UNESCO-IHE INSTITUTE FOR WATER EDUCATION
Distribution of Metals in the Montenegrin Part of Skadar Lake
Mirjana VEMIC
MSc Thesis ES 11.52
April 2011
Netherlands and Western Balkans Environmental Network
Distribution of Metals in the Montenegrin Part of Skadar Lake
Master of Science Thesis by
Mirjana VEMIC
Supervisor
Prof. P.N.L. Lens, PhD, MSc (UNESCO-IHE)
Mentor
D.P.L. Rousseau, PhD, MSc (UNESCO-IHE)
Examination committee
Prof. P.N.L. Lens, PhD, MSc (UNESCO-IHE), Chairman D.P.L. Rousseau, PhD, MSc (UNESCO-IHE) G. Du Laing, PhD, MSc (Ghent University)
This research is done for the partial fulfillment of requirements for the Master of Science degree at the UNESCO-IHE Institute for Water Education, Delft, the Netherlands
Delft April 2011
The findings, interpretations and conclusions expressed in this study do neither necessarily reflect the views of the UNESCO-IHE Institute for Water Education, nor of the individual members of the MSc committee, nor of their respective employers.
With my infinite gratitude dedicated to my father Vukic and mother Zorica for their unlimited love and support throughout all my life and their motivation and encouragement to always reach my dreams.
Abstract
Metal contamination in Skadar Lake, the largest lake on the Balkan Peninsula, was studied during this research. Skadar Lake, which is a transboundary lake shared between two countries - Montenegro and Albania, represents an area of regional importance with a high level of species diversity, as well as diversity of habitats and landscapes, therefore it has a mosaic spread of ecosystems. The lake is famous for a wide range of endemic and rare, or even endangered plant and animal species. Especially due to the bird fauna, the lake has a highly significant international importance. The Montenegrin part of the lake has been designated as National Park in 1983 and has been included in the Ramsar list in 1995.
Different anthropogenic pressures have influenced the fragile equilibriums of the lake ecosystem, either directly or indirectly. For understanding the existing and potential effects of metals on the aquatic environment and human life, it was necessary to investigate the current concentrations and spatial distribution characteristics of metal pollutants in Skadar Lake. At selected sites metal levels in different compartments (water, sediment and lake biota) were monitored with main focus on the food chain, in order to determine the risk of metal bioaccumulation.
Results showed low levels of metals in the lake surface and bottom water but high levels of some metals in lake sediments. Sequential extraction indicates that the mineral fraction bounds the majority of chromium, nickel and copper in lake‘s sediment. Based on the guidelines for freshwater ecosystems, adverse effects are expected to happen due to high concentrations of nickel in all ten stations from Skadar Lake. Metals (Fe, Pb, Cd, Cr, Mn) were present in both plant samples (Phragmites communis and Vallisneria spiralis), at high concentrations, but they were not detected in two types of endemic fish‘s tissue (gill, muscle, liver), indicating that consumption is safe for humans.
Presented research was focused more on the preliminary investigation of the metals in Skadar Lake, and due to some limitations in the period of research (absence of some species from the food chain, insufficient time for research, doing the research only in Montenegrin part of the lake, not considering the tributaries), might not give an accurate insight in the metal concentrations and loads. In order to determine and quantify the latter, detailed identification of sources of metals, spatially and temporally distributed monitoring campaigns for the entire lake (including the Albanian part), and investigation of complete ecological food chains can be recommended for further research.
Keywords: Metals, Skadar Lake, sequential extraction of metals (SEM), bioaccumulation
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Acknowledgements
It is a pleasure to thank those who made this research possible. Firstly, I am heartily thankful to my supervisors, Dr. Diederik Rousseau and Prof. Piet Lens for their valuable suggestions, remarks, technical advices, patience and encouragement throughout this research. Thank you so much, it was pleasure working with you.
The presented study would not have been possible without the support of the Netherlands and Western Balkan Environmental Network project (www.newenproject.org). Accordingly, I am highly indebted to all the partners of the NEWEN project for funding my MSc studies and research, and I would like to put across my sincere gratitude to all people that are behind this project. Special thanks for UNESCO-IHE partners, Dr. Diederik Rousseau, Dr. Tineke Hooijmans and Dr. Henk Lubberding. Thank you for giving me the opportunity to be part of this project.
I am also deeply grateful to all staff members at the Biotechnical Institute in Podgorica (Montenegro), especially to my project coordinator Svetlana Perovic (and her husband Andrej Perovic) without whose cooperation I could not have gotten such relevant data. Thank you so much for helping me that my sampling campaign goes in the best manner and with great pleasure.
Additionally, I would like to thank all institutions, organizations and individuals who helped that my fieldwork and data collection goes in the best manner. Firstly, special thanks to "Skadar Lake National Park" staff members for their technical support and their permission to do research on Skadar Lake. Secondly, many thanks go to Prof. Gijs Du Laing from Ghent University (Laboratory of Analytical Chemistry and Applied Ecochemistry), for analyzing part of my data. Vule, thank you so much for your kind assistance and support during sampling. You made my data collection quicker and easier. Many thanks to Prof. Slavoljub Mijovic for providing me with all the necessary background information's about the lake, and to Dr. Zorica Leka for allowing me to use Technical Institute laboratory facilities during my sampling campaign. Thank you very much, without your help this research would not have been possible.
I want to express my deep appreciation to all UNESCO-IHE laboratory staff, Fred Kruis, Peter Heerings, Lyzette Robbemont, Frank Wiegman and Ferdi Battes, for their disponibility and advices. I owe my deepest gratitude to Don van Galen whose encouragement, supervision and support from the preliminary to the concluding level of my research enabled me to successfully complete my laboratory work. Thank you so much, your assistance was priceless!
Last but not least, my acknowledgements go to all my friends and colleagues for their encouragement and support during this research.
Thank you!
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Table of Contents
Abstract...... i Acknowledgements...... iii List of tables...... viii List of figures...... ix List of symbols...... x 1 INTRODUCTION...... 1 1.1 Background and problem statement...... 1
1.2 Research objectives...... 3
1.3 Research questions...... 3 2 LITERATURE REVIEW...... 5 2.1 Definitions of metals, their toxicity and sources...... 5 2.1.1 Natural and anthropogenic sources of metals...... 6
2.2 Metals in the aquatic ecosystem...... 7 2.2.1 Metals in the water...... 7 2.2.2 Metals in the sediment...... 7
2.3 Bioaccumulation and biomagnifications in aquatic ecosystem...... 8 2.4 Metals pollution in Balkan region...... 9 2.4.1 Mining and environment...... 10 2.4.2 Transition vectors...... 10 2.4.3 Mining sites in the Western Balkans...... 12 2.4.4 The importance of river transport...... 12 2.4.5 Environmental situation in Balkan region today...... 12 2.4.6 Situation in Montenegro...... 13 3 STUDY AREA...... 17 3.1 General informations about Skadar Lake...... 18 3.1.1 Location and area...... 19 3.1.2 Topography...... 19 3.1.3 Geology and geomorphology...... 20 3.1.4 Climate...... 20 3.1.5 Water resources...... 21 3.1.6 Flora and fauna...... 22 3.1.7 Administrative areas...... 23 3.1.8 Economy...... 23
3.2 Main pressures on the Skadar Lake Ecosystem...... 24 3.2.1 Mineral waste oils in the Zeta Plain...... 24 3.2.2 Wastewater from cities and towns in basin...... 25 3.2.3 The Aluminum Plant Podgorica (KAP)...... 26 3.2.4 Agriculture in the Zeta Plain...... 26
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3.3 Effects of pollution on the Skadar Lake Ecosystem...... 27
3.4 Preliminary evaluation of heavy metal pollution in Skadar Lake...... 28
4 METHODOLOGY...... 31 4.1 Transects, stations, compartments and parameters...... 31
4.2 Sample collection and preservation...... 34 4.2.1 Physico-chemical parameters...... 34 4.2.2 Lake water...... 34 4.2.3 Lake sediment...... 34 4.2.4 Lake biota...... 36
4.3 Laboratory experiments...... 37 4.3.1 Digestion of samples and biomass...... 37 4.3.2 Water samples...... 37 4.3.3 Analysis of metals...... 37 4.3.4 Loss on Ignition (LOI)...... 38 4.3.5 Sequential Extracted Metals (SEM)...... 38 4.3.6 Quality control...... 39
5 RESULTS...... 41 5.1 First sampling campaign in Skadar Lake (Montenegrin part)...... 41 5.1.1 Physical-chemical parameters in lake water...... 41 5.1.2 Metals in surface and bottom water...... 41 5.1.3 Metals in plants...... 44 5.1.4 Metals in fish...... 46 5.1.5 Metals in sediment...... 47
5.2 Second sampling campaign in Skadar Lake (Montenegrin part)...... 50 5.2.1 Physical-chemical parameters in lake water...... 50 5.2.2 Total metal levels in sediment...... 50 5.2.3 Loss on Ignition of sediment samples...... 52 5.2.4 Sieving analysis of lake sediment...... 53 5.2.5 Metals in sieving fractions of sediment...... 53 5.2.6 Sequential extraction of metals (SEM)...... 57
6 DISCUSSION...... 63 6.1 Water quality in Skadar Lake...... 63 6.2 Sediment Quality in Skadar Lake...... 65 6.2.1 First screening of sediment...... 65 6.2.2 Second screening of sediment...... 67
6.3 Spatial distribution of heavy metals in the sediment...... 73 6.3.1 Factors controlling variability in sediment...... 73 6.3.2 Variations of metals along the same transect...... 73
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6.4 Metals in plants...... 74
6.5 Metals in fish...... 75 7 CONCLUSIONS AND RECOMENDATIONS...... 77
REFERENCES...... 79 ANNEXURES...... 85 Annex A...... 85
Annex B...... 87
Annex C...... 88
Annex D...... 89
Annex E...... 91
Annex F...... 95
Annex G...... 96
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List of tables
Table 3-1: Main characteristics of Skadar Lake...... 18 Table 3-2: Preliminary evaluation of metal concentrations in Skadar Lake...... 28 Table 4-1: Sampling stations in Skadar Lake...... 32 Table 5-1: Ratio between surface (n=3) and bottom water (n=3) considering the concentrations of calcium, magnesium, potassium, nickel, manganese and aluminum...... 44 Table 5-2: Metal concentrations (n=1) in plants species (Phragmites communis and Vallisneria spiralis) from Skadar Lake compared with the standards (Bigdeli et al. 2008)...... 46 Table 5-3: Average metal concentrations (mg/kg dry weight) ± standard deviation (n=3) for Skadar Lake sediment during the first sampling campaign...... 49 Table 5-4: Average metal concentrations (mg/kg dry weight) ± standard deviation (n=2) for Skadar Lake sediment during the second sampling campaign...... 51 Table 5-5: Correlations (r) between organic matter and metals in Skadar lake sediment...... 52 Table 5-6: Correlation coefficient (r) between total metals in Skadar Lake sediment...... 53 Table 5-7: Metal concentrations (mg/kg dry weight) in different sieving fractions of Skadar Lake sediment during the second sampling campaign (n=1)...... 55 Table 5-7 (Continued): Metal concentrations (mg/kg dry weight) in different sieving fractions of Skadar Lake sediment during the second sampling campaign (n=1)...... 56 Table 5-8: Ratio between sum of metal concentrations in all sieving fractions (n=1) and total metal concentrations in the sediment (n=2)...... 57 Table 6-1: Comparison between metal concentrations found in this research on Skadar Lake and metal concentrations found in some similar lakes from the region...... 71
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List of figures
Figure 2-1: Hazardous industrial sites, water pollution and mining hot spots in the Balkan region...... 11 Figure 2-2: Map of Montenegro and position of Skadar Lake...... 14 Figure 3-1: Geographic positions of Montenegro and Skadar Lake...... 17 Figure 3-2: Skadar Lake catchment along with main tributaries Moraca, Zeta and Cijevna...... 21 Figure 3-3: Main pressures on Skadar Lake Ecosystem...... 27
Figure 4-1: Sampling stations in Skadar Lake (Transect I-S1-S3); (Transect II-S4-S6); (Transect III-S7-S10)...... 35 Figure 4-2: Schematic presentation of sampling campaign and methodology used for analyzing samples from Skadar Lake...... 35 Figure 5-1: Average concentrations ± standard deviations (n=3) for total calcium, magnesium and potassium in surface layer of Skadar Lake water (mg/l)...... 42 Figure 5-2: Average concentrations ± standard deviations (n=3) for total calcium, magnesium and potassium in bottom layer of Skadar Lake water (mg/l)...... 42 Figure 5-3: Average concentrations ± standard deviations (n=3) for total nickel, manganese and aluminum in surface layer of Skadar Lake water (μg/l)...... 43 Figure 5-4: Average concentrations ± standard deviations (n=3) for total nickel, manganese and aluminum in bottom layer of Skadar Lake water (μg/l)...... 43 Figure 5-5: Concentrations (n=1) of total nickel, copper, zinc, lead, cadmium and chromium in two plant species (Phragmites communis and Vallisneria spiralis) from transect I, II and III in Skadar Lake, expressed as mg/kg dry weight...... 45 Figure 5-6: Concentrations (n=1) of total potassium, calcium, magnesium, iron, silica, aluminum and in two plant species (Phragmites communis and Vallisneria spiralis) from transect I, II and III in Skadar Lake, expressed as mg/kg dry weight...... 45 Figure 5-7: Concentrations (n=1) of total potassium, calcium, magnesium, iron, silica, aluminum manganese and zinc in fish species (Perca fluviatilis, Rutilus karamani, Squalius cephalus, Scardinius knezevici and Alburnus scoranca) from Skadar Lake...... 46 Figure 5-8: Concentrations (n=1) of total nickel, copper, lead, cadmium and chromium in fish species (Perca fluviatilis, Rutilus karamani, Squalius cephalus, Scardinius knezevici and Alburnus scoranca) from Skadar Lake, expresed as mg/kg dry weight...... 47 Figure 5-9: Average percentage of sediment organic matter (n=2) in ten stations of Skadar Lake...... 52
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Figure 5-10: Percentage of different sieving fractions of the sediment from all ten sampling stations...... 54 Figure 5-11: Total concentration of metals (% of mg/kg dry weight) in different extraction fractions of the sequential extraction procedure...... 58 Figure 6-1: Aluminum processing plant with the red mud basin and Moraca River (on the right)...... 67 Figure 6-2: Total concentration of metals (% of mg/kg dry weight) in different sieving fractions of the sediment...... 69
List of symbols
DO Dissolved Oxygen EC Electrical Conductivity ICP Inductively Coupled Plasma LOI Loss on Ignition MARS 5 Microwave Accelerated Reaction System PE Polyethylene PEC Probable Effect Concentration SEM Sequential Extraction of Metals SQG Sediment Quality Guidelines TEC Threshold Effect Concentration
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1 Chapter One - Introduction
1.1 Background and problem statement
The presented MSc thesis research has been done as a part of the NEWEN project (Netherlands and Western Balkan Environmental Network). This project is an environmental cooperation and capacity building programme with partners from six universities in the Western Balkans and three universities and institutes in the Netherlands, aiming at improvement of the environmental situation in the Western Balkan region. MSc thesis research that will be presented further in the text was done in Montenegro, on Skadar Lake Ecosystem which is considered to be one of the main "hot spots" in the Montenegro region. Lake Skadar, the largest lake on the Balkan Peninsula, is a transboundary lake shared between two counties, Montenegro and Albania. The Lake Basin belongs to the Adriatic watershed and it is rich with water sources and courses of various sizes (Keukelaar et al., 2006). Skadar Lake represents an area of regional importance with a high level of species diversity, as well as diversity of habitats and landscapes, therefore it has a mosaic spread of ecosystems. The lake is famous for a wide range of endemic and rare, or even endangered plant and animal species. Especially due to the bird fauna, the lake has a highly significant international importance. The Montenegrin part of the lake is designated as National Park 1983 and included in the Ramsar list in 1995 (Knezevic, 2009).
Human activities have a considerable impact on the Skadar Lake ecosystem, either directly or indirectly. Moraca River, the main tributary of the lake, brings most of the pollutants into the lake from Aluminum Company (KAP), Podgorica landfill and the city drainage collector, wastewater from the cities and towns in the basin, mineral waste oils in the Zeta Plain and agriculture from the Zeta Plain (Misurovic, 2002). Among all that different kinds of pollutants, in this research accent was put on the determination of (heavy) metal concentrations in Skadar Lake.
According to Duffus (2002), over the past two decades the term "heavy metals" has been used increasingly in various publications and in legislation related to chemical hazards and the safe use of chemicals. It is often used as a group name for metals and semimetals (metalloids) that have been associated with contamination and potential toxicity or ecotoxicity. At the same time, legal regulations often specify a list of heavy metals to which they apply. Such lists may differ from one set of regulations to the other, or the term may be used without specifying which heavy metals are covered. In other words, over the 60 years or so the term "heavy metals" has been given such a wide range of meanings by different authors that it is effectively meaningless. Furthermore, the term "heavy metal" has never been defined by any authoritative body such as IUPAC (Duffus, 2001), and this has led to general confusion regarding the significance of the term. In order to avoid any kind of confusion, in this research instead of the term "heavy metals", the term "metals" will be used for addressing metal pollutants in Skadar Lake.
(Heavy) metals are environmentally stable and non-biodegradable substances, which are toxic to the living beings and tend to accumulate in plants and animals thus potentially causing chronic adverse effects on human health. They are natural components found in the Earth's crust, thus closely related to biota.
Mirjana VEMIC 1 Apart from natural processes that may enrich environment with metals, they are also introduced through a variety of anthropogenic sources such as mining, ultimate disposal of treated and untreated waste, combustion, extraction, agricultural runoff, transportation etc (Lars, 2003).
Focusing on the aquatic ecosystems, metals are regarded as serious pollutants because of their environmental persistence, toxicity, and ability to be incorporated into food chains. Toxic metals are usually present at low concentration in aquatic ecosystems, but deposits of anthropogenic origin can raise the metal concentration, thus creating environmental problems in lakes, rivers and coastal zones (Kamala-Kannan et al., 2008). Though some of the metals like Cu, Fe, Mn, Ni and Zn are essential as micro nutrients for plants and microorganisms, many other metals like Cd, Cr and Pb are proved detrimental beyond a certain limit (Lokeshwari and Chandrappa, 2006).
Pollution of the aquatic environment, especially lakes, with metals is a major factor posing serious threat to the survival of aquatic organisms. Lakes have complex and fragile ecosystem, as they have lower self cleaning ability (than rivers for example), and therefore readily accumulate pollutants. These pollutants, once they enter in the aquatic environment, are distributed among the aqueous phase, suspended particles and sediments (Pertsemli and Voutsa, 2007). Furthermore, it is well-known that sediments have a great capacity to accumulate and integrate metals and organic pollutants even from low concentrations in the overlying water column (Tam and Wong, 2000).
Environmental effects and biological consequences that could occur due to possible changes in water chemistry, would lead to a deterioration in water quality and reduce its biological productivity. When the uptake by aquatic organisms occurs on regular basis transference of contaminants in the food chain will increase their toxicity, a process which is classically known as bioaccumulation (Duborija et al., 2005).
During the period 1974- 1975 the concentration of metals (Mn, Ni, Zn, Cr, Co, Pb, Cd, Fe, Hg etc.) in Skadar Lake water has been 0.02-4.6 mg/l (Filipovic, 1981). A more recent analysis of lake water and sediments has show a significant increase in concentrations of these metals (Filipovic and Topalovic, 2002).
For understanding the existing and potential effects on aquatic environment and human life from toxic metal pollutants, therefore, it is necessary to investigate the current concentrations and spatial distribution characteristics of metal pollutants in the Skadar Lake. Considering the above reasons it was deemed necessary to carry out a detailed investigation process in different compartments (water, sediment, lake biota) of Skadar Lake (Montenegrin part), with main focus on the food chain, in order to determine the risk of metals bioaccumulation.
2 MSc Thesis Taking into account the above mentioned problems, the following research objectives and questions were developed.
1.2 Research objectives
1. Evaluation of metal concentrations (K, Ca, Mg, Ni, Cu, Fe, Zn, Pb, Cd, Mn, Cr, Si, Co and Al) in different compartments of Skadar Lake (water, sediment, biota) in order to find out potential risk of bioaccumulation
1.3 Research questions
1. What are the concentrations of metals (K, Ca, Mg, Ni, Cu, Fe, Zn, Pb, Cd, Mn, Cr, Si, Co and Al), in both surface and bottom water layer in ten selected sampling locations from Transect I, Transect II and Transect III in Skadar Lake? Is there any notable difference in concentration between surface and bottom water layer?
2. What are the concentrations of metals (K, Ca, Mg, Ni, Cu, Fe, Zn, Pb, Cd, Mn, Cr, Si, Co and Al), in the sediment (in both the raw sediment sample and in the sediment fractions), in ten selected sampling locations from Transect I, Transect II and Transect III in Skadar Lake?
3. What are the concentrations of metals (K, Ca, Mg, Ni, Cu, Fe, Zn, Pb, Cd, Mn, Cr, Si, Co and Al) in the shore aquatic plant (Phragmites communis) and in the benthic aquatic plant (Vallisneria spiralis) in Transect I, Transect II and Transect III from Skadar Lake?
4. What are the concentrations of metals (K, Ca, Mg, Ni, Cu, Fe, Zn, Pb, Cd, Mn, Cr, Si, Co and Al) in fish species (Rutilus karamani, Alburnus scoranza, Scardinius knezevici, Squalius cephalus, Perca fluviatilis) in different organs like liver, muscles and gills?
5. For which sediment extraction fraction are metals (K, Ca, Mg, Ni, Cu, Fe, Zn, Pb, Cd, Mn, Cr, Si, Co and Al) showing the highest bonding affinities in ten selected sampling locations from Transect I, Transect II and Transect III in Skadar Lake?
6. In which sieving size of sediment are metals (K, Ca, Mg, Ni, Cu, Fe, Zn, Pb, Cd, Mn, Cr, Si, Co and Al) showing the highest concentrations in ten selected sampling locations from Transect I, Transect II and Transect III in Skadar Lake?
7. Which sites have the major metals problems regarding water and sediment? Which metals are exceeding the guidelines for freshwater ecosystems?
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4 MSc Thesis 2 Chapter Two - Literature Review
2.1 Definition of metals, their toxicity and sources
Metals are widespread pollutants of great environmental concern as they are non-degradable, toxic and persistent with serious ecological ramifications on aquatic ecology (Jumbe and Nandini, 2009). Definitions of (heavy) metals vary considerably covering a number of concepts such as density, atomic weight, atomic number, other chemical properties, or they are defined without a clear basis other than toxicity. According to Duffus (2002) ―heavy‖ in conventional usage implies high density (>7 g/cm3), while ―metal‖ refers to the element or an alloy of metallic elements. For the purposes of this research the term metal will refer more to potential toxicity in aquatic ecosystems in a broad way, rather than adhering to strict chemical definitions.
Under certain environmental conditions, metals may accumulate to a toxic concentrations and cause ecological damages (Freedman, 1989). Toxicity is defined as the capacity to cause injury to a living organism with reference to the: (i) quantity of substance administered or absorbed; (ii) way in which the substance is administered (inhalation, ingestion, topical application, injection) and distributed in time (single or repeated doses); (iii) type and severity of injury; (iv) time needed to produce the injury; (v) nature of the organism(s) affected, and (vi) other relevant conditions (IUPAC, 1997). The ability of a metal ion to cause toxicity is related with its presence as well as with other factors including available concentrations or the mode and kinetics of uptake (Duffus, 2002; Nieboer and Richardson, 1980). Therefore, metals which are essential to biological system become toxic when their concentration exceeds the correct nutrition by the organisms.
Living organisms require varying amounts of metals. In very small amounts, some metals support life. But when taken in large amounts, they can become toxic. Iron, cobalt, copper, manganese, molybdenum, and zinc are required by humans. Other metals such as mercury, plutonium, and lead are toxic metals that have no known vital or beneficial effect on organisms, and their accumulation over time in the bodies of animals can cause serious illness. Certain elements that are normally toxic are, for certain organisms or under certain conditions, beneficial (Hunter, 1992). Metals are natural constituents of rocks and soils and enter the environment as a consequence of weathering and erosion (Förstner, 1990). Metal pollution of water systems can arise from different point and non-point sources, which can be either anthropogenic or natural by nature. The main sources of metal in the environment can be grouped as: (i) geological weathering; (ii) industrial processing of metal ores and metals; (iii) use of metals and metal components; (iv) leaching of metals from solid waste dumps, and (v) discharge of untreated wastewater (Förstner and Wittmann, 1983).
Mirjana VEMIC 5 2.1.1 Natural and anthropogenic sources of metals
Metals, which include copper (Cu), zinc (Zn), lead (Pb), mercury (Hg), nickel (Ni), cobalt (Co), and chromium (Cr), are common trace constituents in the Earth crust. Their concentrations in the ambient environment have increased dramatically since the Industrial Revolution, as have lead and copper since Roman times. Geological weathering of rocks is the main natural source of metals in soil and water. Naturally, geological weathering can result in high levels of metals in water bodies coming from metal bearing formations.
However, it is commonly observed that the geochemical contribution of metals to the water system through the weathering of bedrock is overshadowed in many areas by the anthropogenic input from a wide variety of sources (Bubb and Lester, 1991). Human activities such as municipal, industrial wastes, beside the atmospheric rout have also introduced large quantities of metals into environment. Mining, processing, and smelting activities are often associated with metal pollution in soils, sediments, surface and ground water. Other possible sources of contamination are untreated municipal wastewater, urban storm water runoff, leaching from solid waste dump sites, erosion from contaminated areas, or from atmospheric deposition due to enhanced industrial activity.
Industrial effluents will lead to metal pollution depending on the type of industry and the quality of the wastewater discharged. The most classic example is the discharge of the catalyst, methylated mercury chloride, into Minamata Bay, Japan, from a factory manufacturing plastics. Bioaccumulation in fish led to mercury poisoning of the local community, due to the increased amount of the Hg consumed via fish (Förstner and Wittmann, 1983).
Finally, the existence of hot spots, such as dump sites, can exert important pressure on the aquatic environment through: direct runoff, leaching in groundwater, or direct transportation by wind. The influence of these sources is proportional to the distance from water bodies, size of waste piles, and storage conditions at the dump site.
In order to draw the line between anthropogenic and natural concentrations, background concentrations should be known in order to determine elevated concentrations of metals in soils stemming from anthropogenic inputs (Hamon et al., 2004). Background levels of metals in sediment depend on bedrock properties.
6 MSc Thesis 2.2 Metals in the aquatic ecosystem
2.2.1 Metals in the water
Metals in the water phase are generally bound to particulate matter, which eventually settles down and is incorporated into sediments (Peng et al., 2008). Settling time depends on the size of the particles, and availability for uptake by aquatic organisms increases when retention time in the aquatic phase is higher. Thus, suspended particles play an important role in controlling the reactivity, transport, and biological impacts of heavy metals and provide a crucial link for chemical constituents between the water column, bed sediments, and food chain (Turner and Millward, 2002).
Other parameters that can influence metal concentrations in the water phase are pH, dissolved oxygen (DO), and electrical conductivity (EC). Low pH means more hydrogen ions in the water phase, thus more competition between the ions and the metals (cations) for binding sites (sediment, organic matter, clay minerals, or Fe-Mn oxides). Generally, at low pH values, more metals can be found in the water phase. Similarly, high metals in the water result in more ions in the dissolved form of the water, increasing in this way conductivity values. Finally, changes in dissolved oxygen can modify the pH and conductivity in water, influencing in this way the metal presence in water. These factors may be modified within the hydrological system by bedrock, soil and sediment characteristics which buffer water pH and determine the natural occurrence of inorganic/organic constituents in the water body (Bubb and Lester, 1991).
2.2.2 Metals in the sediment
Sediments are the most important sink for metals (Peng et al., 2008; Rauret, 1998), but may also become a source under certain conditions, especially in heavily contaminated areas or in drastically changed environments (Rauret, 1998). The mobility of metals is mainly determined by pH, oxidation, and resuspension (Förstner and Wittmann, 1983), as well as by the characteristics of both the sediments and the chemicals involved.
In determination of the bioavailability of metals besides the physical, chemical and biological characteristic of the interstitial water and the sediment, the chemical partitioning of metals between different forms is very important (Luoma, 1983; Nriagu, 1989). The leachable metals fraction are defined as that including exchangeable carbonate bond, iron and manganese oxide-bound and organically bound fractions (Nriagu, 1990). Metals in contaminated sediments may return to the sediment-water interface through diffusion (Van Den Berg et al., 1999), sediment re-suspension (Hulscher et al., 1992), or biological activity such as bioturbation (Wilson and Chang, 2000). Once at the sediment-water interface or in the water column, metals are more likely to be transported and to enter the food web. Therefore, in areas with metal pollution, it is important to inventory the concentration and spatial distribution of each metal to evaluate the potential for remobilization, transport, and biological uptake. Metals of anthropogenic origin are more loosely bound in sediments and thus are more readily available to organisms (Schropp and Windrom, 1988). Sediment analysis is more indicative than water analysis for evaluating the degree of contamination in the aquatic medium. Concentrations of toxic elements are usually higher in sediments with less possibility of contamination of samples during handling and processing, and the analytical methods are simpler. Additionally, sediment can serve as a source to trace short and long term contamination (Wittmann and Förstner, 1975).
Mirjana VEMIC 7 A summary of the most well known fractions and their relationship with heavy metals bounding affinities can be as follows:
Fraction 1 – Exchangeable: Sediments or their major constituents (clays, hydrated oxides of iron and manganese, humic acids) are capable of absorbing cations from the solution and also releasing equivalent amounts of other cations into the solution (Förstner and Wittmann, 1983; Tessier et al., 1979). The affinity of cations toward exchangers can be influenced by: (i) Valence of metal (proportional increase of affinity with oxidation number), (ii) Concentration of solution (iii) Presence of hydrolyzed cations (exchanged more than uncomplexed ones) (Förstner and Wittmann, 1983).
Fraction 2 - Bound to Carbonates: This fraction can be easily susceptible to changes of pH in sediment (Tessier et al., 1979), where a lowering of sediment pH may give rise to mobilization of heavy metals bound to carbonates (Förstner and Wittmann, 1983). On the other hand, calcium carbonate in alkaline waters can precipitate, transporting heavy metals into the sediment. This process is mainly related with the solubility of the heavy metal in carbonates.
Fraction 3 - Bound to Iron and Manganese Oxides: Iron and manganese oxides exist as nodules, concretions, cement between particles, or simply as a coating on particles (Tessier et al., 1979). Several studies have shown that Fe/Mn-oxides likely contribute to the binding of lead, zinc, nickel, and cobalt (Brooks and Herman, 1998; Pokrovsky and Schott, 2002; Tokalioglu et al., 2000; Yin et al., 2008).
Fraction 4 - Bound to Organic Matter/Sulfide: Metals may also be associated with various forms of organic material (Tokalioglu et al., 2000). Due to its great affinity for metals, organic matter plays an important role in the transport behavior of metals in a water sediment system (Li et al., 2007). Under oxidizing conditions in natural waters, degradation of organic matter can occur, leading to a release of soluble trace metals (Tessier et al., 1979).
Fraction 5 - Residual: If the above mentioned fractions are removed from the sediment, the residual solid should contain mainly primary and secondary minerals, which may hold metals within their crystal structure (Tessier et al., 1979). According to (Rauret, 1998) metals in sediments are mainly bound to silicates and primary minerals forming relatively immobile species, whereas in polluted sediments metals are generally more mobile and bound to other fractions.
2.3 Bioaccumulation and biomagnification in aquatic ecosystems
The process of transferring the contaminants in the food chain and increasing their toxicity is called bioaccumulation. This causes an increased chemical concentration in an aquatic organism compared to that in water, due to uptake by all exposure routes including dietary absorption, transport across respiratory surfaces and dermal absorption, being thus viewed as a combination of bioconcentration and food uptake (Gobas and Morrison, 2000). On the other hand, biomagnification can be regarded as a special case of bioaccumulation in which the chemical concentration in the organism exceeds that in the organism's diet (Gobas and Morrison, 2000). Therefore, for an evaluation of the biological impacts of metal pollution, the actual metal concentrations should be taken into consideration (Amundsen et al., 1997).
8 MSc Thesis Metals are among the pollutants that should be monitored in order to obtain a coherent and comprehensive overview of the quality status of aquatic systems (Pertsemli and Voutsa, 2007). Some contaminants are conserved as they pass from organism to organism in a food chain, possibly resulting in progressively higher concentrations at higher trophic levels (Mackay and Fraser, 2000). Analyzing the levels of metals in the biota and not finding elevated concentrations, does not necessary mean that risks for potential toxicity and therefore bioaccumulation in food chain do not exist. Thus, alternative ways have been developed to relate metals in the sediment and the potential toxicity for the organisms living in the sediment. In aquatic ecosystems, plants uptake metals mainly through their root system, as a natural requirement for their growth. If sediment is contaminated then the amount of metals available for plants will be high. Contamination of the aquatic ecosystem may affect the health of fish, either directly by uptake from the water, or indirectly through their diet (Kime et al., 1996; van der Oost et al., 1996). Metals may enter fish bodies in three possible ways: through the body surface, gills or the digestive tract (Dallinger and Kautzky, 1985). While for the skin and gills, dissolved concentration of the metals is taken into account; for the digestive track the contaminated food source is the cause of increased toxicity level.
Since sediments play a very important role in physicochemical and ecological dynamics, any change in toxic concentrations of metal residues on the sediments will affect the natural aquatic life support systems (Jumbe and Nandini, 2009). The total concentration, especially in sediment, provides the common link between available metals for uptake and toxicity. Using sediment as a measure of the bioavailability is not correct as long as different sediments exhibit different degrees of toxicity for the same total quantity of a metal (Di Toro et al., 1992).
2.4 Metal pollution in Balkan region
The countries of the Western Balkans and their European Union neighbors share the same air, the same rivers, and the same seas. At the same time, the region contains unique habitats in its dunes, estuaries, coastal lagoons, wetlands, and forests, and these merit conservation not only for the sake of the region, but for the rest of Europe and the wider world too. Thus, respect for the environment is a pre-condition for the health of everyone in the region.
Countries in the region need to pay attention to environmental issues before the situation worsens. Neglecting of environmental considerations has left many scars, both visible and invisible: where pollution is high, it is not merely an economic or aesthetic issue, but a serious health threat. For instance, a hundred years of mining has left polluted air, lifeless rivers, damaged and destroyed agricultural soil, with over 11,000 tons of waste per citizen of the city (UNEP, 2006). The huge environmental problems will not disappear with reductions of production in the mining and smelting company, but the risks of ecological accidents are often getting higher and the consequences of those accidents can be catastrophic for the Western and Central Balkan region.
Over the last few years UNEP and its ENVSEC partners have been working to identify and reduce transboundary environmental risks from hazardous mining operations in South Eastern Europe, with the focus on Albania, Bosnia and Herzegovina, the Former Yugoslav Republic of Macedonia, Kosovo (Territory under Interim UN Administration), Montenegro and Serbia. This has been achieved by collecting, analyzing and distributing valuable environmental data, facilitating knowledge exchange, and creating partnerships within the region and beyond (UNEP, 2006).
Mirjana VEMIC 9 2.4.1 Mining and environment
Almost all societies depend on the availability and use of mined products such as minerals and metals. They are the basis of the wealth and they ensure economic development all over the world. But the expansion of mining operations into environmentally sensitive and fragile areas has increased the level of environmental destruction and the impact on basic ecosystem services and biodiversity. After all mining entails the exploitation of non-renewable resources (Ilyin et al., 2010).
Mining and mineral processing has played a vital part in the history and economy of the Western Balkans. Richly endowed with mineral resources such as copper, chromate, lead and zinc, it boasts some of the largest deposits in Europe (Ilyin et al., 2010). In the 20th century the mining industry played a vital role in former Yugoslavia and Albania but with the disintegration of the Yugoslav common market, economic conditions in the region deteriorated and in the early 1990s the Balkan economy declined sharply (UNDP, 2007). Industrial output dropped significantly, with a widespread shutdown of operations such as mining. In environmental terms this cuts both ways. With the dramatic drop in industrial output, pollution decreased. But at the same time plants were either abandoned or privatized under conditions that did not clearly establish environmental liability (Williams, 2010). Hazardous industrial sites, water pollution and mining hot spots in the Balkan region are presented in figure 2.1.
The mining industry has been involved in some of the most widely publicized environmental disasters. Well-known examples of mining-related environmental accidents and long-term deterioration include Rio Tinto (a river in southern Spain), the colliery spoil heap failure at Aberfan (Wales), or the Baia Mare cyanide spill in Romania (Ilyin et al., 2010).
A wide range of mining sites do not meet today‘s standards for sustainable mine management. Environmental problems, such as water and soil pollution from metals, are the result of sub- standard operations and improper mine closure. Environmental problems at mine sites are: waste, air pollution, adverse impact on land use and biodiversity, water pollution and availability, hazardous materials, noise and vibration, energy use, visual impacts. Today, mining and quarrying accounts for only 1.2% of total GDP in the Western Balkans (Williams, 2010).
2.4.2 Transition vectors
It has been demonstrated that waterways (fluvial transport) are the dominant vector for exposure, at all levels of interest. The potential toxicity of mine water and its adverse affects on the environment can be ascribed to four common characteristics in such effluents: acidity, iron and its precipitates, metals (e.g. cadmium, zinc, copper, lead etc.) and turbidity (Krasniqi et al., 2010).
Airborne toxic emissions from smelters transported in the atmosphere also have been a very significant issue in the past. However, in the Western Balkans numerous smelter operations have ceased operations. In general the regional and transboundary importance of airborne emissions seems to have decreased in importance (UNDP, 2007). Another important vector appears to be toxic-particulate pollutant transport as dust, which has a largely local or sub- regional effect (Williams, 2010).
10 MSc Thesis
Figure 2-1: Hazardous industrial sites, water pollution and mining hot spots in the Balkan region (Source: http://maps.grida.no/go/graphic/balkans-hazardous-industrial-sites-water-pollution- and-mining-hot-spots)
Mirjana VEMIC 11 2.4.3 Mining sites in the Western Balkans
The mineral extractive industries, focusing primarily on mining for base and precious metals and metallurgy, have a long history in the Western Balkans, reaching back through historical records to at least the 5th century BC. In the period up to the early 1990s, mining, minerals processing and downstream exploitation of the metals, established the region as a major European source of copper, lead, and zinc (Krasniqi et al., 2010). The region, and in particular Albania, was also a major world producer of chromate. In Serbia for example, copper mining at the Bor deposit is believed to have prehistoric beginnings. It is also believed that the Crveni Breg (lead and silver mine) and the Šuplja Stena (mercury mine), on the Avala in the vicinity of Belgrade, were in operation prior to recorded history (Williams, 2010). While traces of very old mining exploitation and metallurgy are still visible at many localities, and are likely to contribute to the environmental risk portfolio of mining sites in some ways, it is post-1945 activities which have generated the most serious mining legacies for the region (Ilyin et al., 2010).
Thousands of old ―abandoned‖ or ―orphaned‖ sites are scattered all over the region. On such sites, with no liable legal owner, the necessary measures to close the site (stabilization, water management, replanting of vegetation, etc.), minimize the risk of accidents and prevent environmental pollution have often not been taken (UNDP, 2007). Taking them now is very expensive. Coping with this situation is complicated, with a large number of sites with serious environmental impacts, high remediation costs and the liable owners missing (Williams, 2010).
2.4.4 The importance of river transport
The potential toxicity of mine water and its adverse affects on the environment can be ascribed to its four main characteristics that are acidity, iron and its precipitates, metals (e.g. cadmium, zinc, copper, lead etc.) and turbidity. Sulphate is another regular component in mine water as it is formed during pyrite oxidation. Not all of these components have to be present in mine water in order to cause harm but in most cases they are found in combination with each other (UNDP, 2007).
Fluvial transport mechanisms for tailings wastes have a pivotal importance for both regional and transboundary pollution risk in the Western Balkans (UNDP, 2007). Very large volumes of polluted materials can be involved with catastrophic damage to downstream land, property and ecosystems associated with the physical impacts of such accidents. Biochemical and eco- toxicological effects of these pollutants can be catastrophic and can extend far beyond the zone physically affected by such materials. Additionally physical, biochemical and eco- toxicological effects can be long term (Ilyin et al., 2010).
2.4.5 Environmental situation in Balkan region today
Badly operated or abandoned mining sites have already caused severe pollution, some with impacts reaching across national boundaries: heavy metal spills from Baia Borsa tailings in Romania; the cyanide spill from Baia Mare in Romania; heavy metal spills from Sasa tailings in Macedonia; and various releases at Majdanpek and Veliki Majdan in Serbia, and Mojkovac in Montenegro (Williams, 2010).
12 MSc Thesis The ENVSEC initiative identified over 180 separate operations, some with many individual sites of activity. About a third of these appeared to be of significant environmental and security concern, and nearly a fifth was deemed to involve potential transboundary risks. On many abandoned sites the necessary measures for proper closure (e.g. stabilization, water management, replanting of vegetation, etc.), to minimize the risk of accidents and to prevent environmental pollution were never taken. Implementing them now is very expensive (UNDP, 2007). Coping with the present situation is complicated, with a large number of sites with serious environmental impacts, high remediation costs and the liable owners missing. In most cases the government is held accountable (UNEP, 2006).
2.4.6 Situation in Montenegro
Red bauxite, together with coal, represents the key strategic mineral raw material in Montenegro. The country‘s main bauxite mines, which were operated by Rudnici Boksita Niksic, are located in Montenegro‘s Niksic area. Primary aluminum was produced by DP Kombinat Aluminjuma, which had smelting facilities at Podgorica. This smelter has capacity to produce over 100 000 t/yr primary aluminum (Williams, 2010). Lignite deposits are also located at the Pljevlja region, while brown coal deposits are found in the Berane area. In addition, several peat deposits are located in the Skadar Lake basin. Montenegro also has some lead and zinc deposits. Although there has been oil drilling along the coast of Montenegro for the last 50 years, no economically viable deposits have so far been discovered.
Prospecting for oil and gas nevertheless continues in promising areas, such as Budva and Bar (UNEP, 2006). In addition gravel and sand deposits are abundant in the bed of the Moraca River, near Podgorica, in the Lim valley, and in the upper reaches of the Tara River. Mining is concentrated at several sites in this small country, with bauxite exploitations mostly having a visual impact on the natural landscape but with lead and zinc mines leaving serious environmental legacies. Gravel and sand excavations are also major problems in the south of the country (Ilyin et al., 2010).
The lead and zinc tailing storage facility for material from the closed Brskovo mine is located directly on the Tara River and for the most part inside the town of Mojkovac. The Tara River and its gorges are on the Unesco World Heritage List (UNEP, 2006). About 3.5 million ton of toxic mining and processing waste has accumulated in a tailings pond near Mojkovac in the course of Pb-Zn mining operations. Its Tailings Mine Impoundment (TMI) occupies an area between the right bank of the Tara and the western perimeter of the town itself. The impoundment covers 19 ha, containing about 2 million m3 of impounded tailings (Williams, 2010).
The red-mud storage facilities (bauxite residue from alumina production) from the alumina/aluminum plant are located 10 km from Podgorica, in the Zeta Valley upstream from Lake Skutari (Ilyin et al., 2010). More than 7 million ton of red mud has accumulated at two adjoining dumps, both of which are contaminating groundwater, with the effects apparent in the lake (Williams, 2010). Apart from the serious problems associated with leachate with a pH as high as 13 and a significant fluorine, phenolic, arsenic and cyanide content, about 10 ton of PCBs have also been dumped here, contaminating the groundwater (UNEP, 2006). Work to recover the PCBs was carried out in 1998. About 1.5 ton of broken barrels were recovered but 9 ton are still missing. A sarcophagus was built to contain the barrels and contaminated soil.
Mirjana VEMIC 13 Sediment samples taken in the lake reportedly indicated PCB levels as high as 0.1 ppm at the time, with fish samples as high as 3 mg/kg. At present the mines and flotation facilities of the lead and zinc mill are closed and toxic tailings are dumped on the bank of the river Cehotina. Of particular interest for rehabilitation is the presence of trace metals in the tailings, reportedly of commercial interest (germanium, uranium). This suggests there may be several options for securing and/or remediating the tailing facility in the future (Ilyin et al., 2010).
Figure 2-2: Map of Montenegro and position of Skadar Lake (Source: http://mapsof.net/uploads/static-maps/montenegro_map.png)
14 MSc Thesis
Mirjana VEMIC 15
16 MSc Thesis 3 Chapter Two - Study Area
Montenegro, meaning "Black Mountain", is situated in the southeast of Europe, in the west of the Balkan Peninsula on the 42˚ 53' N latitude and 20˚ 21' E longitude. Montenegro has a total border length of 625 km and is bordered by Croatia to the west (25 km), Bosnia and Herzegovina (225 km) to the northwest, Serbia (124 km) and Kosovo (79 km) to the northeast, and Albania (172 km) to the southeast. In the west, the Adriatic Sea separates Italy from Montenegro, which has a coastline of 293.5 km.
With an area of 13.812 km2 Montenegro has 4 national parks (Durmitor 390 km2; Lovćen 64 km2; Biogradska Gora 54 km2; Skadar Lake 400 km2), more than 30 major rivers and more than 50 natural lakes. The most Skadar Lake important lakes are Skadar Lake, Black Lake and Biogradsko Lake. Montenegro's capital and largest city is Podgorica.
Montenegro
Figure 3-1: Geographic positions of Montenegro and Skadar Lake (Source: Keukelaar et al., 2006)
Mirjana VEMIC 17 3.1 General informations about Skadar Lake
Skadar Lake is the largest natural, freshwater lake of tectonic-karst origin in the Balkan Peninsula. The lake was declared as a national park in 1983 (Official Gazette SRCG 1991), and in 1995 as a wetland site of international significance and importance – a so-called Ramsar site (Site 3YU003).
Besides for neighboring countries, Montenegro and Albania, Skadar Lake is an important area in a larger regional aspect too. It is an important reserve of biodiversity and a migratory road for many animal species of the region. This area represents also particular values in hydrologic and ecologic aspects, taking into account connection of the lake with a larger hydrographic net in the Balkan through Drini River (Ohrid and Prespa lakes) and with the Adriatic Sea through Bojana River (APAWA and CETI, 2007). Some of the lakes main characteristics are presented in table 3-1.
Table 3-1: Main characteristics of Skadar Lake
Characteristics Value Unit Lake surface area2,3 419.4 km2 Average altitude1,3 4.7-9.8 m.a.s.l Coastline1 168 km Mean depth2,3 6 m Maximum depth1 60 m Volume1,2,3 1.7 - 4.0 km3 Catchment area1,2,3 5.490 km2 Residence time1 2-3 year 1(Karaman, 1981); 2(Keukelaar et al., 2006); 3(Knezevic, 2009);
Lake itself is unusual due to mutual vicinity of different living areas and their chains of feeding. The most pronounced difference between Skadar Lake and the other three national parks in Montenegro (Durmitor, Biogradska Gora, Lovcen), is its exceptional richness of ornithofauna and ichtyofauna, as well as abundant marsh vegetation (Keukelaar et al., 2006). The habitats that the lake contains are: 22500 hectares of water covered surfaces; 5200 hectares of flood surfaces; 12500 hectares stone and rocks on the south side of the coast as well as islands mainly out of rock; and 8128 hectares of ornithological reserves (Karaman, 1981). Skadar Lake is a free flowing type of lake. It changes its water completely about two, two and a half times per year. It is a significant hydrological area and an important physical- geographic phenomenon. The climate is Mediterranean with mild and rainy winters. The average temperature of the water in January is 7.3° C. The summers are dry and hot with a maximum temperature of over 40° C, with the water temperature just slightly above 27° C. The average temperature of the air is around 14.9° C (Karaman, 1981).
18 MSc Thesis 3.1.1 Location and area
Lake Skadar, the largest lake on the Balkan Peninsula, is located on the border between Montenegro and Albania at 40º 10' north latitude and 19 º 15' east longitudes. Skadar Lake Basin is located in the Dinaric mountain system, which includes several mountain ranges paralleling the Adriatic Sea in the southwest. Mountains rise steeply from the lakes southwestern shores, while northern and northeastern shores are flat providing an extensive semi-littoral zone. The lakes southwestern near-shore zone is characterized by a series of islands and cryptodepresions, many of which contain sublacustrine springs (Karaman, 1981).
Skadar Lake belongs to the Drin River basin and, furthermore, lake drains through the Bojana River into the Adriatic Sea. The area of Skadar Lake varies from 359.2 km2, at its minimum water level, to 500.6 km2 at maximum water level, while its average area is 419.4 km2 (Table 3-1). The area of the Skadar Lake watershed is 5.490 km2, of which 4.460 km2 belongs to Montenegro (81.2% of the total watershed territory), and 1,030 km2 to Albania (18.8% of the total watershed territory). Length of coast is 168 km (110.5 km on Montenegrin side, 57.5 in Albania). The lake volume varies between 1.7 km3 in dry periods to 4.0 km3 during wet periods (Karaman, 1981; Knezevic, 2009; Keukelaar et al., 2006). The lake water level also varies seasonally from 4.7 to 9.8 m above sea level. Furthermore, the average depth of the lake is 6 meters. The distance between the mouth of the Crnojevica River (northwestern lake edge) and the lake‘s outlet (Buna-Bojana River) is 44 km (maximum length), while its greatest width is 13 km (Knezevic, 2009).
3.1.2 Topography
Basin of Skadar Lake is located south of the Dinaric Alps and orientated northwest-southeast, parallel to the current shore of the Adriatic Sea coast. Limestone and associated karstic processes determine the geomorphology. On the northern and north-eastern side of the lake, the flat Zeta Plain and the main inflowing rivers (Moraca and Zeta) are located (Figure 3-2). Their deposits (deltas) and the lower edge of the Zeta Plain have created a wide marsh belt that is regularly flooded. Skadar Lake is separated in the southwest by steep hills from the Adriatic Sea: the Tarabos and Rumia mountains. This zone is only 10 to 15 km wide but with peaks up to 1600 m. Along the lake‘s coastline series of islands are elongated (Knezevic, 2009).
Located in the outer part of the south-eastern Dinaric Alps, the lake occupies the lower parts of the Zetsko-Skadar depression, which is in one part a crypto-depression (which means that in some part the bottom of the lake is below sea level). Those places are named sublacustrine springs or eyes (―oka‖). In this lake about 30 such eyes have been found. The deepest eye is called Radus, about 60 meters deep and is an exceptionally rich with fish. Next to this eye, other well known eyes are: Karuc (28 meters deep), Volac (24 meters deep), Krnjicko (24 meters deep) etc. (Keukelaar et al., 2006).
Mirjana VEMIC 19 3.1.3 Geology and geomorphology
Although its precise origin is unknown, most of the hypotheses on the origin of Skadar Lake state its tectonic–karstic origin, formed during Tertiary or Quaternary periods. Subsequent glacial and fluvial erosion, coupled with the chemical and physical weathering (karstification) of the limestone to form karst topology, occurred in the rocks surrounding Lake Skadar and its drainage basin forming a holokarst region (Karaman, 1981). Tectonic depressive movements later created a large flooded area. The actual lake area is surrounded by calc and dolomite formations of Palaeozoic, Mesozoic and Tertiary. The geomorphology of Skadar Lake basin is mostly determined by limestone and associated karstic processes (APAWA and CETI, 2007). Furthermore, The Balkan Peninsula experienced a strong orogenic period (period of mountain formation) during the late Tertiary and early Quaternary periods, resulting in the Dinaric Alps. Glaciations has eroded and reshaped the landscape in the Pleistocene. The Zeta Plain and the Lake Skadar area are filled with some Tertiary, but mainly Quaternary deposits of fluvial and glacial origin (gravel and sand), sometimes cemented into conglomerates and sandstones. The youngest deposits in the Zeta Plain are loess, now only preserved in the foothills. The Zeta Plain and the Lake Skadar area are still sinking. That is best seen on the Crnojevica River, currently almost dry and with the lower section a bay of the lake. Something similar happened to the Malo Blato, a river transformed into a lake next to Lake Skadar. Also as a result of the sinking, lake water moves ‗upstream‘ into the valleys, giving a wavy coastline on the west side (Keukelaar et al., 2006; Karaman, 1981).
3.1.4 Climate
Lake Skadar is a water body lying in an area that has an extremely high evaporation rates (Karaman, 1981). The dominant climate on Skadar Lake and in its surroundings is an Adriatic variant of the Mediterranean climate, thus strongly modified by the effects of high surrounding mountains. This modification is reflected in its temperature as well as in the precipitation regime. With respect to the maritime area, here are present warmer and drier summers, wetter and colder winters, harder frosts and often appearance of snow (Misurovic, 2002).
The annual potential of the sun radiation is 2054 kW/m2, which is considered as a high amount, with a high importance as an ecologic factor for the area. The annual average number of sunny days on the lake is 116.4, while number of the cloudy days is 73 – 106. Wind activity is determined by cyclonic factors of the Mediterranean and Balkan, but also by the local factors. In Montenegrin part winds that predominate is from north-east and south- west. There are 15 types of winds, which are known on Skadar lake basin, of which Murlan and Shirok are the most important (Misurovic, 2002). The annual average temperature of the air is 14ºC - 16ºC. The highest average temperature is usually recorded in August and the lowest average in January. Temperature in winter is low, due to the high elevations and predominant easterly and northerly winds, but at lake level above freezing point (Knezevic, 2009). The highest values of air humidity are recorded in November (77%), while the lowest in July (55%). The average of the annual rainfall on the lake is between 1750 mm and 2500 mm, but within the basin some areas receive over 3,000 mm (APAWA and CETI, 2007).
20 MSc Thesis 3.1.5 Water resources
Lake receives its water mainly by the 99 km long Moraca River (supplies the lake with approximately 66% of its water), which has its source in the central Montenegrin mountains and is altered by four hydropower plants (Andrijevo, Raslovići, Milunovići and Zlatica). The remaining water in lake is divided as follows: 9% from precipitation, 18% from wells, and 5- 7% from other water flows (mainly Zeta River). The area of the Moraca River watershed is 2,627.70 km2, while Zeta River watershed occupies 1,215.80 km2 (Keukelaar et al., 2006; Karaman, 1981).
Other water sources in the Skadar Lake watershed are smaller but significant. It is important to mention that the water level in the lake depends also on the discharge of the river Drim from Albania during the winter. When the water level in the lake is low compared to the water level in the Drim, Drim water enters into the lake. This inflow takes place only within a period of about three months: from December to February (Neziri and Gössler, 2006). Furthermore, groundwater also provides the lake with water. Two main groundwater sources can be distinguished: aquifers in the Zeta Plain and karstic springs, mainly on the south- western side of the lake (Misurovic, 2002). Lake Skadar discharges its water through the transboundary Bojana River (44 km; average flow 320 m³/s) into the Adriatic Sea. The high oscillation of the water level of Skadar Lake and the oscillation of the water flow of the Moraca and Zeta rivers are determined by high precipitation in winter and a lack of precipitation in summer (Knezevic, 2009).
Figure 3-2: Skadar Lake catchment along with main tributaries Moraca, Zeta and Cijevna (Source: Mijovic et al., 2006)
Mirjana VEMIC 21 3.1.6 Flora and fauna
The number of plants and animal species proves that Skadar Lake is a hot spot of biodiversity in Europe. The Skadar Lake region is located in a zone where two major zoogeographic areas meet: the Palaearctic region (Europe, Asia, the Mediterranean and North Africa) and the Palaetropic region (Africa) (Karaman, 1981). Due to that lake is famous for a wide range of endemic and rare, or even endangered plant and animal species. Heterogeneity of the plant and animal world is presented in the large number of species in this area (930 species of algae, 497 vascular plants, 430 species of zooplankton and micro fauna, 60 species of fish, 51 species of herpetofauna, 282 species of birds, 50 species of mammals, for a total of 2300 species), indicating the wealth of Skadar Lake in the plant and animal world (Karaman, 1981).
Especially due to the bird fauna, the lake has a highly significant international importance. Skadar Lake attracts birds that are flying long migratory routes, as it is providing good nesting and colonization conditions. Ninety percent of the bird species are regionally and intercontinentally mobile, thus linking the regions of neighboring continents (Europe, Asia and Africa). The avifauna shows a large number of species: 282 species belonging to 18 taxonomic orders (APAWA and CETI, 2007).
Furthermore, Skadar Lake has a high variety of fish fauna, as the result of a good communication with the sea, and of an extensive network of rivers and streams. Its ichtyofauna includes highland coldwater fish species, warm freshwater fish species and several brackish species (Keukelaar, 2006). From ichthyologic studies carried out by both states (Montenegro and Albania) it appears that the lake has 60 fish species belonging to 17 families. The relatively high number of endemic species (15 species according to Maric, 1995) makes the lake significant on regional level. For a relatively warm lake, the number of fish species is considered high. About 10 species are commercially exploited (e.g. carp, bleak and eel). Two fish families are especially important: cyprinid (most abundant in species) and salmon fish (which are much rarer in the lake due to their specific requirements) (Shumka et al., 2008).
As stated before, this research has a goal to check is there bioaccumulation present in Skadar Lake food chain. For that purpose two plant species and five fish species were chosen. Due to the time of sampling (winter) it was not possible to get representative samples of other species from the food chain (like mollusks, Cladoceras, Copepodas). Aquatic plants that were examined for metal concentration were Phragmites communis, mainly because it's large abundance on the shore of Skadar Lake (particularly in the area of Moraca River Mouth), and benthic aquatic plant Vallisneria spiralis, because of its large abundance on the bottom of the lake. Since the last link in the Skadar Lake food chain is fish species, literature review had shown that the most abundant fish family in Skadar Lake is Cyprinid family. Among this family, species Rutilus karamani, Alburnus scoranza, Scardinius knezevici, Squalius cephalus and Perca fluviatilis are present in the largest number in the lake and they are mainly consumed as food by the local population. Due to that reason above mentioned five fish species were chosen for determination of metal concentrations.
22 MSc Thesis 3.1.7 Administrative areas
Watershed area of Skadar Lake has a total surface about 5,500 km2 in which about 500,000 inhabitants are situated. The area of Skadar Lake National Park is located on the territories of three municipalities of Montenegro: Podgorica, Bar and Cetinje. The highest number of inhabitants is concentrated in the settlements which belong to municipality Podgorica (10,288), while the rest belongs to municipalities Bar (1,668) and Cetinje (518) (Keukelaar et al., 2006).
The current population growth in Montenegro is 0.03% and the life expectancy is 73 years. In Albania it is respectively 0.6% and 74 years. The population living below the poverty line in both countries is about 30%. Analyses of demographic moves, which involved settlements which administratively belong to the Park, as well as the settlements which directly recline on the Park, indicated absolute decreasing in the number of inhabitants in long-term period. In the last ten years, decrease is slower, what is more result of demographic exhaustion than undertaken actions on consolidation and improvement of Lake and its surrounding (APAWA and CETI, 2007; Misurovic, 2002).
3.1.8 Economy
Economy of the Skadar Lake region in Montenegro is modest. Main activities are agriculture, fishery and tourism. Lake hinterland has modest agricultural potentials consist of small lands in slews, formed on the thin substratum. Larger complexes do not exist, except in a Zeta Plain, which is outside of the National Park area, but leaning on it. These agricultural fields are the most quality ones, with aluvian soils, in area of 17.000 ha. Agriculture is the main source of income for households in Malesija (more than 61% of monthly household income comes from agriculture), Zeta (30%) and Krajina (17%). Others (2%) produce agricultural products but only for personal consumption. Those involved in agricultural production professionally, currently use pesticides and plan to continue to use pesticides in the future as well (Keukelaar, 2006).
Fishing is the most prevalent in the Zeta Plain and the Krajina (50% of surveyed households derive all their income from this activity). The only industrial drive is plant for fish processing, in Crnojevica River. Plant is working over decades with variable results, faced with problems, above all, raw material (fish) from the lake and labor. As reported by the households surveyed, income from this activity has decreased in the last years, due to lack of recourses and changing of working engagement toward other activities (Keukelaar, 2006; Knezevic, 2009).
Indirect income from tourism is gained through the sale of fish, olive oil, and fruits to the tourists coming to visit Krajina and Vranjina. In general, reported as a secondary activity for the majority of households in this area, 20% of households derive their annual income from tourism. The reasons for low utilization of tourism as financial support for households are the short tourist season, poor organization and insufficient accommodations.
Mirjana VEMIC 23 3.2 Main pressures on the Skadar Lake Ecosystem
According to Mijovic (2006), Skadar Lake natural resources such as biodiversity (plants, fishes, birds and waterfowl, amphibians, reptiles, invertebrates), water, agriculture, mineral resources, fertile soils, cultural and historical heritage, offer the possibility to develop sustainable activities like eco-tourism and rural tourism, ecological agriculture, fishing, hunting and peat exploitation. The physicochemical-biological characteristics of the water in Skadar Lake are the result of inflow of the major tributaries, inflow from sublacustrine springs, exchange between sediments and overlying waters, the extensive flooding of the terrestrial environment, chemical exchange between waters and the extensive beds of aquatic macrophysics and anthropogenic pollution (Mijovic et al., 2006). During the past three decades the lake and its basin have experienced varying states of pollution. A well defined pollution trend for the basin as a whole is difficult to establish on the basis of the fragmented and inconsistent data sets.
During the period 1974- 1975 the concentration of metals (Mn, Ni, Zn, Cr, Co,Pb, Cd, Fe, Hg etc.) in lake Skadar water has been 0.02-4.6 mg/l (Filipovic, 1981). According to Filipovic and Topalovic (2002), the recent analysis of lake water and sediments are showing significant increase in concentrations of these metals. The increase in the pollution in the recent time would be generally expected, due to of poor management of industrial and municipal wastes, the development in the last decade on both Albanian and Montenegrin part of the lake area, and the increasing population.
Several sources of pollution are present in the Skadar Lake Basin. Most pollutants of surface water, groundwater, soil and air in the basin originate from Podgorica, situated on the Moraca River terraces in the Zeta Plain. On the Albanian side the main polluter is the city of Skadar with its solid waste and wastewater (Karaman, 1981). The main sources of pollution in Skadar Lake Basin are: 1) Mineral waste oils in the Zeta Plain; 2) Wastewater from the cities and towns in the basin; 3) The Aluminum Plant Podgorica (KAP = Kombinat Aluminijuma Podgorica); 4) Agriculture in the Zeta Plain;
3.2.1 Mineral waste oils in the Zeta Plain
Waste oils are spilled all around in the Zeta Plain without any control. Automobile garages and carwash services in Podgorica, Tuzi, Golubovic and other villages in the Zeta Plain are the main source of these pollutants. Analysis of soil and sediment samples confirmed the contamination. As an example of localized pollution, the soil in the KAP area near the ‗Mazut‘ (crude oil) station is polluted with spilled oil, and one of the piezometer boreholes in the area contained crude oil. Other notable sources of pollution are waste oils and tar spills from the boilers of the tobacco factory in Podgorica, the winery ―Plantaze‖ and the metal industry ―Radoje Dakic‖ (Keukelaar et al., 2006).
24 MSc Thesis 3.2.2 Wastewater from the cities and towns in the basin
Wastewaters from the cities and towns are major sources for surface and groundwater pollution in the Skadar Lake basin. Millions of cubic meters of untreated or poorly treated municipal wastewater from the cities and towns on the banks of Moraca and Zeta rivers are discharged each year into these main tributaries of the lake. These are predominantly of household origin, combined with the rain water. Furthermore, municipal wastes also include waste from commercial enterprises and small industries, bio-medical/ hospital waste, waste from demolition of houses and from streets sweeping. They contribute mainly to the microbiological contamination of water with suspended matters, BOD, COD, NH4, nitrates, nitrites, mineral oils, sulphides, phenols and phosphates (Keukelaar, 2006; Rastall et al, 2004).
Pollution of groundwater (leakage, percolation), air pollution (gas emissions) and risk of diseases (for both man and animals) are the main issues. The waste problem is caused by: ineffective legal framework for waste management, inadequate waste collection system (especially in rural areas), lack of awareness among most people about proper waste management, improper separation of wastes and uncontrolled disposal sites, insufficient funds and technical means to deal with the waste disposal. The data on municipal wastes is fragmentary. The waste generated per person per day is estimated to be close to 1 kg in Montenegro, while in the Albanian cities this is about 0.7-0.8 kg/ person/ day (Keukelaar et al., 2006).
Capital city, Podgorica, has a wastewater treatment plant but it has a capacity to treat only about 50% of the city wastes. Wastewaters from Cetinje and Niksic are discharged into open drains without any purification. For over 30 years municipal wastes from Podgorica have been dumped at the Cemovsko Polje landfill site, situated about 8 km east of the city. At this site annually about 400,000 cubic meters of unsorted waste is dumped on about 54 hectares. Information on the quantity of waste dumped is inconsistent. The prolonged storage of untreated waste at this location has contaminated the underlying soil and groundwater. Furthermore, industrial wastewater from the candy factory, stone quarries, pig farm and dairy farm in Spuz (Danilovgrad) are disposed into the municipal waste collector drains, which ultimately discharges into the Zeta River (Rastall et al, 2004).
The solid municipal waste, both in Albania and Montenegro, is generally disposed of at open landfill sites, which are close to the cities, without insulation layer as protection of groundwater, soil contamination and without an environment protection system.
While recyclable materials such as paper, glass, plastic and metals are to a certain extent recovered from the disposal sites, solid hazardous materials like used batteries, neon and mercury lamps, transformers, condensers, refrigerators are left on the dumping sites. There are no facilities for the treatment and disposal of hazardous materials, nor are there any proper storage facilities for these wastes in Albania or Montenegro. This has resulted in a built-up of hazardous wastes, especially at industrial sites (Keukelaar, 2006).
Mirjana VEMIC 25 3.2.3 The Aluminum Plant Podgorica (KAP)
The KAP, situated in the Zeta Plain south of Podgorica, is a major source of pollutant inputs into the lake. This aluminum processing plant is about 32 years old (production at its full capacity begun in 1973) and has several production facilities: production of aluminum and anode, extraction of aluminum, electrolysis, foundry, cold rolling, foil rolling, forging, and quality control. The plant is operating at its full capacity and produces about an annual average of 400,000 tons of cathode production residue (‗red mud‘) (Rastall et al, 2004).
The present disposal sites are two basins, and for the ‗red mud‘ there is a threat for groundwater contamination and leachate reaching the water of Skadar Lake. The first basin has a liner – an insulation layer – to protect against the seepage of heavy metals into groundwater.
The second basin, which does not have any protection layer, possesses a serious threat of groundwater contamination, including drinking water, because the plant is located near (about 10 km) the city. At present, about 4 million tons of red mud has already been dumped over 220,000 m2 in the second basin (Keukelaar et al., 2006). Additionally, water quality investigations conducted in 1991-1996 showed significant contamination of groundwater in the Zeta Plain. The KAP is currently in the process of privatization, in accordance with the privatization plan and policy of the Montenegrin Government.
3.2.4 Agriculture in the Zeta Plain
Most agricultural activities take place in the lower part of the Zeta Plain and on the east side of Lake Skadar. Where agriculture farming is practiced, the use of pesticides and chemical fertilizers by inadequately trained farm workers, using poorly maintained or outdated equipments presents a threat to the lake due to the contamination through surface run-off and infiltration into groundwater (high percolation rates).
Analyses of the soils in this area and the lake water show significant presence of pollutants and nutrients that are possibly caused by pesticides and fertilizers. Furthermore, there is a large vineyard – ―the Plantaze‖ - of about 4,000 hectares, situated close to Podgorica. Irrigation using trickles and sprinklers is practiced. The ―AD Plantaze‖ of Agrocombinat ―13 July‖ utilizes annually 130 kg of cristaline fertilizer and 350 kg of Nitrogen, Phosphorous and Kalium (NPK) fertilizers on 1,050 ha. The ‗Plantaze‘ uses 16 tons ―Tiolit‖ sulphur preparation, 16 tons of copper lime, 10 tons of insecticides and 5 tons of fungicides (new generation of III and IV groups of poison products) annually. The use of pesticides is low, less than 0.5 kg/ha, but it can cause contamination of groundwater, and ultimately the lake water, because of highly porous (intergranular) soil (Knezevic, 2005).
26 MSc Thesis
Montenegro
Figure 3-3: Main pressures on Skadar Lake Ecosystem (Source: Keukelaar et al., 2006)
3.3 Effects of pollution on the Skadar Lake Ecosystem
Despite the proclamation of Montenegro as an eco-state, the Skadar Lake National Park is being polluted with the high concentration of pollutants (especially metals). Metals in the Skadar Lake not only pose a serious risk for the lakes water and sediment, but also for the biota. The consequences of metals contamination are yet to be established. Ingestion of metals by aquatic life can cause bioaccumulation, thus humans can be also affected as a last consumer in the food chain (Redford et al., 1997; Derraik, 2002).
Considering the described situation in Skadar Lake and in line with the general objective, it was necessary to set monitoring campaigns to investigate the levels of metals, in order to identify the "hot spots" in the different compartments of the lake. Also, it is important to detect the sources of pollution, which are probably mainly related with the tributaries that discharge in the Montenegrin part of the lake. The latter are considered to bring the major transport of elevated levels of heavy metals due to their vicinity with the lake.
Mirjana VEMIC 27 3.4 Preliminary evaluation of heavy metal pollution in Skadar Lake
Some fragmented monitoring data from Skadar Lake are presented in table 3-2. Sites with major problems should be analyzed in more details in terms of spatial and temporal variability, mechanisms which determine the mobilization of heavy metals and their presence in different compartments of the lake, and at the end the influence on the lake‘s biota.
Consensual sediment quality guidelines (SQGs) were developed by MacDonald et al. (2000) in order to provide a basis for the sediment toxicity classification (table 2-2). The threshold effect concentrations (TECs) were intended to identify contaminant concentrations below which harmful effects on sediment dwelling organisms were not expected. Moreover, probable effect concentrations (PECs) were intended to identify contaminant concentrations above which harmful effects on sediment-dwelling organisms were expected to occur frequently (MacDonald et al., 2000).
Table 3-2: Preliminary evaluation of metal concentrations in Skadar Lake
Parameters Reference Zn Cd Cu Cr Co Pb Fe Water (mg/l) Filipovic (1981) 0.001- - 0.020- 0.000- 0.0001 0.010 0.02- 0.088 0.2 0.001 0.08 Water (mg/l) CETI (1990-1995) 0.0006- 0.009- 0.06-0.1 0.005- - 0.08- 0.002- 0.001 0.01 0.04 0.01 0.02 Water (mg/l) CETI (1998-2002) 0.003- 0.002- 0.002- 0.002- - 0.005- 0.08- 0.01 0.01 0.01 0.008 0.1 0.145 Water (mg/l) CETI (2005) <0.05 <0.001 <0.01 <0.05 - <0.001 0.07 Sediment (mg/kg) CETI (1993-1996) 1.0-14.60 0.92- 0.40- 4.60- - 12.40- 12-29.4 1.66 19.40 8.60 30 Sediment (mg/kg) Bekteshi (2003) 2.24- 2.17 0.57- 6.7 16.8 14.10- 26.17 15.42 23.12 34 Cyprinus carpio CETI (2001) - 0.00 - 0.12 - 0.00 4.50 (mg/kg) Cyprinus carpio CETI (1993-1996) 19.9- - - <0.05 - <0.10 - (mg/kg) 20.07 Macrophytes CETI (1993-1996) 12.90- 0.92- 0.01- 0.19- - 25.71 0.09- (mg/kg) 34.30 2.76 0.38 1.15 0.63 TEC (mg/kg) Mac Donald et al. 121 0.99 - 43.4 31.6 35.8 - (2004)
PEC (mg/kg) Mac Donald et al. 459 4.98 - 111 149 128 - (2004)
28 MSc Thesis
Mirjana VEMIC 29
30 MSc Thesis 4 Chapter Four - Methodology
4.1 Transects, stations, compartments and parameters
Based on the previous studies done on Skadar Lake and after good literature review, major contaminants in the lake and the areas with major metal problems were identified. Two sampling campaigns were launched conditioned by the research period assigned. First sampling campaign was launched in October 2010-November 2010 when water, sediment and biota samples were taken for total metal determination. Second sampling campaign was launched in January 2011 when sediment samples were taken for more detail sediment analysis like sequential extraction of metals (SEM), sieving analysis and total metal determination.
After identification of the major polluted areas in the lake three sampling transects were defined: first (T1) in the area between Vranjina and Virpazar, next to the Moraca River mouth (first line of pollution); second one (T2) between Radus and Plavnica, on 2 kilometers distance (second line of pollution); and third one (T3) between Krnjice and Gostilje, closer to the border with neighboring Albania (third line of pollution).
In these three transects (T1, T2, T3) overall ten stations were chosen. Transect T1 has three representative stations: first (Moraca 1) on the left branch of Moraca River, second (Moraca 2) on the right branch of Moraca River, and third (Virpazar) between the branches, on the opposite side close to the Virpazar shore. Transect T2 also has three representative stations: first (Radus) on the left side of the lake, second (Plavnica) on the right side of the lake, and third (Radus-Plavnica) in between first and second stations. Transect T3 has four representative stations: first (Gostilje) on the right side of the lake, second (Krnjice) on the left side of the lake, and other two stations (Gostilje-Krnjice 1 and 2) are located in between those two stations (see figure 4-1).
Physical-chemical parameters that were measured at all sampling stations in the surface water are pH, EC, temperature, and dissolved oxygen (DO). It is well known that pH is important for desorption of the metals. For example low pH effects in more hydrogen ion in the water and more competition between the latter and the metals for the binding sites in the sediment. Furthermore, in high EC values, heavy metals ions in the water will also be in competition with the cations. Thus, high EC values will lead to more metals in the dissolved form. Low DO values will lead to decrease of redox potential, and this means release of metals in the water phase.
Metals that were analyzed are K, Ca, Mg, Ni, Cu, Fe, Zn, Pb, Cd, Mn, Cr, Si, Co and Al. These metals were analyzed due to the high expected presence in the lake, either from natural or anthropogenic sources.
Above parameters were investigated in different compartments of the lake. The compartments that were investigated are: 1. Lake water (surface and bottom water); 2. Lake sediment; 3. Lake biota (plant species: Phragmites communis, Vallisneria spiralis; fish species: Rutilus karamani, Alburnus scoranza, Scardinius knezevici, Squalius cephalus, Perca fluviatilis).
Mirjana VEMIC 31 For quality control of the water, sediments and biota samples, replicates were taken. Thus, from each sampling station, three replicates were collected. Replicates were taken in order to see the heterogeneity and spatial variability of samples. Samples were labeled and transferred to laboratory of Bio-Technical University in Podgorica (Montenegro) for further processing and preparation for sending to UNESCO-IHE laboratory.
Table 4-1 shows the names of ten sampling stations along with their code and coordinates. Coordinates were measured using the GPS navigation system.
Table 4-1: Sampling stations in Skadar Lake Sampling station name Station code Coordinates ○ Moraca 1 S1 42 16‘ 32.97‖ N 19○ 07‘ 16.27‖ E
○
1 Moraca 2 S2 42 15‘ 43.82‖ N T 19○ 08‘ 39.56‖ E ○ Virpazar S3 42 14‘ 40.61‖ N 19○ 06‘ 26.00‖ E ○ Radus S4 42 14‘ 42.08‖ N 19○ 10‘ 40.23‖ E
○
2 Radus-Plavnica S5 42 14‘ 44.40‖ N T 19○ 11‘ 10.37‖ E ○ Plavnica S6 42 15‘ 37.95‖ N 19○ 11‘ 39.23‖ E ○ Gostilje S7 42 15‘ 27.29‖ N 19○ 15‘ 29.41‖ E ○ Gostilje-Krnjice 1 S8 42 14‘ 07.48‖ N
○
3 19 14‘ 42.11‖ E
T ○ Gostilje-Krnjice 2 S9 42 12‘ 28.20‖ N 19○ 14‘ 04.06‖ E ○ Krnjice S10 42 10‘ 23.61‖ N 19○ 12‘ 44.05‖ E
32 MSc Thesis
S1 S2 S6
S7 S5
S3 S8
S4
S9
S 10
Figure 4-1: Sampling stations in Skadar Lake (Transect I-S1-S3); (Transect II-S4-S6); (Transect III-S7-S10); (Source: Knezevic, 2009)
Mirjana VEMIC 33 4.2 Sample collection and preservation
From every sampling location samples were taken within the area of 20-50 m2, and furthermore three replicates of every parameter (physico-chemical parameters, water, sediment and biota) were taken. All sampling stations in the lake were positioned where water was 5-10 m deep. First sampling campaign took place in the middle of November 2010, and the average temperatures for this time of the year were between 11-15○C. Second sampling campaign took place in the middle of January 2011, and the average temperatures were between 8-10○C.
4.2.1 Physico-chemical parameters
All the physicochemical parameters (temperature, pH, EC, DO) were measured in situ in the surface water (1 m depth) of Skadar Lake with temperature, pH, EC, and DO meter (MULTILAB). In every station physico-chemical parameters were measured (10 stations x 3 replicates = 30 measurements).
4.2.2 Lake water
Water samples were collected from the surface and the bottom (5-10 m depth) of the lake using a Nansen bottle (10 stations x 3 surface and 3 bottom samples = 60 samples). Water samples were then transferred in polyethylene bottles (PE bottles) of 100 ml, accompanied with a PE cap. Then samples were preserved with few drops of nitric acid (HNO3 65%), till they reach a pH of 2 (APHA, 1985). All the bottles used for storage were previously rinsed with weak acid to prevent contamination. Samples were labeled and transferred to Bio- Technical University in Podgorica.
4.2.3 Lake sediment
As sediment characteristics change slowly, this makes it easier to collect representative samples which, for example, are less dependent on seasonal variations and serve as a storage compartment for long-term release (Van Der Oost et al., 1996).
First Sampling Campaign: Around 1000 g of sediment from each sampling station was collected with the help of Eckman grab sampler (three replicates, each approximately 300g). Overall 30 sediment samples were taken (10 stations x 3 replicates = 30 samples). The sediment grabber with sediment was emptied in plastic container where plants and big stones were removed. Then the collected samples were immediately transferred to polyethylene bags, labeled and transferred to laboratory of Bio-Technical University in Podgorica for further processing.
Second Sampling Campaign: Around 1000 g of sediment from each sampling station was collected with the help of Eckman grab sampler (two replicates, each approximately 500g). Overall 20 sediment samples were taken (10 stations x 2 replicates = 20 samples). Collected samples were immediately transferred to polyethylene bags and additional air was removed with the hand vacuum cleaner. Samples were labeled and transferred to laboratory of Bio- Technical University in Podgorica for further processing.
34 MSc Thesis
Lake Skadar
Physical-Chemical Water Sediment Biota
Parameters (pH, DO, EC, T) Samples Samples Samples in Surface Water
Surface Bottom For For Plants Fish Water Water Total SEM Samples Samples HM
○ ○ Prese rved with Nitric Dried at 105 C for 24h Dried at 105 C for 24h Acid (65%)
Digested in MARS 5 for 1 hour
Sealed in Vacuum
Bags, Wet Sieved
and Dried at 70○ C Total Heavy Metal Analysis for 24h with ICP-AES
Five Steps Sequential Extraction
Procedure
Figure 4-2: Schematic presentation of sampling campaign and methodology used for analyzing samples from Skadar Lake
Mirjana VEMIC 35 4.2.4 Lake biota
Another important compartment that was analyzed is the biota compartment of the lake. From the lake biota, accent was put on plant and fish species. The original idea was to take a look at whole food chain in the lake, but unfortunately due to the time of sampling some biota representatives that were planned to be taken were not present (e.g. pelagic crustaceans (Daphnia sp.) due to their small biomass it was impossible to take representative sample; small benthic mollusk (Valvata piscinalis) was not present in the lake sediment even after repetition of sediment sampling for 60 times).
Plant samples: In the sampling campaign two representative plant species were taken. Firstly, large reed (Phragmites communis) belts are present in the shore of Lake Skadar, thus they represents the first line organisms which uptake the heavy metals from the tributaries inflow. Reeds were removed from the sediment (from the lake shore, from every transect three plant replicates were taken), and roots/bottom part was cleaned with the lake water in order to clean them from the sediment particles. Then the bottom part (below water surface) was divided from the upper part (above the water surface) and sealed in PE bags. Plants were separated into upper and bottom part in order to see an overview of the metal uptake in different parts of the plant, and furthermore to show the potential risk of bioaccumulation if the allowed levels are exceeded. Leafs were not present in the plants, due to the period of sampling. Secondly, the benthic aquatic plant (Vallisneria spiralis) was also collected for metals analysis. This plant was collected from the lake bottom (while taking sediment samples), from every transect three plant replicates, and sealed in PE bags with the hand- vacuum cleaner. Samples were labeled and transferred to laboratory of Bio-Technical University in Podgorica for further processing.
Fish samples: Overall three kilograms of fish mixture was taken for metal analysis. Five fish species were collected - Rutilus karamani, Alburnus scoranza, Scardinius knezevici, Squalius cephalus and Perca fluviatilis. These fish species are the most important one in the lake because they represent the most exploited fish species for human consumption. Feeding patterns of these fish species (plant, invertebrates, and small sediment animals) indicate that they are good representatives for determination of metal concentrations (Shumka et al., 2008). Fish catch was placed in the hand-fridge at 4˚C and transferred to the laboratory for further processing.
36 MSc Thesis 4.3 Laboratory experiments
Laboratory work took place in two laboratories. In first sampling campaign samples were firstly processed (drying of samples, weighting, dissection of fish samples, packing) in the laboratory in the Bio-Technical University in Podgorica (Montenegro), where the sediment, fish and plant samples were pre-prepared for digestion. Second part of the laboratory work (digestion of samples, dilution preparation, ICP measurements) was done in the laboratory of UNESCO-IHE in Delft (the Netherlands). In the second sampling campaign all laboratory work (sieving, drying of samples, weighting, SEM, LOI, ICP) was done in the laboratory of UNESCO-IHE.
4.3.1 Digestion of sediment and biomass
First Sampling Campaign: Collected sediment and plant samples were dried in thermostat for 24 hours at the temperature of 105○C. Stones, parts of plants and other impurities of the raw sediment sample were removed carefully and the samples were grinded with the help of an agatar mortar. After mixing it well a representative sample of 50 g was taken.
From the fish samples muscles, liver and the gills were removed and dried at 105°C for 24 h. Finally, samples were weighted and representative sample of 50 g was taken.
Second Sampling Campaign: Part of collected sediment samples were wet sieved (sieving sizes: <0.45μm; 0.45- 106 μm; 106-500 μm; 500 μm -1mm; >1mm), then dried in thermostat for 24 hours at the temperature of 70○C. Stones, parts of plants and other impurities of the raw sediment sample were removed carefully and the samples were grinded with the help of an agatar mortar. After mixing it well a representative sample was taken. Other part was kept under anoxic conditions and used for SEM (see later in text).
For digestion procedure, approximately 0.5 grams of dry sediment, plant and fish samples were used. Digestion was conducted with the help of 10 ml HNO3 (65%) in digestion tubes of a MARS 5 microwave, which is working under high temperature and pressure. Afterwards, samples were transferred in 50 ml volumetric flasks and filled up to the mark with demineralised water and few drops of HNO3 (65%) were added. Before heavy metal analysis, the samples were left undisturbed for 24 hours. For every concentrated sample dilutions were also made. All dilutions were done with demineralised water.
4.3.2 Water samples
Since water samples were preserved with HNO3, they did not need any kind of preparation for metal analysis, and they been measured directly in Inductively Coupled Plasma (ICP).
4.3.3 Analysis of metals
The metal analysis was performed in Inductively Coupled Plasma (ICP), Perkin -Elmer Optima 3000. Metals that were analyzed are K, Ca, Mg, Ni, Cu, Fe, Zn, Pb, Cd, Mn, Cr, Co and Si. Additionally, Aluminum was analyzed in Belgium at Ghent University (Laboratory of Analytical Chemistry and Applied Ecochemistry).
Mirjana VEMIC 37 4.3.4 Loss On Ignition (LOI)
Pre-weighed, dried sediment samples at 70 ºC, were placed in aluminum cups and combusted at 520 ºC in a furnace for four hours. The weight lost on ignition represents the volatile solids and gives a rough approximation of the amount of organic matter present in the solid fraction of the sediment (APHA, 2005).
4.3.5 Sequential Extracted Metals (SEM)
The use of sequential extractions provides detailed information about the origin, mode of occurrence, biological and physicochemical availability, mobilization, and transport of trace metals. Sequential extraction method was applied according to (Tessier et al., 1979) and also following the modified procedure from (Kelderman and Osman, 2007). According to this procedure there are five groups of binding forms of metals in sediments.
Initially, 10 grams of wet sediment were used and all the steps were performed in same centrifuge tubes in order to avoid weight loss. The first two steps were carried out in a glove box (anaerobic chamber); third and fourth steps in a shaker water bath and at the end the residual fraction is digested in a microwave accelerated reaction system (MARS 5). Different steps performed included:
(F1) - Exchangeable fraction: In wet sediment sample (10g) 20 ml of magnesium chloride solution (1M MgCl2) had been added. That mixture was stirred continuously for one hour with the help of a magnetic stirrer. The sample was then centrifuged (3500 rpm for 30 minutes) and the supernatant was acidified with few drops of HNO3 (65%).
(F2) - Bound to carbonate fraction: The residue from F1 was exposed to 20 ml of sodium acetate solution (1.0M NaAc adjusted to pH 5.0 with HAc), while continuous stirring is applied for three hours. The sample was then centrifuged (3500 rpm for 30 minutes) and the supernatant was acidified with few drops of HNO3 (65%).
(F3) - Bound to Fe-Mn oxides fraction: The residue from F2 was mixed with 20 ml of hydroxylamine hydrochloride (0.04M NH2OHxHCl) in 25% acetic acid (HAc v/v). Agitation for six hours was then maintained in the shaker water bath under 93ºC. The sample was centrifuged (3500 rpm for 30 minutes) and the supernatant was acidified with few drops of HNO3 (65%).
(F4) - Bound to organic matter fraction: The residue from F3 was oxidized with hydrogen peroxide in acidic media pH<2 (6 ml 0.02M HNO3 followed by 10 ml of 30% H2O2) and left for one hour at room temperature. Agitation for two hours was then maintained in the shaker water bath under 86ºC. Furthermore, 6 ml of H2O2 (30%) was added in the sample, and agitation in the water bath was maintained for three more hours. After cooling, 10 ml of 3.2M NH4Ac in 20% HNO3 was added and the sample was diluted by adding 40 ml of demineralised water and stirred continuously for 30 minutes at room temperature. The sample was then centrifuged (3500 rpm for 30 minutes) and the supernatant was acidified with few drops of HNO3 (65%).
38 MSc Thesis (F5) - Residual fraction: The residue from F4 was digested with 10 ml 65% HNO3 in a microwave accelerated reaction system (MARS 5), under high pressure and temperature, to extract metals attached to detritus or minerals. Then the sample was transferred to a 50 ml volumetric flask and left overnight. All the supernatants from each extraction step were analyzed with ICP (Inductively Coupled Plasma) for determination of trace metals.
4.3.6 Quality control
Blanks were prepared in each digestion procedure for total heavy metal analysis. They consist of HNO3 (65%) used to check the level of heavy metals in the acid. Furthermore, during digestion, one of the samples was used as a control sample for temperature and pressure. All dilutions for total heavy metal analysis were done with demineralised water. During the ICP measurements, after every ten samples, standard was measured in order to check quality of work.
In order to assure anoxic conditions for some parts of the experiment, frequent maintenance of the glove box was necessary. This implied repeated flushing (two times per week) of the main glove box area with nitrogen gas and removal of the air by vacuum pumps. Also, every time the small chamber was opened to take out/bring in samples/materials, the vacuum/nitrogen gas flushing procedure was followed in order to preserve the main chamber under anoxic conditions. Moreover, for calibration purposes, standards were prepared from the stock solutions (provided by UNESCO-IHE laboratory) for all the metals.
Mirjana VEMIC 39
40 MSc Thesis 5 Chapter Five - Results
5.1 First sampling campaign in Skadar Lake (Montenegrin part)
During November 2010, water (surface and bottom water), sediment, plant and fish samples were taken from three transects in Skadar Lake, from overall 10 sampling stations (see Chapter 4, figure 4.1). Each sample was taken in three replicates there where the lake was 5-10 meters deep. In this section, results of a screening of water, sediment, plants and fish samples are presented with regards to total aluminum, chromium, copper, cobalt, iron, manganese, nickel, potassium, calcium, magnesium, lead, cadmium, silica and zinc concentrations.
5.1.1 Physical-chemical parameters in lake water
During the first sampling campaign parameters measured in situ in surface water of Skadar Lake were pH, electrical conductivity and temperature. Surface temperature and pH were relatively constant with temperature in between 11.2-14.5ºC and pH in between 6.72-7.43. Electrical conductivity manifested wider range of values with the lowest value in Gostilje- Krnjice 1 (S8) with value of 235 μS/cm, and the highest value in Plavnica (S6) with value of 400 μS/cm. In general the highest values of electrical conductivity were found in sampling stations from Transect 2 (see details in Annex A, table 1).
5.1.2 Metals in surface and bottom water
Water samples were collected from the surface (1 m depth) and the bottom (5-10 m depth) layer of the lake. Analysis of those samples showed that even though fourteen metals were analyzed, only six of them were detected in the surface and bottom layer of Skadar Lake water. Fist analysis of water samples took place in UNESCO-IHE laboratory and the instrument used for the analysis was Inductively Coupled Plasma (ICP), Perkin-Elmer Optima 3000. From fourteen metals analyzed, only four of them were measured. Those are potassium, calcium, magnesium, and manganese. The other ten metals were below detection limits of the instrument used for analysis. Detection limits of the instrument used for the analysis are presented in Annex B (table 1), were they been compared with Flemish basic freshwater quality standards (VLAREM II, 2010), and for all these metals detection limits were below the recommended standards.
Concentrations of cadmium, lead, cobalt, nickel and aluminum were again measured at Ghent University, in Belgium, in Laboratory of Analytical Chemistry and Applied Ecochemistry. First three metals were below detection limits of the instrument, and last two (nickel and aluminum) were detected in the water samples.
Mirjana VEMIC 41 Potassium has shown relatively low values; between 0.01 and 2.18 mg/l in surface lake water and between 0.20 and 3.51 mg/l in bottom lake water. Magnesium and calcium were found in all sampling sites with relatively constant values, between 4.81-8.34 mg/l and 36.65-62.07 respectively (see figure 5.1 and 5.2). When we compare the surface and the bottom layer of the lake water (see ratios in table 5.1), there is no big difference in metal concentrations.
100 5.0 90 4.5 80 4.0 70 3.5 Ca 60 3.0 50 2.5 Mg 40 2.0 30 1.5 K 20 1.0
Concentration (mg/l) Concentration 10 0.5 0 0.0 1 2 3 4 5 6 7 8 9 10 Sampling stations
Figure 5-1: Average concentrations ± standard deviations (n=3) for total calcium, magnesium and potassium in surface layer of Skadar Lake water (mg/l). Concentrations of calcium and magnesium presented on the primary axis, while concentrations of potassium are presented on the secondary axis
100 5.0 90 4.5 80 4.0 70 3.5 Ca 60 3.0 50 2.5 Mg 40 2.0 30 1.5 K
20 1.0 Concentration (mg/l) Concentration 10 0.5 0 0.0 1 2 3 4 5 6 7 8 9 10 Sampling stations
Figure 5-2: Average concentrations ± standard deviations (n=3) for total calcium, magnesium and potassium in bottom layer of Skadar Lake water (mg/l). Concentrations of calcium and magnesium presented on the primary axis, while concentrations of potassium are presented on the secondary axis
42 MSc Thesis Concentrations of manganese, nickel and aluminum are presented in figures 5.3 and 5.4. In bottom water these metals (Mn, Ni and Al) were present in higher values than in the surface lake water. Aluminum is the most prominent metal among them and it reaches value of 92.67 μg/l in surface water layer of station Moraca 2 (S2), and value of 119.33 μg/l in bottom water layer of station Radus-Plavnica (S5).
120
100
80 Ni
60 Mn
40 Al
20 Concentration (μg/l) Concentration 0 S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 Sampling stations
Figure 5-3: Average concentrations ± standard deviations (n=3) for total nickel, manganese and aluminum in surface layer of Skadar Lake water (μg/l).
120
100 Ni 80
60 Mn
40 Al
20 Concentration (μg/l) Concentration 0 S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 Sampling stations
Figure 5-4: Average concentrations ± standard deviations (n=3) for total nickel, manganese and aluminum in bottom layer of Skadar Lake water (μg/l).
Manganese reaches its peak in station Plavnica (S4) in both, surface and bottom water, with values of 20.33 μg/l and 31.00 μg/l respectively. Nickel had its highest values in station Moraca 1 (S1), with concentrations of 14.57 μg/l in surface water layer and 12.33 μg/l in bottom water layer of Skadar Lake.
Mirjana VEMIC 43 Table 5-1: Ratio between surface (n=3) and bottom water (n=3) considering the concentrations of calcium, magnesium, potassium, nickel, manganese and aluminum
Station Ratio Ratio Ratio Ratio Ratio Ratio (Ca) (Mg) (K) (Ni) (Mn) (Al) S1 0.91 0.88 0.02 1.18 0.69 1.08 S2 0.90 0.96 1.09 0.31 0.51 0.86 S3 0.88 0.97 0.02 0.40 0.25 0.00 S4 0.90 0.90 0.71 0.38 0.66 0.00 S5 0.89 0.96 0.92 0.86 0.22 0.39 S6 0.92 0.91 1.21 0.84 0.73 0.90 S7 0.99 0.89 0.24 0.21 0.74 0.57 S8 0.95 0.90 0.00 0.89 0.50 0.00 S9 0.92 0.89 0.00 0.38 0.43 1.04 S10 0.99 0.99 0.41 0.34 0.81 0.00
5.1.3 Metals in plants
Reed (Phragmites communis) was collected near the lake shore from 1-2 m depth, and from every sampling transect (transect I, II and III), only from locations where plant was present, three plant replicates were taken. Benthic aquatic plant (Vallisneria spiralis) was also collected for metal analysis. This plant was collected from the lake bottom (while taking sediment samples), from every transect three plant replicates, also only from locations where plant was present. For metal analysis, plants were dried at 105○C for 24h and later a mixture of the three replicates was used for digestion, in order to have representative sample of the whole transect.
Benthic aquatic plant (Vallisneria spiralis), has in general higher concentration of metals compared to the reeds (Phragmites communis). Nickel, lead, chromium, iron, manganese, magnesium, silica and aluminum have the higher values in Vallisneria spiralis from transect I, while potassium, zinc and copper have the highest values in Vallisneria spiralis from transect III. The highest values occur in transect I, possibly due to the constant loading of pollution from main tributaries.
44 MSc Thesis 40 35 30 25 20 15
10 Concentration (mg/kg) Concentration 5 0 Ni Cu Zn Pb Cd Cr T1 Vallisneria spiralis T2 Vallisneria spiralis T3 Vallisneria spiralis T1 Phragmites communis T2 Phragmites communis T3 Phragmites communis
Figure 5-5: Concentrations (n=1) of total nickel, copper, zinc, lead, cadmium and chromium in two plant species (Phragmites communis and Vallisneria spiralis) from transect I, II and III in Skadar Lake, expressed as mg/kg dry weight.
67919 58456 77868 18000 16000 14000 12000 10000 8000 6000 4000 Concentration (mg/kg) Concentration 2000 0 K Ca Mg Fe Si Al Mn T1 Vallisneria spiralis T2 Vallisneria spiralis T3 Vallisneria spiralis T1 Phragmites communis T2 Phragmites communis T3 Phragmites communis
Figure 5-6: Concentrations (n=1) of total potassium, calcium, magnesium, iron, silica, aluminum and in two plant species (Phragmites communis and Vallisneria spiralis) from transect I, II and III in Skadar Lake, expressed as mg/kg dry weight. Values of Ca (mg/kg) for Vallisneria spiralis are presented on top of the graph in small colored squares.
Mirjana VEMIC 45
Results presented in table 5.2 show that among all metals iron, lead, cadmium, manganese and chromium have elevated values compared to the permitted standard (Bigdeli et al. 2008).
Table 5-2: Metal concentrations (n=1) in plants species (Phragmites communis and Vallisneria spiralis) from Skadar Lake compared with the standards (Bigdeli et al. 2008). Values that are exceeding permitted levels are presented in bold. Plant species Ni Cu Fe Zn Pb Cd Mn Cr Al T1 Vallisneria spiralis 38.45 12.82 16774 30.53 6.43 0.42 922 24.71 4312 T2 Vallisneria spiralis 31.75 13.84 7278 30.10 4.44 0.52 736 18.93 3766 T3 Vallisneria spiralis 30.98 16.99 9111 38.24 4.75 0.64 749 15.39 3355 T1 Phragmites communis 1.24 2.26 284 33.72 2.78 0.07 25.14 1.64 301 T2 Phragmites communis 2.09 2.82 610 14.29 2.00 0.04 51.52 2.57 435 T3 Phragmites communis 1.76 2.93 456 27.19 2.56 0.05 37.38 2.24 387 Standards for metals in plants 67 73 425 100 0.3 0.1 500 0.5 - (Bigdeli et al. 2008), mg/kg
5.1.4 Metals in fish
Five fish species were collected for metal analysis-Rutilus karamani, Alburnus scoranza, Scardinius knezevici, Squalius cephalus and Perca fluviatilis. These fish species are the most important ones in the lake because they represent the most exploited fish species for human consumption. Results had show that aluminum is present in Rutilus karamani with the value of 137.2 mg/kg, and manganese with the value of 19.72 mg/kg. In other fish species values of these metals are quite lower but prominent (see Annex C, table 2). Comparing to the available standards, zinc is the metals exceeding permittable limits.
1366 1574 1683 1805 1588 500 450 400 350 300 250 200 150 100 50 Concentartion (mg/kg)Concentartion 0 K Ca Mg Fe Si Al Mn Zn Alburnus scoranca Scardinius knezevici Squalius cephalus
Rutilus karamani Perca fluviatilis
Figure 5-7: Concentrations (n=1) of total potassium, calcium, magnesium, iron, silica, aluminum manganese and zinc in fish species (Perca fluviatilis, Rutilus karamani, Squalius cephalus, Scardinius knezevici and Alburnus scoranca) from Skadar Lake, expressed as mg/kg dry weight. Values for Ca (mg/kg) for all five fish species are presented on the top of the graph in small colored squares.
46 MSc Thesis
11.5 7 6 5 4 3 2
Concentration (mg/kg) Concentration 1 0 Ni Cu Pb Cd Cr Alburnus scoranca Scardinius knezevici Squalius cephalus Rutilus karamani Perca fluviatilis
Figure 5-8: Concentrations (n=1) of total nickel, copper, lead, cadmium and chromium in fish species (Perca fluviatilis, Rutilus karamani, Squalius cephalus, Scardinius knezevici and Alburnus scoranca) from Skadar Lake, expresed as mg/kg dry weight. Values for Ca (mg/kg) for Scardinius knezevici are presented on the top of the graph in small red square.
5.1.5 Metals in sediment
Lake sediment was collected in three replicates from each sampling station, stored in PE bags and transferred to the laboratory. There it was dried, grinded and digested. Analysis was done for thirteen metals using an ICP-AES and the results are shown in this section (table 5.3).
In order to identify polluted sites in Skadar Lake concentrations of the metals obtained from the analysis were compared with the existing standards. Considering this fact, sediment quality guidelines for freshwater ecosystems, mainly focused on potential adverse effects on sediment dwelling organisms (Jaagumagi, 1992; MacDonald et al., 2000), were used for comparison. Referring to the same authors, consensus based SQGs (sediment quality guidelines) were developed for each contaminant (Table 5.3). These guidelines include a threshold effect concentration (TEC; below which adverse effects are not expected to occur) and a probable effect concentration (PEC; above which adverse effects are expected to occur more often than not). Also Dutch standards for soil/sediment remediation (VROM, 2000) were used to evaluate the results. Target values indicate the level at which sustainable soil/sediment quality is achieved, whereas the intervention values indicate when the functional properties of the soil/sediment are seriously impacted or threatened (VROM, 2000).
Low concentrations compared with the standards were found for zinc, lead, cadmium and iron except for site S5 (Fe is having a value of 20789 mg/kg). The guidelines used do not include aluminum, silica, magnesium, calcium and potassium, thus comparison in this case is not possible. Detection limits of the metals along with the permitted standards (Jaagumagi, 1992; MacDonald et al., 2000) can be found in Annex B (table 1). Conclusion is that analytic equipment is sensitive enough to detect all measured metals.
Mirjana VEMIC 47 As it is shown in table 5.3, high concentrations of nickel, copper, chromium and manganese are present in the lake sediment. Manganese levels are exceeding TEC values in S1-491.78 mg/kg, and S5-733.46 mg/kg. Chromium standards are not exceeded except in station S2. In every other station values are above TEC levels. Based on the guidelines for freshwater dwelling organisms, adverse effects are expected to happen due to high concentrations of nickel in S1 (104.79 mg/kg), S4 (91.62 mg/kg), S5 (116.41 mg/kg), S6 (87.55 mg/kg), S8 (72.04 mg/kg) and S9 (65.49mg/kg). In other stations (S2, S3, S7 and S10), concentrations of nickel are above the TEC values. Copper values are also quite high in eight sampling stations (exceeding the TEC values), but in the S7 and S10 values are low.
Regarding the need for intervention, all sites have concentrations below the target value meaning that no interventions are needed.
48 MSc Thesis Table 5-3: Average metal concentrations (mg/kg dry weight) ± standard deviation (n=3) for Skadar Lake sediment during the first sampling campaign Site K Ca Mg Fe Cu Zn Pb Cd Mn Cr Ni Al
S1 1078±117 110464±2929 16448±779 16711±1173 24.1±3.1 40.1±4.0 13.2±8.8 0.31±0.5 491.8±60.9 68.2±2.5 104.8±5.4 11964±21
S2 1293±89 55709±6827 10363±1192 11688±1448 20.9±3.2 41.2±3.4 12.2±3.6 0.68±0.5 253.5±26.1 23.6±2.2 28.1±2.8 10676±2
S3 1819±281 73738±13412 13042±1419 15077±2103 26.1±5.1 53.3±8.2 14.3±4.4 0.78±0.9 310.6±32.3 29.2±2.7 35.5±4.0 14767±14
S4 1047±49 77168±3526 10127±560 15865±913 24.8±1.9 47.9±2.5 15.9±2.3 0.43±0.6 198.6±15.5 91.4±1.7 91.6±6.5 11732±4
S5 1463±34 12404±8304 17340±1189 20789±1231 33.0±2.5 1.4±0.4 14.5±5.3 0.41±0.9 733.5±28.3 74.5±3.3 116.4±5.5 15233±5
S6 734±159 165855±13195 15305±791 13615±1316 19.0±2.8 29.2±3.0 0.1±3.5 0.00±0.8 412.4±14.1 55.2±4.8 87.5±8.8 79±3
S7 773±554 97847±59607 7386±3919 10734±7224 14.6±11.7 31.8±26.6 14.6±4.0 0.46±0.5 420.5±301.8 33.6±21.5 46.3±31.1 12566±21
S8 1076±125 77915±24342 8448±1305 13653±2235 19.1±2.7 38.3±3.4 12.7±1.4 0.36±0.3 344.2±270.0 63.7±9.8 72.1±8.1 9830±8
S9 1044±148 76774±18877 7907±1113 12857±2319 18.4±1.9 33.5±2.7 12.1±5.4 0.32±0.5 409.4±247.1 52.1±9.6 65.5±6.0 9494±2
S10 800±145 79281±5868 10291±855 9804±529 11.1±1.6 21.5±0.8 7.2±3.0 0.23±0.6 349.9±29.0 37.1±1.2 44.2±3.0 7013±1
TEC 200002 1211 0.991 4602 43.41 22.71 1202 262 162 PEC 437702 4591 4.981 11002 1111 48.61 8202 1102 Target value 1233 0.83 1003 353 Intervention 7203 3803 2103 value 1 MacDonald et al., (2000) Legend: 2 Jaagumagi (1992) Sites exceeding TEC values 3 VROM(2000) Sites exceeding PEC values
Mirjana VEMIC 49 5.2 Second sampling campaign in Skadar Lake (Montenegrin part)
The second sampling campaign was launched in January 2011 when sediment samples were taken for sequential extraction of metals (SEM), loss on ignition, sieving and total metal analysis. From each sampling site (S1-S10) two replicates were taken. Samples for SEM were stored and transported in anoxic conditions and were kept upon arrival in the laboratory, in glove box flushed with nitrogen gas in order to maintain anoxic conditions. With this procedure (SEM) metal distribution in different fractions of the sediment was investigated. Last year a similar analysis (SEM) was done on sediments of Ohrid Lake (Malaj, 2010), and during that study a quality control experiment confirmed that this way of sampling, storing and transporting did not significantly alter the characteristics of sediment.
5.2.1 Physical-chemical parameters in lake water
During the second sampling campaign parameters measured in situ in surface water of Skadar Lake were pH, electrical conductivity, temperature and dissolved oxygen. Surface temperature and pH were relatively constant with temperature in between 8.0-9.3ºC and pH in between 7.6-8.9. Again, electrical conductivity showed wider range of values, with the lowest value in Gostilje (S7) with value of 267 μS/cm, and the highest value in Plavnica (S6) with value of 407 μS/cm. Dissolved oxygen showed relatively constant values between 10.3- 11.3 mg/l in all stations except in Plavnica (S6) where the values of dissolved oxygen were the lowest (7.8 mg/l) (details in Annex A, table 2).
5.2.2 Total metal levels in sediment
During the second sampling campaign, lake sediment was collected in two replicates from each sampling station (ten stations), immediately stored in PE bags and transferred to the laboratory. Part of the samples dried for 24h at 70○C, grinded and digested. Analysis was done for fourteen metals using an ICP-AES and the results are shown in this section (Table 5.4).
Low concentrations compared with the standards (MacDonald et al., 2000; Jaagumagi, 1992) were found for zinc, lead and cadmium. Elevated concentrations of nickel, copper, chromium, manganese and iron (S1-20521.19 mg/kg; S8-22904.71 mg/kg; S9-23013.91 mg/kg) are present in the lake sediment. Chromium levels are exceeding TEC values in all sampling stations (S1-S10). Based on the guidelines for freshwater dwelling organisms, adverse effects are expected to happen due to high concentrations of nickel in all sampling stations due to the exceedance of PEC values. Regarding the need for intervention, all sites have concentrations below the target value meaning that no interventions are needed. Copper values are also quite high in eight sampling stations (exceeding the TEC values), except in the station S2 where the value can be neglect (9.24 mg/kg). Regarding manganese five sampling stations are having values above TEC level and these concentrations are quite close to the PEC levels (especially the stations from transect 3: S8 820.54 mg/kg; S9-832.53 mg/kg; S10-878.34 mg/kg).
50 MSc Thesis Table 5-4: Average metal concentrations (mg/kg dry weight) ± standard deviation (n=2) for Skadar Lake sediment during the second sampling campaign Site K Mg Fe Cu Zn Pb Cd Mn Cr Ni Al Co
S1 1236±336 18652±1426 20521±2352 27.1±2.6 50.9±5.3 8.4±0.4 0.25±0.1 624.6±74.2 79.2±7.8 126.8±12.5 5452±766 13.9±1.2
S2 427±58 11817±752 9654±309 9.2±1.4 25.7±1.3 12.7±6.9 0.34±0.8 370.5±0.0 34.6±2.4 63.3±2.5 10532±80 7.4±0.9
S3 1183±58 11520±714 17326±1028 23.9±1.2 46.0±2.9 15.9±6.6 0.40±0.3 808.6±48.5 52.7±3.1 84.4±4.2 11949±23 11.1±1.3
S4 1112±195 9658±1193 13907±1389 26.4±1.5 61.8±0.7 16.7±6.0 0.51±1.0 246.4±23.6 73.8±6.1 73.9±8.9 12306±191 7.5±0.5
S5 1127±158 8559±4110 12191±5414 20.8±9.8 50.4±19.1 18.0±4.7 0.51±0.1 201.5±95.9 65.1±28.7 64.9±32.9 12524±159 8.4±4.5
S6 1040±193 11497±1631 15987±1993 30.5±2.9 74.3±16.5 18.1±4.8 0.55±0.3 277.8±23.2 73.2±2.6 76.6±2.9 13785±154 8.6±1.1
S7 2084±303 12774±387 19399±1051 36.9±1.7 60.3±1.9 21.1±3.3 0.57±0.2 239.1±6.3 63.7±3.0 85.3±3.6 15189±186 13.5±0.1
S8 1708±20 13302±23 22905±38 32.4±0.2 55.5±0.8 21.4±5.6 0.59±0.7 820.5±16.5 66.5±0.0 110.9±0.2 17207±182 13.8±0.2
S9 1848±208 13412±53 23014±176 33.0±0.4 56.1±0.6 23.5±3.8 0.98±0.5 832.5±7.5 67.9±2.0 111.7±1.4 18385±93 12.2±0.7
S10 1443±138 12256±413 19467±629 27.7±0.6 51.1±1.6 25.4±2.7 0.52±0.6 878.4±29.1 57.9±2.7 90.9±2.5 20303±143 10.5±0.6
TEC 200002 31.61 1211 35.81 0.991 4602 43.41 22.71 162 1202 262 162 PEC 437702 1491 4591 1281 4.981 11002 1111 48.61 502 1102 8202 1102 Target value 363 1403 1003 353 93 Intervention 1903 7203 3803 2103 2403 value 1 MacDonald et al., (2000) Legend: 2 Jaagumagi (1992) Sites exceeding TEC values 3 VROM (2000) Sites exceeding PEC values
Mirjana VEMIC 51
5.2.3 Loss on Ignition (LOI) of sediment samples
Organic matter in the sediment was analyzed in ten sampling stations (each two replicates) of the lake and was found to be in a range from 4.72-21.49 % respectively (Figure 5-9).
25
20
15
10 Percentage (%) Percentage 5
0 S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 Stations Figure 5-9: Average percentage of sediment organic matter (n=2) in ten stations of Skadar Lake
LOI was checked for correlation with the total metals in the sediment (Table 5.5). Results are showing that there is no correlation between organic matter and metals.
Table 5-5: Correlations (r) between organic matter and metals in Skadar lake sediment
Ni Cu Fe Zn Pb Cd Mn Cr Al n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s. LOI 0.05 0.01 0.01 0.01 0.02 -0.04 0.11 0.05 -0.01 n.s. Correlation is not significant at the 0.05 level
Furthermore, correlations between metals from the sediment were done in order to identify their relationships with each other (Table 5.6).
Significant correlations were found between nickel in the sediment and copper, iron and chromium. Copper is found to relate highly with iron and zinc but less with aluminum. Iron is related with zinc, manganese and chromium. Furthermore, zinc is found to be in relation with lead, cadmium and aluminum. Lead is found to be strongly correlated with aluminum and cadmium, while the latter is related with aluminum.
52 MSc Thesis Table 5-6: Correlation coefficient (r) between total metals in Skadar Lake sediment
Ni Cu Fe Zn Pb Cd Mn Cr Al Ni 1.00 Cu 0.56* 1.00 Fe 0.79* 0.94** 1.00 Zn 0.25n.s. 0.90** 0.75* 1.00 Pb 0.01n.s. 0.43n.s. 0.34n.s. 0.65* 1.00 Cd -0.51n.s. 0.34n.s. 0.06n.s. 0.66* 0.71* 1.00 Mn 0.60n.s. 0.43n.s. 0.59* 0.11n.s. 0.00n.s. -0.29n.s. 1.00 Cr 0.89*. 0.48n.s. 0.67* 0.31n.s. 0.18n.s. -0.39n.s. 0.24n.s. 1.00 Al 0.00n.s. 0.55* 0.44n.s. 0.72* 0.94** 0.77* 0.20n.s. 0.06n.s. 1.00 * Correlation is significant at the 0.01 level (2-tailed); **Correlation is significant at the 0.001 level n.s. (2-tailed); Correlation is not significant;
5.2.4 Sieving analysis of lake sediment
Sediment samples (from one replicate) were wet sieved (sieving sizes: <045μm; 0.45- 106 μm; 106-500 μm; 500 μm -1mm; >1mm), then dried in thermostat for 24 hours at the temperature of 70○C. Stones, parts of plants and other impurities of the raw sediment sample were removed carefully and the samples were grinded with the help of an agatar mortar. After mixing it well a representative sample was taken and total metal analysis was conducted with ICP-AES. Percentage of different sieving fractions of the sediment, from all ten sampling stations, is graphically presented in figure 5.10 (more details presented in Annex D, table 1 and 2). From figures it can be noted that sediment in the Skadar Lake is mainly distributed in three smallest sieving fractions (colloid, clay and silt). Variations between sampling stations are visible, thus for example in station Moraca 1 (S1-left branch of the Moraca River) sediment is mainly present in silt fraction, while in the station Moraca 2 (S2-right branch of the Moraca River) sediment is present in silt and sand fraction (medium sand).
5.2.5 Metals in sieving fractions of the sediment
After wet sieving procedure, analysis of total metal content was conducted using the ICP- EAS. Analysis was conducted in every sieving fraction of the sediment and for every sampling station (ten sampling stations). Results are presented in table 5.7. Metals had showed quite variation in distribution patterns in different sieving fractions of sediment. Since nickel, chromium and copper previously had shown the highest concentrations in sediment (in both sampling campaigns) their distribution patterns will be presented in discussion part of the thesis (figure 6.1). Additionally, sum of metal concentrations in all sieving fractions of the sediment was compared with results from previous total metal analysis. Ratios are presented in table 5.8.
Mirjana VEMIC 53
S1 S2
S3 S4
S5 S6
S7 S8
S9 S10 9
Figure 5-10: Percentage of different sieving fractions of the sediment from all ten sampling stations
54 MSc Thesis
Table 5-7: Metal concentrations (mg/kg dry weight) in different sieving fractions of Skadar Lake sediment during the second sampling campaign (n=1)
Sieving K Ca Mg Ni Cu Fe Zn Pb Cd Mn Cr Co Al fractions 1.<0.45 μm 2618 92745 16333 141.57 55.49 25421 69.22 10.88 10882 804 95.20 17.25 21470 1. 0.45-106 μm 1500 103377 18949 112.95 36.49 19371 46.72 5.72 5722 592 76.45 12.48 12945 1. 106-500 μm 858 117590 18680 113.00 22.47 16692 36.23 <0.05 <0.05 448 71.89 12.05 2026 1. 500-1 μm 1482 59298 10464 108.48 78.36 15894 37.43 12.57 12573 614 85.38 19.30 31871 1. >1 mm 845 86367 12405 143.59 79.06 19085 64.53 85.47 85470 875 106.41 27.78 13547 2. <0.45 μm 1615 133904 18942 137.62 41.62 22104 59.90 8.67 1.24 671 85.05 15.62 15428 2. 0.45-106 μm 913 114378 22087 110.39 25.51 16335 37.11 <0.05 0.46 442 71.99 11.78 10482 2. 106-500 μm 370 159753 11221 56.53 9.28 8699 19.13 <0.05 0.19 312 34.56 5.78 5823 2. 500-1 μm 387 206800 11120 59.58 7.28 9626 17.34 <0.05 0.96 357 33.91 5.65 5756 2. >1 mm 494 202215 12581 72.35 10.02 11329 26.88 8.77 0.29 404 43.74 7.80 4961 3. <0.45 μm 1449 139411 11235 84.22 31.08 17098 47.25 5.49 1.37 779 61.57 8.73 15098 3. 0.45-106 μm 1381 171400 12412 89.88 27.33 19319 48.93 11.28 1.07 876 61.48 11.77 15077 3. 106-500 μm 1378 153222 11865 88.09 26.95 18876 52.25 22.85 0.88 851 60.74 13.09 1054 3. 500-1 μm 1581 177814 10387 88.74 44.04 17407 55.96 24.83 3.31 774 70.86 7.95 56622 3. >1 mm 1103 243235 10823 114.71 29.41 16500 90.20 38.24 4.90 693 124.51 10.78 39117 4. <0.45 μm 1406 97475 12776 118.74 38.45 18873 74.08 11.84 1.07 296 108.54 12.72 15922 4. 0.45-106 μm 895 106909 11689 89.06 28.79 14836 54.70 11.71 1.54 236 86.08 11.80 11996 4. 106-500 μm 722 122798 11037 73.97 26.32 12602 53.62 7.24 0.88 224 69.96 9.88 5929 4. 500-1 μm 747 150391 6950 60.37 59.39 12798 75.93 13.89 1.86 250 69.37 8.61 9765 4. >1 mm 506 163378 4314 42.68 29.59 10810 45.51 18.07 1.07 237 52.64 6.05 8271 5. <0.45 μm 1085 92138 10306 91.09 26.83 14643 54.65 11.39 0.59 228 77.82 9.90 13366 5. 0.45-106 μm 1139 107183 11877 88.98 28.54 15181 57.76 17.34 0.67 235 83.91 9.39 13218 5. 106-500 μm 915 106250 10059 72.72 27.18 12966 49.21 15.28 1.69 216 69.74 8.73 7361 5. 500-1 μm 897 128709 8228 70.23 33.24 13911 56.55 113.58 1.64 245 74.18 8.67 9826 5. >1 mm 597 137162 6052 59.27 34.65 13098 65.44 38.90 0.48 278 78.47 7.43 10135
Mirjana VEMIC 55 Table 5-7 (Continued): Metal concentrations (mg/kg dry weight) in different sieving fractions of Skadar Lake sediment during the second sampling campaign (n=1)
Sediment K Ca Mg Ni Cu Fe Zn Pb Cd Mn Cr Co Al fractions 6. <0.45 μm 981 112019 11653 87.02 27.21 14855 54.81 6.63 1.54 237 81.83 14.04 1211 6. 0.45-106 μm 743 1895 96718 142.45 144.31 4546 22.08 91.70 17.08 230 329.63 949.61 18339 6. 106-500 μm 968 129320 11902 79.03 26.31 14174 60.97 18.25 0.68 242 75.15 8.45 7834 6. 500-1 μm 1057 119543 8486 75.10 42.66 15426 62.90 24.50 0.69 281 86.01 11.41 11309 6. >1 mm 756 157293 6332 62.09 34.93 14692 61.61 27.35 <0.05 277 71.59 8.64 9328 7. <0.45 μm 1967 81029 11245 119.94 44.38 17977 72.57 12.08 1.03 267 129.21 11.52 19662 7. 0.45-106 μm 1653 67285 13095 84.00 34.95 16971 65.52 12.95 1.90 228 66.38 11.43 18285 7. 106-500 μm 2071 64971 14648 95.45 41.84 20313 75.05 17.74 2.37 252 75.14 15.28 3946 7. 500-1 μm 1533 58785 9190 105.47 54.86 21761 77.73 30.77 4.25 246 121.66 20.45 42510 7. >1 mm 1757 67272 11287 121.97 57.58 22344 111.74 67.42 4.92 266 170.08 21.21 26325 8. <0.45 μm 2022 130837 14590 121.03 33.60 24349 60.78 11.76 0.80 857 77.27 16.84 18003 8. 0.45-106 μm 474 12200 2509 43.16 13.01 5914 23.60 4.21 0.00 128 31.27 5.34 18726 8. 106-500 μm 1592 148985 12444 102.49 33.76 21845 55.90 8.49 0.74 823 66.24 13.56 606 8. 500-1 μm 2069 282536 15803 171.69 77.76 29246 92.65 18.38 2.39 1286 136.95 22.06 33455 8. >1 mm 468 284763 3023 35.83 20.08 7519 39.37 6.30 1.18 468 28.35 10.63 42519 9. <0.45 μm 2052 131337 14501 119.16 33.43 24002 63.47 14.37 0.80 834 80.24 17.37 19061 9. 0.45-106 μm 1646 137400 13090 109.30 36.20 22760 57.50 11.30 1.00 877 74.30 16.10 17300 9. 106-500 μm 1511 149449 11614 95.50 31.83 20733 53.94 9.08 1.56 798 63.12 14.31 435 9. 500-1 μm 650 123242 4335 46.60 30.27 9259 29.93 7.71 2.04 443 31.29 10.32 20181 9. >1 mm 213 291806 2502 24.79 11.34 5768 47.48 <0.05 1.68 371 26.89 9.24 21722 10. <0.45 μm 1418 155842 12298 93.97 25.29 18352 61.59 14.66 1.72 845 70.69 13.89 14463 10. 0.45-106 μm 1424 159837 12170 89.45 28.19 19918 54.16 14.81 0.51 918 63.89 13.39 15517 10. 106-500 μm 1130 167490 11254 83.17 24.71 17357 48.29 15.59 0.19 838 57.03 12.26 5522 10. 500-1 μm 643 261463 7274 103.66 37.80 13670 78.05 <0.05 <0.05 674 171.95 18.29 167073 10. >1 mm 1442 193547 10613 92.09 34.62 17739 76.28 8.55 3.42 828 75.43 13.25 1202
56 MSc Thesis Table 5-8: Ratio between sum of metal concentrations in all sieving fractions (n=1) and total metal concentrations in the sediment (n=2)
Stations Ratio Ratio Ratio Ratio Ratio Ratio Ratio Ratio Ratio Ratio Ratio Ratio Ratio Ratio (K) (Ca) (Mg) (Ni) (Cu) (Fe) (Zn) (Pb) (Cd) (Mn) (Cr) (Si) (Co) (Al)
S1 1.18 0.87 0.82 0.98 2.01 0.94 1.00 3.42 1.34 1.07 1.10 1.40 1.27 3.00 S2 1.77 0.83 1.29 1.38 2.03 1.41 1.25 0.69 1.84 1.18 1.55 1.34 1.27 0.81 S3 1.17 1.16 0.98 1.10 1.32 1.03 1.28 1.29 1.84 0.98 1.44 1.72 0.94 2.13 S4 0.77 1.17 0.97 1.04 1.38 1.01 0.98 0.75 2.52 1.01 1.05 1.66 1.30 0.84 S5 0.82 1.17 1.09 1.18 1.45 1.15 1.13 2.18 1.98 1.20 1.18 0.97 1.05 0.86 S6 0.73 0.79 2.35 1.94 1.80 0.74 0.44 1.86 2.16 0.75 1.76 0.76 1.16 0.85 S7 0.86 1.08 0.93 1.23 1.27 1.02 1.33 1.34 1.05 1.06 1.77 1.43 1.18 1.46 S8 0.78 1.20 0.73 0.86 1.10 0.78 0.98 0.46 1.73 0.87 1.02 1.04 0.99 1.32 S9 0.66 1.14 0.69 0.71 0.87 0.72 0.90 0.45 1.44 0.80 0.81 1.10 1.11 0.86 S10 0.84 1.16 0.87 1.02 1.09 0.89 1.25 0.53 0.17 0.93 1.52 1.38 1.35 2.01
5.2.6 Sequential extraction of metals (SEM)
During the second sampling campaign sequential extraction of metals (SEM) was carried out on each of both replicate sediment samples collected at the ten sites.
Figure 5.11 (A-J) presents the total concentration of metals (% of mg/kg dry weight) in different extraction fractions of the sequential extraction procedure. From the charts it is visible that cadmium is mostly bound to the organic matter fraction (IV) and it is also bound to iron and manganese (III), but in lower amounts. Chromium is present in three extraction fractions (III, IV and V), with its highest values in residual fraction (V). Copper and iron are mostly captured in residual fraction, while manganese is bound (apart from residual fraction) to the carbonate extraction fraction. Lower amounts of nickel are present in exchangeable fraction (I) and bound to carbonates. Potassium and zinc are showing similarities with copper and iron.
Mirjana VEMIC 57 S1 100% S1-V
80% S1-IV
60% S1-III
40% S1-II
Percentage (%) Percentage 20% S1-I 0% Cd Cr Cu Fe Mn Ni Pb Zn
Metals
S2 100% S2-V 80% S2-IV 60% S2-III 40% S2-II
20% Percentage (%) Percentage S2-I 0% Cd Cr Cu Fe Mn Ni Pb Zn Metals
S3 100% S3-V 80% S3-IV 60% S3-III 40% S3-II
Percentage (%) Percentage 20% S3-I 0% Cd Cr Cu Fe Mn Ni Pb Zn Metals
Figure 5-11: Total concentration of metals (% of mg/kg dry weight) in different extraction fractions of the sequential extraction procedure; S1-Moraca 1; S2-Moraca 2; S3-Virpazar; S4- Radus; S5-Radus-Plavnica; S6-Plavnica;S7-Gostilje;S8-Gostilje-Krnjice 1; S9-Gostilje- Krnjice 2; S10-Krnjice
58 MSc Thesis S4 100% S4-V 80% S4-IV 60% S4-III 40% 20% S4-II
0% S4-I Percentage (%) Percentage Cd Cr Cu Fe Mn Ni Pb Zn Metals
S5 100% S5-V 80% S5-IV 60% S5-III 40% S5-II 20% S5-I
0% Percentage (%) Percentage Cd Cr Cu Fe Mn Ni Pb Zn Metals
S6 100% S6-V 80% S6-IV 60% S6-III 40% S6-II 20% 0% S6-I
Percentage (%) Percentage Cd Cr Cu Fe Mn Ni Pb Zn Metals
S7 100% S7-V 80% S7-IV 60% S7-III 40% S7-II 20% S7-I
0% Percentage (%) Percentage Cd Cr Cu Fe Mn Ni Pb Zn Metals
Figure 5-11 (Continued): Total concentration of metals (% of mg/kg dry weight) in different extraction fractions of the sequential extraction procedure; ; S1-Moraca 1; S2-Moraca 2; S3- Virpazar; S4-Radus; S5-Radus-Plavnica; S6-Plavnica;S7-Gostilje;S8-Gostilje-Krnjice 1; S9-Gostilje-Krnjice 2; S10-Krnjice
Mirjana VEMIC 59 S8 100% S8-V 80% S8-IV 60% S8-III 40% S8-II
Percentage (%) Percentage 20% S8-I 0% Cd Cr Cu Fe Mn Ni Pb Zn
Metals
S9 100% S9-V 80% S9-IV 60% S9-III 40% S9-II 20% Percentage (%) Percentage S9-I 0% Cd Cr Cu Fe Mn Ni Pb Zn Metals
S10 100% S10-V
80% S10-IV
60% S10-III 40% S10-II
Percentage (%) Percentage 20% S10-I 0% Cd Cr Cu Fe Mn Ni Pb Zn Metals
Figure 5-11 (Continued): Total concentration of metals (% of mg/kg dry weight) in different extraction fractions of the sequential extraction procedure; S1-Moraca 1; S2-Moraca 2; S3- Virpazar; S4-Radus; S5-Radus-Plavnica; S6-Plavnica;S7-Gostilje;S8-Gostilje-Krnjice 1; S9-Gostilje-Krnjice 2; S10-Krnjice
60 MSc Thesis
Mirjana VEMIC 61
62 MSc Thesis 6 Chapter Six - Discussion
6.1 Water quality in Skadar Lake
Six metals that were found in Skadar Lake water were potassium, calcium, magnesium, manganese, nickel and aluminum, while eight others (chromium, copper, cobalt, iron, lead, cadmium, silica and zinc) were not detected.
Metals content in the water phase depends on their solubility in water. Most of the metals are generally bound to particulate matter, which settles quickly thereby transforming the sediment of the lake into the most important sink of metals (Peng et al., 2008). The high transparency of Skadar Lake confirms the low concentration of particles in the water. That allows us to assume that the absence of above mentioned eight metals in surface and bottom water may be due to the fact that they simply are not present in the lake water, or that they settle down so quickly and interact with the sediment, thus disappearing from the water phase.
Some other parameters that can influence metal concentrations in the water phase are pH, dissolved oxygen (DO), and electrical conductivity (EC). Low pH means more hydrogen ions in the water phase, thus more competition between the ions and the metals (cations) for binding sites (sediment, organic matter, clay minerals, or Fe-Mn oxides). Generally, at low pH values, more metals can be found in the water phase. Similarly, high metals in the water result in more ions in the dissolved form of the water, increasing in this way conductivity values. Finally, changes in dissolved oxygen can modify the pH and conductivity in water, influencing in this way the metal presence in water (Bubb and Lester, 1991). Looking at the physical-chemical data from this research, it seems that stations from transect II have metals mostly distributed in the water phase (thus available for the uptake), because pH values in these stations are lowest comparing to the other sampling stations. Findings of DO and EC in these stations indicates the same conclusion, due to the fact that EC values are the highest in these stations (transect II), and DO values are the lowest.
Focusing on the Skadar Lake water samples, among six detected metals potassium has shown the lowest concentrations; between 0.2 and 2.18 mg/l in surface lake water and between 0.20 and 3.51 mg/l in bottom lake water. When we compare the surface and the bottom layer of the lake water (see ratio in table 5.1), there is no big difference when we look at the concentrations of magnesium, calcium. These metals have peak concentrations in Moraca 1 (S1), Moraca 2 (S2) and Radus (S4), belonging to transect I and transect II. Some possible explanations of their high concentrations in the lake water could be geological weathering of rocks, stormwater runoff, erosion etc.
Concentrations of manganese, nickel and aluminum are showing more variability. In the bottom water layer these metals (Mn, Ni and Al) were present in higher values than in the surface lake water layer. Levels of dissolved oxygen at the lake bottom are normally lower than at the surface, thus anoxic conditions are expected to influence the higher presence of some metals in water.
Mirjana VEMIC 63 Also, lake dynamics can influence the concentrations of metals (e.g. presence of wind, waves, turbulence in the lake, stratification, lake velocity, seasonal variations in the lake). Aluminum is the most prominent metal among them and it reaches its maximum value of 92.67 μg/l in surface water layer of station Moraca 2 (S2), and value of 119.33 μg/l in bottom water layer of station Radus-Plavnica (S5).
Manganese reaches its peak in station Plavnica (S4) in both, surface and bottom water (20.33 μg/l and 30.01 μg/l respectively), while nickel had its highest values in station Moraca 1 (S1) (14.57 μg/l and 12.33 μg/l respectively). One explanation could be the incoming waste water that is coming from the Podgorica town, carried by the Moraca River, the main tributary of the lake, which makes two branches (one close to S1 and another one close to S2) and discharges into the lake. However, a concentration of metals in the river itself is not available as it was not part of this research.
All six metals that were present in the water phase (surface and bottom layer) have concentrations that are below the standards for freshwater aquatic life (VLAREM II, 2010), therefore no problem should arise from their presence in water. Thus, their presence might be related more with natural concentration rather than anthropogenic inputs.
Similarly with the findings from Skadar Lake, the findings from the neighboring lake (Ohrid Lake) indicate that only two metals were found in the lake water (manganese and iron), while six others (aluminum, copper, nickel, chromium, cobalt, zinc) were not detected. Iron was detected in all the water samples (0.05-1.2 mg/l), while manganese was only found in five sites (0.08-0.2 mg/l) (Malaj, 2010). Zaw and Chizwell (1999) found the same range for iron (0.2-1 mg/l) and lower concentrations for manganese (0.05 mg/l) for the Hinze dam used for drinking water supply. On the other hand, Watzin et al., (2002) found 12 mg/l of iron, 5.3 mg/l of nickel and 0.07 mg/l of cobalt in water of Ohrid Lake. By looking at those data, one can tell that Skadar Lake has much more lower concentrations of metals compared to the Ohrid Lake.
Physical-chemical parameters that were measured in the surface water (1m depth) of the Montenegrin part of Skadar Lake were pH, dissolved oxygen, electrical conductivity and temperature.
The lowest pH values (6.72 in first campaign and 7.6 in second campaign) were found in station Plavnica (S6). Dissolved oxygen showed did not show large fluctuations and varied from 10.3-11.3 mg/l in all stations except in Plavnica (S6) where the values of dissolved oxygen were the lowest (7.8 mg/l). This may be due to the fact that in this station there is constant loading of organic matter in the lakes water (lots of restaurants on the shore), which lowers the oxygen level in the water. Values of electrical conductivity manifested a wider range of values, with the lowest value in Gostilje-Krnjice 1 (S8) with value of 235 μS/cm, and the highest value in Plavnica (S6) with value of 407 μS/cm. In general the highest values of electrical conductivity were found in sampling stations from Transect 2 in both sampling campaigns. Surface temperature was relatively constant with temperature in between 11.2- 14.5ºC during the first sampling campaign (November, 2010) and in between 8.00-9.3ºC during the second sampling campaign (January, 2011). As it is mentioned before in the text, solubility of metal pollutants is highest in transect II of Skadar Lake due to the influence of physical-chemical parameters.
64 MSc Thesis 6.2 Sediment quality in Skadar Lake
6.2.1 First screening of sediment
When we look at the sediment samples analyzed in the first campaign in Skadar Lake (chapter 5, table 5.3), concentrations of nickel exceeded the PEC (probable effect concentrations) for sites S1, S4, S5, S6, S8 and S9. Based on the guidelines for freshwater dwelling organisms (MacDonald et al., 2000; Jaagumagi, 1992), adverse effects are expected to happen due to high concentrations of nickel in S1 (104.79 mg/kg), S4 (91.62 mg/kg), S5 (116.41 mg/kg), S6 (87.55 mg/kg) , S8 (72.04 mg/kg) and S9 (65.49 mg/kg). In the other sites (S2, S3, S7 and S10) nickel concentrations are exceeding TEC (threshold effect concentrations).
Low concentrations compared with the standards were found for zinc, lead, cadmium and iron except for site S5 (Fe is having a value of 20789 mg/kg and exceeding TEC). The guidelines used do not include aluminum, thus comparison was not possible for this metal.
As it is shown in table 5.3, manganese levels are exceeding TEC values in S1-491.78 mg/kg, and S5-733.46 mg/kg. Chromium levels are above TEC in every station except in station Moraca 2 (S2). Copper values are also quite high in eight sampling stations (exceeding the TEC values), except in S7 and S10.
In the table 5.3, it is notable that the sites that are having highest metal concentrations are located mostly in transect I (in stations Moraca 1, Moraca 2 and Virpazar) and transect II (stations Radus, Radus-Plavnica and Plavnica).
One of the reasons for such pattern is probably closeness of the incoming tributaries that are responsible for bringing the main load of pollution into Skadar Lake. The main tributary, Moraca River, comes from the capital city (Podgorica) and on its way to the lake it passes next to the Aluminum processing plant (see figure 6.1). Thus the river picks up the pollutants and carries them downstream into Skadar Lake. Since the river is making two branches before entering the lake, the lake receives pollutant inputs from two different sides (one close to S1 and other close to S2).
The above mentioned stations from transect I (stations Moraca 1, Moraca 2 and Virpazar ) can be considered as a "hot spots" of metal pollution in Skadar Lake (first line of pollution) which may be the reasons of high metal concentrations found deeper in the lake (in transect II and III). However, several additional transportation factors of metals should be taken into consideration like changes in seasonal flow patterns of the streams; runoff, leaching, and erosion from the close dump sites; and also tides and currents in the lake.
The latter are important especially in explaining the presence of heavy metals in stations from transect III. All the other streams passing nearby dump sites are potential polluters of the lake. Therefore, transportation processes within the lake can help distribute the heavy metals spatially.
Mirjana VEMIC 65 Sieving analysis of the lake sediment (see later in the text) showed that sediment composition variations between sampling stations are visible. However, when we look at all three transects in the lake, transect I has its sediment mainly in the silt and sand fraction, transect II has a mixture of three smallest sieving fractions (silt, clay and colloid), while transect III has its sediment mainly in sand fraction.
These foundings correlate with the study done by Lee (1995) who explained that a lake is basically three dimensional, unsteady and turbulent. According to that, sand fraction in transect I can be explained with the current coming from the Moraca River, which result in settling down bigger particles first and transporting smaller particles deeper in the lake. On the other side, current in the lake that comes from Albanian side (Drina River), has a same effect on distribution of sediment particles, thus sediment in the transect III is mostly sand by nature. Where these two currents meet, lake sediment is mostly composed of smallest particles (silt, clay and colloid in transect III). Small particles settle down slowly but they resuspend more easily in the lake, which has effect on metal distribution and availability.
Figure 6-1: Aluminum processing plant with the red mud basin and Moraca River (on the right) (Source: Jelena Perunicic, private collection)
During the processing of data, also the Dutch standards for soil/sediment remediation (VROM, 2000) were used to evaluate the results, including target and intervention values to examine the current situation in Skadar Lake. Target values indicate the level at which sustainable soil/sediment quality is achieved, whereas the intervention values indicate when the functional properties of the soil/sediment are seriously impacted or threatened (VROM, 2000). For all metals present in the lake all concentrations were below intervention values, while target values were exceeded for nickel in all stations.
66 MSc Thesis 6.2.2 Second screening of sediment
Second screening of sediment (table 5.4) was launched in January 2011 when sediment samples were taken for loss on ignition (LOI), sieving, total metal analysis and sequential extraction of metals (SEM).
Loss on ignition showed that organic matter in the sediment of the Skadar Lake was found to be in a range from 4.72-21.49 % respectively. Variation between replicates was not notable. Like it was suspected, organic matter has its peak in stations S4, S5, S6 and S7 (transect II).
Possible explanation for this pattern is that in this region there are many restaurants present and many of them discharge organic waste directly into the lake water. Additionally, when we look at the values of physical-chemical parameters in this transect it is clear that oxygen and pH have lowest values. Conductivity, on the other hand, in this transect has its peak.
LOI was checked for correlation with the total metals in the sediment (details in table 5.5). In natural waters there is a strong affinity between metals and organic matter. In general, organic matter in sediments tends to decrease availability of metals to the sediment dwelling organisms because of formation of complexes between metals and organic matter (Campbell et al., 1988). Dissolved and particulate organic carbon in the water column act like scavengers for metals, and the scavenged metals may be incorporated into the bottom of sediment (Forstner and Wittmann, 1981). However, results that are presented in table 5.5 are showing that organic matter is not showing correlation with the sediment metal concentrations.
Sieving analysis conducted (wet sieving-sieving sizes: <0.45μm; 0.45-106 μm; 106-500 μm; 500 μm-1mm; >1mm), showed that sediment in Skadar Lake is mainly distributed over the three smallest sieving fractions (colloid, clay and silt). Variations between sampling stations are visible, thus in station Moraca 1 (S1-left branch of the Moraca River) sediment is mainly present in silt fraction (clay and colloid fraction are following), while in the station Moraca 2 (S2-right branch of the Moraca River) sediment is present in silt and sand fraction (medium sand).
Station Virpazar (S3) has sediment mostly composed of silt and clay, while stations Radus (S4), Radus-Plavnica (S5), Plavnica (S6) and Gostilje (S7) have almost the same distribution of sediment particles, and they are having sediment distributed in silt, clay and colloid fraction. Stations Gostilje-Krnjice 1 (S8), Gostilje -Krnjice 2 (S9) and Krnjice (S10) have sediment mostly present in the form of sand (medium and very coarse). Thus, sediment from transect I and transect II have metals much higher bound to their particles compared to the sediment from transect II, where composition of sediment in smallest fraction allows resuspension of metals in the water column. Metals in contaminated sediments may return to the sediment-water interface through diffusion (Van Den Berg et al., 1999), sediment re- suspension (Hulscher et al., 1992), or biological activity such as bioturbation (Wilson and Chang, 2000). Once at the sediment-water interface or in the water column, metals are more likely to be transported and to enter the food web. Therefore, in areas with metal pollution, it is important to inventory the concentration and spatial distribution of each metal to evaluate the potential for remobilization, transport, and biological uptake. Metals of anthropogenic origin are more loosely bound in sediments and thus are more readily available to organisms (Schropp and Windrom, 1988).
Mirjana VEMIC 67 100% >1 mm
80% 500-1 μm 60% 106-500 μm 40% 0.45-106 μm 20%
0% <0.45 μm
Ni
100% >1 mm 80% 500-1 μm 60% 106-500 μm 40% 0.45-106 μm 20%
0% <0.45 μm
Cr
100% >1 mm 90% 80% 70% 500-1 μm 60% 50% 106-500 μm 40% 30% 0.45-106 μm 20% 10% <0.45 μm 0%
Cu
Figure 6-2: Total concentration of metals (% of mg/kg dry weight) in different sieving fractions of the sediment.
68 MSc Thesis When wet sieving analysis was done, in every sieving fraction of the sediment and for every sampling station (ten sampling stations) total metal content was determined. Results of this analysis are presented in figure 5-10 and table 5.7. Metal that is present in high concentrations in all sieving fractions and in all sampling stations is nickel, with a maximum concentration of 171.69 mg/kg in Gostilje-Krnjice 1 (S8). Copper and chromium are following, with also high metal concentrations in almost every sampling station. Due to that reason (high concentrations), concentration of these metals in every sieving fraction, in ten stations from Skadar Lake is presented in figure 6.2.
From the figures it appears that in S6 all three metals are captured in highest concentrations in sand fraction. When we look at the other stations, distribution of these three metals is relatively equal among all sieving fractions.
Total metal analysis during the second sampling campaign (January, 2011) was conducted after the flooding season, thus results for metal concentrations that are presented in table 5.4 are showing differences, specifically in concentrations of nickel, when they are compared with the first sampling campaign (table 5.3). In second sampling campaign all sampling stations have exceeded PEC values.
Low concentrations compared with the standards (MacDonald et al., 2000; Jaagumagi, 1992), were found for zinc, lead and cadmium. Elevated concentrations of nickel, copper, chromium, manganese and iron (S1-20521.19 mg/kg; S8-22904.71 mg/kg; S9-23013.91 mg/kg) are present in the lake sediment. Chromium levels are exceeding TEC values in all sampling stations (S1-S10). Based on the guidelines for freshwater dwelling organisms, adverse effects are expected to happen due to high concentrations of nickel in all sampling stations due to the exceeding of PEC values. Regarding the need for intervention (VROM, 2000), all sites have concentrations below the target value meaning that no interventions are needed. Copper values are also quite high in nine sampling stations (exceeding the TEC values), except in station S2 where the value is lower (9.24 mg/kg). Regarding manganese five sampling stations are having values above TEC level and these concentrations are quite close to the PEC levels (especially the stations from transect III: S8 820.54 mg/kg; S9-832.53 mg/kg; S10-878.34 mg/kg). Findings about metal concentrations from the Skadar Lake are compared with the findings from the similar lakes from the region (table 6.1). When we look at those data it appears that Skadar Lake is still quite unpolluted lake compared to the other ones.
When we look at the correlations between metals (table 5.6) in the Skadar Lake sediment, significant correlations were found between concentrations of nickel with copper, iron and chromium. Copper is found to relate highly with iron and zinc but less with aluminum. Iron is related with zinc, manganese and chromium. Furthermore, zinc is found to be in relation with lead, cadmium and aluminum. Lead is found to be strongly correlated with aluminum and cadmium, while the latter is related only with aluminum. However when we look at the correlations between metals and electrical conductivity, pH and temperature it can be seen that correlations are not significant, indicating that other factors play much more important role in metal distribution.
Mirjana VEMIC 69 Regarding the comparison between first and second sampling campaign in the Skadar Lake, it is clearly notable in the results that values of almost all metals have been higher in the second screening of sediment. This may be due to the above mentioned flooding season that happened in the lakes surroundings. Moraca and Zeta rivers could be the main cause for this difference in results, due to the fact that they bring into the lake a mixture of different kinds of pollutants from upstream cities, industries and agricultural areas. As lake is quite clear (low turbidity), it appears that particles (and the associated metals) settle down quickly to the lake sediment.
Additionally, when we look at the metal distribution between the lake transects, it is notable that in the second screening concentrations of metals are equally high in all transects, while in the first screening transect I was the most polluted one. Explanation for this pattern could be the high water circulation (floods) before the second sampling campaign.
Table 6-1: Comparison between metal concentrations found in this research on Skadar Lake and metal concentrations found in some similar lakes from the region Lakes Cd Cr Co Pb Cu Zn Fe Ni As Reference
Lake Ohrid 1.6 166.0 34.0 7.0 5.0 30.0 - 682.0 < Environmental (Memelisht) 0.1 Institute, 2004 Lake Ohrid - 800 155 - - - 9 500 - (Pojske) Hydromet. Institute,2004 Lake Ohrid - 1100 35 - - - 4.2 2000 - (Guri I Kuq)
Lake Skadar 0.05- 10.28- - 3.71- 7.65- 25.70- - 23.66- - Stesevic (Albania/Montenegro 1.01 82.60 60.14 28.53 87.23 136.1 et al.,(2007) ) 2 0.1 - 5.7-66 1.7- 2.4 - 0.7-36 13- - 4.4- - Nguyen Lake Balaton 0.7 17 160 150 5.5 et al., (2008) (Hungary)
Lake Koronia 0.97 27.27 - 16.30 14.76 72.12 - - - Fytianos and (North Greece) – – – – – Lourantou 1.01 37.03 24.46 18.77 99.60 (2004) Lake Skadar 0.25- 34.64- 7.35- 8.37- 9.24- 25.68- 9654- 63.13- - This study (Montenegrin part) 0.51 79.15 13.9 25.4 36.92 74.32 2301 126.7 (2011) 4 4 6 TEC 0.99 43.4 - 35.8 31.6 121 - 22.7 9.7 MacDonald 9 et al., (2000) PEC 4.98 111.0 - 128.0 149.0 459 - 48.6 33. 0
70 MSc Thesis Sequential extraction of metals (SEM) was done for all ten sampling stations in two replicates. Eight metals were examined: cadmium, chromium, copper, iron, manganese, nickel, lead and zinc. For all the sites low affinity of metals was noticed for the exchangeable fraction (F-I). Similar pattern is present for the F-II (bound to carbonates) with the exception of manganese in some stations from transect II and transect III (S5, S6, S7, S8) This means that metals can be bound and released more easily from these two fractions, and also for F-II, the amount of carbonates in the sediment can determine the binding ability of the metals. Even if in literature (Förstner and Wittmann, 1983; Pardo et al., 1990) it is commonly found that these two fractions (exchangeable fraction and carbonate fraction) are considered to be the most indicative ones for recent anthropogenic pollution, this might not be the truth in all cases. Forstner and Wittmann (1983) explain that binding fractions III–V can replace the more labile fractions in the competition for available binding sites. When the mineralogical fraction of the residues (from dump sites, rivers) enters in the lake as a source of pollution; it can be considered from SEM analysis that metals are bound with the mineralogical/inert fraction. In fact that is not the case because the residues in this case are considered as pollutants and can be found in high amounts. Therefore, not finding a metal bound to these two fractions does not necessarily means that the anthropogenic source of pollution is not recent, but it mainly depends on form of the contaminant that the lake is exposed to. Cadmium is present in all sampling stations and mainly bound to the organic matter (F-IV), while lower amounts of this metal are seen in exchangeable fraction (F-I) and bound to the Fe-Mn oxides (F-III). Chromium is most abundant in the residual phase, followed by the organic/sulfide fraction, and the metal is least abundant in the fraction with iron and manganese oxides. The high presence of chromium in the residual fraction can be associated with the weathering of rocks, like chlorite or montmorillonite (Yücesoy and Ergin, 1992) and 2+ 3+ exchange with Mg and Fe . The binding of chromium with organic matter/sulfide fraction and fraction with iron and manganese oxides is relatively low compared with the residual one. Also, correlation presented in the table 5.5 is showing that there is no high correlation of metals with this fraction (organic fraction). Stations S4, S5 and S6 are showing almost equal distribution of above mentioned three fractions.
No clear distinction is made during the five-step sequential analysis between the bounding with the organic matter or the sulfide fraction, as also stated by Kelderman and Osman (2007). The impossibility to divide these two fractions does not help to distinguish how chromium (or the other heavy metals) is related with each of them. Yin et al., (2008) for instance found that chromium is not bound to sulfide or pyrite but mainly with silicate minerals or carbonates. Kiratli and Ergin (1996) also stated that chromium of non-detrital origin exists in anoxic depositional settings, predominantly as reduced species, in the form of iron and manganese oxides.
The pattern for copper is that high affinity of metal was found for the organic/sulfides fraction and the metal was less abundant in the residual fraction. However, in transect I more dominant fraction is residual fraction, especially for S2 where almost all metal concentration is in this fraction (F-V). In transect II and III more dominant is organic/sulfides fraction.
Iron is present in the residual fraction in all stations from first two transects (I and II), except in station S6 that is having smaller amounts of metal in the organic/sulfides fraction. Transect III has iron distributed among residual, bound to organic/sulfides and bound to Fe-Mn oxides fractions.
Mirjana VEMIC 71 Manganese has shown wide variety of distribution in different extraction fractions of the sediment in every station. Thus, in S1 it is mainly present bound to organic/sulfides fraction, while in S2 manganese is present only in residual fraction. Station S3 and S4 are showing almost equal distribution of this metal among two above mentioned fractions (F-IV and F-V). In stations S5, S6 and S8 as an addition to distribution of manganese in these two fractions, it is also distributed in carbonate fraction (around 20%). Stations S9 and S10 are having metal distributed in all fractions (F-I, F-III, F-IV, F-V) except of fraction F-II (bound to carbonate). Station S7 has metal distributed in all fractions (F-II, F-III, F-IV, F-V) except of fraction F-I (exchangeable fraction).
Pattern for nickel is similar to the one for chromium, thus metal is most abundant in the residual phase, followed by the organic/sulfide fraction, and the metal is least abundant in the fraction with iron and manganese oxides. In S2 the organic/sulfide fraction is absent. Reasons for such a distribution of nickel may be in the fact that materials coming from rivers, erosion, and runoff water or with other sources of input in the lake can become part of the sediment and enrich it with nickel.
Nickel is dominant in the anoxic waters both as a Ni-sulfide mineral (NiS) and as a co- precipitate with the Fe-sulphides (Di Toro et al., 1992, DiToro et al., 1990, Kiratli and Ergin, 1996). Nickel is extracted in the form of iron-nickel mineral, which is first present in the watershed area of the lake and thus can be found naturally in the sediment. Secondly, different sources can transport the mineral in the lake from the dumps sites which were mainly with iron-nickel residues. However, even if correlated with iron and manganese, the binding affinity is much smaller in the reducible fraction compared with the residual or organic/sulfide one.
Lead is showing quite high variations in bounding affinities to the extraction fractions, but those variations are mostly applied on the hole transect. Thus in transect I lead is mainly bound to the Fe-Mn oxides, while metal bounding to the organic/sulfide and residual fraction is following. In transect II lead is present in two fractions (F-III and F-IV) and the more dominant one is organic/sulfide fraction (F-IV). In transect III equal distribution of metal among latter fractions is present.
Just like nickel and chromium, zinc is also a metal that is mostly abundant in the residual phase, followed by the organic/sulfide fraction, and the metal is least abundant in the fraction with iron and manganese oxides. This pattern is changing only in S2 and S3 in which one fraction is missing (F-IV).
Several studies (Förstner and Wittmann, 1983; Pardo et al., 1990; Tessier et al., 1979) demonstrated that binding in F-V fraction (residual fraction) is related with relatively unpolluted sediments and that the metals cannot be remobilized when they are attached to the mineral fraction. This due to the fact that residual solid contains mainly primary and secondary minerals, which occlude the metals within their crystal structures (Tokalioglu et al., 2000).
72 MSc Thesis As mentioned above chromium, nickel, and zinc are found more in the residual fraction of Skadar Lake sediment for almost all sampling stations. The residual are transported in the lake from pollution sites in the form of mineral, which is ―read‖ by SEM as the residual fraction. Furthermore, in what was considered as a less polluted site, all the metals had a higher affinity for the organic/sulfidic and reducible fraction. Therefore, it can be again proven that the contamination level found is mainly related with the transportation mechanisms of the lake. Finally, in order to draw a firm conclusion, further research needs to be done especially with regard to the natural background levels of these heavy metals in lake and other binding mechanisms of heavy metals in the sediment.
6.3 Spatial distribution of metals in the sediment
6.3.1 Factors controlling variability in sediment
Spatial distribution in sediments can be controlled by grain size and organic matter content of the sediment (Gorenc et al., 2004; Kishe and Machiwa, 2003); or the differences in chemical composition of sediment (Kishe and Machiwa, 2003). Organic matter in the sediment was analyzed in ten sampling stations (from three transects) of the lake and was found to be in a range from 4.72-21.49 % respectively. Highest values are found to be in the transect II (S4- S6). Even through correlations between the organic matter and total metals did not show any significance, in this transect (transect II) some metals showed high affinities for the organic fraction (see SEM results above). As far as grain sizes are concerned, sieving analysis conducted (wet sieving-sieving sizes: <0.45μm; 0.45-106 μm; 106-500 μm; 500 μm- 1mm; >1mm), had shown that sediment in the Skadar Lake is mainly distributed in three smallest sieving fractions (colloid, clay and silt), and in general the highest metal concentrations are present in these fractions.
6.3.2 Variations of metals along the same transect
All sites do not follow the same pattern of metal distribution in the sediment. Proportional decrease of heavy metal concentrations was expected with increase of the distance from the main tributaries especially for sites that are considered not to be additionally polluted by anthropogenic sources. The idea is that sites located near the shore and the discharge points of the rivers or other sources of pollution can be more influenced due to the direct impact from these sources. However, several authors (Förstner and Wittmann, 1983; Gorenc et al., 2004; Tam and Wong, 1995) suggest the formation of the so called ―hot spots‖ in the sediment, where the concentrations in different depths are different from the linear decrease of concentration with distance from the shore and depth.
This fluctuation in metal concentrations is mainly expressed in variations between transects (I, II and III), and not so much the fluctuation in the stations from the same transect. Even though there are some small differences in concentrations of metals in some stations, in general all the stations from same transect act more or less the same. Possible reason for some small fluctuations noticed between the sampling stations is related with the fact that vegetation in the bottom of the sediment, especially the presence of Vallisneria spiralis was making sediment difficult to sample.
Mirjana VEMIC 73 Also, one other explanation for fluctuation of metal concentrations can be related with currents and tides in the lake that can transport metals from other contaminated spots of the lake. Considering the size of the lake, tides can also alter the metal balances in the sediment. Metals are transported on fine particulate and organic materials carried by the tides and this has been shown to cause spatial heterogeneity in metal concentrations (Mackey and Hodgkinson, 1995). This is also a demonstration of the importance of sampling along transects, and that transportation mechanisms can expand pollution in lakes. Furthermore, temporal variation is possible explanation too. This can be explained with the difference in time between two sampling campaigns (2 months).
6.4 Metals in plants
Reed (Phragmites communis) was collected near the lake shore from 1-2 m depth from every transect (from three different sites along the transects) in three replicates and analyzed for metal concentrations. Leafs were not present due to the time of sampling (November 2010; January 2011). Benthic aquatic plant (Vallisneria spiralis) was also collected for metal analysis. This plant was collected from the lake bottom (while taking sediment samples), from every transect three plant replicates, also only from locations where plant was present. For metal analysis mixture of all three replicates was used in order to have representative sample of the plants for the whole transect.
Results showed that the benthic aquatic plant (Vallisneria spiralis), has higher concentration of all metals compared to reed (Phragmites communis) , and the highest values are in transect I, possibly due to the constant loading of pollution from main tributaries. This can be also explained with the distribution of the heavy metals in sediment, which comes from anthropogenic sources. Vallisneria spiralis and Phragmites communis were found to be influenced more by heavy metals in sediment than by those in water, consequently, bioaccumulation is greater when sediments are contaminated (Bonanno and Lo Giudice, 2010). The same situation stands for Skadar Lake plants, because high metals concentrations were not found in water, but only in the sediment. If we compare levels of metals in plants with the levels of metals in sediment, they increase proportionally.
Bonanno and Lo Giudice (2010) refer to Allen (1989) for quantification of levels above which metals can be toxic. Ni is thought to be poisonous over 5 mg/kg, while Cr concentrations are toxic to plants when they exceed the 0.5 mg/kg level. Thus, in the case of Skadar Lake sites, the levels found in the plants exceed largely the threshold values, for Vallisneria spiralis for both nickel and chromium and for Phragmites communis only for chromium. Furthermore, iron has it permissible level at 425 mg/kg, manganese at 500 mg/kg, lead at 0.3 mg/kg and cadmium at 0.1 mg/kg. In Skadar Lake plant Vallisneria spiralis all mentioned metals have concentrations which are much higher than permissible limits, while Phragmites communis has exceeded metal concentrations only for cadmium.
It is common knowledge that metal concentrations in aquatic plants vary considerably according to the plant part as well as to the type of element (Bragato et al., 2006), thus comparison with threshold values is not in all cases correct. Also, the availability of metals can change under variable redox, pH, and salinity conditions, thus increasing the bioavailability in the plant (Du Laing et al., 2009; Keller et al., 1998).
74 MSc Thesis Additionally, Du Laing et al. (2009) found for intertidal marshes along the river Scheldt that sediment characteristics like clay content or organic matter can influence (disproportionally) the aboveground part of the plant. Also, when comparing between the different case studies, the type of destruction method should be taken into account, because not all of them can give the same results for the same samples (Du Laing et al., 2003). The authors further explain that the best destruction method is the microwave destruction, which is the one used in this study. Taking into consideration the above mentioned results and argumentations, Phragmites communis and Vallisneria spiralis are found to be contaminated with chromium, nickel, iron, manganese, lead and cadmium, with potential risk for their population and for the variety of organisms that have it as their natural habitat.
6.5 Heavy metals in fish
Five fish species were collected for trace metals analysis- Rutilus karamani, Alburnus scoranza, Scardinius knezevici, Squalius cephalus and Perca fluviatilis. These fish species are the most important ones in the lake because they represent the most exploited fish species for human consumption. They belong to the Cyprinidae family, and they are all bentho- pelagic fish species that feed mainly on cladocerans and copepods. They breed in lake shores or they migrate to lake tributaries to spawn in shallow riffles. After breeding they deposits eggs on sand, gravel, and rock bottoms at about 0.3 m depth.
Results showed that zinc have elevated values in all fish species. Aluminum is present in Rutilus karamani with the value of 137.2 mg/kg, and manganese with the value of 19.72 mg/kg. In other fish species values of these metals are quite lower but still prominent. Zinc is exceeding permissible levels in all fish species.
A more detailed investigation should also be made for the ecological chain of the fishes. Metal concentrations should be measured also for the species that they feed with, like zooplankton, benthic invertebrates, fish larvae, or freshwater prawns.
Mirjana VEMIC 75
76 MSc Thesis 7 Chapter Seven - Conclusions and recommendations
7.1 Conclusions
Six metals that were found in the Skadar Lake water were potassium, calcium, magnesium, manganese, nickel and aluminum, while eight others (chromium, copper, cobalt, iron, lead, cadmium, silica and zinc) were not detected. Sediment served in this case as the major sink for metals especially in the sites influenced by contaminated sources like waste dumps or discharging points from the tributaries.
Based on the guidelines for freshwater ecosystems, adverse effects are expected to happen due to high concentrations of nickel in all ten stations from Skadar Lake. Concentrations of nickel are exceeding PEC (potential effect concentration) in every sampling location.
Distribution of chromium and copper indicates that TEC (threshold effect concentration) are exceeded in every sampling station from Skadar Lake, thus attention should be put on controlling the loading of these metals into the lake, in order to avoid exceeding of PEC (like it is the case with nickel).
Sieving analysis conducted showed that sediment in Skadar Lake is mainly distributed in three smallest sieving fractions (colloid, clay and silt). Variations between stations are visible, but in general sediment from transect I is composed of silt and sand, transect II mainly is distributed over the three smallest fractions (silt, clay and colloid), and transect III is composed of clay and sand.
Sequential extraction indicates that the mineral fraction bounds the majority of chromium, nickel and copper in lake‘s sediment.
Loss on ignition showed that organic matter in the sediment of Skadar Lake was found to be in a range from 4.72-21.49 % respectively. Like it was suspected, due to the presence of restaurants in this area that discharge organic waste directly into the lake water, organic matter has its peak in stations from transect II.
Metals (Fe, Pb, Cd, Cr, Mn) were present in both plant samples (Phragmites communis and Vallisneria spiralis), at high concentrations, but they were not detected in two types of endemic fish‘s tissue (gill, muscle, liver), indicating that consumption is safe for humans.
Mirjana VEMIC 77 7.2 Recommendations
In order to have proper and complete information about distribution patterns of metals and their exact concentrations in Skadar Lake, and also for the cause of preserving good environmental condition of the lake, the following strategies should be implemented:
Setting of monitoring campaigns, distributed first temporally with taking into consideration the stratification periods of the lake. Secondly, spatial investigations are required for the entire water column and sediment in Skadar Lake, including Montenegrin and Albanian part.
Identification of all the tributaries of the lake, their flow and seasonal patterns is necessary. Furthermore, concentration of metals in the tributaries itself was not measured, as it was not the part of this research, and it would be really helpful to have these information's in order to make a good estimation of metal concentrations in the lake.
Continued monitoring and identification of the sources of metals and their contribution to the lake.
Assessment of the other sources of pollution like runoff, leaching or erosion.
Geological investigations with regard to the metal background levels in the watershed and also investigation of historical backgrounds in the lake with the help of core sediments in the lake would be very interesting.
Further research concerning sediment composition and texture in order to find out more on the bounding mechanisms in the lake.
Evaluating full ecological chains in the lake for potential bioaccumulations/biomagnifications.
Performing toxicity tests on sensitive species in order to find out metal related toxicity.
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Mirjana VEMIC 81 Neziri A, Gössler W. Determination of heavy metals in water and sediments of Drini River, Buna River and Lake Shkodra. BALWOIS—Conf. on Water Observation and Information System for Decision Support, 25–29 May 2004, Ohrid, FY Republic of Macedonia; 2004. 7 pp Nieboer E, Richardson DHS (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: 3-26 Nguyen HL, Leermakers M, Osán J, Török S, Baeyens W (2005) Heavy metals in Lake Balaton: water column, suspended matter, sediment and biota. Science of The Total Environment 340: 213-230 Nriagu JO (1989) A global assessment of natural sources of atmospheric trace metals. Nature, 338: 47-49. Nriagu JO, Pacyna J (1990) Quantitative assessment of worldwide contamination of air, water and soil by trace metals, Nature, 333: 134-139. Pardo R, Barrado E, Lourdes P, Vega M (1990) Determination and speciation of heavy metals in sediments of the Pisuerga river. Water Research 24: 373-379 Peng K, Luo C, Lou L, Li X, Shen Z (2008) Bioaccumulation of heavy metals by the aquatic plants Potamogeton pectinatus L. and Potamogeton malaianus Miq. and their potential use for contamination indicators and in wastewater treatment. Science of The Total Environment 392: 22-29 Pertsemli E, Voutsa D (2007) Distribution of heavy metals in Lakes Doirani and Kerkini, Northern Greece. Journal of Hazardous Materials 148: 529-537 Pokrovsky OS, Schott J (2002) Iron colloids/organic matter associated transport of major and trace elements in small boreal rivers and their estuaries (NW Russia). Chemical Geology 190: 141-179 Rastall AC, Neziri A, Vukovic Z, Jung C, Mijovic S, Nikcevic S, Erdinger L (2004) The identification of readily bioavaiable pollutants in Lake Shkodra/Skadar using semipermeable membrane devices (SPMDs), bioassays and chemical analysis. Environmental Science and Pollution Research Vol.11, No. 4, 240-253 Rauret G (1998) Extraction procedures for the determination of heavy metals in contaminated soil and sediment. Talanta 46: 449-455 Redford,D.P.,Trulli,H.,Trulli,W.,1997. Sources of plastic pellets in the aquatic environment. In: Coe,J.M.,Rogers,D.B. (Eds.), Marine Debris: Sources,Impacts,and Solutions. Springer-Verlag, New York,pp. 335–343. Schropp SJ and Windom HL (1988) A guide to the interpretation of metal concentrations in estuarine sediments, in Georgia, 53 pp. Shumka S, Grazhdani S, Cake A, Mali S (2008) Fishery of some tributaries and reservoirs and zooplankton at the Drini catchment: The needs of better conception of the monitoring practices at transboundary water bodies. Natura Montenegrina, Podgorica 8 (1): 41-50 Stesevic, D., Feiler, U., Sundic, D., Mijovic, S., Erdinger, L., Seiler, T.-B., et al. (2007). Application of a new sediment contact test with myriophyllum aquaticum and of the aquatic lemna test to assess the sediment quality of Lake Skadar. Journal of Soils and Sediments, 7(5), 342-349. Tam NFY, Wong YS (2000) Spatial variation of heavy metal in surface sediments of Hong Kong mangrove swamps. Env. Pollut. 110: 195-205 Tessier A, Campbell PGC, Bisson M (1979) Sequential extraction procedure for the speciation of particulate trace metals. Analytical Chemistry 51: 844-851
82 MSc Thesis Tokalioglu S, Kartal S, Elçi L (2000) Determination of heavy metals and their speciation in lake sediments by flame atomic absorption spectrometry after a four-stage sequential extraction procedure. Analytica Chimica Acta 413: 33-40 Turner A, Millward GE (2002) Suspended particles: Their role in estuarine biogeochemical cycles. Estuarine, Coastal and Shelf Science 55: 857-883 UNDP (2007) Case studies on remediation of environmental hot spotsin the Western Balkan. Report: 67-98 UNEP (2006) Mining and the environment in the Western Balkans. Report: 56-102 Van Den Berg GA, Loch JPG, Van Der Heijdt LM and Zwolsman JJG (1999) Mobilisation of heavy metals in contaminated sediments in the river Meuse, The Netherlands, Water Air Soil Pollut. 116 (3–4), 567–586. Van der Oost R, Opperhuizen A, Satumalay K, Heida H, Vermeulen NPE (1996) Biomonitoring aquatic pollution with feral eel (Anguilla anguilla) I. Bioaccumulation of PCBs, OCPs, PCDDs and PCDFs. Aquatic Toxicology 35: 21-46 VLAREM II (2010) Decision of the Flemish Government 537 of 21/05/2010 concerning general and sectoral regulations with regards to Environmental Issues. Belgian Government Gazette, 09/07/2010 (revision of the original decision from 1995) [in Dutch] VROM (2000) (Ministry of Housing. Spatial Planning and Environment), Circular on target values and intervention values for soil remediation. Netherlands Government Gazette p. 39 Watzin MC, Puka V, Naumoski T (2002) Lake Ohrid and its watershed, State of the environmental report. Lake Ohrid conservation project, Tirana, Albania and Ohrid, Macedonia. Williams S (2010) Case studies on remediation of environmental hot spots in the Western Balkans. ISBN 978-9940-9245-7-7: 18-182 Wilson DJ and Chang E (2000) Bioturbation and oxidation of sulfide in sediments, Acad. Sci. 75 (3–4), 76–85 Wittmann GTW, Förstner U (1975) Metal enrichment of sediment in inland waters-The Hartbeespooort Dam. Water SA 1: 76-82 Yin H-B, Fan C-X, Ding S-M, Zhang L, Zhong J-C (2008) Geochemistry of iron, sulfur and related heavy metals in metal-polluted Taihu Lake sediments. Pedosphere 18: 564-573 Yücesoy F, Ergin M (1992) Heavy-metal geochemistry of surface sediments from the southern Black Sea shelf and upper slope. Chemical Geology 99: 265-287 Zaw M, Chiswell B (1999) Iron and manganese dynamics in lake water. Water Research 33: 1900-1910
Mirjana VEMIC 83
84 MSc Thesis ANNEX A
Table 1: Physical-chemical parameters (pH; EC (μS/cm); T (○C)) of the first sampling campaign in the Skadar Lake
Sampling Stations Replicates pH EC T S1 - Moraca 1 1a 6.94 294 11.4 1b 7.43 285 11.6 1c 6.82 289 11.3
S 2 - Moraca 2 2a 6.88 288 11.4 2b 6.91 284 11.7 2c 6.85 282 11.2 S 3 - Virpazar 3a 7.05 304 13.3 3b 7.23 298 13.2 3c 6.98 301 13.3 S 4 - Radus 4a 7.03 288 12.1 4b 6.87 296 12.5 4c 7.08 275 12.3 S 5 - Radus-Plavnica 5a 7.40 325 12.4 5b 7.32 323 12.6 5c 7.38 336 12.4
S 6 - Plavnica 6a 6.78 398 13.3 6b 6.72 392 13.1 6c 6.85 400 13.6
S 7 - Gostilje 7a 7.24 252 12.5 7b 7.28 250 12.6
7c 7.32 257 12.9 S 8 - Gostilje-Krnjice 1 8a 7.33 238 14.1 8b 7.42 242 14.5 8c 7.32 235 14.1 S 9 - Gostilje-Krnjice 2 9a 7.22 267 12.8
9b 7.25 273 12.5 9c 7.20 261 13.1 S 10 - Krnjice 10a 7.40 254 13.2 10b 7.43 263 13.5 10c 7.35 258 13.4
Mirjana VEMIC 85 Table 2: Physical-chemical parameters (pH; EC (μS/cm); T (○C); DO (mg/L)) of the second sampling campaign in the Skadar Lake
Sampling Stations Replicates pH EC T DO
S1 - Moraca 1 1a 8.1 293 8.6 10.3 1b 8.2 289 8.7 10.4 1c 8.5 297 8.6 10.4 S 2 - Moraca 2 2a 8.1 284 8.5 10.5 2b 8.3 287 8.5 10.5 2c 8.4 286 8.6 10.3 S 3 - Virpazar 3a 8.7 294 8.5 10.7 3b 8.1 297 8.4 10.5 3c 8.8 295 8.5 10.7 S 4 - Radus 4a 8.4 284 8.3 10.4 4b 8.1 289 8.5 10.3 4c 8.4 285 8.3 10.4 S 5 - Radus-Plavnica 5a 8.8 280 8.4 10.6 5b 8.9 286 8.3 10.6 5c 8.8 289 8.3 10.5 S 6 - Plavnica 6a 7.7 403 9.2 7.8 6b 7.9 405 9.3 7.9 6c 7.6 407 9.3 7.8 S 7 - Gostilje 7a 7.9 269 8.4 10.5 7b 7.8 267 8.4 10.7 7c 7.7 271 8.5 10.5 S 8 - Gostilje-Krnjice 1 8a 7.9 289 8.3 10.7 8b 7.9 290 8.0 10.7 8c 7.7 294 8.0 10.8 S 9 Gostilje-Krnjice 2 9a 7.8 297 8.1 10.8 9b 7.6 301 8.1 10.8 9c 7.8 305 8.2 10.6 S 10 - Krnjice 10a 8.7 287 8.8 11.2 10b 8.9 286 8.9 11.1 10c 8.7 287 8.8 11.3
86 MSc Thesis ANNEX B
Table 1: Comparison between detection limits of ICP used for metals analysis and guidelines for water, sediment, fish and plant metal concentrations
Detection Standards for Standards for Standards Standards Limits of ICP freshwater soil for fish for plants at UNESCO- systems (1 MacDonald et (Fevzi et (Bigdeli et IHE laboratory (VLAREM II, al., 2000; al., 2007) al.,,2008) (μg/l) 2010) 2 Jaagumagi, (mg/kg) (mg/kg) (μg/l) 1992) PEC (mg/kg)
Ni 50 50 48.61 67 752 Cr 20 50 1111 0.5 1102 Co 50 7 502 10 50 Cu 10 50 1491 73 1102 Al 100 Fe 50 200 437702 425 Mn 50 200 11002 500 Pb 100 50 1281 1.5 0.3 Cd 10 1 4.981 0.2 0.1 Zn 50 50 4591 150 100 8202
Mirjana VEMIC 87 ANNEX C
Table 1: Trace metals concentrations in plants species (Phragmites communis and Vallisneria spiralis) from Skadar Lake Plant species Ni Cu Fe Zn Pb Cd Mn Cr Al mg/kg mg/kg mg/k mg/kg mg/kg mg/kg mg/kg mg/k mg/k g g g T1 Vallisneria 38.4 12.82 16774 30.53 6.43 0.42 922.33 24.71 4312 spiralis T2 Vallisnerai 31.7 13.84 7278 30.10 4.44 0.52 736.31 18.93 3766 spiralis T3 Vallisneria 30.9 16.99 9111 38.24 4.75 0.64 749.71 15.39 3355 spiralis Phragmites 1.24 2.26 284 33.72 2.78 0.07 25.14 1.64 301 communis top Phragmites 2.09 2.82 610 14.29 2.00 0.04 51.52 2.57 435 communis bottom
Table 2: Trace metals concentrations in fish species (Perca fluviatilis, Rutilus karamani, Squalius cephalus, Scardinius knezevici and Alburnus scoranca) from Skadar Lake Fish species Ni Cu Fe Zn Pb Cd Mn Cr Al mg/kg mg/kg mg/kg mg/kg mg/kg mg/kg mg/kg mg/kg mg/kg Alburnus 1.09 4.98 152.34 99.59 0.29 0.10 14.63 1.83 35.57 scoranca Scardinius 1.17 11.50 197.50 532.63 0.42 0.03 18.23 1.73 64.30 knezevici Squalius 0.98 4.18 178.01 366.47 0.27 0.03 16.77 1.51 39.16 cephalus Rutilus 1.10 6.00 212.91 142.28 0.43 0.14 19.72 1.52 137.2 karamani 0 Perca 0.80 5.05 186.41 243.06 0.41 0.07 17.46 1.49 56.55 fluviatilis
88 MSc Thesis ANNEX D
Table 1: Sieving analysis done in first five sampling stations (S1-S5) presented in percentage (%)
Stations + Sieving Size Fractions Weights (g) Total Weights (g) %
S1 <0.45 μm 8.25 61.16 13.49 S1 0.45-106 μm 5.71 9.34 S1 106-500 μm 46.93 76.73 S1 500-1 μm 0.15 0.25 S1 >1 mm 0.12 0.20
S2 <0.45 μm 11.72 117.67 9.96 S2 0.45-106 μm 5.54 4.71 S2 106-500 μm 63.98 54.37 S2 500-1 μm 31.01 26.35 S2 >1 mm 5.42 4.61
S3 <0.45 μm 2.91 22.37 13.01 S3 0.45-106 μm 10.82 48.37 S3 106-500 μm 8.44 37.73 S3 500-1 μm 0.16 0.72 S3 >1 mm 0.04 0.18 S4 <0.45 μm 3.36 25.21 13.33 S4 0.45-106 μm 3.99 15.83 S4 106-500 μm 15.88 62.99 S4 500-1 μm 1.01 4.01 S4 >1 mm 0.97 3.85
S5 <0.45 μm 2.74 17.8 15.39 S5 0.45-106 μm 3.01 16.91 S5 106-500 μm 9.96 55.96 S5 500-1 μm 1.4 7.87 S5 >1 mm 0.69 3.88
Mirjana VEMIC 89 Table 1 (Continued): Sieving analysis done in last five sampling stations (S6-S10) presented in percentage (%)
S6 <0.45 μm 2.06 24.52 8.40 S6 0.45-106 μm 4.25 17.33 S6 106-500 μm 15.69 63.99 S6 500-1 μm 1.94 7.91 S6 >1 mm 0.58 2.37
S7 <0.45 μm 2.04 18.81 10.85 S7 0.45-106 μm 4.01 21.32 S7 106-500 μm 12.39 65.87 S7 500-1 μm 0.25 1.33 S7 >1 mm 0.12 0.64
S8 <0.45 μm 17.26 33.65 51.29 S8 0.45-106 μm 5.46 16.23 S8 106-500 μm 10.51 31.23 S8 500-1 μm 0.28 0.83 S8 >1 mm 0.14 0.42
S9 <0.45 μm 16.5 29.3 56.31 S9 0.45-106 μm 5.61 19.15 S9 106-500 μm 6.65 22.70 S9 500-1 μm 0.43 1.47 S9 >1 mm 0.11 0.38
S10 <0.45 μm 6.85 24.75 27.68 S10 0.45-106 μm 12.14 49.05 S10 106-500 μm 5.49 22.18 S10 500-1 μm 0.03 0.01 S10 >1 mm 0.24 0.97
90 MSc Thesis ANNEX E
Table 1: Average concentrations ± standard deviation (n=3) of surface water layer including all three replicates (μg/l) Replicates K Ca Mg Ni Cu Fe Zn Pb Cd Mn Cr Si 1A top water 195 45520 5194 27 9 -64 -21 -109 -1 7 -12 -13 1B top water 19 45710 4642 13 -2 -41 -33 -34 -9 8 -14 -12 1C top water 0 47650 4659 15 -5 -73 -29 -19 -11 7 -15 -12 SD 107.5 1178.7 313.9 7.6 7.4 16.5 6.1 48.2 5.3 0.6 1.5 0.6 Average 71.3 46293.3 4831.6 18.3 0.6 -59.3 -27.6 -54.0 -7.0 7.3 -13.6 -12.3
2A top water 1171 49840 8029 4 -6 -44 -33 -85 -9 10 -15 -1.3 2B top water 98 49490 7836 -4 -4 -59 -30 -40 -9 10 -12 -10 2C top water 204 52330 8179 5 -8 -41 -26 -66 -20 12 -14 -11 SD 591.3 1548.6 171.9 4.9 2.0 9.6 3.5 22.6 6.4 1.2 1.5 5.3 Average 491 50553.3 8014.7 1.6 -6 -48 -29.7 -63.7 -12.7 10.7 -13.7 -7.4 3A top water 190 43990 5261 0 -5 -122 -43 26 -6 2 -16 -12 3B top water -79 44870 5310 -1 -6 -122 -34 -5 -10 3 -16 -12 3C top water -92 45220 5236 -1 -9 -111 -31 -22 -15 3 -13 -10 SD 159.2 633.7 37.6 0.6 2.1 6.4 6.2 24.3 4.5 0.6 1.7 1.2 Average 6.3 44693.3 5269.0 -0.7 -6.6 -118.3 -36.0 -0.3 -10.3 2.7 -15.0 -11.3
4A top water 1439 55170 6581 -2 -2 -96 -30 -43 -10 21 -15 -10 4B top water 1199 54900 6518 -1 -6 -96 -38 -65 -12 20 -10 -12 4C top water 1294 58330 6629 3 -6 -103 -30 -45 -5 20 -13 -11 SD 120.9 1907.2 55.7 2.6 2.3 4.0 4.6 12.2 3.6 0.6 2.5 1.0 Average 1310.67 56133.3 6576.0 0.0 -4.6 -98.3 -32.6 -51.0 -9.0 20.3 -12.6 -11.0
5A top water 60 47680 4890 -10 -2 -161 -36 -19 -6 -2 -16 -10 5B top water 5883 46080 4799 13 -7 -68 -30 6 -14 7 -13 -13 5C top water 584 47790 4751 2 -5 -75 -17 -38 -14 7 -11 -12 SD 3221.3 957.1 70.6 11.5 2.5 51.8 9.7 22.1 4.6 5.2 2.5 1.5 Average 2175.6 47183.3 4813.3 1.6 -4.6 -101.3 -27.6 -17.0 -11.3 4.0 -13.3 -11.6
Mirjana VEMIC 91
Table 1(Continued): Average concentrations ± standard deviation (n=3) of surface water layer including all three replicates (μg/l) Replicates K Ca Mg Ni Cu Fe Zn Pb Cd Mn Cr Si 6A top water 139 45840 5098 0 -6 -85 -33 2 -4 8 -14 -13 6B top water 478 45750 5070 7 -8 -37 -27 -29 -9 9 -14 -12 6C top water 97 48900 5139 -1 -6 -66 -28 -24 -16 10 -15 -100 SD 208.9 1793.2 34.7 4.4 1.2 24.2 3.2 16.6 6.0 1.0 0.6 50.5 Average 238 46830 5102.3 2 -6.6 -62.6 -29.3 -17 -9.6 9 -14.3 -41.6 7A top water 139 45840 5098 0 -6 -85 -33 2 -4 8 -14 -13 7B top water -33 36940 5211 1 -7 -91 -40 -42 -11 5 -11 -100 7C top water 193 41160 5501 2 -4 -63 -30 -56 -23 4 -10 -10 SD 118.0 4452.0 207.9 1.0 1.5 14.7 5.1 30.3 9.6 2.1 2.1 51.1 Average 99.6 41313.3 5270.0 1.0 -5.6 -79.6 -34.3 -32.0 -12.6 5.6 -11.6 -41.0 8A top water 21 35990 5051 -2 -6 -102 -33 -56 -9 3 -15 -11 8B top water -163 35850 5252 2 -6 -111 -34 8 -16 3 -13 -10 8C top water 115 38110 5383 -6 -6 -106 -34 -43 -12 1 -12 -13 SD 139.3 1145.9 83.2 4.0 0.0 2.8 0.2 26.6 2.2 1.0 0.7 1.5 Average -9 36650 5228.6 -2 -6 -106.3 -33.7 -30.3 -12.3 2.3 -13.3 -11.3 9A top water 45 42170 5013 6 -6 -101 -37 -14 -8 3 -14 -12 9B top water -109 37500 5299 4 -7 -89 -32 -44 -7 3 -15 -13 9C top water -16 38390 5229 -1 -6 -71 -42 -46 -19 4 -13 -11 SD 77.6 2479.6 149.1 3.6 0.6 15.1 5.0 17.9 6.7 0.6 1.0 1.0 Average -26.6 39353.3 5180.3 3.0 -6.3 -87.0 -37.0 -34.6 -11.3 3.3 -14.0 -12.0 10A top water 84 41700 4967 3 -7 -97 -35 -58 -12 3 -14 -12 10B top water 473 47440 7027 -18 -13 -93 -15 0 4 12 12 -933 10C top water -62 36580 5194 -2 -8 -110 -34 -23 -12 2 -14 -11 SD 276.5 5432.9 1129.5 11.0 3.2 8.9 11.3 29.2 9.2 5.5 15.0 532.0 Average 165 41906.6 5729.3 -5.6 -9.3 -100 -28 -27 -6.6 5.6 -5.3 -318.6
92 MSc Thesis
Table 1: Average concentrations ± standard deviation (n=3) of bottom water layer including all three replicates (μg/l) Replicates K Ca Mg Ni Cu Fe Zn Pb Cd Mn Cr Si 1A bottom water 9009 45690 4838 15 5 -85 -24 -38 -13 6 -15 -11 1B bottom water 308 60300 6903 9 -7 -76 0 35 3 19 16 -788 1C bottom water 1202 46280 4672 10 -8 -78 -30 2 -8 7 -17 6 SD 4786.4 8270.0 1242.9 3.2 7.2 4.7 15.9 36.6 8.2 7.2 18.5 453.6 Average 3506.3 50756.7 5471.0 11.3 -3.3 -79.7 -18.0 -0.3 -6.0 10.7 -5.3 -264.3 2A bottom water 532 50430 7444 -2 -2 -11 -30 -56 -16 16 -14 -10 2B bottom water 645 66040 9952 -4 -13 15 -2 -10 4 32 15 -363 2C bottom water 175.0 52250.0 7626.0 9.0 -5.0 -25.0 -36.0 4.0 -11.0 15.0 -13.0 -11.0 SD 245.3 8535.7 1398.4 7.0 5.7 20.3 18.1 31.4 10.4 9.5 16.5 203.5 Average 450.7 56240.0 8340.7 1.0 -6.7 -7.0 -22.7 -20.7 -7.7 21.0 -4.0 -128.0 3A bottom water 244 45100 4873 -5 -5 -77 -30 -3 -17 5 -14 -11 3B bottom water 806.0 59560.0 6454.0 -11.0 -18.0 -63.0 -5.0 -30.0 2.0 18.0 14.0 -765.0 3C bottom water 148.00 47440.00 5011.00 11.00 0.00 -47.00 -26.00 -34.00 -15.00 9.00 -13.00 -12.00 SD 355.4 7761.7 875.7 11.4 9.3 15.0 13.4 16.9 10.4 6.7 15.9 435.0 Average 399.3 50700.0 5446.0 -1.7 -7.7 -62.3 -20.3 -22.3 -10.0 10.7 -4.3 -262.7 4A bottom water 1680.0 55100.0 6432.0 16.0 1.0 -65.0 -29.0 -28.0 -3.0 26.0 -8.0 -12.0 4B bottom water 2306.00 74950.00 8952.00 -10.00 -17.00 -91.00 -8.00 -19.00 3.00 42.00 17.00 657.00 4C bottom water 1517 56150 6482 0 -4 -102 -32 -60 -13 25 -12 -10 SD 416.5 11169.6 1440.7 13.1 9.3 19.0 13.1 21.5 8.1 9.5 15.7 385.7 Average 1834.3 62066.7 7288.7 2.0 -6.7 -86.0 -23.0 -35.7 -4.3 31.0 -1.0 211.7 5A bottom water 308.00 47290.00 4452.00 3.00 -4.00 -52.00 -39.00 -53.00 -13.00 13.00 -14.00 -12.00 5B bottom water 298 62830 6083 -4 -17 -14 3 -24 3 27 15 -722 5C bottom water 126 48330 4535 9 -6 -8 -31 -68 -3 14 -12 -13 SD 102.3 8687.4 918.6 6.5 7.0 23.9 22.3 22.4 8.1 7.8 16.2 409.6 Average 244.0 52816.7 5023.3 2.7 -9.0 -24.7 -22.3 -48.3 -4.3 18.0 -3.7 -249.0
Mirjana VEMIC 93
Table 2 (Continued): Average concentrations ± standard deviation (n=3) of bottom water layer including all three replicates (μg/l) Metals (μg/L) K Ca Mg Ni Cu Fe Zn Pb Cd Mn Cr Si 6A bottom water 207 45930 5049 2 -2 -79 -31 5 -9 8 -14 -10 6B bottom water 165 58690 6571 -20 -23 -49 -8 -61 1 20 16 -772 6C bottom water 218 47300 5110 1 -7 -62 -30 -88 -18 9 -11 -15 SD 28.0 7005.1 861.7 12.4 11.0 15.0 13.0 47.8 9.5 6.7 16.5 438.5 Average 196.7 50640.0 5576.7 -5.7 -10.7 -63.3 -23.0 -48.0 -8.7 12.3 -3.0 -265.7 7A bottom water 371 36340 5236 12 0 -76 -24 -27 -12 4 -15 -10 7B bottom water 522 47950 7089 -8 -17 -58 -4 -14 7 15 14 -830 7C bottom water 375 37980 5342.00 8 -5 -76.00 -26.00 -37.00 -9.00 4.00 -14.00 -7.00 SD 86.0 6283.3 1040.6 10.6 8.7 10.4 12.2 11.5 10.2 6.4 16.5 474.3 Average 422.7 40756.7 5889.0 4.0 -7.3 -70.0 -18.0 -26.0 -4.7 7.7 -5.0 -282.3 8A bottom water 226 34830 5157 -6 -6 -109 -32 -96 -3 1 -13 -13 8B bottom water 454.0 46310.0 7017.0 -14.0 -21.0 -121.0 2.0 25.0 2.0 11.0 15.0 -991.0 8C bottom water 305.00 35150.00 5204.00 5.00 -4.00 -115.00 -37.00 -43.00 -6.00 2.00 -14.00 -9.00 SD 115.8 6537.6 1060.6 9.5 9.3 6.0 21.2 60.7 4.0 5.5 16.5 565.8 Average 328.3 38763.3 5792.7 -5.0 -10.3 -115.0 -22.3 -38.0 -2.3 4.7 -4.0 -337.7 9A bottom water 338.0 42990.0 5003.0 -1.0 -3.0 -99.0 -36.0 -58.0 -12.0 8.0 -12.0 -11.0 9B bottom water 479 47140.00 7153.00 -14.00 -20.00 -124 -8 35 0.00 11.00 14.00 -977.00 9C bottom water 344 37750 5330 6 -3 -77 -34 39 -16 4 -16 -21 SD 79.7 4705.5 1158.5 10.1 9.8 23.5 15.6 54.9 8.3 3.5 16.3 554.9 Average 387.0 42626.7 5828.7 -3.0 -8.7 -100.0 -26.0 5.3 -9.3 7.7 -4.7 -336.3 10A bottom water 269.0 43530.0 5026.0 3.0 -7.0 -104.0 -43.0 -70.0 -23.0 8.0 -13.0 -21.0 10B bottom water 568.00 46830.00 7113.00 -22.00 -18.00 -119.00 -9.00 -37.00 3.00 11.00 15.00 -994.00 10C bottom water 364 34590 5159 -2 -5 -114 -40 -70 -15 2 -12 -13 SD 152.8 6332.9 1168.4 13.2 7.0 7.6 18.8 19.1 13.3 4.6 15.9 564.1 Average 400.3 41650.0 5766.0 -7.0 -10.0 -112.3 -30.7 -59.0 -11.7 7.0 -3.3 -342.7
94 MSc Thesis ANNEX F
Table 1: Summary of average concentrations (n=3) of surface water layer (μg/l) Stations K Ca Mg Ni Cu Fe Zn Pb Cd Mn Cr Si Co Al S1 71.3 46293.3 4831.7 14.6 < 0.5 < 0.5 < 0.5 < 0.5 < 0.5 7.3 < 0.5 < 0.5 < 0.5 81.3 S2 491.0 50553.3 8014.7 2.5 < 0.5 < 0.5 < 0.5 < 0.5 < 0.5 10.7 < 0.5 < 0.5 < 0.5 92.7 S3 6.3 44693.3 5269.0 2.5 < 0.5 < 0.5 < 0.5 < 0.5 < 0.5 2.7 < 0.5 < 0.5 < 0.5 < 0.5 S4 1310.7 56133.3 6576.0 3.0 < 0.5 < 0.5 < 0.5 < 0.5 < 0.5 20.3 < 0.5 < 0.5 < 0.5 < 0.5 S5 2175.7 47183.3 4813.3 3.0 < 0.5 < 0.5 < 0.5 < 0.5 < 0.5 4.0 < 0.5 < 0.5 < 0.5 46.0 S6 238.0 46830.0 5102.3 2.8 < 0.5 < 0.5 < 0.5 < 0.5 < 0.5 9.0 < 0.5 < 0.5 < 0.5 81.7 S7 99.7 41313.3 5270.0 1.8 < 0.5 < 0.5 < 0.5 < 0.5 < 0.5 5.7 < 0.5 < 0.5 < 0.5 34.0 S8 < 0.5 36650.0 5228.7 1.6 < 0.5 < 0.5 < 0.5 < 0.5 < 0.5 2.3 < 0.5 < 0.5 < 0.5 < 0.5 S9 < 0.5 39353.3 5180.3 2.0 < 0.5 < 0.5 < 0.5 < 0.5 < 0.5 3.3 < 0.5 < 0.5 < 0.5 55.0 S10 165.0 41906.7 5729.3 1.5 < 0.5 < 0.5 < 0.5 < 0.5 < 0.5 5.7 < 0.5 < 0.5 < 0.5 < 0.5
Table 2: Summary of average concentrations (n=3) of bottom water layer (μg/l) Stations K Ca Mg Ni Cu Fe Zn Pb Cd Mn Cr Si Co Al S1 3506.3 50756.7 5471.0 12.3 < 0.5 < 0.5 < 0.5 1.0 < 0.5 10.7 < 0.5 < 0.5 < 0.5 75.3 S2 450.7 56240.0 8340.7 8.3 < 0.5 < 0.5 < 0.5 0.5 < 0.5 21.0 < 0.5 < 0.5 < 0.5 107.7 S3 399.3 50700.0 5446.0 6.2 < 0.5 < 0.5 < 0.5 1.2 < 0.5 10.7 < 0.5 < 0.5 < 0.5 86.3 S4 1834.3 62066.7 7288.7 8.0 < 0.5 < 0.5 < 0.5 1.4 < 0.5 31.0 < 0.5 < 0.5 < 0.5 23.7 S5 244.0 52816.7 5023.3 3.5 < 0.5 < 0.5 < 0.5 0.6 < 0.5 18.0 < 0.5 < 0.5 < 0.5 119.3 S6 196.7 50640.0 5576.7 3.3 < 0.5 < 0.5 < 0.5 0.2 < 0.5 12.3 < 0.5 < 0.5 < 0.5 90.3 S7 422.7 40756.7 5889.0 8.6 < 0.5 < 0.5 < 0.5 0.8 < 0.5 7.7 < 0.5 < 0.5 < 0.5 59.7 S8 328.3 38763.3 5792.7 1.8 < 0.5 < 0.5 < 0.5 0.7 < 0.5 4.7 < 0.5 < 0.5 < 0.5 38.7 S9 387.0 42626.7 5828.7 5.2 < 0.5 < 0.5 < 0.5 5.3 < 0.5 7.7 < 0.5 < 0.5 < 0.5 53.0 S10 400.3 41650.0 5766.0 4.6 < 0.5 < 0.5 < 0.5 0.3 < 0.5 7.0 < 0.5 < 0.5 < 0.5 20.3
Mirjana VEMIC 95 ANNEX G
Table 1: Average concentrations ± standard deviation (n=3) of sediment samples from first campaign (mg/kg) Stations K Ca Mg Ni Cu Fe Zn Pb Cd Mn Cr Si Al S1 1078 110464 16449 104.8 24.1 16711 40.1 13.2 0.3 492 68.2 1189 11964 S2 1293 55709 10363 28.1 20.9 11688 41.2 12.2 0.7 253 23.6 1278 10676 S3 1819 73738 13042 35.5 26.0 15077 53.3 14.3 0.8 311 29.2 1509 14767 S4 1047 77168 10127 91.6 24.9 15866 48.0 15.9 0.4 199 91.4 1653 11732 S5 1463 124043 17340 116.4 33.0 20789 51.4 14.5 0.4 733 74.5 1580 15233 S6 734 165855 15305 87.5 19.0 13615 29.2 0.1 0.0 412 55.2 1212 79 S7 773 97847 7386 46.3 14.6 10734 31.8 14.6 0.5 421 33.6 1032 12567 S8 1076 77915 8448 72.0 19.0 13653 38.3 12.8 0.4 344 63.7 1963 9830 S9 1044 76774 7907 65.5 18.4 12857 33.5 12.0 0.3 409 52.1 1815 9494 S10 800 79282 10291 44.2 11.1 9804 21.5 7.2 0.2 350 37.0 1961 7013
Table 2: Average concentrations ± standard deviation (n=2) of sediment samples from second campaign (mg/kg) Stations K Ca Mg Ni Cu Fe Zn Pb Cd Mn Cr Si Co Al S1 1236 105703 18652 126.76 27.10 20521 50.98 8.37 0.25 625 79.16 1309.8 13.94 5452 S2 427 197527 11817 63.31 9.24 9654 25.68 12.70 0.34 371 34.64 426.8 7.35 10532 S3 1183 152775 11521 84.43 23.98 17326 46.03 15.94 0.40 809 52.74 787.4 11.09 11950 S4 1112 109821 9659 73.90 26.38 13907 61.78 16.71 0.51 246 73.77 439.6 7.54 12306 S5 1127 97616 8559 64.95 20.79 12192 50.37 18.04 0.51 202 65.13 938.1 8.38 12524 S6 1040 131570 11497 76.65 30.55 15987 74.32 18.11 0.55 278 73.19 1006.9 8.57 13785 S7 2084 62655 12774 85.35 36.92 19399 60.34 21.06 0.57 239 63.68 759.7 13.48 15190 S8 1708 143447 13302 110.87 32.44 22905 55.49 21.37 0.59 821 66.55 919.7 13.84 17207 S9 1849 145687 13412 111.68 33.01 23014 56.12 23.49 0.98 833 67.88 810.8 12.18 18385 S10 1443 161261 12256 90.91 27.72 19467 51.10 25.41 8.52 878 57.95 827.4 10.55 20303
96 MSc Thesis