Heavy metal contamination and toxicity Florence Alex Mamboya

Heavy metal contamination and toxicity
Studies of Macroalgae from the Tanzanian Coast
Florence Alex Mamboya
Stockholm University
©Florence
Alex Mamboya, Stockholm 2007
ISBN 91-7155-374-6 pp. 1–48
Front cover illustration: Submerged green and brown macroalgae
from the Western Indian Ocean. Photo by Katrin Österlund
Printed in Sweden by Universitetsservice, US-AB, Stockholm 2007
Distributor: Stockholm University library
My father, Mzee Alex Roweta
My mother, Mama Ernester Ma-Rimoy
My lovely wife, Elizabeth, and
our beloved sons, Edwin and Collins
Abstract
Concentrations of various metals are elevated above background levels in several intertidal areas along the Tanzanian coasts. However,
there is little available information concerning the toxicity of these
metals and how the uptake of these metals by bioindicators are influenced by external factors, such as heavy rains and increased coastal
eutrophication, which tend to fluctuate.
The present study focused on the uptake and toxicity of Cu and Zn in
two common macroalgal species, Padina gymnospora (Phaeophyta)
and Ulva reticulata (Chlorophyta). Laboratory studies were performed
where metal content, growth (DGR), maximal quantum yields
(Fv/Fm) and protein expression patterns (in Ulva) were measured as a
response to exposure to Cu and Zn. The levels of metals accumulated
in algal tissues correlated well to exposure concentrations and the
longer the exposure time, the greater the uptake. However, an increased nutrient load (tested on Padina) or dilution of the seawater
(tested on Ulva) affected both uptake of metals and their toxic effects.
Here, DGR was more affected than Fv/Fm, suggesting DGR to be the
more sensitive indicator of Cu and Zn toxicity. As shown by 2-D gel
electrophoresis, more than ten proteins were up-regulated in U. reticulata after being exposed to Cu (1 μg/L), while at higher concentrations
(10 and 100 μg/L) of Cu numerous proteins were down-regulated.
P. gymnospora was also used as a bioindicator to monitor long-term
(1994–2005) and seasonal in-year variations in heavy metal concentrations in the Zanzibar Channel. No clear overall trends were revealed, but analysis of the combined dataset clearly pinpointed the
most contaminated sites. It was concluded that seasonal and long-term
variations, as well as environmental conditions need to be taken into
consideration when using macroalgae as bioindicators.
Key words: Heavy metals, Padina gymnospora, Ulva reticulata, salinity,
nutrients, proteins, uptake, growth, Fv/Fm, Zanzibar Channel
List of papers
This thesis is based on the following papers, which will be referred to
in the text by their Roman numerals.
I. Mamboya, F.A., H.B. Pratap, M. Mtolera, and M. Björk. 2007.
Accumulations of copper and zinc and their effects on growth
and maximum quantum yield of the brown macroalga Padina
gymnospora. Western Indian Ocean J. Mar. Sci. (In press).
II. Mamboya, F.A., T.J. Lyimo, and M. Björk. 2007. Long-term
and seasonal variations of heavy metal concentrations in a
brown macroalga, the case of Padina gymnospora in the Zanzibar Channel (Submitted).
III. Mamboya, F.A., T.J. Lyimo, and M. Björk. 2007. Copper affects protein expression pattern and maximum quantum yield
in the green macroalga Ulva reticulata. (In manuscript).
IV. Mamboya, F.A., T.J. Lyimo, T. Landberg, and M. Björk. 2007.
Influence of combined changes in salinity and ambient copper
concentrations on growth and copper accumulation in the
tropical green macroalga Ulva reticulata. (Submitted).
My contribution to the above papers:
Paper I: participated in planning and performed all laboratory work,
except the heavy metal analysis; wrote most of the manuscript.
Paper II: participated in planning, did most of the fieldwork, and
wrote most of the manuscript; performed all laboratory work and data
analysis.
Paper III: did most of the planning and writing; performed all laboratory work and most of the sample and data analysis.
Paper IV: did most of the planning and writing, and performed all
experiments and data analysis; participated in the Cu analysis.
An additional relevant paper not included in the thesis:
Engdahl, S., F.A. Mamboya, M. Mtolera, A.K. Semesi and M. Björk (1998).
The brown macroalgae Padina boergesenii as an indicator of heavy metal
contamination in the Zanzibar Channel. Ambio 27 (8): 694–700.
Contents
Abstract ........................................................................................................... v
List of papers.................................................................................................. vi
Contents ........................................................................................................ vii
Abbreviations ............................................................................................... viii
1. Introduction .................................................................................................9
1.1. Heavy metals ...................................................................................................... 9
1.2. Bioaccumulation of heavy metals ..................................................................... 10
1.3. Factors affecting heavy metal accumulation by macroalgae............................ 11
1.4. Toxicity of heavy metals.................................................................................... 13
1.5. External factors affecting metal toxicity ............................................................... 15
1.6. Heavy metals along the Tanzanian coast ........................................................... 17
2. Objectives .................................................................................................19
3. Comments on materials and methods ......................................................20
3.1. Description of the study site................................................................................ 20
3.2. Collection of macroalgae .................................................................................... 20
3.3. Experimental set-up for heavy metal exposure ................................................... 21
3.4. Influence of nutrients on accumulation and toxicity of Cu and Zn (Paper II)......... 22
3.5. Influence of salinity on accumulation and toxicity of Cu (Paper IV) ...................... 22
3.6. Preparation for heavy metal analysis .................................................................. 23
3.7. Determination of daily growth rate (DGR; Papers I and IV) ................................. 23
3.8. Measurements of maximum quantum yield (Fv/Fm; Papers I and III).................. 23
3.9. Protein expression profiling (Paper III) ................................................................ 24
3.10. Statistical data analysis..................................................................................... 24
4. Results and discussion .............................................................................25
4.1. Heavy metal uptake............................................................................................ 25
4.2. Effects of nutrient levels on bioaccumulation of metals........................................ 25
4.3. Effects of salinity on bioaccumulation of heavy metals ........................................ 26
4.4. Effects of heavy metals on DGR and maximum quantum yield ........................... 27
4.5. Effects of nutrient levels and salinity on toxicity of metals.................................... 28
4.6. Effects of heavy metals on protein expression pattern ........................................ 29
4.7. Seasonal accumulation of heavy metals by macroalgae ..................................... 31
5. General conclusions..................................................................................34
6. Future perspectives...................................................................................35
7. Acknowledgements ...................................................................................36
8. References................................................................................................38
Abbreviations
2D PAGE
AAS
Two-dimensional gel electrophoresis
Atomic absorption spectrophotometer
DGR
Daily growth rate
DOM
Dissolved organic matter
EC50
Fv/Fm
Effect concentration of toxicant causing 50% inhibition
Maximal quantum yield
HN
High nutrient concentration
ICP-OES
IEF
Inductively Coupled Plasma - Optical Emission
Spectrometer
Isoelectric focusing
IN
Intermediate nutrient concentration
LN
Low nutrient concentration
MALDI TOF
MS
Mass-assisted laser desorption ionization time of
flight
Mass spectrometer
NN
No added nutrient
NOEC
Non-observable effect concentration
PAM
Pulse amplitude modulated
PE
Photosynthetic efficiency
PEA
Plant efficiency analyser
SDS
Sodium dodecyl sulphate
ROS
Reactive oxygen species
pI
Isoelectric point
UEA
Ulex europaeus agglutinin
1. Introduction
1.1. Heavy metals
Heavy metals are by definition metals having densities higher than 5 g
mL−1 (Sorentino, 1979), for example, Fe, Cu, Pb, Cd, Hg, Ni, Zn, and
Mn. Approximately fifty three of the ninety naturally occurring elements are called heavy metals (Weast, 1984), and many of these, such
as Cu, Mn, Fe, and Zn, are essential micronutrients, but can become
toxic at concentrations higher than the amount required for normal
growth (Nies, 1999). Other heavy metals, such as Cd, Hg, and Pb,
have so far unknown roles in living organisms, and are toxic even at
very low concentrations (Wood, 1974; Nies, 1999). Since many heavy
metals can be very toxic and thus may threaten the health of organisms,
studies have been conducted to investigate heavy metal levels in environmental samples, as well as heavy metal accumulation in and effects
on organisms, and factors affecting heavy metal accumulation by various organisms. However, studies conducted in tropical environments are
rare (Machiwa, 1992; Ferletta et al., 1996; Engdahl et al., 1998; Machiwa, 2000).
Several activities can contribute to heavy metal pollution in the marine
environment, for example, seafloor and bedrock dredging, shipping
activities, industrial and urban effluents, mining, agricultural fertilizer
use, and burning of fossil fuels (Machiwa, 1992; UNEP, 1997; Lionetto et al., 2003). Natural weathering of rocks is yet another source
(Pyle and Mather, 2003).
In the marine environment, heavy metals may occur as dissolved free
metal ions or as complex ions, chelated with certain inorganic ligands
such as Cl−, OH−, CO3−, and NO3−; and sometimes heavy metals can
form complexes with organic ligands such as fulvic acid, amines, humic acids, and proteins (Beijer and Jerenlöv, 1979; Batley et al.,
2004). Heavy metals can be present in various particulate forms: as
colloids or aggregates, bound into particles, precipitated as metal coat9
ings on particles, incorporated into organic matter such as algae, and
held in crystalline detrital particles (Beijer and Jerenlöv, 1979). The
physical and chemical forms of heavy metals in the marine environment are governed by environmental variables such as salinity, temperature, pH, redox potential, organic and particulate matter, biological activities, and metal properties (Lobban and Harrison, 1994).
1.2. Bioaccumulation of heavy metals
Unlike many other pollutants in the environment, heavy metals are
non biodegradable (Kaewsarn and Yu, 2001). Remediation processes
for heavy metal-polluted ecosystems are difficult, and expensive.
Heavy metals can also be accumulated by some organisms either directly (e.g., in the case of macroalgae) or through the food chain,
eventually posing a serious health risk to inhabitants of an ecosystem,
including humans (Galloway et al., 1982; Angelone and Bini, 1992;
Chan et al., 2003). The bioaccumulation of toxicants, such as heavy
metals, by living organisms is often a good integrative indicator of exposure, and has been extensively used to assess contamination levels of
heavy metals in polluted ecosystems (Phillips and Rainbow, 1994).
Macroalgae are major primary producers in the marine environment and
play an important role in food chains. Since marine pollution is most
serious in coastal waters adjacent to major pollutant sources, macroalgae
from marine environment are particularly suitable for pollution studies.
Additionally, they have the ability to accumulate high levels of various
metals in their cell walls (Burdin and Bird, 1994; Salgado et al., 2005).
Macroalgae, especially from the Phaeophyceae, have, besides negatively
charged polysaccharides, special compartments (physodes) that enhance
their ability to accumulate high concentrations of heavy metals (Salgado
et al., 2005, Fig. 1).
The accumulation of heavy metals by macroalgae can take place either
passively or actively (Eide et al., 1980). Macroalgae have been used in
studying the contamination status of coastal ecosystems, due to their
ability to accumulate and tolerate high metal concentrations (Bryan,
1983; Wekwe et al., 1989; Ferletta et al., 1996; Amado Filho et al.,
1999; Ho, 1990; Muse et al., 1999). Unlike several other bioindicators
of heavy metal contamination (such as filter-feeding animals), macroalgae accumulate only metal ions that are dissolved in the seawater
(Luoma, 1983; Luoma et al., 1982).
10
Figure 1. Differential interference
contrast light microscopy image of transverse cryosections of P. gymnospora. 1.
Bar: 60 μm. 2. Fluorescence microscopy
image of the transverse cryosection of P.
gymnospora observed in 1 incubated with
UEA, displaying intense labelling in cortical
and medullar cells. Bar: 60 μm. 3. Detailed
image of the cell walls (arrowheads), physodes (P), and nuclear region (N). Bar: 30
μm. 4. Same region as in 3 observed by
means of fluorescence microscopy,
revealing the intense labelling of UEA in
the cell walls (arrowheads) and physodes
(P) of cortical and medullar cells, and the absence of labelling in the nuclear region
(N). Bar: 30 μm. Reprinted with minor modifications from Salgado et al. (2005)
from Springer, NewYork.
1.3. Factors affecting heavy metal accumulation by macroalgae
In marine environments, the concentration of heavy metals is largely
governed by the biological, chemical, and physical characteristics of
the surrounding seawater (Wangersky, 1986). For example, Rice and
Lapointe (1981) found that light and nitrogen availability positively
affected rates of uptake of Fe, Mn, Zn, Cd, and Rb in Ulva fasciata,
and it has been demonstrated that uptake of Cd in Ulva fasciata increased with increased ambient concentration of nitrate in the growth
medium (Lee and Wang, 2001). However, in the freshwater plant
Ipomoea aquatica (water spinach), heavy metal accumulation was
negatively affected by increased nutrient levels, the uptake of Hg, Cd
and Pb decreasing when the nutrient levels were higher (Göthberg et
al., 2004). Phytoplankton studies of the influence of major nutrients
on heavy metal bioaccumulation demonstrated that nutrient enrichment increased concentrations of Cd and Zn uptake (Wang and Dei,
2001). In environments with high nutrient levels, metal uptake can be
inhibited because of complex formation between nutrient and metal
ions (Göthberg et al., 2004; Haglund et al., 1996; Paper I).
Growth rate is another factor that reportedly affects heavy metal accumulation in macroalgae, Cd and Rb levels decreasing and Mn levels
increasing as the specific growth rate increases. This probably indicates the metabolic regulation of these metals (Rice, 1984), or possibly the presence of a “dilution factor” (Greger et al., 1991; Wang and
11
Dei, 1999; Göthberg et al., 2004) as a result of an increase in the
heavy metal-to-biomass ratio.
Salinity is yet another factor reported to affect heavy metal bioavailability. However, most studies of salinity have described its effects on
heavy metal accumulation by animals; information concerning the
effects on macroalgae is scarce (Munda 1986; Nugegoda and Rainbow, 1989; Anderson et al., 1995; Shazili, 1995; Ozoh, 1994; Lee et
al., 1998; Wang and Dei, 1999). Paper IV describes the influence of
salinity on Cu uptake by a tropical marine macroalga, Ulva reticulata.
The pH and redox potential affects the bioavailability of metals in
solution: at high pH elements are present as cations, while at low pH
the bioavailability of metals ions is enhanced (Peterson et al., 1984). It
is known, however, that metals in seawater may exist in either particulate, or dissolved form mainly determined by the properties of a particular metal and other factors, such as pH, salinity, redox potential,
ionic strength, alkalinity, persistent organic and particulate organic
matter, and biological activity (Stokes, 1983).
Humic substances in the aquatic environment may influence the accumulation of metal ions (Koukal et al., 2003). It has been demonstrated that the bioavailability and toxicity of heavy metals are reduced through complex formation with dissolved organic matter
(DOM) hence, reduce the concentration of free ionic metals in the
aquatic environment (Tubbing et al., 1994; Kim et al., 1999; Guo et
al., 2001). DOM may also block the accumulation of some heavy metals by blocking the algal surface sites (Campbell et al., 1997; Guo et
al., 2001). Temperature affects the metabolic rate of organisms, and
hence also their heavy metal uptake (Lemus and Chung, 1999). Indeed, temperature also affects the water chemistry hence the distribution of organisms in an ecosystem (Countant, 1987). On the otherhand
according to Zumdahl (1992), seasonal variation in temperature does
not affect heavy metal accumulation. Sometimes, heavy metal bioaccumulation has been regarded as both species specific and metal specific (Lee et al., 1998).
Knowledge of the factors affecting heavy metal bioaccumulation by
macroalgae enhances our understanding of the usefulness and limitations of using macroalgae as bioindicators of heavy metals in the marine environment. Thus, Papers I and IV report on how two macronu12
trients (phosphates and nitrates) and salinity affect the bioaccumulation of selected metals. This might provide insight into what regulates
uptake in the field, since the inflow of pollutants to the environment is
not as isolated compounds, but rather as combinations of several.
1.4. Toxicity of heavy metals
Several studies have reported on the toxic effects of heavy metals on
various species of macroalgae (Markham et al., 1980; Amado Filho et
al., 1993, 1996; Kangwe, 1999). Most studies of macroalgae have
been done in temperate regions, while information regarding tropical
environments, especially the Western Indian Ocean is scarce. As well,
it has been found that different species may respond differently when
exposed to different heavy metals (Carreras and Pignata, in press). It
has previously been reported that even the same species growing in
different areas subject to different environmental parameters may respond differently to heavy metal contamination (Hall et al., 1979).
Furthermore, several other factors, such as concentration of dissolved
metal, pH, salinity, temperature and nutrients, are known to influence
the toxicity of certain metals to macroalgae (Rai et al., 1981; Florence
et al., 1984; Munda and Hudnik, 1988; Langston, 1990).
Thus, the mechanism of heavy metal toxicity to plants is not yet well
understood. Research into how various species from different areas
respond to various heavy metals at different levels could help improve
our understanding of the action of heavy metals on, and the tolerance
of heavy metals by, macroalgae. Papers I, III, and IV describe the
hitherto unexamined toxic effects of selected metals on tropical
macroalgae from the Zanzibar Channel in the Western Indian Ocean.
The toxicity of heavy metals in macroalgae has been reported to follow the general order of Zn < Pb < Ag < Cd < Cu < Hg, which may
vary depending on experimental conditions and macroalgal species
(Rai et al., 1981, Kangwe, 1999). Heavy metals are among the major
environmental hazards due to their affinity for metal sensitive groups,
such as thiol groups. Heavy metals block functional groups of proteins, displace and/or substitute essential metals, induce conformational changes, denature enzymes and disrupt cells and organelle integrity (Hall, 2002). Different heavy metals have been reported to affect macroalgae by interacting with enzymes and inhibiting their normal functions (Van Assche and Clijsters, 1990).
13
Heavy metal toxicity is often linked to the formation of free reactive
oxygen radicals causing the inhibition of macroalgae development
(Collén et al., 2003; Pinto et al., 2003). Molecular oxygen is unreactive with organic molecules because it has two unpaired unpaired
valence shell electrons in outer shell. However, when activated
through reduction it forms reactive oxygen species (ROS) such as superoxide radical (O2-), singlet oxygen (1O2), hydrogen peroxide
(H2O2), hydroxyl radical (OH.) and finally water (H2O) (Fig. 2). Reactive oxygen species are toxic because they have ability to interact rapidly with biological molecules (proteins, lipids, DNA) causing
oxidative stress which can result into cell death via apoptosis or
necrosis (Kannan and Jain, 2000).
Oxidative stress occurs as a result of imbalance between the
production of reactive oxygen and a biological system's ability to
readily detoxify the reactive intermediates or easily repair the resulting
damage. Damage as a result of oxidative stress can occur in biological
molecules such as DNA, proteins and lipids (Fig. 2). Oxidative attack
on proteins results in site-specific amino acid modifications, fragmentation of the peptide chain, aggregation of cross-linked reaction products, altered electrical charge and increased susceptibility to proteolysis. The oxidative degradation of protein is enhanced in the presence
of metal cofactors that are capable of redox cycling, such as iron. In
these cases, the metal binds to a divalent cation binding site on the
protein. The metal then reacts with hydrogen peroxide in a Fenton
reaction to form a hydroxyl radical that rapidly oxidises an amino acid
residue at or near the cation binding site of the protein (Stadtman,
1986). Detoxifications of ROS are mainly by production of
antioxidants such as enzymes (superoxide dismutase, catalase,
peroxidase), thioredoxin superfamiliy, glutathione and vitamin E
(Dowling and Sheen, 2006).
14
Figure 2. Oxidative stress is elicited by ROS derived by a univalent
reduction of O2. ROS can be induced by endogenous or external
sources such as heavy metals, PAHs, polyaromatic hydrocarbons.
(Source: Dowling and Sheehan, 2006).
More specifically in macroalgae, heavy metal toxicity is known, for
example, to inhibit growth and photosynthesis, reduce chlorophyll
content, affect reproduction, interfere with cell permeability, cause the
loss of K ions, affect protein synthesis and degradation, and cause
oxidation and lipid peroxidation (Sorentino, 1979; Strömgren, 1980;
Rai et al., 1981; Kremer and Markham, 1982).
1.5. External factors affecting metal toxicity
Toxicity of heavy metals in macroalgae in marine environments largely
depends on the biological availability of the heavy metals (Campbell,
1995; Sunda and Huntsman, 1998), which is determined by both their
physical and chemical states (Langston, 1990). For example, the toxicity
of heavy metals may be reduced when they are adsorbed to suspended
organic matter, thus reducing their ionic fraction in the water column.
Both pH and redox potential affect the toxicity of heavy metals by
15
limiting their availability (Peterson et al., 1984). At low pH, metals
generally exist as free cations; at alkaline pH, however, they tend to
precipitate as insoluble hydroxides, oxides, carbonates, or phosphates.
Thus, measurements of total heavy metal concentrations in the water
column sometimes may not correlate with toxicity in macroalgae
(Florence et al., 1984); this may explain a situation in which two studies
examining the effect of a metal at the same concentration may obtain
different results.
Little is known of how salinity and temperature influence the toxicity of
metals in macroalgae. Munda (1984) noted the effect of salinity on the
bioaccumulation of Mn, Zn, and Co by Enteromorpha intestinalis and
Scytosiphon. Andersson and Kautsky (1996) reported that the addition of
20 µg Cu/L of water caused an approximately 70–80% decline in the
germination of Fucus vesiculosus zygotes at 6 psu and also at 20 psu
(the latter being higher than optimum). At a salinity close to optimum
(14 psu), no negative effects on germination were noted when 20 µg
Cu/L of water was added. These results suggest that the degree of
salinity stress acting on the zygotes is a more important determinant of
their response to Cu than is the influence of salinity on metal
availability.
Rai et al. (1981) reported both a reduction and increase in metal toxicity
in algae with increased temperature. The increase in toxicity with
increased temperate is due to higher energy demand, which causes a
higher respiration rate in the organism (Rai et al., 1981). In addition,
temperature is might affect the chemistry of water (Fritioff et al., 2005)
which might influence heavy metal toxicity. However, the reasons for
the decrease in toxicity with increased temperature are not well
understood (Förstner and Wittmann, 1979).
High concentrations of nutrients, such as phosphates and nitrates, have
been reported by several authors to reduce the toxicity of heavy metals
(Paper I; Haglund et al., 1996). The presence of other pollutants in the
growth medium can also affect the toxicity of heavy metals. Whereas the
presence of 2,4-dichlorophenoxy acetic acid (2,4-D) was found to
reduce the toxicity of both nickel and aluminium in marine
phytoplankton, Cu decreased the toxicity of the herbicide paraquat to
freshwater phytoplankton (Rai et al., 1981). Interactions between metals
occurring together in the environment, for example, via metal−metal
antagonistic or synergistic effects, have also been reported (Strömgren,
16
1980; Rai et al., 1981; Lewis and Cave, 1982; Stauber and Florence,
1985; Munda and Hudnik, 1986).
The Tanzanian coast has many estuaries, sewage outfall areas, and
intertidal areas with freshwater intrusion where fluctuations of nutrients
and salinity are frequent. In this work the effects of nutrient levels on the
accumulation and toxicity of Cu and Zn are investigated for P.
gymnospora in Paper I, while the influence of salinity on the toxicity of
Cu to Ulva is examined in Paper IV.
nn
. Number of people x 1000
1.6. Heavy metals along the Tanzanian coast
Tanzania borders on the Indian Ocean on its eastern side, with an
extended coastline of approximately 800 km. Highly populous Dar es
Salaam, the biggest city in the country, lies on the Indian Ocean.
Disposal of untreated industrial waste and raw sewage is common in the
city, due to a lack of proper sanitary facilities. The Zanzibar Islands are
Year
Figure 3. Population growth in Tanzania.
Source: http://fr.wikipedia.org/wiki/Tanzanie
17
situated to the east of Dar es Salaam, separated from it by the Zanzibar
Channel. Tanzania’s population is growing rapidly and almost linearly
(Fig. 3), the current population coming to approximately 37 million
people (Census, 2003). Many people are now migrating to coastal urban
areas increasing the coastal population; this population growth is
associated with environmental degradation, wastes being disposed
untreated into the environment. Industrial emissions and effluents are
increasing due to an increase in industrial activities, especially in Dar es
Salaam. Mining, shipping, and agricultural activities are also increasing
in line with population growth. All these activities pose a serious threat
to the environmental, especially the aquatic environment. Amongst the
pollutants that are expected to increase in amount are heavy metals and
nutrients from industrial and sewage effluents.
Despite the increased exposure of the Tanzanian coast to pollution, few
studies have investigated the levels and effects of pollution in the area’s
marine biota (Machiwa, 1992; Ferletta et al., 1996; Engdahl et al., 1998;
Mremi and Machiwa, 2003). The available studies are limited to shortterm sampling and only a few examine toxicity effects. Hence, no longterm or seasonal studies have been conducted to evaluate variations in
pollutant levels. Few studies have examined heavy metal levels in
sediments or biota, and the effects of heavy metals and factors that might
affect their uptake and bioaccumulation are poorly understood. No
information is available as to whether pollution is increasing or
decreasing along the Tanzanian coast.
The present study attempts to determine seasonal and long-term
fluctuations of the levels of various metals in the Zanzibar Channel,
using the previously recommended macroalga P. gymnospora as a
bioindicator. It was also of interest to determine the effects on selected
macroalgae species of heavy metals that were found in high
concentrations in the environment. Papers I, III, and IV examine the
toxicity of heavy metals and factors that affect their uptake and
toxicity in macroalgae. Paper II demonstrates the use of P.
gymnospora for the seasonal and long-term monitoring of heavy metal
contamination, and its potential as a pollution bioindicator in the
Zanzibar Channel.
18
2. Objectives
I.
II.
III.
IV.
To map heavy metal contaminations along the coasts of
Tanzania and to determine variations in concentrations over
time (Paper II)
To evaluate how surrounding factors, such as increased
nutrient levels and fluctuations in salinity, affect metal
accumulation patterns in selected macroalga (Papers I and
IV)
To determine physiological responses induced by the heavy
metals found to have increased in Zanzibar Channel (Papers
I, III, and IV).
To evaluate the potential of macroalgae as natural
bioindicators for heavy metal pollutions.
19
3. Comments on materials and methods
The following is a summary description of the materials and methods
used; detailed information is provided in Papers I−IV.
3.1. Description of the study site
The Zanzibar Islands are separated from the Tanzanian mainland by the
Zanzibar Channel (Fig. 4). The Channel is mostly dominated by the East
African Coastal Current, which has a net northward flow. Of the study
sites selected, some are subject to human environmental degradation
pressures near Dar es Salaam and the town of Zanzibar, while others are
located away from anthropogenic input. Approximately six study sites in
Dar es Salaam (the Mbudya Island, Ocean Road, Kunduchi, Yacht Club,
and Oyster Bay sites) and five in the Zanzibar Islands (the Bawe,
Chapwani, Maruhubi, and Nungwi sites) (Fig. 4) were selected for this
study.
3.2. Collection of macroalgae
Fresh submerged P. gymnospora samples (Fig. 5) were collected for
laboratory experiments from Bawe Island (one of the Zanzibar Islands), at low tide (Paper I), while U. reticulata samples (Fig. 6) were
collected from the intertidal area in Oyster Bay, Dar es Salaam (Papers III and IV, Fig. 4). Padina was selected because previous studies
showed that is suitable as bioindicators of heavy metals compared to
other macroalgae investigated (Ferletta et al., 1996). U. reticulata was
used in this study because it is available in many intertidal areas including polluted and areas with high variability of salinities. U. reticulata samples were transported to Stockholm University and cultured
under tropical conditions in a climate chamber using natural seawater.
To allow acclimatization, the macroalgae were cultured in Plexiglas
cylinders and left for at least two weeks before starting experiments.
The field study involved collecting P. gymnospora samples for the
seasonal and long-term monitoring (Paper II) of heavy metals in the
Zanzibar Channel. This was done at various study sites in 1994, 1997,
20
1998, 2002, 2003, and 2005 (Fig. 4). Samples for seasonal study were
collected in 1998 (Paper II).
Figure 4. Sampling sites located in the Zanzibar Channel. Sampling
points are indicated by black dots. Note the Zanzibar Channel separating Zanzibar Island and Dar es Salaam mainland, Tanzania.
3.3. Experimental set-up for heavy metal exposure
The set-ups for Papers I, II, and IV involved three replicates and for
Paper I, six replicates, of controls and each treatment, several repetitions
being made of the experiments. Before exposure to any toxicant, set-ups
were left for 24 hr to acclimatize. The macroalgae were exposed to
selected concentrations of Cu or Zn; Cu was added in the form of CuCl2
(Paper I, Merck, Darmstadt, F.R. Germany; Paper III and IV, Sigma
Aldrich, Steinhelm, Germany) while Zn was added in the form of ZnCl2
(Merck, Darmstadt, F.R. Germany) in natural filtered seawater.
Measurements of toxicity (Fv/Fm and daily growth rate – DGR) were
made for certain durations presented in Papers I, II, and IV. Samples
were taken and processed/preserved for heavy metal analysis (Papers I,
21
III, and IV) and protein extraction (Paper III).
Figure 5. Padina gymnospora
(Brown macroalga)
Source:
http://www3.gettysburg.edu/~rcavalie/bda_i/109.html
Figure 6. Ulva reticulata
(Sea Lettuce, Green macroalga)
3.4. Influence of nutrients on accumulation and toxicity of Cu and
Zn (Paper II)
P. gymnospora specimens growing in a medium containing either 500
µg Cu l−1 or 1000 µg Zn l−1 were exposed to three different
concentrations of nitrate and phosphate. The sources of the nitrate and
phosphate nutrients were sodium nitrate (NaNO3, Merck, Darmstadt,
F.R. Germany) and hydrous sodium hydrogen phosphate
(NaHPO4.12H2O, Merck, Darmstadt, F.R. Germany), respectively.
Three different nutrient concentrations were defined as (i) high nutrient
(HN), containing 20 mg/L nitrate and 2 mg/L phosphate; (ii)
intermediate nutrient (IN), containing 10 mg/L nitrate and 1 mg/L of
phosphate; and (iii) low nutrient (LN), containing 1 mg/L nitrate and 0.1
mg/L phosphate. While measuring for toxicity (Fv/Fm and DGR),
samples for heavy metal analysis were taken as described in Paper I.
3.5. Influence of salinity on accumulation and toxicity of Cu
(Paper IV)
Tests of the influence of salinity were made at 20, 25, 30, 35, and 40 psu
S at different concentrations of copper; the controls contained no added
22
Cu. U. reticulata samples were exposed to different levels of salinity at
different concentrations of Cu for 7 d; the exposure concentrations of Cu
used were 0, 5, 50 and 500 μg Cu/L. The measurement of wet weight for
DGR determination was done after 7 d. After the experiments, samples
were washed and analysed for Cu concentration.
3.6. Preparation for heavy metal analysis
Field samples (Paper II) and laboratory samples (Papers I, II, and IV)
were ground in a porcelain mortar; the homogenous algal powder
(approximately 0.1−0.5 g) was digested using concentrated nitric acid
and perchloric acid. After dilution with double distilled water, the
resulting solutions were filtered and analysed for heavy metal
concentrations using Inductively Coupled Plasma Optical Emission
Spectrometer (ICP-OES, Spectro, Marlborough, MA, USA ) (Paper I)
or atomic absorption spectrophotometer (AAS, Paper III and IV,
Spectra AA-100 and GTA 100, Varian, Springvalve, Australia; Paper
II, AnalytikJena novAA 400, Jena, Germany). Both ICP-OES and
AAS were used in this study because they are sensitive instruments able
to detect low concentrations of heavy metals in environmental samples
and they do not require a big sample size.
3.7. Determination of daily growth rate (DGR; Papers I and IV)
Daily growth rate (DGR), expressed as a percentage, was estimated
according to Lignell et al. (1987), Mtolera et al. (1995), and Haglund et
al. (1996) using the following formula:
DGR = [(Wt/Wo)1/t−1] × 100
where Wt represents fresh weight at time t, Wo represents initial fresh
weight, and t is time in days.
3.8. Measurements of maximum quantum yield (Fv/Fm; Papers I
and III)
The maximum quantum yield (Fv/Fm) has been shown to be a suitable
measure of stress in plants caused by various stressors (Kangwe, 2006;
Beer and Björk, 2003). Measurements were made using a plant
efficiency analyser (PEA, Hansatech Instruments Ltd, Lynn, Norfolk,
UK) (Paper I) and a Diving Pulse Amplitude Modulation Fluorometer,
(Diving PAM, Walz, Effeltrich, Germany) (Paper III). For more details
23
about the techniques, see Beer and Björk (2000) and Beer et al. (2001).
Before measurement, plants were dark adapted for 15 min.
3.9. Protein expression profiling (Paper III)
Proteomics is the new technique applied by toxicologist to achieve highthroughput analysis of effects of toxicants on protein populations and
sub-populations with the potential to identify novel biomarkers or
toxicity targets (Dowlig and Sheehan 2006; Jamers et al., 2006; Lay et
al., 2006; Gianazza et al., 2007). Limited studies are available on the
use of proteomic tools in the marine environmental studies. Therefore in
this study, 2D gel electrophoresis, PdQuest software programme and
Mass Spectrometry, MALDI TOF (Voyager-DE STR mass
spectrometer, Applied Biosystems, Foster City, CA USA) were
employed as tools to investigate the effect of different concentrations of
Cu in U. reticulata as a baseline.
3.10. Statistical data analysis
Two-way ANOVA testing was used to test the effects of two variables
on growth (Fv/Fm) or on the uptake of metals. For multiple comparisons
testing, the post-hoc HSD test was used to test the level of significant
and non-significant effects after ANOVA testing (Siegel and Castellan,
1988). Cluster analysis was employed in assessing homogenous groups
(sampling stations) for overall levels of heavy metal concentrations from
1994 to 2005 (Paper III). Spearman rank correlation tests were
performed to determine the correlation between effects (e.g., growth and
accumulated Cu in algal tissue). Student’s t-test was used to compare the
measurable effects between different treatments. All statistical tests were
performed using the Statistica, version ’99, software package (StatSoft,
Tulsa, OK, USA), at a 95% significance level.
24
4. Results and discussion
4.1. Heavy metal uptake
The uptake of two metals, Cu and Zn, by the brown alga P. gymnospora
was investigated in Paper I; the uptake of Cu by U. reticulata was
investigated in Papers III and IV. It was found that both macroalgae
accumulated metals proportionally to the concentration in the growth
medium (p < 0.05); the higher the exposure concentration of heavy
metals in the growth medium, the greater the accumulation of metal. It
was also found that the exposure time substantially influenced the
accumulation (Papers I and IV). Many studies have previously reported
the ability of various macroalgae to accumulate metals from surrounding
media. This ability to accumulate is usually attributed to the presence of
charged polysaccharides in the cell walls. However, brown macroalgae
have been reported to have higher accumulation ability than green algae
do. This is explained by the presence in their cell wall material of
polysaccharides (alginates and sulphated fucans) with a higher affinity
for cations, and by the presence of so-called physodes – bodies
containing phenolic compounds, which are known to accumulate large
amounts of heavy metals (Salgado et al., 2005; Andrade et al., 2002;
Farina et al., 2003).
4.2. Effects of nutrient levels on bioaccumulation of metals
Although the accumulation of metals by P. gymnospora from the
surrounding medium was proportional to the available concentration of
metals, the addition of nutrients, such as nitrate and phosphate,
significantly inhibited the accumulation (Paper I). It was found that the
higher the concentration of nutrients in the growth medium, the lower
the accumulation of both Cu and Zn (Paper I). It is impossible, from this
data, to explain exactly why the accumulation decreased with nutrient
additions. However, when a similar effect was found in the red alga
Gracilaria tenuistipitata, it was suggested to be due to complex
25
formation, competition for the binding sites in the cell wall during
accumulation, or simply the improving overall nutrient status of the alga
(Haglund et al., 1996). Macroalgae accumulate only the available
dissolved metal ions from the growth medium (Luoma, 1983).
If this decrease in accumulation due to nutrient levels also exists in the
field, it would have implications when macroalgae are used as indicators
of heavy metals: if high levels of both metals and nutrients are present
together in the environment, the estimated environmental levels of heavy
metals could be underestimated. The experimental nutrient
concentrations used in this study, however, are higher than would
normally be found in the field, though such levels could well be found
near raw sewage outfalls, which could directly raise nutrient
concentrations in the surrounding seawater. Reported nutrient levels in
coastal waters near different parts of Zanzibar and Dar es Salaam are
quite low (Lugomela et al., 2002; Björk et al., 1995; Hamisi et al., 2004),
and are unlikely to affect bioaccumulation significantly. However, in an
area such as Ocean Road (Paper II), which is indeed close to a major
sewage outlet area, the metal levels might have been underestimated. In
another case, nutrient enrichment has been reported to enhance the
accumulation of various heavy metals in phytoplankton (Wang and Dei,
2001). In the present study, the uptake of Cd and Zn was enhanced by
the addition of nitrate but not of phosphate.
4.3. Effects of salinity on bioaccumulation of heavy metals
Salinity significantly influenced the accumulation of Cu in U. reticulata.
Just as decreased nutrients caused an increase in metal accumulation,
decreased salinity enhanced the accumulation of Cu in the algal thallus
(Fig. 7, Paper IV). Previously reported results (mostly for animals)
regarding heavy metal accumulation at varying salinities, display a
similar pattern, high salinity inhibiting accumulation (Wright, 1977;
Amiard-Triquet et al., 1991). However, still other studies found that
salinity had no influence on the accumulation of copper in Atherinops
affinis and the Ragworm (Anderson et al., 1995; Ozoh, 1994).
In tropical regions with estuaries, large river mouths, and freshwater
intrusion into intertidal areas, U. reticulata might be used to study metal
accumulation, because it can endure in these areas better than many
other macroalgae can. In Tanzania, for example, at the mouth of the
River Msimbazi, which might be highly polluted because it drains urban
26
and industrial areas (including former waste dumping sites), it is
impossible to find P. gymnospora, which has been recommended as a
bioindicator; hence, alternative algae that thrive in this area, such as
Ulva, could be used instead.
Figure 7. Effect of salinity on the accumulation of Cu in Ulva reticulata
exposed to different Cu concentrations for 7 day (N = 6).
4.4. Effects of heavy metals on DGR and maximum quantum
yield
Cu is ranked among the most toxic metals to plants (Rai et al., 1981). It
was evident in this study that Cu inhibited both DGR (Papers I and IV)
and Fv/Fm. Cu toxicity displayed a dependency on exposure time: the
longer the exposure time, the greater the inhibition (Papers I and III). We
observed a strong negative correlation between DGR in U. reticulata
and both the level of Cu in the medium and the accumulated Cu in the
algal tissue; this indicates that the inhibition of DGR was due to Cu
(Table 1, Paper IV). Along with the decrease in both DGR and Fv/Fm,
high concentrations of Cu were found to induce chlorosis. Toxicity of
heavy metals such as Cu has been linked with the production of free
oxygen radicals, which are known to induce the peroxidation of
membrane lipids (Halliwell and Guttereridge, 1984; Vavilin et al., 1998;
Monnet et al., 2006). Despite Cu being an important micronutrient, at
27
elevated concentrations it has been found to inhibit growth by replacing
cofactors in key enzymes (e.g., Mg, an essential element in chlorophyll
molecules), disrupting photosynthetic activity and other important
cellular processes (Küpper et al., 1996; Strömgren, 1980). Cu was
demonstrated to affect the growth and photosynthesis performance of
Gracilaria tenuistipitata exposed to different Cu concentrations; it was
concluded that Cu might have inhibited the electron transport chain of
the light reaction of photosynthesis at the site of the secondary quinine
acceptor in photosystem II (Haglund et al., 1996).
Table 1. Correlation between Cu concentration in thalli and DGR of
U. reticulata exposed to different concentrations of Cu at different
salinities. (Significant correlation at
P<0.05)
20 psu
25 psu
30 spu
35 psu
40 psu
N
20
20
20
20
20
r
-0.930
-0.909
-0.756
-0.823
-0.669
2
r
0.865
0.827
0.571
0.676
0.448
p
<0.01
<0.01
<0.01
<0.01
<0.01
Similarly, Zn also inhibited both DGR and Fv/Fm in P. gymnospora
(Paper I); however, it was found to be less toxic than Cu was, producing
less inhibition of both DGR and Fv/Fm. In addition, the concentration of
Zn was far higher than that of Cu, yet Cu proved to be the more toxic.
Similar findings regarding the possible effect of Zn on growth were
reported previously for Padina spp., in which growth inhibition occurred
with exposure to 20 μg/L of Zn (Amado Filho et al., 1996, 1997). In
both cases, i.e., Cu and Zn exposures (Paper I), the inhibitory effect was
higher in terms of DGR than of Fv/Fm, indicating that growth is the
more sensitive indicator of heavy metal toxicity. This may be because
the toxicity of these metals did not only target photosynthetic pathways,
but also inhibited other metabolic processes. This has also been reported
in the related green macroalgae Enteromorpha intestinalis, in which
growth was more inhibited by Cu than photosynthesis was (Lewis et al.,
1998).
4.5. Effects of nutrient levels and salinity on toxicity of metals
The toxicity of heavy metals to algae is reportedly influenced by several
factors. In this work, both nutrient addition to the growth medium and
changes in salinity were found to affect the toxicity of heavy metals
28
significantly. Nutrient additions to the growth medium increased growth
in P. gymnospora (Paper I). Similarly, increases of salinity of up to 35
psu enhanced the DGR in U. reticulata (Paper IV), after which the DGR
dropped. Both increased nutrient levels (Paper I) and increased salinity
(Paper IV) negatively affected metal accumulation, and as a result, the
degree of inhibition was significantly lower (Fig. 8, Paper IV). It would
be interesting in the future to test the effects of the two variables
together, because in an estuarine environment low salinity would
enhance accumulation while concurrent high anthropogenic nutrient
input would have a decreasing effect on heavy metal accumulation.
Figure 8. Effect of salinity on Cu toxicity, expressed as effect on DGR
of U. reticulata exposed to different concentration s of Cu at varying
salinities.
4.6. Effects of heavy metals on protein expression pattern
Recently, there has been interest in studying the effects of environmental
parameters and toxicants on gene and protein expression in various
organisms (Aina et al., 2007; Silvestre et al., 2006; Lay et al., 2006;
Jamers et al., 2006; Gianazza et al., 2007). However, limited attention
has been paid to the effects of heavy metals on marine organisms
(Jamers et al., 2006). In addition, the protein expression pattern arising
from Cu exposure is poorly understood in plants. The present study
(Paper III) presents some findings regarding the protein expression
29
pattern of U. reticulata exposed to Cu, as determined using twodimensional gel electrophoresis. It appears that the protein expression
pattern can be at least as sensitive as other sensitive toxicological testing
methods reported for assessing Cu toxicity in macroalgae.
Exposure of U. reticulata to the lowest experimental concentration of
Cu, i.e., 1 μg/L, for 24 h, caused drastic changes in the protein
expression pattern (Fig. 9). Several newly induced proteins appeared
after the exposure, and although they have not yet been identified, they
could be part of a defence mechanism against Cu toxicity. At higher
levels of exposure to Cu, more changes were evident, in which a large
numbers of protein spots were down-regulated and a few up-regulated.
The up-regulated proteins are possibly proteins responsible for
counteracting Cu toxicity, while the down-regulation of proteins could
represent a toxicity mechanism. Cu toxicity is largely linked to the
production of oxygen free radicals (reactive oxygen species, ROS),
which are known to induce proteotoxicity and other major protein
expression changes in living organisms (Cu has been reported to
increase the protein expression of defence proteins against oxidative
stress (Collén et al., 2003; Weber et al., 1991). However, when the
concentration of Cu is high, it has been reported to suppress the
expression of proteins, including stress and anti-oxidative proteins
(Okamoto et al., 1996; Pinto et al., 2003). Also, the tissue protein
content has been shown to decrease with Cu exposure (Weber et al.,
1991; Collén et al., 2003; Malea et al., 2006). In the macroalgae
Gracilaria and in some higher plants, for example, rice, Cu exposure
can result in a decrease of protein content of up to 50% after 24 h
(Collén et al., 2003).
The down-regulation of five successfully identified proteins, namely,
actin, lectin, protein synthesis elongation factor, ATP synthase beta
subunit, and ATP binding proteins, occurred with increased Cu
concentration. As mentioned earlier, this could be due to proteotoxicity
as a result of Cu exposure. Information regarding the effect of Cu on
protein levels is lacking, making it difficult to pinpoint the mechanism
underlying the observed down-regulation and upregulation of various
proteins. However, a similar study of Mytilus edulis exposed to Cu
found that actin expression decreased significantly (Manduzio et al.,
2005). In some organisms, Cu has been found to induce highly
conserved stress proteins, especially the well-known HSP70 (Parsell and
Lingist, 1993; Tedengren et al., 1999); however, a study by Lewis et al.,
30
2001 on effect of Cu on induction of HSP70 in Enteromorpha
intestinalis (now renamed Ulva) found no significant changes in HSP70
levels due to Cu exposure. Consequently, response to toxicants at the
protein level might be species and protein specific.
The present study has demonstrated that Cu and Zn induce toxic effects in the studied algae; however, this finding does not disqualify
these algae for use as bioindicators of heavy metals in the Zanzibar
Channel, since they were able to tolerate even the highest levels
tested. In the natural environment, it is rare to find concentrations
close to the highest concentrations tested in this study, except in
highly polluted environments. For example, concentrations of approximately 454 µg Cu/L and 329 µg Zn/L were reported along sewage-impacted shoreline of Mauritius (Daby, 2006). What is probably
more important is the consideration of factors that influence bioaccumulation; this would allow the better interpretation of results from
two different sites, or from one site but obtained on two different
sampling occasions subject to dissimilar factors (e.g., salinity and nutrient concentrations).
4.7. Seasonal accumulation of heavy metals by macroalgae
Macroalgae have been used in studying the seasonal and long-term
variations of heavy metal levels in the marine environment (Villares et
al., 2002; Ho et al., 1990; Haritonidis and Malea, 1999; Malea and
Haritonidis, 1999a). Most of these studies were conducted in temperate
areas, and a few studies observed that some heavy metals fluctuated
seasonally (Malea and Haritonidis, 1999a), while others found no clear
seasonal patterns but rather monthly irregular fluctuations (O’Leary and
Breen, 1998; Malea and Haritonidis, 1999b). In the present study, heavy
metal levels were found to vary with no clear seasonal pattern. A few
metals at certain study sites displayed a tendency to increase during
rainfall, but this was not significant. Concentrations of various metals
were higher at those sites located near anthropogenic inputs than at sites
located away from such inputs. The observed fluctuation of heavy metal
levels could be influenced by factors that affect heavy metal
bioavailability. However, a known major factor affecting heavy metal
bioavailability is the heavy metal concentration itself in the environment.
Hence, sources of contamination at local study sites could have had an
influence on the observed fluctuations.
31
Figure 9. Protein expression pattern revealed in the 2D PAGE of Ulva
reticulata. Immobiline dry strip pI range 4−7, SDS Gel 12.5%. Gel
stained with SYPRO Ruby. White arrows indicate spots excised for
identification with MALDI TOF mass spectrometer (Paper III).
The heavy metal concentrations in the Zanzibar Channel fluctuated
significantly (Two Way ANOVA, p<0.05) between both years and sites
(Paper II). Even though some sites were close to each other, for
example, the Oyster Bay and Ocean Road or the Chapwani and
Maruhubi sites, the long-term heavy metal concentration patterns at
them could differ completely. These differences were likely caused by
localized sources of metals influencing the long-term trends of heavy
metal levels at the study sites. Environmental factors affecting
bioavailability might also have influenced the long-term fluctuation of
metal levels at the study sites.
The Ocean Road site was found to have higher levels of heavy metals
than the other sites did, most likely because of its proximity to a trunk
sewer pipe and to the Dar es Salaam harbour. A similar study site in
Greece was also found to receive a large amount of heavy metals from a
sewer pipe and harbour (Haritonidis and Malea, 1995). Compared to the
Dar es Salaam study sites, the Zanzibar sites were found to have lower
concentrations of heavy metals. This is not surprising, because Dar es
Salaam has more anthropogenic pollution than Zanzibar does, due to its
higher number of industries and larger population. An overall similarity
analysis of heavy metal levels at the various study sites for all years
between 1994 and 2005 grouped sites according to close similarity in
32
heavy metal concentration patterns (Fig. 10, Paper II). Ocean Road was
singled out as the most contaminated study site. Study sites such as
Nungwi and Maruhubi, which were found to have lower concentrations
of various metals, were placed into a single group. Comparison with the
results of other studies indicates that the Zanzibar Channel is less
contaminated with various metals than many other such areas.
450
400
350
Linkage Distance
300
250
200
150
Mbudya Island
Oysterbay
Nungwi
Bawe
Maruhubi
Kunduchi
Chapwani
Yatch club
50
Ocean Road
100
Study sites
Figure. 10. Similarity classification (cluster analysis) of the sampling
stations for different concentrations of heavy metals in P. gymnospora
collected from the Zanzibar Channel from 1994 to 2005.
33
5. General conclusions
In summary, the present study found that, even though macroalgal
accumulation of heavy metals largely depends on the available
concentration in the growth medium, factors such as salinity and nutrient
concentration can affect the accumulation significantly. This is
important to consider when using these algae as indicators of heavy
metal loads in field environments where there are high salinity
fluctuations or high nutrient inputs.
Seasonal and long-term studies of heavy metals in the Zanzibar Channel
revealed significant monthly and yearly fluctuations of concentrations,
probably due to variations in local sources of heavy metals in the study
areas and/or the influence of abiotic and biotic factors on heavy metal
accumulation. The Ocean Road and Chapwani study sites were found to
be the most contaminated sites in Dar es Salaam and Zanzibar,
respectively, because they were close to harbours and raw sewage
inputs. It is recommended that the high fluctuations in metal content of
bioindicators is considered in future studies involving macroalgae as
indicators of heavy metals, since the choice of sampling time might
affect the accumulation levels of the alga. To determine accurately
whether heavy metal contamination is increasing or decreasing in a
certain area, one should follow the trends for a number of years.
The level of DGR and Fv/Fm inhibition caused by the metals depended
on the metal concentration in the growth media. The DGR of P.
gymnospora was thus a more sensitive indicator of stress than was
Fv/Fm. However, high salinity and high nutrient concentrations in the
growth medium reduced the toxicity of the metals. The protein
expression pattern changed drastically in U. reticulata, even at 1µg Cu
/l; in the future, this phenomenon could possibly be developed into a
useful indicator of heavy metal stress in macroalgae.
34
6. Future perspectives
1. It will be important to examine in the future, seasonal and long
term studies to monitor the status of heavy metal concentrations in order to evaluate increasing or decreasing of heavy
metals.
2. Other environmental factors, such as temperature and light,
and their effects on the accumulation and toxicity of heavy
metals are poorly understood, especially in combination with
other factors, and hence merit research efforts.
3. Separating proteins by fractionation in the future would be
more reproducible, allowing for more comprehensive analysis
of the expression of many proteins via 2D PAGE.
4. The identification of proteins was limited in this study by limited database resources. However, by employing other identification techniques, for example, using a tandem mass spectrometer along with a sequence similarity search, it might be
possible to identify more proteins, which could help explain
the mechanisms involved in Cu toxicity responses. In addition,
not all the proteins spots were excised for identification, so
there is still a chance in the future to identify other more conserved proteins using MALDI TOF.
5. Protein database resources are updated frequently, and new
protein sequence information is added as available. This creates another opportunity to identify more as-yet-unidentified
proteins in the future, using the peptide mass data we already
possess.
35
7. Acknowledgements
First: Prof. Mats Björk, my supervisor – you have been the key person
in my academic career. Thanks for initiating the project and introducing me to many interesting new fields of study. You have generously
devoted much time and energy to make sure that I feel comfortable,
academically and socially, by providing support, advice, guidance,
and all that was necessary for the successful completion of this study.
I thank you so much for all that you have done for me. Special thanks
also go to my co-supervisors, Dr. Thomas Lyimo and Prof. Ulla Rasmussen, for their academic assistance during the course of my studies.
I wish to express my sincere gratitude to Prof. Birgitta Bergman for
accepting me as a Ph.D. student in the Plant Physiology Section, Botany Department. I thank her for spending time reading my thesis and
for her useful comments.
I acknowledge with thanks SIDA/SAREC as the major sponsor of my
Ph.D. studies. Thanks also go to Dr. Alfonse Dubi, Director of the
Institute of Marine Sciences, for accepting me as a SIDA/SARECsponsored Ph.D. student and for providing all the necessary facilities
for my laboratory and field studies. I gratefully acknowledge Dr. Dubi
and the co-ordinators of the SIDA/SAREC Marine Science Bilateral
Programme (Sweden and Tanzania) for all their efforts to make the
programme run smoothly.
Many thanks are extended to WIOMSA, for the support that enabled
me to attend various scientific meetings and a training course during
my Ph.D. programme. To Dr. Julius Francis and Dr. Melcksedeck
Osore, I extend sincere thanks for much understanding, support, and
co-operation.
Special thanks and appreciation goes to Maria Greger, Ass. Prof. Botany Department, SU for providing laboratory facilities for heavy metal
analysis, for reading our papers and for her useful comments.
.
36
I must also thank my M.Sc. supervisors, Dr. H.B. Pratap and Dr. Marten Mtolera, for their academic advice and many contributions.
Juma Kangwe, my special friend: I hope you still remember the good
times we had together here in Sweden. Thanks for your good company
that made me feel happy and energetic.
During my laboratory work, I received assistance from many friends
and colleagues; to mention but a few: Tommy, Martin Ekman, Dimitra, Ran Liang, Gustaf Sandh, Sara Johansson, Johan Klint, and Mia
Bengtson – thank you all for your assistance.
Thanks to all the staff at the Botany Department, SU, especially at the
Plant Physiology Section, for being so friendly and helpful during my
stay at the Department. I wish to express special thanks to the heads,
staff, and postgraduate students at IMS, the Departments of Botany
and MBB, University of Dar es Salaam, for their friendly welcome
and for hosting my studies in Tanzania. During my fieldwork, I received assistance from Dotto, Mwandya, Saleh, Mr. Muhhid, and Idd
of IMS; Mr. Muhagama, and Dr. Buriyo – I thank you all for your
help. Mr. Alhaji Kombo (IMS) thanks for the Map.
In Mat’s Group I am indebted to the following: 1. Former Ph.D. students: Frida Hellblom, Herman Carr, Jacqueline Uku, and Juma
Kangwe; 2. Current Ph.D. students: Sware E. Semesi and Lina Rasmusson; 3. Mats’s visiting scientists: Dr. Salomao Bandeira and Dr.
Shalena Fokeera-Wahedally. I wish to thank you all for your pleasant
company, discussion, and collaboration.
I must express gratitude to my wife, Elizabeth, for being so special to
me, for being so understanding, strong, loving, and caring. I thank you
so much for taking care of yourself and our kids Edwin and Collins
during my absence. I will always be there for you, Aika Mae. Thanks,
Edwin, for always looking at my photos daily and for running to answer my phone calls.
Thanks to my parents, Mzee Alex Roweta and Mama Ernester Marimoy, and to my sisters and brothers for your prayers, caring, and support all along. Above all I thank God for blessing this long and exciting journey, Amen.
37
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