0.001, R2=0.990) with the total concentration of Cu in sediment. Furthermore, the level of this metal (Cu) in the gill tissue of male showed a good correlation (P<0.05, R2=0.960) with the one in the sediment. Among all the metals related to the slag, Fe is the only metal that presented a firm relation in the exoskeleton of male Nephrops (P<0.01, R2=0.983) and the total concentration of this metal in sediment. Correlation coefficients are given in the Annex. 154

5.1.3.8 Estimation of pollution state in crustaceans

The concentrations of bioaccumulated metals in this study were compared with similar crustacean species studied in the Mediterranean (Table 44). The comparison demonstrated that the concentrations of Cr and Ni in M. rugosa (all the studied tissues) from N. Evoikos Gulf were higher than those from the other crustaceans. In case of Mn, higher concentration of this metal than those measured in our study was detected in Potamonautes warrenti (Yildaz, 2012; Lozano, 2010). Moreover, the concentration of Cu in N. norvegicus samples measured by Canli, (1998) was higher in both sexes in gills and carapace (but not in muscle), than that of the present study. In addition, two other crustacean species, Palaemon.sp (Lozan, 2010) and Scylla.sp presented higher concentration of Cu.

The concentrations of metals in the muscle tissue of the crustacean samples in this study were also compared with previous studies from the same area in the N.Evoikos Gulf

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Table 43: Concentration of metals (µg/g dry wet) in different species of crustaceans. 1=October sampling, 2=April sampling, 3=sampling from the Germiston lake, 4=sampling from the Potchefsroom dam, Exo= exoskeleton. P.semisulcatus=Penaeus semisulcatus, N. norvegicus=Nephrops norvegicus, P.warrenti =Potamonautes warrenti, P.elegans= Palaemon elegans,C.pagarus =Cancer pagurus, P.serratus=Palaemon serratus, P.adspersus=Palaemon adspersus, S.serata=Scylla serata, N. granulata =Neohelice granulate.

species sex tissue Cr Ni Cu Fe Zn Mn Ref P.semisulcatus M1 Muscle 12.6±1.3 3.4±3.9 41.0±5.4 21.5±4.0 6.1±1.3 Yildaz, 2012 Gill 78.2±38.9 33.8±3.3 44.3±17.6 272±50 196±19 F1 Muscle 9.3±6.3 3.6±3 14.9±0.7 24.7±9.3 6.1±1.6 Gill 47±11 33.2±5.7 27.9±16.1 231±72 159±9 M2 Muscle - 0.6±0.4 17.2±4.4 5.9±1.8 6.1±2 Gill - 23.5±1.1 32.1±7.6 186±24 167±33 F2 Muscle - 0.6±0.2 17.5±1.6 6.8±1.8 4.8±1.8 Gill - 28.5±1 30.8±17.2 235±11 142±0.1 N. norvegicus M Muscle 25±12 37±46 59±10 Canli, 1993 Gill 250±105 916±998 161±104 Carapace 47±26 214±151 37±17 F Muscle 27±15 21±24 63±13 Gill 207±106 1196±998 156±148 Carapace 48±24 156±148 32±16 P.warrenti M3 Muscle 477±235 681±468 Sanders, 1998 F3 Muscle 416±206 643±320 M4 Muscle 406±157 233±110 F4 Muscle 529±451 243±113

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species sex tissue Cr Ni Cu Fe Zn Mn Ref P.elegans M Muscle 11.6±9.6 134±53.3 81.1±69.2 81.5±15.7 Lozano, 2010 F Muscle 84±4.3 116±38.6 61.1±38.8 75.2±18.1 C.pagurus M Muscle 0.93±0.28 0.48±0.14 6±1 0.27±0.005 Barrento,et al, 2009) F Muscle 0.86±13 0.37±0.06 5.5±0.2 0.22±0.003 P. serratus M Muscle 9.9±6.4 149±25.1 117±45.5 93.5±4.4 F Muscle 6.1±1.8 137±33.5 109±29.2 77.8±13.2 P.adspersus M Muscle 7.6 131.8 171.3 83.8 F Muscle 13.1±8.1 205±126 230±123 138±95.9 12.1±1.01 (Mohapatra et al., 2009) Scylla serata M Muscle 131.2±9.6 171±12.3 301±21.2 18.2±1.31 Exo 53.2±7.3 132±11.7 72.3±11.2 13.3±1.96 F Muscle 139±104 187±21.1 322±21.5 28.3±2.37 Exo 50.9±4.2 129±9.6 79.6±10.2 12.1±1.01 N. granulata Muscle F 0.66±0.4 122±19.7 Simonetti, 2012 Muscle M ND 156±20 Muscle F 1.9±0.03 136±19.3 Muscle M 0.48±0.001 172±67.7

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Table 44: Concentration of heavy metals (µg/g dry wet) in muscle of different species collected from the contaminated and reference area of N.Evoikos Gulf. T.lastoviza=Trigloporus lastoviza, E. guranardus =Eutrigla guranardus, M.barbatus=Mullus barbatus, P.erythrinus=Pagellus erythrinus, S.vulgaris=Solea vulgaris, A.laterna= Arnoglossus laterna, C.linguatula=Citharus linguatula, T.capelanus=Trisopterous capelanu, M.merluccius= merluccius merluccius, L.depurator=Liocarcinus depurator, M.rugosa=Munida rugosa, N.norvegicus=Nephrops norvegicus

Contaminated area Reference area Species Ni Fe Cr Cu Mn Ni Fe Cr Cu Mn Ref T.lastoviza 1.5±0.5 8.6±6.0 1.7±0.9 0.91±0.31 1.5±1.1 2.2±0.5 15.8±4.4 2.0±0.9 0.02±0.01 1.0±0.5 HCMR.1992 E.gurnadus 1.8±0.5 22.7±6.7 2.3±0.9 0.02±0.0 1.3±0.5 3.3±1.1 21.4±4.5 3.4±1 0.02 2.6±1.2 M.barbatus 1.9±0.7 28.4±1.7 1.6±0.8 3.19±2.69 1.0±0.3 1.2±0.4 23.8±2.7 1.5±0.5 3±0.8 1.0±0.3 P.erythrinus 0.5±0.3 9.5±2.2 1.7±0.6 0.5±0.1 0.7±0.2 S.vulgaris 4.4±1.6 52.6±12.8 4.8±1.4 0.02±0.01 1.3±0.2 A.laterna 3.2±2 31.8±15.9 3.1±1.1 0.08±0.18 2.8±1.0 C.linguatula 2.1±1.1 10.7±4.2 2.2±1.0 0.1±0.2 2.7±1.8 T.capelanus 3.4±1.2 27.6±7.5 2.8±1.9 0.02±0.0 1.1±0.3 M.merluccius 1.9±0.2 21.9±8.6 1.7±0.4 0.45±0.74 0.5±0.1 L.depurator 5.0±2.7 20.2±6.4 2.4±0.5 22.64±6.33 4.7±2.7 8.2±6.1 33.9±16.3 4.7±2.9 23.1±9.3 11.8±11.7

M.rugosa 3.2±1.1 24.5±15.1 4.3±1.2 20.53±8.52 1.5±0.7 2.9±1.2 57.5±33.4 3.6±1.1 14.9±3.5 2.5±1.5 N.norvegicus 3.8±1.2 97.4±35.8 3.3±1.0 15.50±5.61 13.4±19

A.laterna 3.7±0.5 363±5.3 1.4±0.2 3.2±0.5 32.6±5.8 1.2±0.3 HCMR.2005 E. guranardus 3.6±0.6 104±63 1.8±0.4 3.2±0.3 41±7.4 1.6±0.5 M.barbatus 2.9±0.1 69.3±31 1.2±0.1 5.9±0.1 58.9±0.8 1.2±0.5 L.depurator 3.2±1.4 28.1±4.1 1.6±0.1 3.8±1.4 41.1±25 1.5±0.4 M.rugosa 3.3±0.4 706±83 1.9±0.2 3.3±0.7 42.4±21 1.8±0.3 N.norvegicus 3.4±0.4 90.2±56 1.3±0.1 T.capelanus 3.1±0.7 72±37 1.8±1.2 2.1±0.3 23.2±8.1 2.±0.8 158

The concentration of Ni and Cr in M.rugosa from both the contaminated and the reference area in this study were slightly higher than, the same species from the previous studies. The only exception was found in L.depurator, which higher concentration of these metals was detected in 1992 at the reference area (HCMR, 1992). Moreover, the concentration of Mn in all the crustacean species in this study is higher than the one from the fish samples (Table 45); however it seems that the level of this metal in the L. depurator from 1992 sampling is slightly higher in the reference area than the same specimen in our study. Furthermore, considerably high concentration of Mn was measured in the N.norvegicus in this study from the reference area. In addition to Mn, the two metals of Fe and Cu show remarkable higher concentrations in this study than the previous ones.

The distribution in variation of bioaccumulated heavy metals in the muscle tissue of the crustacean species from the both contaminated and the reference area were demonstrated in Tables 46 and 49.

Table 45: Variation in concentrations of metals (µg/g dry wt) in soft tissue of Liocarcinus depurator from the contaminated area (2003-2015).

Year Fe Ni Cr Zn Mn Cu References 2003 17.3±4.4 3.4±0.4 0.7±0.15 Catsiki, 2003 2004 41±25.3 3.8±1.4 1.4±0.4 Catsiki, 2004 2005 312±186 11.1±1.2 12.2±8 Catsiki, 2005 2006 50.8±5.7 9.3±1.1 3.6±0.5 Catsiki, 2006 2008 49.7±24 2.1±1 0.9±0.5 27.3±4.5 Catsiki, 2008 2009 44.6±16.2 4.8±1.5 0.8±0.4 57.6±5.2 5.5±2.8 28.3±4.1 Catsiki, 2009 2010 39.2±20.6 53.9±7 3.9±5.1 29±5.5 Catsiki, 2010 2011 55.1 0.87 1.67 51.43 5.08 21.77 Catsiki, 2011 2012 44.1±39 4.3±3.6 0.9±0.6 49.6±11.4 5.1±4.6 46.1±15.2 Catsiki, 2012 2013 134.8±2.7 2.5±0.3 2.8±0.1 72.1±4.2 7.5±4.9 37.4±4.1 Catsiki, 2013 2014 30.2±1.1 1.8±0.4 0.3±0.02 32.9±14 Catsiki, 2014 2015 23.2±5.5 0.6±0.3 0.7±0.5 60.6±5.3 2.1±0.8 28.3±3.7 Catsiki, 2015

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Table 46: Variation in concentrations of metals (µg/g dry wt) in soft tissue of Liocarcinus depurator from reference area (2003-2015).

Year Fe Ni Cr Zn Mn Cu Reference 2003 13.6±0.7 2.5±0.1 0.7±0.2 Catsiki,2003 2004 28.1±4.2 3.3±1.4 1.5±0.1 Catsiki,2004 2005 69.8±45.6 8.5±0.7 2.8±0.7 Catsiki,2005 2006 33.5±14.4 9.1±0.1 2.8±0.3 Catsiki,2006 2007 78±34.2 0.9±0.3 0.6±0.2 60.9±5.7 2.7±1.3 34.3±3.9 Catsiki,2007 2008 54.8±25.1 1.2±0.5 0.6±0.2 29.1±4.8 Catsiki,2008 2009 18.4±2.6 1.6±0.2 0.2±0.06 46.4±26.4 13.4±20.1 24±1.6 Catsiki,2009 2010 14.5±0.6 39.1±14.4 2±0.7 24.2±0.04 Catsiki,2010 2011 32.8 1.7 0.47 80.19 2.7 36.4 Catsiki,2011 2012 59.8±44.3 2.8±1.6 0.8±0.1 68.9±5.4 3.3±1.5 41.4±11.7 Catsiki,2012 2013 44.3±39.9 2.1±0.8 2±1 70.4±6.1 1.9±0.7 35.1±3 Catsiki,2013 2014 64.2±70.4 1.4±0.5 0.7±0.7 71.1±8.3 36.6±6.7 Catsiki,2014 2015 18.9±4.3 1.2±0.2 0.3±0.1 56.7±6.9 0.6±0.6 29.3±4.8 Catsiki,2015

Table 47: Variation in concentrations of metals (µg/g dry wt) in soft tissue of Munida rugosa from reference area (2002-2015).

Year Fe Cr Ni Zn Cu Mn Reference 2002 14.1±2.7 6.3±0.7 3.7±0.5 Catsiki and Kozanoglou, 2002 2003 23.3±8.6 1±0.1 2.4±0.1 Catsiki, 2003 2004 42.4±20.7 1.7±0.3 3.2±0.7 Catsiki, 2004 2005 140.1±61.6 3.4 9.6±1.1 Catsiki, 2005 2006 36.3±12.9 2.8±0.2 8.1±1.3 Catsiki, 2006 2007 88.5±38.3 1±0.1 0.8±0.2 42±4.8 23.3±2.8 1.6±0.5 Catsiki, 2007 2008 48.2±30.5 0.4±0.2 1±0.6 38.9 21±1 2.2 Catsiki, 2008 2009 37.9±21.9 0.5±0.2 0.6±0.3 35.8±4.7 20.6±3.9 0.8±0.7 Catsiki, 2009 2010 31.6±22.8 0.5±0.5 1.4±1.2 40.1±1.4 25.5±3.6 1.7±0.3 Catsiki, 2010 2011 120.7±63.7 1±0.3 1.2±0.4 76.5±9.2 27.4±4.5 2.9±0.7 Catsiki, 2011 2012 89.8±30.6 1.1±0.5 1.3±0.4 41.3±0.7 28.1±2.8 3.1±1.2 Catsiki, 2012 2013 60.1±32.3 0.8±0.1 1.2±1 52.8±18.7 23.7±1.8 1.6±0.6 Catsiki, 2013 2014 192±48.4 1.2±0.6 2.2±0.6 78.1±51.7 27.5±2.3 Catsiki, 2014 2015 12.6±5.7 1.1±0.1 0.4±0 43.2±0.5 18.1±0.6 0.1±0.1 Catsiki, 2015 160

Table 48: Variation in concentrations of metals (µg/g dry wt) in soft tissue of Munida rugosa from contaminated area (2002-2015).

Year Fe Cr Ni Zn Cu Mn Reference 2002 32.4±23.6 6.6±0.8 4.1±0.9 Catsiki and Kozanoglou, 2002 2003 37±24.8 1.3±0.6 3±0.2 Catsiki, 2003 2004 70.4±82.5 1.8±0.2 3.2±0.4 Catsiki, 2004 2005 352±105 15.9±7.1 10.1±0.7 Catsiki, 2005 2006 84.4±46.4 4.5±1.1 8.5±0.4 Catsiki, 2006 2007 9.6±6.8 4.1±3.4 0.6±0.1 48.7±0.9 29±5.8 1.3±0.3 Catsiki, 2007 2008 134±92 0.2±0.2 1.9±0 18.3±1 Catsiki, 2008 2009 95.6±47.2 4.4±5.7 3.6±1.9 42.5±1.8 21.2±4.3 3.3±2.5 Catsiki, 2009 2010 42.5±12.4 1.3±0.4 0.8±0.1 39.3±1.8 24.1±4.2 1.8±0.2 Catsiki, 2010 2011 76.1±82.6 3.1±3.2 1.3±0.8 58.6±3.8 28.8±3.8 2.9±1.1 Catsiki, 2011 2012 50.5±11.7 1.8±1 1.1±0.3 41.4±2 34.8±8 4.5±3.7 Catsiki, 2012 2013 129±92 2.8±1.5 0.7±0.1 42.3±1.1 28.2±3.6 1.6±0.4 Catsiki, 2013 2014 67.4±33.7 1.8±1 0.6±0.2 53±2.4 27.7±3.4 Catsiki, 2014 2015 75.1±54.4 2.3±1.2 0.8±0.2 46.9±2 20.3±4.8 1.8±0.9 Catsiki, 2015

The results included the concentration of both metals related (Ni, Mn, Cr, Fe) and non- related to the metallurgy industry (Zn and Cu) since 2002 until 2015. As it is shown in Tables 47, 48 to 50 similar trends in the concentration of metals were discovered in the all the three species. Therefore, a slight decreased in bioaccumulation of metals related to the smelting plant (Ni, Fe, Cr) were found in the contaminated area in the two species of M. rugosa and L.depurator. Similar to the contaminated area, in the reference area the concentration of the two metals of Ni and Cr were decreased in the two species of L.depurator and N.norvegicus. In contract, Fe showed an increasing trend in M.rugosa. As for the two metals of Zn and Cu, an increasing gradient in the concentration were detected in both sampling areas.

The only exception was found in the two collected species from the contaminated area attributed to the Mn level. Therefore, the concentration of this metal in M.rugosa in the both areas was constant during the decades of monitoring, however; the concentration of this metal in L.depurator followed a decreasing slope.

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Table 49: Variation in concentrations of metals (µg/g dry wt) in soft tissue of Nephrops norvegicus from reference area (2002-2015).

Year Fe Ni Cr Cu Mn Zn Reference 2002 26.3±11.3 3.7±0.3 Catsiki and Kozanoglou, 2002 2004 90.3±56.3 3.4±0.4 1.3±0.1 Catsiki, 2004 2005 122±94 9.2±1 2.6±0.7 Catsiki, 2005 2006 91.1±1.2 10.6±0.3 3±0.1 Catsiki, 2006 2007 118±32.9 1.2±0.6 1.2±0.5 15.8±2 3±0.9 38.3±5.2 Catsiki, 2007 2008 460.3±14.3 1.1±0.5 1.6±0.2 11.1±2 Catsiki, 2008 2009 59.2±35.3 3.9±1.9 1.1±0.1 19.9±7 24.5±28.1 47.1±9.8 Catsiki, 2009 2010 25.1±6.1 34.4±4 3.8±0.8 38.4±19.4 Catsiki, 2010 2012 75.6±8 2.8±0.9 1.2±0.4 30.2±6 5.1±0.8 46.2±4.4 Catsiki, 2012 2013 174.3±97.9 2.9±2.1 1.1±0.4 41.3±6 4.4±1.3 53±4.1 Catsiki, 2013 2015 102±18.8 1±0.3 1.1±0.2 18.3±2 2±1.5 48±0.7 Catsiki, 2015

5.1.3.9 Discussion

Metal ions have a biologic significance is a contradictory concept. No life can develop and survive without the participation of metal ions. Some of the metals are categorized as essential elements, thus elements are essential when their consistency present in all healthy living tissues within a zoological family and deficiency symptoms are noted in terms of depletion or removal of these elements (Vlahogianni et al., 2007). All the heavy metals that were the subject of this study are essential, however, it should be considered that toxicity occurs when the total rate of uptake exceed the combined rates of detoxification and excretion, causing the metal to accumulate in metabolically available form (Luma and Rainbow, 2008).

Heavy metals abundance, which is one of the consequences of industrialization, can easily be traced in seawater and sediments as well as in the aquatic organisms. One of the most endangered organism categories is that of the benthic invertebrates with restricted mobility living on benthic. They frequently come in contact with bottom bed mud which is often the final destination of various pollutants (Chen et al., 2005; Rainbow, 1997; Simonetti et al., 2012).

According to Szefer et al. (1990), knowledge of the distribution of metals in isolated tissues of marine organisms is useful in order to identify specific organs that may be particularly

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selective and sensitive to accumulation of heavy metals. Recently, several researchers have reported that some elements, taken up primarily by tissues of aquatic organisms, are transported (translocation) to other tissues (Edmonds et al., 1993; Pourang and Amini, 2000). In this study three different tissues: gill, muscle and exoskeleton from three different species of crustaceans were studied. Crustacean gills are multifunctional organs; they are essential for osmotic and ionic homeostasis as well as for gas exchange (Freire et al., 2008). They are the first tissues exposed to metals and subsequently the ions distributed to the whole body part through blood. From bloodstream, metals can also reach other important organs such as brain and muscle. Muscle is important to be monitored because it is the subject of human consumption, and finally, the exoskeleton is a tissue from which heavy metals and other contaminants are frequently excreted (Alcorlo et al., 2006). The highest concentration of almost all metals in the three species of crustaceans in both sexes and locations were found in gill, followed by exoskeleton and finally by muscle.

The accumulation and distribution of metals in benthic invertebrates‘ tissues are influenced by several factors; including environmental conditions (season, location, depth, salinity, temperature and anthropogenic sources, (Rainbow, 2002) metal concentrations in the surrounding water or sediment, metabolic pathways involving the accumulated metals (Tunca, et al., 2013). and finally the ecological needs (feeding, moulting, reproductive cycle), sex and size (Canli and Atli, 2003).

As it is discussed before, Larymna bay subjected to the long term ferronickel smelting plant activities (more than 45 years) and the continuous offshore slag dumping is known as one of the heavily polluted areas in Europe (Kontos and Zevgolis, 2004). Hence, very high concentration of all metals in the three species of crustaceans, not only in the slag dumping area (Contaminated area), but also in the reference area is related to the activity of the metallurgy plant. In addition we can‘t neglect that the area is rich in natural ores of laterite (Anagnostou, 1986).

Season may influence body burdens of heavy metals. The seasonal metal variability may results from both biological cycle and changes in growth rate (Pourang et al., 2004) of the organisms or from the changes in the availability of metals in the organism‘s environment. Besides, it is well known that the seasonal spawning of gametes in benthic invertebrates usually changes the relative proportion of body tissue weight within different organs (Pourang et al., 2004). Ambient temperature also has an important effect on metal speciation, because most chemical reaction rates are highly sensitive to temperature changes (Elder, 1989). Temperature 163

may affect quantities of metal uptake by an organism, because biological process rates typically double with every 10 ºC temperature increment (Prosi, 1989). In this study, the maximum concentrations of metals in muscle of all the three species of crustaceans from both areas were mostly found in spring (in 22 cases). This sampling was carried out in late spring therefore the water was warmer than winter sampling which has taken part in March. Therefore higher metabolisms rates expected in the samples from spring than those from winter.

The bioavailability of metals in the environment is another important factor affects directly on the metal uptake by benthic invertebrates. Sediment is the final destination of all pollutants inserted to the seawater. However, some factors such as the organic and inorganic constituents of the sediment relatively affect the binding affinities for metals (Gupta and Singh, 2011). The metals are showing some interactive effects, which vary from synergistic (when the concurrent presence of one metal enhances the bioaccumulation of another), to antagonistic (when the concurrent presence of another metal decrease the bioaccumulation) of the first. The results obtained from the correlation analysis of metals in the three species showed that in most cases Zn and Fe had a synergetic relation. Nevertheless, the synergistic effects were detected between the metals related to the slag dumping such as Fe and Mn or Fe and Cr. These positive correlations between metals indicate that, they might share common routes of uptake. However, it should be taken into consideration that metals do not consistently interact competitively or synergistically at the exposure concentration (Patil et al., 2013). The very strong negative correlation of uptake metals by different tissues of all three studied Crustaceans, particularly by Munida rugosa and Liocarcinus depurator from the contaminated area and those in sediment and bottom seawater suggests that; as in this study the bottom water was taken one meter above the sediment, thus the bioavailability of metal elements might be low in this depth. Moreover the sediment that was mentioned in this study due to the long term of dumping is no longer the natural sediment of the area. Therefore, the metals associated with sediments may interact with the benthic invertebrate in two ways: metals may be released from the sediments into the interstitial water to which the crustacean are exposed due to the formation of dissolved concentration gradients at different sediment depths. Low oxygen concentrations with associated changes in redox potential are more likely in sediment interstitial water than in overlying seawater. As a result of these processes, trace element concentrations in porewater (interstitial water) may be one to two orders of magnitude higher than those in the overlying water (Presley et al., 1972., Loring and Hill, 1992., Gavriil and Angelidis, 2006). Another important factor is that the release of metals from sediments into seawater depends not only on the physicochemical

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characteristics of the adjacent water but also on the strength of chemical binding of the metals in the sediment.

The bioavailability of sediment-associated metals after ingestion of the sediment is another considerable issue. This bioavailability is affected by the amount of the present metal but more particularly is controlled by the relative strength of metal binding in the sediment and the crustacean‘s digestive processes. Metal binding is affected by sediment grain size, particularly co-varying with sediment organic content, the presence of other metals such as lead and iron, as well as ambient environmental conditions such as pH, Ec, etc. (Luoma and Fisher, 1997., Bryan and Langston, 1992., Marsden and Rainbow, 2004).

The BCFs provide a first attempt to assess the potential of organisms for bioaccumulation of metals (Jung and Zauke, 2008). Gills are the first target organ of metal uptake and accumulation in the marine environment. Note that the larger surface area with mucus sheets in the mussel gill explains why this organism is a better accumulator of metals from ambient seawater than the remaining soft tissue. High level of BCFs in metals related to the slag, such as Mn, Fe Ni and Cr in the crustacean female samples indicate that probably female specimens due to their particular biological cycle are accumulating metals more than males. The high concentration of these metals in the exoskeleton is mainly related with the absorption rather than the bioaccumulation. (Knowlton, 1983., Alcorlo et al., 2006) The differences in the concentration of absorbed metals in this tissue are probably due to the molting process. Since crustaceans have several molting stages in their life, the bioaccumulation process in the exoskeleton is also cycling and each time, new bioaccumulation starts in a new skeleton. Therefore in this study no accurate relationship was found between the sex and the metal absorption.

Although it is considered that ecological needs (sex and size) (Canli and atili, 2003) are the other important factors which effect on the accumulation of metals, nevertheless, in many studies such as Canli, (1993), Nugegoda and rainbow, (1989) no significant differences in metal bioaccumulation were detected between different sex and sizes of the two shrimp species Nephrops norvigicus and Palalemon elegans. Kannan at al (1995), who worked with the marine crab Tachypleus tridentatus also did not observed differences in accumulation of Cd, Co, Cu, Fe, Hg, Mn, Ni and Pb between sexes. In a similar way, Sastre et al (1999) did not find differences in accumulation of Cd, Hg, Pb and Cu within crab Callinectues Spp genders. In other cases however, females have been found to contain higher levels of metals than males i.e. Cd, Cu, Ni and Zn in the brown shrimp Penaeus californiensis (Páez-Osuna and Tron-Mayen, 1995) Zn in Pleoticus muelleri (Jeckel et al., 1996), Fe in the freshwater river crab, Potamonautes warreni 165

(Sanders et al., 1998) and Ag in the American lobster, Homarus americanus (Chou et al., 2000) This variation in metals uptake between two genders may be related to growth rate differences between male and female. Male Nephrops norvegicus grow much faster than females (Davies and McKie, 1983; Howard, 1989). In addition, it was shown that the maximum carapace length of males in Liocarcinus depurator (Abelló et al., 1990) and Munida rugosa (Claverie and Smith, 2007) is higher than females. Canli 1993 has observed that animals of the same age would have different sizes; males being larger than females of the same age. This means that females of the same size as males have lived in contaminated environment concentrations for a longer time. The small individuals are more than the larger ones thus the concentration of metals could be due to the activity of animals. In accordance with the above findings, studied species of crustaceans in this research mostly contained higher concentration of metals in females than males. In addition all the studied samples of female specimens had lower total length and were smaller than males.

However statistically significant differences between the sexes were only found for some metals such as Zn, Cu and Mn. As it was said before, Zn and Cu are the essential elements in hemocyanin and enzymes activity (Rainbow, 2007) and in most studies no differences were discovered between sexes because these essential metals can be regulated and do not accumulate in decapods crustaceans until certain environmental threshold levels are reached. Mn is also an important micronutrient in the aquatic environment. It is a constituent of many enzymes and activator of enzyme systems and is responsible for the hardness of the shell in crustacean (Bryan and Ward, 1965). Decapods are capable of absorbing large amounts of Mn from the stomach and removing access metal by urinary excretion via annus and across the body surface (Bryan and ward, 1965). Besides, low Mn content in soft tissues is certainly a common feature in decapods (Martin, 1947).Thus the high level of Mn in female is probably due to the greater physiological demand during the reproduction period and probably has higher intake of Mn via enriched diet (Miramand et al, 1991). Relative to that, in another study high concentration of Mn was found in rock crabs that carry external fertilized eggs for at least one month during the reproductive period (Rainbow, 1997). Moreover, chen (2005), found 2–2.5 times higher concentration of Mn in the three tissues of female rock crab (Thalamita Creneta) than those of males. The high concentration of Zn in the muscle tissue of Munida might be attributed to the fact that Zn is selectively concentrated in the ovary, suggesting that substantial amounts of this element are required for successful oogenesis and this element could be involved in the structural stabilization of storage proteins utilized during embryogenesis (Busselen, 1971). In this respect, little is known about the mating behavior of M. rugosa from the sampling area (Evoikos gulf) and Aegean Sea. However similar to other species of this Genus; M. intermedia and M.

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subrugosa , it is suggested that adult females have ripe ovaries in autumn and become ovigerous in winter (Tapella et al., 2002) Consequently the high concentration of Mn in gill of female Munida in this study in the winter samples is due to the high physiological demands and the development of the ovaries or the higher concentration of metals in this season than spring Furthermore, Mn accumulation in the exoskeleton might be related to the duration of the hypoxia environment (Baden and Neil, 2003).

Fe is another essential element for the physiology needs of crustaceans which constitutes the nucleus of enzymes and pigments (Martin, 1973) and plays a central role in electron transfer in the cytochromes during cellular respiration. The deficiency of Fe may raise the susceptibility of the marine organisms to other toxicants, for example, Fe deficiency substantially increased absorption of dietary cadmium in experimental animals (Jonnalagadda and Prasada, 1993). The burrowing of the crustaceans in sediment and increase in turbulence may enhance the flux of iron from the sediment to the water (Starkel, 1985). This is the case we face in our study area; the slag which is replacing sediments, covers around 30 km2 of the bottom in the Gulf and is highly enriched with Fe, Mn, Ni and Cr (Simboura et al., 2007; Zaharaki and Komnitsas, 2009) The physiochemical conditions of this area with respect to the salinity, oxygen concentration and redox potential, could easily raise the Fe concentration in the interstitial water. The Fe content in female Munida in both gill and muscle from the spring sampling is higher than in males. Same as Munida, N. norvigicus also showed a high concentration of this element in female specimen in all tissues in spring. Meanwhile, high concentration of Fe in female L. depurator was detected in winter. Similar results were found in P. warreni from Germiston Lake (Sanders et al.,1998) in the gill tissue of Nephrops norvigicus (Canli, 1993) and the muscle tissue of Scylla serata (Mohapatra, Rautray, Patra, Vijayan, & Mohanty, 2009). Similarly to Mn, it is suggested that Fe concentration increased in female during the gonad development and egg-laying stages so as to use the energy reserves for the reproduction function.

It is worth mentioning that the metals Ni and Cr for technical reasons were only measured in Munida and not the other two crustacean species. Cr is an important trace element for many organisms, but has toxic and mutagenic effects in higher concentrations (Srinath et al. 2002). Besides, high concentration of this metal has been found to produce endocrine disruption in the crab Ucides cordatus as well as glycemia (Dias Correˆa et al. 2006). In all three crustaceans‘ species, gills were the prime sites of Cr accumulation. The maximum concentration of Cr was detected in female from the winter sampling in both gill and muscle, but no statistical differences found between the sexes. Our result is also in agreement with the bioaccumulation of Cr in the crayfish (Astacus leptodactylus) from Lake of Hirfanli in Turkey (Tunca, et al., 2013). 167

Nickel (Ni) is a dietary requirement to some animals, although it is toxic in higher concentrations (Zaroogian and Johnson, 1984; Denkhaus and Salnikow, 2002). Consequently, lack or excess of Ni can have adverse biological effects (Simonetti et al., 2012) for example it has been observed that in Acartia pacifica, the egg hatching success and the number of nauplii hatched decreased with increasing Ni concentrations at environmental, implying that the process of yolk accumulation (vitellogenesis) was affected (Hook and Fisher, 2001). The concentration of Ni in gill in the male Munida in winter was slightly higher than female. In contrary in muscle samples of female Munida the level of Ni was higher. These results are in agreement to Beltrame, 2010 from Neohelice granulate crab from Mar Chiquita Lagoon, Argentina which found the greater concentration of Ni in male tissues (Beltrame et al., 2010).

It is well known that dietary exposure is the major route for metal bioaccumulation in many marine organisms (Borgmann 2000; Wang and Ke, 2002) This fact pointed out the assimilation efficiency of contaminants, which is critical for understanding both their bioaccumulation and trophic transfer ability in aquatic invertebrates (Wang and Fisher, 1999). The relative importance of food or solution as routes of metal uptake will also vary between different genders as the females may have restricted feeding during egg incubation. During this period females often behave cryptically as the feeding opportunities reduce (Hartnoll, 2006). The reproductive period of the studied crustacean samples is mostly happening from September to February, therefore the feeding rate of females samples reduce during autumn and winter and increase after the spawning during spring and early summer. Hence, the feeding limitation in females during cold seasons could be another reason in high concentrations of the metals in muscle tissue during spring time which they should gain energy for the reproduction cycle for other half of the year.

Concerning the differences among the species no remarkable differences were found among them. On the other hand all the three species are sensetive indicator of environmental monitoring, nevertheless Nephrops showed to bioaccumulate the metals slightly higher than the other two species from the reference area and therefore it might be a reason that this species was not captured at all from the contaminated area.

The results from the temporal trends in bioaccumulated heavy metals in the soft tissue of the three crustacean species from the both areas during 13 years of continues study (2002-2015) indicate that the concentration of metals related to the smelting plant activities such as Ni, Cr and somehow in Mn decreased which shows that the situation has improved. In opposite the concentrations of Zn and Cu which mostly attributed to the anthropogenic sources of 168

contamination has dramatically increased. This phenomenon probably related to the increase in number of fish frams and untreated water effluent from the residency areas around the gulf. Since the dumping practice continuous, actual recovery rates cannot be precisely estimated in this area.

Nephrops norvegicus is the only interests species in commercial fisheries, therefore, the average concentration of Zn, Cu Fe and Mn from the muscle tissue were compared to FAO,1993 and WHO,1998) standards (Chapter 1-Introduction). The results showed lower concentration of these metals than the permissible intake concentration suggested by FAO and WHO except Mn. The average concentration of this metal is 3.54 (µg/g wet wt) which is higher than the permissible concentration proposed for Mn by USDA, (2009) (Mn, 0.15 (µg/g wet wt)). Similar results were obtained for the other studied crustacean species. The average concentration of Ni (µg/g wet wt) and Cr in muscle of Munida were also lower than the toxic limit. The only elevated concentration of studied metals was found in the average level of Cr (µg/g wet wt) from 2009. It should be taken into consideration that fisheries activities is forbidden in the slag dumping, but according to the results the whole area is affected by the smelting plant operation and since the smelting plant operates and the dumping practice continues, therefore actual recovery rates cannot be estimated.

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5.2 INSHORE AREA (Coastal area)

Apart from the by-product deposition in the offshore area of Larymna bay, serious in situ evidence indicated that the operation of the smelting plant influences the environmental quality of the inshore bay. Thus, the seawater and benthic biota of the coastline of Larymna bay was also studied.

5.2.1 Inshore water

As mentioned in the methodology chapter, coastal seawater samples were collected seasonally from sevens stations (Figure 6.). Additionally, two stations far from the metallurgy were selected as a reference area. In total 29 samples of water were analyzed for dissolved and particulate metals, as well as for suspended particulate material.

5.2.1.1 Dissolved metals

The average, maximum and minimum concentrations of Fe, Ni, Cr, Mn, Zn and Cu in seawater collected from the whole coastline of Larymna bay are presented per season in Table 51. It should be mentioned that the concentration of total Cr was not determined in water samples collected during the first sampling (autumn 2009) and also not from the reference stations.

For the majority of metals related to the smelting plant activity (Fe, Cr and Mn) the highest average concentration of dissolved metals was measured in autumn 2010. Furthermore, the minimum and maximum concentrations of these metals varied from 17-40 µg/l respectively.

The highest average concentrations of dissolved Zn and Ni were recorded in spring 2010. In the case of Ni, there were no great differences among the average seasonal concentrations. In contrast to Ni, the dissolved concentration of Zn during spring was about 2 folds higher than the other seasons. Cu was the only metal that showed the highest average concentration in autumn 2009.

The Non parametric Kruskal-Wallis statistical test was applied to investigate any significant variation among the average seasonal concentrations of dissolved metals. The data from the reference areas have been excluded for this statistical test.

The results showed no statistical differences except for Cu between autumn 2009 and autumn 2011 (P<0.05).

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Table 50: Seasonal variations of dissolved metal concentrations in coastal water around the smelting plant. Values are expressed in µg/l. high concentrations are shown in bold. ND= no data.

Seasons Autumn 2009 Winter 2010 Spring 2010 Autumn 2010 Fe Average±SD 8.1±9.4 8.8±4.9 21.3±40.1 44.3±69.7 Min 3.1 4.6 3.8 4.7 Max 27.4 16.2 112 191 Average±SD 3.01±1.1 2.9±1.4 3.2±0.9 3.1±2.9 Ni Min 1.7 0.63 2.4 0.21 Max 4.9 5.2 5.1 8.0 Average±SD 0.46±0.2 0.77±1.0 1.3±1.6 Cr Min ND 0.20 0.25 0.28 Max 0.85 3.0 4.8 Average±SD 2.5±1.2 0.82±0.5 2.0±1.8 2.5±3.1 Mn Min 1.3 0.07 0.92 0.08 Max 4.7 1.6 6.1 7.1 Average±SD 5.1±2.1 2.3±1.8 9.0±2.1 2.9±1.6 Zn Min 2.8 0.63 6.35 0.5 Max 7.8 4.08 11.6 5.6 Average±SD 0.94±0.4 0.27±0.3 0.71±0.3 0.65±0.5 Cu Min 0.6 0.03 0.4 0.11 Max 1.5 0.85 1.14 2.2

The average concentrations of dissolved metals per station during the four studied seasons of monitoring are illustrated in Figure 44. It is worth noting that in autumn 2009 (first sampling) and autumn 2010 (last sampling) due to the bad weather conditions, water samples from stations LA3 and LA2 were not taken.

The results reveal that generally, the metals related to the smelting plant by-products (Fe, Mn, Ni, Cr) had significantly increased concentrations in all stations in autumn 2010. The graphs of these metals illustrate this pattern. Moreover, they all present remarkably high level of metals in LA4 and LA5 (the nearest stations to the smelting plant).

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Ni Fe

Mn Cr

Zn Cu

Figure 44: Average concentrations of dissolved metals per season and station. Ref presents the reference area.

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It is observed that, apart from the exceptional peak in station LA4, the horizontal distribution of Fe seems to be quite homogeneous, while the distribution of Mn and mainly Ni is dispersed gradually.

On the contrary the horizontal distribution of Zn showed an erratic pattern during the four seasons of monitoring; however relative similarities were detected in some stations in autumn and winter 2010. The highest concentration of Zn in all stations in spring 2010 was considerable. It should be taken into consideration that Zn is not assumed as one of the slag related metals; however, its high concentration in three stations of LA3, LA4 and LA5 which are the nearest ones to the smelting plant worth noting.

The horizontal distribution of Cu showed also an erratic pattern during the four seasons of monitoring. The average concentration of Cu in autumn 2009, autumn and spring 2010 presumed to follow similar patterns from LA1 to LA5 stations, but the last two stations of LA6 and LA7 showed completely different patterns. High concentration of Cu in autumn 2010 was also found.

The kruskal Wallis test, presented statistically significant differences (P<0.05) in the concentrations of Ni and Cr between the stations. The differences were mostly applied between the enriched stations of LA4, LA5 and the other stations with the lower concentrations.

5.2.1.2 Particulate metals

The average, minimum and maximum concentrations of particulate metals in different seasons are presented in Table 52. The maximum concentrations along with the highest averages are indicated in bold. Seawater samples from LA3 and LA2 during autumn 2009 and autumn 2010 due to rainy weather were not collected. It is clearly seen that the highest concentrations of all particulate metals, similar to the dissolved ones, were detected in autumn 2010.

The Kruskal-Wallis test revealed significant differences (P<0.05) only for Ni between autumn 2010 and the other seasons. The seasonal variations of heavy metals per station are presented in Figure 45.

Unlike the dissolved metals, the distributions of particulate metals per station and season seem to follow similar patterns. The remarkably high concentrations of metals in autumn 2010 and the distinguished peak in LA4 applied not only for metals related to the slag, but also for Zn which is mostly attributed to various and ubiquitous anthropogenic activities. The second station with high concentration of Mn, Cu and Zn was LA3. 173

Table 51: Basic statistics of particulate metals concentrations per season, expressed in µg/l high concentrations are in bold.

Seasons Autumn 2009 Winter 2010 Spring 2010 Autumn 2010 Fe Averages±SD 26.8±27.8 27.3±41.3 44.2±47.3 265±381 Min 9.97 5.95 8.26 9.2 Max 82.9 19.50 141 840 Average±SD 0.88±1.8 0.86±1.6 0.31±0.2 3.8±6.07 Ni Min 0.06 0.10 0.10 0.25 Max 4.56 4.51 0.83 16.8 Average±SD 0.65±0.98 0.63±1.3 0.95±1.2 7.6±11.2 Cr Min 0.05 0.09 0.08 0.11 Max 2.64 3.66 2.99 27.8 Average±SD 0.63±0.67 0.67±0.94 1.42±2.05 5.5±6.8 Mn Min 0.14 0.10 0.17 0.23 Max 1.89 2.73 5.9 18.5 Average±SD 0.43±0.1 0.36±0.15 0.35±0.31 0.78±0.96 Zn Min 0.3 0.25 0.20 0.20 Max 0.58 0.67 1.06 2.74 Average±SD 0.14±0.08 0.09±0.03 0.3±0.42 0.3±0.19 Cu Min 0.07 0.05 0.05 0.07 Max 0.24 0.16 1.2 0.63

In accordance to Figure 45 the distribution of Fe and Cr, along with Ni and Mn were perfectly matched. Significant seasonal differences (P<0.05) were detected in the concentrations of Ni and Cr. Regarding the sampling stations the highest concentrations of metals were mostly found between the two enriched stations of LA3 and LA4 and the other stations.

The distribution and concentrations of particulate Fe, Mn, Cr and Ni showed clear decrease from the smelting plant station (LA4) to the furthest stations.

The distribution of the average percentage of dissolved and particulate metals is recorded in Table 53. Concerning the stations it is clearly presented that the highest percentage of particulate Ni and Cr was found in station LA4, but some increased percentages were also found in stations LA3 and LA6. For the other metals there is no clear trend of dissolved to particulate distribution between stations.

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Ni Fe

Mn Cr

Zn Cu

Figure 45:.Graphs of particulate metal concentrations per season and station. Ref represents the reference area.

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Generally most of the metals showed higher percentage of the dissolved form compared to the particulate. This does not apply to Fe, which did not follow the same trend as the other metals related to the smelting plant by-products and higher percentage of particulate Fe was detected. Besides, among all the stations some high percentages of particulate metals were also detected in LA3 as well as the two stations of LA4 and LA5. The percentage of both dissolved and particulate Fe was constant during the different seasons. In contrast to Fe results, Ni, Cr and Mn indicated slightly decrease in the percentage of dissolved forms and increase of the particulate ones from autumn 2009 to autumn 2010. Cu and Zn did not show any particular pattern. The order of magnitude in average percentage of dissolved metals are Zn>Ni>Cu>Mn>Cr>Fe.

Table 52: Average percentage of dissolved and particulate metals in coastal area of Larymna Bay during four seasons. ND=no data. Dis = dissolved metals. Par = particulate metals. R = for reference area.

Metals Station Type Fe Ni Cr Mn Cu Zn LA1 %Dis 31 84 70 73 84 90 LA2 %Dis 28 89 68 60 67 86 LA3 %Dis 18 87 51 37 60 88 LA4 %Dis 30 61 28 56 71 81 LA5 %Dis 23 88 52 66 86 93 LA6 %Dis 27 74 52 64 65 80 LA7 %Dis 34 93 60 74 83 94 R %Dis 64 97 ND 38 92 85 LA1 %Par 69 16 47 27 16 10 LA2 %Par 72 11 55 40 33 14 LA3 %Par 82 13 49 63 40 12 LA4 %Par 70 39 79 44 29 19 LA5 %Par 77 12 61 34 14 7 LA6 %Par 73 26 61 36 35 20 LA7 %Par 66 7 55 26 17 6 R %Par 36 3 ND 62 8 15

5.2.1.3 Suspended particulate material (SPM)

Suspended particulate material (SPM) is defined as fine solid inorganic particles of non- biogenic, biogenic, lithogenic, etc….origins suspended in water. Table 54 presents the 176

distribution of SPM (mg/l) in different seasons and stations. Except for autumn 2009 with the lowest average concentrations of SPM, the other seasons showed somehow constant concentrations. The highest SPM concentrations were found in LA3 in both winter and spring samplings and LA1 in winter 2010.

Table 53: Concentration of Suspended particulate matter (SPM) (mg/l) in different seasons and stations. ND= no data. R=reference station

Season Stations Autumn 2009 Winter 2010 Spring 2010 Autumn 2010 LA1 2.1 11.3 5.5 5.5 LA2 4.2 8.3 6.3 ND LA3 ND 12.6 16.1 6.0 LA4 3.2 8.4 5.4 6.2 LA5 4.0 6.2 5.3 5.8 LA6 4.0 5.5 5.9 7.3 LA7 1.1 5.8 5.7 6.0 R ND 4.1 ND ND Average±SD 3.1±1.3 8.3±2.8 7.2±4 6.1±0.6

Table 54: Range of Suspended particulate matters (SPM) concentrations (mg/l) from offshore and inshore area.

Surface area Reference 2009 2010 2011 5.7-10.1 5.2-6.9 2.8-7.9 This study-off shore Bottom area 6.0-6.7 4.6-6.8 1.3-6.9 This study-off shore Autumn 2009 Winter 2010 Spring 2010 Autumn 2010 1.1-4.2 4.1-12.6 5.5-16.1 5.5-7.3 This study-In shore

The SPM results from offshore and inshore area were compared in Table 55. The lowest and the highest concentration of SPM were detected in the inshore sampling.

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5.2.1.4 Investigation of metal level relations in inshore seawater and estimation of pollution state

A cluster analysis and correlation coefficient analysis was carried out to identify any analogous behavior patterns between the different sites and metals (carrasco et al, 2003). Both correlation and cluster analysis are indicating similarities between the variables (Gore, 2000). In this study both methods were used to confirm relationships in the data.

Table 55: Spearman correlation coefficient A) dissolved metals B) particulate metals from the seawater sample of costal area.

Correlations Fe Cr Ni Mn Cu Zn Fe 1.000 Cr .636** 1.000 Ni .520** .689** 1.000 Mn .454* .718** .743** 1.000 Cu -.070 -.114 -.068 .308 1.000 Zn -.228 .003 .204 .325 .339 1.000

**. Correlation is significant at the 0.01 level (2-tailed). *. Correlation is significant at the 0.05 level (2-tailed). A) The Spearman correlation coefficient of dissolved metals Correlations Fe Ni Cr Mn Zn Cu Fe 1.000 Ni .759** 1.000 Cr .887** .764** 1.000 Mn .945** .803** .893** 1.000 Zn .426* .289 .275 .375 1.000 Cu .413* .130 .301 .352 .339 1.000 **. Correlation is significant at the 0.01 level (2-tailed). *. Correlation is significant at the 0.05 level (2-tailed). B) The Spearman correlation coefficient of particulate metals 178

Euclidean distances and Ward‘s method were applied for cluster analysis. The Reference stations data were eliminated for the analysis of correlation coefficients and cluster so as to investigate the metal similarities in the coastal area around the smelting plant.

The results from the spearman correlation coefficient of both dissolved and particulate metal (Table 56 A & B) show a very strong relation (P<0.01) particularly among the metals related to the smelting plant by-products. Fe is the only metal correlated to particulate Zn and Cu. Moreover, the results from the cluster analysis mostly supported the correlation coefficient findings. Therefore, as it is shown in the Figure 46A, the three metals Fe, Cr and Mn were grouped together tightly. Meanwhile, Ni –a metal related to the smelting plant- was grouped with the two other metals of Zn and Cu which apparently are not strongly related to the metallurgic industry. This is probably because of the stronger preference for the dissolved phase that Ni, Cu and Zn shared in the inshore stations.

The dendrogram classification of particulate metals (Figure 45B) is slightly different than the dissolved. In accordance, the two metals of Fe and Cr were classified together in one cluster and the other metals of Ni, Mn and Zn were correlated in another one. Cu was grouped separately probably due to the much lower absolute particulate values than all the other metals.

In order to evaluate spatial and temporal similarities among the stations cluster analysis was again performed. Figure.47 introduces which stations in dissolved and particulate metals were grouped together. Based on the concentrations of dissolved metals, it is detected that most of the coastal stations expect for LA4 were grouped together; LA4 and reference sites comprise two individuals‘ clusters. The arrangement of the stations, according to the concentration of particulate metals was somehow similar with the dissolved ones, however in particulate metals the reference site were grouped with the other coastal stations and LA4 and LA3 are the only individual clusters.

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Dendrogram Dendrogram Ward's Method,Euclidean Ward's Method,Euclidean

3 10

2.5 8

2 6

1.5 4

Distance Distance 1 2 0.5 0

0

Ref

LA1 LA3 LA5 LA2 LA6 LA7 LA4

Ni

Fe Cr

Zn Cu Mn A) Dendrogram of dissolved metals A) The dendogram of stations in based on the concentration of dissolved metals Dendrogram Dendrogram Ward's Method,Euclidean Ward's Method,Euclidean

4 10

8 3 6 2

4

Distance Distance 1 2

0

0

Ref

LA1 LA5 LA2 LA7 LA6 LA3 LA4

Ni

Cr

Fe

Zn Cu Mn B) The dendogram of stations in based on the concentration of particulate B) Dendrogram of particulate metals metals Figure 46: Dendrogram classifies stations according to A) Figure 47: Dendrogram classifies stations according to A) dissolved metals, B) particulate metals. dissolved metals, B) particulate metals.

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5.2.1.5 Spatial configuration of heavy metal in coastal areas

MDS (Multidimensinal scaling) is a mathematical technique which generates a spatial configuration map where the distance between data reflects the relationship between individual variables (He, Zhao, Gao, Hu, & Qiu, 2010).

The two dimension plots of MDS simulate the distribution of particulate and dissolved metals in the coastal area (Figure 48). All data has been log transferred and Euclidean distance was applied.

Stress is a goodness of fit that MDS tries to minimize; it varies between 0 to1, the value near to 0 indicates better fit. The 2D stress in this graph is less than 0.04 which performed a good fitness of data. The spatial metal concentrations in particulate and dissolved metals illustrated a strong separation between the two forms of metals. Furthermore, it indicated much firmer similarities among the concentration of particulate metals than the dissolved ones. The vectors representing the metals were mostly pointed to the enriched station LA4.

Transform: Log(X+1) Resemblance: D1 Euclidean distance 2D Stress: 0.04 type Dis Zn Ni LA4 Part

Mn Cu LA3 LA4 LA5 LA7 LA1 LA6 LA4 LA7LA2 LA2 LA6LA1 LA5 LA5 LA5 LA4 LA7 LA3 LA4 LA6 LA7 LA1 LA4 LA4 LA1 LA3

Fe Ref LA6 LA3 LA5 LA2 LA4 LA1 LA3 LA3 LA5 LA1 LA6 LA6 LA2LA1 LA5LA7LA7 LA5LA7LA7LA2LA6 LA2 LA6 LA1 Ref

Figure 48: The two dimension distribution (MDS) of dissolved and particulate metals from the coastal area.

5.2.1.6 The ratio of contamination

The partition coefficients (kd) are used to represent the ratio of contaminant mass in the solid phase (particulate matters) to that in the water phase.

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Log Kd Autm 09 Wint-10 Spri-10 Autm-10 6.5

6

5.5

5

4.5

4

3.5 Zn Cu Ni Mn Cr Fe

A) Partition Coefficient in different seasons

Log kd Zn Cu Ni Mn Cr Fe 6.5

6

5.5

5

4.5

4

3.5 LA1 LA2 LA3 LA4 LA5 LA6 LA7 R

B) Partition Coefficient in different stations

Figure 49: Logarithim of partition coefficient (log Kd ) of metals in four seasons from the coastal area.

During transport of metals in soils and surface water systems, metal sorption to the solid matrix results in a reduction in the dissolved concentration of metal and this affects the overall rate of metal transport. Therefore, partition coefficient (kd) provides information about the geochemical fate of metals. Partition coefficients are given as log Kd and are defined as:

kd = Mp/Md

In which Mp is the concentration of metals in the particulate forms which express in

µg/kg and Md is the concentration of dissolved metals and express in µg/l (García-Rico et al., 2011). 182

The average ratios of Kd in the coastal area and the reference station are demonstrated in

Figure 49. It is clearly seen that no remarkable differences found in Kd of metals; neither among different stations (the coastal stations and the reference area), nor among different seasons. The comparison of the results of the inshore study with the results from the dumping off-shore area

(offshore area-chapter 3) showed that the Kd of Ni, Cr, Mn and Fe from the coastal area is slightly higher than the dumping area. The magnitude order of the Kd of metals from the coastal and slag dumping areas are not matched as well. Hence, the Kd order in the coastal area is as follows: Fe>Cr>Mn>Cu>Ni>Zn, while in slag dumping area is: Cu>Fe>Cr>Mn>Zn>Ni. It should be noticed that the reference area also presented the similar ratio of (log kd) with the coastal stations.

5.2.1.7 Discussion

The coastal area of Larymna bay apart from many other possible pollution sources, hosts two main pathways of metal contamination; point source contamination mostly related to the smelting plant- by products and the other non–point source inputs attributed to municipal effluents, aquaculture, antifouling paints and agriculture activities.

SPM in seawater mostly originates from the fine sediment (mud) in the bottom and fluvial inflow which is classified by the grain size. In particular, SPM distribution in the water column influences the plankton primary production by regulating the light penetration depth in seawater (Loring, 1991), furthermore SPM can absorb and thus transport some human-made contamination such as heavy metals (Haarich et al. 1993) persistence organic pollution and radionuclides (Nies, Harms, Karcher, Dethleff, & Bahe, 1999).

The increasing pattern of SPM in the coastal area from autumn 2009 to autumn 2010 is probably due to terrestrial, atmospheric and biological inputs. The remarkably high concentrations of SPM provided, in the two stations of LA3 and LA7, are probably related to the biological particles (fish feces and food residues), from the nearby fish-culture activities. LA3 is also located near a seasonal stream, thus considerable high concentration of metals such as Cu, Zn and Mn in the spring sampling might be related to the land washed out. The two stations of LA4 and LA5 might be a good example of atmospheric and land input sources. Both stations were located near the smelting plant and the village. Thus, strong winds and rainfall with direction toward the bay could carry the particles from land and the smelting plant chimney to the seawater. The influence of weather conditions might be another issue in increasing the SPM, since the average concentration of SPM was increased in winter (Table 54). In this respect, not

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only the SMP concentration but also the average percentages of particulate metals in these two stations near the smelting plant are quite higher than the other stations.

The comparison results of SPM concentration from this study with other studies from the industrialized and polluted areas in Greece show that, the range of SPM concentrations in the present research (1.1-15.1 mg/l) is slightly higher than the results from the dumping area of Larco smelting plant during the same period (5.23-10.07 mg/l) (Table 56) and Saronikos Gulf (0.05-1.84 mg/l) (Price et al., 2005), however it seems that the Thermaikos Gulf is characterized by a much higher range of SPM (17.3-32.3 mg/l) due to the existence and influence of major rivers (Violintzis et al., 2009).

The percentage of particulate metals were lower comparing to dissolved ones, but on the other hand the partition coefficient (log Kd) which is indicating the ratio mass of particulate to dissolved metals were recorded relatively high in all stations. It is worth noticing that the high levels of kd indicate high metal adsorption in sediments, therefore less free heavy metals available to aquatic organisms (García-Rico et al., 2011; Gavriil & Angelidis, 2005). Most probably the high concentrations of particulate metals in this study are attributed to the metallurgy operation. Therefore it can be considered that Kd is related to the particle contents which is almost metal materials from the smelting plant.

Precipitation, large water inputs from the streams and seasonal anthropogenic activities play an important role in governing heavy metal distributions in water (García-Rico et al., 2011; Varol, 2013). Autumn is the season in which rain often occurs and increases the wash out process of different materials on its way. High concentration of metals, especially the ones associated with slag (Fe,Cr, Mn &Ni) in both dissolved and particulate forms were recorded in Autumn 2010 more than the same season in 2009. It is suggested that both atmospheric rainfall and heavy winds could reinforce such a condition. The strong winds would transport the dust and small particulates around the smelting plant easily into the water. The by-product is stored in the open area near the smelting plant before being loaded to the barges and discharged in the seawater. Therefore, the finer particles could easily be mobilized and washed off during the wet seasons. Another scenario for the high concentrations of particulate metals might be the transport of fine particles from the dumping site toward the coast during the deposition procedure.

High concentrations of dissolved metals such as Fe, Cr, Ni and Mn in the coastal area could be attributed to the non-treatment of wastewaters discharged directly from the smelting plant to the bay and the discharge of domestic effluents from the residential area into the seawater (García-Rico et al., 2011). The high concentration of dissolved Zn and Cu in LA3, LA5 184

and LA7 could be due to the vicinity of these stations to the village of Larymna and the aquaculture activities around the bay.

Strong positive correlation coefficient of metals related to smelting plant-by product in both dissolved and particulate metals indicate common origin i.e. the smelting plant.

The correlation of Zn and Cu in particulate forms with Fe, could be explained by the high concentration of Fe in the area, and its tendency to form colloids in fresh water and coagulate into larger particles in salt water co-precipitating all other metals along (Wei et al., 2011)..

The cluster analysis from the coastal area as well as the slag-dumping area showed that Zn/Ni and Mn were grouped together. Good correlation of Zn with the metals related to the smelting plant slag could lead this opinion that the laterite ore exploited in the smelting plant is quite enriched with this element as well as Fe, Ni, Cr and Mn. High concentration of Zn was detected in the stations near the smelting plant but also in the dumping area which could confirm the above theory.

The results from MDS, from one side showed clearly the spatial distribution and separation of dissolved and particulate metals related to and not related to the smelting plant, and from the other hand supported the findings from the line plots and cluster analysis with the high concentration of metals in LA4 in both dissolved and particles forms. As a result this station did not group with any other station along from the coastal area.

The average concentrations of dissolved and particulate metals from the present study are compared with the results from two other contaminated coastal areas in Saronikos Gulf (western industrial zone of Saronikos Gulf) and Maliakos Gulf (Table 57 and 58) along with the US EPA criterions and EU water framework directive (WFD). West Saronikos Gulf receives considerable pollution loads due to industrial and oil refinery activities (Paraskevopoulou, 2009). It also has a similar geological background with the Larymna Bay with increased occurrence of serpentinite rocks and enriched Ni and Cr content. Maliakos Gulf is a semi enclosed gulf in centre of Greece which connected the Aegean Sea. The most important activities in this area are agriculture and livestock farming (Rousselaki, 2007).

The results show that the dissolved and particuleate concentrations of all metals are much enriched in Larymna bay than west Saronikos Gulf; however Maliakos Gulf presented higher concentration of Mn, Cu and Zn in the dissolved fraction and Zn in the particulate form which can be attributed to inputs from Sperchios river and the agricultural area since Cu and Zn are

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known additives in fertilizers (Dassenakis et al., 1995). The concentration of both chronic and acute US EPA criterions (CCC µg/l & CMC µg/l) (Chapter one Introduction) and EU water framework directive (WFD) were higher than the average concentration of trace metals from the coastal area.

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Table 56: Comparison of average concentrations of dissolved metals (µg/l) from coastal areas in Greece. The high concentrations are in bold

Metals Ni Fe Mn Cr Zn Cu Location References Aut-9 3.0 8.1 2.5 - 5.1 0.94 Larymna Bay Present study Aut-10 3.1 44.3 2.5 1.3 2.9 0.65 Larymna Bay Present study Spri-10 3.2 21.3 2.0 0.77 9.0 0.71 Larymna Bay Present study Wint-10 2.9 8.8 0.82 0.46 2.3 0.27 Larymna Bay Present study Aut-06 1.7 - 1.9 0.28 3.1 0.48 W-Saronikos Gulf Paraskevopoulou,2009 Aut-07 1.9 - 1.7 0.55 1.5 0.35 W-Saronikos Gulf Paraskevopoulou,2009 Sum-06 1.3 - 1.3 0.29 2.1 0.53 W-Saronikos Gulf Paraskevopoulou,2009 Sum-07 2 - 2.2 0.19 3.1 0.44 W-Saronikos Gulf Paraskevopoulou,2009 Wint-06 1.4 - 1.5 0.25 4.8 0.23 W-Saronikos Gulf Paraskevopoulou,2009 Wint-07 1.8 - 1.2 0.24 1.04 0.23 W-Saronikos Gulf Paraskevopoulou,2009 Spri-05 - - 3.2 - 4.1 0.75 Maliakos Gulf Rousselaki,2007 Sum-05 - - 3.2 - 2.2 1.7 Maliakos Gulf Rousselaki,2007 Aut-05 - - 3.8 - 11.3 1.6 Maliakos Gulf Rousselaki,2007 Wint-05 - - 0.82 - 2.5 0.9 Maliakos Gulf Rousselaki,2007

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Table 57: Comparison of average concentrations of particulate metals (µg/l) from coastal areas in Greece. The high concentrations are in bold.

Metals Ni Fe Mn Cr Zn Cu Location References

Aut-9 0.88 26.8 0.63 0.65 0.43 0.14 Larymna Bay Present study Aut-10 3.8 265 5.5 7.6 0.78 0.3 Larymna Bay Present study

Spri-10 0.31 44.2 1.42 0.95 0.35 0.3 Larymna Bay Present study Wint-10 0.86 27.3 0.67 0.63 0.36 0.09 Larymna Bay Present study

Aut-06 1.7 59.3 1.2 0.28 0.39 0.16 W-Saronikos Gulf Paraskevopoulou,2009 Aut-07 1.7 60.8 0.64 0.30 0.42 0.09 W-Saronikos Gulf Paraskevopoulou,2009 Sum-06 0.36 23.4 0.37 0.19 0.25 0.26 W-Saronikos Gulf Paraskevopoulou,2009 Sum-07 1.9 24 0.40 0.24 0.70 0.08 W-Saronikos Gulf Paraskevopoulou,2009 Wint-06 0.70 16.4 0.58 0.36 0.31 0.08 W-Saronikos Gulf Paraskevopoulou,2009 Wint-07 0.44 25.2 0.34 0.34 0.75 0.06 W-Saronikos Gulf Paraskevopoulou,2009 Spri-05 - 103 3.3 - 1.0 0.29 Maliakos Gulf Rousselaki,2007 Sum-05 - 95.2 5.1 - 1.7 0.27 Maliakos Gulf Rousselaki,2007 Aut-05 - 60.7 2.7 - 1.0 0.16 Maliakos Gulf Rousselaki,2007 Wint-05 - 87.5 7.8 - 2.0 0.21 Maliakos Gulf Rousselaki,2007

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5.2.2 Gastropod samples from the coastal zone

In addition to the seawater samples two different gastropod species, Phorcus turbinatus and Patella caerulea, were collected from seven stations around the Larymna bay and two stations farther from the smelting plant as reference sites.

5.2.2.2 The concentration of heavy metals in gastropods - Spatial and temporal distribution

In total, 181 Ph. turbinatus samples corresponding to 2715 individuals and 254 of P. caerulea corresponding to 2550 individuals were measured for Fe, Mn, Zn and Cu. Due to the high number of biota samples, only 70 samples from both species were analyzed for Cr and Ni. These samples were selected from the stations of LA4 and LA5 located near the smelting plant and from LA1 that is the farthest station in northern site of the smelting plant. Summary statistics of heavy metals concentrations in gastropod samples are presented in Table 59. The values are expressed in κg/g dry. It is clearly observed that the two gastropod species bioaccumulated metals in different degree: P. caerulea had higher Zn and Fe concentrations mostly in the contaminated area, whilst Ph. turbinatus accumulated higher concentrations of Mn and Cu. In this respect; the differences between the two species are considerable. Therefore the concentrations of Mn and Cu in Ph. turbinatus are respectively two and five folds higher than those in P. caerulea. The concentrations of Zn and Fe in P. caerulea are also twice higher than that in Ph. turbinate. Table 58: Summary statistics of heavy metal concentrations in P. caerulea and Ph. turbinatus (in μg/g dry weight) in the contaminated and the reference areas.

Contaminated area Zn Mn Fe Cu Ni Cr P. caerulea Mean 65.1 8.4 4552 12.2 28.5 27.2 SD 7.3 5.3 3530 5.3 22.6 16.4 Ph. turbinatus Mean 57.9 20.9 1684 68.1 62.4 82.7 SD 20.9 13.3 13.3 12.7 74.6 104 Reference area Zn Mn Fe Cu Ni Cr P. caerulea Mean 48.4 12.6 1719 9.2 13.3 9.5 SD 9.8 3.7 299 2.7 4.8 7.9 Ph. turbinatus Mean 46.5 17.9 1023 49.1 13.1 13.5 SD 5.3 2.3 276 12.9 1.9 3.9

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Concerning the differences in the concentrations of heavy metals between the two areas (contaminated and reference) it is revealed that no remarkable variations were found between them except for the two metals of Ni and Cr. Therefore in the contaminated area the concentration of Ni and Cr in Patella is about 3 folds and in Phorcus is almost 7 folds higher than those from the reference area.

The distribution of heavy metals in each station along the coastline during different seasons of samplings is presented in Figure 50A, B and C.

Phorcus turbinatus Patella caerulea

Fe

Mn

Figure 50A: Heavy metals distribution in Ph. turbinatus and P. caerulea (µg/g dry wt) in different sampling stations and seasons. REF1 and REF2 indicate the Reference areas.

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Cu

Zn

Ni

Figure 50B: Heavy metals distribution in Ph. turbinatus and P. caerulea (µg/g dry wt) in different sampling stations and seasons. REF1 and REF2 indicate the Reference areas.

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Cr

Figure 50c: Heavy metals distribution in Ph. turbinatus and P. caerulea (µg/g dry wt) in different sampling stations and seasons. REF1 and REF2 indicate the Reference areas.

According to Figure 50A, B and C the similar spatial and seasonal pattern of Fe and Mn which are related to the smelting plant activity in Ph. turbinatus can be clearly observed. The same patterns were noticed for P. caerulea with the exception of the very high values in LA2 during winter 2010.

The geographical distribution of metals apparently indicates that during the whole study period the sampling stations located closer to the metallurgy (station LA4 and station LA5) are the most contaminated places by Fe , Mn, Ni and Cr. Zn, also showed high concentrations in both species collected at the vicinity of the smelting plant (station LA4). On the other hand Cu presented quite completely different pattern; the higher concentrations were detected in both species in stations LA6 and LA7. In addition Ph. turbinatus collected at station LA3 was heavily impacted by Cu presenting higher concentrations even than those at stations LA6 and LA7. Kruskal Wallis test revealed that all the observed differences were statistically significant (P<0.05).

Concerning the season, it is observed that the two studied gastropods reached their maximum values during different seasons. Therefore, P. caerulea followed the seasonal pattern of seawater presenting the highest levels during autumn and winter (2009-2010). On the contrary the highest metal concentrations in Ph. turbinatus were measured in spring. For both species the seasonal differences were statistically significant according to the Kruskal Wallis test (P<0.05).

Note that data from the reference stations were excluded from the statistical tests, in order to have a better understanding of the contaminated area. 192

5.2.2.3 Relations of metals in gastropods and the ambient environmental

In order to find out any relation between the high concentrations of metals in the tissues of gastropods and the elevated dissolved metal concentrations in seawater, correlation analysis was performed (Table 60).

Table 59: Spearman correlations among: a) the concentration of metals in seawater with those in gastropod tissues, b) among bioconcentrated metals (M= Ph. turbinatus, P= P. caerulea, W= Seawater). The significant values are in bold. A) Ph. turbinatus Correlations Fe-M Cu-M Zn-M Mn-M Ni-M Cr-M Fe-M 1 Cu-M -.031 1 Zn-M .564** -.136 1 Mn-M .895** .079 .524** 1 Ni-M .768** -.132 .741** .515 1 Cr-M .934** .369 .180 .812** .541 1 Fe-w -.180 .048 -.075 -.124 -.146 -.237 Ni-w .081 -.172 .113 .098 .241 .078 Cr-w -.148 .160 -.046 -.101 -.060 -.124 Mn-w -.114 .097 -.031 -.066 -.137 -.085 Cu-w -.124 .046 -.147 -.122 -.410 -.066 Zn-w .343 -.055 .450* .470* .062 .116 **. Correlation is significant at the 0.01 level (2-tailed). *. Correlation is significant at the 0.05 level (2-tailed).

B) P. caerulea Correlations Zn-P Mn-P Fe-P Cu-P Ni-P Cr-P Zn-P 1 Mn-P .632** 1 Fe-P .726** .906** 1 Cu-P .035 .031 .085 1 Ni-P .305 .153 .191 -.213 1 Fe-w .036 -.043 .074 .674** -.311 Cr-P .913** .899** .904** .603* .398 1 Cr-w -.053 -.098 -.043 .648** -.415 -.762* Ni-w -.010 -.133 -.036 .207 -.261 -.291 Mn-w -.048 -.156 -.080 .351 -.433 -.482 Cu-w -.057 -.187 -.182 -.100 .166 -.235 Zn-w .612** .376 .389* -.190 .021 .470 **. Correlation is significant at the 0.01 level (2-tailed). *. Correlation is significant at the 0.05 level (2-tailed).

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The two species of gastropods seem to present different sensitivity to seawater quality; however for both species a significant positive correlation was found between concentrations of t dissolved Zn in seawater and the accumulated ones in their soft tissues. The only difference was the statistical probability. In Ph.turbinatus the significant differences were discovered in P<0.05, but for P. caerulea the level of positive correlation was found in P<0.01 (Table 60 A&B.). Besides, a strong negative correlation was detected between bioacumulated Cr in Patella with the same metal in the seawater. Regarding the interrelation of the bioaccumulated metals, positive significant correlation was detected for both species between Fe, Mn and Cr. Moreover, it seems that the bioaccumulated Fe in Phorcus not only make a strong correlation (P<0.01) with the metals related to the smelting plant, but also with Zn which is known as a metal related to anthropogenic pollution.

On the other hand, Zn also presented a good relation with bioacumulated Mn and Ni. Similar to Phorcus, the bioaccumulated Zn in the soft tissues of Patella showed strong relation (P<0.01) with Mn and Cr. Cu as well as Zn had a good correlation with the two metals Fe and Cr.

Finally, the magnitude order of the bioaccumulated metals in the two species were different: in P. caerulea metal concentrations decrease in the following order: Fe>Zn>Cu>Mn while in Ph. turbinatus they followed a quite different order: Fe>Cu>Zn>Mn.

In order to establish a better relationship between the different parameters in this study, a cluster analysis was performed. This is a unique method which groups the cases that share the same variables (Core, 2000). Ward‘s method and Euclidean distance were applied for this analysis.

Dendrogram Dendrogram Ph. Ward'sturbinatus Method,Euclidean P. caeruleaWard's Method,Euclidean 12 12

10 10

8 8

6 6

Distance Distance 4 4

2 2

0 0

LA1 LA2 LA7 LA3 LA6 LA4 LA5

LA1 LA3 LA4 LA5 LA6 LA7 LA2

REF1 REF2 REF2 REF1 Figure 51: Cluster analysis in Ph turbinate and P caerulea, based on the sampling stations. REF indicates the two reference stations

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The cluster analysis was performed for grouping the sampling stations as well as the bioaccumulated metals in the soft tissues of gastropods (Figures 51.and 52). As Ni and Cr were not measured in all the sampling stations, these two metals were excluded. Based on the first cluster it is observed that in Ph. turbinatus the sampling stations are classified into three groups. The first group contained the two closer stations to the smelting plant that are considered as contaminated area (LA4 and LA5), the second included the two reference areas and the third grouped the stations that are far from the smelting plant (LA1, LA2, LA3, LA6 and LA7).

The dendogram for P. caerulea is similar to that of Ph. turbinatus, thus the coastline stations are also grouped into three clusters; however the order is different for this species.

Dendrogram Dendrogram Ph. turbinatusWard's Method,Euclidean P. Ward'scaerulea Method,Euclidean 2.4 2.5

2 2 1.6 1.5

1.2 Distance Distance 1 0.8 0.5 0.4

0 0

Fe

Zn

Cu

Fe

Zn

Mn Cu Mn Figure 52: Cluster analysis in Ph turbinate and P caerulea, based on the bioaccumulated metals.in the soft tissue

The two reference areas formed a group similar to Phorcus, but in this case they were grouped together with one station far from the metallurgy (station LA2), the second group contained the remained northern stations (LA1 and LA3). Meanwhile, the third group included the stations close to the smelting plant in the eastern part (LA4, LA5, LA6 and LA7). Finally the clusters classified the metals presented some similarities for the two species. Thus for both species Fe and Zn were grouped together, also confirmed by the results from the correlation coefficient. Meanwhile, in Ph. turbinatus Cu made an individual cluster and in P. caerulea so does Mn.

The Bioconcentration Factor (BCF) may be used to evaluate the bioaccumulation ability of a species in an ecosystem (Conti, 2003). The formula used for the calculation is BCF=Co/Csw:

Co is the mean concentration of metals in the organism in µg/g dw

and Csw is the equivalent concentration in seawater in µg/l (Conti, 2007).

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The calculated Bioconcentration Factor (BCF) for the two studied gastropods indicates that Ph. turbinatus presented higher bioaccumulation capacity for Mn and Cu in both contaminated and the reference areas, while P. caerulea got better ability in accumulating Fe in the contaminated area (Table 61).

Table 60: Bioconcentration factor (BCF) calculated for the gastropods Ph. turbinatus and P. caerulea.

Bioconcentration Factor Contaminated area Reference area Fe Mn Zn Cu Ni Cr Fe Mn Zn Cu Ni Ph. turbinatus 180 13.0 12.5 312 16.5 58 412 170 53 160 5.3 P. caerulea 570 5.9 13.5 20.5 8.4 33.3 144 148 51.7 32.7 7.2

5.2.2.4 Temporal evolution of bioaccumulated metals

In total 356 pooled samples of Phorcus turbinatus and 391 Patella caerulea were analyzed for heavy metals during the period of 1993 to 2010. The average concentrations of heavy metals in different species are presented in Table 62. It is easily observed that the concentration of bioaccumulated metals in the both gastropod species fluctuated significantly over time. Generally an increasing trend for almost all metals expects for Mn and Zn was discovered in Patella. These metals present a decreasing gradient

As for Phorcus similar pattern was found. Thus the concentrations of most of the metals increased during this period. Zn concentration, however, seems to be constant.

The kruskal Wallis tests showed significant differences in P<0.05 in the concentration of Cr, Cu, Ni and Zn during the sampling times. The post hoc test indicated that in most cases the significant differences was found between the samples from autumn 2010 with those from1993 and 1998.

Table 63 and 64 presented the levels of heavy metals in the two species of gastropod in this study with the similar ones from the other areas. Regarding the results this is obviously showed that the concentration of all metals related to the by- products of smelting plant are enormously higher in the both gastropod species collected from the Larymna bay than the other areas

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Table 61: Average concentration of bioaccumuated metals in Patella from Larymn bay. The values are expressed in µg/g d wt.

Species Year Fe Cr Cu Zn Ni Mn Refrence Patella.Sp 1993 2435±762 19.4±13.2 6.2±1.3 Kozanoglou,1995 Patella.Sp 1998 2631±1538 13.2±8.7 8.6±5.4 9.7±3.7 Koukouliabas,1999 Patella.Sp 2004 3087±1276 12.4±4.9 11.7±2.4 123±24.4 15.1±4.8 9.5±3.9 HCMR,2004 Patella.Sp 2005 7230±5758 20.5±13.5 9.1±1.6 144.2±43.9 30.2±19.4 29.7±28.9 HCMR,2004 Patella. Caerulea 2009 4480±3447 23.1±9.4 12.8±6.4 83.6±95.3 19.6±15.5 5.8±3.3 This study Patella. Caerulea 2010 6307±3918 31.9±19.6 13.4±4.7 68±15.2 23.9±17.1 12.6±7.5 This study Ph.turbinatus 1993 653±116 10.4±3.4 69±14.7 51.8±1.9 Kozanoglou,1995 Ph.turbinatus 1998 753±212 8.2±5.7 49.9±11.8 7.4±3.6 Koukouliabas,1999 Ph.turbinatus 2009 1099±1124 28.3±24.7 64.7±17 50.3±14.8 16.4±10.6 14.4±10.4 This study Ph.turbinatus 2010 1441±1592 56.9±65.8 75.2±9.7 53.3±12 15.1±9.7 22.9±20.7 This study

Table 62: Range and average concentration of bioaccumuated metals in Ph turbinatus in different areas. The values are expressed in µg/g d wt .* Ph.turbinatus was known before as Monodonta turbinate

Area Species Cu Cr Ni Zn Fe Mn References Anavisos-Greece Ph.turbinatus Range 5.5-7.06 0.16-0.3 0.46-0.63 4.2-4.3 38.0-45.5 0.93-1.02 This study Mean±SD 6.04±0.6 0.19±0.04 0.57±0.08 4.2±0.0 42.6±2.9 0.95 Larymna Bay-Greece Ph.turbinatus Range 36.5-93.7 5.8-366 2.9-259.8 20-212 71.1-699.8 1.6-43.7 This study Mean±SD 74.2±10.9 82.7±89.4 62.4±40.8 59.8±17.08 2046±2080 20.7±13.9 Favignana Island- M.turbinata* Mean±SD 10.9±7.7 0.31±0.21 31±15 Conti, 2001 Italy Ustica Island-Italy M.turbinata* Range 18.7-32.6 0.49-1.2 54.9-72.2 Conti, 2007 M.turbinata* Mean±SD 22.14±5.9 0.68±0.27 64.02±6.7 Linosa Island-Italy M.turbinata* Range 12.3-34.9 0.15-1.3 50.2-60.9 Conti, 2009 M.turbinata* Mean±SD 20.2±10.8 0.64±0.47 55.4±4.9 N. Evoikos Gulf- M.turbinata* Mean±SD 67.2±5.4 16.3±10.4 58.7±8.8 1292±803 Kozanglou Greece et al, 1997 197

Table 63: Range and average concentration of bioaccumuated metals in P.caerulea and P. aspera in different areas. The values are expressed in µg/g dw

Area species Cu Cr Ni Zn Fe Mn References Saronikos Gulf- Patella Sp. Range 5-77.4 0.43-9.4 6.02-31.6 43.2-367 97-3045 Kontopoulos et al, Greece 2003 Mean±SD 17.7±19.2 3.4±1.9 13.2±6.1 134±79.01 1129±789 N. Evoikos Gulf- P.aspera Mean±SD 7.8±2.9 16.8±12.8 56.5±14.3 Kozanglou et al, Greece 1997 Favignana Island- P. Caerulea Range 1.21-2.35 0.19-0.46 3.5-14.6 Campanella, 2001 Italy Mean±SD 1.6±0.45 0.31±0.11 6.60±4.6 Linosa Island-Italy P. Caerulea Range 2.8-9.7 0.13-0.75 38.4-51.5 Mean±SD 5.9±2.7 0.42±0.29 43.20±4.9 Conti, 2007 Ustica Island-Italy P.caerulea Range 4.9-9.5 0.28-0.95 44.2-56.6 Mean±SD 6.4±1.8 0.55±0.27 51.4±5.3 Conti, 2007 Artemida-Greece Patella Sp Mean±SD 6±1 7±3 9±3 48±4 16±5 Kelepertzis, 2013 Iskenderum Gulf- P. caerulea Mean±SD 2.3±0.82 1.1±0.65 11.8±2.8 190.8±31.2 Aslantyavsosa, Turkey 2007 Larymna Bay P. caerulea Range 1.2-53.6 6.1-47.9 9.04-87.8 7.5-126 693.3-16232 1.6-33.6 This study Mean±SD 12.5±3.5 26.8±16.4 28.9±22.8 66.6±11.2 4731.2±1878.8 8.6±4.5

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As for Phorcus, the concentration of both groups of metals, related and non-related to the smelting plant expect for Zn are higher in this study. Slightly elevated concentration of Zn was found in Lisona Island from Italy than this research.

5.2.2.5 Discussion

Dumping activities, apart from the enrichment of the marine environment with metals and other contaminants, have several side effects such as increase in turbidity and the consequently decrease in illumination (Nicolaidou et al., 1989). Furthermore, the effects of tailings are comparable to those of organic and inorganic pollution since they cause ecological deterioration as reduction of the biodiversity and the total number of individuals (Nicolaidou et al., 1989).

The enrichment of the marine environment with metals results to elevate the bioaccumulation in marine life that becomes potentially toxic if the concentrations above a certain level (Méndez, Racotta, Acosta, & Rodríguez-Jaramillo, 2001). Marine gastropods are recognized to accumulate high metal concentrations (Wang & Ke, 2002) due to their sedentary life cycle (Nicolaidou et al., 1989; Wang & Wong, 2006) and they are used as efficient bioindicators. It is known that several factors influence metal bioaccumulation by marine organisms such as seasonal dietary and food chain (Nicolaidou et al. 1989; Wang and Wong, 2006), climate conditions (Popham and D‘Auria, 1983), physiology of species (Crothers, 2001), body tissues (Li et al., 2009) and of course the closeness to a pollution source, which is probably the most important factor.

In this study the two common and accessible marine gastropods of the Mediterranean, Phorcus turbinatus and Patella caerulea, have been chosen to investigate the impact of the smelting plant located on the coastal zone of Larymna bay (Greece). The concentration of bioaccumulated metals in the studied gastropods are considered high although the known pollution source -the slag dumping area- is located about 8 Km far away from the coastline (Simboura et al., 2007). Moreover, the sampling stations of the northern part of the Larymna bay (stations LA1 and LA2) that are closer to the dumping site did not display any metal enrichment; instead at the cluster analysis LA2 was grouped together with the reference areas. On the contrary both Ph. turbinatus and P. caerulea collected from the two nearest stations to the smelting plant (LA4 and LA5) were heavily affected by Fe, Mn, Ni and Cr, the metals abundant in the ores used by the smelting plant to produce steel. This phenomenon could probably be related to different evidence such as; the direct release of cooling and untreated waters from the smelting plant, the escape of metalliferous dust (Nicolaidou et al., 1998) during transportation and other activities or the fallout of smoke from the chimney of the metallurgy. Although it is confirmed by the smelting plant authorities that metals in the smoke are trapped by special filters in the chimney,

199 there is always possibility of metals to escape to the open air, agglomerate to particles and due to gravity and rain likely fallout close to the smelting plant. Zn also was found in high concentrations in both species collected at the vicinity of the metallurgy. Although this metal is not abundant in the refined ores, it is usually measured in elevated concentrations near the smelting plant which is probably due to side activities (Voutsinou, 1993). Zn and Cu are both considered as metals related to urbanization. It is worth mentioning that the smelting plant is currently located at the border of the Larymna village and the worker‘s residential area, thus different metal sources may exist in this area. Nevertheless, the high concentrations of Cu in both gastropods were determined near aquacultures (stations LA3, LA6 and LA7) which probably attributed to the antifouling paints used in the fish nets.

The temporal variation of bioaccumulated metals in the soft tissues of the two studied gastropods from 1993 to 2010 show an increasing gradient in the metals related to the smelting plant, which probably contributed to the A) increased in activities of the smelting plant B) inefficient operation in achieving the qualified amount of metals from the ore laterite C) old establishment D) improper keeping of slag in the open area around the smelting plant. Moreover an increase in concentration of Zn in the recant years along with the other metals related to the smelting plant in the both gastropods species may suggest that this metal may have the same source as the slag related metals.

The cluster analysis for both gastropod species indicated that the stations closer to the smelting plant grouped together which reveals how strongly affected these stations from the smelting plant. Thus, it is clear that the metallurgy do impact the coastal zone at a distance of at least 1 km, contaminating the coastal biocenoses with Fe, Mn, Ni, Cr and Zn.

Concerning the seasonality of metal bioaccumulation, P. caerulea followed the seasonal pattern of seawater presenting the highest levels during autumn and winter. On the contrary, the highest metal concentrations in Ph. turbinatus were measured in spring, short after the wet seasons. Therefore, it seems that the two studied gastropods reached their maximum metal values during different seasons, however the season with the maximum concentration of metal are mostly related to rain and runoff. The increase in water runoff charged with different particles introduces a large quantity of suspended particulate materials, as well as dissolved ones, into the water which could reinforce metal uptake. Our findings are supported by Fowler and Oregioni (1976) who found that the higher levels of trace metals in mussels living in the northwestern Mediterranean Sea occurred in the late winter during the time of heavy rain. Popham (1980) also discovered that trace metal concentrations increased in seawater and mussels after a rainfall. The essential metals such as Cu, Fe, Mn, and Zn in the whole body of Onchidium struma increased in

200 autumn (Li et al., 2009). Moreover, Patella vulgata and Fucus samples collected from a contaminated area by radionuclides, presented high concentrations of Cd, Co, Cr, Cu, Fe, Ni, Pb and Zn in winter (Miramand and Bentley, 1992).

An important assumption underlying the use of bioindicators is that metal concentration in their tissues reflects the metal bioavailable level in the ambient environment (Wang & Ke, 2002). Hence, the bioconcentration factor (BCF) is an important issue which depends on the concentration of metals in the environment and could also be influenced by the passage of contaminant through the trophic chain (Conti and Cecchetti 2003; Conti 2007). High BCF indicates the high potential of an organism to concentrate heavy metals in its tissues. Thus it is useful to express results in terms of Bioconcentration factor (BCF) to investigate the magnitude of metal uptake (Dummee et al., 2012).

The result from the BCF calculation indicated that P. caerulea accumulated Fe three folds more than Ph. turbinatus. In opposite, Phorcus indicated higher BCF concentration of Mn, Cu, Ni and Cr than Patella. In case of Cu level, similar results were reported by Conti (2007), who found the higher concentration of BCF in this metal for Phorcus than for Patella. Even when metals in the ambient seawater are in low levels, the relevant concentrations in the bioindicator tissues may be significant. Furthermore the uptake of some metals by marine organisms may induce that of other metals. For example Cd and Zn uptake in marine invertebrates are often coupled. The opposite, a competition between different metals, becomes probable in heavily polluted areas (Conti, 2007), such as in the present study.

The strong positive correlations between Zn in the seawater samples and those in the soft tissues of both Phorcus and Patella may reflect the high metal level in the ambient water or in the trophic chain (P.S. Rainbow, 1997; Wang & Wong, 2006). Among the bioaccumulated metals in the soft tissues of both gastropod, good correlation was found between Zn, Mn and Fe which might be due to their chemical similarities (Chong & Wang, 2001; Wang & Dei, 1999). Besides, these metals are abundant in the ores refined by the metallurgy; consequently they are systematically released into the marine environment and finally found into the soft tissues of the studied gastropods in high concentrations.

There are very few studies dealing with the interaction of metals in marine biota (Wang & Ke, 2002). Particularly in marine gastropods it is difficult to discern any pattern due to the chemical properties of the metals, probably because these animals may regulate Zn & Cu to maintain a relatively constant concentration through seasons (Wang & Wong, 2006). Any differences in metal chemistry can be confounded during transfer through the food chain. Wang (2002) believed that trophic transfer is an important mechanism of overall metal bioaccumulation

201 in marine invertebrates. Besides, some metals are regulated by the organism itself. Meanwhile, metal concentrations in the predators may not necessarily reflect the bioavailable metal levels in the prey (Depledge & Rainbow, 1990; Wang & Ke, 2002).

Seasonal diet availability and food is another important factor affecting the bioaccumulation of metals in organisms. Therefore, the reason for the observed differences could be related to the fact that although both studied species are herbivores (eating from microscopic plants) and found together on rocky sea beds of the intertidal and tidal bed of the medio- littoral zone (Conti & Finoia, 2010), they are not having similar diets. In this respect, Gause (1934) suggests that there are no similar species living in the same habitat and feeding on the same food, because, if they did so, they would be in competition and, over time they would exclude each other. Hence, it is probable that these two species do not feed in exactly the same habitat or in the same manner and same food. Besides the depth of grazing on the rock surface of these species is different as well.

Patella is apart from being a native common species in the Mediterranean Sea is a very popular food in many countries in south Europe (Nakhle, 2006; Aslanyavrusu, 2010; Culha, 2010; Conti, 2009); however, these two species are rarely consumed by Greeks. The results from this study were compared with the FAO, (1993), WHO, (1989) and European Union (2008a). In both species of gastropods the average concentration of metals related to the smelting plant (Fe, Cu, Mn, Cr and Ni) is higher than the concentrations proposed by the above organizations.

Based on the above findings, it seems that Ph. turbinatus reflects quite better the environmental conditions near a smelting plant and could be considered as more efficient bioindicator for this kind of investigation. However it is not proper to propose only a single species for monitoring metal pollution since each species offers supplementary information.

In conclusion, the results of the bioaccumulation study on the coastal gastropods Patella caerulea and Phorcus turbinatus clearly indicate the impact of the ferronickel smelting plant on the coastal zone.

202 5.3 Biomarkers

5.3.1 The biochemical effect on marine organisms-Biomarkers of exposure

The ecological impact of the smelting plant operation on the marine organisms was investigated by the study of the activities of three different biomarkers. The aquatic animals were taken from the inshore and offshore areas of Larymna bay. Phorcus turbinatus was selected as a native benthic gastropod species from the inshore area and Munida rugosa as a benthic crustacean from the offshore area (from both the slag deposition and the reference areas). In addition, Sparus aurata, a commonly cultivated and commercially important fish species, collected from a fish farm was another species selected to investigate the activities of antioxidate enzymes.

Different tissues from each species were selected for this investigation. The non- parametric Kruskal-Wallis and Mann–Whitney tests were performed to investigate any significant differences in the enzymes activities between contaminated and reference areas or between tissues. In this study, the Acetylcholinesterase activity is expressed in nmoles ACTC/min/mg proteins, the catalase activity in units/mg proteins and the glutathione S- transferase activity in nmoles CDNB conjugate/min/mg proteins.

5.3.2 Biomarkers’ responses

5.3.2.1 Munida rugosa

The enzymes‘ activities were determined in both genders and in different tissues including gills, eyes and liver. The Acetylcholinesterase (AChE) was measured in the gill and the eye tissues, the Catalase (CAT) and Glutathione S-transferase (GST) activities were measured in the liver from both the contaminated and the reference areas. The results of the enzymes activities are presented in Figure 53.

203

Figure 53: Biomarkers activities in M.rugosa from the North Evoikos Gulf. [(CON)=contaminated/slag dumping area, R= reference area; (CAT)= catalase activity, (GST) =glutathione S-transferase activities (AChE)= acetylcholinesterase activity, M-G=male gill, F- G=female gill, M-E=male eye, F-E=female eye, M-L=male liver, F-L= female liver.

The results clearly showed no considerable differences, neither between the two genders, nor between the two areas of study. The only significant difference (P<0.05) was detected in the AChE activity in the gill tissue of male specimens between the contaminated and the reference areas. The samples from the contaminated area, are presenting almost half AChE activity than those from the reference area. Opposite to AChE, the Catalase (CAT) and (glutathione S- transferase) GST activities did not show any significant difference neither between genders nor between areas.

5.3.2.2 Phorcus turbinatus

The entire soft body tissue of this gastropod was analyzed for the activities of AChE and GST. The results of the enzyme activities are presented in Figure 54.

204

Figure 54: Biomarkers activities of Ph.turbinatus from the coastal line of Larymna bay. [(CON)= contaminated/slag dumping area (R)= reference area; (GST)= glutathione S-transferase activity, (AChE)= acetylcholinesterase activity.CON-LA1=station LA1 in contaminated area.CON-LA4=station LA4 in contaminated area.

In respect to Figure 54, the concentration of the two measured enzymes (AChE and GST) were higher in the reference area than the slag dumping area. The results from Kruskal-Wallis test also provided significant differences (P<0.05) in the level of both enzymes from these areas. On the other hand no remarkable variation was found in the concentration of both enzymes from the two stations of LA1 and LA4 in the contaminated area. It is worth noticing that the LA4 station is located near the smelting plant, but the LA1 is the farthest station from the village and the metallurgy.

Regarding the AChE, the variation in activities between the two areas (contaminated and reference) is considerable with about 19 folds difference.

5.3.2.3 Sparus aurata

The activities of AChE, CAT and GST were measured in Seabream samples from a fish farm in the Larymna bay. In order to compare and evaluate the results, samples were also taken from a fish farm in Anavissos that is considered as a reference area.

AChE was measured in gills and muscle, while the activities of both CAT and GST were measured in the liver tissue (Figure 55).

The results of the AChE activity measurements presented great variation between tissues and areas. Muscle had higher AChE concentration than gills. Gills on the other hand exhibited statistically higher AChE activity values (P<0.05) in samples from the reference area. In addition to AChE, statistically significant differences were found in the activities of the other two

205 enzymes; CAT and GST. However in the case of the latter biomarkers the results are contradictory. Concerning the CAT activities, the concentration of enzyme in the contaminated area was about 320 folds higher than that in the reference area. Similar to CAT, GST also showed about 7 folds higher concentration in the dumping area than the reference one.

Figure 55: Biomarkers activities of S. aurata. (CONT)= the contaminated/slag dumping area, (R)= the reference area; (GST)= glutathione S-transferase activity, (AChE)= acetylcholinesterase activity, CON-LA1 and CON-LA4=stations LA1 and LA4 in the contaminated/ slag dumping area.

206

5.3.3 Investigation the relations of metal level with biomarker responses

In order to clarify the influences of environment on the enzymes activities, the non- parametric spearman‘s correlation coefficients test was performed between the concentration of metals measured in sediment, seawater and biota‘s tissues.

5.3.3.1 Relationships with metals in Seawater

In this section the non-parametric spearman correlation coefficient among the enzymes activities and the concentrations of metals was assigned for M. rugosa from the offshore area and Ph. turbinata from the coastal line. To investigate this correlation the results from the concentrations of metals from both coastal and offshore seawater samples were used. Regarding Ph. turbinata the correlation was carried out between the samples collected near the smelting plant (station LA4) as the contaminated area and those collected at station LA1 as the less contaminated (Figure 6 location of sampling area). Similarly, regarding M. rugosa, the correlation was performed between the biota samples from the slag dumping area and those from the reference area. The seawater bottom samples were also collected from the same area as the biota samples (Figure 6.). The correlation analysis is presented in Table 16 to 27 in Annex

It is worth noticing that in this analysis in M.rugosa, the factor of sex was neglected, thus the correlation was not performed separately for each sex. Concerning the gill samples, the only fairly strong negative correlation (P<0.05, R2=-0.743) was found between the AChE activities in gill samples from the reference area with the concentration of Cr in the seawater sample (Table 16 in Annex). On the opposite AChE activities in the eye samples of Munida specimens did not show any significant correlation with the seawater samples (Table 17.in Annex).

GST activity in the liver of M. rugosa specimens from the reference area showed strong positive correlations with the dissolved concentration of Mn (P<0.05, R2=0.769) and Zn (P<0.01, R2=0.872). On the other hand no significant correlation was found between the activity of this enzyme and the metal levels in the contaminated area.

Similar to GST, no correlation was found between the CAT levels in the liver of M.rugosa and the metal levels in the seawater, neither from the contaminated, no from the reference areas (Table 18 in Annex).

Finally, the enzymes‘ activity in Ph. turbinatus presented very few negative statistically significant correlations with metals in seawater. One is the strong negative correlation of AChE

207 activity in specimens from LA1 (less contaminated station) with Cu in seawater (P<0.01, R2=- 0.900) and the other is the firm significant correlation between the GST in the liver and the concentration of Ni in LA4 station (the station near the smelting plant which represent as the contaminated area) (Tables 20 and 21 in Annex).

5.3.3.2 Relationships with metals in Sediment

Sediment samples were collected from the slag depositing and the reference area (Offshore area), therefore in this case the correlation was just performed between the total concentration of heavy metals in sediment and the enzymes activities in M. rugosa.

The results of non-parametric Spearman correlation between the sediment samples from the two areas (contaminated and reference) and the activities of all three enzymes (AChE, GST and CAT) did not indicate any significant correlation (Tables 22, 23, 24, 25 in Annex).

5.3.3.3 Relationships with metals in biota

The non-parametric Spearman correlation was carried out between the concentration of bioaccumulated heavy metals in different tissues of biota samples and the enzymes activities to identify any significant relation.

In Ph. turbinatus, the negative significant correlation (R=-0.900, P<0.05) was found between the GST activity and the Mn concentration in the soft tissue from the LA4 station (the contaminated station near the smelting plant) and between AChE and the biocumulated Cr (R=- 1.000, P<0.01) in LA1 station (Table 26 in Annex).

Regarding S. aurata, no significant correlation was also found between the CAT, GST and AChE values and the concentration of metals in the liver and muscle tissues. Meanwhile, AChE activities in gill presented negative correlation (R=-1.000, P<0.01) with the concentration of Mn in the same tissue (Table 27.in Annex).

5.3.3.4 Relationships among data variables by the use of Multidimensional scaling (MDS)

Multidimensional scaling (MDS) is an exploratory data analysis technique which condenses large amounts of data into a relatively simple spatial map. In other words, it is a mathematical technique which generates a spatial configuration map, where the distance between data reflects the relationship between individual variables (Mugavin, 2008). In addition, it provides a visual representation of dissimilarities (or similarities) among objects, cases or, more broadly, observations. On the other hand, this technique attempts to find structure in data by

208 rescaling a set of dissimilarities measurements into distances assigned to specific locations in a spatial configuration (Giguère, 2006; Tsogo et al., 2000).

In this study, MDS was performed for all the three selected species to confirm the differences in the metal compositions in the soft tissues and the enzyme activities in the two contaminated and the reference areas.

The spatial distribution of metal concentrations and the enzymes activities in all the selected species showed a clear separation between the two areas (contaminated and reference). In addition, the direction of vectors indicates the correlation between variables. For instance, in M.rugosa (Figure 56) the vectors are indicating that CAT, GST and AChE g (gill) activities along with Zn, Mn, Ni and Fe, show higher concentration in the samples from the reference area (MR). In opposite, the vector indicates that AChE e (Eye) along with Cu and Cr revealed the elevated concentration in the organisms from the contaminated area (MC). Moreover, the size of vectors assumes the strength of their influence. Therefore, Fe, Ni and GST with the longest vector are creating the strongest influence.

Standardise Samples by Total Resemblance: D1 Euclidean distance

2D Stress: 0.04

MR

Fe Ni MR Mn GST MR AChE gMR Zn

MC MC MR MC MR MR CAT MR AChE e MC

MC Cr Cu MR MC

MC

Figure 56: Multidimensional scaling (MDS) results from the concentration of metals (Fe, Cu, Mn, Zn, Cr, Ni) in the soft tissue of M.rugosa and biomarkers (CAT, GST, AChE gill, AChE eye).

Figure 57 present the results from the MDS technique from S.aurata. It is clearly seen that the two populations of S.aurata from the reference (SR) and the contaminated area (SC) are separated.

209 Standardise Samples by Total Resemblance: D1 Euclidean distance 2D Stress: 0

S R AChE-M SCAT C L S C S R MnZn M M S R GST L S C SS C C

Cu M S R Fe M S R

AChE G

Figure 57: Multidimensional scaling (MDS) results from the concentration of metals (Fe, Cu, Mn, Zn, Cr, Ni) in the soft tissue of S.aurara and biomarkers (CAT, GST, AChE gill, AChE eye).

Standardise Samples by Total Resemblance: D1 Euclidean distance 2D Stress: 0.01 LA4 LA4 LA4 R LA4 R

R Fe CuMn R CrZn

AChE

R

GST

LA1LA1

Ni LA1

LA1 LA1

Figure 58: Multidimensional scaling (MDS) results from the concentration of metals (Fe, Cu, Mn, Zn, Cr, Ni) in the soft tissue of Ph.turbinatus and biomarkers (CAT, GST, AChEgill, AChE eye).

210 The high and abundant loading variables of both enzymes and the heavy metals except for AchE and Fe are toward the samples from the contaminated area. On the hand higher concentration of Fe and AChE in soft tissue and gills, respectively, is clearly apparent in the samples from the reference area (Figure 57).

As for Ph. turbinatus, not only the contaminated and the reference area were clearly separated but also the two contaminated stations (LA1 and LA4) are districted from each other. The abundant vectors for most of the metal elements are toward the LA4, the station near the smelting plant (Figure 58). However, the enzymes variables are toward the samples from the reference area on the opposite side of the metals concentrations.

5.3.3.5. Discussion

The use of biological responses in marine pollution studies has been repeatedly advocated, however, it is hampered by the inferences of natural intrinsic and environmental variability (ICES, 2013). In addition to other different general measurements to assess the environmental status, biomarkers are considered as a useful approach not only in polluted environments with high sensitivity of particular relevance but also in low impacted areas to enhance the capability to detect an environmental disturbance. In this respect, the enzymatic activities of two benthic invertebrate species and one commercial fish from the local fish farm as sensitive species were investigated. A suite of pollutants act upon different receptors and present different modes of action, causing variety of biochemical effects in organisms, thus in biomonitoring studies a combined biomarker approach becomes relevant in order to find more sensitive biomarkers and new strategies to protect the non target organisms (Walker et al., 2001; brooks et al., 2009., Bellas et al., 2014).

Acetylencholinestrase (AChE) has been widely used as a biomarker of neurotixic effects. For decades, the activity of this enzyme has been used as a specific and sensitive biomarker of exposure to organophosphorus pesticides and carbamates (Kristoff et al., 2006.,Bianco et al., 2013). However, recent studies have shown that other types of pollutants such as heavy metals, surfactants and PAHs (Guilhermino et al., 1998; Akcha et al., 2000; Elumalai et al., 2002., (Tsangaris et al., 2007) may also inhibit AChE activity. Thus AChE inhibition has been suggested as indicative of general stress (Broge et al., 2006). In accordance, our results showed significant inhibition in AChE activities in gill of M.rugosa and the soft tissue of Ph. turbinatus and S.aurata from the contaminated area. A very low activity of this enzyme in Ph.turbinatus (21 folds) and in gill of Munida (6 folds) in the contaminated area, confirmed the idea that this enzyme is a good biomarker for the heavy metals presence, as well as the other organic pollutants. As far as biomarker concerns, AChE responses to metals varied in different tissues.

211 The results from other experiment carried out in different tissues of brain, muscle and gill in Solea solea, revealed significant differences in these tissues from the northern Costa Brava site which was exposed to metal pollution (Vieira et al., 2009). Besides, an efficient brain AChE compensatory mechanisms were also noted in zebrafish under chronic exposure to metals (Richetti et., al, 2011., Siscar et al., 2013). In respect, in this study the AChE inhibition in gill tissues indicated better activities and reflecting the environmental conditions than the eye and muscle samples.

In relation to our study Cunha (2007) found the inhibition of ChE activity in two mollusks species Monodonta lineata and Nucella lapillus exposed to copper. Moreover, the author suggested that the copper pollution should be in high concentration (Cunha et al., 2007) to induce such a response. The gradual decrease in AChE activities of transplanted and native mussel populations in Larymna Bay in comparison to the reference area (Tsangaris, 2007) and the inhibition of this enzyme in vitro mussel experiment (Najimi et al., 1997) and in clams in vivo (Hamza-Chaffaei et al., 1999) are some other apparent examples of AChE inhibition activities presented in the metal polluted areas. On the other hand, other studies showed no variation in the activity of this enzyme in organisms exposed to metals in vitro situation or collected from polluted areas. Tsangaris et al.,(2015) observed no remarkable differences in this enzyme activity in the Liocarcinus depurator collected from the same areas and season as our crustacean samples (M. rugosa) from this study. In response, this author suggested that the lack of AChE function may be related to the fact that this enzyme is not influenced by metals as much as by organic pollutants. It seems that ChE responses seem to vary according to the taxonomic group; contaminant class and exposure time (Saenz et al., 2010). With respect to the significant differences in inhibition of AChE in both M. rugosa and the gastropod Ph. tubinatus in our study from the smelting plant area and the previous studies on the mussel, it is supposed that L.depurator might not be a good sensitive species for AChE activities.

The inhibition mechanism of AChE activity may be explained by the fact that metals can bind to functional group of the protein, such as imidazole, sulfyhdryl and carboxyl group (Najimi et al., 1997). Hence, once the enzyme is bound to some of these functional groups, its catalytic activity could be compromised, leading to the loss of enzyme function. On the other hand, some authors reinforce the idea that the direct inhibitory effect of metals seems to be more due to the denaturation of the enzyme than to the binding of metals at the specific target sites of enzyme, causing the loss of its activity. While some also suggest that there may be an inhibitory effect of metals on esterase activity due to the possible presence of cystein residues near the active site of the enzymes (Keay and cook, 1956; Frasco et al., 2007). It should be taken into consideration that the inhibitory activity of this enzyme is also time-dependent, thus more protein loses its native

212 conformation, much difficult it is for the protein to recover enzymatic activities which happens when the metal concentration increases to the level that can affect enzymes conformation. In this case new and stronger interactions can be established between metals and the acetylcholine receptors, blocking the binding of the acetylcholine to its receptor. Eventually as a part of prolong effect of metals on the receptor, less acetylcholine is produced that causes a progressive decrease in AChE (Pretto et al 2010; Lima and Bernardez, 2011).

GST and CAT are both known as antioxidant enzymes which operate as the defense wall against the ROS (Reactive oxygen species) The activities of these two enzymes are a bit complicated as the direction of change of responses (increase or decrease) can be variable (Martínez-Álvarez et al., 2005). Sheehan and Power (1999) concluded that a part of variation recorded for oxidative stress biomarkers may be explained by season, species, modification of food availability, spawning period, type of pollution and other environmental factors. In our study significantly elevated levels of GST and CAT were only found in the liver of S.aurata samples from the fish farm in the N.Evoikos Gulf. Female M.rugosa also showed slightly higher concentration of GST in this area. The elevation in glutathione may help protect the cell from lipid peroxidation and aid in maintenance of mitochondrial calcium content (Oliva et al., 2012). Thus, the high activities of GST and in particular CAT (320 folds induction) detected in the liver of S.aurata, apart from the metal enriched environmental status of the study area, may be due to the large proportion of ROS generated in this tissue, probably related to the low food availability and poor environmental condition. It is worth mentioning that the S.aurata samples in this study are taken from a cage culture thus the high dense population and limitation of food availability in this type of fish culture is a general phenomenon. The influence of food viability on antioxidant enzymes is well described in other studies; for instance Bayir (2011) showed that the antioxidant defenses such as CAT increased in brown trout (Salmo trutta) as a result of food deprivation, while GST decreased (Bayir et al., 2011). Pascual et al. (2003) found that the direction of variation of some antioxidant activities such as GPX and GST may vary according to the level and duration of the food deprivation period. In addition, number of studies showed that some metal ions, such as copper, nickel, lead and cadmium decrease the glutathione activity in cell playing a primary role in the overall toxic manifestations (Thomas and Juedes, 1992, Amado et al. 2006). Decrease in GST activity in Ph.turbinatus collected from the contaminated area might be due both to food deprivation and to the high concentrations of metals in the area. In line with the results from our mollusc species reduced GST activities were also found in the transplanted mussels (Mytilus galloprovincialis) in the contaminated area of Larymna Bay (Tsangaris et al., 2010) and in Dog whelk (Nucella lapillus) exposed to Cu. Glutathion was shown to inhibit free radical formation by copper ions in the response of hydrogen peroxide and stabilize oxidation

213 state (Jaishankar et al., 2014). Amado et al., (2006) observed decreasing of GST activity in Micropogonias furnieri from a Cu polluted area in two different seasons of winter and summer. Gravato et al., (2006) observed the same effect in Anguilla anguilla exposed to high concentration of Cu. Other studies on marine mussels confirmed the decrease in GSH level in several tissues when exposed to Cu. (Regoli and Principato, 1995). The administration of nickel to rats results in lipid peroxidation enhanced and decreased in glutathione activity (Oxidation mechanism in the toxicity). Importantly, glutathione acts in a negative feedback manner on its own synthesis, so the elevations in rate of synthesis caused by xenobiotics may reflect a change in set point and decrease in its activity and concentration (Oliva et al., 2012).

A number of studies have shown that differences in sex and body size can influence enzymatic activities and complicate the interpretation of biomarker results (Van der Oost et al., 2003). In the present study GST and CAT activities varied between sexes: Female Munida showed higher (but not significantly higher) enzymes activities than males, which suggest that female sex hormones might also influence this enzyme activity (Koenig and Solé, 2012). Similar results were also observed in the bioaccumulated metals in different tissues of this species.

MDS results show a clear separation between the populations from the reference and the contaminated area for all the studied species. In S.aurata higher concentration of Fe and AChE in soft tissue and gills is apparent in the samples from the reference area and elevated GST and metals toward contaminated area; which provides enough evidence that the slag dumping area is much influenced by the deposited slag than the reference area does. Besides the loading vectors are more likely parallel and the samples from the contaminated area were captured inside the cycle, which supports the correlation among these factors (Figure 56). In Ph.turbinatus the abundant vectors for most of the metal elements were toward the LA4 which indicated that this area is more enriched by metals than the other two sites (Figure 57). However, the enzymes variables were toward the samples from the reference area on the opposite side of the metals concentrations. Thus it seems that the enzymes and the metals concentrations are following a reverse rout, therefore they do not show any correlation. As for M.rugosa GST, Fe and Ni are the most significant variables with the highest loading value. Note that these metals are the most significant ones in determining the pollution distribution in this area. Thus, the presence of the variables from the reference area inside the cycle, revealed heavily contaminated this area is.

None of the measured biomarkers showed any positive correlation with the metal concentrations in the sediment and seawater. Since the metal bioavailability in sediments depends both on the concentrations of metals in sediments and on its characteristics and fluxes (sediment to pore water and the overlying seawater fluxes) (Chapman et al., 1998). Furthermore, metals are

214 additionally taken up from food which is an important source of metals in crustaceans (Bondgaard and Bjerregaard, 2005.,Cotou et al., 2012).

Finally, the differences observed in the effects of heavy metals on various enzyme activities in the studied marine species from the Larymna Bay probably depend on the type of metal cation, the concentration of bioaccumulated metals in the tissues, the speciation within the cellular environment and the different patterns of metal cation interaction with intracellular components (Viarengo, 1994) and the taxnonomic group (Saenz et al., 2010). The N.Evoiks Gulf is an oligotrophic area which receives different sources of contamination not only from the smelting plant, but also from the municipal sewage of the nearby residency area, from a number of fish farms and from agriculture activities (Scoullos and Dassenakis, 1983; Tsangaris et al., 2007). Nevertheless, the smelting plant activities and the continuous slag depositing is the most prominent pollution in this area.

215 6. CONCLUSIONS

The Larymna bay in N. Evoikos gulf hosts different sources of pollution. One of the most prominent one is the by-product (slag) of the biggest smelting plant in Greece. The long term deposition of metallurgy by-product inside the gulf provoked a number of researches in this area to estimate and monitor the severe impact of deposited slag on the marine environment and organisms.

In line with previous researches, this study was carried out to integrate the latest findings and evaluate better the influence of slag from various aspects including sediments, ambient seawater, different marine organisms and finally the pollutant stress responses on cellular.

The three years of study from the offshore area where the dumping takes place to the coastal zone where the smelting plant is located showed that:

Sediment:

 Due to the long term deposition no natural surface sediment was found in the dumping area. The reference area was also influenced.  The slag is a metal brittle material with a low concentration of fine fraction.  High concentration of Ni, Fe, Mn and Cr in both total and labile forms in sediment attributed to the smelting plant.  Higher concentration of Hg in the reference area than the contaminated area is related to the natural source of this metal in the area.

 Elevated concentration of CO3% and TOC% and the clustering of these two parameters are probably related to the non stop dumping of slag and hypoxic area.  Strong correlation and clustering of Zn/Ni suggested the association of Zn in the metaliferous laterite ore.  Temporal distribution of metals showed a gradual increasing in the concentration of Fe, Mn, Ni and Cr in the recent years.

 Based on the Enrichment factor (EF) and Geo-accumulation Index (Igeo), it is suggested that the slag dumping area is heavily enriched with the metals related to the smelting plant.  The concentration of two metals, Ni and Cr, are highly above the US EPA standards and indicated certain adverse biological effects.

216 Offshore and Coastal seawater

 High concentration of metals related to the smelting plant in the bottom water is mostly attributed to increased desorption of metals from fresh dumped slag.  The high concentration of particulate metals may be attributed to the selective fractionation of particles while the dumping is taking palce.  High concentration of dissolved Zn in the area may lead to the high level of this metal in the origin source of laterite ore or other unknown sources in this area.  The clustering of Zn with Ni and Cr in both water samples from the offshore and coastal areas also suggest to the same origin of this metal with the metals related to the smelting plant.  High concentrations of particulate metals related to the smelting plant in the coastal area are probably attributed to the weather condition and rain, heavy wind, dust transportation from both chimney and the unsafe storage of the by-product in open areas near the smelting plant establishment.  High concentration of dissolved metals such as Ni, Cr, Fr and Mn near the smelting plant is perhaps attributed to the direct release of waste or run-off waters into the bay.  The concentration of Ni, Zn and Cr from both sampling areas were lower than the chronic and acute standard of US EPA and Directive 2000/60/EC.

Crustaceans:

 The highest concentration of metals in both sexes and the ones related to the dumped slag were found in gill.  Higher concentration of metals in the tissue of crustaceans detected from the spring samples due to the high temperature and high rate of metabolisms.  Synergetic relation of Fe/Zn in crustacean samples might be related to the share common routs of uptake.  Absence of positive correlation between the concentration of metals in the tissues and those in the bottom water and sediment probably attributed to the positive infusion of metals from sediment to the porewater.  Higher level of Bioconcentration factor (BCF) measured in gill was probably because of the vaster surface of this organ expose to pollutants.  Higher concentrations of metals such as Fe, Mn, Zn and Cu in females are probably related to the biological needs and reproductive period.  The 13 years of temporal trend in bioaccumulated heavy metals in soft tissue of crustacean samples indicated a decrease gradient in concentration of metals related to the

217 metallurgy (Ni, Fe and Cr). In opposite a sharp decrease in concentration of Zn and Cu was found.  The concentration of metals in the soft tissue of all crustacean samples expect for Mn was within the safe range of metals intake for human.

Gastropods:

 High concentration of metals such as Fe, Cr, Mn and Ni in the samples near the smelting plant is related to the smelting plant activities.  High concentrations of Cu were detected in the samples collected near the fish farm activities.  High concentrations of metals were detected in autumn samples.  By comparing the BCF results it is detected that Patella is a good indicator for Mn, however Phorcus showed high level of Fe, Cu, Ni and Zn.  The poor correlation of bioaccumulated metals with the sediment and seawater suggest that seasonal dietary and food are the important source in uptake of metals.

Biomarkers:

 The inhibition of AChE activities in gills of M.rugosa and soft tissue (muscle) of Ph. turbinatus and S.aurata from the contaminated area indicated that this enzyme is a good biomarker for heavy metals as well as organic pollutants.  Decreased in GST activity in Ph.turbinatus from the contaminated area might be due both to food deprivation and to the high concentrations of metals in the area.  High activities of GST and CAT detected in the liver of S.aurata, apart from the metal enriched environmental status of the study area, may be due to the large proportion of ROS (Reactive oxygen species) generated in this tissue, probably related to the low food availability and poor environmental condition.  GST and CAT activities in Female Munida are higher than males which suggest that female sex hormone might influence these enzyme activities.  None of the measured biomarkers showed positive correlations with the metals concentrations in sediment and bottom water which suggests that diet is an important source of metals in marine organisms.

218 7. Future study

In accordance to the results from this study, the following points are suggested for future research:

 Closer monitoring of the surface sediment and efforts to take cores from a wider area in N. Evoikos area, in order to map the area that is affected by the depositing slag in details.

 Perform in vitro sediment toxicity experiments with the fresh slag I. Measure the concentrations of heavy metals in the slag before introducing to the water II. Determine the levels of heavy metals in the first critical hours of introducing the slag into the water

 Regular monitoring of the concentrations of heavy metals in other benthic and demersal species of area.  Regular monitoring of the chimney of the smelting plant and measuring the concentration of metals in the air and the filters  Regular medical monitoring of the local in particular the children and determination of the concentrations of metals related to the smelting plant activities in their body fluids.

219 8. REFERENCES

Abelló, P., Carbonell, A., Torres, P. (2002). Biogeography of epibenthic crustaceans on the shelf and upper slope off the Iberian Penin- sula Mediterranean coasts: implications for the establishment of natural management areas. Sci. Mar. 66(suppl. 2): 183-198 Abelló, P., Sardá, F. (1989). Some observations on the biology and fishery of Squilla mantis L. in the Catalan area (NW Mediter- ranean). In: Ferrero E.A. (ed.), Biology of stomatopods. Mucchi, Modena, pp. 229-239 Abelló, P., Pertierra, J., Reid, D. (1990). Sexual size dimorphism, relative growth and handedness in Liocarcinus depurator and Macropipus tuberculatus (Brachyura: Portunidae). Scientia Marina. Abrahim, G. M. S., Parker, R. J. (2008). Assessment of heavy metal enrichment factors and the degree of contamination in marine sediments from Tamaki Estuary, Auckland, New Zealand. Environmental Monitoring and Assessment. 136(1–3), 227–38. http://doi.org/10.1007/s10661- 007-9678-2 Akcha, F., Izuel, C., Venier, P., Budzinski, H., Burgeot, T., Narbonne, J.F. (2000). Enzymatic biomarker measurement and study of DNA adduct formation in benzo[a]pyrene- contaminated mussels, Mytilus galloprovincialis . Aquat. Toxicol. 49, 269-287 Alcorlo, P., Otero, M., Crehuet, M., Baltanás, A., & Montes, C. (2006). The use of the red swamp crayfish (Procambarus clarkii, Girard) as indicator of the bioavailability of heavy metals in environmental monitoring in the River Guadiamar (SW, Spain). Science of the Total Environment, 366(1), 380– 90. http://doi.org/10.1016/j.scitotenv.2006.02.023 Allen, G., Burton, Jr. (2002). Sediment quality criteria in use around the world. Limnology, 3(2), 65–76. http://doi.org/10.1007/s102010200008 Alloway B.J., Ayres D.C. (1997). ‗‗Chemical Principles of Environmental Pollution‘‘, p. 190-217, Blackie Academic & Professional: an imprint of Chapman and Hall. Alyakrinskaya, I. O. (2010). Some adaptations of Monodonta turbinata (Born, 1780) (Gastropoda, Prosobranchia, Trochidae) to feeding and habitation in the littoral zone. Biology Bulletin, 37(1), 63–68. http://doi.org/10.1134/S1062359010010097 Amado, L. L., Da Rosa, C. E., Leite, A. M., Moraes, L., Pires, W. V., Pinho, G. L., Martins, C. M., Robaldo, R. B., Nery, L. E., Monserrat, J. M., Bianchini, A., Martinez, P. E., Geracitano, L. A. (2006). Biomarkers in croakers Micropogonias furnieri (Teleostei: Sciaenidae) from polluted and non-polluted areas from the Patos Lagoon estuary (Southern Brazil): evidences of genotoxic and immunological effects. Mar. Pollut. Bull. 52 (2), 199–206. Ambrose, R. B. (2005). Partition coefficients for metals in surface water, soil and waste. U.S. Environmental Protection Agency Office of Research and Development Washington, (July). Anagnostou, Ch. (1986). The relation between Neo, techtonic and sediments genesis in N. Euvoikos Gulf. Bull.Geol.Soe.Greece, 20, 269–280. Andrade, J. P., Erzini, K., Palma, J. (1996). Gastric evacuation and feeding in the gilthead sea bream reared under semi-intensive conditions. Aquaculture International, 4: 129–141.

220 Andrew, J. E., Anras, M. L. B. G., Kadri, S., Holm, J., Huntingford, F. A. (2003). Feeding responses of hatchery-reared gilthead sea bream (Sparus aurata L.) to a commercial diet and natural prey items. Marine and Freshwater Behaviour and Physiology, 36: 77–86. Angelidis, M. O., Aloupi, M. (2000). Geochemical Study of Coastal Sediments Infuenced by River- Transported Pollution : Southern Evoikos Gulf , Greece. Marine Pollution Bulletin, 40(1), 77–82. Anger, K. (2001). The biology of decapod crustacean larvae. Crustacean Issues, 14(February), 417. http://doi.org/10.1651/0278-0372(2005)025 ATSDR. (2004). Toxicological profile for Copper. U.S Public Health Service, Agency for Toxic Substances and Disease Registry, (September), 121–189. http://doi.org/doi:10.1201/9781420061888_ch106 ATSDR. (2005). Toxicological Profile for Nickel. U.S Public Health Service, Agency for Toxic Substances and Disease Registry, (August), 397. http://doi.org/http://dx.doi.org/10.1155/2013/286524 Ayas, D. (2010). Distribution and morphometric characteristics of Patella species (Archaeogastropoda) in Mersin-Viranşehir region of the Northeastern Mediterranean sea. Journal of FisheriesSciences.com. 4(2), 171–176. http://doi.org/10.3153/jfscom.2010017 Baden, S. P., Eriksson, S.P. (2006). Role, routes and effects of manganese in crustaceans,in Oceanography and Marine Biology: An annual review, 44, 61-83, (Eds) Gibson, R. N., Atkinson, R. J. A, Gordon, J. D. M. Taylor and Francis, 528p. Baden, S. P., Neil, D. M. (2003). Manganese accumulation by the antennule of the Norway lobster Nephrops norvegicus (L.) as a biomarker of hypoxic events. Marine Environmental Research, 55(1), 59–71. http://doi.org/10.1016/S0141-1136(02)00216-7 Bailie, D., Hynes, R., Prodöhl, P. (2011). Genetic parentage in the squat lobsters Munida rugosa and M. sarsi (Crustacea, Anomura, Galatheidae). Marine Ecology Progress Series, 421, 173–182. http://doi.org/10.3354/meps08895 Bakir, F., Damluji, SF., Amin-Zaki, L. et al. (1973). "Methylmercury poisoning in Iraq" (PDF). Science. 181 (4096): 230–41. Ballatori, N. (2002). Transport of toxic metals by molecular mimicry. Environmental Health Perspectives. 110(SUPPL. 5), 689–694. http://doi.org/10.1289/ehp.02110s5689 Balomenos, E., Panias, D., Mud, R. (2013). Iron recovery and production of high added value products from the metallurgical by- products of primary aluminium and ferro-nickel industries. 3th International Slag Valorisation Symposium 161–172. Balopoulos, E., Papageorgiou, E. (1989). Water masses and flow features in the north Euboikos Gulf (eastern Mediterranean Sea): an initial assessment. Vie Milieu, 41(2/3): 169-176. Balopoulos, E., Papageorgiou, E. (1991).Water masses and flow features in the North Evoikos Gulf (Eastern Mediterranean), an initial assessment. Vie Milieu. 41, 169-176 Bannister, J. V. (1975). Shell parameters in rela- tion to zonation in mediterranean limpets, Marine Biology, 31(1): 63-67 Bayir, A., Sirkecioglu, A. N., Bayir, M., Haliloglu, H. I., Kocaman, E. M., Aras, N. M. (2011). Metabolic responses to prolonged starvation, food restriction, and refeeding in the brown trout, Salmo trutta:

221 oxidative stress and antioxidant defenses. Comp. Biochem. Physiol. Part B: Biochem. Mol. Biol. 159, 191-196. Bell, M. C., Redant, F., Tuck, I. (2006). Nephrops Species, In B. Phillips (ed), Lobsters, Biology, management, Aquaculture and Fisheries, Oxford: Blackwell Publishing Bellas, J., Albentosa, M., Vidal-Liñán, L., Besada, V., Franco, M. Á., Fumega, J., Beiras, R. (2014). Combined use of chemical, biochemical and physiological variables in mussels for the assessment of marine pollution along the N-NW Spanish coast. Marine Environmental Research, 96, 105–17. http://doi.org/10.1016/j.marenvres.2013.09.015 Beltrame, M. O., De Marco, S. G., Marcovecchio, J. E. (2010). Influences of sex, habitat, and seasonality on heavy-metal concentrations in the burrowing crab (Neohelice granulata) from a coastal lagoon in Argentina. Archives of Environmental Contamination and Toxicology. 58(3), 746–56. http://doi.org/10.1007/s00244-009-9405-9 Beyer, J., Petersen, K., Song, Y., Ruus, A., Grung, M., Bakke, T., Tollefsen, K. E. (2014). Environmental risk assessment of combined effects in aquatic ecotoxicology: a discussion paper. Marine Environmental Research, 96, 81–91. http://doi.org/10.1016/j.marenvres.2013.10.008 Bianco, K., Yusseppone, M. S., Otero, S., Luquet, C., Ríos de Molina, M. D. C., Kristoff, G. (2013). Cholinesterases and neurotoxicity as highly sensitive biomarkers for an organophosphate insecticide in a freshwater gastropod (Chilina gibbosa) with low sensitivity carboxylesterases. Aquatic Toxicology (Amsterdam, Netherlands), 144–145, 26–35. http://doi.org/10.1016/j.aquatox.2013.09.025. Bockendahl, H., Ammon, R. (1965). Cholinesterases, Methods of Enzymatic Analysis (Second Printing, Revised), Bergmeyer, H.-U., Ed. Academic Press, 1965; pp 771-775. Bondgaard, M., Bjerregaard, P. (2005). Association between cadmium and calcium uptake and distribution during the moult cycle of female shore crabs, Carcinus maenas: an in vivo study. Aquatic Toxicology, 71, 17−28 Bordbar, L., Dassenakis, M., Catsiki, A., Megalofouno, P. (2015). Influence of a Ferronickel Smelting Plant Activity on the Coastal Zone through Investigation of Metal Bioaccumulation on Two Gastropod Species (Patella caerulea and Phorcus turbinatus). Journal of Environmental & Analytical Toxicology, s7. http://doi.org/10.4172/2161-0525.S7-004 Borgmann, U. (2000) Methods for assessing the toxicological significance of metals in aquatic ecosystems: Bio-accumulation- toxicity relationships, water concentrations and sediment spiking approaches. Aquat Ecosyst Health Manag 3:277–289 Branch, G. M. (1981). The biology of limpets: physical factors, energy flow, and ecological interactions Oceangr. Mar. Biol. Ann. Rev. 19 p235 – 380 Bridge, J., Demico, R. (2008). Earth surface processes, landfroms and sediment deposits, pg 34, Cambridge University Press, 795 p. Broeg, K., Lehtonen, K. K. (2006). Indices for the assessment of environmental pollution of the Baltic Sea coasts: Integrated assessment of a multi-biomarker approach. Mar Pollut Bull 53:508– 522 Brooks, S., Lyons, B., Goodsir, F., Bignell, J., Thain, J. (2009). Biomarker responses in mussels, an integrated approach to biological effects measurements. J. Toxicol. Environ. Health Part A 72, 196-208.

222 Bruland, K. W. (1983). Trace elements in seawater. In: Chemical oceanography, (Ed). J. P. Riley and R. Chester, Academic Press, London Bryan, G. H. (1979). Bioaccumulation in marine organisms. Philosophical Transactions of the Royal Society of London. 286, 483−505. Bryan, G. W., Ward, E. (1965). The absorption and loss of radioactive and non-radioactive manganese by the lobster, Homarus vulgaris. Journal of Marine Biological Association, UK 45, 65–95. Bryan, G. W., Langston, W. J. (1992). Bioavailability, accumulation and effects of heavy metals in sediments with special reference to United Kingdom estuaries: a review. Environ. Pollu. 76, 89– 131. Busselen, P. (1971). The presence of haemocyanin and serum proteins in the eggs of Carcinus maenas, Eriocheir sinensis and Portunus holsatus. Comp Biochem Physiol 38A: 317 328 Byerley J. J., Scharer J. M. (1992). Natural release of copper and zinc into the aquatic environment. Hydrometallurgy, 30, 1-3, 107-126 Campanella, L., Orazio, D. D., Petronio, B. M., Pietrantonio, E. (1994). Proposal for a metal speciation study in sediments. Analytica Chimica Acta, 309(95), 387–393. Campbell, J. A. (1983). The Geochemical Ocean Sections Study—GEOSECS. In Chemical Oceanography, J.P. Riley & R. Chester (Eds), Vol. 8, 89–155. London: Academic Press. Campbell, P. G. C. (1995). Interactions between trace metals and aquatic organisms: a critique of the free- ion activity model, in ‗‗Metal Speciation and Bioavailability in Aquatic Systems‘‘, (Eds) Tessier A., Turner D.R, IUPAC Series on Analytical and Physical Chemistry of Environmental Systems. Volume 3, p. 45-102, John Wiley and Sons. Canli, M., Furness, R. W. (1993). Influences of Sex and Size on Metal Accumulation and Tissue Distribution in the Norway Lobster Nephrops. Marine Environmental Research. 36, 217–236. Canli, M., Atli, G. (2003). The relationships between heavy metal (Cd, Cr, Cu, Fe, Pb, Zn) levels and the size of six Mediterranean fish species. Environmental Pollution. 121(1), 129–136. http://doi.org/10.1016/S0269-7491(02)00194-X Carrasco, M., López-Ramírez, J.A., Benavente, J., López-Aguayo, F., Sales, D. (2003). Assessment of urban and industrial contamination levels in the bay of Cádiz, SW Spain. Marine Pollution Bulletin. 46(3), 335–45. http://doi.org/10.1016/S0025-326X(02)00420-4 Catsiki, V A., Vakalopoulou, C., Moraitou-Apostolopoulou, M., Verriopoulos, G. (1993) Monodonta turbinata (Born); toxicity and bio accumulation of Cu and Cu + Cr mixtures. Toxicological & Environmental Chemistry. 37:173–184. Catsiki, V.A., Strogguloudy, E., Bei, F. (1998). ―Study the bioaccumulation of heavy metals‖. In. Simboira, N. ―Environmnetal monitoring study of the impacts of the disposal slag in Larymna Bay‖. Final Technical report. (Eds) Hellenic center for marine research (In Greek)

Catsiki, V.A. (2003).‖Ecotoxicology‖. In. Simboura, N. ―Research on the environmental impact of disposal slag in North Evoikos Gulf‖. Yearly Technical report, HCMR (In Greek)

223 Catsiki, V.A. (2005).‖Ecotoxicology‖. In. Simboura, N. ―Research on the environmental impact of disposal slag in North Evoikos Gulf‖. Final Technical report, HCMR (1999-2004) (In Greek)

Catsiki, V.A. (2006).‖Ecotoxicology‖. In. Simboura, N. ―Research on the environmental impact of disposal slag in North Evoikos Gulf‖. Final Technical report, HCMR (2005-2006) (In Greek)

Catsiki, V.A. (2007). ―Study the bioaccumulation of heavy metals in aquaculture fish, benthic and demersal organisms in the Larymna Bay during 2005-6‖. In. Simboura, N. ―Research the environmental impact of slag in North. Evoikos Gulf‖. Final technical report, HCMR July 2007 (In Greek)

Catsiki, V.A. (2008). ―Study the bioaccumulation of heavy metals in aquaculture fish, benthic and demersal organisms in the Larymna Bay during 2005-6‖. In. Simboura, N. ―Research the environmental impact of slag in North. Evoikos Gulf‖. Final technical report, HCMR, October 2008 (In Greek)

Catsiki, V.A. (2009). ―Study the bioaccumulation of heavy metals in aquaculture fish, benthic and demersal organisms in the Larymna Bay in 2008‖. In. Simboura, N. ―Research the environmental impact of slag in North. Evoikos Gulf‖. Final technical report, HCMR, November 2009 (In Greek)

Catsiki, V.A. (2010). ―Study the bioaccumulation of heavy metals in aquaculture fish, benthic and demersal organisms in the Larymna Bay during 2009‖. In. Simboura, N. ―Research the environmental impact of slag in North. Evoikos Gulf‖. Final technical report, HCMR November 2010 (In Greek)

Catsiki, V.A. (2011). ―Study the bioaccumulation of heavy metals in aquaculture fish, benthic and demersal organisms in the Larymna Bay during 2011‖. In. Simboura, N. ―Research the environmental impact of slag in North. Evoikos Gulf‖. Final technical report, HCMR August 2010 (In Greek)

Catsiki, V.A. (2012). ―Study the bioaccumulation of heavy metals in aquaculture fish, benthic and demersal organisms in the Larymna Bay during 2012‖. In. Simboura, N. ―Research the environmental impact of slag in North. Evoikos Gulf‖. Final technical report, HCMR (In Greek)

Catsiki, V.A. (2013). ―Study the bioaccumulation of heavy metals in aquaculture fish, benthic and demersal organisms in the Larymna Bay during 2012‖. In. Simboura, N. ―Research the environmental impact of slag in North. Evoikos Gulf‖. Final technical report, HCMR, February 2013 (In Greek)

Catsiki, V.A. (2014). ―Study the bioaccumulation of heavy metals in aquaculture fish, benthic and demersal organisms in the Larymna Bay during 2014‖. In. Simboura, N. ―Research the environmental impact of slag in North. Evoikos Gulf‖. Final technical report, HCMR (In Greek)

224 Catsiki, V.A. (2015). ―Study the bioaccumulation of heavy metals in aquaculture fish, benthic and demersal organisms in the Larymna Bay during 2015‖. In. Simboura, N. ―Research the environmental impact of slag in North. Evoikos Gulf‖. Final technical report, HCMR (In Greek)

Chapman, P. M., Wang, F, Janssen, C., Persoone G., Allen, H. E. (1998). ―Ecotoxicology of Metals in Aquatic Sediments: Binding and Release, Bioavailability, Risk Assessment, and Remediation.‖ Canadian Journal of Fisheries and Aquatic Sciences 55: 2221-2243. Chen, K. Y., Young, C. S., Jan, T. K., Rohatgi, N. (1974). Trace Metals in Wastewater Effluents, Journal (Water Pollution Control Federation). Water Environment Federation 46, 12, pp. 2663-2675. Chen, M.-H., Chen, C.-Y., Chou, H.-Y., Wen, T.-C. (2005). Gender and size effects of metal bioaccumulation on the rock crab, Thalamita crenata, in Dapeng Bay, southwestern Taiwan. Marine Pollution Bulletin, 50(4), 463–9. http://doi.org/10.1016/j.marpolbul.2005.01.012 Chester, R., 2000, 2003. Marine Geochemistry. Blackwell Science Ltd, Oxford, 2nd ed., pp. 506.Clark, Chiarelli, R., Roccheri, M. C. (2014). Marine Invertebrates as Bioindicators of Heavy Metal Pollution. Open Journal of Metal, (December), 93–106. http://doi.org/10.4236/ojmetal.2014.44011 Chong, K., Wang, W. X. (2001). Comparative studies on the biokinetics of Cd, Cr, and Zn in the green mussel Perna viridis and the Manila clam Ruditapes philippinarum. Environmental Pollution. 115(1), 107–121. http://doi.org/10.1016/S0269-7491(01)00087-2 Chou, C.L. (2000) Personal communication. Bedford Institute of Oceano- graphy, Department of Fisheries and Oceans, Maritime Region, P.O. Box 1006, Dartmouth, NS, Canada B2Y 4A2. Christophoridis, C., Dedepsidis, D., Fytianos, K. (2009). Occurrence and distribution of selected heavy metals in the surface sediments of Thermaikos Gulf, N. Greece. Assessment using pollution indicators. Journal of Hazardous Materials. 168(2–3), 1082–91. http://doi.org/10.1016/j.jhazmat.2009.02.154 Claverie, T., Smith, I. P. (2007). Functional significance of an unusual chela dimorphism in a marine decapod: specialization as a weapon? Proceedings. Biological Sciences / The Royal Society, 274(1628), 3033–8. http://doi.org/10.1098/rspb.2007.1223 Claverie, T., Smith, I. P. (2009). Morphological maturity and allometric growth in the squat lobster Munida rugosa. Journal of the Marine Biological Association of the United Kingdom. 89(6), 1189. http://doi.org/10.1017/S0025315409000277 Conti M., Cecchetti, C. (2003). A biomonitoring study: trace metals in algae and molluscs from Tyrrhenian coastal areas. Environmental Research. 93(1), 99–112. http://doi.org/10.1016/S0013- 9351(03)00012-4 Conti, M E. (2007). A biomonitoring study : trace metals in seagrass, algae and molluscs in a marine reference ecosystem Marta Iacobucci and Gaetano Cecchetti. Int.J.Environmental and pollution. 29:308–332. Conti, M. E., Finoia, M. G. (2010). Metals in molluscs and algae: a north-south Tyrrhenian Sea baseline. Journal of Hazardous Materials, 181(1–3), 388–92. http://doi.org/10.1016/j.jhazmat.2010.05.022 Cornell, R.M., Schwertmann, U. (2003). The Iron Oxides: Structure, Properties, Reactions, Occurrences and Uses, p509-511, John Wiley & Sons, p 667.

225 Costa, M., Klein, C. B. (2006). Toxicity and Carcinogenicity of Chromium Compounds in Humans, Critical Reviews in Toxicology. 36, 2, 155-163. Cotou, E., Henry, M., Zeri, C., Rigos, G., Torreblanca, A., Catsiki, V.A. (2012). Short-term exposure of the European sea bass Dicentrarchus labrax to copper-based antifouling treated nets: copper bioavailability and biomarkers responses. Chemosphere, 89(9), 1091–7. http://doi.org/10.1016/j.chemosphere.2012.05.075 Cox, P. A. (1995). ‗‗The Elements on Earth. Inorganic Chemistry in the Environment‘‘, p. 26-29, 64-70, 78-80, 105-106, 114-115, 154, 155-157, 177-183, 186-190, 192-193, 196, 277, 278-280, Oxford University Press. Crothers, J. H. ( 2001). Common topshells : an introduction to the biology of Osilinus lineatus with notes on other species in the genus. Field studies 115–160. Cubbage, J., Batts, D., Briendenbach, S. (1997). Creation and analysis of freshwater sediment quality values in Washington State. Environmental Investigations and Laboratory Services Program, Washington Department of Ecology, Olympia, WA, USA Cunha, I., Mangas-Ramirez, E., Guilhermino, L. (2007). Effects of copper and cadmium on cholinesterase and glutathione S-transferase activities of two marine gastropods (Monodonta lineata and Nucella lapillus). Comparative Biochemistry and Physiology. Toxicology & Pharmacology : CBP, 145(4), 648–57. http://doi.org/10.1016/j.cbpc.2007.02.014 Dassenakis, M., Andrianos, H., Depiazi, G., Konstantas, A., Karabela, M., Sakellari, A, Scoullos, M. (2003). The use of various methods for the study of metal pollution in marine sediments, the case of Euvoikos Gulf, Greece. Applied Geochemistry, 18(6), 781–794. http://doi.org/10.1016/S0883- 2927(02)00186-5 Dassenakis, M., Degaita, A., Scoullos, M. (1995). Trace metals in sediments of a Mediterranean estuary affected by human activities (Acheloos river estuary, Greece). The Science of the Total Environment. 168(1), 19–31. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/7610382 Dassenakis, M., Krasakopoulou, E., Matzara, B. (1994). Chemical Characteristics of Aetoliko. Marine Polllution Bulletin. 28(7), 427–433. Denkhaus, E., Salnikow, K. (2002). Nickel essentiality, toxicity, and carcinogenicity. Crit Rev Oncol Hematol. 42:35–56 Depledge, M. H., Rainbow, P. S. (1990). Models of regulation and accumulation of trace metals in marine invertebrates. Comparative Biochemistry and Physiology - C Pharmacology Toxicology and Endocrinology. 97(1), 1–7. http://doi.org/10.1016/0742-8413(90)90163-4 Dias Corre ,ˆA. J., Ramos da Silva, M., Bastos da Silva, A.C., Arau ´jo de Lima, S. M., Malm, O., Allodi, S. (2005). Tissue distribution, subcellular localization and endocrine disruption patterns induced by Cr and Mn in the crab Ucides cordatus. Aquat Toxicol 73:139–1 Drever, J. I. (1997). The Geochemistry of Natural Waters: Surface and Groundwater Environments. Prentice Hall. Drinia, H., Anastasakis, G. (2012). Benthic foraminifer palaeoecology of the Late Quaternary continental outer shelf of a landlocked marine basin in central Aegean Sea, Greece. Quaternary International, 261, 43–52. http://doi.org/10.1016/j.quaint.2011.07.003

226 Duc, T. A., Loi, V. D., Thao, T. T. (2013). Partition of heavy metals in a tropical river system impacted by municipal waste. Environmental Monitoring and Assessment. 185(2), 1907–25. http://doi.org/10.1007/s10661-012-2676-z Dummee, V., Kruatrachue, M., Trinachartvanit, W., Tanhan, P., Pokethitiyook, P., Damrongphol, P. ( 2012). Bioaccumulation of heavy metals in water, sediments, aquatic plant and histopathological effects on the golden apple snail in Beung Boraphet reservoir, Thailand. Ecotoxicology and Environmental Safety. 86:204–212. http://doi.org/10.1016/j.ecoenv.2012.09.018 Edmonds, J. S., Francesconi, K. A. (1993). Arsenic in seafoods: Human health aspects and regulations. Marine Pollution Bulletin. 26(12), 665-674. Elder, J. F. (1989). Metal Biogeochemistry in Surface-water Systems – A Review of Principles and Concepts. U.S. Geo- logical Survey Circular. 1013, 43 pp Ellman, G. L., Courtney, K. D., Andres, V. Jr, Featherstone, R. M. (1961). A new and rapid colorimetric determination of acetylcholinesterase activity, Biochemical Pharmacology, vol. 7, pp 88-95. Elumalai, M., Antunes, C., Guilhermino, L. (2002). Single metals and their mixtures on selected enzymes of Carcinus maenas. Water Air Soil Pollut. 141, 273–280. European Union. (2008a). Commission Regulation (EC) No. 629/2008. Setting maximum levels for certain contaminants in foodstuffs. Official Journal of the European Union L 173 (pp. 6–9). European Union. (2008b). Commision Directive 2008/100/EC amending Council Directive 90/496/EEC on nutrition labelling for foodstuffs as regards recommended daily allowances, energy conversion factors and definitions. 28 October 2008 FAO/WHO Expert Committee on Food Additives, 1989. WHO Technical Report Series 759. WHO, Geneva, Switzerland Farmer, A. S. D. (1974). Reproduction in Nephrops norvegicus (Decapoda: Nephropidae), Journal of Zoology. vol. 174, pp. 161- 183 Flemming, C. A., Trevors, J. T. (1989). Copper toxicity and chemistry in the environment: a review. Water, Air and Soil Pollution. 44 : 143-158. Folk, R. L. (1954). The distinction between grain size and mineral composition in sedimentary-rock nomenclature. JGeol. 62:344–359 Förstner, U., Wittmann, G. T. W. (1979). "Heavy Metals in Aquatic Organisms." Metal Pollution in the Aquatic Environment. Springer Berlin Heidelberg. 271-323. Förstner, U., Wittmann, G. T. W. (1983). Metal Pollution in the Aquatic Environment, p. 162, Springer Verlag . Foster, P. (1995). Minor and Trace Elements in the Shell of Patella vulgata (L). Marine environmental research. 40 (1). 55-76. Fowler, S. W., Oregioni, B. (1976). Trace metals in mussels from the N.W. Mediterranean. Mar. Pollut. Bull. 7, 26-29. FSANZ (Food Standards Australia NewZealand). (2002). Contaminants and natural toxicants: Standard 1.4.1 of the Australia New Zealand Food Standards Code Volume 2. FSANZ, Australia

227 Frasco, M. F., Fournier, D., Carvalho, F., Guilhermino, L. (2005). Do metals inhibit acetylcholinesterase (AChE)? Implementation of assay conditions for the use of AChE activity as a biomarker of metal toxicity. Biomarkers. 10, 360–375. Freire, C. A., Onken, H., McNamara, J. C. (2008). A structure-function analysis of ion transport in crustacean gills and excretory organs. Comparative Biochemistry and Physiology. Part A, Molecular & Integrative Physiology. 151(3), 272–304. http://doi.org/10.1016/j.cbpa.2007.05.008 Freire, J. (1996). Feeding ecology of Liocarcinus depurator (Decapoda: Portunidae) in the Ria de Arousa (Galicia, north-west Spain): effects of habitat, season and life history. Marine Biology. 126(2), 297–311. http://doi.org/10.1007/BF00347454 Frumkin, H., Gerberding, J. L. (2008). Draft toxicological profile for chromium. U.S. DEPARTMENT OF HEALTH AND HUMAN SERVICES Public Health Service Agency for Toxic Substances and Disease Registry, (September). Fukue, M., Kita, K,. Ohtsubo, M., Chaney, R. (2006). Contaminated Sediments: Evaluation and Remediation Techniques. ASTM publisher USA-388 p Fukue, M., Mulligan, C. N., Sato, Y., Fujikawa, T. (2007). Effect of organic suspended solids and their sedimentation on the surrounding sea area. Environmental Pollution (Barking, Essex : 1987), 149(1), 70–8. http://doi.org/10.1016/j.envpol.2006.12.015 García-Rico, L., Tejeda-Valenzuela, L., Jara-Marini, M. E., Gómez-Álvarez, A. (2011). Dissolved and particulate metals in water from Sonora Coast: a pristine zone of Gulf of California: metals in water from Sonora Coast. Environmental Monitoring and Assessment, 176(1–4), 109–23. http://doi.org/10.1007/s10661-010-1570-9 Gaudette, H. E., Flight, W.R., Toner L., Folger, D.W. (1974). An inexpensive titration method for the determination of organic carbon in recent sediments, J. Sed. Petr., 44, 249-253 Gavriil, A. M., Angelidis, M. O. (2005). Metal and organic carbon distribution in water column of a shallow enclosed Bay at the Aegean Sea Archipelago: Kalloni Bay, island of Lesvos, Greece. Estuarine, Coastal and Shelf Science. 64(2–3), 200–210. http://doi.org/10.1016/j.ecss.2005.02.015 Gavriil, A. M., Angelidis, M. O. (2006). Metal diagenesis in a shallow semi-enclosed marine system in the Aegean Sea, Greece. Estuarine, Coastal and Shelf Science. 70(3), 487–498. http://doi.org/10.1016/j.ecss.2006.06.029 Gedik, K., Boran, M. (2013). Assessment of metal accumulation and ecological risk around rize harbor, Turkey (southeast black sea) affected by copper ore loading operations by using different sediment indexes. Bulletin of Environmental Contamination and Toxicology, 90(2), 176–81. http://doi.org/10.1007/s00128-012-0894-2 Gilkes, R. J., McKenzie, R. M. (1988). Geochemistry and mineralogy of Mn in soils, chapter 2, p. 23-35, in Manganese in soils and plants, (Eds) Graham R.D., Hannam R.J., Uren N.C., Springer Science and Business Media B.V. Glasby, G. P. (1984). Manganese in the marine environment, in Oceanography and Marine Biology: An annual review, 22, 165-191, (Eds) Barnes H., Barnes M., Aberdeen University Press, 694p . Gore, R. H. (1979). Larval development of Galathea rostrata under laboratory conditions, with a discussion of larval development in the Galatheidae (Crustacea Anomura). Fish Bull 76:781–806

228 Gore, P. A. (2000). Cluster Analysis, 297–321. http://doi.org/10.1016/B978-012691360-6/50012-4 Gosar, M., Miler, M. (2011). Anthropogenic metal loads and their sources in stream sediments of the Meţa River catchment area (NE Slovenia). Applied Geochemistry. 26(11), 1855–1866. Gowland, B. T. G., Moffat, C. F., Stagg, R. M., Houlihan D. F,. Davies, I. M. (2002). Cypermethrin induces glutathione S-transferase activity in the shore crab, Carcinus maenas. Marine Environmental Research. vol. 54, pp. 169-177. Gravato, C., Teles, M., Oliveria, M. A., Santos, M. A. (2006). Oxidative stress, liver biotransformation and genotoxic effects induced by copper in Anguilla anguilla L.—the influence of pre-exposure to b-naphthoflavone. Chemosphere 65, 1821–1830. Guertin, J. (2005). Toxicity and Health Effects of Chromium (All Oxidation States). Chromium (VI) Handbook, 215–234. http://doi.org/doi:10.1201/9780203487969.ch6 Guilhermino, L., Barros, P., Silva, M. C., Soares, A. M. V. M. (1998). Should the use of inhibition of cholinesterases as a specific biomarker for organophosphate and carbamate pesticides be questioned? Biomarkers 3, 157–163. Gupta, S. K., Singh, J. (2011). Evaluation of mollusc as sensitive indicatior of heavy metal pollution in aquatic system: A review. Special issue on environmental management for sustainable development. 2(1), 49-57, ISSN: 0976 – 3104 Haarich, M. k., Wolfgang, K., K r a u s e, M., Z a u k e, G., S c h m i d t, D. (1993). Heavy Metal Distribution in Different Compartments of the Northern North Sea and Adjacent Areas. Ocean Dynamic. UDC 551,464,6; North Sea. pp: 313-336 Halliwell, B., Gutteridge, J. M. C. (2015). Free radicals in biology and medicine, Oxford University Press, 905 pages. Published Online: 16 MAR 2015, DOI: 10.1002/9780470015902.a0002269.pub3 Hamza-Chaffai, A., Roméo, M, Gnassia-Barelli, M., El Abed, A. (1998). Effect of copper and lindane on some biomarkers measured in the clam Ruditapes decussates, Bulletin of Environmental Toxicology, vol. 61.pp: 397–404. Hamza-Chaffai, A., Amiard, J. C., Cosson, R. P. (1999). Relationship between metallothioneins and metals in a natural population of the clam Ruditapes decussatus from Sfax coast: a non-linear model using Box-Cox transformation. Comparative Biochemistry and Physiology 123:153-163. Hartnoll, R. G. (2006). Reproductive investment in Brachyura. Hydrobiologia. 557(1), 31–40. http://doi.org/10.1007/s10750-005-9305-6 Haven, N. (1999). The influence of size distribution on the particle concentration effect and trace metal partitioning in rivers. Geochimica et Cosmochimica Acta. 63(1), 113–127. Hayes, J.D., Flanagan, J.U., Jowsey, I.R. (2004). Glutathione transferases, Annual Review of Pharmacology and Toxicology. vol. 45, pp. 51-88. HCMR 1992. In: Papathanassiou, E. (Ed.), Environmental study of the Northern Evvoikos gulf. Technical Report HCMR, 243 pp.(in Greek) HCMR, 1998. In: Simboura, N. (Ed.), Study of the environmental impact of dumping metalliferous waste in the area of Larymna. Technical Report HCMR, 156 pp. (in Greek).

229 HCMR, 2000. In: Simboura, N. (Ed.), Study of the environmental impact of dumping coarse metalliferous waste in N. Evvoikos gulf. Technical Report HCMR, 63 pp. (in Greek). HCMR, 2001 In: Simboura, N. (Ed.), Study of the environmental impact of dumping coarse metalliferous waste in N. Evvoikos gulf. Technical Report HCMR, 60 pp. (in Greek). HCMR, 2002. In: Simboura, N. (Ed.), Study of the environmental impact of dumping coarse metalliferous waste in N. Evvoikos gulf. Technical Report HCMR, 70 pp. (in Greek). HCMR, 2003. In: Simboura, N. (Ed.), Study of the environmental impact of dumping coarse metalliferous waste in N. Evvoikos gulf. Technical Report HCMR, 60 pp. (in Greek). HCMR, 2005. In: Simboura, N. (Ed.), Study of the environmental impact of dumping coarse metalliferous waste in N. Evvoikos gulf. Final Technical Report HCMR, 145 pp. (in Greek). HCMR, 2007. In: Simboura, N. (Ed.), Study of the environmental impact of dumping coarse metalliferous waste in N. Evvoikos gulf. Final Technical Report HCMR, 63 pp. (in Greek). HCMR, 2008. In: Simboura, N. (Ed.), Study of the environmental impact of dumping coarse metalliferous waste in N. Evvoikos gulf. Final Technical Report HCMR, 69 pp. (in Greek). HCMR, 2010. In: Simboura, N. (Ed.), Study of the environmental impact of dumping coarse metalliferous waste in N. Evvoikos gulf. Final Technical Report HCMR, 81 pp. (in Greek). HCMR, 2011. In: Simboura, N. (Ed.), Study of the environmental impact of dumping coarse metalliferous waste in N. Evvoikos gulf. Final Technical Report HCMR, 67 pp. (in Greek). HCMR, 2012. In: Simboura, N. (Ed.), Study of the environmental impact of dumping coarse metalliferous waste in N. Evvoikos gulf. Final Technical Report HCMR, 58 pp. (in Greek). HCMR, 2005. In: Simboura, N. (Ed.), Study of the environmental impact of dumping coarse metalliferous waste in N. Evvoikos gulf. Final Technical Report HCMR, 145 pp. (in Greek). HCMR, 2013a. In: Tsangaris, C. (Ed.), Study of the environmental impact of dumping coarse metalliferous waste in N. Evvoikos gulf. Final Technical Report HCMR, 57 pp. (in Greek). HCMR, 2013b. In: Tsangaris, C. (Ed.), Study of the environmental impact of dumping coarse metalliferous waste in N. Evvoikos gulf. Final Technical Report HCMR, 60 pp. (in Greek). HCMR, 2014. In: Tsangaris, C. (Ed.), Study of the environmental impact of dumping coarse metalliferous waste in N. Evvoikos gulf. Final Technical Report HCMR, 26 pp. (in Greek). HCMR, 2015a. In: Tsangaris, C. (Ed.), Study of the environmental impact of dumping coarse metalliferous waste in N. Evvoikos gulf. Final Technical Report HCMR, 44 pp. (in Greek). HCMR, 2015b. In: Tsangaris, C. (Ed.), Study of the environmental impact of dumping coarse metalliferous waste in N. Evvoikos gulf. Final Technical Report HCMR, 31 pp. (in Greek). HCMR, 2015c. In: Tsangaris, C. (Ed.), Study of the environmental impact of dumping coarse metalliferous waste in N. Evvoikos gulf. Final Technical Report HCMR, 27 pp. (in Greek). He, Z., Zhao, J., Gao, F., Hu, Y., Qiu, G. (2010). Monitoring bacterial community shifts in bioleaching of Ni-Cu sulfide. Bioresource Technology. 101(21), 8287–93. http://doi.org/10.1016/j.biortech.2010.05.047 Hook, S. E., Fisher, N. S. (2001). Reproductive toxicity of metals in calanoid copepods. Mar. Biol. 138, 1131–1140

230 Horvat, M., Covelli, S., Faganeli, J., Logar, M., Mandić, V., Rajar, R., Širca, A., Ţagar, D. (1999). Mercury in contaminated coastal environments; a case study: the Gulf of Trieste, The Science of the Total Environment. 237/238, 43-56 ICES. (2013). Report of the ICESWorking Group on Biological Effects of Contaminants (WGBEC), 10e15 March 2013, San Pedro del Pinatar, Spain. ICES CM 2013/ SSGHIE: 04, p. 37 Ingersoll, C. G., Ivey, C. D., Brunson, E. L, Hardesty, D. K., Kemble, N. E. (2000). Evaluation of toxicity: Whole-sediment versus overlying-water exposures with amphipod H. azteca. Environmental Toxicology and Chemistry. 19: 2906-2910. IOFR, 1985. In: Chronis, G. (Ed.), Study of the environmental impact of dumping metalliferous waste in the southern part of N. Evvoikos gulf. Technical Report IOFR, 254 pp. (in Greek). Irukayama, T.T.K. (1977). Minamata disease. Methylmercury poisoning in Minamata and Niigata, Japan. Viii, pp 317, ISBN 0-444-99816-0.

Issaro, N., Abi-Ghanem, C., Bermond, A. (2009). Fractionation studies of mercury in soils and sediments: a review of the chemical reagents used for mercury extraction. Analytica Chimica Acta. 631(1), 1– 12. http://doi.org/10.1016/j.aca.2008.10.020 Jaishankar, M., Tseten, T., Anbalagan, N., Mathew, B. B., Beeregowda, K. N. (2014). Toxicity, mechanism and health effects of some heavy metals. Interdisciplinary Toxicology. 7(2), 60–72. http://doi.org/10.2478/intox-2014-0009 Jaworska, N., Anastasova, A. C. (2009). A Review of Multidimensional Scaling ( MDS ) and its Utility in Various Psychological Domains, Tutorilas in Quantitative Methods for Psychology. Vol. 5(1), p. 1‐10. Jeckel, W. H., Roth, R. R., Ricci, L. (1996). Patterns of trace-metal distribution in tissues of Pleoticus muelleri (Crustacea: Decapoda: Solenoceridae). Marine Biology, 125(2), 297–306. http://doi.org/10.1007/BF00346310 Jonnalagadda, S. B., Prasada Rao, P. V. V. (1993). Toxicity, bio- availability and metal speciation. Comp. Biochem. Physiol. C 106, 585—595 Jung, K., Zauke, G.-P. (2008). Bioaccumulation of trace metals in the brown shrimp Crangon crangon (Linnaeus, 1758) from the German Wadden Sea. Aquatic Toxicology (Amsterdam, Netherlands), 88(4), 243–9. http://doi.org/10.1016/j.aquatox.2008.05.007 Kalantzi, I., Black, K. D., Pergantis, S. A., Shimmield, T. M., Papageorgiou, N., Sevastou, K., Karakassis, I. (2013). Metals and other elements in tissues of wild fish from fish farms and comparison with farmed species in sites with oxic and anoxic sediments. Food Chemistry, 141(2), 680–694. http://doi.org/10.1016/j.foodchem.2013.04.049 Kannan, K., Yasunaga, Y., Iwata, H., Ichihashi, H., Tanabe, S., Tatsukawa, R. (1995). Concentrations of heavy metals, organochlorines, and organotins in Horseshoe Crab, Tachypleus tridentate, from Japanese coastal waters. Arch Environ Contam Toxicol 28:40–47 Katoh, E. (2011). Sex, pheromone and aggression in Norway lobster (Nephrops norvegicus): For a better future of Scampi, PhD Thesis, University of Hull. (June), 1–173.

231 Keay, L., Crook, E. M. (1965). Effect of metals ions in hog liver esterase. Archives of Biochemistry and Biophysics. 111 (3), 626-634. Kersten, M., Anagnostou, Ch. (1994). Metal fluxes from a slag dump site in Northern Euboikos Bay. In: Varnavas, S.P. (Ed.), Proc. 6th Int. Conf., Delphi, Environmental contamination Greece, pp. 371– 373. Kingston, H. M., Barnes, I. L., Brady, T. J., Rains, T. C., Champ, M. A. (1978). Separation of eight transition elements from alkali and alkaline earth elements in estuarine and seawater with chelating resin and their determination by graphite furnace atomic absorption spectrometry. Anal. Chem. 50 (14), 2065–2070. Knowlton, M. F., Boyle, T. P., Jones, J. R. (1983). Uptake of lead from aquatic sediment by submersed macrophytes and crayfish. Arch Environ Contam Toxicol.12: 535–41. Koenig, S., Solé, M. (2012). Natural variability of hepatic biomarkers in Mediterranean deep-sea organisms. Marine Environmental Research. 79, 122–31. http://doi.org/10.1016/j.marenvres.2012.06.005 Koukouliapas, K. (1999). ―Concentrations of heavy metals in the two gastropods of Monodonta turbinata and Patella aspera from the polluted coastal area‖. Master dissertation in Oceanography. University of national and kapodistrian university of Athens Kontos, J., Zevgolis, E. N. (2004). Control of hazards in the Greek ferronickel industry. Conference Proceeding : Advances in Mineral Resources Managament and Environmental Geotechnology Conference, Hania 2004, Greece, 221–226. Kotas, J., Stasicka, Z. (2000). Chromium occurrence in the Environment and Methods of its Speciation. Environmental Pollution. 107: 263-283 Kozanglou, C., Catsiki, A.V. (1997). Impact of products of ferronickel smelting plant to the marine benthics life. Chemosphere. 34 (12), 2673–2682. Kouzanouloua, A. (1995). ―Concentrations of heavy metals in benthic organisms from two coastal areas in Greece, Ι coast of Kerkura, ΙΙ coast line of North Evoikos (Larymna)‖. Master dissertation in Oceanography. University of national and kapodistrian university of Athens. Kouzanoulou, A., Catsiki, V.A. (2001).‖ Study the bioaccumulation of heavy metals in aquaculture fish, benthic and demersal organisms in the studies area‖. In. Simboura, N. ―Research the environmental impact of slag in North. Evoikos Gulf. Final technical report. (Eds). Hellenic center for marine research (In Greek)

Krčmar, D., Prica, M., Dalmacija, B., Watson, M., Tričković, J., Rajić, L., Tamaš, Z. (2013). Correlation of different pollution criteria in the assessment of metal sediment pollution. Journal of Environmental Science and Health. Part A, Toxic/hazardous Substances & Environmental Engineering. 48(4), 380–93. http://doi.org/10.1080/10934529.2013.727757 Krewski, D., Yokel, R. A., Nieboer, E., Borchelt, D., Cohen, J., Kacew, S., Rondeau, V. (2009). Human Health Risk Assessment For Aluminium, Aluminium Oxide and Aluminium Hydroxide. Department of Epidemiology and Community Medicine, Faculty of medicine, University of Ottawa, Ottawa, Ontario, Canada (Vol. 10). http://doi.org/10.1080/10937400701597766.

232 Kristoff, G., Verrengia Guerrero, N. R., Pechén de D‘Angelo, A. M., Cochón, A. C. (2006). Inhibition of cholinesterase activity by azinphos-methyl in two freshwater invertebrates: Biomphalaria glabrata and Lumbriculus variegatus. Toxicology 222, 185–194. Kroes, R., Kozianowski, G. (2002). Threshold of toxicological concern in food safety assessment. Toxicol Lett. 127: 43-6. Kumar, V., Elliott, W. B. (1973). The acetylcholinesterase of Bungarus fasciatus venom, European Journal of Biochemistry. 34, 586-592. Kumar, V., Kalita, J., Misraa U.K., Bora, H. K. (2015). A study of dose response and organ susceptibility of copper toxicity in a rat model. Journal of Trace Elements in Medicine and Biology. 29, 269– 274. Lane, T.W., Morel, F. M. M. (2000). A biological function for cadmium in marine diatoms, PNAS. 97, 4627-4631. Langston, W. J., Spence, S. K. (1995). ‗‗Biological Factors Involved in Metal Concentrations observed in Aquatic Organisms‘‘, in ‗‗Metal Speciation and Bioavailability in Aquatic Systems‘‘, Eds Tessier A., Turner D.R, IUPAC Series on Analytical and Physical Chemistry of Environmental Systems, Volume 3, p. 408-465, John Wiley and Sons. Li, X., Jia, L., Zhao, Y., Wang, Q., Cheng, Y. (2009). Seasonal bioconcentration of heavy metals in Onchidium struma (Gastropoda: Pulmonata) from Chongming Island, the Yangtze Estuary, China. Journal of Environmental Sciences. 21(2), 255–262. http://doi.org/10.1016/S1001- 0742(08)62260-3 Lima, L. R. P. D. A., Bernardez, L. A. (2011). Characterization of the lead smelter slag in Santo Amaro, Bahia, Brazil. Journal of Hazardous Materials. 189(3), 692–699. http://doi.org/10.1016/j.jhazmat.2011.02.091

Livieratos, E., 1979. The M2 Sea-Tidal propagation in the Euvoikos Bay. Technika Chronika 3, 49–55. Long, E. R., Morgan, L. G. (1991). The potential for biological effects of sediment-sorbed contaminants tested in the National Status and Trends Program. National Oceanic and Atmospheric Administration Technical Memorandum, NOS OMA 52, Seattle, WA, USA Long, E. R., Bin, C., Smith, S. L., Calder, F. D. (1995). Incidence of Adverse Biological Effects within Ranges of Chemical Concentrations in Marine and Estuarine Sediments. Environmnetal Management. 19(1), 81–97. Loring, D. H., Rantala, R. T. T. (1992). Manual for the geochemical analyses of marine sediments and suspended particulate matter. Earth-Science Reviews. 32(4), 235–283. http://doi.org/10.1016/0012-8252(92)90001-A. Loring, D. H., Hill, S. (1992). Factors controlling the bioavailability of heavy metals in sediments. Report for the ICES working group on Biological Contaminants. ICES, Copenhagen. Loring, D. H. (1995). Manual for geochemical anylyses of marine sediments and suspended particulates matters. United Nations environment program (UNEP). Luís, A. T., Durães, N., Fernandes Pinheiro de Almeida, S., Ferreira da Silva, E. (2016). Integrating geochemical (surface waters, stream sediments) and biological (diatoms) approaches to assess

233 AMD environmental impact in a pyritic mining area: Aljustrel (Alentejo, Portugal), Journal of Environmental Sciences. 42, 215-226. Luoma, S. N. (1996). The developing framework of marine ecotoxicology: pollutants as a variable in marine ecosystems?. Journal of Experimental Marin Biology and Ecology. 200, 29-55. Luoma, S. N., Fisher, N. S. (1997). Uncertainties in assessing contaminant exposure from sediments. In: Ingersoll, C.G., Dillon, T., Biddinger, G.R. (Eds.), Ecological Risk Assessment of Contaminated Sediments. Society of Environmental Toxicology and Chemistry Special Publication Series, Pensacola, FL, pp. 211–237. Luoma, S. N., Rainbow, P. S. (2008). Metal contamination in aquatic environments: science and lateral management. Cambridge University Press, Cambridge, UK Lyons, C., Dowling, V., Tedengren, M., Gardeström J., Hartl M .G. J., O'Brien N., Van Pelt, F. N. A. M., O'Halloran J., Sheehan D. (2003). Variability of heat shock proteins and glutathione S-transferase in gill and digestive gland of blue mussel, Mytilus edulis. Marine Environmental Research, vol. 56, pp. 585-597. MacDonald, D. D., Carr, R. S., Calder, F. D., Long, E. R., Ingersoll, C. G. (1996). Development and evaluation of sediment quality guidelines for Florida coastal waters. Ecotoxicology 5:253-278 Macdonald, D. D., Ingersoll, C. G., Berger, T. A. (2000). Environmental C ontamination and Development and Evaluation of Consensus-Based Sediment Quality Guidelines for Freshwater Ecosystems. Environmental Contamination Toxicology. 31, 20–31. http://doi.org/10.1007/s002440010075 Macõâas-zamora, J. V., Villaescusa-celaya, J. A., Mun, A. (1999). Trace metals in sediment cores from the Campeche shelf, Gulf of Mexico. Environmental Pollution. 104, 69–77. Maiorano, P., Capezzuto, F., Sion, L., D‘onghia, G., Tursi, A. (2013). Spatio-temporal changes of Munida Rutllanti zariquiey-alvarez, 1952 (decapoda: Galatheidae) in the north-western Ionian Sea (Central Mediterranean). Mediterranean Marine Science. 14(3), 42–48. http://doi.org/10.12681/mms.619 Manahan, S. E. (1994). The Geosphere and Geochhemistry, ―Environmental Chemistry‖ P 463, Lewis Publishers 811p. Marmolejo-Rodríguez, A. J., Prego, R., Meyer-Willerer, A., Shumilin, E., Cobelo-García, A. (2007). Total and labile metals in surface sediments of the tropical river-estuary system of Marabasco (Pacific coast of Mexico): Influence of an iron mine. Marine Pollution Bulletin. 55(10–12), 459–68. http://doi.org/10.1016/j.marpolbul.2007.09.008 Marsden, I., Rainbow, P. (2004). Does the accumulation of trace metals in crustaceans affect their ecology—the amphipod example? Experimental Marine Biology and Ecology. 300(1–2), 373– 408. http://doi.org/10.1016/j.jembe.2003.12.009 Martin, J. L. M. (1974). Metals in Cancer irroratus (Crustacea: Decapoda): concentrations, concentration factors, discrimination factors, correlations. Mar Biol. 28:245 251 Martínez-Álvarez, R., Morales, A., Sanz, A. (2005). Antioxidant defenses in fish: biotic and abiotic factors. Rev. Fish Biol. Fish. 15, 75-88

234 Martínez-sánchez, M J., Navarro, M. C., Pérez-sirvent, C., Marimón, J., Vidal, J. (2008) Assessment of the mobility of metals in a mining-impacted coastal area (Spain, Western Mediterranean ). Journal of Geochemical Exploration. 96:171–182. http://doi.org/10.1016/j.gexplo.2007.04.006 Mason, A. Z., Jenkins, K. D. (1995). ‗‗Metal detoxification in Aquatic Organisms‘‘, in ‗‗Metal Speciation and Bioavailability in Aquatic Systems‘‘, Eds Tessier A., Turner D.R, IUPAC Series on Analytical and Physical Chemistry of Environmental Systems, Volume 3, p. 482-491, John Wiley and Sons,. Chi- Chester, UK, pp. 479–608. Mason, k., Simkiss, A. (1982). Sites of mineral deposition in metal accumulaing cells. Expeimental Cell Reserch. 139 (383-391) Melidonis, N. G. (1960). Nickeliferous iron-ores of Central Euboea. Geol. Reconn., Inst. Geol. Subsurface Res. 32: 1-35. Méndez, L., Racotta, I. S., Acosta, B., Rodríguez-Jaramillo, C. (2001). Mineral concentration in tissues during ovarian development of the white shrimp Penaeus vannamei (Decapoda: Penaeidae). Marine Biology. 138:687–692. http://doi.org/10.1007/s002270000508 Michibata, H., Terada, T., Anada, N., Yamakawa, K., Numakunai, T (1986). "The accumulation and distribution of vanadium, iron and manganese in some solitary ascidians". Biol. Bull. 171, 672 Michalopoulos, P., Karageoris, A., Kanelopoulos, T., Anagnostou, Ch., C, Zeri. (2005). ―Environmental study of the Northern Evvoikos Gulf‖. Technical Report HCMR, pp. 22-39

Millero, F. J. (2001). The physical chemistry of natural waters, pp 654 Wiley-Interscience press, University of California. Minervini, R., Giannotta, M., Falciai, L. (1982). A preliminary report on the decapod crustaceans in the estuarine area of the Tiber. Quad. Lab. Tecnol. Pesca. 3 (2-5): 305-318. Miramand, P., Bentley, D. (1992). Heavy metal concentrations in two biological indicators (Patella vulgata and Fucus serratus) collected near the French nuclear fuel reprocessing plant of La Hague. Science of the Total Environment, 111, 135–149. Retrieved from http://www.sciencedirect.com/science/article/pii/004896979290352S Mohapatra, A., Rautray, T. R., Patra, A. K., Vijayan, V., Mohanty, R. K. (2009). Trace element-based food value evaluation in soft and hard shelled mud crabs. Food and Chemical Toxicology. 47(11), 2730–2734. http://doi.org/10.1016/j.fct.2009.07.037 Monitoring Chemical Pollution in Europe‘ s Seas. (2011). (November), Board-ESF, M. p 108 Moreira, S. M., Guilhermino, L. (2005). The use of Mytilus galloprovincialis acetylcholinesterase and glutathione S-transferases activities as biomarkers of environmental contamination along the northwest. Environmental monitoring and Assessment. 105: 309-325. Mota, A. M., Correia Dos Santos, M. M. (1995). ‗‗Trace metal Speciation of Labile Chemical Species in Natural Waters: Electrochemical Methods‘‘, in ‗‗Metal Speciation and Bioavailability in Aquatic Systems‘‘, (Eds) Tessier A., Turner D.R, IUPAC Series on Analytical and Physical Chemistry of Environmental Systems, Vol. 3, p. 206-207, John Wiley and Sons. Mudgal, V., Madaan, N., Mudgal, A., Singh, R. B., Mishra, S. (2010). Effect of Toxic Metals on Human health. The Open Nutraceuticals Journal. 3, 94–99. http://doi.org/10.2174/1876396001003010094

235 Najimi, S., Bouhaimi, S., Daubeze, M., Zekhnini, A., Pellerin, J., Narbonne, J. F., Moukrim, A. (1997). Use of acetylcholinesterase in Perna perna and Mytilus galloprovincialis as a biomarker of pollution of Agadir marine bay (South Marocco). Bulletin of Environmental Contamination and Toxicology. 58, 901–912. Nicolaidou, A., Pancucci, M.A., Zenetos, A. (1989). The impact of dumping coarse metalliferous waste on the benthos in Evoikos Gulf, Greece. Marine Pollution Bulletin. 20(1), 28–33. http://doi.org/10.1016/0025-326X(89)90274-9 Nicolaidou, A. Nott, J A. (1998). Metals in Sediment, Seagrass and Gastropods near a Nickel Smelter in Greece : Possible Interactions. Marine polllution bulletin 36(5), 360-365. Nieboer, E., Richardson, D. H. S. (1980). The replacement of the nondescript term ―heavy metals‖ by a biologically and chemically significant classification of metal ions. Environmental Pollution. Series B, Chemical and Physical, 1(1), 3–26. http://doi.org/10.1016/0143-148X(80)90017-8 Nies, H., Harms, I. H., Karcher, M. J., Dethleff, D., Bahe, C. (1999). Anthropogenic radioactivity in the Arctic Ocean--review of the results from the joint German project. The Science of the Total Environment, 237–238, 181–91. http://www.ncbi.nlm.nih.gov/pubmed/10568275 Nott, J .A., Nicolaidou, A. (1989) Metals in gastropods--metabolism and bioreduction. Marine Environmental Research. 28:201–205. Nott, J. A., Nicolaidou, A. (1990). Transfer of metal detoxification along marine food chains. Journal of the Marine Biological Association of the United Kingdom. 70, 905–912. http://doi.org/10.1017/S0025315400059130 Nriagu, J. O. (1988). ‗‗Production and uses of chromium, in: Chromium in the natural and human environments‘‘, (Eds) Nriagu J. O., Nieboer E., p. 81-103, John Wiley & Sons Inc. Nriagu, J. O. (1989). A global assessment of natural sources of atmospheric trace metals. Nature. 338, 47 - 49 Nriagu, O. N. (1989). Natural versus anthropogenic emissions of trace metals to the atmosphere. In Control and Fate of Atmospheric Trace Metals, J. M. Pacyna & B. Ottar (Eds), 3–13. Dordrecht: Kluwer Academic Publishers. Nugegoda, D., Rainbow, P. S. (1989). Effects of salinity changes on zinc iptake and regulation by the decapod crustaceans Palaemon elegans and Palaemonetes varians. Mar. Ecol. Prog. Ser. 51, 57– 75. Okay, O. S., Pekey, H., Morkoc, E., Basak, S., Baykal, B. (2008). Metals in the surface sediments of Istanbul Strait (Turkey). Journal of Environmental Science and Health. Part A, Toxic/hazardous Substances & Environmental Engineering. 43(14), 1725–34. http://doi.org/10.1080/10934520802330412 Oliva, M., José Vicente, J., Gravato, C., Guilhermino, L., Dolores Galindo-Riaño, M. (2012). Oxidative stress biomarkers in Senegal sole, Solea senegalensis, to assess the impact of heavy metal pollution in a Huelva estuary (SW Spain): Seasonal and spatial variation. Ecotoxicology and Environmental Safety. 75(1), 151–162. http://doi.org/10.1016/j.ecoenv.2011.08.017 OSPAR. (2000). Commission for the Protection of the Marine Environment of the North-East Atlantic Quality Status Report

236 Pacyna, E., Pacyna, J., Steenhuisen, F., Wilson, S. (2006). Global anthropogenic mercury emission inventory for 2000. Atmos. Environ. 40, 4048 – 4063 Pacyna, E. G., Pacyna, J. M., Fudala, J., Strzelecka-Jastrzab, E., Hlawiczkac, S. Panasiuk D., Nitter S., Preggere T., Pfeiffer H., Friedrich R. (2007). Current and future emissions of selected heavy metals to the atmosphere from anthropogenic sources in Europe. Atmospheric Environment. 41, 38, 8557-8566. Páez-Osuna, F., Tron-Mayen, L. (1995). Distribution of heavy metals in tissues of the shrimp Penaeus californiensis from the northwest coast of Mexico. Bulletin of Environmental Contamination and Toxicology. 55(2), 209–15. http://www.ncbi.nlm.nih.gov/pubmed/7579925 Palpandi, C., Kesavan, K. (2012) Heavy metal monitoring using Nerita crepidularia-mangrove mollusc from the Vellar estuary, Southeast coast of India. Asian Pacific Journal of Tropical Biomedicine. 2:S358–S367. Papaioannou, J., Makropoulos, K., Kouskouna, V. (1994). Revision of some historical earthquakes in central Greece. XXIV European Seismological Commission general assembly. Sep 19 - 24 1994, Athens, Proceedings, 1711-1713. Papanastassiou, D., Stavrakakis, G., Makaris, D. (2001). Recent micro-earthquake activity at Northern Evoikos Gulf, central Greece. Bulletin of Geological Society of Greece 34, 1567-1572. Papathanassiou, E., Catsiki, A. V., Bei, F. (1992). ―Toxicology‖. In. Papathanasoiu, E. ―The Environmental study of North Evoikos Gulf ―.Final Technical report, (Eds) Hellenic center for marine research (In Greek)

Papathanasiou, E., Zenetos, A. (2005). State of the Hellenic Marine Environment. Hellenic Center for Marine Research, Report, (Eds.) HCMR. 358p. Athens Papathanasiou, E., Zenetos, A., Vassilopoulou, V., Tseres, G. (2007). State of Hellenic Fisheries. Institute of Marine Biological Resources. Report (Eds.) HCMR. 466 p. Athens Paraskevopoulou, V. (2009). Trace metals distribution and chemical behaviour in a coastal marine area affected by industrial pollution CNW Saronikos Gulf, PhD thesis in Greek, National and Kapodistrian University of Athens Pascual, P., Pedrajas, J. R., Toribio, F., López-Barea, J., Peinado, J. (2003). Effect of food deprivation on oxidative stress biomarkers in fish (Sparus aurata). Chem.-Biol. Interact. 145, 191-199 Patil, Y. P., Pawar, S. H., Jadhav, S., Kadu, J. S. (2013). Biochemistry of metal absorption in Human Body : Reference to check Impact of Nano Particles on Human Being. International Journal of scientific Reasearch Publications. 3(4), 1–5. Pavarino, E. C., Russo A., Silva Galbiatti A. L., Pereira Almeida, W., Goloni Bertollo, E. M. (2013). Glutathione: Biosynthesis and Mechanism of Action in Glutathione: Biochemistry, Mechanisms of Action and Biotechnological Implications (pp. 1-34), (Eds): Labrou, N., Flemetakis, Em., Series: Microbiology Research Advances Biochemistry Research Trends, Nova Science Publishers. Payne, J. F., Mathieu A., Melvin, W., Fancey, L. L. (1996). Acetylcholinesterase, an old biomarker with a new future? Field trials in association with two urban rivers and a paper mill in Newfoundland, Marine Pollution Bulletin, Vol. 32, pp. 225-231.

237 Pekey, H., Karakaş, D., Ayberk, S., Tolun, L., Bakoğlu, M. (2004). Ecological risk assessment using trace elements from surface sediments of Izmit Bay (Northeastern Marmara Sea) Turkey. Marine Pollution Bulletin. 48(9–10), 946–53. http://doi.org/10.1016/j.marpolbul.2003.11.023 Pekey, H. (2006). The distribution and sources of heavy metals in Izmit Bay surface sediments affected by a polluted stream. Marine Pollution Bulletin. 52(10), 1197–208. http://doi.org/10.1016/j.marpolbul.2006.02.012 Percin, F., Sogut, O., Altinelataman, C., Soylak, M. (2011). Some trace elements in front and rear dorsal ordinary muscles of wild and farmed bluefin tuna (Thunnus thynnus L. 1758) in the Turkish part of the eastern Mediterranean Sea. Food and Chemical Toxicology. 49(4), 1006–1010. http://doi.org/10.1016/j.fct.2011.01.007 Persaud, D., Jaagumagi, R., Hayton, A. (1993) Guidelines for the protection and management of aquatic sediment quality in Ontario. Water Resources Branch, Ontario Ministry of the Environment, Toronto, Canada Pfeifer, H. R., Derron M. H., Schlegel, D., Rey, C., Atteia O., Dalla Piazza R., Dubois J. P., Mandia, Y. (2000). Chapter 2 Natural trace element input to the soil-sediment-water-plant system: examples of background and contaminated situations in Switzerland, Eastern France and Northern Italy in Trace Metals in the Environment. (Eds) Markert, B., Friese K, Vol 4, 2000, Pages 33–86, Elsevier. Pickering, W. F. (1986). Metal ion speciation; soils and sediments. (A review) Ore Geol Rev:1:83–146 Popham, J. D., D‘Auria, J. M. (1983). Combined effect of body size, season, and location on trace element levels in mussels (Mytilus edulis). Archives of environmental contamination and toxicology. 12:1– 14. http://www.ncbi.nlm.nih.gov/pubmed/6830305 Pourang, N., Amini, G. (2000). Distribution of trace elements in tissues of two shrimp species from Persian Gulf and effects of storage temperature on elements transportation. Water, Air, and Soil Pollution. 229–243. Pourang, N., Dennis, J. H., Ghourchian, H. (2004). Tissue distribution and redistribution of trace elements in shrimp species with the emphasis on the roles of metallothionein. Ecotoxicology. 13(6), 519– 533. http://doi.org/10.1023/B:ECTX.0000037189.80775.9c Presley, B. J., Kolodny, Y., Nissenbaum, A., Kaplan, I. R. (1972). Early diagenesis in a reducing fjord, Saanich Inlet, British Columbiad II. Trace element distribution in interstitial water and sediment. Geochimica et Cosmochimica Acta. 36, 1073e1090. Pretto, A., Loro, V. L, Morsch, V. M, Moraes, B. S, Menezes, C., Clasen, B., Hoehne, L., Dressler, V. (2010). Acetylcholinesterase activity, lipid peroxidation, and bioaccumulation in silver catfish (Rhamdia quelen) exposed to cadmium. Archives Environ Contamination and Toxicol. DOI 10.1007/s00244-009-9419-3

Price, N. B., Karageorgis, A. P., Kaberi, H., Zeri, C., Krasakopoulou, E., Voutsinou-Taliadouri, F., Pagou, K. (2005). Temporal and spatial variations in the geochemistry of major and minor particulate and selected dissolved elements of Thermaikos Gulf, Northwestern Aegean Sea. Continental Shelf Research, 25(19–20), 2428–2455. http://doi.org/10.1016/j.csr.2005.08.007

238 Proctor, D. M., Otani, J. M., Finley, B. L., Paustenbach, D. J., Bland, J. A., Speizer, N., Sargent, E. V. (2002). Is hexavalent chromium carcinogenic via ingestion? A weight-of-evidence review. Journal of Toxicology and Environmental Health, Part A. 65, 10, 701-746 Prosi, F. (1989). Factors controlling biological availability and toxic effects of lead in aquatic organisms. Total Environ. 79, 157–69. Puig, P., Palanques, A., Sanchez-cabeza, J. A. (1999). Heavy metals in particulate matter and sediments in the southern Barcelona sedimentation system (North-western Mediterranean). Marine Chemistry. 311–329. Rainbow, P. S. (1995). Biomonitoring of Heavy Metal Availability in the Marine Environment, Marine Pollution Bulletin. 31, 193-192. Rainbow, P. S. (1995). Physiology, physicochemistry and metal uptake—A crustacean perspective. Marine Pollution Bulletin. 31(1–3), 55–59. http://doi.org/10.1016/0025-326X(95)00005-8 Rainbow, P. S. (1997). Ecophysiology of Trace Metal Uptake in Crustaceans. Estuarine, Coastal and Shelf Science. 44(2), 169–175. http://doi.org/10.1006/ecss.1996.0208 Rainbow, P. (2002). Trace metal concentrations in aquatic invertebrates: why and so what? Environmental Pollution. 120(3), 497–507. http://doi.org/10.1016/S0269-7491(02)00238-5 Rainbow, P. S. (2007). Trace metal bioaccumulation: models, metabolic availability and toxicity. Environment International, 33(4), 576–82. http://doi.org/10.1016/j.envint.2006.05.007 Rainbow, P. S., Luoma, S. N. (2011). Metal toxicity, uptake and bioaccumulation in aquatic invertebrates- -modelling zinc in crustaceans. Aquatic Toxicology (Amsterdam, Netherlands), 105(3–4), 455–65. http://doi.org/10.1016/j.aquatox.2011.08.001 Regoli, F., Principato, G. (1995). Glutathione, glutathione-dependent and antioxidant enzymes in mussel, Mytilus galloprovincialis, exposed to metals under field and laboratory conditions implications or the use of biochemical biomarkers. Aquat. Toxicol. 31, 143–164 Reimann, C., De Caritat, P. (2005). Distinguishing between natural and anthropogenic sources for elements in the environment: regional geochemical surveys versus enrichment factors. The Science of the Total Environment. 337(1–3), 91–107. http://doi.org/10.1016/j.scitotenv.2004.06.011 Relić, D., Dordević, D., Popović, A., Blagojević, T. (2005). Speciations of trace metals in the Danube alluvial sediments within an oil refinery. Environment International. 31(5), 661–9. http://doi.org/10.1016/j.envint.2004.11.003 Richard, F. C., Bourg, A. C. M. (1991). Aqueous Geochemistry of Chromium: A review, Water Research. Vol. 25, No 7, p. 807-816 Richetti, S. K., Rosemberg, D. B., Ventura-Lima, J., Monserrat, J. M., Bogo, M. R., Bonan, C. D., (2011). Acetylcholinesterase activity and antioxidant capacity of zebrafish brain is altered by heavy metal exposure. Neurotoxicology. 32, 116–122 Riley, J. P., Taylor, D. (1968). Chelating resins for the concentration of trace elements from seawater and their analytical use in conjunction with A.A.S. Anal. Chim. Acta. 40, 479–484

239 Roberts, S., Jackson, J. (1991). Active 760 normal faulting in central Greece: an overview. In: Roberts, A., Yielding, M., Freeman, G. (Eds.), The Geometry of Normal Faults, pp. 125-142. Geological Society Special Publication 56 Roose, P., Brinkman, U. A. (2005). Trends in Anal. Chem. 24:897 Rouselaki E., 2007. Levels and fluctuations of heavy metals in the estuary of Sperchios river and Maliakos Gulf, Master dissertations. National and Kapodistrian University of Athens (in Greek) Rufino, M. M., Maynou, F., Abelló, P., Yule, A. B. (2004). Small- scale non-linear geostatistical analysis of Liocarcinus depurator (Crustacea: Brachyura) abundance and size structure in a western Mediterranean population. Mar. Ecol. Prog. Ser., 276: 223-235 Rufino, M. M., Abelló, P., Yule, A. B., Torres, P. (2005). Geographic , bathymetric and inter-annual variability in the distribution of Liocarcinus depurator ( Brachyura : Portunidae ) along the Mediterranean coast of the Iberian Peninsula. SCI.MAR. 69(4), 503–518. Russo, R. C. (2002). Development of marine water quality criteria for the USA. Marine Pollution Bulletin. 45(1–12), 84–91. http://www.ncbi.nlm.nih.gov/pubmed/12398371 Sáenz, L. A., Seibert, E. L., Zanette, J., Fiedler, H. D., Curtius, A. J., Ferreira, J. F., et al. (2010). Biochemical biomarkers and metals in Perna perna mussels from mariculture zones of Santa Catarina, Brazil. Ecotoxicology and Environmental Safety. 73 (5), 796-804 Saleh, F. Y., Parkerton, T. F., Lewis, R. V., Huang, J. H., Dickson, K. L. (1989). Kinetics of chromium transformations in the environment. Sci. Total Environ. 86, 1–2, 25–41 Salminen, R. (Chief-editor). (2005). Nickel (Ni), in Geochemical Atlas of Europe. Part 1 - Background Information, Methodology and Maps. p. 271-276, ISBN 951-690-913-2 (electronic version), http://www.gtk.fi/publ/foregsatlas/ Salomons, W., Förstner, U. (1984). ‗‗Metals in the Hydrocycle‘‘, p. 1-4, 7, 10-11, 18, 25, 63-73, 97, 149, 269, Springer-Verlag. Sanders, J., Preez, H. H. D., Van Vuren, J. H. J. (1998). The freshwater river crab, Potamonautes warreni, as a bioaccumulative indicator of iron and manganese pollution in two aquatic systems. Ecotoxicology and Environmental Safety. 41(2), 203–214. http://doi.org/10.1006/eesa.1998.1699 Sarkar, F. H., Li, Y. (2006). Using chemopreventive agents to enhance the efficacy of cancer therapy. Cancer Res. 66: 3347-50. Sastre, M. P., Reyes, P., Ramos, H., Romero, R., Rivera, J. (1999). Heavy metal bioaccumulation in Puerto Rican Blue Crabs (Callinectes spp.). Bull Mar Sci. 64:209–217 Schiff, K., Diehl, D., Valkirs, A. (2004). Copper emissions from antifouling paint on recreational vessels. Mar Pollut Bull. 48, 371–377 Schmidt, W. L. (1965) Crustaceans. The University of Michigan Press, Ann Arbor, MI Scoullos, M., Dassenakis, M. (1983). Trace metals in a tidal Mediterranean embayment. Marine Pollution Bulletin. 14(1), 24–29. http://doi.org/10.1016/0025-326X(83)90091-7 Scoullos, M. J., Sakellari, A., Giannopoulou, K., Paraskevopoulou, V., Dassenakis, M. (2007). Dissolved and particulate trace metal levels in the Saronikos Gulf, Greece, in 2004. The impact of the

240 primary Wastewater Treatment Plant of Psittalia. Desalination, 210(1–3), 98–109. http://doi.org/10.1016/j.desal.2006.05.036 Selvaraj, K., Mohan, V. R., Szefer, P. (2004). Evaluation of metal contamination in coastal sediments of the Bay of Bengal, India: geochemical and statistical approaches. Mar Pollut Bull. 49: 174–185 Sheehan, D., Power, A. (1999). Effects of seasonality on xenobiotic and antioxidant defence mechanisms of bivalve molluscs. Comp. Biochem. Physiol. Part C Toxicol. Pharmcol. 123, 193-199. Simboura, N., Papathanassiou, E., Sakellariou, D. (2007). The use of a biotic index (Bentix) in assessing long-term effects of dumping coarse metalliferous waste on soft bottom benthic communities. Ecological Indicators, 7(1), 164–180. http://doi.org/10.1016/j.ecolind.2005.11.006 Simkiss, K., Taylor, M. G. (1995). ‗‗Transport of Metals across membranes‘‘, in ‗‗Metal Speciation and Bioavailability in Aquatix Systems‘‘. (Eds) Tessier, A., Turner, D. R, IUPAC Series on Analytical and Physical Chemistry of Environmental Systems, Volume 3, p. 22-38, John Wiley and Sons. Simonetti, P., Botté, S. E., Fiori, S. M., Marcovecchio, J. E. (2012). Heavy-metal concentrations in soft tissues of the burrowing crab Neohelice granulata in Bahía Blanca estuary, Argentina. Arch Environ Contam Toxicol. 62(2), 243–53. http://doi.org/10.1007/s00244-011-9692-9 Siscar, R., Torreblanca, A, Palanques, A., Solé, M. (2013). Metal concentrations and detoxification mechanisms in Solea solea and Solea senegalensis from NW Mediterranean fishing grounds. Marine Pollution Bulletin. 77(1–2), 90–9. http://doi.org/10.1016/j.marpolbul.2013.10.026 Sokolowski, A., Wolowicz, M., Hummel, H. (2001). Distribution of dissolved and labile particulate trace metals in the overlying bottom water in the Vistula river plume (southern Baltic Sea). Marine Pollution Bulletin. 42(10), 967–80. http://www.ncbi.nlm.nih.gov/pubmed/11693652 Srinath, T., Verma, T., Ramteke, P. W., Garg, S. K. (2002). Chromium (VI) biosorption and bioaccumulation by chromate resistant bacteria. Chemosphere. 48(4):427–435 Starkel, W. M. (1985). Predicting the effect of macrobenthos on the sediment/water flux of metals and phosphorus. Can. J. Fish. Aquat. Sci. 42, 95—100 Stephan, C. E., Mount, D. I., Hansen, D. J., Gentile, J. H., Chapman, G. A., Brungs, W. A. (1985). Guidelines for deriving numerical national water quality criteria for the protection of aquatic organisms and their uses. Duluth, U.S. Environmental Protection Agency, Environmental Research Laboratory. 98 pp Study of Environmental Monitoring. (2011). Report of the Commissioner of the Environment and Sustainable Development. Chapter 5. Office of the Auditor General of Canada Ottawa, Ontario. (www.oag-bvg.gc.ca/) Switala, J., Loewen, P. C. (2002). Diversity of properties among catalases, Archives of Biochemistry and Biophysics. Vol. 401, pp. 145-154. Szefer, P., Szefer, K. (1990). Metals in molluscs and associated bottom sediments of the southern Baltic. Helgola¨nder Meeresuntersuchun- gen. 44, 411–424. Takeuchi, T., Morikawa, N., Matsumoto, H., Shiraishi, Y. (1962). A pathological study of Minamata disease in Japan, Acta Neuropathologica. 2, 1, 40–57 Taebi, A., Droste, R. L. (2004). Pollution loads in urban runoff and sanitary wastewater. Science of the Total Environment. 327, 1–3, 175–184

241 Taebi, A., Droste, R. L. (2004). First flush pollution load of urban stormwater runoff. Journal of Environmental Engineering and Science. 3(4), 301–309 Tapella, F., Gustavo, A. L., Romero, M. C., Thatje, S. (2002). Reproductive biology of the crab Munida subrugosa (Decapoda : Anomura : Galatheidae ). J. Mar.Biol.ASS. UK, 589–595. Tchounwou, P. B., Yedjou, C. G., Patlolla, A. K., Sutton, D. J. (2012). Molecular, Clinical and Environmental Toxicology, 101, 1–30. http://doi.org/10.1007/978-3-7643-8340-4 Tepavitcharova, S., Todorov, T., Dassenakis, M., Paraskevopoulou, V. (2010). Chemical speciation in waters influenced by lead–zinc metallurgical industry. Environmental Monitoring and Assessment. 169, 27–36. The Joint Food and Agriculture Organization/World Health Organization Expert Committee on Food Additives (JECFA). (2003). In Summary and conclusions of the 61st meeting of the joint FAO/WHO expert committee of food additives. JECFA/61/ Sc, Rome, Italy. 10–19.06.03. pp. 1– 22. Thomas, D.J., Juedes, M.J. (1992). Influence of lead on the glutathione status of the Atlantic croaker tissues. Aquat. Toxicol. 23, 11–30 Tranchina, L., Basile, S., Brai, M., Caruso, A., Cosentino, C., Miccichè, S. (2008). Distribution of Heavy Metals in Marine Sediments of Palermo Gulf (Sicily, Italy). Water, Air, and Soil Pollution. 191(1– 4), 245–256. http://doi.org/10.1007/s11270-008-9621-3 Tsangaris, C., Hatzianestis, I., Catsiki, V. A., Kormas, K., Strogyloudi, E., Neofitou, C., Andral B., Galgani F. (2011). Active biomonitoring in Greek coastal waters: Application of the integrated biomarker response index in relation to contaminant levels in caged mussels. Science of the Total Environment. 412-413, 359–365. Tsangaris, C., Kaparou, D., Bordbar, L., Simboura, N., Karris, G. (2015). An integrated investigation of biomarkers‘ response in crabs (Liocarcinus depurator) and benthic indices at a metalliferous waste discharge area in North Evoikos gulf, Greece. Toxicological & Environmental Chemistry, 2248. 1–16. http://doi.org/10.1080/02772248.2015.1095919 Tsangaris, C., Kormas, K., Strogyloudi, E., Hatzianestis, I., Neofitou, C., Andral, B., Galgani, F. (2010). Multiple biomarkers of pollution effects in caged mussels on the Greek coastline. Comparative Biochemistry and Physiology – Part C Toxicology and Pharmacology. 151(3), 369–378. http://doi.org/10.1016/j.cbpc.2009.12.009 Tsangaris, C., Papathanasiou, E., Cotou, E. (2007). Assessment of the impact of heavy metal pollution from a ferro-nickel smelting plant using biomarkers. Ecotoxicology and Environmental Safety. 66(2), 232–43. http://doi.org/10.1016/j.ecoenv.2006.03.011 Tsubaki, T., Irukayama, K. (1977). Minamata Disease, Kodansha, Tokyo and Elsevier, Amsterdam. Tunca, E., Üçüncü, E., Kurtuluş, B., Ozkan, A. D., Atasagun, S. (2013). Accumulation trends of metals and a metalloid in the freshwater crayfish Astacus leptodactylus from Lake Yeniçağa (Turkey). Chemistry and Ecology, 29(8), 754–769. http://doi.org/10.1080/02757540.2013.810724 Tunca, E., Ucuncu, E., Ozkan, A. D., Ulger, Z. E., Cansizoǧlu, A. E., Tekinay, T. (2013). Differences in the accumulation and distribution profile of heavy metals and metalloid between male and female crayfish (Astacus leptodactylus). Bulletin of Environmental Contamination and Toxicology. 90(5), 570–577. http://doi.org/10.1007/s00128-013-0960-4

242 Turekian, K. K. (1976). Oceans. Englewood Cliffs, New Jersey, Prentice-Hall, Inc Turekian, K. K. (1977). The fate of metals in the oceans. Geochim. Cosmo. Chim. Acta. 41, 1139–44. Turner, D. R. (1995). ‗‗Problems in Trace metal Speciation Modelling‘‘, in ‗‗Metal Speciation and Bioavailability in Aquatix Systems‘‘, (Eds) Tessier A., Turner D.R, IUPAC Series on Analytical and Physical Chemistry of Environmental Systems, Volume 3, p. 150-152, 192, John Wiley and Sons. Tylmann, W., Lysek, K., Kinder, M., Pempkowiak, J. (2011). Regional Pattern of Heavy Metal Content in Lake Sediments in Northeastern Poland. Water, Air, and Soil Pollution. 216(1–4), 217–228. http://doi.org/10.1007/s11270-010-0529-3 UNEP. (2013). Global Mercury Assessment: Sources, Emissions, Releases and Environmental Transport. UNEP Chemicals Branch, Geneva, Switzerland Ungaro, N., Marano G., Vlora, A., Passarella, G. (1999). Distribution patterns of two species of swimming crabs (Portunidae), Liocarcinus depurator (L.) and Macropipus tuberculatus (Roux), in the southwestern Adriatic Sea (Mediterranean Sea). In: Von Paupel-Klein, J. C., Schram, F. R. (Eds.), The Biodiversity Crisis and Crustacea: Proceedings of the Fourth International Crustacean Congress, Amsterdam, The Netherlands, July 20-24, 1998, pp. 131-138. A.A. Balkema, Rotterdam US Environmental Protection Agency. (2000). Bioaccumulation testing and interpretation for the purpose of sediment quality assessment: status and needs. EPA 823-R-00-001. Washington, DC USDA. (2009). National Nutrient Database for Standard Reference, Release 22. Fish, Tuna, Fresh, Bluefin, Raw. pp. 1–3. http://www.nal.usda.gov/fnic/foodcomp/ cgi-bin/list_nut_edit.pl.> Valavanidis, Ath., Vlahogianni, Th., Dassenakis, M., Scoullos, M. (2006). Molecular biomarkers of oxidative stress in aquatic organisms in relation to toxic environmental pollutants, Ecotoxicology and Environmental Safety. 64, 178–189. Van der Oost, R., Beyer, B., Vermeulen, N. P. E. (2003). Fish bioaccumulation and biomarkers in environmental risk assessment: A review, Environmental Toxicology and Pharmacology, 13, 57- 149. Van Dover, CL., Williams, A. B. (1991). Egg size in squat lobsters (Galatheoidea): constraint and freedom. In: Wenner A, Kuris A (Eds) Crustacean egg production. Crustacean issues, Vol 7. Balkema, Rotterdam, p 143–156 Variagin, M. (1972). Tides and tidal data from Greek harbours. Hellenic Hydrographic Service Vol 9, 117 Varol, M. (2013). Dissolved heavy metal concentrations of the Kralkızı, Dicle and Batman dam reservoirs in the Tigris River basin, Turkey. Chemosphere. 93(6), 954–62. http://doi.org/10.1016/j.chemosphere.2013.05.061 Vemberg, W. B., Calabresse, A., Thurberg, F. P., Vemberg, F. J. (1982). Physiological Mechanisms νf Marine Pollutant Toxicity. Academic, New York Verta, M., Salo, S., Korhonen, M., Porvari, P., Paloheimo, A., Munthe, J. (2010). Climate induced thermocline change has an effect on the methyl mercury cycle in small boreal lakes. Science of the Total Environment. 408, 3639-3647.

243 Viarengo, A., Arena, N., Canesi, L., Alia, F. A, Orunesu, M. (1944). Structural and biochemical alterations in the gills of copper-exposed mussels. (Eds): In: Renzoni A, Mattei N, Lari L. Contaminants in the environment. Boca Raton: Lewis Publishers. p. 135–44 Viarengo, A., Lowe, D., Bolognesi, C., Fabbri, E., Koehler, A. (2007). The use of biomarkers in biomonitoring: a 2-tier approach assessing the level of pollutant-induced stress syndrome in sentinel organisms. Comp Biochem Physiol. 146C:281–300. Vieira, L. R., Gravato, C., Soares, A. M. V. M., Morgado, F., Guilhermino, L. (2009). Acute effects of copper and mercury on the estuarine fish Pomatoschistus microps: linking biomarkers to behaviour. Chemosphere. 76, 1416–1427 Violintzis, C., Arditsoglou, A., Voutsa, D. (2009). Elemental composition of suspended particulate matter and sediments in the coastal environment of Thermaikos Bay, Greece: delineating the impact of inland waters and wastewaters. Journal of Hazardous Materials. 166(2–3), 1250–60. http://doi.org/10.1016/j.jhazmat.2008.12.046 Vlahogianni, T., Dassenakis, M., Scoullos, M. J., Valavanidis, A. (2007). Integrated use of biomarkers (superoxide dismutase, catalase and lipid peroxidation) in mussels Mytilus galloprovincialis for assessing heavy metals‘ pollution in coastal areas from the Saronikos Gulf of Greece. Marine Pollution Bulletin. 54(9), 1361–71. http://doi.org/10.1016/j.marpolbul.2007.05.018 Voigtmann, R., Uhlenbruck, G. (1971). Purification of a pseudocholinesterase from the haemolymph of Helix pomatia, Z. Naturforsch, Vol. 26b, pp. 1374–1375. Voutsinou-Taliadouri, F., Varnavas, S. P. (1993). Geochemical study of sediments from northern Euboekos Bay, Greece, with regard to the presence of submarine mineral deposits. Marine Geology. 110(1–2), 93–114. http://doi.org/10.1016/0025-3227(93)90108-8 Voutsinou-Taliadouri, F., Varnavas, S. P. (1987). Marine Mineral Resources in Eeastern Mediterranean Sea II: An Iron, Chromium and Nickel Deposite in the Northen Euboikos Bay, Greece. Marine Mining. 6, .290-259 Walker, C. H., Hopkin, S. P., Sibly, R. M., Peakall, D. B. (2001). Principles of Ecotoxicology, second Ed. Taylor & Francis, London. Walker, C. H., Hopkin, S. P., Sibly, R. M., Peakall, D. B. (2006). Principles of Ecotoxicology, CRC Presss. Taylor & Francis Group, Boca Raton. Wang, W. (1987). Factors affecting metal toxicity to (and accumulation by) aquatic organisms — Overview, Environment International. 13, 6, 437-457. Wang, W. X., Fisher, N. S. (1999). Assimilation efficiencies of chemical contaminants in aquatic invertebrates: a synthesis. Environ Toxicol Chem. 18: 2034–45 Wang, W. X., Ke, C. (2002). Dominance of dietary intake of cadmium and zinc by two marine predatory gastropods. Aquatic Toxicology. 56:153–165. http://doi.org/10.1016/S0166-445X(01)00205-3 Wang, W. X., Wong, P. (2006). Dynamics of trace metal concentrations in an intertidal rocky shore food chain. Marine Pollution Bulletin. 52:332–337. http://doi.org/10.1016/j.marpolbul.2005.10.023 Wang, W. X., Dei, R. C. H. (1999). Factors affecting trace element uptake in the black mussel Septifer virgatus. Marine Ecology Progress Series. 186:161–172. http://doi.org/10.3354/meps186161

244 Warnken, K. W., Gill, G. A., Griffin, L. L., Santschi, P. H. (2001). Sediment-water exchange of Mn, Fe, Ni and Zn in Galveston. Marine Chemistry. 70: 215-231. Wassef, E., Eisawy, A. (1985). Food and feeding habits of wild and reared gilthead bream Sparus aurata (L). Cybium. 9: 233–242. Wei, C. L., Jiann, K. T., Wen, L. S., Tsai, J. R., Sheu, D. D. (2011). Removal fluxes of Mn and Fe from the nearshore waters along the west coast of Taiwan. Marine Pollution Bulletin. 62(5), 1081–7. http://doi.org/10.1016/j.marpolbul.2011.01.039 Wen, L. S., Santschi, P., Gill, G., Paternostro, C. (1999). Estuarine trace metal distributions in Galveston Bay: importance of colloidal forms in the speciation of the dissolved phase. Marine Chemistry. 63(3–4), 185–212. http://doi.org/10.1016/S0304-4203(98)00062-0 Williams, S. P. (2001). Occupational health and speciation using nickel and nickel compounds as an example, in Trace Element Speciation for Environment, Food and Health, (Eds), Ebdon, L., Pitts, L., Cornelis, R., Crews, H., Donard, O. F. X., Quevauviller, P., Royal Society of Chemistry. Witters, H. E. (1998). Chemical Speciation Dynamics and Toxicity Assessment in Aquatic Systems. Ecotoxicology and Environmental Safety. 41, 90-95. www.unep.gr. United nation environmental program. www.pops.int. Stockholm Convention on Persistent Organic Pollutants Xing, W., Liu, G. (2011). Iron biogeochemistry and its environmental impacts in freshwater lakes. Fresenius Environmental Bulletin. 20(6), 1339–1345. Yarsan, E., Yipel, M. (2013). The Important Terms of Marine Pollution ―Biomarkers and Biomonitoring, Bioaccumulation, Bioconcentration, Biomagnification‘. Molecular Biomarkers and Diagnosis. 1– 4. http://doi.org/10.4172/2155-9929.S1-003 Zabetoglou, K., Voutsa, D., Samara, C. (2002). Toxicity and heavy metal contamination of surficial sediments from the Bay of Thessaloniki (Northwestern Aegean Sea) Greece. Chemosphere. 49(1), 17–26. http://www.ncbi.nlm.nih.gov/pubmed/12243326 Zaharaki, D., Komnitsas, K. (2009). Effect of additives on the compressive strength of slag-based inorganic polymers. Global Nest Journal. 11(2), 137–146. Zaroogian, G. E., Johnson, M. (1984). Nickel uptake and loss in the bivalves Crassostrea virginica and Mytilus edulis. Arch Environ Contam Toxicol. 13:411–418 Zhou, Q., Zhang, J., Fu, J., Shi, J., Jiang, G. (2008). Biomonitoring: An appealing tool for assessment of metal pollution in the aquatic ecosystem. Analytica Chimica Acta. 606, 135-150. Zhou, J. L., Liu, Y. P., Abrahams, P. W. (2003). Trace metal behaviour in the Conwy estuary, North Wales. Chemosphere. 51(5), 429–40. http://doi.org/10.1016/S0045-6535(02)00853-6 Zhu J., Wu C. F., Li X., Wu G. S., Xie, S., Hu, Q. N., Deng, Z., Zhu, M. X, Luo, H. R., Hong, X. (2013). Synthesis, biological evaluation and molecular modeling of substituted 2-aminobenzimidazoles as novel inhibitors of acetylcholinesterase and butyrylcholinesterase, Bioorganic & Medicinal Chemistry. vol.21, pp. 4218-4224.

245 Zhuang, P., Li, Z. A., McBride, M. B, Zou, B., Wang, G. (2013). Health risk assessment for consumption of fish originating from ponds near Dabaoshan mine, South China. Environ Sci Pollut Res Int. 20: 5844-5854 Zoumis, T., Schmidt, A., Grigorova, L., Calmano, W. (2001). Contaminants in sediments: remobilisation and demobilization. Science of the Total Environment. 266, 195–202.

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9 Annex

247

Table 1: Pearson correlation of gill in Munida rugosa with the bottom water in the contaminated area. Sp=spring, G=gill, Wi=winter, WT=Bottom Water

Correlations Zn- Zn- Mn- Mn- Fe- Fe- Cu- Cu- Zn- Mn- Fe- Cu- Zn- Mn- Fe- Cu-

SpG WiG SpG WiG SpG WiG SpG WiG SpWt SpWt SpWt SpWt WiWt WiWt WiWt WiWt Zn-SpG 1 Zn-WiG .724 1 Mn-SpG .651 .764 1 Mn-WiG .917* .656 .536 1 Fe-SpG .329 -.080 -.206 .588 1 Fe-WiG .275 .010 .265 .482 .314 1 Cu-SpG -.272 -.318 -.059 -.270 -.446 .581 1 Cu-WiG .519 .933** .776 .423 -.389 .040 -.042 1 Zn-SpWt .013 .510 .265 -.161 -.391 -.804 -.626 .503 1 Mn-SpWt -.215 .263 .485 -.363 -.581 -.448 -.239 .400 .725 1 * Fe-SpWt .491 .496 .903 .289 -.283 .068 -.115 .512 .299 .627 1 Cu-SpWt .041 .323 -.362 .106 .125 -.405 -.356 .232 .388 -.276 -.604 1 Zn-WiWt -.246 .269 -.166 -.359 -.354 -.878* -.522 .281 .886* .456 -.166 .674 1 * * Mn-WiWt -.873 -.388 -.500 -.912 -.616 -.557 .170 -.154 .370 .394 -.397 .238 .617 1 Fe-WiWt -.131 .069 .486 -.413 -.860* -.131 .388 .325 .265 .688 .666 -.568 .026 .286 1 * * Cu-WiWt -.187 -.015 .555 -.310 -.583 .145 .348 .208 .084 .725 .721 -.795 -.226 .144 .865 1 **. Correlation is significant at the 0.01 level (2-tailed). *. Correlation is significant at the 0.05 level (2-tailed).

Table 2: Pearson correlation of muscle in Munida rugosa with the bottom water in the contaminated area. Sp=spring, M=muscle, Wi=winter, Wt=Bottom Water

Correlations Zn- Zn-WiM Mn-SpM Mn-WiM Fe-SpM Fe-WiM Cu-SpM Cu-WiM Zn-SpWt Mn-SpWt Fe-SpWt Cu-SpWt Zn-WiWt Mn-WiWt Fe-WiWt Cu-

SpM WiWt Zn-SpM 1 Zn-WiM .096 1 Mn-SpM .521 .238 1 Mn-WiM .383 .627 .286 1 Fe-SpM .685 -.159 .731 .096 1 Fe-WiM .926** .072 .385 .602 .576 1 Cu-SpM .125 -.952** -.189 -.606 .188 .100 1 Cu-WiM -.483 .627 -.286 .568 -.506 -.261 -.784 1 Zn-SpWt -.024 .368 .618 .280 -.067 -.063 -.336 .081 1 Mn-SpWt .096 .628 .300 .169 -.316 -.061 -.457 .059 .725 1 Fe-SpWt -.194 .698 -.327 .492 -.743 -.081 -.630 .666 .299 .627 1 Cu-SpWt -.104 -.226 .648 -.005 .547 -.095 .047 -.089 .388 -.276 -.604 1 Zn-WiWt .166 .080 .841* .125 .347 .075 -.062 -.248 .886* .456 -.166 .674 1 Mn-WiWt .682 .024 .788 -.115 .672 .401 .162 -.722 .370 .394 -.397 .238 .617 1 Fe-WiWt .571 .734 .188 .607 -.074 .535 -.505 .159 .265 .688 .666 -.568 .026 .286 1 * Cu-WiWt .255 .675 -.140 .216 -.367 .133 -.458 .152 .084 .725 .721 -.795 -.226 .144 .865* 1 **. Correlation is significant at the 0.01 level (2-tailed). *. Correlation is significant at the 0.05 level (2-tailed).

Table 3: Pearson correlation coefficient of exoskeleton in Munida rugosa with the bottom water in the contaminated area. Sp=spring, E=exosketon, Wi=winter, Wt=Bottom Water

Correlations Zn- Zn- Mn- Mn- Fe- Fe- Cu- Cu- Zn- Mn- Fe- Cu- Zn- Mn- Fe- Cu- SpE WiE SpE WiE SpE WiE SpE WiE SpWt SpWt SpWt SpWt WiWt WiWt WiWt WiWt Zn-SpE 1 Zn-WiE -.242 1 Mn-SpE -.279 -.104 1 Mn-WiE -.294 -.377 -.614 1 Fe-SpE -.205 .264 .442 -.348 1 Fe-WiE -.008 -.003 .945** -.826* .397 1 Cu-SpE .097 .601 -.170 -.256 .740 -.079 1 Cu-WiE -.185 -.473 .237 .337 .457 .049 .017 1 Zn-SpWt .272 .596 -.443 -.118 .494 -.307 .921** -.006 1 Mn-SpWt .102 .465 -.049 -.330 .719 .040 .914* -.140 .725 1 Fe-SpWt -.604 .556 -.103 .108 .388 -.209 .515 -.271 .299 .627 1 Cu-SpWt .526 .226 -.361 -.119 -.289 -.171 .046 -.029 .388 -.276 -.604 1 Zn-WiWt .519 .365 -.324 -.213 .399 -.146 .721 .183 .886* .456 -.166 .674 1 Mn-WiWt .554 -.103 .408 -.607 .613 .557 .434 .372 .370 .394 -.397 .238 .617 1 Fe-WiWt -.494 .405 .548 -.385 .883* .462 .600 .165 .265 .688 .666 -.568 .026 .286 1 . Cu-WiWt -.328 .229 .442 -.362 .646 .395 .456 -.142 .084 .725 .721 -.795 -.226 .144 .865* 1 **. Correlation is significant at the 0.01 level (2-tailed). *. Correlation is significant at the 0.05 level (2-tailed).

Table 4: Pearson correlation in Liocarcinus depurator with the bottom water in the contaminated area from winter sampling. E=exoskeleton, M=muscle, G=gill, Wi=winter, Wt=Bottom Water

Zn- Zn- Zn- Mn- Mn- Mn- Fe- Fe- Fe- Cu- Cu- Cu- Zn- Mn- Fe- Cu- WiE WiG WiM WiE WiG WiM WiE WiG WiM WiE WiG WiM WiWt WiWt WiWt WiWt Zn-WiE 1 Zn-WiG .239 1 Zn-WiM .948* .080 1 Mn-WiE .996** .213 .922* 1 Mn-WiG -.210 -.411 -.131 -.203 1 Mn-WiM -.112 .877 -.216 -.145 -.618 1 Fe-WiE .282 .915* .200 .237 -.666 .913* 1 Fe-WiG .032 -.694 .160 .043 .885* -.896* -.826 1 Fe-WiM .889* -.138 .975** .870 -.091 -.388 .016 .272 1 Cu-WiE .100 -.468 .219 .097 .932* -.743 -.646 .958* .273 1 Cu-WiG -.724 .402 -.773 -.738 -.385 .751 .430 -.692 -.836 -.666 1 Cu-WiM .961** .444 .877 .952* -.135 .042 .409 -.016 .765 .123 -.624 1 Zn-WiWt .881* .325 .781 .885* -.633 .144 .464 -.370 .731 -.363 -.414 .800 1 Mn-WiWt .749 .308 .870 .685 -.275 .143 .510 -.096 .800 .029 -.420 .730 .642 1 Fe-WiWt -.156 -.414 .105 -.207 -.328 -.073 -.032 -.078 .224 -.197 .098 -.335 -.009 .336 1 Cu-WiWt -.366 -.501 -.108 -.409 -.236 -.106 -.153 -.035 .028 -.176 .213 -.538 -.208 .124 .974** 1 **. Correlation is significant at the 0.01 level (2-tailed). *. Correlation is significant at the 0.05 level (2-tailed).

Table 5: Pearson correlation of gill in Nephrops norvegicus with the bottom water in the reference area. G=gill, Wi=winter, Sp=spring, Wt=Bottom Water

Correlations Zn- Zn- Mn- Mn- Fe- Fe- Cu- Cu- Zn- Mn- Fe- Cu- Zn- Mn- Fe- Cu- SpG WiG SpG WiG SpG WiG SpG WiG SpWt SpWt SpWt SpWt WiWt WiWt WiWt WiWt Zn-SpG 1 Zn-WiG .986 1 Mn-SpG -.570 -.426 1 Mn-WiG .967 .911 -.762 1 Fe-SpG .824 .719 -.935 .942 1 Fe-WiG -.124 -.286 -.745 .135 .460 1 Cu-SpG .403 .549 .522 .155 -.186 -.958 1 Cu-WiG .330 .481 .588 .076 -.263 -.978 .997 1 Zn-SpWt .354 .503 .567 .102 -.239 -.972 .999* 1.000* 1 Mn-SpWt .655 .521 -.994 .827 .968 .669 -.428 -.498 -.476 Fe-SpWt .851 .926 -.054 .688 .404 -.626 .823 .776 .792 .161 1 Cu-SpWt .428 .273 -.987 .645 .865 .844 -.654 -.712 -.694 .963 -.110 1 Zn-WiWt .046 -.119 -.847 .301 .604 .985 -.895 -.928 -.918 .785 -.485 .923 1 Mn-WiWt .496 .632 .431 .256 -.084 -.923 .995 .983 .988 -.332 .878 -.573 -.845 1 Fe-WiWt .636 .500 -.997 .813 .961 .687 -.449 -.519 -.497 1.000* .137 .970 .800 -.355 1 Cu-WiWt .160 -.005 -.903 .408 .691 .959 -.838 -.879 -.867 .851 -.381 .961 .993 -.778 .864 1 *. Correlation is significant at the 0.05 level (2-tailed).

Table 6: Pearson correlation of muscle in Nephrops norvegicus with the bottom water in the reference area. M=muscle, Wi=winter, Sp=spring, Wt=Bottom Water

Correlations Zn- Zn- Mn- Mn- Fe- Fe- Cu- Cu- Zn- Mn- Fe- Cu- Zn- Mn- Cu- SpM WiM SpM WiM SpM WiM SpM WiM SpWt SpWt SpWt SpWt WiWt WiWt WiWt Zn-SpM 1 Zn-WiM .390 1 Mn-SpM -.561 .543 1 Mn-WiM -.127 .864 .892 1 Fe-SpM .992 .503 -.453 -.001 1 Fe-WiM -.521 .582 .999* .913 -.410 1 Cu-SpM -.750 .315 .968 .751 -.662 .955 1 Cu-WiM .837 -.177 -.922 -.649 .762 -.903 -.990 1 Zn-SpWt .498 -.603 -.997* -.923 .386 -1.000* -.947 .891 1 Mn-SpWt .526 .988 .410 .777 .628 .452 .168 -.025 -.476 1 Fe-SpWt .924 .009 -.835 -.496 .869 -.808 -.946 .983 .792 .161 1 Cu-SpWt .278 .993 .639 .918 .396 .675 .426 -.292 -.694 .963 -.110 1 Zn-WiWt -.114 .870 .886 1.000** .011 .907 .742 -.639 -.918 .785 -.485 .923 1 Mn-WiWt .628 -.471 -.997 -.852 .526 -.991 -.986 .951 .988 -.332 .878 -.573 -.845 1 Cu-WiWt .001 .921 .827 .992 .126 .853 .660 -.546 -.867 .851 -.381 .961 .993 -.778 1 *. Correlation is significant at the 0.05 level (2-tailed). **. Correlation is significant at the 0.01 level (2-tailed).

Table 7: Pearson correlation of exoskeleton in Nephrops norvegicus with the bottom water in the reference area. M=muscle, Wi=winter, Sp=spring, Wt=Bottom Water

Correlations Zn- Zn- Mn- Mn- Fe- Fe- Cu- Cu- Zn- Mn- Fe- Cu- Zn- Mn- Fe- Cu- SpE WiE SpE WiE SpE WiE SpE WiE SpWt SpWt SpWt SpWt WiWt WiWt WiWt WiWt Zn-SpE 1 Zn-WiE -.468 1 Mn-SpE -.977 .269 1 Mn-WiE .756 -.932 -.599 1 Fe-SpE .768 -.925 -.614 1.000* 1 Fe-WiE -.131 .938 -.083 -.748 -.736 1 Cu-SpE .972 -.663 -.900 .889 .897 -.361 1 Cu-WiE -.985 .307 .999* -.631 -.645 -.043 -.916 1 Zn-SpWt .956 -.705 -.873 .914 .921 -.415 .998* -.891 1 Mn-SpWt -.712 -.288 .845 -.078 -.096 -.603 -.526 .822 -.476 1 Fe-SpWt .579 -.991 -.393 .972 .967 -.884 .755 -.429 .792 .161 1 Cu-SpWt -.874 -.021 .957 -.342 -.359 -.367 -.735 .945 -.694 .963 -.110 1 Zn-WiWt -.994 .366 .995 -.678 -.691 .020 -.940 .998* -.918 .785 -.485 .923 1 Mn-WiWt .899 -.807 -.785 .966 .971 -.552 .977 -.810 .988 -.332 .878 -.573 -.845 1 Fe-WiWt -.728 -.265 .857 -.102 -.120 -.584 -.546 .836 -.497 1.000* .137 .970 .800 -.355 1 Cu-WiWt -.974 .257 1.000** -.589 -.604 -.095 -.894 .999* -.867 .851 -.381 .961 .993 -.778 .864 1 *. Correlation is significant at the 0.05 level (2-tailed). **. Correlation is significant at the 0.01 level (2-tailed).

Table 8: Pearson correlation of muscle in Munida rugosa with the bottom water in the reference area. M=muscle, Wi=winter, Sp=spring, Wt=Bottom Water

Correlations Zn- Zn- Mn- Mn- Fe- Fe- Cu- Cu- Zn- Mn- Fe- Cu- Zn- Mn- Fe- Cu- SpM WiM SpM WiM SpM WiM SpM WiM SpWt SpWt SpWt SpWt WiWt WiWt WiWt WiWt Zn-SpM 1 Zn-WiM .531 1 Mn-SpM .322 -.631 1 Mn-WiM -.215 .714 -.994 1 Fe-SpM .264 -.677 .998* -.999* 1 Fe-WiM -.374 -.985 .758 -.825 .796 1 Cu-SpM -.144 -.915 .891 -.936 .916 .972 1 Cu-WiM -.726 .197 -.885 .827 -.855 -.366 -.576 1 Zn-SpWt .998* .480 .377 -.272 .320 -.319 -.086 -.765 1 Mn-SpWt -.423 .543 -.994 .976 -.986 -.682 -.836 .930 -.476 1 Fe-SpWt .826 .916 -.267 .373 -.325 -.831 -.676 -.213 .792 .161 1 Cu-SpWt -.650 .298 -.928 .881 -.904 -.461 -.658 .995 -.694 .963 -.110 1 Zn-WiWt -.893 -.093 -.713 .631 -.670 -.083 -.316 .958 -.918 .785 -.485 .923 1 Mn-WiWt .995 .612 .228 -.118 .168 -.463 -.240 -.655 .988 -.332 .878 -.573 -.845 1 Fe-WiWt -.445 .523 -.991 .970 -.981 -.664 -.822 .939 -.497 1.000* .137 .970 .800 -.355 1 Cu-WiWt -.836 .022 -.789 .716 -.750 -.197 -.423 .984 -.867 .851 -.381 .961 .993 -.778 .864 1 *. Correlation is significant at the 0.05 level (2-tailed).

Table 9: Pearson correlation of gill in Munida rugosa with the bottom water in the reference area. G=gill, Wi=winter, Wt=Bottom Water

Correlations Zn-WiG Mn-WiG Fe-WiG Cu-WiG Zn-Wi-Wt Mn-Wi-Wt Fe-Wi-Wt Cu-Wi-Wt Zn-WiG 1 Mn-WiG .383 1 Fe-WiG .654 .949 1 Cu-WiG -.854 -.808 -.952 1 Zn-Wi-Wt -.992 -.264 -.554 .782 1 Mn-Wi-Wt .905 .739 .914 -.994 -.845 1 Fe-Wi-Wt -.719 .367 .056 .251 .800 -.355 1 Cu-Wi-Wt -.971 -.152 -.455 .705 .993 -.778 .864 1

Table 10: Pearson correlation of exoskeleton in Munida rugosa with the bottom water in the reference area. E=exoskeleton, Wi=winter, Wt=Bottom Water

Correlations Zn-WiE Mn-WiE Fe-WiE Cu-WiE Zn-Wi-Wt Mn-Wi-Wt Fe-Wi-Wt Cu-Wi-Wt Zn-WiE 1 Mn-WiE .885 1 Fe-WiE .712 .957 1 Cu-WiE -.888 -1.000** -.955 1 Zn-Wi-Wt .308 -.170 -.449 .163 1 Mn-Wi-Wt .249 .671 .857 -.666 -.845 1 Fe-Wi-Wt .817 .455 .177 -.461 .800 -.355 1 Cu-Wi-Wt .415 -.056 -.343 .049 .993 -.778 .864 1 **. Correlation is significant at the 0.01 level (2-tailed).

Table 11:Pearson correlation of gill in Liocarcinus depurator with the bottom water in the reference area. G=gill,Sp=spring, Wi=winter, Wt=Bottom Water

Correlations Zn- Zn- Mn- Mn- Fe- Fe- Cu- Cu- Zn- Mn- Fe- Cu- Zn- Mn- Fe- Cu- SpG WiG SpG WiG SpG WiG SpG WiG SpWt SpWt SpWt SpWt WiWt WiWt WiWt WiWt Zn-SpG 1 Zn-WiG -.608 1 Mn-Sp-G .970 -.784 1 Mn-WiG .426 .459 .192 1 Fe-SpG .991 -.710 .994 .299 1 Fe-WiG .972 -.404 .884 .627 .931 1 Cu-SpG .874 -.145 .728 .812 .799 .964 1 Cu-WiG .534 .346 .311 .992 .414 .719 .878 1 Zn-Sp-Wt .336 .544 .095 .995 .204 .548 .751 .976 1 Mn-Sp-Wt -.988 .480 -.921 -.559 -.958 -.996 -.938 -.657 -.476 1 Fe-Sp-Wt -.309 .943 -.533 .728 -.436 -.077 .192 .638 .792 .161 1 CuSp-Wt -.911 .227 -.782 -.761 -.847 -.983 -.997 -.835 -.694 .963 -.110 1 Zn-Wi-Wt -.682 -.167 -.482 -.952 -.576 -.835 -.951 -.983 -.918 .785 -.485 .923 1 Mn-Wi-Wt .184 .668 -.062 .968 .049 .411 .639 .929 .988 -.332 .878 -.573 -.845 1 Fe-Wi-Wt -.984 .458 -.911 -.579 -.951 -.998* -.946 -.675 -.497 1.000* .137 .970 .800 -.355 1 Cu-Wi-Wt -.761 -.052 -.579 -.911 -.666 -.893 -.980 -.955 -.867 .851 -.381 .961 .993 -.778 .864 1 *. Correlation is significant at the 0.05 level (2-tailed).

Table 12: Pearson correlation of muscle in Liocarcinus depurator with the bottom water in the reference area. M=muscle, Sp=spring, Wi=winter, Wt=Bottom Water

Correlations Zn-SpM Zn-WiM Mn-SpM Mn-WiM Fe-SpM Fe-WiM Cu-SpM Cu-WiM Zn-SpWt Mn-SpWt Fe-SpWt Cu-SpWt Zn-WiWt Mn-WiWt Fe-WiWt Cu-WiWt Zn-SpM 1 Zn-WiM -.507 1 Mn-SpM .401 -.993 1 Mn-WiM .123 .793 -.860 1 Fe-SpM -.849 .886 -.825 .420 1 Fe-WiM .650 .326 -.436 .834 -.150 1 Cu-SpM .390 -.991 1.000** -.866 -.818 -.446 1 Cu-WiM .589 .398 -.505 .875 -.073 .997* -.514 1 Zn-Sp-Wt .993 -.607 .508 .003 -.906 .554 .498 .487 1 Mn-Sp-Wt -.366 .988 -.999* .878 .803 .469 -1.000* .536 -.476 1 Fe-Sp-Wt .860 .005 -.124 .613 -.459 .947 -.135 .919 .792 .161 1 CuSp-Wt -.602 .993 -.973 .718 .933 .215 -.970 .290 -.694 .963 -.110 1 Zn-Wi-Wt -.864 .872 -.808 .394 1.000* -.178 -.801 -.101 -.918 .785 -.485 .923 1 Mn-Wi-Wt .999* -.475 .367 .159 -.829 .677 .356 .618 .988 -.332 .878 -.573 -.845 1 Fe-Wi-Wt -.389 .991 -1.000** .867 .817 .448 -1.000** .516 -.497 1.000* .137 .970 .800 -.355 1 Cu-Wi-Wt -.800 .923 -.870 .497 .996 -.064 -.864 .013 -.867 .851 -.381 .961 .993 -.778 .864 1 *. Correlation is significant at the 0.05 level (2-tailed). **. Correlation is significant at the 0.01 level (2-tailed).

Table 13: Pearson correlation of exoskeleton in Liocarcinus depurator with the bottom water in the reference area. E=exoskeleton, Sp=spring, Wi=winter, Wt=Bottom Water

Correlations Zn-SpE Zn-WiE Mn-SpE Mn-WiE Fe-SpE Fe-WiE Cu-SpE Cu-WiE Zn-SpWt Mn-SpWt Fe-SpWt Cu-SpWt Zn-WiWt Mn-WiWt Fe-WiWt Cu-WiWt Zn-SpE 1 Zn-WiE .982 1 Mn-SpE .966 .997* 1 Mn-WiE .995 .958 .935 1 Fe-SpE .577 .412 .345 .656 1 Fe-WiE .767 .631 .573 .827 .967 1 Cu-SpE -.431 -.252 -.182 -.519 -.986 -.910 1 Cu-WiE .993 .953 .928 1.000* .670 .837 -.535 1 Zn-Sp-Wt .355 .172 .100 .446 .968 .873 -.997 .463 1 Mn-Sp-Wt .653 .785 .828 .575 -.241 .015 .401 .560 -.476 1 Fe-Sp-Wt .852 .738 .687 .900 .919 .989 -.839 .908 .792 .161 1 CuSp-Wt .426 .590 .647 .334 -.492 -.254 .632 .317 -.694 .963 -.110 1 Zn-Wi-Wt .044 .233 .303 -.055 -.790 -.607 .882 -.074 -.918 .785 -.485 .923 1 Mn-Wi-Wt .497 .324 .254 .581 .995 .938 -.997* .596 .988 -.332 .878 -.573 -.845 1 Fe-Wi-Wt .635 .770 .814 .555 -.264 -.009 .423 .539 -.497 1.000* .137 .970 .800 -.355 1 Cu-Wi-Wt .159 .343 .410 .060 -.715 -.512 .822 .041 -.867 .851 -.381 .961 .993 -.778 .864 1 *. Correlation is significant at the 0.05 level (2-tailed).

Table 14: Pearson correlation in Munida rugosa with the sediment in the contaminated area. E=exoskeleton, G=gill, M=muscle.

Correlations Zn-E Mn-E Fe-E Cu-E Zn-G Mn-G Fe-G Cu-G Ni-G Cr-G Zn-M Mn-M Fe-M Cu-M Ni-M Cr-M

Zn-E 1 Mn-E -0.386 1 Fe-E -0.465 0.61 1 Cu-E -0.587 0.264 .828* 1 Zn-G 0.413 -0.57 -0.57 -0.486 1 Mn-G 0.255 0.053 -0.3 -0.491 .745* 1 Fe-G 0.476 -0.158 -0.12 -0.502 0.347 0.34 1 Cu-G -0.135 0.492 -0.12 -0.086 -0.317 0.09 -0.654 1 Ni-G -.805* 0.736 0.365 -0.14 -0.611 -0.1 0.363 0.141 1 Cr-G 0.666 -0.276 -0.33 -0.519 .860* .835* 0.692 -0.428 -0.21 1 Zn-M 0.374 0.013 0.064 0.172 0.388 0.4 -0.222 0.167 -0.6 0.209 1 Mn-M 0.029 0.274 -0.04 0.083 0.231 0.46 -0.586 0.68 -0.41 0.035 .772* 1 Fe-M 0.306 0.267 -0.09 -0.073 0.192 0.35 -0.433 0.605 -0.31 -0.094 .730* .880** 1 Cu-M -0.602 0.617 .918** .883** -0.599 -0.4 -0.342 -0.009 0.296 -0.459 0.136 0.08 -0.014 1 Ni-M 0.395 -0.16 -0.46 -0.358 .731* .774* -0.135 0.342 -0.59 0.511 .738* .803* .724* -0.42 1 Cr-M 0.002 0.307 -0.02 0.104 0.196 0.45 -0.599 0.697 -0.38 0.013 .760* .999** .874** 0.11 .782* 1 *. Correlation is significant at the 0.05 level (2-tailed). **. Correlation is significant at the 0.01 level (2-tailed).

Table 15:.Pearson correlation in Munida rugosa with the sediment in the reference area. E=exoskeleton, G=gill, M=muscle.

Correlations Zn-E Mn-E Fe-E Cu-E Zn-G Mn-G Fe-G Cu-G Ni-G Cr-G Zn-M Mn-M Fe-M Cu-M Ni-M Cr-M Zn-E 1 Mn-E .594 1 Fe-E .109 .478 1 Cu-E -.593 -.704 -.737 1 Zn-G .083 .627 .612 -.264 1 Mn-G .144 .820* .472 -.625 .420 1 Fe-G -.054 .711 .421 -.491 .405 .979** 1 Cu-G -.064 .046 -.464 .625 .403 -.206 -.164 1 Ni-G .193 .686 .462 -.700 .133 .925** .884* -.498 1 Cr-G -.035 .595 .705 -.530 .476 .787 .777 -.315 .807 1 Zn-M -.445 .247 .362 .075 .780 .317 .417 .421 -.015 .246 1 Mn-M -.450 -.404 -.009 .589 .430 -.552 -.464 .598 -.765 -.303 .569 1 Fe-M -.530 .204 .021 .103 .336 .522 .654 .238 .270 .193 .770 .094 1 Cu-M .300 .327 -.406 .309 .418 -.038 -.063 .901* -.333 -.345 .325 .331 .206 1 Ni-M .491 -.159 -.797 .319 -.497 -.457 -.531 .359 -.416 -.735 -.583 -.162 -.399 .515 1 Cu -.741 -.183 .039 .321 -.017 .283 .424 -.054 .325 .559 .212 .028 .371 -.382 -.556 .511 Ni .825* .343 -.134 -.541 -.423 .146 -.014 -.383 .331 -.137 -.728 -.793 -.461 -.018 .578 -.570 Cr .311 .820* .224 -.383 .667 .687 .653 .358 .396 .256 .593 -.107 .607 .606 -.083 .410 *. Correlation is significant at the 0.05 level (2-tailed). **. Correlation is significant at the 0.01 level (2-tailed)

Table 16: Spearman correlation of AChE in gill of Munida rugosa with seawater from the contaminated and the reference area. C=contaminated, R=Reference

Correlations A-C A-R Ni-C Fe-C Cr-C Mn-C Zn-C Cu-C Ni-R Fe-R Cr-R Mn-R Zn-R Cu-R A-C 1.000 A-R -.257 1.000 Ni-C .406 -.218 1.000 Fe-C .232 -.315 -.630 1.000 Cr-C .348 -.267 -.556 .975** 1.000 Mn-C .348 -.267 -.556 .975** 1.000** 1.000 Zn-C .696 -.242 .926** -.358 -.235 -.235 1.000

Cu-C -.441 -.025 .226 -.679 -.730* -.730* -.101 1.000 Ni-R -.655 .247 -.252 -.420 -.588 -.588 -.588 .599 1.000 Fe-R -.348 .655 -.630 .210 .185 .185 -.556 -.478 .252 1.000 Cr-R .339 -.743* .637 -.133 -.186 -.186 .531 .311 -.090 -.877** 1.000 Mn-R .541 -.655 -.048 .778* .746* .746* .175 -.615 -.540 -.238 .444 1.000 Zn-R -.655 .247 -.252 -.420 -.588 -.588 -.588 .599 1.000** .252 -.090 -.540 1.000 Cu-R -.655 .247 -.252 -.420 -.588 -.588 -.588 .599 1.000** .252 -.090 -.540 1.000** 1.000 *. Correlation is significant at the 0.05 level (2-tailed). **. Correlation is significant at the 0.01 level (2-tailed).

Table 17: Spearman correlation of AChE in eye of Munida rugosa with seawater from the contaminated and the reference area. C=contaminated, R=Reference

Correlations A-C A-R Ni-C Fe-C Cr-C Mn-C Zn-C Cu-C Ni-R Fe-R Cr-R Mn-R Zn-R Cu-R A-C 1.000 A-R .371 1.000 Ni-C .406 .182 1.000 Fe-C .058 .473 -.630 1.000 Cr-C .000 .497 -.556 .975** 1.000 Mn-C .000 .497 -.556 .975** 1.000** 1.000 Zn-C .348 .327 .926** -.358 -.235 -.235 1.000

Cu-C .177 -.124 .226 -.679 -.730* -.730* -.101 1.000 Ni-R .464 .327 .259 -.383 -.407 -.407 .037 .881** 1.000 Fe-R -.696 -.691 -.630 .210 .185 .185 -.556 -.478 -.778* 1.000 Cr-R .754 .667 .556 -.185 -.210 -.210 .432 .528 .802* -.975** 1.000 Mn-R .754 .521 .630 -.062 -.136 -.136 .556 .050 .235 -.654 .728* 1.000 Zn-R .290 -.158 .852** -.778* -.802* -.802* .630 .377 .210 -.383 .407 .630 1.000 * Cu-R -.232 -.036 -.926** .753* .630 .630 -.852** -.352 -.333 .556 -.432 -.309 -.728* 1.000 **. Correlation is significant at the 0.01 level (2-tailed). *. Correlation is significant at the 0.05 level (2-tailed).

Table 18: Spearman correlation of GST in hepatopancreas of Munida rugosa with seawater from the contaminated and the reference area. C=contaminated, R=Reference

Correlations GST-C GST-R Ni-C Fe-C Cr-C Mn-C Zn-C Cu-C Ni-R Fe-R Cr-R Mn-R Zn-R Cu-R GST-C 1.000 GST-R .143 1.000 Ni-C -.145 .821** 1.000 Fe-C .073 -.496 -.579 1.000 Cr-C -.073 -.513 -.526 .982** 1.000 Mn-C -.073 -.513 -.526 .982** 1.000** 1.000 Zn-C -.291 .718* .947** -.351 -.263 -.263 1.000

Cu-C .110 .104 .133 -.631 -.667* -.667* -.133 1.000 Ni-R .073 .068 .158 -.368 -.386 -.386 -.035 .916** 1.000 Fe-R .073 -.410 -.579 .123 .105 .105 -.526 -.453 -.719* 1.000 Cr-R .073 .427 .526 -.105 -.123 -.123 .439 .489 .737* -.982** 1.000 Mn-R .218 .769* .737* -.105 -.158 -.158 .684* -.027 .105 -.632 .684* 1.000 Zn-R .073 .872** .895** -.719* -.737* -.737* .737* .240 .123 -.368 .386 .737* 1.000 Cu-R .291 -.616 -.895** .737* .632 .632 -.842** -.293 -.263 .474 -.368 -.368 -.737* 1.000 **. Correlation is significant at the 0.01 level (2-tailed). *. Correlation is significant at the 0.05 level (2-tailed).

Table 19: Spearman correlation of CAT in hepatopancreas of Munida rugosa with seawater from the contaminated and the reference area. C=contaminated, R=Reference

Correlations CAT-C CAT-R Ni-C Fe-C Cr-C Mn-C Zn-C Cu-C Ni-R Fe-R Cr-R Mn-R Zn-R Cu-R CAT-C 1.000 CAT-R .400 1.000 Ni-C -.600 -.255 1.000 Fe-C .800 .400 -.579 1.000 Cr-C .600 .473 -.526 .982** 1.000 Mn-C .600 .473 -.526 .982** 1.000** 1.000 Zn-C -.500 -.109 .947** -.351 -.263 -.263 1.000

Cu-C -.103 .012 .133 -.631 -.667* -.667* -.133 1.000 Ni-R .300 .303 .158 -.368 -.386 -.386 -.035 .916** 1.000 Fe-R -.100 -.279 -.579 .123 .105 .105 -.526 -.453 -.719* 1.000 Cr-R .300 .206 .526 -.105 -.123 -.123 .439 .489 .737* -.982** 1.000 Mn-R .300 -.230 .737* -.105 -.158 -.158 .684* -.027 .105 -.632 .684* 1.000 Zn-R -.600 -.570 .895** -.719* -.737* -.737* .737* .240 .123 -.368 .386 .737* 1.000 Cu-R .700 .133 -.895** .737* .632 .632 -.842** -.293 -.263 .474 -.368 -.368 -.737* 1.000 **. Correlation is significant at the 0.01 level (2-tailed). *. Correlation is significant at the 0.05 level (2-tailed).

Table 20: Spearman correlation of GST in of Ph.turbinatus with seawater from two stations of LA1 and LA4 in the Larymna Bay. Gs= Glutathione s transferase

Correlations GS-LA1 GS-LA4 Fe-LA1 Ni-LA1 Cr-LA1 Mn-LA1 Cu-LA1 Zn-LA1 Fe-LA4 Ni-LA4 Cr-LA4 Mn-LA4 Cu-LA4 Zn-LA4 GS-LA1 1.000 GS-LA4 -.200 1.000 Fe-LA1 -.300 -.300 1.000 Ni-LA1 -.100 .400 .500 1.000 Cr-LA1 .000 .600 -.100 .800 1.000 Mn-LA1 .100 -.100 .400 .800 .700 1.000 Cu-LA1 .500 .100 -.600 -.700 -.500 -.800 1.000

Zn-LA1 .700 .200 -.800 -.100 .400 .100 .400 1.000 Fe-LA4 -.500 .600 .400 .300 .000 -.300 .000 -.600 1.000 Ni-LA4 .500 -.900* .400 -.200 -.500 .200 .000 -.100 -.500 1.000 Cr-LA4 .100 -.100 .400 .800 .700 1.000** -.800 .100 -.300 .200 1.000 Mn-LA4 -.700 -.200 .800 .100 -.400 -.100 -.400 -1.000** .600 .100 -.100 1.000 Cu-LA4 .700 .200 -.800 -.100 .400 .100 .400 1.000** -.600 -.100 .100 -1.000** 1.000 Zn-LA4 -.200 .000 .700 .900* .600 .900* -.900* -.300 .100 .100 .900* .300 -.300 1.000 *. Correlation is significant at the 0.05 level (2-tailed). **. Correlation is significant at the 0.01 level (2-tailed).

Table 21: Spearman correlation of AChE in Ph.turbinatus with seawater from stations of LA1 and LA4 in the Larymna Bay. A=Acetylcholinesterase

Correlations A-LA1 A-LA4 Fe-LA1 Ni-LA1 Cr-LA1 Mn-LA1 Cu-LA1 Zn-LA1 Fe-LA4 Ni-LA4 Cr-LA4 Mn-LA4 Cu-LA4 Zn-LA4 A-LA1 1.000 LA4-A .400 1.000 Fe-LA1 -.400 .500 1.000 Ni-LA1 .000 .000 .500 1.000 Cr-LA1 .600 -.300 -.100 .800 1.000 Mn-LA1 -.400 -.300 .400 .800 .700 1.000 Cu-LA1 .200 -.300 -.600 -.700 -.500 -.800 1.000 A Zn-LA1 .200 -.900* -.800 -.100 .400 .100 .400 1.000 Fe-LA4 .600 .700 .400 .300 .000 -.300 .000 -.600 1.000 Ni-LA4 -1.000** -.300 .400 -.200 -.500 .200 .000 -.100 -.500 1.000 Cr-LA4 -.400 -.300 .400 .800 .700 1.000** -.800 .100 -.300 .200 1.000 Mn-LA4 -.200 .900* .800 .100 -.400 -.100 -.400 -1.000** .600 .100 -.100 1.000 Cu-LA4 .200 -.900* -.800 -.100 .400 .100 .400 1.000** -.600 -.100 .100 -1.000** 1.000 Zn-LA4 -.400 .100 .700 .900* .600 .900* -.900* -.300 .100 .100 .900* .300 -.300 1.000 **. Correlation is significant at the 0.01 level (2-tailed). *. Correlation is significant at the 0.05 level (2-tailed).

Table 22: Spearman correlation of AChE in eye of Munida rugosa with sediment from the contaminated and the reference area. C=contaminated, R=Reference, A=Acetylcholinesterase, E=eye

Correlations A-E-C A-E-R Ni-C Mn-C Fe-C Cu-C Zn-C Cr-C Ni-R Mn-R Fe-R Cu-R Zn-R Cr-R A-E-C 1.000 A-E-R .371 1.000 Ni-C .177 -.267 1.000 Mn-C -.530 -.121 -.556 1.000 Fe-C .000 -.315 .556 .037 1.000 Cu-C -.294 .170 -.630 .630 .259 1.000 Zn-C .530 .121 .556 -1.000** -.037 -.630 1.000

Cr-C -.412 -.170 -.333 .778* .556 .852** -.778* 1.000 Ni-R .412 .267 .259 -.556 -.630 -.778* .556 -.926** 1.000 Mn-R .000 -.073 .556 .185 .753* .160 -.185 .407 -.235 1.000 Fe-R -.412 -.121 -.753* .802* -.457 .333 -.802* .383 -.259 -.407 1.000 Cu-R -.294 -.170 -.728* .383 -.432 .259 -.383 .210 -.333 -.778* .778* 1.000 Zn-R -.494 -.281 -.559 .923** -.091 .429 -.923** .637 -.507 -.091 .923** .637 1.000 Cr-R -.294 -.267 -.556 .259 -.111 .333 -.259 .333 -.556 -.654 .556 .926** .507 1.000 *. Correlation is significant at the 0.05 level (2-tailed). **. Correlation is significant at the 0.01 level (2-tailed).

Table 23: Spearman correlation of AChE in gill of Munida rugosa with sediment from the contaminated and the reference area. C=contaminated, R=Reference. G=Gill, A= AChE.

Correlations A-G-C A-G-R Ni-C Mn-C Fe-C Cu-C Zn-C Cr-C Ni-R Mn-R Fe-R Cu-R Zn-R Cr-R A-G-C 1.000 A-G-R .371 1.000 Ni-C .177 -.267 1.000 Mn-C -.530 -.121 -.556 1.000 Fe-C .000 -.315 .556 .037 1.000 Cu-C -.294 .170 -.630 .630 .259 1.000 Zn-C .530 .121 .556 -1.000** -.037 -.630 1.000 Cr-C -.412 -.170 -.333 .778* .556 .852** -.778* 1.000 Ni-R .412 .267 .259 -.556 -.630 -.778* .556 -.926** 1.000 Mn-R .000 -.073 .556 .185 .753* .160 -.185 .407 -.235 1.000 Fe-R -.412 -.121 -.753* .802* -.457 .333 -.802* .383 -.259 -.407 1.000 Cu-R -.294 -.170 -.728* .383 -.432 .259 -.383 .210 -.333 -.778* .778* 1.000 Zn-R -.494 -.281 -.559 .923** -.091 .429 -.923** .637 -.507 -.091 .923** .637 1.000 Cr-R -.294 -.267 -.556 .259 -.111 .333 -.259 .333 -.556 -.654 .556 .926** .507 1.000 *. Correlation is significant at the 0.05 level (2-tailed). **. Correlation is significant at the 0.01 level (2-tailed).

Table 24: Spearman correlation of GST in hepatopancreas of Munida rugosa with sediment from the contaminated and the reference area. C=contaminated, R=Reference, G=GST, L= hepatopancreas

Correlations G-L-C G-L-R Ni-C Mn-C Fe-C Cu-C Zn-C Cr-C Ni-R Mn-R Fe-R Cu-R Zn-R Cr-R G-L-C 1.000 G-L-R .143 1.000 Ni-C .110 .481 1.000 Mn-C .110 -.565 -.487 1.000 Fe-C -.110 -.127 .538 .077 1.000 Cu-C -.257 -.515 -.487 .624 .385 1.000 Zn-C -.110 .684* .538 -.983** -.026 -.607 1.000

Cr-C -.110 -.487 -.260 .797* .571 .883** -.745* 1.000 Ni-R .110 -.008 .077 -.641 -.487 -.675* .504 -.849** 1.000 Mn-R -.110 -.136 .670* .086 .721* .155 -.086 .322 -.197 1.000 Fe-R .257 -.582 -.692* .812** -.333 .265 -.846** .398 -.214 -.369 1.000 Cu-R .110 -.076 -.692* .573 -.333 .299 -.504 .364 -.487 -.695* .726* 1.000 Zn-R .199 -.328 -.431 .934** -.054 .395 -.880** .646 -.647 -.099 .844** .736* 1.000 Cr-R .110 -.228 -.487 .145 .077 .282 -.128 .277 -.214 -.601 .385 .658 .216 1.000 *. Correlation is significant at the 0.05 level (2-tailed). **. Correlation is significant at the 0.01 level (2-tailed).

Table 25: Spearman correlation of CAT in hepatopancreas of Munida rugosa with sediment from the contaminated and the reference area. C=contaminated, R=Reference, CA=CAT, L= hepatopancreas

Correlations CA-C CA-R Ni-C Mn-C Fe-C Cu-C Zn-C Cr-C Ni-R Mn-R Fe-R Cu-R Zn-R Cr-R CA-C 1.000 CA-R .400 1.000 Ni-C -.103 -.315 1.000 Mn-C .410 .412 -.556 1.000 Fe-C .103 -.048 .556 .037 1.000 Cu-C .308 .485 -.630 .630 .259 1.000 Zn-C -.410 -.412 .556 -1.000** -.037 -.630 1.000

Cr-C .410 .315 -.333 .778* .556 .852** -.778* 1.000 Ni-R -.410 -.048 .259 -.556 -.630 -.778* .556 -.926** 1.000 Mn-R .103 .364 .556 .185 .753* .160 -.185 .407 -.235 1.000 Fe-R .205 .121 -.753* .802* -.457 .333 -.802* .383 -.259 -.407 1.000 Cu-R .103 -.267 -.728* .383 -.432 .259 -.383 .210 -.333 -.778* .778* 1.000 Zn-R .335 .128 -.559 .923** -.091 .429 -.923** .637 -.507 -.091 .923** .637 1.000 Cr-R .103 -.388 -.556 .259 -.111 .333 -.259 .333 -.556 -.654 .556 .926** .507 1.000 *. Correlation is significant at the 0.05 level (2-tailed). **. Correlation is significant at the 0.01 level (2-tailed).

Table 26 : Spearman correlation of AChE and GST in soft tissue of Ph.turbinatus with the bioaccumulated metals from the reference area. G=GST, AC= AChE, R=Reference.

Correlations R-G R-AC Zn-R Mn-R Fe-R Cu-R Ni-R Cr-R R-G 1.000 R-AC -.700 1.000 Zn-R -.500 .600 1.000 Mn-R -.100 -.400 -.700 1.000

Fe-R .200 .200 -.100 .100 1.000 Cu-R .400 -.500 .300 -.200 .100 1.000

Ni-R -.600 -.100 -.200 .700 -.500 -.300 1.000 Cr-R .500 -.100 .500 -.800 .100 .700 -.800 1.000

Table 27: Spearman correlation of AChE and GST in S.aurata with the bioaccumulated metals from the fish farm in N.Evoikos Gulf .G=GST, AChEG = AChE in gill tissue, AChEM= AChE in muscle. GSTL= GST in liver. C=Contaminated area.

Correlations . AChEG AChEM GSTL CATL NiC CrC MnC ZnC CuC AChEG 1 AChEM .751 1 GSTL -.243 .454 1 CATL .036 .500 .609 1 NiC -.605 -.143 .645 .031 1 CrC .826 .565 -.283 -.022 -.819 1 MnC .758 .478 -.327 -.017 -.869 .991** 1 ZnC .246 .641 .673 .065 .562 -.005 -.121 1 CuC -.815 -.833 -.135 -.200 .523 -.902* -.856 -.376 1 **. Correlation is significant at the 0.01 level (2-tailed). *. Correlation is significant at the 0.05 level (2-tailed).

Table 28: Spearman correlation of AChE and GST in soft tissue of Ph.turbinatus with the bioaccumulated metals in the same tissue from the two stations of LA1 and LA4 from Larymna Bay. G=GST, AC= AChE,

Correlations LA1-G LA4-G LA1-AC LA4-AC Zn-LA1 Mn-LA1 Fe-LA1 Cu-LA1 Ni-LA1 Cr-LA1 Zn-LA4 Mn-LA4 Fe-LA4 Cu-LA4 Ni-LA4 Cr-LA4 LA1-G 1.000 LA4-G -.200 1.000 LA1-AC -.400 .800 1.000 LA4-AC -.900* .100 .400 1.000 Zn-LA1 .200 .300 .000 -.400 1.000 Mn-LA1 .500 -.600 -.600 -.700 .100 1.000 Fe-LA1 .200 .700 .800 -.500 .700 .100 1.000 Cu-LA1 -.600 .600 .800 .300 .600 -.300 .600 1.000

Ni-LA1 -.800 -.200 .200 .900* -.700 -.400 -.700 .000 1.000 Cr-LA1 .200 -.800 -1.000** .100 -.200 .100 -.800 -.600 .200 1.000 Zn-LA4 -.300 -.200 .200 -.100 .300 .600 .300 .500 .000 -.300 1.000 Mn-LA4 .100 -.900* -.800 -.200 -.100 .800 -.400 -.300 .100 .500 .600 1.000 Fe-LA4 -.500 -.400 .200 .200 -.100 .500 -.100 .300 .400 -.100 .900* .700 1.000 Cu-LA4 -.600 .600 .800 .300 .600 -.300 .600 1.000** .000 -.600 .500 -.300 .300 1.000 Ni-LA4 -.700 .700 1.000** .600 -.300 -.600 .200 .500 .500 -.700 .000 -.600 .100 .500 1.000 Cr-LA4 -.600 .400 .800 .500 -.600 -.300 .000 .200 .600 -.600 .100 -.300 .300 .200 .900* 1.000 *. Correlation is significant at the 0.05 level (2-tailed). **. Correlation is significant at the 0.01 level (2-tailed).