FEB – Fresenius Environmental Bulletin founded jointly by F. Korte

Transcription

FEB – Fresenius Environmental Bulletin
founded jointly by F. Korte and F. Coulston
Production by PSP – Parlar Scientific Publications, Angerstr. 12, 85354 Freising, Germany
in cooperation with Lehrstuhl für Chemisch-Technische Analyse und Lebensmitteltechnologie,
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© by PSP Volume 24 – No 1a. 2015
Fresenius Environmental Bulletin
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0
CONTENTS
FOREWORD
162
REVIEW ARTICLE
RELATIONS BETWEEN ECOSYSTEM AND WIND ENERGY
163
İlhami Kiziroğlu and Ali Erdoğan
ORIGINAL PAPERS
INTEGRATION OF GIS AND REMOTE SENSING WITH
THE USLE MODEL IN THE ASSESSMENT OF ANNUAL SOIL
LOSS AND SEDIMENT INPUT OF ZINAV LAKE BASIN IN TURKEY
172
Hakan Mete Doğan, Orhan Mete Kılıç, Doğaç Sencer Yılmaz, Ekrem Buhan, Fatih Polat and Saliha Dirim Buhan
ACCUMULATION AND BEHAVIOR OF SOME HEAVY METALS
IN THE MAIN COMPONENTS OF ZINAV LAKE BASIN ECOSYSTEM
180
Fatih Polat, Hakan Mete Dogan, Ekrem Buhan, Orhan Mete Kılıç, Doğaç Sencer Yılmaz and Saliha Dirim Buhan
COMPARATIVE ANALYSIS OF BIOTIC INDICES FOR EVALUATION OF
WATER QUALITY OF ESEN RIVER IN SOUTH-WEST ANATOLIA, TURKEY
188
Bülent Yorulmaz, Atakan Sukatar and Murat Barlas
DETERMINING OF AREAS WITH HIGH EROSION RISK IN KÜÇÜK MENDERES RIVER
BASIN (WEST ANATOLIA, TURKEY) BY USING MULTI-CRITERIA DECISION MAKING METHOD
195
Ali Ekber Gülersoy and Mehmet Ali Çelik
TROPHIC STATUS AND THREATS IN ZINAV LAKE (TOKAT/ TURKEY)
203
Saliha Dirim Buhan, Nihal Bektaş, Mehmet Ali T. Koçer,Hakan Mete Doğan, Ekrem Buhan and Fatih Polat
APPLICATION OF MULTI-METAL BIOACCUMULATION INDEX
AND BIOAVAILABILITY OF HEAVY METALS IN Unio sp. (UNIONIDAE)
COLLECTED FROM TERSAKAN RIVER, MUĞLA, SOUTH-WEST TURKEY
208
Tuncer Okan Genç, Fevzi Yilmaz, Burak Evren İnanan, Bülent Yorulmaz and Gökhan Ütük
HEAVY METAL ACCUMULATION IN BIOINDICATORS OF
POLLUTION IN URBAN AREAS OF NORTHEASTERN POLAND
216
Zbigniew Mazur, Maja Radziemska, Joanna Fronczyk and Jerzy Jeznach
HEAVY METAL CONTAMINATION OF PLANT RAW MATERIAL INTENDED FOR FOOD
Elvyra Jariene, Honorata Danilcenk and Maria Jeznach
160
224
COPPER REMOVAL FROM CONTAMINATED GROUNDWATER USING
NATURAL AND ENGINEERED LIMESTONE SAND IN PERMEABLE REACTIVE BARRIERS
228
Joanna Fronczyk, Maja Radziemska and Zbigniew Mazur
THE SALINITY PROBLEM AT YELKOMA LAGOON (YUMURTALIK-ADANA)
AND ITS RESTORATION BY MIXING WITH FRESHWATER FROM CEYHAN RIVER
235
Harun Aydin, Hüseyin Karakuş and Osman Erdem
DETERMINING NATURAL AND CULTURAL CHANGES AROUND WETLANDS
BY MEANS OF REMOTE SENSING TECHNIQUES: A CASE STUDY IN EGIRDIR LAKE
244
Namık Kemal Sonmez, Işın Onur and Sevda Altunbaş
NOTICE
ECOTOURISM, PROTECTED AREAS AND NATURE CONSERVATION
250
Nihan Yenilmez Arpa and Yusuf Ceran
INDEX
258
161
FOREWORD
The view of humanity on the environment has grown in recent times in different dimensions. His
relationship with the environment has led to different echo in the strata of society. Since the destruction of the environment on the one hand caused the emergence of the environmental crisis, on the
other hand leads to reducing the chance of survival of future generations. To avoid this, each individual must look at life in our environment, and protect the creatures bear any responsibility.
This matter needs to be seriously discussed and solutions need to be found both on universal and
local level. International meetings have also the duty to keep such environmental problems before the
eye, and allow the exchange of expert opinion scientists. The 7th International Symposium On Ecology And Environmental Problems will forward the following topics in the Symposium:
Environmental Education; -Environmental Law and Philosophy;- Chemical and physical contamination and measures to be taken in the ecosystem; -Tourism and Environment; -Alternative and
Green Energy; -Biodiversity; -Environment and Settlement;- Ecology and Economy Dilemma; -Energy and Environment; -Health, Nutrition and Environment; -Art and Environment:
What should be done to create a platform for climate change. A certain consensus has to be made
between the economic thinking and ecological thinking. The relationship between human health and
pollution should be thoroughly demonstrated. The impact of genetically modified foods on the environment and not least on the health of humanity is discussed. To protect against the import of genetics-modified organism (GMO) must be made on a legal basis. The protection of the air, water and
soil should be placed on the agenda and discussed in the context of ecological thinking. Some issues
should be directed to the protection of biodiversity and nature protection. In addition, the method of
using the optimal form of alternative energy sources must be carried forward.
The 7th International Symposium On Ecology And Environmental Problems was organized
by the Hacettepe, Akdeniz and Mehmet Akif Ersoy University with the Ministry of Forestry and
Water Affairs in Turkey and 65 national-and international organisations in Manavgat/Antalya/Turkey
from Dezember 18-21, 2013.
The main results of the 7th Symposium are included in this issue of the international journal
“Fresenius Environmental Bulletin”(FEB) and they will contribute undoubtedly to the advancement
of the environmental scientific knowledge and hence, to the improvement of the environment.
Prof. Dr. Ilhami KİZİROĞLU
Chairman of the Symposium
162
RELATIONS BETWEEN ECOSYSTEM AND WIND ENERGY
İlhami Kiziroğlu¹,* and Ali Erdoğan ²
¹ Hacettepe University, Faculty of Education, 06800 Beytepe, Ankara, Turkey
² Akdeniz University, Faculty of Science, Department of Biology, 07058 Antalya, Turkey
Presented at the 7th International Symposium on Ecology and
Environmental Problems (ISEEP2013), December 18-21, 2013, Antalya, Turkey
KEYWORDS: Wind turbines, wind energy, bird migration, monitoring program, bird deaths, bat deaths
ABSTRACT
The study explores the amount of energy production
by wind turbines in Turkey, in Europe, and in several other
countries around the world. Annual production of electric
energy through the wind turbines in Turkey is only 2493
MW, which accounts for only 5.8% of the total energy consumption in the country. We discussed the possibilities of
how this amount can be increased to 10%, over the next 35 years.
Turkey, which has 502 bird species, is a part of the Palearctic, and is located on important bird migration routes.
As a result, birds prefer the migration routes on the areas
where the wind intensity is quite high. Certain areas in Turkey, such as Kapıdagi peninsula, Iskenderun, Izmir, Belen,
or Adana, are located on high wind intensity routes. Continuous monitoring programs for migratory bird species
must be carried out for several years before the wind turbines are built on such areas.
In this work, we discussed the collision effects of wind
turbines on birds and bats, based on information obtained
from literature and field observations both in Turkey and
in several other countries. For example, the wind turbines
in four states in the eastern part of the United States have
only 0.003% share for bird deaths in the region. Similarly,
it was found that only 0.23 individual bats died per year by
wind turbines in Brandenburg, Germany. Similarly, the
death rates of birds and bats caused by wind turbines are quite
low compared to other human factors in Turkey.
The available data indicate that death rates of birds and
bats by wind turbines in relation to the other factors (e.g.
by clashing up against glass windows of buildings) is quite
low. Therefore, public oppositions against wind turbines are
not well justified. Considering the ever lasting energy needs
of mankind and environmental deterioration due fossil
fuels and nuclear energy, establishment of wind turbines as
green energy sources must be encouraged in appropriate
places accomponied by bird observation programs along
with recommended implementations proposed by ornitology reports.
* Corresponding author
1. INTRODUCTION
The energy need of countries is steadily increasing.
This need is met mainly by traditional sources of energy,
as well as coal, petroleum and natural gases. About 81% of
energy used on earth derives from the classic energy
sources, namely, fossil fuels energy. Also, everlasting negative effects of nuclear power plants on the environment have
not yet been eliminated; nevertheless, 6.2% of energy in
the world is met by nuclear energy sources [1]. The increasing trend towards the use of fossil energy sources around the
world has also accelerated their negative influences on environment and human health. All of these factors result in various social and economic problems, including climate
change on global scale. The energy consumption in Turkey
as well as in many other countries of the world is met
mostly by the fossil sources. Because Turkey depends on
imports for most of its energy requirements and allocates
about 72% of its budget for this purpose, and needs to eliminate the negative environmental effects of fossil fuels, the
country needs to invest for alternative energy sources. Turkey has a great chance for alternative green energy and, in
this respect, has to activate its solar energy and wind energy potentials [2-4]. Also Hötker [5] has suggested that,
as already known before, wind turbines have smaller impact on breeding birds than on migrant birds [6-8].
Intensive use of fossil fuels as energy sources has been
the major cause of global climate change, and this destroyed the balance of the environment due to resulting
CO2 derivatives [9, 10]. In particular, lives of almost all
living things have been distorted and influenced through
the negative impacts of fossil energy in the environment
and the ecosystems.
In recent years, wind energy appears to be replacing
the place of the primary energy sources. According to
Moidl et al. [11], about 86 000 MW of wind energy has
been produced per year in Europe. A medium-sized wind
turbine produces enough energy per year to meet the needs
of more than 1250 houses. By the year 2020, the European
163
Union countries aim to increase the amount of wind energy
to meet 20% of their annual energy consumption [12].
This article evaluates the current situation of wind energy in Turkey, Europe and several other countries around
the world. Although expansion of wind energy seems to be
inevitable, the impact of this energy source on the environment also needs to be investigated. This article covers the
relationships between wind turbines and their environments, specifically the effects of wind turbines on birds and
bats.
2. THE CURRENT SITUATION OF
WIND ENERGY PRODUCTION IN
THE WORLD, EUROPE AND TURKEY
The amount of electrical energy from the wind was 40
000 MW in the world, in 2003. Following the announcement of the law on renewable energy in 2004, amount of
wind energy has increased considerably within a relatively
short time. As one result, amounts of CO2 emission in the
world were reduced by about 32 700 000 tonnes (www.igwindkraft.at/fakten). In Tables 1 and 2, the highest wind energy producing countries (and few others for comparative
purposes) are presented in their wind energy production, in
2011, 2012 and 2013 (end of June).
In 2013, five countries in the world have produced
more than 10,000 MW of wind energy. Together, these five
countries produced over 215 726 MW of energy which accounts for 73% of all WE in the world. Here, China is the
first one with 80 824 MW (26.7%), the United States is the
second with 60 009 MW (17.6%), Germany is the third
with 32 422 MW (12.0%), Spain is the fourth with 22 907MW (8.8%), and India is the fifth (19 564 MW, 6.8%) (Table 1).
According to the data from wind farm Atlas TURSAT-2012/2 [4], the amount of energy produced by
wind turbines in Turkey is 2041.35 MW with additional
452.65 MW under process of construction. It can be said
that the amount of current wind energy production was increased by two times in 2010. The amount of electrical energy consumed per year in Turkey is about 43,000 MW.
The amount of wind energy represents only 5.8% of the electrical energy consumed in Turkey. The country must strive
to raise this amount to at least 10% in a very short time.
On the continental scale, European countries produce
32.5% of the world wind energy production, Asian countries 34.6%, North Americab countries 22.4%, and other
countries 8.8 % (Table 2). The European Union countries
get only 10% of their energy needs through the wind. It is
expected that the share of renewable energies (including
wind energy) in the total energy production in the European Union will raise to 20% by 2020. However, propor-
TABLE 1 - The first fifteen wind energy producing countries in the world in 2011, 2012 and 2013 (Status: end of June) and the status of Turkey
(from: [13-14 ]).
Rank
Name of the
country
Total capacity
in June 2013
(Megawatts)
(=MW)
Capacity increase (MW) in
the first half of
2013
Total capacity
at the end of
the 2012
(MW)
Capacity increase (MW)
in the first half
of 2012
Total Capacity at
the end of the
year 2011(MW)
Increase in wind energy
capacity from the end of
2011 by the end of June,
2013
Growth
Growth Rate
(MW)
%
18 460
22.8
13 090
21.8
3 347
10.3
1 411
5,4
3684
18.8
3592
37.4
1538
18.3
1181
15.1
1 313
20,0
651
14.2
185
4.1
1268
31.2
833
27.2
1359
48.7
154
5.8
712.35*
65.1
7 426
28.3
58 538
19.8
1
China
80 824
5 500
75 324
5 410
62 364
2
USA
60 009
1,6
60 007
2 883
46 919
3
Germany
32 422
1 143
31 308
941
29 075
4
Spain
22 907
122
22 785
414
21 673
5
India
19 564
1243
18 321
1 471
15 880
6
UK
9610
1331
8228
822
6018
7
Italy
8415
273
8152
320
6877
8
France
7821
198
7623
650
6640
9
Canada
6578
377
6201
246
5265
10
Denmark
4578
416
4162
56
3927
11
Portugal
4564
22
4542
19
4379
12
Sweden
4066
526
3743
2798
13
Australia
3059
475
2584
2226
14
Brazil
2788
281
2507
118
1429
15
Japan
2655
41
2614
2501
Turkey * 2041.35+452,6¹= 2494**
712.35*
1329
Other Countries
26 204
2030
24 174
3026
18 778
TOTAL
296 255
13 980
282 275
16 376
237 717
End of 2011199 739
Increase
Increase %
End of 2013
318 000
118 261
37,2
*: Turkey Wind Farm Atlas TURSAT_2012\2 (Turkish Wind Energy Atlas_2012\/as of July 2, 2012; * *) as of the end of June 2013 after construction,
WEC production Megawatt (MW)
164
TABLE 2 - World wind energy (WE) production on continental bases in June (Sources: [14, 15].
Countries By Continents
European Countries
Asian Countries
The Countries of North America
Other Countries
TOTAL
Wind Energy (WE) Production In MW
2010-2012
From 2012 to the
% Increase
End of June in 2013
86.075
96 877
32,5
58.641
103 043
34,6
44.189
66 587
22,4
21 023
26 204
8,8
292 711
tion of fossil fuels energy (coal, natural gas and oil) in the
total energy consumption in the Union is still more that
70%. It means that it will take much longer time to replace
fossil fuel energy by renewable energy sources. Therefore, it
appears necessary that each country in the Union should take
initiatives to take necessary steps on this important issue.
In its 2012 report WWEA indicated that wind energy
production in Europe will double in the next five years.
[16], at Global Wind Energy Council on the European wind
energy Conference and exhibition in Copenhagen, noted
that worldwide wind energy production in 2011 increased
40 500 MW (which amounts to 20% increase), and this increases is expected to be even more in the year 2012. Here
the leading country is China. Despite the fact that Europe
is a leading continent with 96 600 MW, China alone is approaching to Europe with 82 000 MW. About 80 countries
in the world, 20 of which with wind energy center (WEC)
capacity of more than 1000 MW, have been producing and
using wind energy. Especially, nuclear power plant accident in Fukushima in 2011 has been an influential factor to
avoid nuclear energy and, at the same time, to urge the development of renewable energy sources. The tsunami and
earthquake completely immobilized few of the 55 nuclear
reactors in Japan. However, all of the 190 WEC started to
fully operate within short time following the earthquake
disaster. Similarly, a wind turbine, which is 300 km away
from the center of earthquake and hit by five-meter-high
tsunami waves, started to operate again only three hours
after the tsunami hits. Fossil energy sources have multidimensional negative impacts on the environment. In addition, their prices are steadily increasing and creating various economic crisis. All these are the indicators why alternative energy sources should be sought and encouraged.
The argument that the fossil energy is cheaper than renewable energy is a wrong statement when considered in
wide time and space perspectives. For example, each person in Europe, on the average, spends about 700 euros
every day for the fossil energy [17].
Germany plays a leading role in Europe in using the
wind energy. Currently, there are twenty-five thousand
wind energy turbines in Germany, and these turbines are
producing more electrical energy than the electrical energy
produced by 30 nuclear reactors [18]. Therefore, replacing
new technological applications instead of the old technology for wind energy turbines are on the agenda [18]. The
WE Production
From 2010 to the
% Increase
End of June 2013
10 802
11.2
16 968
16.5
22 398
33.6
5 181
19.8
same argument is supported by “http:\\ www. Wind-Energie.de-Portal”. Indeed, numbers of WECs using the old technology established since 2002 have been decreasing each
year. This is attributed to low capacities of old technologies
of wind turbines. For this reason, developments of new
technologies on wind turbines have been in process in Gemany. Therefore, establishment of wind turbines with old
technology is not recommended in Turkey. In particular the
low capacity 2.5 and 3.5 MW wind energy should not be
used (see for example: http://www. enercon.de/ de-de/23mw.htm).
3. BIRD MIGRATION PERIODS
AND MIGRATION ROUTES IN TURKEY
Common migration routes of birds proceed north to
south (or vice versa), depending on season, either in spring
or late summer [3, 19-22]. Spring migration season over
Turkey starts in March and lasts until the end of May. Autumn migration starts at the end of August and continues
until the end of October. One of the most important migration routes in Turkey comes from Europe and goes through
Bosphorus strait in the western part of the country. The
other equally important route comes from Caucasia and
goes over Artvin Province in the eastern part. Both of these
routes are used mainly by storks and some predatory birds.
The third route, which is of great importance mainly for
cranes and common quail, comes over Black Sea and
crosses the Anatolian peninsula [19-22] (Map 1 a and b).
Monitoring programs need to be established and continued in areas with high wind intensities where WECs are
already present and/or to be established (for examples, areas such as Kapıdağ peninsula, Iskenderun, Izmir, Adana
and Belen, Map 1 a and b). In Map 1b, the localities of WEC
and the most important routes of migratory birds are specified. An important route for migratory birds (especially of
storks, Eurasian buzzard, black kite, short-toed snake eagle, European honey buzzard, greater spotted eagle, lesser
spotted eagle and white pelicans) is located in the north of
Istanbul. After 2-year monitoring of a wind turbine in the
area, only a few dead seagulls were found, but no dead
storks nor birds of prey were observed.That does not mean
that in the course of time, no collision of birds of prey to
WEC with twelve turbines in Arnavutköy at Istanbul will
occur.
165
The height of wind energy facilities, especially in areas
on migration routes for migratory birds, can present some
disadvantages. They act as obstacles in the sky for free
movement of birds. Therefore, wing rotors reaching
heights of 80-100 m (modern wind turbines can reach up
to 200 m) should be questioned. They may or may not be
harmful to the bird’s navigation, depending on whether
they are on migration routes or not. Maps 1a and 1 b show
the overlapping between the flyway of migratory birds and
the distribution of wind turbines. For example, in recent
years, RES tribunes were built in Kapıdağ peninsula, which
is used by white pelicans as migration route. Monitorings
have just been started in these localities. Therefore, relevant results have not yet published. Here,
Annual Mean Wind Speed (m/s) at 30m
Migratory Routes Of Birds Shown On Turkish Annual Mean Wind Speed Map
Red lines represent primary migratory routes while blue lines represent less used secondary migrotary routes. Blue arrows represent north‐south oriented migrations occuring without a certain route.
Migratory Routes Of Birds Shown On Turkish Wind Power Plant Atlas
Red lines represent primary migratory routes while blue lines represent less used secondary migrotary routes. Blue arrows represent north‐south oriented migrations occuring without a certain route.
MAP 1 - a) Migratory routes of birds shown b) on Turkish annual mean wind speed map (red lines represent primary migratory routes while
blue lines represent less used secondary migrotary routes. Blue arrows represent north-south oriented migrations occuring without a certain
route. White arrow is representing Belen Pass).
166
the altitude of migration route is very important in that radar records cannot be used for low-flying birds. Therefore,
radar data sets for the low flying birds are questioned
whether they could be suitable [23].
Dürr (2013-2014) [43] has given a number of deaths
of birds and bats by years as reported by various sources in
several European countries and several states in Germany.
Such results are not present for Turkey, mainly because
there are no sufficient and reliable monitorings and records
on such deaths. This study evaluates the general information available so far.
The flying altitude of birds is very variable. In general,
small birds fly close to the ground level [24]. Flight altitude
could be quite high for large bird species. Birds migrating
at night can achieve greater height than birds flying at day
time [24]. Bird species flying by wing flapping prefer to fly
at lower altitudes than the birds flying through the help of
air drafts. Birds flying against wind direction also prefer to
fly at lower altitudes. Bird species that prefer wind direction coming behind during migration can achieve 400, 700,
900 m above ground level. It was stated that this level is
achieved especially during the autumn migration [25-26]
(s. Map 1 a and 1 b).
birds are able to assess the technical structures not as a
threat or danger. In particular, they can see wind turbines
as not being a problem in the region and in the thickets of
semi-open areas [6]. Because birds have their own internal
biological clock, they can easily adapt to new conditions
and then exhibit almost natural behavior [3, 8, 31-33]. In
Denmark [34] and Germany [35], it has been found, according to the radar measurements, that birds change their
flight direction before reaching 100 to 200 m close to the
rotor of a WEC, and can thus avoide any probable damage.
The birds can adapt to the regions where WEC power
plants are installed, in the course of time. However, such
cases need to be further clarified in further works and continued monitoring of the respective areas.
According to (http://www.wind-energie.de/en/topics/
protection-of-birds) portal, Arctic geese used to reach altitudes of 500-1000 m, but now they fly about 30-100 m above
ground level around the cities. This is especially common
when organisms do not change their body temperature according to the ambient conditions (homoiothermal) [33].
Even it is known that some species of migratory birds are
able to prefer a new route with new conditions instead of going through a distorted geographic area.
4.2 Death Impact on Birds
4. DEATHS OF BIRDS AND
BATS BY WIND ENERGY TURBINES
During a monitoring of a wind-turbine-farm on Belen
Pass by Hatay [27] (Map 1a), flight altitudes of the following
bird species were determined: white stork, lesser spotted eagle, greater spotted eagle, crane, short-toed snake-eagle,
western marsh harrier, Eurasien hobby, Eurasian sparrowhawk, European honey buzzard, northern harrier, common
buzzard, long-legged buzzard, common kestrel and European beeeater. It was noted that common kestrel, Eurasian
sparrowhawk, however, flew above the height of the turbine
more than the smaller bird species, over 300 NN or 500 m
of sea level. This means that wind turbines have negative
impacts on the life of few small bird species.
It appears that wind energy expansion seems to be inevitable; therefore, the potential impacts of these facilities on
bird species should be investigated before constructions. In
this context, the primary concern focuses on the relationship
between WEC and birds and bats [28, 29]. In addition, bird
species show intra-species variation in their behavioural patterns. For this reason, the respective species of migratory
birds, especially on the optimal migrational zones, need to be
monitored. To prevent the negative effects of wind turbines
on the environment, various investments have been made in
different countries. In spite of this, the prevailing public
opinion and concerns are that wind energy power plants
cause deaths of larger birds and bats.
4.1 The Dilemma of Bird Deaths and Wind Power Plants
Isselbächer [30] pointed out that the wind turbines
have minimal negative impact on native birds. It is because
The numbers of deaths of birds by WEC are not greater
than with other human activities. In this context, one of the
most extreme examples is as follows: in Sweden, 43 bird
died in one night on a WEC, which was under illumination
but not in operation [36]. Similaly, the number of bird
deaths by various human-related factors in the United
States exceeds millions, or even billions. In four States in
the eastern part of the USA, the proportion of bird deaths
by WEC accounts only for 0.003% of all bird deaths in the
region [37]. A study by [38] has shown that, through the
various means, 20 million birds are killed each year. Such
deaths were mostly due to glass constructions on buildings
in residential, high voltage lines, communication lines and
traffic. In other words, the deaths of the birds by the WEC
rotors is much lower than the deaths by various other human factors. According to a study by [39], each year, 60 to
80 million individual birds die by vehicles in traffic, 98 to
980 million by clashes against the glasses on buildings, and
4 to 50 millions by communication lines. On the other
hand, in the same study, it was highlighted that the number
of birds killed by WEC is only 10-40 thousand in a year.
As it is seen, the bird deaths from wind turbines compared
to the other sectors are rather small. It was also found that
the numbers of birds and bats killed by WEC in Turkey is
much lower than the deaths by other sectors; for example,
through the traffic [40]. For these reasons, the number of
birds and bats killed by WEC alone must not prevent the
development of this alternative energy source. Also, it is
assumed that the number of bird deaths from clash against
glass windows of buildings is much larger in Turkey than
deaths due to WECS. Therefore, it is suggested that bird
pictures must be glued on the glasses so that the birds avoid
clashing against the glass windows and walls of particu-
167
larly tall official buildings. If it is assumed that one bird
dies by hitting windows of every single house, it can be
estimated that the number of birds dying in Germany becomes enormous [41]. It was found out by Hötker et al.
[42] that between 1989 and 2004, 269 individual birds were
killed by WEC. Among them, red kite with 41 and buzzard
with 27 individuals reached the highest numbers.
It was also found that the number of birds killed by the
WEC plants, near a village (Fehmarn, Germany), representing an important bird migration route between Scandinavia and Central Europe, was only 13 individuals [44].
This number was much lower than the results obtained
from WEC farms located in the western part of SchleswigHolstein and in Netherlands [45-47]. Birds dying by WEC
in Fehmarn compared to traffic was quite low. The results
of the studies cited above indicate that bird deaths due to
WEC installations is much lower than the bird deaths
caused by other human activities. If one makes a comparison between the mortality rate of birds and bats by traffic
and by WEC installations in Turkey, it could easly be
stated that WEC installation is almost innocent [40].
4.3 The Bat Deaths by Wind Energy Centers
Glasner [26] indicates the number of bats killed per
year in a wind farm is 0.23 individual in Brandenburg in
Germany. It was reported that three individuals of serotine
bat (Eptesicus serotinus) died in a year in North RhineWestphalia in Germany, and the number of deaths of noctule (Nyctalus noctula) was larger [48]. The important reason for low bat deaths was that bats in the air will not move
when the rotors operate at high speeds [49]. Also in August
and September wind speed is quite low (5.5 m/sec) and the
rotors are closed down at this period. However, there is a
possibility that bats die when the rotors exert a pressure on
them as it was observed in Canada [50]. It can well happen
in other countries, too. Indeed, hundreds of dead bats
were observed on a WEC
TABLE 3 - Bird deaths caused by the WEC (SVLU, GV, Brandenburg, 7th October 2013, Dürr)
(http://www.mugv.brandenburg.de/cms/detail.php/bb2.c.451792.de) [43].
Species/Countries
Bubulcus ibis
Anas platyrhynchos
Gyps fulvus
H.albicilla
Milvus milvus
M.migrans
Buteo buteo
Falco tinnunculus
L.arus ridibundus
L. argentatus
L.fuscus
Sterna hirundo
Apus apus
Alauda arvensis
Delichon urbica
Emberiza calandra
Sturnus vulgaris
Countries Total (Europa union)
(B)
(G)
48
76
(S)
96
36
1877
(F)
(UK)
2
87
213
13
4
22
66
13
245
21
2
5
55
220
14
328
72
2
33
787
45
1
200
5
4
154
1
2
75
73
20
72
89
19
23
37
1
28
162
2
26
60
2
8
59
539
878
122
1698
1305
3020
121
1757
1844
3892
243
(B) Belgium; (G) Germany; (S) Spain; (F) France; (UK) United Kingdom; NL)
np=Nonpassers.
(Nl)
(N)
7
2
1
39
3
9
36
1
4
29
8
1
1
2
1
14
46
16
119
102
110
133
148
126
Netherlands; (N) Norway;
(P)
4
1
(Sw)
TOTAL
100
185
1882
24
157
12
246
101
3
3
281
20
321
474
2
889
211
155
5
3
181
22
219
40
6
134
20
212
2
115
141
68
p
2004
59
62
np 6671
200
130
8675
(P) Portugal; (Sw) Sweden; p= Passers;
TABLE 4 - Types of bat species died due to WEC in various countries ([43] (Dürr, 2013).
Countries
B
G
S
F
UK
NL
P
Species
Nyctalus noctula
689
1
12
10
1
N. leislerii
99
19
39
58
152
Eptesicus isabellinus
117
1
Pipistrellus pipistrellus
409
211
226
24
14
200
P. pygmaeus
503
76
34
24
TOTAL
6
1982
1219
743
199
22
689
(B) Belgium; (G) Germany; (S) Spain; (F) France; (UK) United Kingdom; (NL) Netherlands, (P) Portugal; (Sw) Sweden
168
Sw
TOTAL
1
746
371
118
10195
144
5024
1
1
11
farm near Brandenburg, Germany. It was soon understood
that the deaths were not due to physical harm to the bats,
but rather due to pressure on bats while they were resting
under standstill rotors [43] (Table 4).
5. CONCLUSIONS AND RECOMMENDATIONS
In the USA, it is considered that wind turbines can be
deadly for birds and bats, and the focus has been on how to
prevent such deaths. This will take 1- to 1-year periods of
monitoring on seven thousand wind turbines and over one
thousand observations [51].
Observations indicated that birds of prey species are
the most affected by deaths through the clash with rotor
blades. Through observations and scientific studies, it has
been established that maximum 0.5 bird per turbine is under threat in Germany [52-54]. However, certain bird species, such as common quail, corncrake, common buzzard,
long-legged buzzard, Eurasian sparrowhawk, short-toed
snake-eagle, red kite, crane, and various duck species are
affected by wind energy rotor blades. Therefore, it appears
necessary to monitor and study how bird species breeding
in the close vicinity of WEC installations are affected. Such
monitoring studies around WEC installations should also
continue on non-migratory birds as well as migatory birds
both during the summer, spring and autumn periods. The
location of WEC installations must be inspected and monitored by ornithologists, at least for 1- to 2-year periods before the WEC establishments and during the WEC operations [3, 27, 55-57].
Several studies by ornithologists showed that bird species exhibit certain intraspecific variation in their behvioural
patterns, which may results in changes of their interaction
with WEC areas. It appears that WEC areas should be studied and monitored due to ornithological reasons for the period of at least 2 years before and after the WEC establishments [3, 27, 55-57]. Although the available data
strongly indicate that bird deaths caused by wind turbines are
almost negligible compared to deaths caused by other human activities, it is still necessary to obtain new data by
continual monitoring and additional studies.
Dürr [43] has presented various data (see Tables 3 and
4) and evaluated them in his study. When some bird species, such as storks, pelicans, red kites, sea eagles, buzzards
and bee-eaters, are considered in Bosphourus and Belen
Pass in Turkey, similar evaluations as by Dürr [43] seem to
be not possible at this stage, because there are not enough
data based on monitoring and field observations. As such
data become available, we will be able to make more reliable assessments whether or not the WECs have negative,
positive, or neutral impact on the birds.
The candidate zones where WEC facilities to be established need to be investigated in terms of predatory and,
especially, in terms of migratory birds. On this context, especially predatory bird species in danger of extinction
should be given special consideration in monitoring programs. When the results of such studies become apparent,
people will be able to better understand that WEC installations are not as harmful to bird populations as it has been
thought. As the establishment of WEC plants extended, it
is expected that harming affects of fossil fuels energy and
nuclear energy on our environment would decline rapidly.
Energy resources other than coal reserves in Turkey are
rather limited. Turkey has to meet about 72% of its energy
needs through paying foreign exchange money. At present,
annual energy consumption in Turkey is about 43 000MW.
By 2023, it is expected that this amount will double. To meet
the growing needs, Turkey has to make investments for alternative energy sources. As seen in Table 1, the amount of wind
energy production in Turkey has reached 1329 MW in recent
years. On the other hand, potential wind energy sources in
Turkey are estimated to be about 40 times higher, i.e.
around 40 000 to 50 000 MW. Another advantage of Turkey in terms of energy system is that the country has been
surrounded by seas, and efficient WECs can also be established in offshore areas.
169
The authors have declared no conflict of interest.
REFERENCES
[1]
EA (2008) Key World Energy Statistics 2008, Paris, Cedex
2008.
[2]
Turan, L. and Kiziroğlu, I. (2006) Rüzgâr Santralleri, Kuşlar
Ve Türkiye. XVII. Ulusal Biyoloji Kongresi,2006,Aydın
[3]
Kiziroğlu,, İ.(2013) Alternatif Enerji Kaynağı Olarak Rüzgar
Enerjisi ve Ekosistem İlişkisi. 6. Mühendislik ve Teknoloji
Sempozyumu 25-26 Nisan 2013, Çankaya Üniversitesi/Ankara,107-112.
[4]
Turkiye Ruzgar Santralleri Atlası TURSAT_2012/2 (Turkish
Wind Energy Atlas_2012/2)
[5]
Hötker, H. (2006) Auswirkungen des “Repowering” von Windkraftanlagen auf Vögel und Fledermaeuse. NABU, 40 pp.
[6]
Horch, P. and Keller, V. (2005) Windkraftanlagen und Vögel
- ein Konflikt? Eine Literaturrechrche. Schweizerische Vogelwarte Sempach, Sempach.
[7]
Langston, R.W.H. and Pullan, J.D. (2003) Wind farms and
birds: an analysis of the effects of wind farms on birds, and
guidance on environmental assessment criteria and site selection issues. Report written by Bird Life İnternational on behalf
of the bern Convention, Sandy.
[8]
Reichenbach, M. (2003) Windenergie und Vögel-Ausmass
und planerische Bewaeltigung Dissertation. Technische Universitaet, Berlin
[9]
Pall, P., T. DA. Aina., Stone, PA. Stott, T. Nozawa, AGJ. Hilberts, D. Lohmann, MR. Allen (2011) Anthropogenic greenhouse gas contribution to flood risk in England and Wales in
autumn 2000. Nature 470,382-385
[10] Stott, P. A., M. Allen, N. Christidis, R. Dole, M. Hoerling, C.
Huntingford, P. Pall, J.Perlwitz, and D. Stone (2011) Attribution of Weather and Climate-Related Extreme Events. 44pp.
conference2011.wcrp-climate.org.
[11] Moidl, M. S., U. Naeher and G. Scholz (2011) Die starken
Zahlen der Windkraft. IMPRESSUM, Wien.
[12] www.igwindkraft.at/fakten.
[35] Bergen, F. (2001) Untersuchungen zum Einfluss der Errichtung und des Betriebs von Windenergieanlagen auf Vögel im
Binnenland. Dissertation, Ruhr Universität Bochum.
[36] Karlsson, J. (1983) Birds and windpower, pp.12.
[13] http://www.igwindkraft.at/redsystem/mmedia
[14] WWEA (2012) 2012 Half-year Report. World Wind Energy
Association. www.wwindea.org,
[15] WWEA (2013) 2013 Half-year Report. World Wind Energy
Association. www.wwindea.org,
[16] Sawyer, S. (2012) GWEC(Global Wind Energy Council).
[17] Engel, (2012) GWEC(Global Wind Energy Council).
[37] National Academy of Sciences. (2007) Environmental impacts
of wind-energy projects (Washington, DC: National Research
Council).
[38] Arup, O. (2002) Bird Collision With Manmade Structures ,Report No. 3 On Ecological Survey Results (Ref. 076). Agreement No. CE 39/2001, Shenzhen Western Corridor Investigation And Planning. Ove Arup & Partners Hong Kong Limited,
February 2002, 26pp+App.A-Z
[18] Maas, N. (2008) Der Repoweringmarkt für Windkraftanlagen
in Deutschland. Bachelor of Engineering (B.Eng.) Nico Maas,
FB Energie Gebaeude Umwelt, Münster.
[39] Reichenbach, M and Steinborn, H. (2008) Brutvögel und
Feldermäuse im Bereich der geplanten Erweiterung des Windparks Westerburg/Charlottendorf Ost. Faunistisches Gutachten .
[19] Kiziroğlu, İ. (1994) Türkiye’nin Yerli ve Göçmen Kusları
Haritası. Atlas ve Tempo Dergisi Eki olarak.
[40] Kiziroğlu, İ., Turan, L. and Erdoğan, A. (2013) Biological diversity in Turkey and threats on it. (F-2012-528)/ Fresenius
Environmental Bulletin 22(3), 770-778..
[20] Kiziroğlu, İ. (1995) Türkiye’nin Yerli ve Göçmen Kusları
Haritası. Atlas ve Tempo Dergisi Eki olarak.
[21] Kiziroğlu, İ.(2008) Türkiye Kusları Kırmızı Listesi. Red Data
Book for Birds of Türkiye. Ankamat Mat., Ankara, 151 pp.
[22] Kiziroğlu, İ. (2009) Türkiye Kusları Cep Kitabı. Pocketbook
for Birds of Türkiye. Ankara, Ankamat Matbaacılık, 568 pp.
[23] Gatter, W. (2000) Vogelzug und Vogelbestaende in Mitteleuropa. Aula, Wiesbaden.
[24] Berthold, P. (2000) Vogelzug-Steuerung und Evolution aus
heutiger Sicht.Sber.Ges.Naturf.Freunde Berlin (N.F.)39, 4352.
[25] Bruderer, B. and Liechti, F. (2004) Welcher Anteil ziehender
Vögel fliegt im Höhenbereich von Windturbinen? Der Ornithologische Beobachter 101, 327-335.
[26] Glasner, W. (2009) Faunistische Untersuchungen zur Windkraftnutzung im Aachener Norden. Zum Einfluss des weiteren
Ausbaus der Windenergie auf Vögel und Fledermäuse - Gutachten im Auftrag des LA Landschaftsplanung, Oktober
2009, 114 pp.
[27] Erdoğan, A., A. Aslan, H. Sert, S. Kaçar and H. Karaardıç
(2010) Hatay-Senköy'de Kurulması Planlanan Rüzgâr Enerji
Santralinin Yaz Ve Sonbahar Kus Göç Hareketleri Üzerine
Olası Etkilerinin Değerlendirmesi, 60 pp.
[28] Bach, L. (2001) Fledermäuse und Windenergienutzung – reale
Probleme oder Einbildung? Vogelkdl. Ber. Niedersachs, 33,
119-124
[29] Bach, L. and Rahmel, U. (2006) Fledermäuse und Windenergie – ein realer Konflikt? Inform.d.Naturschutz Niedersachs.26.Jg.N147–52.
[30] Isselbächer, K. and Isselbächer, T. (2001) Vogelschutz und
Windenergie in Rheinland-Pfalz. Gutachten im Auftrag des
LfUG, Oppenheim.
[31] Kiziroğlu, İ. (2001) Ekolojik Potpuri. TAKAV Yay., Ankara,
265 pp.
[32] Kiziroğlu, İ.(2014) Ekolojik Potpuri. 2. Ankamat Mat.
Baskıda.(in pres),465pp, Ankara.
[33] Kiziroğlu, İ. (2010) Genel Biyoloji. Canlılar Bilimi. VII Baskı:
Okutman Yayıncılık. Ankara; 610 pp.7.Baskı.
[34] Pedersen, M.B. and Poulsen, E. (1991) Avian responses to the
implementation of the Tjaereborg Wind Turbine at the Danish
Wadden Sea . Dan. Wildtundersogelser 47,1- 44.
170
[41] Richarz, K. and Limbrunner, A. (2003) Fledermäuse. FrankhKosmos
[42] Hötker, H., Thomsen, K.M. and Köster, H. (2004) Auswirkungen regenerativer Energiegewinnung auf die biologische
Vielfalt am Beispiel der Vögel und der Fledermäuse –Fakten,
Wissenslücken, Anforderungen an die Forschung, ornithologische Kriterien zum Ausbau von regenerativen Energiegewinnungsformen. Endbericht. Michael-Otto-Institut im NABU.
[43] Dürr, T. (2013) Daten aus der zentralen Fundkartei der Staatlichen Vogelschutzwarte im Landesamt für Umwelt, Gesundheit und Verbraucherschutz Brandenburg zusammengestellt:
Tobias Dürr; Stand vom: 07. Oktober 2013). Daten aus der
zentralen Fundkartei der Staatlichen Vogelschutzwarte im
Landesamt für Umwelt, Gesundheit und Verbraucherschutz
Brandenburg Stand: 25. September 2013, Tobias.Dürr. [email protected]. http://www.mugv.brandenburg.de/cms/detail.php/bb2.c.451792.de
[44] Grünkorn, T. (2010) “Hotspot Fehmarn“ ausgewählter Ort
2010, „www.land-der-ideen.de“ Preisträger des Wettbewerbes
der Deutschen Bank.
[45] Grünkorn, T., A. Diederichs, D. Poszig, B. Diederichs and G.
Nehls (2005) Entwicklung einer Methode zur Abschätzung
des Kollisionsrisikos von Vögeln an Windenergieanlagen. Untersuchungen im Auftrag des Landesamtes für Naturschutz des
Landes Schleswig-Holstein. *pdf bei „www.bioconsult-sh.de“
[46] Grünkorn, T., A. Diederichs, D. Poszig, B. Diederichs and G.
Nehls (2009) Wie viele Vögel kollidieren mit Windenergieanlagen? Natur und Landschaft 7, 309-314. *pdf bei
http.www.bioconsult-sh.dewww.bioconsult-sh.de
[47] Krijgsveld, K.L., K. Akershoek, F. Schenk, F. Dijk and S.
Dirksen (2009) Collision risk of birds with modern large wind
turbines. Ardea 97(3) 357–366.
[48] Brinkmann, R. (2006) Auswirkungen von Windkraftanlagen
auf Fledermäuse. Naturschutz- Info 2/2006 + 2/2006.
[49] Brinkmann, R. and Schauer-Weißhahn, H. (2006) Untersuchungen zu möglichen betriebsbedingten Auswirkungen von
Windkraftanlagen auf Fledermäuse im Regierungsbezirk Freiburg. Studie im Auftrag des Regierungspräsidiums Freiburg.
[50] Brahic, C. (2008) Wind turbines make bat lungs explode. New
scientist, Aug 2008.
[51] Orloff, S. and Flannery, A. (1992) Wind turbine effects on
avian activity, habitat use and mortality in Altamont Pass and
Solano County wind resources areas 1989-1991. California
Energy Commission, Bio-Systems Analysis, Tiburón, Califonia. 49.
[52] Winden, J., Spaan, A.L. and Dirksen, S. (1999) Nocturnal collision risks of local wintering birds with wind turbines in wetlands. Bremer Beiträge für Naturkunde und Naturschutz 4, 3338.
[53] Green, R. E. (1999) Survival and dispersal of male Corncrakes
Crex crex in a threatened population. Bird Study 46 (supplement), 218-229.
[54] Everaert, J. (2003) Collision victims on 3 wind farms in Flanders (Belgium) in 2002.1nstituut voor Naturbücher, Brüssel.
[55] Daulton M. (2007) Congressional Testimony on Benefits of
Wind Power Before the Committee on Natural Resources Subcommittee on Fisheries, Wildlife and Oceans Impacts of Wind
Turbines on Birds and Bats May 1, 2007
[56] Telleria, J.L. (2009) Potential impacts of wind farms on migratory birds crossing Spain Bird Conservation International
19, 131-136. (http://www.wind-energie.de/en/topics/protection-of-birds),
[57] www.gwec.net,www.ruzgarenerjisibirligi.org.tr,www.tr.wikipedia.org,www.igwindkraft.at/fakten,http://eie.gov.tr
http://www.nabu.de/aktionenundprojekte/weissstorchbesenderung/#karte,www.oekostrom.at,www.aae.at,www.naturkraft.at
Received: April 03, 2014
Revised: August 04, 2014
Accepted: August 13, 2014
CORRESPONDING AUTHOR
Ilhami Kiziroğlu
Dicle Caddesi, No. 2006532
Beysukent/Ankara
TURKEY
Phone +903122351336
E-mail: [email protected]
FEB/ Vol 24/ No 1a/ 2015 – pages 163 - 171
171
INTEGRATION OF GIS AND REMOTE SENSING WITH THE
USLE MODEL IN THE ASSESSMENT OF ANNUAL SOIL LOSS
AND SEDIMENT INPUT OF ZINAV LAKE BASIN IN TURKEY
Hakan Mete Doğan1*, Orhan Mete Kılıç1,
Doğaç Sencer Yılmaz1, Ekrem Buhan2, Fatih Polat3 and Saliha Dirim Buhan4
1
4
Gaziosmanpaşa University, Agricultural Faculty, Soil Science Department, GIS&RS Unit, Taşlıçiftlik, Tokat/Turkey
2
Gaziosmanpaşa University, Agricultural Faculty, Aquaculture and Fisheries Department, Tokat/Turkey
3
Gaziosmanpaşa University, Almus Vocational School, Tokat/Turkey
Gebze Institute of Technology, Faculty of Engineering, Environmental Engineering Department, Gebze/Kocaeli/Turkey
ABSTRACT
The effects of annual soil loss on Zinav Lake Basin located in Tokat Province of Turkey were researched. The annual soil loss of the study area was modelled and mapped by
using the Universal Soil Loss Equation (USLE), Geographic
Information Systems (GIS) and remote sensing (RS). Employing the USLE model, precipitation (R), the degree of
slope and length of slope (L and S), soil erodibility (K), vegetation cover-land use (C), and soil conservation (P) factors
were separately calculated and transformed to raster maps in
GIS environment. Then, all produced factor map layers were
multiplied each other to develop an annual soil loss raster
map. Annual sediment input was calculated by utilizing
Roehl sediment delivery ratio and developed annual soil loss
raster map. The mean annual soil loss of the study area was
determined as 0.037 t.ha-1year-1. Erosion risk classes of the
study area were determined according to European Environment Information and Observation Network for Soil (EIONET-SOIL) Erosion Map, and summarized as five classes
including the 0-0.5, 0.5-1.0, 1.0-2.0, 2.0-5.0 and 5.0-7.1
t.ha-1year-1 intervals. Sediment delivery ratio and mean annual sediment input were determined as 44.67% and 0.181
t.ha-1year-1, respectively.
KEYWORDS: EIONET-SOIL, Geographic Information Systems,
erosion, K-factor, modelling, LANDSAT-7 ETM+, soil accumulation, Zinav Lake.
1. INTRODUCTION
Soil erosion, as a result of anthropogenic activity, in
excess of accepted rates of natural soil formation, causing
a deterioration or loss of one or more soil functions [1]. It
is a gradual process that occurs when the impact of water
or wind detaches and removes soil particles, causing the
soil to deteriorate. The most importantly, erosion generates
many environmental problems such as removal and disappearance of the productive top soil [2, 3], corruption of
river regimes [4], destruction of wetlands [5], short reservoir life and water pollution [6]. Therefore, soil deterioration and low water quality due to erosion and surface runoff
have become the most fundamental problems that threat
ecosystems and life world-wide [3].
The spatial identification of erosion risk areas has a
high priority to combat erosion and is a prerequisite for sustainable ecosystem. The Universal Soil Loss Equation
(USLE), developed by the United States Department of
Agriculture (USDA) in 1930s, has been a widely used
mathematical model that describes soil erosion processes
[7, 8]. The USLE played critical roles in soil and water resource conservation and nonpoint source pollution assessments, including: sediment load assessment and inventory,
conservation planning and design for sediment control,
and for the advancement of scientific understanding [9,
10]. A GIS is a tool that integrates hardware, software, and
data for capturing, managing, analyzing, and displaying all
forms of geographically referenced information [11]. As a
modern and powerful tool GIS allows us to view, understand, question, interpret, and visualize the spatial data in
many ways [12]. Rise and significant improvements in GIS
and RS, as well as the advances made in computing power
have assisted in the orientation of modelling efforts towards the spatial attributes of erosion processes and features [13]. In the past decade, many researchers integrated
the USLE with GIS in order to calculate and map soil
losses properly [6, 8, 14-16]. GIS for water erosion modelling caused the consequences such as facilitating multiplesource data mixing, creating a computing environment for
172
potential model rescaling and increasing complexity in datamethod relations [17].
Zinav Lake is the biggest natural lake in Tokat Province and Kelkit Basin of Turkey. The lake has regional and
national importance because of its natural structure, biological diversity, and unique landscape values. However,
the lake and the lake basin are under the pressure of several
threats such as anthropogenic and natural hazards. One of
these pressures is soil accumulation towards the lake due
to erosion. Although ecologists in the area have been
aware this accumulation process, there is not any study to
quantify this accumulation until now. This is a critical subject for the lake management in the future. In this study, we
aimed to research the effects of annual soil loss and sediment input on Zinav Lake. For this purpose, we employed the
USLE model, GIS and RS to calculate and map soil loss and
to determine sediment input of Zinav Lake Basin.
2. MATERIAL AND METHODS
The study area is located in the Central Black Sea Region of Turkey. Zinav Lake Basin is situated in the northwest
district of Reşadiye county of Tokat province. The precise
position of study area can be described as between the
37o13’-37o21’ East longitudes and 40o32’-40o23’ North latitudes (Figure 1). The total study area is determined as
98.01 km2, and Zinav Lake surface area covers an area of
0.34 km2.
The highest and lowest points of the basin are 1648 m
and 451 m, respectively. The lowest point is located in the
vanishing point of Zinav Lake and Kelkit River. In the
study area, the average annual temperature is 10.3oC, and
average annual rainfall is 482 mm. The majority of rains
fall in the spring. From time to time, less intense but prolonged rains occur in the study area. Zinav Lake is located
FIGURE 1 - Location and topographic features of the study area
173
at higher elevation comparing to the Reşadiye county centre (Figure 1). For this reason, the lake and surrounding
area receive more snow fall compared with the centre of
Reşadiye county, and this snow is long lasting. Both spring
rains and leaked water from the melting snow cause increasing water level in Zinav Lake. This situation promotes
erosion and landslides in the study area [18]. The sedimentation is transferred to the lake by Kelkit river and side gutters around the lake. Main vegetation of the study area is degraded oak and pine forests. The soils of the study area have
been considered to the Brown Forest Soil major group. This
major group corresponds to Eutric Cambisols (CM) according to the WRB Map of World Soil Resources [19-21]. This
soil group holds soils with incipient soil formation. Beginning transformation of soil material is evident from weak,
mostly brownish discolouration and/or structure formation
below the surface horizon [20, 21]. The main land covers
of the study area are forest, rangeland, water surfaces (Lake
Zinav) and rocky areas.
The soil erosion indicator adopted in this study is
the estimated soil loss expressed in ton per hectare per
year (t ha-1 yr-1) as described in detail by Huber et al. [22].
We followed a methodology that integrates the USLE
model with GIS and RS to determine soil erosion. The
USLE model (1) below predicts soil losses depending on
rainfall, soil, and vegetation characteristics [10]. In this
model (1), A is the annual soil loss (t ha-1 yr-1); R is the rainfall-runoff erosivity factor (MJ mm ha−1 h−1 yr−1); K is the
soil erodibility factor (t ha h ha−1 MJ−1 mm−1); L is the slope
length factor; S is the slope steepness factor; C is the covermanagement factor; and P is the support practice (soil conservation) factor. L, S, C, and P are unit-less as factors.
A  R * K * L * S *C * P
(1)
Basically, R (rainfall-runoff erosivity) factor shows the
effects of annual independent rainfalls to soil loss. Duration
and the amount of rainfall, kinetic energy of raindrops, and
the diameters of raindrops are the main factors that affect R
factor [23]. We utilized rain data from Reşadiye meteorological station to calculate R factor. This rain data contains the
average monthly rainfall data between 1970 and 1992. The
Modified Fournier Index (MFI) equation (2) below have
been used to calculate R factor [24-26]. In this equation (2),
Pi is the total rainfall (mm) in the ith month, P is the average
annual rainfall (mm).
12
MFI   Pi 2 / P
(2)
its topography, geology, vegetation, and soil characteristics
to find the best placement of sampling sites. Considering
this stratification, a total of 70 sampling sites were randomly distributed within these strata. Soil samples were
collected with a small shovel to the depth of 15–20 cm.
During the soil sampling process, the soil sampling procedures were utilized [28, 29]. The soil samples (̴ 2.0 kg) were
taken into a plastic bag and labelled. Organic matter, texture, and very fine sand fraction were determined through
the laboratory analyses. We measured soil organic matter
content (%) by following the modified Walkley–Black wet
oxidation procedure [30], and determined the texture by
utilizing the Bouyoucos hydrometer method [31, 32].
Structure codes were determined during the field studies,
and permeability values were calculated by using SPAW
hydrology software [33]. On the basis of these calculated
values, K factor values were determined by using the following equation (3) [8, 10, 23]. In this equation (3), K is
soil erodibility factor (t ha h ha−1 MJ−1 mm−1), M is the
product of the primary particle size fractions ((% modified
silt or the 0.002-0.1 mm size fraction) * (% silt + % sand)),
OM is organic matter content (%), s is structure type code,
and p is permeability class code. Using this equation (3), K
factor values of 70 soil samples were calculated. Then, a K
factor raster map was developed by using ordinary kriging
method with spherical variogram in ArcGIS software [12].
In the interpolation process, we used the highest precision
for derived coverage, and single precision for new coverage.


(3)
L (length of slope) factor is the horizontal distance
from the starting point of surface runoff to the point that
water flow concentrated to a particular canal [10]. We determined the length of slope (L) and the degree of slope (S) factors by using the digital elevation model (DEM). For this
purpose we digitized the topographic map of the study area
with 1/25000 scale, and developed a DEM from this digitized topographic map. We utilized DEM to generate elevation and slope raster maps with 10x10m grid size in
ArcGIS. Then, we calculated L factor values from the created raster maps by using raster calculator function of
ArcGIS and the equation (4) below [10, 34, 35]. In equation (4): L is the length of slope factor, λ is the length of
slope (m), and m is a dimensionless exponent. Coefficient
m depends on the slope steepness value and gets 0.5 (slope
>5%), 0.4 (slope=4%), and 0.3 (slope <3%) values [10, 34,
35].
1
L  ( / 22 .1) m
K (soil erodibility) factor defines the effects of soil features and soil profile characteristics to soil loss. In this
study, K factor was calculated by using the laboratory analysis results of 70 geo-referenced soil samples that we collected by following the stratified random sampling design
[27]. For this aim, we stratified the study area according to

K  2.1  10 4 (12  OM ) M 1.14  3.25( s  2)  2.5( p  3) / 100 * 0.1317
(4)
S (degree of slope) factor shows the effect of slope to
erosion. S factor was calculated by using slope raster map,
the equations (5, 6) below and raster calculator function of
ArcGIS. In equations (5, 6): S is the degree of slope factor,
Ɵ is the angle of slope, and s refers to the land slope. If the
174
land slope is smaller than 9% (s<9%), equation 5 is valid.
If the land slope is equal or bigger than 9% (s≥9%), equation 6 is used [10, 35, 36].
S  10.8Sin  0.03
(5)
S  16.8Sin  0.05
(6)
C (vegetation cover-land use) factor reflects the impacts of surface roughness, plant cover and management
practices. Protection of top soil because of vegetative cover
or surface roughness is evaluated in C factor [10]. In this
study, C factor was calculated by utilizing geo-referenced
land observations, available land cover and forest maps.
We also utilized a LANDSAT-7 ETM+ satellite image
taken on 18 September 2012. The image was corrected by
using a gap fill method [37] before analyzing. Then, the
image was subset to obtain the area of interest (AOI) image
by using a vector AOI created from the border map of the
study area. Consequently, a subset image was created in
ERDAS Imagine software [38]. Utilizing this prepared image, the normalized difference vegetation index (NDVI)
values were calculated. NDVI index equation (7) used in
this process was given below [39-42]. In this equation (7):
B3 and B4 are LANDSAT-7 ETM+ bands 3 and 4, respectively.
NDVI  ( B 4  B3) /( B 4  B3)
(7)
In ERDAS Imagine, NDVI values were stretched to an
unsigned 8-bit image varying between 0 and 256. Utilizing
geo-referenced land observations, available land cover and
forest maps, we classified produced NDVI map in 5 classes
including water surface or rocky areas (NDVI: 0-87), weak
grasslands (NDVI: 88-114), moderately dense rangelands
(NDVI: 115-148), moderately dense oak forest (NDVI: 149189), dense forest (NDVI: 189-255). With this classification, we also updated the land cover map of the study area.
Utilizing land cover map, we determined C factor value of
each class [43]. For this aim, 0.000, 0.280, 0.042, 0.011,
and 0.003 C factor values were assigned to water surface
or rocky areas, dense forest, moderately dense oak forest,
moderately dense rangelands, and weak grasslands land
cover classes, respectively.
(SDR) is the ratio between sediment export and annual soil
loss in a catchment [44]. We calculated SDR by utilizing the
Roehl Equation (8) below [44, 45]. In this equation (8), SDR
is sediment delivery ratio, A is the Zinav Lake Basin area
(km2). Multiplying SDR with mean annual soil loss, we
determined sediment input of Zinav Lake Basin.
SDR  36( A) 0.20
3. RESULTS
Produced raster map layers of K, L*S, and C factors
are shown in Figure 2. R (62.30 MJ mm ha−1 h−1 yr−1) and
P (1.00 unit-less) layers are constant and uniform in the
study area. For this reason R and P were not shown up in
Figure 2. However, pixel values of K factor (soil erobility)
raster map changed between 0.00571 and 0.01320 t ha h
ha−1 MJ−1 mm−1 (Figure 2a) with a 0.00789 t ha h ha−1 MJ−1
mm−1 mean value. L and S factors were expressed together
as the L*S raster map with the changing pixel values between 1.29 and 42.50 (Figure 2c) with a 19.03 mean value.
In C factor raster map (Figure 2d), pixel values varied between 0.000 and 0.280 with a 0.037 mean value.
The annual soil loss (t.ha-1year-1) raster map, developed by multiplying the raster maps of all factors (R, K,
L*S, C, and P) in ArcGIS, was shown in Figure 3. Pixel
values of this raster map changed between 0.0 and 7.1 t.ha1
year-1 with a 0.037 mean value (Figure 3). We classified
the annual soil loss raster map using the class intervals of
European Environment Information and Observation Network for Soil (EIONET-SOIL) Erosion Map [8]. Covered
area by erosion risk classes were summarized in Table 1.
Spatial distribution of erosion risk classes showed that
84.89% of the study area in 0.0-0.5 t.ha-1year-1 risk class.
This was followed by the 2.0-5.0 (6.02%), 0.5-1.0 (4.54%),
1.0-2.0 (2.30%) and 5.0-7.1 (1.90%) erosion classes (Table 1).
Remaining cover area (0.35%) is the lake surface.
TABLE 1 - Cover areas of determined soil loss and sediment input
classes
Soil Loss
(t.ha-1year-1)
0.0-0.5
0.5-1.0
1.0-2.0
2.0-5.0
5.0-7.1
Lake
TOTAL
We considered P (soil conservation) factor as 1, because
there is no soil conservation application in the area [10]. All
calculated factors (R, K, L*S, C, and P) were transformed to
raster map layers by using 10x10 m grid size in ArcGIS software [12]. Using map calculation functions, all map layers
were multiplied each other to develop annual soil loss (A)
raster map in ArcGIS.
Some parts of erosion in a catchment area are hold by
stream bed, the end of slope, and surface pits. Some parts,
however, are exported or delivered. This delivered portion
is important, because this is the portion that will negatively
affect the lake through time. The sediment delivery ratio
(8)
Sediment Input
(t.ha-1year-1)
0.00-0.22
0.22-0.45
0.45-0.89
0.89-2.23
2.23-3.16
Cover Area
(km2)
83.20
4.45
2.25
5.91
1.86
0.34
98.01
Cover Area
(%)
84.89
4.54
2.30
6.02
1.90
0.35
100.00
The mean annual soil loss of the total study area
(98.01 km2) was determined as 0.037 t.ha-1year-1. The SDR
was calculated as 44.67%. The mean annual sediment input
of Zinav Lake Basin was determined as 0.181 t.ha-1year-1 by
multiplying SDR with mean annual soil loss. The annual sediment inputs of the study area were also shown in Figure 3.
175
FIGURE 2 - Raster map layers of R (a), K (b), L*S (c), C (d), and P (e) factors.
FIGURE 3 - Raster map layer of the annual soil loss (t.ha-1year-1) and annual sediment input (t.ha-1year-1).
176
(Project No: ÇAYDAG- 1110Y117); and Orhan Mete Kılıç
for his technical assistance as a part of his Ph.D. thesis.
4. DISCUSSION AND CONCLUSION
In shallow lake ecosystems, the sediment plays an important role in the overall nutrient dynamics [46]. The deposition rate of lake sediments is controlled by many factors
such as lake morphology (area, depth, shoreline’s length),
water circulation, climate conditions, area and land use in
the catchment [47, 48]. Erosion is one of the key factors
that drive sediment delivery ratio and sediment input.
Higher values of these variables will most likely stress the
lake ecosystems and give a chance to be filled through time
[3, 6]. Therefore, the information about soil loss and sediment input of a lake basin plays a crucial role in lake management process.
The early identification of areas susceptible to erosion
is an essential step to take necessary precautions. The
USLE model is one of the most widely used mathematical
model that describes soil erosion processes [7, 8]. The sediment delivery ratio (SDR) developed by Roehl (1962) is
the ratio between sediment export and total soil loss in a
catchment [44, 45]. Utilizing the USLE and SDR annual
soil loss and sediment input values of a lake basin can
safely be calculated. Integration of this model with GIS
and RS opened the ways to develop precise raster maps of
these variables [6, 14-16].
In this study, annual soil loss and sediment input of Zinav Lake Basin were mapped by using the USLE, Roehl`s
SDR, GIS and RS. Integration the USLE model with GIS
and RS tools simplified the mapping process in a reliable
and fast way. Our study showed that majority of the study
area (84.89%) was in 0.0-0.5 t.ha-1year-1 class. This was
found to be the lowest erosion rate compared to EIONETSOIL Erosion Map [8].
Although higher erosion rates (2.0-5.0 and 5.0-7.1)
covered small portions (6.02% and 1.90%) of the lake basin area, the majority of these kinds of areas were found
close to the northwest of Zinav Lake. The erosion rate of
5.0-7.1 t.ha-1year-1 was reported as very critical level according to some researchers in Turkey [49-52]. It was also reported that this is the highest level determined in Europe
(Italy- 6.60 t.ha-1year-1) [8]. Sediment input from these
kinds of areas changed between 0.89-2.23 and 2.23-3.16
t.ha-1year-1 in the study area. In the studies combating erosion, priority should be given to these areas. With some
measures such as forestation of degraded forest, rehabilitation of the side gutters, improvement of the infrastructure of
nearby settlements, and developing a lake management plan,
Zinav Lake and the surrounding area will be able to maintain its existence for future generations without losing its
importance and naturalness.
ACKNOWLEDGEMENTS
The authors thank to the Scientific and Technical Research Council of Turkey (TÜBİTAK) for project funding
177
REFERENCES
[1]
Jones R.J.A., Verheijen F.G.A, Reuter H.I. and Jones A.R.
(2008) Environmental Assessment of Soil for Monitoring Volume V: Procedures and Protocols. EUR23490 EN/5. Office for
the Official Publications of the European Communities, Luxembourg: 165pp.
[2]
Reganold, J.P., Elliott, L.F. and Unger, Y.L. (1987) Long-term
effects of organic and conventional farming on soil erosion.
Nature, 330: 370-372.
[3]
Pimentel, D., Harvey, C., Resosudarmo, P., Sinclair, K., Kurtz,
D., McNair, M., Crist, S., Shpritz, L., Fitton, L., Saffouri, R.
and Blair, R. (1995) Environmental and economic costs of soil
erosion and conservation benefits. Science, 267(5201): 11171123.
[4]
Benda, L., Miller, D., Bigelow, P. and Andras, K. (2003) Effects
of post-wildfire erosion on channel environments, Boise River,
Idaho. Forest Ecology and Management, 178: 105-119.
[5]
Uluocha, N.O. and Okeke, I.C. (2004) Implications of wetlands degradation for water resources management: Lessons
from Nigeria. GeoJournal, 61(2):
[6]
Karabulut, M. and Küçükönder, M. (2008) Kahramanmaraş
ovası ve çevresinde CBS kullanılarak erozyon alanlarının
tespiti. KSÜ Fen ve Mühendislik Dergisi, 11(2): 14-22.
[7]
Hudson, N.W. (1993) Field Measurement of Soil Erosion and
Runoff. Food and Agriculture Organization of the United Nations (FAO), Soils Bulletin 68, Rome: 141 pp.
[ 8] Panagos, P., Meusburger, K., Liedekerke, M.V., Alewell, C.,
Hiederer, R. and Montanarella, L. (2014) Assessing soil erosion in Europe based on data collected through a European network. Soil Science and Plant Nutrition, 60(1): 15-29.
[9]
Wischmeier, W.H. and Smith, D.D. (1960) A universal soil-loss
equation to guide conservation farm planning. Transactions of
7th International Congress of Soil Science, 1: 418-425.
[10] Wischmeier, W.H. and Smith, D.D. (1978) Predicting Rainfall
Erosion Losses: A Guide to Conservation Planning (Agriculture Handbook No. 537). USDA Science and Education Administration, US. Goverment Printing Office, Washington DC:
58 pp.
[11] DeMers, N.M. (2009) Fundamentals of Geographic Information
Systems, 4th Edition. John Wiley & Sons, New York: 443 pp.
[12] ESRI (2005) ArcGIS 9, What is in ArcGIS 9.1. Environmental
Systems Research Institute, Redlands California: 1-123.
[13] De Jong, S.M. (1994) Applications of Reflective Remote
Sensing for Land Degradation Studies in a Mediterranean Environment. PhD Thesis, Utrecht University.
[14] Mati, B.M., Morgan, R.P.C., Gichuki, F.N., Quinton, J.N.,
Brewer, T.R. and Liniger, H.P. (2000) Assessment of erosion
hazard with the USLE and GIS: a case study of the Upper Ewaso
Ng'iro North basin of Kenya. International Journal of Applied
Earth Observations and Geoinformation, 2(2): 78-86.
[15] Cohen, M.J., Shepherd, K.D. and Walsh. M.G. (2005) Empirical reformulation of the universal soil loss equation for erosion risk assessment in a tropical watershed. Geoderma,
124(3-4): 235-252.
[16] Meusburger, K., Konz, N., Schaub, M. and Alewell, C. (2010)
Soil erosion modelled with USLE and PESERA using QuickBird derived vegetation parameters in an alpine catchment. International Journal of Applied Earth Observation and Geoinformation, 12(3): 208-215.
[17] Karydas, C.G., Panagos, P. and Gitas, I.Z. (2014) A classification of water erosion models according to their geospatial
characteristics. International Journal of Digital Earth, 7(3):
229-250.
[18] Zeybek, H.I. (2002) Sinan (Zinav) Gölü (Reşadiye-Tokat).
Türk Coğrafya Dergisi, 38: 105-120.
[19] FAO (1990) WRB Map of World Soil Resources. Food and
Agriculture Organization of the United Nations, Rome. (available at online: www.fao.org/nr/land/soils/soil/wrb-soilmaps/wrb-map-of-world-soil-resources/en/)
[20] Durak A. (1990) Clay mineralogy and classification of Brown
soils and Gray-Podsolic soils of Tokat Region (in Turkish).
Çukurova Üniversitesi, Ziraat Fakültesi Dergisi, 6(1): 275291.
[21] FAO (2014) Mineral Soils Conditioned by Limited Age, Cambisols (CM). Lecture Notes on the Major Soils of the World.
Food and Agricultural Organization Corporate Document Repository,
Rome.
(available
at
online:
http://www.fao.org/docrep/003/ y1899e/y1899e08.htm)
[22] Huber, S., Prokop, G., Arrouays, D., Banko, G., Bispo, A.,
Jones, R.J.A., Kibblewhite, M.G., Lexer, W, Möller, A., Rickson, R.J., Shishkov, T., Stephens, M., Toth, G., Van den Akker, J.J.H., Varallyay, G., Verheijen, F.G.A. and Jones, A.R.
(eds) (2008) Environmental Assessment of Soil for Monitoring: Volume I Indicators and Criteria. EUR 23490 EN/1 Office
for the Official Publications of the European Communities,
Luxembourg, 339 pp.
[23] Renard, K.G., Foster, G.R., Weesies, G.A., McCool, D.K. and
Yoder, D.C. (1997) Predicting Soil Erosion by Water: a Guide
to Conservation Planning with the Revised Universal Soil
Loss Equation (RUSLE). Agriculture Handbook, Washington,
703 pp.
[32] Gee, G.W. and Bauder, J.W. (1986) Particle Size Analysis. In
A Klute (Ed.), Methods of Soil Analysis, Part 1- Physical and
Mineralogical Methods, Second Edition, American Society of
Agronomy Inc., Madison, Wisconsin , 383-411 pp.
[33] Saxton, K.E. and Willey, P.H. (2004) Agricultural wetland and
pond hydrologic analyses using the SPAW model. In: J.L.
D'Ambrosio (Ed.), Self-Sustaining Solutions for Streams, Watersheds and Wetlands, Proceedings of the September 2004
Conference, American Society of Agricultural Engineers
(ASAE), St. Paul, Minessota: 16-23 pp.
[34] Okalp, K. (2005) Soil Erosion Risk Mapping Using Geographic Information Systems: A Case Study on Kocadere
Creek Watershed, İzmir. M.Sc. Thesis, Middle East Technical
University, Ankara.
[35] Oguz, I. (2012) Analyzing variation of sediment yields in wet
and drought years. Journal of Agricultural Sciences, 18(2):
146-156.
[36] McCool, D.K., Brown, L.C., Foster, G.R., Mutchler, C.K. and
Meyer, L.D. (1987) Revised slope steepness factor for the Universal Soil Loss Equation. Transactions of the American Society of Agricultural Engineers, 30(5): 1387-1396.
[37] Bustillos, L.V. (2012) Gap Fill for Landsat 7 Images – A correction of SLC – off.
[38] ERDAS (2003) Erdas Field Guide, Seventh Edition. Leica Geosystems, GIS and Mapping LLC, Atlanta Georgia: pp. 1-672.
[39] Tucker, C.J. (1979) Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment, 8(2): 127-150.
[40] Sabins, F.F. (1987) Remote Sensing Principles and Interpretation, 2nd Edition. Waveland Press Inc., New York: 449 pp.
[41] Jensen, J.R. (1996) Introductory Digital Image Processing: A
Remote Sensing Perspective, 2nd Edition. Prentice Hall, Englewood Cliffs, New Jersey: 316 pp.
[42] Dogan, H. M. (2009) Mineral composite assessment of Kelkit
River Basin by means of remote sensing. Journal of Earth System Science, 118: 701-710.
[24] Renard, K.G. and Freimund J.R. (1994) Using monthly precipitation data to estimate the R-factor in the revised
USLE. Journal of hydrology, 157(1-4): 287-306.
[43] Haan, C.T., Bartfield, B.J. and Hayes, J.C. (1994) Design Hydrology and Seminetology for Small Catchments. Academic
Press: USA.
[25] İrvem, A., Topaloğlu, F. and Uygur, V. (2007) Estimating spatial distribution of soil loss over Seyhan River Basin in Turkey. Journal of Hydrology, 336(1-2): 30-37.
[44] Kasai, M, Marutani, T., Reid, L.M. and Trustrum, N.A.
(2001) Estimation of temporally averaged sediment delivery
ratio using aggradational terraces in headwater catchments of
the Waipaoa River, North Island, New Zealand. Earth Surface
Processes and Landforms, 26: 1-16.
[26] Oduro-Afriyie, K. (1996) Rainfall erosivity map for
Ghana. Geoderma, 74(1): 161-166.
[27] McGrew, J.C.Jr. and Monroe, C.B. (1993) Statistical Problem
Solving in Geography. Dubuque, IA: Wm. C. Brown, 254 pp.
[45] Roehl, J.E. (1962) Sediment sources areas, delivery ratios and
influencing morphological factors. International Association
of Hydrological Sciences Publication, 59: 202–213.
[28] Atesalp, M. (1976) Soil and Water Sampling for Analysis, 1–
25. Ankara: Soil and Fertilizer Research Institute Press, General No.: 68, Farmer Pub. No: 3.
[46] Søndergaard, M., Jensen, J.P. and Jeppesen, E. (2003) Role of
sediment and internal loading of phosphorus in shallow lakes.
Hydrobiologia, 506–509(1-3): 135–145.
[29] Walworth, J.L. (2006) Soil Sampling and Analysis, 1–5. Tucson, AZ: Arizona Cooperative Extension, The University of
Arizona, College of Agriculture and Life Sciences.
[47] Noges, P., Tuvikene, L., Noges, T. and Kisand, A. (1999) Primary production, sedimentation and resuspension in large
shallow Lake Vortsjarv. Aquatic Sciences, 61(2): 168-182.
[30] Nelson, D.W. and Sommers, L.E. (1982) Total Carbon, Organic Carbon, and Organic Matter. In A. L. Page, R. H. Miller,
and D. R. Keeney (Eds.), Methods of Soil Analysis, Part 2 –
Chemical and Microbiological Properties, , 2nd ed. Madison,
WI: American Society of Agronomy, 539–579 pp.
[48] Ott, I., Rakko, A., Sarik, D., Noges, P. and Ott, K. (2005) Sedimentation rate of seston during the formation of temperature
stratification after ice break-up in the partly meromictic Lake
Verevi. Hydrobiologia, 547(1): 51-61.
[31] Bouyoucos, G.J. (1951) A recalibration of the hydrometer
method for making mechanical analysis of soil. Agronomy
Journal 43: 434–438.
178
[49] Ozcan, A.U., Erpul, G., Basaran, M., Erdogan, H.E. (2008)
Use of USLE/GIS technology integrated with geostatistics
to assess soil erosion risk in different land uses of Indagi
Mountain Pass-Çankırı, Turkey. Environmental Geology,
53(8): 1731-1741.
[50] Hacisalihoglu, S., Mert, A., Negiz, M.G. and Muys, B. (2010)
Soil loss prediction using universal soil loss equation (USLE)
simulation model in a mountainous area in Aglasun district, Turkey. African Journal of Biotechnology, 9(24): 35893594.
[51] Gündoğan, R., Akay, A.E., Okatan, A., Yuksel, A. and Oguzkan, E.A. (2010). Land suitability evaluation for reducing soil
losses in Kahramanmaras, Turkey. Fresenius Environmental
Bulletin, 19(11a): 2678-2689.
[52] Erkal, T., Unal, Y. (2012) Soil erosion risk assessment in
the Sincanli Sub-Watershed of the Akarcay Basin (Afyonkarahisar, Turkey) using the Universal Soil Loss Equation
(USLE). Ekoloji 21(84): 18-29.
Revised: May 21, 2014
Accepted: June 25, 2014
Hakan Mete Dogan
Gaziosmanpaşa University
Agricultural Faculty
Soil Science Department
GIS&RS Unit
Taşlıçiftlik, Tokat
TURKEY
Phone: + 90 356 252 16 16 (internal: 2280)
Fax: + 90 356 252 14 88
FEB/ Vol 24/ No 1a/ 2015 – pages 172 - 179
179
ACCUMULATION AND BEHAVIOR OF SOME HEAVY METALS IN
THE MAIN COMPONENTS OF ZINAV LAKE BASIN ECOSYSTEM
Fatih Polat1, Hakan Mete Dogan2*, Ekrem Buhan3,
Orhan Mete Kılıç2, Doğaç Sencer Yılmaz2 and Saliha Dirim Buhan4
1
Gaziosmanpaşa University, Agricultural Faculty, Soil Science Department, GIS&RS Unit, Taşlıçiftlik, Tokat/Turkey
3
2
4
ABSTRACT
Effects of Pb, Cu, Zn, Fe, Co, Al, and Cd on the ecosystem of Zinav Lake Basin in Turkey were investigated
between 2011 and 2013. The ecosystem was handled in
seven main components including fish, macrophyte, sediment, water, benthos, plankton, and soil. Heavy metal contents of the main components were determined by using Inductively Coupled Plasma-Mass Spectrometer (ICP-MS)
and field samples. Relationships among the heavy metal contents of ecosystem components were investigated by using
correlation (Pearson) analysis. Hierarchical Clustering
Analysis (HCA) was applied to delineate the similar subgroups of ecosystem components according to their
heavy metal contents. Fe, Al and Zn were found maximum
and Cd was found minimum in almost all ecosystem components except plankton. The accumulation order of heavy
metal levels was found as soil > sediment > macrophyte >
fish = benthos > water > plankton (except Fe). Al, Co, Cu,
Fe, and Zn behave similar in the ecosystem. However, Pb
and Cd did not show any similarities neither to each other
nor to the others.
KEYWORDS: Heavy metal, ecosystem, Hierarchical Clustering
Analysis, Zinav Lake Basin.
1. INTRODUCTION
Heavy metal pollution of aquatic ecosystems is a potential problem [1]. Heavy metals may enter into aquatic
ecosystems from several ways such as anthropogenic
sources, industrial wastewater discharges, sewage wastewater, fossil fuel combustion and atmospheric deposition
[2-6]. Trace amounts of heavy metals are always present
in fresh waters from terrigenous sources such as weathering
of rocks resulting into geo-chemical recycling of heavy
metal elements [7, 8]. Sometimes, trace elements may be
immobilised within the stream sediments and thus could be
involved in absorption, co precipitation, and complex formation [1, 9, 10].
Risk posed by sediment-associated chemicals to aquatic
organisms is best understood through an evaluation of sediment quality which is known as the Sediment Quality Triad
(SQT). SQT includes sediment chemistry, toxicity, and benthic community. Measurements of these three areas are integrated to reach conclusions based on the degree of risk indicated by each measurement and the confidence in each
measurement [11].
Heavy metals such as cadmium (Cd), lead (Pb), copper
(Cu) and zinc (Zn) are principal pollutants of aquatic ecosystems because of their environmental persistence, toxicity and great potential of accumulation in the food
chains [12, 13]. The quantity and form of the element in water, sediment, or food will determine the degree of accumulation, and different aquatic organisms often respond to this
external contamination in different ways [14]. Many heavy
metals accumulate in the soil and sediments of water bodies. The lower aquatic organisms absorb and transfer them
through the food chain to higher trophic levels. Some kinds
of toxic sediments kill benthic organisms, reducing the food
available to larger animals such as fish. Some contaminants
in the sediment are taken up by benthic organisms in a process called bioaccumulation [15]. Therefore, the determination of heavy metals accumulation in the nutrient cycle,
water, sediment and soil is very important.
Zinav Lake is an important natural lake in Tokat Province and Kelkit Basin. The lake has regional and national
importance. At present, the lake and the lake basin are under
the threats of household discharge, spoiled water quality, incorrect fish species modifications, unsound land management, and forest destruction. Understanding the nature of
180
heavy metal pollution is important for sustainable management of the lake. Consequently, this study aimed to research accumulation and behavior of heavy metals in the
ecosystem of Zinav Lake Basin.
This study was conducted in Zinav Lake Basin located
in the Central Black Sea Region of Turkey (Figure 1). Zinav
Lake is situated in the northwest district of Reşadiye
county of Tokat province in Turkey. The precise position
of study area can be described as between the 40o26’35”40o27’10” North latitudes and 37o16’05”-37o16’38” East
longitudes. The study was performed at 5 stations on the
lake and 3 stations around the lake (Figure 1), and field
samples were collected between June 2011 and May
2013.
Ecosystem in the lake basin was firstly divided into
seven categories including (1) fish, (2) macrophyte, (3)
sediment, (4) water, (5) benthos, (6) plankton, and (7) soil.
Water samples were collected from the aforementioned stations, and were poured into 100 ml bottles after filtering.
In order to perform heavy metal analysis, water samples
immediately were acidized by adding 1 ml HCl (Merck).
This prevented biological activities of bacteria and microorganisms, and did not allow metals to turn into other
forms of metals. The treated samples were stored at +4 °C
in the refrigerator until the analysis [16]. Sediment and soil
samples were collected from each sampling site at a depth
of 0 - 5 cm and stored in a plastic bottle. At least three composite soil and sediment samples were collected from each
sampling stations. Each sediment and soil sample was airdried in the laboratory at room temperature and was passed
through a 2 mm sieve before extraction. The dry weight
of each sample was measured after 12 hours of drying in
an oven at 105°C. At each sampling time, algae biomass
samples from the same sites were collected using plankton
nets (30 μm in mesh size) and about 100 g immediately
stored in dry ice and later frozen at -20°C prior to heavy
metal extraction. 250 ml of
FIGURE 1 - Location, topographic features, and sampling stations of the study area.
181
water samples were collected and immediately fixed by a
freshly prepared 0.7% Lugol’s solution and stored prior to
phytoplankton identification and analysis. Other (benthos,
fish species, macrophyte species) samples were stored in
dry ice and later freezer at -80 ºC until analysis.
3. RESULTS
During the sampling process, five fish species (Capoeta banarescui, Carassius auretus, Carassius carassius,
Carassius gibelio, Squalius cephalus) and five macrophyte species (Myriophyllum spicatum, Nasturtium officinale, Phragmites australis, Potamogeton natans, Potamogeton pectunatus) were identified in the study area. The average heavy metal contents of ecosystem components and
their ranges are given in Table 1 and Figure 2. The order of
heavy metal concentrations was determined as Fe > Zn >
Al = Cu = Pb = Co = Cd in water, Zn > Fe > Al > Pb > Co
> Cu > Cd in fish species, Al > Fe > Zn > Cu > Pb > Co >
Cd in macrophyte species, Fe > Cu > Pb > Al = Co = Zn =
Cd in plankton, Fe > Al > Zn > Cu > Pb > Co > Cd in benthos, Al > Fe > Zn > Cu > Co > Pb > Cd in sediment, and Fe
> Al > Zn > Cu > Co > Pb > Cd in soil.
De-ionized water (18.2 MΩ) from a Milli-Q system
(Millipore, Belford, MA, USA) was used to prepare all
aqueous solutions. All mineral acids and oxidants (HNO3,
HCl) used were of the highest quality (Suprapure, Merck,
Darmsadt, Germany). All the plastic and glassware were
cleaned by soaking (with contact) overnight in a 10% (w/v)
nitric acid solution and then rinsed with deionized water.
Heavy metal analysis was performed by using Inductively
Coupled Plasma-Mass Spectrometer (ICP-MS) equipment
in the Soil Laboratory of Gaziosmanpaşa University, Tokat.
After removing samples from the freezer, the samples
were deforested for a while. From each sampled category,
1g amount was taken and soluted in 15 ml aqua regia (a
mixture of 3 volume HCl and 1 volume HNO3) and dried
to determine heavy metal (Pb, Cu, Zn, Fe, Co, Al, Cd) contents of the categories. After rinsing with 10 ml 2 M HNO3,
the resulting suspension was filtered with the blue-band filter paper. Finally, filtered amount leaved to evaporate to
reach 6-8 ml, and remaining amount (6-8 ml) was completed
to 10 ml with distilled water. Heavy metal contents of prepared solution were determined in ICP-MS [17].
The order of heavy metal levels in the ecosystem components was found as soil > sediment > macrophyte > fish
= benthos > plankton (except Fe) (Figure 3). Except plankton, Fe, Al and Zn were found maximum in almost all ecosystem components. On the contrary, Cd was determined
as minimum. This status may be due to their common occurrence in the basic rock.
Pearson correlation analysis results among heavy metal
variables indicated statistically significant relationships
(Table 2). Al showed strong positive correlations with Co
(0.793), Cu (0.836), Fe (0.889) at 1% level. Strong positive
correlations were also observed between Co and Cu
(0.734); Co and Fe (0.827); Co and Zn (0.793); Cu and Fe
(0.857); Fe and Zn (0.716) at 1% level.
Pearson correlation analysis was adopted to analyse
and establish inter-metal relationship of the lake water.
Pearson correlation analysis was also employed to research
the possible relationships between ecosystem components in
respect to their heavy metal contents. Hierarchical Cluster
Analysis (HCA) was performed to further classify the elements into groups representing different sources on the basis of similarities in their chemical properties [18]. HCA
for identifying relatively homogeneous groups of variables
were selected in this study by using an algorithm that starts
with each variable in a separate cluster and combines clusters until only one is left. Before Cluster analysis, the variables were standardized by means of Z-scores; then Euclidean distances for similarities in the variables were calculated. A dendrogram was constructed to assess the cohesiveness of the clusters formed, in which correlations among
ecosystem components can readily be seen. SPSS software [19] was employed for all statistical analyses.
Pearson correlation analysis results among ecosystem components also indicated statistically significant relationships
(Table 3). Water positively correlated with soil (0.752) and
benthos (0.758) at 5% levels, and plankton (0.943) at 1%
level. Soil showed strong positive correlations with sediment (0.946), benthos (1.000) and macrophyte (0.878) at
1% levels, and plankton (0.833) at 5% level. Strong positive correlations were also observed between sediment and
benthos (0.945), sediment and macrophyte (0.985), benthos and macrophyte (0.878) at 1% levels. Finally, a positive
relationship between benthos and plankton were found statistically significant (0.834) at 5% level.
TABLE 1 - The average heavy metal contents (mg/kg) of ecosystem components in Zinav Lake.
ECOSYSTEM COMPONENT
WATER
FISH
MACROPHYTE
PLANKTON
BENTHOS
SEDIMENT
SOIL
Al
Cd
Co
Cu
Pb
Zn
Fe
0.00
6.65
578.85
0.01
1124.15
11980.00
7802.33
0.00
0.13
0.22
0.01
1.08
0.00
0.02
0.00
0.63
0.81
0.01
2.11
5.48
6.09
0.00
0.47
1.98
6.03
9.16
55.48
27.92
0.00
1.36
1.45
3.34
3.89
0.05
3.86
0.03
34.08
28.71
0.01
47.65
78.08
83.28
0.08
11.56
402.37
960.00
1862.16
11105.80
12907.67
182
FIGURE 2 - The heavy metal ranges in the Zinav Lake Basin
FIGURE 3 - The heavy metal levels in the main ecosystem components of Zinav Lake
183
TABLE 2 - Pearson Correlation Analysis results of heavy metal variables
Al
Cd
Co
1.000
Al
-0.018
1.000
Cd
0.252
1.000
Co
0.793**
0.028
Cu
0.836**
0.734**
0.054
Fe
0.889**
0.827**
-0.165
0.395*
0.002
Pb
0.678**
0.311
Zn
0.793**
**. Correlation is significant at the 0.01 level (2-tailed).
*. Correlation is significant at the 0.05 level (2-tailed).
Cu
Fe
1.000
0.857**
0.136
0.628**
1.000
0.045
0.716**
Pb
Zn
1.000
-0.069
1.000
TABLE 3 - Pearson Correlation Analysis results of ecosystem components
WATER
SOIL
1.000
WATER
1.000
SOIL
0.752*
0.525
SEDIMENT
0.946**
*
BENTHOS
0.758
1.000**
0.398
MACROPHYTE
0.878**
0.449
0.105
FISH
PLANKTON
0.943**
0.833*
**. Correlation is significant at the 0.01 level (2-tailed).
*. Correlation is significant at the 0.05 level (2-tailed).
SEDIMENT
BENTHOS
MACROPHYTE
FISH
PLANKTON
1.000
0.945**
0.985**
0.069
0.609
1.000
0.878**
0.121
0.834*
1.000
0.086
0.468
1.000
0.131
1.000
FIGURE 4 - Dendrograms of the ecosystem components in respect to their heavy metal contents. (Method: Hierarchical Cluster Analysis,
Furhest Neighbor, Measure: Euclidean distance).
184
HCA was used to create a hierarchical classification of
the ecosystem components in respect to their heavy metal
contents, and the groups with their containing subgroups
were displayed as a dendrogram (Figure 4). Dendrograms
of heavy metals showed how the cases (ecosystem components) were clustered together at each stage of the cluster
analysis. In each dendrogram, the most similar ecosystem
components were first joined to form the first cluster, which
was then considered as a new object. All the subgroups were
joined in the final cluster, containing all the ecosystem components (Figure 4). The lowest distance values indicated
the closest ecosystem component similarity, while the greatest distance values suggested the least habitat resemblance
of these components.
Considering ecosystem components, behaviors of Al,
Co, Cu, Fe, and Zn showed similar characteristics. Within
this group, soil and sediment components established a cluster, while the other components constituded another cluster.
However, Pb and Cd did not show any similarities neither to
each other nor to the others. In Pb, soil, benthos and plankton
formed the first cluster, while water, sediment, macrophyte,
and fish formed the second cluster. In Cd, all components
except benthos established one cluster (Figure 4). Conformity between HCA (Figure 4) and correlation (Table 2) results
was observed. Close relationships among Al, Co, Cu, Fe,
and Zn variables in both HCA and correlation analysis indicated that these heavy metals mostly accumulate in the
soil and sediment.
The exact correlation between soil and benthos (1.00) in
Table 3 can be explained by HCA results of Pb (Figure 4).
The HCA clearly displayed the Pb accumulation from soil to
benthos or vice versa. High correlations of benthos-macrophyte (0.878) and benthos-plankton (0.834) pairs (Table 3)
reflected to the HCA results of Cu (Figure 4). The HCA
clearly delineated the accumulation of Cu from benthos to
macrophyte and plankton. Similarly, Cd accumulation
(from soil to plankton and macrophyte or from macrophyte
and plankton to soil) and Cd and Pb accumulations (from
sediment to macrophyte or from macrophyte to sediment)
were detected.
Five fish species (Capoeta banarescui, Carassius auretus, Carassius carassius Carassius gibelio Squalius cephalus) and five macrophyte species (Myriophyllum spicatum,
Nasturtium officinale, Phragmites australis, Potamogeton
natans, Potamogeton pectunatus) were identified in the
ecosystem of Zinav Lake Basin. Ecosystem was researched
in seven main components including (1) fish, (2) macrophyte, (3) sediment, (4) water, (5) benthos, (6) plankton,
and (7) soil. Within these components, the order of heavy
metal concentrations was determined as Fe > Zn > Al = Cu
= Pb = Co = Cd in water, Zn > Fe > Al > Pb > Co > Cu > Cd
in fish species, Al > Fe > Zn > Cu > Pb > Co > Cd in macrophyte species, Fe > Cu > Pb > Al = Co = Zn = Cd in plank-
ton, Fe > Al > Zn > Cu > Pb > Co > Cd in benthos, Al > Fe
> Zn > Cu > Co > Pb > Cd in sediment, and Fe > Al > Zn >
Cu > Co > Pb > Cd in soil. It was reported that the concentration of different metals in water, plankton, and fish tissues
followed the same order (Zn > Cu > Pb > Cd) in Lake Manzala [20]. Consequently, our study produced similar results
for fish and water components.
The orders of heavy metal levels in the ecosystem
components of Zinav Lake were also determined. Except
Fe, the levels of all investigated heavy metals ordered as
soil > sediment > macrophyte > fish = benthos > plankton.
High levels of heavy metals in the sediments, up to 10 000
times higher than in water, were found in Lake Barato [21].
In general, metal content in sediments is indicative of the degree of pollution and serves as a source of precipitation or
solubilization into water depending on the physico-chemical circumstances (pH, tempearture etc.) and the uptake by
benthic organisms [22]. In this study, the presence of
higher heavy metal in sediments was found consistent with
other related studies. However, the Fe contents of lake sediments were found higher than some literature reports [2325]. Results of Zn, Cu and Pb at sediment were found as
78.08 mg/kg, 55.48 mg/kg and 0.048 mg/kg, respectively.
Comparing these results to some literature [23], Zn and Cu
levels of lake sediments were found higher, while lower Pb
level was observed. Our results indicated that Pb level in soil
(3.86 mg/kg) was found higher than sediment. Sekabira et al.
[1] reported that the reason for accumulation of heavy metals in the Nakivubo stream sediments could be co-precipitation of Pb, Cd and Zn with Mn and Fe hydroxides along
with scavenging of other metals. The reason of heavy metal
accumulation in Zinav Lake sediment can be explained
similarly because alkaline status of lake (pH>8) [26].
We found Fe, Al and Zn maximum in almost all ecosystem components, but plankton. On the contrary, Cd was
determined as minimum. In a study conducted in the Anzali
wetland of Iran, different accumulation (higher Pb and
lower Cu) rates of heavy metals in smaller Mytilaster lineatus (bivalve molluscs) were observed [27]. It was also reported that the quantity of heavy metals in plankton usually
depends on their concentration in water and partially on
sediment [28]. These cases can explain the minimum detection levels of heavy metals in plankton. In a similar study, it
was also reported that the organic matter, accumulated at
the bottom of the lake and river, increase the concentration
of the heavy metals in sediment [24]. These results confirmed our findings.
We determined that Al showed strong positive correlations with Co (0.793), Cu (0.836), Fe (0.889) at 1% level.
Moreover, we also observed strong positive correlations
between Co and Cu (0.734); Co and Fe (0.827); Co and Zn
(0.793); Cu and Fe (0.857); Fe and Zn (0.716) at 1% level.
Relationships among heavy metals may signify that each
paired elements has identical source or common sink in the
lake sediments [1, 29, 30]. Good correlations among Co,
Cu, Fe, Zn, and Th might be from the same source of contamination [31]. These kinds of correlations suggested that
185
contamination input is mainly due to atmospheric deposition, human settlement and agriculture activity [31]. Zinav
lake and the lake basin are under the threats of household
discharge, spoiled water quality, incorrect fish species
modifications, unsound land management, and forest destruction at the present time [26]. Consequently, strong positive correlations between heavy metals in the Zinav lake
ecosystem are compatible with above literature.
We observed conformity between HCA and correlation indicating that Al, Co, Cu, Fe, and Zn mostly accumulated in the soil and sediment in the Zinav Lake Basin. Similarly, it was reported that the cluster trees can confirm correlations [32], and it was stated that metals are commonly
found adsorbed in sediments [22]. These findings showed
that organic matter plays an important role in heavy metal
concentrations within sediment. Each biomonitor, such as
benthic fauna, fish, bivalves and algae, responds differently to a particular metal fraction of an aquatic system
[22]. Our results showed that heavy metals have different
accumulation characteristics in the ecosystem components.
The directions of accumulation were determined as; from
soil to benthos or vice versa in Pb, from benthos to macrophyte and plankton or vice versa in Cu, from soil to macrophyte and plankton or vice versa in Cd, from sediment to
macrophyte or vice versa in Cd and Pb. Accumulation of
Pb and Cu in benthos was also reported in a research conducted in macro invertebrates of Anzali wetland [27].
Some kinds of toxic sediments kill benthic organisms, reducing the food available to larger animals such as fish
[15]. We observed that Pb, Cd and Cu in benthos, macrophytes, soil and sediment have clear connection to the
catchment area of Zinav lake. Consequently, the results of
the present study supplied detailed information for understanding the threats focused on the lake basin.
According to the FAO food standards for fish, levels
of Zn, Cu, Fe and Al were found below the thresholds of
concern, whereas Co was found slightly above the acceptable limits for human consumption (40, 30, 100 and 13.8
mg/kg wet weight for Zn, Cu, Fe and Al, respectively) [33,
34]. Among the heavy metals, Pb and Cd have been classified as toxic metals that cause chemical hazards and therefore maximum residual levels have been prescribed for human consumption by various agencies of food standards
[35-37]. In this study, Cd contents (0.13 mg/kg) in the samples were found much less than the permitted level (0.50
mg/kg) [33]. However, we observed that Pb level in fish is
higher than the permissible limits of FAO (0,50 mg/kg)
[30]. Measures should be taken to reduce this amount. A
study conducted in Gediz river showed that eutrophication
due to the accumulation of organic matter at the bottom of
the river caused an increase of metal concentrations [24].
Consequently, increasing heavy metal concentrations in the
Zinav Lake may be related to lake's trophic status. Another
study carried out in the river Laire estuary ecosystem illustrated that transfer of Cd, Pb, Cu and Zn through the trophic
chain of the river estuary ecosystem caused bioaccumulation in fish [38]. However, fish had no correlations with the
other ecosystem variables in this study. This can be explained by the feeding behavior of fish in the lake. Fish are
different from the other components of Zinav Lake ecosystem. Feeding from the lake is limited for fish because of
hypertrophic and anoxic conditions. For this reason, fish
generally move on to the mouth of the rivers or rivers that
are often cleaner for feeding. This situation causes low
heavy metal accumulation in fish.
Heavy metal pollution of lakes has a seriously detrimental impact on people and ecosystems. Ecosystem contamination by heavy metals is an important issue because of
the potential accumulation in biosystems, through contaminated water. A better understanding of heavy metal sources
and their behaviors in ecosystem components is extremely
important in ongoing risk assessments and sustainable managements of lake ecosysytems. In this point of view, the results of this study supplied valuable detailed information.
ACKNOWLEDGEMENTS
The authors thank the Scientific and Technical Research Council of Turkey (TÜBİTAK) for project funding
(Project No: ÇAYDAG- 1110Y117); and Saliha Dirim Buhan
for her technical assistance as part of her Ph.D. thesis.
186
REFERENCES
[1]
Sekabira, K., Oryem Origa, H., Basamba, T.A., Mutumba, G.,
Kakudidi, E. (2010) Assessment of heavy metal pollution in the
urban stream sediments and its tributaries. International Journal of
Environmental Science and Technology, 7(3): 435-446.
[2]
Linnik, P.M. and Zubenko, I.B. (2000) Role of bottom sediments
in the secondary pollution of aquatic environments by heavy metal
compounds. Lakes and Reservoirs Management, 5(1): 11-21.
[3]
Campbell, L.M. (2001) Mercury in Lake Victoria (East Africa):
Another Emerging Issue for a Beleaguered Lake? Ph.D. dissertation, University of Waterloo, Ontario, Canada.
[4]
Lwanga, M.S., Kansiime, F., Denny, P. and Scullion, J. (2003)
Heavy metals in Lake George, Uganda with relation to metal concentrations in tissues of common fish species. Hydrobiologia, 499
(1-3), 83-93.
[5]
El Diwani, G. and El Rafie, S. (2008) Modification of thermal and
oxidative properties of biodiesel produced from vegetable oils. International Journal of Environmental Science Technology, 5(3):
391-400.
[6]
Al-Momani, I.F. (2009) Assessment of trace metal distribution and
contamination in surface soils of Amman. Jordan Journal of Chemistry, 4(1): 77-87.
[7]
Muwanga, A. (1997) Environmental Impacts of Copper Mining at
Kilembe, Uganda: A Geochemical Investigation of Heavy Metal
Pollution of Drainage Waters, Stream, Sediments and Soils in the
Kilembe Valley in Relation to Mine Waste Disposal. Ph.D. dissertation, Universitat Braunschweig, Germany.
[8]
Zvinowanda, C.M., Okonkwo, J.O., Shabalala, P.N. and Agyei,
N.M. (2009) A novel adsorbent for heavy metal remediation in
aqueous environments. International Journal of Environmental
Science and Technology, 6(3): 425-434.
[9]
Okafor, E.C. and Opuene, K. (2007) Preliminary assessment of
trace metals and polycyclic aromatic hydrocarbons in the sediments. International Journal of Environmental Science and Technology, 4(2): 233-240.
[10] Mohiuddin, K.M., Zakir, H.M., Otomo, K., Sharmin, S. and Shikazono, N. (2010) Geochemical distribution of trace metal pollutants in water and sediments of downstream of an urban river. International Journal of Environmental Science and Technology,
7(1): 17-28.
[11] Erdoğan, M. (2009) Monitoring and Statistical Assessment of
Heavy Metal Pollution in Sediments Along İzmir Bay Using ICPMS. Ph.D. dissertation, The Graduate School of Engineering and
Sciences of İzmir Institute of Technology, İzmir, Turkey.
[12] Copat, C., Bella, F., Castaing, M., Fallico, R., Sciacca, S. and Ferrante, M. (2012) Heavy metals concentrations in fish from Sicily
(Mediterranean Sea) and evaluation of possible health risks to consumers. Bulletin of Environmental Contamination and Toxicology, 88: 78-83.
[13] Farombi, E.O., Adelowo, O.A. and Ajimoko, Y.R. (2007) Biomarkers of oxidative stress and heavy metal levels as indicators
of environmental pollution in African Cat Fish (Clarias gariepinus) from Nigeria Ogun River. International Journal of Environmental Research and Public Health, 4(2): 158–165.
[14] Begum, A., HariKrishna, S. and Khan, I. (2008) Chemical composition of rainwater in South Bangalore, Karnataka. Rasayan Journal of Chemistry, 1(4): 774-781.
[15] Begum, A., HariKrishna, S. and Khan, I. (2009) Analysis of heavy
metals in water, sediments and fish samples of Madivala Lakes of
Bangalore, Karnataka. International Journal of Chemical Technology Research, 1(2): 245-249.
[16] Tuncer, S. and Uysal, H. (1983) Etude des Metaux Lourds Chez
les Mollusques Dans les Differentes Zones de la Baie d'Izmir (Turquie). VIes Journees D'etudes Sur les Pollutions Marines en Mediterranee, Cannes: pp. 307-313.
[17] Anonymous (1984). Determination of total Cadmium, Zinc, Lead
and Copper in Selected Marine Organisms by Flameless Atomic
Absorption Spectrophotometry. Reference Methods for Marine
Pollution Studies No. 11. United Nations Environment Programme
(UNEP), Geneva: 18 pp.
[18] Ragosta, M., Caggiano, R. and Macchiato, M. (2008) Trace elements in daily collected aerosol: level characterization and source
identification in a four-year study. Atmospheric Research, 89(12): 206-217.
[19] SPSS (2007) SPSS 16.0 for Windows. SPSS Inc.: Chicago.
[20] Bahnasawy, M., Khidr, A.A. and Dheina, N. (2011) Assessment of
heavy metal concentrations in water, plankton, and fish of Lake
Manzala, Egypt. Turkish Journal of Zoology, 35(2): 271-280.
[21] Jumbe, A.S. and Nandini, N. (2009) Heavy metals analysis and
sediment quality values in Urban Lakes, American Journal of Environmental Sciences, 5 (6): 678-687, 2009.
[22] Ogoyil, D.O., Mwita, C.J., Nguu, E.K. and Shiundu, P.M. (2011)
Determination of heavy metal content in water, sediment and microalgae from Lake Victoria, East Africa. The Open Environmental Engineering Journal, 4: 156-161.
[23] Mendil, D., Uluözlü, Ö.D. (2007) Determination of trace metal
levels in sediment and five fish species from lakes in Tokat, Turkey. Food Chemistry, 101: 739-745.
[24] Öner, Ö. and Çelik, A. (2011) Gediz Nehri Aşağı Gediz Havzası'ndan alınan su ve sediment örneklerinde bazı kirlilik parametrelerinin incelenmesi. Ekoloji, 78: 48-52.
[25] Tariq, J., Jaffar, M., Ashraf, M. (1991) Trace metal concentration,
distribution and correlation in water, sediments and fish from the
Ravi river, Pakistan. Fisheries Research, 19:131-139.
[26] Buhan, E., Doğan, H.M. and Polat, F. (2014) Zinav Gölü ve
Havzasında Su Kalitesi ve Balık Topluluklarının Zamansal ve
Alansal Değişimleriyle Ekolojik Risklerinin Belirlenmesi. TÜBİTAK, CAYDAG, Research Project (No: 110 Y 117) Final Report.
187
[27] Pourang, N. (1996) Heavy metal concentrations in surficial sediments and benthic macroinvertebrates from Anzali Wetland, Iran.
Hydrobiologia, 331(1-3): 53-61.
[28] Elmacı, A., Teksoy, A., Olcay Topac, F., Ozengin, N., Kurtoğlu,
S. and Savaş Başkaya, H. (2007) Assessment of heavy metals in
Lake Uluabat, Turkey. African Journal of Biotechnology, 6: 22362244.
[29] Singh, M., Muller, G. and Singh, I.B. (2002) Heavy metals in
Freshly deposited stream sediments of rivers associated with urbanization of the Ganga plain, India. Water, Air, and Soil Pollution, 141(1-4): 35-54.
[30] Nyangababo, J.T., Henry, I. and Omutunge, E. (2005) Heavy metal
contamination in plants, sediments and air precipitation of
Katonga, Simiyu and Nyando wetlands of Lake Victoria Basin,
East Africa. Bulletin of Environmental Contamination and Toxicology, 75(1): 189-196.
[31] Hamzah, Z., Saat, A., Wood, A.K., Abu Bakar, Z. (2011) Sedimentation, Heavy Metals Profiles and Cluster Analysis of a Former Tin
Mining Lake. International Journal of Environmental Science and
Development, 2(6): 448-453.
[32] Nguyen, H.L., Leermakers, M., Elskens, M., De Ridder, F., Doan,
T.H. and Baeyens, W. (2005) Correlations, partitioning and bioaccumulation of heavy metals between different compartments of Lake
Balaton. Science of the Total Environment, 341 (1-3): 211-226.
[33] FAO, (1983) Compilation of legal limits for hazardous substances
in fish and fishery products. Food and Agriculture Organisation
(FAO) Fishery Circular, No. 464, pp. 5-100.
[34] IAEA (2003) World-Wide İntercomparison Exercise For The Determination of Trace Elements and Methylmercury in Fish Homogenate International Atomic Energy Agency- 407 Report No
IAEA/ AL/144 IAEA/MEL/72.
[35] Rejomon, G., Nair, M. and Joseph, T. (2010) Trace metal dynamics in fishes from the southwest coast of India. Environmental
Monitoring and Assessment, 167(1-4): 243-255.
[36] Nauen, C.E. (1983) Compilation of Legal Limits for Hazardous
Substance in Fish and Fishery Products. Food and Agricultural Organization of the United Nations, Rome: 102 pp.
[37] EC. (2001) European Commision Regulation (EC) No. 466/2001
of 8 March 2001. Official Journal of the European Communities,
1.77/1.
[38] Amirad, CA-T., Mateyaer, C. (1980) Marich J, Bio-accumulation
of heavy metals in the trophic chain of the river Laire estuary. Water Res. 14: 665-72.
Revised: June 24, 2014; August 04, 2014
Hakan Mete Dogan
Gaziosmanpaşa University
Agricultural Faculty
Soil Science Department
GIS&RS Unit
Taşlıçiftlik, Tokat
TURKEY
Phone: + 90 356 252 16 16 (internal: 2280)
Fax: + 90 356 252 14 88
FEB/ Vol 24/ No 1a/ 2015 – pages 180 – 187
COMPARATIVE ANALYSIS OF BIOTIC INDICES FOR
EVALUATION OF WATER QUALITY OF ESEN RIVER
IN SOUTH-WEST ANATOLIA, TURKEY
Bülent Yorulmaz1,*, Atakan Sukatar2 and Murat Barlas1
1
Muğla Sıtkı Koçman University Science Faculty Biology Department, 48000 Kötekli Muğla, Turkey
2
Ege University Science Faculty, Biology Department, 35100, Bornova, Izmir, Turkey
ABSTRACT
Five biotic indices were used for the assessment of water quality of Eşen River in South-west of Turkey. The
classification of water quality based on benthic macroinvertebrate and physical and chemical parameters were also
done. Taxonomic composition of benthic macroinvertebrate fauna was used for calculation of the following biological indices; Saproby Index (SI), Biological Monitoring
Working Party (BMWP), Average Score Per Taxon
(ASPT), Family Biotic Index (FBI) and Belgian Biotic Index (BBI). Electrical conductivity, temperature, dissolved
O2 content, pH, BOD5, Cl-, NH4-N, NO2-N, NO3-N,
PO4-P were analyzed. According to the data obtained, the
water quality of Eşen River varied from poor to high ecological status. The indices SI, BMWP, BBI and ASPT were
the more adequate estimate of water quality in accordance
with physicochemical characteristics of the examined watercourse.
KEYWORDS: Biotic Indices, Benthic macroinvertebrates, Esen
River, Water Quality.
1. INTRODUCTION
Freshwater ecosystems, especially rivers are most
threatened ecosystem in the world. Ideally, the quality of
running waters should be assessed by the use of physical,
chemical and biological parameters in order to provide a
complete spectrum of information for appropriate water
management [1]. The methods used now to determine the
environmental state of a river or a river basin is mostly biological-control methods based on the assessment of the
flora and fauna of water bodies. [2]. In the past decades,
hundreds of biological monitoring approaches have been
developed to assess the water quality, about 60% of which
are based on macroinvertebrate analysis [3-5]. Most of the
biological approaches are particular to specific geographic
regions. Various European countries use various indices
with different levels of identification of organisms, and different assumptions of final interpretation of results [6]. For
example the Saproby indices [7] in Germany, Biological
Monitoring Working Party- BMWP and Average Score
Per Taxon-ASPT [5] in England, Biotic Index for Pampean rivers and streams-IBPAMP [8] in Argentina, Belgian
Biotic Index-BBI [9] in Belgium, seem to give the most
reliable results specific to geographic regions. Unification
of river classification and the use of a common biotic index
are impossible due to different geographic distribution of macroinvertebrate species, and biotypological differences
among the rivers [6]. Yet a biological water quality approach
particular to Turkey has not been developed. Biological water quality classification studies started in the 1990’s in Turkey. Some researchers used a number of biotic indices such
as BMWP, ASPT, FBI and SI for assessment of water quality of rivers [10-19].
According to the European Union water framework directive, all member states are obliged to evaluate environment status according to do criteria imposed by EU directives including river quality by the end of 2015 [20].
The objective of this study is to compare the results of
five water quality approaches and to determine the most adequate estimate of water quality in accordance with physicochemical characteristics of the examined watercourse.
2. MATERIALS AND METHODS
Eşen River is the largest river in West Mediterranean
river basin of Turkey with the total length of 146 km. The
most important pollution sources on Eşen River are overirrigation, gravel gathering, dams and extensive using of
chemicals for farming in the surrounding. Benthic samples
were collected from seven stations that represent the river.
188
Macroinvertebrate communities along the stream were
sampled monthly between June 2003 and June 2005, using
a bottom kick net (500 μm mesh). The samples were taken
from an area of nearly 100 m2 in order to include all possible microhabitats at each station. In some areas with the
presence of large stones, the collected macroinvertebrates
were first picked out and washed into the kick net in order
to remove pupae and other attached individuals. In addition, macroinvertebrate samples were separated from the
macrophytes and the sediment using sieves (250 μm). Collected organisms were immediately fixed in formaldehyde
(4%) in the field and then transferred to 70% ethyl alcohol.
The macroinvertebrates were sorted, identified to the lowest possible taxon (species, genus or families) and counted
under a stereomicroscope. Simultaneous with macroinvertebrate sampling, water samples were taken and analyzed
for the following parameters, BOD5, Cl-, NH4-N, NO2-N,
NO3-N, PO4-P. All analyses were done in accordance to
national standards. Water temperature (ºC), pH, dissolved
oxygen (DO mgL-1) and electrical conductivity (EC μScm-1)
were measured in the field by portable equipments. Water
quality assessment by physico-chemical parameters was
done according to Klee (1991) and Water Pollution Control
Regulation (WPCR) (1988) [21, 22].
2.1 Biotic indices
This study is restricted to indices focused on the determination of water quality. The following five indices were
tested: Average Score Per Taxon (ASPT) [6], the Saproby
Index (SI) [7], Belgian Biotic Index (BBI) [5], Biological
Monitoring Working Party (BMWP) [4], Family Biotic Index [23]. Correlation analysis was based on Pearson’s and
multiple regression analysis from SPSS version 11.5.
3. RESULTS AND DISCUSSION
The results of physical and chemical parameters variables measured at the seven stations are presented in Table 1.
The lowest EC value was measured in the first station while
the highest value was found in the seventh station and
ranges between 145 Scm-1 and 622 S cm-1. The water temperature varied from 9, 9 ºC to 25, 8 ºC. The highest DO
was found at the third station and varied between 4, 7 mg L-1
and 13, 8 mg L-1. The highest biological oxygen demand was
measured in sampling point six (5, 04 mg L-1). The average of
pH values were similar among the sampling points and
ranged between 7, 05 and 8, 64. The highest ammonium nitrogen (NH4-N) was measured in sampling point seven as 4,
35 mg L-1, but the highest average was measured in sampling
point six as 1, 73 mg L-1. The average nitrite nitrogen (NO2N) was high in sampling points three, six and seven but the
highest level was measured in sampling point three and six
as 0, 0329 mg L-1. The highest average nitrate nitrogen
(NO3-N) was found in sampling point six as 5, 97 mg L-1,
but the highest nitrate nitrogen value was measured in sampling point seven. The highest level of phosphorus (PO4-P)
was measured in sampling point three as 2, 32 mg L-1 (Table 1).
In this study, 22061 individuals were collected. A total
of 111 benthic macroinvertebrate taxa consisting of 48 genera
and 63 species, which belong to classes Turbellaria, Gastropoda, Bivalvia, Hirudinea, Malacostraca and Insecta
were identified. Of these, 86%, 84 (96 taxa) belong to Insecta (Fig. 2). It was determined that, Gammarus sp. was
dominant on 1, 2, 4 and 5, while Chironomus sp. was dominant on 3, 6 and 7 sampling points.
FIGURE 1 - Study area and the sampling points (■ Sampling points,
Settlement Areas).
The advantage of monitoring by the use of bioindicators is that biological communities reflect overall ecological quality. Moreover monitoring integrates the effects of
different stressors providing a broad measure of their impact
and an ecological measurement of fluctuating environmental
conditions [1, 9, 24-27]. Biological water quality can be assessed by various kinds of organisms such as macrophytes,
phytoplankton, diatoms, macroinvertebrates and fishes for
regular observations. The use of benthic macroinvertebrates as biological indicators has been widely applied in
river quality assessment, because these organisms are relatively sedentary and thus are unable to avoid deteriorating
water/sediment quality. These organisms, having relatively
long life-spans, reveal marked responses to stress depending
on their species-specific sensitivity/tolerance levels and
189
TABLE 1 – Maximum, minimum and mean levels of physicochemicals according to the sampling points
1
2
3
4
5
Min*.
145.0
224.0
268.0
146.0
344.0
Mean
277.2
321.4
340.6
234.6
427.1
EC μScm-1
Max.
400.0
425.0
458.0
387.0
590.0
Min.
10.8
12.4
10.0
9.9
13.1
ºC
Mean
11.6
12.9
14.8
10.5
14.3
Max.
11.9
14.6
19.0
12.4
18.5
Min.
6.8
6.2
6.6
6.4
4.7
DO mgL-1
Mean
9.0
8.5
9.2
8.8
6.1
Max.
10.5
10.5
13.8
10.3
7.7
Min.
7.2
7.1
7.5
7.2
7.1
pH
Mean
7.7
7.5
7.9
7.7
7.5
Max.
8.6
8.3
8.6
8.6
8.3
Min.
0.8
0.7
0.3
0.6
0.5
BOD5 mgL-1
Mean
2.4
2.5
2.7
2.5
1.4
Max.
3.9
4.3
4.3
4.5
2.5
Min.
0.2
0.2
0.2
0.2
0.3
Cl- mgL-1
Mean
1.7
1.7
1.5
1.5
2.1
Max.
4.0
2.5
2.0
2.5
4.0
Min.
BDL
BDL
BDL
BDL
BDL
NH4-N mgL-1
Mean
0.6
0.7
0.8
0.8
0.5
Max.
2.5
1.5
2.5
2.2
2.5
Min.
BDL
BDL
BDL
BDL
BDL
NO2 -N mgL-1
Mean
0.006
BDL
0.006
BDL
BDL
Max.
0.01
BDL
0.033
BDL
BDL
BDL
BDL
BDL
BDL
Min.
BDL
NO3 -N mgL-1
Mean
2.2
2.7
2.9
2.7
1.9
Max.
8.8
8.0
8.8
7.5
8.8
BDL
BDL
BDL
BDL
Min.
BDL
PO4 -P mgL-1
Mean
0.2
0.001
0.2
0.03
0.04
Max.
0.3
0.014
2.3
0.4
0.7
*minimum level as Min, average level as Mean and maximum level as Max were given. BDL: Below detection limits
6
345.0
487.0
590.0
13.4
19.3
25.8
6.3
8.4
11.3
7.3
7.7
8.3
0.8
2.6
5.0
0.2
2.5
6.0
0.2
1.7
3.6
BDL
0.003
0.033
0.6
6.0
12.5
BDL
0.05
0.8
7
289.0
416.1
622.0
13.7
19.8
24.7
6.1
8.3
10.6
7.5
7.9
8.6
0.5
2.4
4.8
1.5
2.7
4.0
BDL
1.4
4.4
BDL
0.002
0.02
N.A
4.9
15.0
BDL
0.005
0.04
Number of Taxa
30
25
20
15
10
5
0
FIGURE 2 - Distribution of Taxa on Order Base.
TABLE 2 - Assessment of water quality according to applied indices.
1
2
3
4
WPCR
VG
VG
VG
VG
Klee (1991)
G
G
G
G
Sİ.
G
G
M
G
FBI
G
G
G
G
BMWP
G
G
G
G
ASPT
VG
VG
VG
VG
BBI
G
G
G
G
Letters refer to water quality: Very Good: VG, Good: G, Moderate: M, Poor, and Very Poor: VP.
190
5
VG
VG
VG
G
G
VG
G
6
G
M
M
M
P
M
M
7
VG
G
P
M
P
G
M
play a vital role in cycling nutrients and materials between
the underlying sediment and the overlying water column
[3-7, 28, 29]. According to our study water quality varied
from poor (according to BMWP and SI 7th and according
to BMWP 6th) to very good. However there was a disagreement between the in-dices applied. The physical chemical
parameters based indices showed higher results than the biological based indices. According to BMWP the 6th station
was found “poor” water quality class while it was found
“moderate” water quality class according to other biotic indices. The 7th station was found “poor” according to SI and
BMWP indices and “moderate” according to FBI and BBI.
However the 7th was found “good” water quality class according to ASPT (Table 2).
Table 3 summarizes the correlations of both biological
and physicochemical indices. It was found that two physicochemical indices have significant correlation value (r
value 0,582, p<0.01). Among physicochemical indices and
biotic indices, the significant correlation was found between Klee (1991) and SI (r value 0,224, p<0.05). Among
biotic indices the highest significant correlation was found
between BMWP and BBI (r value 0, 827, p<0.01) followed
by BMWP and ASPT (r value 0, 708, p<0.01). The significant negative correlation was found between SI and BBI,
BMWP, ASPT respectively. A negative correlation was
found between physicochemical indices (WPCR and Klee)
and BMWP, ASPT, BBI. However an increase in the results
in BBI index and BMWP, ASPT scores systems shows good
ecological quality while an increase in SI, FBI, and physicochemical based indices shows bad ecological quality.
The highest ecological water quality was determined
in the 5th station according to SI in winter season. The most
effective physicochemical parameter on SI was temperature (Table 4). All biotic indices were in accordance between each other except FBI.
According to correlation of between physical-chemical parameter and indices, significant correlation value was
determined between NH4-N with both physicochemical indices (Klee (1991) and WPCR), followed by NO3-N.
Among biotic indices the significant correlation was found
between NO2-N and SI (r value 0,229, p<0.05), between
BOD5 and SI (r value 0,188, p<0.05). The significant correlation was found between temperature and SI because sabroby value increases with temperature [7]. Benthic macroinvertebrate species are differentially sensitive to many
biotic and abiotic factors in their environment [30, 31]. In
many studies diversity indices are also used for assessing
water quality but the biotic index and score systems are
better for assessing organic pollution and eutrafication but
poor for assessing toxic and physical pollution [3].
From the biotic indices and scores systems applied at
Eşen river, SI BMWP, BBI and ASPT were adequate in
assessing water quality while FBI was inadequate.
Kantzaris et al. [29] applied nine biotic indices and scores
at two rivers in Greece and BMWP, ASPT and LQI were
determined inadequate in assessing water quality while
BBI and IBE were suitable. Zeybek et al. [32] applied five
versions of BMWP and three versions of ASPT indices in
Değirmendere Stream and obtained different score values
because, in their view, applied ver sions were based on
the geological and ecological futures of their source countries.
TABLE 3 - Correlation assessment between biotic indices and physicochemical indices.
WPCR
KLEE
SI
WPCR
1
,582**
,128
KLEE
1
,224*
SI
1
FBI
BMWP
ASPT
BBI
** and * Correlation is significant at the 0.01 and 0.05 levels (2-tailed).
FBI
,152
,157
,349**
1
BMWP
-,340**
-,186*
-,558**
-,228*
1
ASPT
-,222*
-,112
-,535**
-,317**
,708**
1
BBI
-,273**
-,173
-,571**
-,241**
,827**
,681**
1
TABLE 4 - Assessment of Pearson correlation between physicochemical parameters and biotic indices
WPCR
KLEE
SI
BOD5
,144
,265**
,188*
**
**
Temperature
,355
,307
,609**
pH
,188*
,177
,190*
TH
,134
-,017
,134
NH4-N
,747**
,827**
,182*
NO3-N
,735**
,816**
,175
NO2-N
-,032
,055
,229*
PO4-N
-,080
,106
-,015
** and * Correlation is significant at the 0.01 and 0.05 levels (2-tailed).
FBI
,095
,127
-,006
-,022
,147
,098
,117
,138
191
BMWP
,020
-,574**
-,028
-,339**
-,329**
-,290**
-,154
,045
ASPT
-,036
-,478**
-,075
-,170
-,173
-,157
-,232*
,024
BBI
-,088
-,587**
-,023
-,332**
-,226*
-,200*
-,183*
,076
FIGURE 3 - The distribution of the results of the biological water quality according to seasons and stations.
192
4. CONCLUSION
The results of this study verify the use of macroinvertebrates as bioindicators for the assessment of water quality
in southwest of Turkey. In conclusion, the indices SI,
BMWP, BBI and ASPT seem to be more suitable than FBI.
The biotic indices and scores were also found consistent
with the physicochemical parameters but FBI was the least
sensitive to pollution. The biotic indices used in our research were designed in order to sample many sites, in
many countries with minimal effort [29]. However there is
a need for the establishment of a Turkish Biotic Index
which takes into account regional macroinvertebrates, their
abundance, biology and ecology. This proposed Index can
more or less differ from those used in the development of
the other biotic indices.
ACKNOWLEDGEMENTS
The authors are thankful for the financial support of
the Unit of Scientific Research Projects of Mugla Sitki
Kocman University (Project No: 2003/10).
[8]. Capitulo, A.R., Tangorra, M., Ocon, C. (2001). Use of Benthic
Macroinvertebrates to Assess the Biological Status of Panpean
Streams in Argentina. Aquatic Ecology 35, 109-119.
[9]. De Pauw, N. and Heylen, S. (2001) Biotic Index for Sediment
Quality Assessment of Watercourses In Flanders, Belgium.
Aquatic Ecology 35, 121-133.
[10]. Kazancı, N., Izbırak, A., Çaglar, S.S., and Gökçe, D. (1992).
Hydrobiological Investigation of the Aquatic Ecosystem of the
Köycegiz-Dalyan Specially Protected Area, Özyurt Printinghouse, Ankara.
[11]. Girgin, S., Kazancı,N., and Doğan, O. (1997). A New Approach to the Irrigation Water Quality Criteria in Turkey: Ankara Stream, International Conference on “Water Management, Salinity and Pollution Control Towards Sustainable Irrigation in The Mediterranean Region” Vol. II Water Quality
and Pollution Control, Bari Italy, pp. 43-54.
[12]. Kazancı, N. and Girgin, S. (2001) Physico-Chemical Characteristics of Thermal Springs in Köyceğiz Dalaman Basins In
South-Western Turkey And Recommendations For Their Protection. Water Science and Technology 45 (5), 211-221.
[13]. Barlas, M., İmamoğlu, Ö. and Yorulmaz, B. (2002). The Determination of Water Quality in Tersakan Stream (MuğlaDalaman). XVI. Biology Congress, Abstract Booklet, 4-7 September 2002, Malatya/Turkey.
[14]. Girgin, S., Kazancı, N. and Dügel, M. (2003). Science and
Technology Ordination and Classification of Macroinvertebrates and Environmental Data of a Stream in Turkey. Water
Science and Technology 47 (7) 133–139.
[15]. Duran, M., Tuzen, M. and Kayım, M. (2003). Exploration of
Biological Richness and Water Quality of Stream Kelkit, Tokat-Turkey. Fresenius Environmental Bulletin 12(4), 368-375
REFERENCES
[1]. Georgudaki, I., Kantzaris, J., Katharios, V., Georgiadis, P.,
and Montesantou, B. (2003). An Application of Different Bioindicators for Assessing Water Quality; A Case Study in The
Rivers Alfeios and Pineios (Peloponnisos, Greece). Ecological
Indicators 2, 345-360.
[2]. Semenchenko, P. and Moroz, M. (2005). Comparative Analysis of Biotic Indices in the Monitoring System of Running Water in a Biospheric Reserve Water Quality And Protection: Environmental Aspects 32 (2), 223–226.
[3]. Kalyoncu, H. and Zeybek, M. (2011). An Application of Different Biotic And Diversity Indices for Assessing Water Quality: A Case Study in The Rivers Çukurca and Isparta (Turkey).
African Journal of Agricultural Research 6(1), 19-27.
[4]. Armitage, P.D., Moss, D., Wright, J.F., Furse, M.T. (1983).
The Performance of A New Biological Water Quality Score
System Based on Macroinvertebrates Over a Wide Range of
Unpolluted Running Water Sites. Water Research 17, 333347.
[5]. De Pauw, N. and Hawkes, H.A. (1993). Biological Monitoring
of River Water Quality. in: Walley, W.J., Judd, S. (Eds.), River
Water Quality Monitoring and Control. Aston University, Birmingham, pp. 87–112.
[6]. Korycińska, M. and Królak, E. (2006). The Use of Various Biotic Indices for Evaluationof Water Quality in the Lowland
Rivers of Poland (Exemplified by the Liwiec River). Polish
Journal of Environmental Studies 15 (3), 419-428.
[7]. DIN 38410-2 (1990). Biological-Ecological Analysis of Water
(Group M); Determination of the Saprobic Index (M2), German Standard Methods for the Examination of Water, Waste
Water and Sludge, p. 10.
193
[16]. Dügel, M. and Kazancı, N. (2004). Assessment of Water Quality of the Büyük Menderes River (Turkey) by Using Ordination and Classification of Macroinvertebrates and Environmental Variables. J. Freshwater Ecology 19, 605-612.
[17]. Duran, M. (2006). Monitoring Water Quality Using Benthic
Macroinvertebrates and Physicochemical Parameters of the
Behzat Stream (Tokat, TURKEY). Polish J. Environmental
Studies 15(5), 709-717.
[18]. Kazancı, N., Dügel, M., and Girgin, S. (2008). Determination
of Indicator Genera of Benthic Mactoinvertebrate Communities in Running Waters in Western Turkey. International Review of Hydrobiology 1(1), 1-16.
[19]. Kalyoncu, H., Akyıldırım, G.H. and Yorulmaz, B. (2009b).
The Comparison of the Diversity and Biotic Indices With
Physical-Chemical Analyses Applied to Macroinvertebrate
Community to Determine the Water Quality. International
Conference On Lakes and Nutrient Loads- Pocrades, Agricultural University of Tirana, Albania, 24-26.
[20]. Water Framework Directive (WFD) (2000). Directive of the
European Parliament and of the Council (establishing a framework for Community action in the field of water policy), Official Journal of the European Communities, 327/1, 72 p.
[21]. Klee, O., (1991). Angewandte Hydrobiologie.- G. Theieme
Verlag, 2. neubearbeitete und erweiterte Auflage, StuttgartNew York, 272 p.
[22]. Water Pollution Control Regulation (WPCR) (1988). Official
Gazette Ankara, Issue: 19919.
[23]. Hilsenhoff, W. L. (1988) Rapid Field Assessment of Organic
Pollution with a Family-Level Biotic Index. J. N. Am. North
American Benthological Society 7(1), 65-68.
[24]. Metcalfe-Smith, J.L. (1994). Biological Water Quality Assessment of Rivers: Use of Macroinvertebrate Communities. In:
The Rivers Handbook 2. (Calow, P. Petts, G.E. eds.), Blackwell Scientific Publications, Oxford, pp. 144-170
[25]. Knoben, R.A.E., Roos, C., Oirschot, M.C.M. (1995). Biological Assessment Methods for Watercourses. UN/ECE Task
Force on Monitoring and Assessment 3, P.O. box 17, 8200 AA
Lelystads, The Netherlands, p. 86.
[26]. Ghetti, P.F. (1997). Application Manual: Extended Biotic Index – Macroinvertebates In Quality Control Of Running Water
Environments, in Italian [Macroinvertebrati Nel Controllo
Della Qualita Di Ambienti Di Acque Correnti. Indise Biotico
Esteso (I.B.E). Manuale Di Applicazione. Provincia Autonoma Di Trento]. Trento Italy, 222 pp.
[27]. Scuri, S., Torrisi, M., Cocchioni., Dell’Uomo. (2006). The European Water Framework Directive 2000/60/EC in the evaluation of the ecological status of watercourses. Case study: The
River Chienti (Central Apennies, Italy). Acta Hydrochimica
Hydrobiologica 34, 498-505.
[28]. Jose Antonio de-la-Ossa-Carretero, Jean-Claude Dauvin
(2010). A Comparison of Two Biotic Indices, AMBI and
BOPA/BO2A, for assessing the Ecological Quality Status
(EcoQS) of Benthic Macro-invertebrates, Transitional Waters
Bulletin 1, 12-24.
[29]. Kantzaris,V. Iliopoulou- Georgudaki, J., Katharios, P.,
Kaspiris, P. (2002). A Comparison Of Several Biotic Indices
Used For Water Quality Assessment At The Greek Rivers,
Fresenius Environmental Bulletin 11 (11), 1000 – 1007.
[30]. Friberg, N., Sandin L., Furse, MT., Larsen, ES., Clarke, R.T,
Haaese P (2006). Comparison of Macroinvertebrate Sampling
Methods in Europe. Hydrobiologia 566, 365-378.
[31]. Ortiz, J.D., and Puig, M.A. (2007). Point Source Effects on
Density, Biomass and Diversity of Benthic Macroinvertebrates in a Mediterranean Stream. River Research and Applications 23, 155-170.
[32]. Zeybek, M., Kalyoncu, H., Karakaş, B., Özgül, S. (2014). The
Use of BMWP and ASPT Indices for Evaluation of Water
Quality (According To Macroinvertebrates) in Değirmendere
Stream (Isparta, Turkey). Turkish Journal of Zoology DOI:
10.3906/zoo-1310-9.
Bülent YORULMAZ
Muğla Sıtkı Koçman University
Science Faculty
Biology Department
48000 Kötekli, Muğla
TURKEY
FEB/ Vol 24/ No 1a/ 2015 – pages 188 - 194
194
DETERMINING OF AREAS WITH HIGH EROSION RISK IN
KÜÇÜK MENDERES RIVER BASIN (WEST ANATOLIA, TURKEY)
BY USING MULTI-CRITERIA DECISION MAKING METHOD
Ali Ekber Gülersoy1,* and Mehmet Ali Çelik2
1
Dokuz Eylul University, Buca Faculty of Education, Department of Primary Education, 35150, Buca, Izmir, Turkey
2
Kilis 7 Aralık University, Faculty of Arts and Sciences, Department of Geography, 79100, Kilis, Turkey
1. INTRODUCTION
ABSTRACT
The objective of this study was to determine the current situation of erosion in Küçük Menderes River Basin
which is a site rich in natural environment potential, and to
semtinize possible dangers which the erosion would create
in the future, from a geographical-ecological point of view.
In the research, Landsat satellite images were utilized to
determine the relationship of the current land use-erosion,
and “Multi-criteria analysis method” was used in the process
of these images. Beside this, parameters such as slope, exposure, lithology etc., which are influential in erosion, were
created on GIS software. Finally, areas with high erosion
risk in Küçük Menderes River Basin were determined by
overlaying each parameter. There is an inconsistency between the land capability classes and their usage in the basin.
While the area occupied by Ist, IInd, IIIrd and IVth class lands
which should be used as agricultural fields covers 32%, the
area occupied by the agricultural fields covers currently
40%. This situation shows that, in the area, lands have not
been used according to their capability classes. In other
words, 4% of IIIrd class lands is used as the agricultural
area; 16% of IVth class lands as forest; 38% of VIth class
lands as the agricultural; 44% as forest-maquis; 11% of
VIIth class lands as the agricultural, and 46% of VIIIth class
lands as settlement area. The said situation leads to aggravation exacerbation of erosion in the area. As a matter of
fact, severe erosion being seen at 30% of the basin affects
the lands that are inclined and devoid of vegetation cover.
As a result of land degradation and erosion, the basin lands
become unuseable losing their natural feature. To prevent
this loss is possible by establishing a pattern of land use
which is suitable for natural environment potentials of the
lands (land capability classes).
KEYWORDS: Küçük Menderes River Basin, Erosion, GIS-Remote
sensing, Basin Planning.
Soil, pedogenesis of which has occurred over thousands
of years, is in danger of disappearing in a lot of regions in
the World today [1, 2]. Soil erosion is one of the most important environmental problems in arid and semi-arid regions especially. For this reason, we also confront with the
erosion as an important problem in Turkey where arid and
semi-arid regions cover a wide area. A great part of Turkey
is faced with the danger of erosion [3-6].
Since Geographic Information Systems (GIS) technology has various skills such as storing, producing, analyzing
and visualizing spatial data, it possesses a great potential to
solve and manage spatial problems [7]. Importance of Geographic Information Systems in planning and in protecting
natural landscape is increasing day by day. Estimation of
erosion in an area is a very large and effective method in
workings for soil protecting and planning, and more effective and more correct data are obtained by using GIS technology. Both in Turkey and abroad, erosion risk analyses
of many areas are carried out by ensuring integration of
CBS and remote sensing data [8-13]. Used within this technology, Multi-Criteria Decision Making Method has features such as evaluating alternatives in solving the problem
in line with the decision maker's priorities and selecting the
most satisfying one, etc. For this reason, it provides the user
with considerable flexibility (offers choices) in creating risk
and sensitivity maps at planning works and thus giving a
better and flexible response to the user's determinations.
Considering the erosion's emergence depending on numerous reasons, Multi-Criteria Decision Making Method seems
highly suitable for evaluating the emerging differences locally by so many alternatives in creating erosion risk maps.
Therefore, this method was used while erosion risk map was
being created in this study.
The objective of this study was to determine the current situation of erosion in Küçük Menderes River Basin
which is a site rich in natural environment potential, and
195
FIGURE 1 - Location map of study area.
to semtinize possible dangers which erosion would create
in the future, from a geographic-ecologic point of view.
Sometimes for economic purposes, different land-use
policies within different administrative boundaries can be
implemented, which causes different soil losses within the
same ecosystems. In this study, Küçük Menderes River Basin was investigated. The objective of the study was to present erosion risk in Küçük Menderes River Basin and to
emphasize factors (land cover, geology, topography etc.)
having influence over spatial differences in soil loss. In this
study, it was aimed to determine areas of potential and true
erosion danger with the help of Geographic Information System (GIS) and Remote sensing methodologies.
To determine erosion risk of the study area, parameters
of slope, lithology, vegetation cover, rainfall and exposure
were used. Topography maps of 1/25.000 belonging to the
study area were digitized on ArcGIS 10.x programme for
the purpose of producing slope and exposure maps which
were used as a parameter in the study, and databases of
these parameters were established. The similar method was
also used to establish database of lithological features
which is an-other parameter that was utilized in presenting
the erosion risk of the study area. In determining lithological features, ''Geology and Lithology Map of Küçük Menderes River Basin'' prepared by Gozenc (1978) was used as
underlay [14].
Another influential factor used in the study is rainfall.
For establishing database related to this data, annual average precipitation data belonging to the study area were obtained from Turkish State Meteorological Service. Distri-
bution map of precipitation for the area was created via Inverse Distance Weighted (IDW) method by considering altitude factor, as well. Beside this, MODIS satellite image
dated August 23, 2012 was used to present land cover density of Küçük Menderes River Basin. NDVI (Normalized
Difference Vegetation Index) image was created to measure closure status of land cover by utilizing from satellite
image of the year 2012. Clearance or closure of the land in
the study area in terms of vegetation cover was addressed
within three different categories: a) Bare surfaces, b) Fewer
vegetation cover, c) Areas with dense vegetation cover.
There is an inconsistency between the land capability
classes and their usage in the basin. While the area occupied by Ist, IInd, IIIrd and IVth class lands which should be
used as agricultural fields covers 32%, the area occupied
by the agricultural fields covers currently 40%. This situation shows that, in the area, lands have not been used according to their capability classes. In other words, 4% of
IIIrd class lands is used as the agricultural area; 16% of IVth
class lands as forest; 38% of VIth class lands as the agricultural; %44 as forest-maquis; 11% of VIIth class lands as the
agricultural, and %46 of VIIIth class lands as settlement
area. The said situation increases erosion risk in the research area.
3.1. Slope
In the research area, the erosion risk is high on the
south slopes of Bozdağlar Mountains and the north slopes
of Aydın Mountains, where the cleavage degree geomorphologically and the plain density have increased, and
TABLE 1 - The Slope classes and reclassify values in the study area.
Slope Classification
Level-Very low slope (% 0-3)
Moderate slope (% 3-12)
Steep slope (% 12-25)
Very steep slope (% 25-40)
Sheer ( > % 40)
Reclassify Value
20
40
60
80
100
196
GIS Value
10
FIGURE 2 - The Slope map of the study area.
TABLE 2 - Elevation zones and reclassify values in the study area.
Elevation (m)
0-185
185-450
450-750
750-1150
> 1150
Reclassify Value
20
40
60
80
100
GIS Value
9
FIGURE 3 - DEM (Digital Elevation Model) map of the study area.
which have the slope above 10-12%. It is moderate along
the alluvial deposits, lower hilly areas and the erosional
surfaces, where the slope is below 10-12%. The erosion
risk is rather low on the basin bottom where the slope is
less and the deposition is in the forefront.
the areas which are at altitudes between 450-750 m. The erosion poses a serious threat on these areas, which are
cleaved narrowly and deeply by rivers, and the basin densities of which are high. It is low on the areas with an elevation below 450 m. in the research area.
3.2. Elevation
3.3. Vegetation
In the basin, the erosion risk is high on the areas that are
inclined, the average elevation of which is above 750 m. and
the vegetation cover density is low, while it is moderate on
While, in Küçük Menderes River Basin, the erosion
risk is high around residential areas where the vegetation
cover has been destroyed, and on the areas which are de-
197
TABLE 3 - NDVI classes and reclassify values in the study area.
NDVI
Dense vegetation
Vegetation
Non-vegetation
Reclassify Value
25
50
100
GIS Value
7
FIGURE 4 - NDVI map of the study area
TABLE 4 - Geological elements in the study area and their reclassify values
Geology
Alluvion
Limestone-Conglomerate
Rhyolite-Andesite-Granite-Quartzite
Gneiss-Mica schist-Marble
Reclassify Value
25
50
75
100
FIGURE 5 - Geology map of study area.
198
GIS Value
5
void of vegetation cover and where the slope is in excess
of 10 %, it is moderate in the areas which are free from
destruction, and the closure of vegetation cover of which is
at an intermediate level. The erosion risk is low on the areas
where there is a respectively high rainfall and density of
vegetation cover.
3.4. Geology
While, in the research area, the erosion risk is very high
on slopes that are inclined and devoid of vegetation cover,
which consist of metamorphic elements (gneiss, mica schist
and marble), it is high on slopes which are inclined and consist of volcanic rocks (rhyolite, andesite, granite) and quartzites. The erosion risk is moderate over sedimentary elements
(limestone, conglomerate) which show a spreading on a
narrow area whereas it is rather low on the basin bottom
and banks, which consist of alluvions.
3.5. Rainfall
The research area receives an average rainfall around
724 mm. While north, northwest and west slopes of Aydın
Mountains which are facing air masses coming from north,
northwest and west receive higher rainfall (750-850 mm),
the south and depression areas of Bozdağlar Mountains receive less rainfall (550-650 mm). Within this scope, erosion
risk is high on the south of the basin on which the amount and
density of precipitation are high (above 800 mm.) whereas it
is moderate on the north of the basin receiving respectively
less rainfall (below 750-800 mm.). The erosion risk is low
on the areas receiving a rainfall below 700 mm.
3.6. Aspect
In the basin, the erosion risk is generally high on the
inclined slopes which consist of metamorphic and volcanic
elements which face south, the vegetation cover of which
has been destroyed, and which receive respectively less rain,
while it is at a moderate level on slopes that face east-west,
which receive rainfall on the basin average (724 mm), and
which the closure of vegetation cover of is moderate. On the
other hand, it is low on north-facing slopes, where the rainfall is more (750-780 mm.) than the basin average and of
which the closure of vegetation cover is high.
TABLE 5 - Rainfalls and reclassify values in the study area.
Rainfall (mm)
650-700
700-750
750-800
800-850
> 850
Reclassify Value
20
40
60
80
100
GIS Value
3
FIGURE 6 - Rainfall map of the study area.
TABLE 6 -Reclassify values according to the exposure in the study area.
Aspect
North
East-West
South
Reclassify Value
25
50
100
199
GIS Value
2
FIGURE 7 - Exposure map of the study area.
3.7. Erosion Risk Mapping
Erosion risk maps were determined according to variables of geology, exposure, vegetation, slope, elevation
and rainfall in Küçük Menderes River Basin. The erosion
risk is high on the areas with high slope, rainfall and elevation, which are devoid of vegetation cover, with south-facing, and consisting of metamorphic-volcanic elements. It is
moderate over the sedimentary-volcanic elements, with the
intermediate degree of slope, which receive a rainfall on
the basin average and which are at altitudes between 450750 m., with an intermediate degree of vegetation cover
closure and with east-west-facing. The erosion risk is low
on the areas consisting of alluvional-colluvial elements,
with a low slope, which have an altitude below 450 m.,
where there is a respectively dense of vegetation cover, and
with north-facing.
FIGURE 8 - Erosion risk map of the study area.
TABLE 7 - Areas (ha) and ratios (%) which are covered by erosion risk classes in the study area.
Erosion Risk
Low erosion
Moderate erosion
Moderately high erosion
High erosion
Extremely high erosion
Area (ha)
140675,4
89826,9
47938,2
31620,9
16938,6
200
%
43,02
27,47
14,66
9,67
5,18
FIGURE 9 - Agricultural activities which are done around Beydağ Dam are increasing the erosion and siltation, and consequently lifetime of
the dam is getting shorter.
FIGURE 10 - Agricultural activities being done over the inclined lands, in the north of D310 State Highway, in the south of Derebaşı Village,
Tire County lead the fertile soil to erode.
201
4. CONCLUSIONS
In this study, the areas the erosion risk of which is high
in the Küçük Menderes River Basin were determined by
using the multi-criteria (geology, exposure, vegetation,
slope, elevation and rainfall) decision making method. The
areas where the erosion risk is high are sites with high
slope, rainfall and elevation, which are devoid of vegetation cover, with south-facing, and which consist of metamorphic-volcanic elements. The areas with moderate erosion risk are the sites, which have intermediate level of
slope, which are at altitudes of 450-750 m., which have a
moderate closure of vegetation cover, receive rainfall on
the basin average, with east-west facing, and which consist
of sedimentary-volcanic elements. As for the areas with
low erosion risk are sites, where the slope is low, which
have an altitude below 450 m., where there is a respectively
dense of vegetation cover, with north-facing, and which
consist of alluvial-colluvial elements.
In the research area, non-use of lands in a suitable way
for their natural environment potential leads to the aggravation of erosion. As a matter of fact, severe erosion being
seen at 30% of the basin affects lands that are inclined and
devoid of vegetation cover. As a result of land degradation
and erosion, the basin lands become unusable losing their
natural feature. To prevent this loss is possible by establishing a pattern of land which is suitable for natural environment potentials (land capability classes) of the lands.
[8]
Ekinci, D. (2005). “Erosion Analysis of Kozlu River Basin
with the GIS-Based a Modified a RUSLE Method”. Istanbul
University Journal of Geography, Department of Geography,
Faculty of Arts, 13, 109-119.
[9]
Kheira R.B., Abdallaha, C., Runnstromb, M. and Martenssonb
U. (2008). Designing erosion management plans in Lebanon
using remote sensing, GIS and decision-tree modeling. Landscape and Urban Planning 88, 54–63
[10] Karabulut, M. and Küçükönder, M. (2008). Detection of Erosion Areas around Kahramanmaraş City and its Plain by Using
GIS. Kahramanmaraş Sütçü İmam University Journal of Physical and Engineering Sciences, 11, 14-22.
[11] Sönmez, M. E., Çelik, M. A. and Seven, M. (2013). Detection
of Erosion Risk Areas of Kilis Central District with the Help
of Geographic Information Systems and Remote Sensing.
Kahramanmaraş Sütçü İmam University Journal of Social Sciences, 1, 1-21.
[12] Alexakis, D.D., Hadjimitsis, D.G. and Agapiou, A. (2013). Integrated Use of Remote Sensing, GIS and Precipitation Data
for the Assessment of Soil Erosion Rate in the Catchment Area
of “Yialias” in Cyprus. Atmospheric Research, 131, 108–124.
[13] Conoscenti, C., Angileri, S., Cappadonia, C., Rotiglianoa, E.,
Agnesi, V and Märker, M., (2014). Gully Erosion Susceptibility Assessment by means of GIS-Based Logistic Regression: A Case of Sicily (Italy). Geomorphology, 204, 399–411.
[14] Gözenç, S. (1978). The Use and Classification of the Land on
Küçük Menderes River Basin. İ.Ü. (Istanbul University) Publishing House of Arts Faculty, Istanbul, Turkey (in Turkish).
REFERENCES
[1]
Vrieling, A., Jong S.M., Sterk G. and Rodrigues S.C. (2008).
Timing Of Erosion and Satellite Data: A Multi-Resolution Approach To Soil Erosion Risk Mapping. International Journal of
Applied Earth Observation and Geo-information, 10, 267-281.
[2]
Nigel, R. and Rughooputh S.D.D.V. (2010). Soil Erosion Risk
Mapping with New Datasets: An Improved Identification and
Prioritisation of High Erosion Risk Areas. Catena, 82, 191-205.
[3]
Tağıl, Ş. (2009). Spatial Distribution and Affecting Factors of
Soil Loss on Çakırdere and Yahu Dere Stream Basins
(Balıkesir). Balıkesir University Journal of Institute of Social
Sciences, 22, 23-39.
[4]
Gülersoy, A.E. (2008). Relationships between Natural Environment Conditions and Land Use in Bakırçay Basin. Institute
of Educational Sciences, D.E.U., Unpublished Doctoral Dissertation, Izmir (in Turkish).
[5]
Doğan, O. (2011). Reasons, and Solution Suggestions of Erosion in Turkey. Education in the Light of Science and Intelligence, 134, 62-69.
[6]
Tunç, E. and Çelik, M. A. (2014). Monitoring The Effects of
Rainfall Conditions on Wheat (Triticum Aestivum L.) Fields
Using Modis Data in Araban/Gaziantep, Turkey. Fresenius
Environmental Bulletin, 23 (3), 728-737.
[7]
Zhang, Y.J., Li, A.J. and Fung, T. (2012). Using GIS and
Multi-Criteria Decision Analysis For Conflict Resolution in
Land Use Planning Original Research Article. Procedia Environmental Sciences, 13, 2264-2273.
202
Ali Ekber Gülersoy
Dokuz Eylul University
Buca Faculty of Education
Department of Primary Education
Buca, Izmir
TURKEY
Phone: ++90 232 301 22 99
FEB/ Vol 24/ No 1a/ 2015 – pages 195 – 202
TROPHIC STATUS AND THREATS
IN ZINAV LAKE (TOKAT/ TURKEY)
Saliha Dirim Buhan1, Nihal Bektaş1, Mehmet Ali T. Koçer2,*,
Hakan Mete Doğan3, Ekrem Buhan4 and Fatih Polat5
1
2
Mediterranean Fisheries Research Production and Training Institute, Antalya/Turkey,
3
Gaziosmanpaşa University, Agricultural Faculty, Soil Science Department, Tokat/Turkey
4
5
ABSTRACT
This study was conducted to determine water quality
and trophic state of Zinav Lake, which is located in the
Central Black Sea Region of Turkey. Temperature, dissolved oxygen, pH, electrical conductivity, total suspended
solids, Secchi disc transparency, ammonia, nitrite, nitrate,
total nitrogen, soluble reactive phosphorus, total phosphorus
and chlorophyll a were monitored in water column from
January 2012 to June 2013. The study showed that Zinav
Lake was a dimictic lake, which circulated in spring and fall,
and thermally stratified in summer. Hypo-limnetic oxygen
deficit and metalimnetic oxygen minima were the cases.
Vertical distribution of dissolved oxygen and pH and ammonia accumulation in hypolimnion represented eutrophic/hypereutrophic conditions. The values and ratios of nutrients
and chlorophyll a and Secchi disc transparency clearly
classified Zinav Lake as eutrophic/hypereutrophic. Allochthonous loadings of organic matter and nutrients from the
catchment and the changes in hydrological cycle by a
power plant seem to be the most threatening factors controlling the water quality and trophic state in Zinav Lake.
system and from terrestrial loadings from catchment area [1].
Many factors such as location, climate, vegetation and land
cover/land use in the catchment area have significant influences on the water quality of small lakes [2, 3], considering
they have the high hydrologic and nutrient processing rates
[4]. Allochthonous organic matter, however, supplements
primary production in small lakes, particularly in forested
areas [5]. Therefore, organic matter and nutrient loadings from
catchment area result in generally higher productivity of
small lakes as compared with large ones [6]. Despite diminished characteristics, small lakes were neglected by ecologists
partly due to presuming that ecosystems with a small areal extent cannot play a major role in global processes. However,
recent analyses have shown that they play an unexpectedly
significant role in global cycles [7].
Zinav Lake is a small lake with a surface area of
0.34 km2, located in the Central Black Sea Region of Turkey.
There is a lack of data on the lake in spite of the biggest natural
lake in Kelkit River Basin. In this context, this study aimed
to investigate the vertical and seasonal fluctuations of physicochemical properties and trophic state of Zinav Lake.
KEYWORDS:
Zinav Lake, water quality, nutrients, trophic state, threats
Zinav Lake as abovementioned is located in the Central Black Sea Region of Turkey. The precise position of
study area can be described as between the 37o13’-37o21’
North latitudes and 40o32’-40o23’ East longitudes (Figure 1).
1. INTRODUCTION
Although the magnitude and proportion of the sources
vary widely among different systems, aquatic ecosystems
receive organic matter from primary production within
The study was carried out between January 2012 and
June 2013. Surface water samples were collected from three
areas of the lake representing inlet, outlet and mid-point,
while vertical samples from 0, 5, 10 meters and deep water
at the mid-point. Temperature, pH and electrical conductivity were measured in situ by using YSI-556 MPS multiparameter measurement instrument (YSI, USA). Total sus-
203
pended solids, ammonia, nitrite, nitrate, soluble reactive
phosphorus, total nitrogen and phosphorus were determined with suitable methods and test kits using by DR2800
benchtop spectrophotometer (Hach-Lange Germany). Chlorophyll a concentrations were calculated by monochromatic
method after extraction with 90% acetone solution and absorbance measurement by the spectrophotometer [8].
Normality was firstly tested for each variable using the
Shapiro-Wilk test. Comparisons of sampling sites and nonnormally distributed water quality parameters were made
using a signed rank test (Wilcoxon) and followed by post
hoc test (Tukey’s HSD). Wilcoxon test did not detect significant differences between the three surface water sampling sites in terms of physicochemical variables (P > 0.05).
Therefore, water quality of the lake was evaluated according
to mid-point measurements. All statistical tests were carried
out using JMP 8.0 software package.
3. RESULTS
Vertical change of temperature was significantly different (P<0.01), with higher values at surface water than
10 meters and deep water (Table 1). Vertical temperature
profile displayed a clear seasonal variation, revealing the periods of mixing and thermal stratification (Figure 2a). In
summer, temperature sharply decreased from surface water
to bottom, and the greatest thermal discontinuity was observed in July. Temperature of surface water gradually decreased in late summer, and thermal stratification was lost
during the fall circulation. Measurements showed that the fall
turnover was completed in November, and almost a thermal
homogeneity in water column prevailed during winter.
Dissolved oxygen sharply decreased in metalimnion
during thermal stratification, and the hypolimnetic oxygen
deficit was the case (Figure 2b). The large volume of anoxic
hypolimnion which was expanded above 5 meters in July resulted in low dissolved oxygen concentrations in entire water
column during the fall circulation. Despite low concentrations in deep water, high concentrations of dissolved oxygen in surface layers were observed during winter.
FIGURE 1 - Location and topographic features of the study area
TABLE 1 - Summary of monitored parameters in water column during the study period (parameters not connected by same letter in same line
are significantly different; P < 0.05)
Parameters
0m
5m
a
10 m
ab
7,7±2,7
20 m
b
6,5±1,9b
Temperature (°C)
13,7±7,1
10,5±4,6
Dissolved oxygen (mg/L)
9,4±2,9a
6,8±3,4ab
5,1±3,8bc
3,2±2,5c
pH
8,4±0,4a
8,2±0,5ab
8,0±0,5bc
7,6±0,5c
Electrical conductivity (µS/cm)
255±26
249±29
226±26
234±41
Total suspended solids (mg/L)
16±11
15±11
14±8
18±11
Ammonia (mg NH4 -N/L)
0,082±0,100
a
0,128±0,098
a
0,352±0,190
b
0,480±0,181b
Nitrite (mg NO2--N/L)
0,009±0,007
0,009±0,007
0,014±0,011
0,023±0,023
0,165±0,108
0,218±0,143
0,266±0,163
0,279±0,227
2,2±1,4
2,2±1,3
2,3±1,0
4,0±1,8
Soluble reactive phosphorus (mg PO4 -P/L)
0,111±0,056
0,085±0,029
0,112±0,051
0,113±0,057
Total phosphorus (mg P/L)
0,125±0,140
0,109±0,069
0,127±0,010
0,201±0,193
+
-
Nitrate (mg NO3 -N/L)
Total nitrogen (mg N/L)
-3
Chlorophyll a (µg/L)
Secchi disc transparency (cm)
27,4±22,5
105±38
204
a
15,3±11,1
ab
5,3±4,8
b
1,9±1,4b
FIGURE 2 - Depth-time diagrams of (a) temperature (°C), (b) dissolved oxygen (mg/L), (c) pH and (d) chlorophyll a (µg/L).
Vertical pH was measured between 7.1 and 9.2 and
significantly higher (P<0.001) at surface water than 10 meters and deep water (Table 1). While it was homogenous in
winter and spring, low values were observed in hypolimnion during thermal stratification and fall turnover (Figure
2c). Electrical conductivity was between 164 and 310
µS/cm, with higher values at surface layers than deep. However, differences of electrical conductivity among monitored
depths were not significant (P>0.05). Although suspended
solids was higher in deep water than the upper column (Table 1), there were no significant differences (P>0.05).
Despite low pH values, ammonia nitrogen levels were
clearly higher in deep water than surface layers (Table 1).
It was determined at high concentrations in deep water with
low dissolved oxygen concentrations during all the study period. Therefore, ammonia of 10 meters and deep water did
significantly differ from surface water and 5 meters
(P<0.001). Nitrite and nitrate nitrogen exhibited an increasing trend with depth (Table 1). Mean concentration of total
dissolved inorganic nitrogen (NH4+NO2+NO3) represented
only a small fraction of total nitrogen (12-27%) in water column. Indeed, total nitrogen in Zinav Lake was determined at
high concentrations in a range of 0.7 and 6.1 mg/L. The
ratio of soluble reactive phosphorus to total phosphorus
was relatively high (56-89%). However, vertical changes
of abovementioned nitrogen and phosphorus fractions
were statistically insignificant (P>0.05).
Chlorophyll a showed a clear decreasing trend with
depth (Table 1). Chlorophyll a at surface water was significantly higher than 10 meters and deep water (P<0.001). In
fact, chlorophyll a was observed at high concentrations in
surface layers during thermal stratification and fall and
spring turnover periods (Figure 2d). Secchi disc transparencies, measured between 40 and 180 centimeters, were not
linearly related with chlorophyll a (r2=0.02; P>0.05). However, suspended solids at surface layers significantly affected Secchi disc transparency (r2=0.55; P<0.01).
Vertical temperature profile revealed that Zinav Lake
was a dimictic lake which was entirely mixed by a combination of convection currents and wind-induced epilimnetic
circulation in the spring and fall. The rise in thermal gradient, accepted as much more than 1 °C per meter [9], was
maximum between 5 and 10 meters representing metalimnion
depth during summer. Epilimnion narrowed to a depth of 34 meters in July.
Distribution of dissolved oxygen in water column provided an evidence for trophic status of the lake. A hypolimnetic oxygen deficit observed during thermal stratification
period was probably due to the oxidative consumption of organic matter as an indicator of eutrophic/hypereutrophic
lakes [10]. Moreover, decomposition of settling material in
the denser water of the metalimnion could evoke a metalimnetic oxygen decrease in the lake. Indeed, increasing
eutrophication leads to an increase in hypolimnetic oxygen deficit [11]. This clinograde curve of dissolved oxygen during thermal stratification was suggested as an indicator of eutrophic conditions [9, 12]. In addition, the
large volume of anoxic hypolimnion resulted in low concentrations of dissolved oxygen in water column during the
fall turnover. It was reported that byproducts of anaerobic
metabolism accumulated annually in the hypolimnion
cause low dissolved oxygen concentrations in the upper
waters during fall turnover [13].
Since dissolved oxygen and pH are mainly influenced
by photosynthesis in epilimnion and the decomposition in
205
hypolimnion, they vary similarly in eutrophic lakes [14].
Therefore, a decrease in pH which was observed in hypolimnion during thermal stratification and fall turnover in Zinav
Lake was probably due to an increase in carbon dioxide concentrations released from decomposition processes. In fact,
eutrophic lakes with a clinograde pH curve possess a clinograde oxygen curve [9].
High amounts of organic matter together with distribution of dissolved oxygen and pH in water column seemed
to be responsible for high concentrations of ammonia in deep
water of Zinav Lake. An accelerated accumulation of ammonia in hypolimnion takes place under anoxic conditions
and this condition cause a marked release of ammonia from
the sediment because of reduced absorptive capacity [9].
Thus, it was suggested that the degree of development of
the anoxic layer is the primary factor controlling the extent
of hypolimnetic ammonia accumulation [15].
Although vertical pattern of ammonia evidently showed
eutrophic conditions, higher concentrations of nitrate in deep
water than surface were incompatible with these conditions
[9]. This case might be due to nitrate assimilation by photosynthesis in surface layers of water column and prevailing low denitrification rate in deep layers of Zinav Lake. It
is well known that dominant fraction of dissolved inorganic
nitrogen in oxygen rich lakes is nitrate whereas in anoxic
or oxygen fair conditions ammonia is dominant [16].
The concentrations of nutrients and chlorophyll a and
Secchi disc transparency may be used to estimate trophic
state. Ratio of nitrogen to phosphorus can also be used to
indicate if a particular nutrient is limiting since they are
major limiting nutrients in growth of primary production.
Measurements of total nitrogen and total phosphorus are
also useful for determining trophic state [16, 17]. Therefore, it is clear that Zinav Lake was classified as eutrohic/hypereutrophic based on mean Secchi disc transparency and average concentrations of total nitrogen, total
phosphorus and chlorophyll a [9, 18-20]. The ratio of total
nitrogen to total phosphorus in surface water (17:1 by mass)
suggested that nitrogen was the limiting nutrient in Zinav
Lake [21, 22]. In addition to above indicators, dissolved inorganic nutrients are also useful in trophic state determinations. If soluble reactive phosphorus are at high values (>0.1
mg/L), it indicates a phosphorus enriched system [17]. Soluble reactive phosphorus made up much of the total phosphorus in Zinav Lake.
In conclusion, Zinav Lake was characterized as dimictic
lake, mixed twice a year and stratified in summer. Although
metalimnion was observed between 5 and 10 meters during
summer, epilimnion narrowed to a depth of 3-4 meters in
July. Vertical distribution of dissolved oxygen and pH and
ammonia accumulation in hypolimnion represented eutrophic/hypereutrophic conditions. Hypolimnetic oxygen
deficit and metalimnetic oxygen minima were the cases due
to the oxidative consumption of organic matter. The values
and ratios of nutrients and chlorophyll a and Secchi disc
transparency clearly classified Zinav Lake as eutrophic/hypereutrophic. Nitrogen was the limiting nutrient.
Water quality and trophic state in Zinav Lake are
threatened by human activities by highly increasing terrestrial suspended solids and organic matter loadings as well
as changing natural hydrological cycle. Streams flowing
into the lake carrying rural and agricultural wastes from the
towns and villages located upper parts of the basin are responsible for terrestrial loadings. As a common process in
the basin, land clearing for agriculture also exacerbates
erosion and accelerates sediment transport into the lake.
But operation of a hydropower plant installed to outlet of
the lake causes the changes in flow rates and ways of the
currents in the lake. Therefore, terrestrial loadings from
catchment, biological processes, stratification and circulation in water column seem to be the most important factors
controlling the water quality and trophic state in Zinav
Lake.
ACKNOWLEDGEMENT
The authors thank to The Scientific and Technical Research Council of Turkey for their grand (TUBITAK, Project No: ÇAYDAG-110Y117 ). The authors also thank
staff of Mediterranean Fisheries Research Production and
Training Institute for their invaluable help with data collection and laboratory analyses. This study is also a part of the
doctoral thesis of Saliha Dirim Buhan.
206
REFERENCES
[1]
Carpenter, S.R., Kitchell, J. F. and Pace, M.L. (2002) Pathways of organic carbon utilization in small lakes: Results from
a whole-lake 13C addition and coupled model. Limnology and
Oceanography 47, 1664-1675.
[2]
Mallory, M.L., McNicol, D.K., Cluis, D.A. and Laberge, C.
(1998) Chemical trends and status of small lakes near Sudbury,
Ontario, 1983-1995: evidence of continued chemical recovery.
Canadian Journal of Fisheries and Aquatic Sciences 55(1), 6375.
[3]
Müller, B., Lotter, A.F., Sturm, M. and Ammann, A. (1998)
Influence of catchment quality and altitude on the water and
sediment composition of 68 small lakes in Central Europe.
Aquatic Sciences 60, 316-337.
[4]
Hanson, P.C., Carpenter, S.R., Cardille, J.A., Coe, M.T. and
Winslow, L.A. (2007) Small lakes dominate a random sample
of regional lake characteristics. Freshwater Biology 52(5),
814-822.
[5]
Sand-Jensen, K. and Staehr, P. (2007) Scaling of Pelagic Metabolism to Size, Trophy and Forest Cover in Small Danish
Lakes. Ecosystems 10, 128-142.
[6]
Tilzer, M.M. (1990) Specific Properties of Large Lakes. In:
Large Lakes (Tilzer, M. and Serruya, C., Eds.),. Springer Berlin Heidelberg,Germany, 39-43.
[7]
Downing, J.A. (2010) Emerging global role of small lakes and
ponds: little things mean a lot. Limnetica 29(1), 9-24.
[8]
APHA, AWWA and WEF (1998). Standard Methods for the
Examination of Water and Wastewater. 20th edition. American Public Health Association, Washington, DC.
[9]
Wetzel, R.G. (2001) Limnology: Lake and River Ecosystems.
3rd edition. Academic Press, San Diego, CA.
[10] Walker, W.W. (1979) Use of Hypolimnetic Oxygen Depletion
Rate as a Trophic State Index for Lakes. Water Resources Research 15(6), 1463-1470.
[11] Matzinger, A., Müller, B., Niederhauser, P., Schmid, M. and
Wüest, A. (2010) Hypolimnetic oxygen consumption by sediment-based reduced substances in former eutrophic lakes.
Limnology and Oceanography 55(5), 2073-2084.
[12] Goldman, C.R. and Horne, A.J. (1983) Limnology. McGrawHill, New York.
[13] Effler, S.W. and Matthews, D.A. (2008) Implications of redox
processes for the rehabilitation of an urban lake, Onondaga
Lake, New York. Lake and Reservoir Management 24, 122138.
[14] Rogora, M., Garibaldi, L., Morabito, G., Arisci, S. and Mosello, R. (2002) Present trophic level of Lake Alserio (Northern Italy) and prospect for its recovery. Journal of Limnology
61(1), 27-40.
[15] Satoh, Y., Ura, H., Kimura, T., Shiono, M. and Seo, S.K.
(2002) Factors controlling hypolimnetic ammonia accumulation in a lake. Limnology 3, 43-46.
[16] Dodds, W.K. (2002), Freshwater Ecology: Concepts and Environmental Applications. Academic Press, San Diego, CA.
[17] Dodds, W.K. (2003) Misuse of inorganic N and soluble reactive P concentrations to indicate nutrient status of surface waters. Journal of the North American Benthological Society
22(2), 171-181.
[18] OECD (1982) Eutrophication of waters: Monitoring, assessment and control. OECD Cooperative programme on monitoring of inland waters (Eutrophication control), Environment
Directorate, OECD, Paris.
[19] Hakanson, L. and Jansson, M. (1983) Principles of Lake Sedimentology. Springer, Berlin, Germany.
[20] Nürnberg, G.K. (1996) Trophic state of clear and colored, soft
and hardwater lakes with special consideration of nutrients,
anoxia, phytoplankton and fish. Journal of Lake and Reservoir
Management 12, 432-447.
[21] Downing, J.A. and McCauley, E. (1992) The nitrogen: phosphorus relationship in lakes. Limnology and Oceanography
37(5), 936-945.
[22] Guildford, S.J. and Hecky, R.E. (2000) Total nitrogen, total
phosphorus, and nutrient limitation in lakes and oceans: Is
there a common relationship? Limnology and Oceanography
45, 1213-1223.
Accepted: July 07, 2014
Mehmet Ali T. Koçer
Mediterranean Fisheries Research Production and
Training Institute
Yeşilbayır Mah. Akdeniz Bul.No:2
Döşemealtı, Antalya
TURKEY
Phone: +90-242-251 05 85
Fax: +90-242-251 05 84
FEB/ Vol 24/ No 1a/ 2015 – pages 203 – 207
207
APPLICATION OF MULTI-METAL BIOACCUMULATION
INDEX AND BIOAVAILABILITY OF HEAVY METALS IN Unio sp.
(UNIONIDAE) COLLECTED FROM TERSAKAN RIVER, MUĞLA,
SOUTH-WEST TURKEY
Tuncer Okan Genç*, Fevzi Yilmaz, Burak Evren İnanan, Bülent Yorulmaz and Gökhan Ütük
Muğla Sıtkı Koçman University, Science Faculty, Biology Department 48000 Kötekli Muğla, Turkey
ABSTRACT
In the present study, some heavy metals (Cd, Co, Cr,
Cu, Fe, Mn, N, Pb and Zn ) were seasonally determined in
some tissues of mussel Unio sp. from Tersakan River,
which is an important agricultural, a visiting area and, a
water source for irrigation. Generally, higher concentrations of the measured metals were found in the winter and
autumn compared with those during spring and summer.
High levels of heavy metals were found in shell samples,
while low levels in muscle. The measured metal concentrations in tissues is not differed significantly for all metals
except Fe between autumn and spring but significant differences for Cd, Co, Cr, Cu, Mn and Pb were observed between summer and autumn (p<0.05). Individual MultiMetal Bioaccumulation Index (MBI) values varied from
0.01 to 0.44, 0.00 to 0.47, and 0.00 to 0.43 in muscle, gill,
and shell respectively. Generally, IMBI values calculated
in muscle were higher than gill and shell. According to the
acceptable values for human consumption designated by
various health organizations, concentrations of some metals such as Cd and Pb were more than the designated limits,
especially in summer.
KEYWORDS: Bioaccumulation, heavy metals, multi-metal bioaccumulation index, Unio sp.
1. INTRODUCTION
Effluents from textile, leather, tannery, electroplating,
galvanizing, pigment and dyes, metallurgical and paint industries and other metal processing and refining operations
contain considerable amounts of toxic heavy metal ions
which are both potential hazards for human health and for
other life forms. The river systems may be excessively contaminated with toxic heavy metals released from do* Corresponding author
mestic, industrial, mining and agricultural effluents. The
contamination of freshwaters with a wide range of pollutants has become a matter of great concern over the last few
decades, not only because of the threat to public water supplies, but also with of the damage caused to the aquatic life
[1-5].
Bivalves are well known for their biological features
of concentrating some substances especially heavy metals
in their tissues [6]. Because of their main characteristics
such as being sedentary organisms, their longevity, their
abundance and availability throughout the year, their environmental tolerance, they are often chosen for biomonitoring studies. Besides, bivalves have good net accumulation
capacities. The importance of bivalves in pollution impact
studies has been shown within the scope of the International Mussel Watch Programme [7-8]. Although all heavy
metals are potentially harmful to most organisms at some
level of exposure and absorption [9], some heavy metals
such as Zn, Cu and Co are essential in trace amounts for
normal growth and development. However, others such as
Hg, Cd and Pb have no known biological role [10, 11].
Since it is edible and marketed commercially, the determination of contaminant levels in mussel species provides the
assessment of possible toxicant risk to public health [12].
This study includes the metal bioaccumulation of Cd,
Co, Cr, Cu, Fe, Mn, N, Pb and Zn in Unio sp. tissues and
evaluating Individual Multi-Metal Bioaccumulation Index
for influence of heavy metal pollution.
2.1 Study Area
In this study, heavy metals (Cd, Co, Cr, Cu, Fe, Mn,
Ni, Pb and Zn ) contents in Unio sp. were investigated. The
mussels were collected from the Tersakan River polluted
by agricultural and domestic wastes. The Tersakan River is
a temperate stream (36°45'51"N, 28°49'20"E), which is im-
208
pacted by unpredictable environmental conditions associated with a Mediterranean climate. Its length is 30 km and
this stream has temporal and spatial water flow variations
throughout the water course (48–780 m3/s) [13]. The
lower section of the stream was channelized by local authorities to prevent seasonal floods. The stream flows into Mediterranean Sea. Vegetation is usually abundant throughout
the stream banks and depth varies between 0.5–2 m. The
sampling site was characterized by muddy substrate, limited
vegetation and slow flow velocity. It had recently been affected by heavy floods, which occur seasonally because of
high annual precipitation [14].
(gradually increased) until all the materials were dissolved.
After digestion, the digested samples were diluted with distilled water appropriately in the range of standards which
were prepared from stock standard solution of the metals
(Merck). The extraction procedure for the tissues included
multi-acid digestion (HNO3-HClO4-HCl) and analysis for
Cd, Co, Cr, Cu, Fe, Mn, N, Pb and Zn, by ICP-AES (Perkin-Elmer Corp., Nonvalk, CT, USA). Verification of the
method was demonstrated by analysis of an independent
standard reference material (DORM-2, certified dogfish
muscle tissue, National Research Council, Ottowa, Ontario, Canada).
2.2 Collection of Mussel and Physical and Chemical Analyses
2.4 Data Analyses
Freshwater mussels Unio sp. were caught by bucket.
20 freshwater mussels were selected from each caught in
the Tersakan River in every season of 2010. Physico-chemical parameters (pH, temperature, conductivity and dissolved O2) of freshwater on sampling days were determined
by a Hach Lange HQ40D model. The samples were brought
to the laboratory at the same day, weighed, measured and
dissected. During the sampling, physico-chemical parameters of the water were analyzed on the selected station using
LCK cuvette test on Hach Lange DR 2800 model Spectrophotometer. LCK codes used were as follows: LCK 305 for
(NH4N), LCK 339 for (NO3), LCK 342 for (NO2), LCK
348 for (PO4P), LCK 328 for (K), LCK 327 for (Ca) and
(Mg). The analysis of each parameter was performed as described on standard cuvette tests.
Kruskal-Wallis following Man-Whitney-U test were
used to determine whether metal concentrations differ
significantly between tissues and season at a level of a =
0.05. Paired relationships between environmental parameters and metal concentrations were revealed using Pearson’s
correlation tests. These statistical calculations were performed with SPSS 20.0 for Windows. The individual mean
(multi-metal) bioaccumulation index (IMBI) was calculated as [15]:
IMBI
∑
1 i/Cimax
n
With N the total member of metals, Ci the individual
metal concentration of heavy metal i, Cimax the maximal observed concentration of heavy metal i and 0<IMBI<1.
2.3 Digestion and Heavy Metal Determination
Tissues of the same species from the same station were
pooled to make 20 subsamples. They were transferred to an
oven set (120 °C) to dry. Drying continued until all the wet
tissues reached a constant weight. Dry tissue samples were
put into digestion flasks and 2 ml hydrogen peroxide and
8 ml nitric acid (Merck) were added. The digestion flasks
were then put on a microwave digestion unit at 120 °C
3. RESULT AND DISCUSSION
3.1 Environmental Parameters and Shell Allometric Parameters
Some physicochemical parameters of water in the
study area and also shell allometric parameters of samples
are presented in Table 1 and Table 2, respectively.
TABLE 1 - Seasonally water parameters of Tersakan River (average±SEM, n=3)
Autumun
Winter
Spring
Ammonium (NH4N) mg/L
0.16±0.1
0.32±0.1
0.21±0.2
Summer
0.01±0.1
Nitrate (NO3) mg/L
0.55 ±0.2
1.85±0.2
0.27±0.1
0.71±0.1
Nitrite (NO2) mg/L
0.03±0.1
0.03±0.1
0.01±0.1
0.04±0.1
Orthophosphate (PO4P) mg/L
0.35±0.1
0.37±0.1
0.10±0.1
0.06±0.1
Potassium (K+) mg/L
0.35±0.1
1.71±0.2
3.30±0.1
0.23±0.1
Total hardness dH
21.80±0.1
40.20±0.3
49.4±0.3
22.80±0.2
Calcium (Ca) mg/L
54.40±0.2
41.10±0.3
31.70±0.2
46.20±0.2
Magnesium (Mg) mg/L
61.40±0.3
149±0.4
195±0.4
70.90±0.3
Dissolved Oxygen (mg/L)
8.50±0.1
6.83±0.2
10.24±0.3
9.54±0.2
Temperature T ºC
21.9±0.2
15.60±0.2
22.4±0.2
26.30±0.2
7.41±0.2
7.30±0.2
7.48±0.1
7.12±0.2
pH
-1
Conductivity (µS cm )
889±0.4
887±0.3
820±0.3
920±0.4
BOI5
4.02±0.2
0.96±0.1
2.50±0.2
4.14±0.2
209
TABLE 2 – The seasonal averages of some allometric parameters in Unio sp. from Tersakan River (average±SEM, n=60)
Autumn
Winter
Spring
Summer
Shell Length (mm)
66.30±0.2
66.51±0.3
55.97±0.6
54.08±0.6
Shell Width (mm)
26.63±0.1
26.61±0.1
20.35±0.1
20.36±0.2
It is well documented that salinity of waters is an important factor influencing concentrations of selected metals
in the soft tissue of Unio sp. in the Tersakan River. The
mean concentrations of Cd, Co, Cr, Mn, Ni, Pb and Zn are
the higher in autumn and summer than winter and spring.
Similarly, the values of conductivity, NO3, NO2 and Ca+2
in autumn and summer are also higher than the values in
the other seasons.
According to Broman et al. [16], the bioavailability of
Cd in soft tissues of mussels is dependent on salinity of the
adjacent water. The tissue Cd levels of Unio sp. collected
from the Tersakan River in summer and winter were high
when salinity levels were high. However Co, Cr, Mn, Ni,
Pb and Zn concentration were determined higher in autumn
and summer when salinity was high. Broman et al. [16] observed that there was no relationship between Zn levels and
salinity. However, our results showed a positive relationship between Zn levels and salinity. Although some other
environmental factors such as salinity, pH, hardness and
temperature play significant roles in metal accumulation,
other studies showed that accumulation of heavy metals in
a tissue is mainly dependent upon water concentrations of
metals and exposure period [17].
There were also seasonal differences between mussels
of different size classes from the sampling site. The longest
shell length of Unio sp. (70.58 mm) was measured in autumn while the shortest length (50.3 mm) was measured in
summer. The longest shell width (40.35 mm) was in summer while the shortest shell width (24.3 mm) was in winter.
The maximum mussel weight was determined in spring as
57 g and minimum weight of mussel also was determined
in summer as 29 g. The longest shell height of mussel
(43.82 mm) was measured in autumn while the shortest
height (20.11mm) was measured in summer.
Shell Height (mm)
42.33±0.1
42.24±0.1
34.04±0.5
33.53±0.5
Mussel Weight (g)
40.150±0.4
40.70±0.6
42.05±0.7
37.05±1.0
There was a statistically significant negative relationship between the metal content of the tissues and the mussel length, width, height and weight for all metals in autumn (p <0.05). Significantly positive relationships were
found in winter between only Cr and weight (r < 0.509),
while negative relationships were found between Pb-width
and Cd-width (r < -0.511 and r < -0.598). Additionally in
spring, almost all metals except Fe, Mn and Ni have negative correlation with length, width, height and weight (r <
- 0.334). Finally, in summer Ni showed positive correlations with weight and length (respectively, r <0.287 and r
<0.335). Other trace metals showed weak relationships.
The amount of heavy metal bioaccumulation in the tissues may vary depending on length and weight of samples
[18, 19]. Our data showed that almost all trace metals have
negative relationships with mussel size in every season;
only some of the metals such as Cr (in winter) and Ni (in
summer) had positive correlations.
3.2 Seasonal Variations of Metal Accumulation
Mean concentrations of all metal accumulation in autumn, winter, spring and summer is shown in Table 3. Investigated concentrations of heavy metals in tissues have
shown seasonal variation and the following maximum concentrations were found: 2.660 µg/g dry weight (d.w.) for
Cd, 4.786 µg/g d.w. for Co, 24.430 µg/g d.w. for Cr,
291.179 µg/g d.w. for Cu, 1921.090 µg/g d.w. for Fe,
752.840 µg/g d.w. for Mn, 112.440 µg/g d.w. for Ni,
15.960 µg/g d.w. for Pb and 460.330 µg/g d.w. for Zn.
We have reported that almost all metals displayed the
maximum values during the winter, except Fe, Cd and Co.
The maximum metal concentration for Fe was determined
in spring while the highest Cd and Co concentrations were
found in summer.
TABLE 3 - Mean concentrations (µg/g dry weight) of metal accumulation of Unio sp. on seasonally (average±SEM, n=60)
Metals
Autumn
Winter
Spring
0.131±0.01a
0.255±0.02b
0.081±0.03c
Cd
0.501±0.08a
0.526±0.09a
0.488±0.07ab
Co
a
a
4.274±0.56
5.618±0.74
2.891±0.40a
Cr
6.973±0.83a
17.999±5.52a
6.084±0.94a
Cu
533.804±66.73a
575.360±65.48a
373.690±47.43a
Fe
490.940±183.84a
206.590±23.04a
179.472±24.22ab
Mn
46,060±9.42a
12.937±2.35a
10.738±1.51a
Ni
15.960±1.91a
5.870±1.98a
1.892±0.34a
Pb
a
a
460.300±35.75
135.560±21.01
17.752±4.59a
Zn
The same superscript letters in the same row are not significantly different at p<0.05
210
Summer
1.075±0.07d
1.551±0.12b
8.404±0.33b
2.800±0.85b
352.141±38.92a
114.236±15.44b
10.910±1.54a
3.938±0.34b
17.907±4.11a
TABLE 4 - Mean concentrations (µg/g dry weight) values of metals in tissues of Unio sp. in Tersakan River (average±SEM, n=240)
Tissue
Muscle
Gill
Shell
Cd
Co
Cr
Cu
Fe
Mn
Ni
0.296±0.04a 0.284±0.06a 2.284±0.39a 3.070±1.97a 77.879±7.41a
12.975±6.26a
4.646±1.13a
0.478±0.05b 0.861±0.11b 6.206±0.41b 6.420±3.61b 627.184±38.887b 231.742±13.358b 7.536±0.77b
0.382±0.06a 1.155±0.08c 7.403±0.54b 15.902±1.24c 671.182±55.05b 268.291±18.24b 20.821±1.77c
Result show that the highest accumulation of heavy
metal for each season was Fe. Fe is generally the most
abundant metal in all of the reservoirs because it is one of
the most common elements in the earth’s crust [20]. According to Cabrera et al. [21] pyrite oxidation produces sulphate and the Fe2+ ion, which is oxidized to Fe3+ by microorganisms such as Thiobacillus ferrooxidans. Results
shown that all metal concentrations decreased from winter
to spring, but again increased from spring to summer.
According to the correlations of heavy metal concentrations in Unio sp. from the Tersakan River, in autumn the
data showed very high levels of correlation with mostly positive values for Cr and Fe (p <0.05, r<0.812). These two
metals were in positive correlation with all metals. Additionally, Ni and Pb also showed positive correlations
with all metals except for Cd and Zn in autumn (r<0.961).
There are significant positive correlations between the tissues
in winter and spring (r<0.642). In this season, Co and Cr are
positively correlated with almost all metals. Significantly
positive relationships were found in summer between CuFe, Cu-Ni, Mn-Cr, Mn-Zn (respectively, r<0.482, 0.459,
0.457, 0.535). On the other hand, in summer there was a significant high level correlation between Fe and Cr. (p < 0.05,
r<0.943).
The heavy metal accumulation of Cu, Mn, Ni, Pb and
Zn in winter was found higher when comparing seasons.
There were significant differences between the seasons and
the tissues for each of the heavy metals (p<0.05). The concentrations of Fe, Ni and Zn in Unio sp. from the Tersakan
River were not found to be statistically significant among
all seasons (p<0.05). The heavy metal concentrations in
tissues did not differed significantly for all metals except
for Fe between autumn and spring (p<0.05) but significant
differences for Cd, Co, Cr, Cu, Mn and Pb were observed
between summer and autumn. All heavy metal elements
were not significantly different in tissues between autumn
and winter (p<0.05).
In general, Cd and Pb have been found at higher levels
in the tissues in Unio sp. in almost every season. A number
of studies have shown that the concentrations of Cd and Pb
in aquatic organisms depend mainly on their environmental
levels [22-24]. The present study shows that levels of all
metals were significantly higher in tissues in autumn and
summer. High levels of metals in tissues on different season
could be originated from different sources around the study
area. These sources are motor oil and ballasts water for Pb
and Cd in summer when tourist activities are increased.
Also Cd could occur from phosphorus fertilizer used in agriculture in summer and autumn. Moreover, other heavy
Pb
1.016±0.23a
2.123±0.3b
4.157±0.21c
Zn
4.215±2.13a
56.627±7.28b
8.475±2.48c
metals, such as Zn as composed fertilizer, Mn and Cu as
micro elements fertilizer, are used in citrus fruits plantations
and green-houses around the Tersakan River where agricultural activities become more intense especially in autumn
and winter.
3.3 Metal Accumulations in Tissues
The mean concentrations of all metals in the gill, shell
and muscle of Unio sp. collected from Tersakan are given
in Table 4. We determined that maximum metal concentration of Co, Cu, Mn, Pb and Zn were the highest in gill tissues but the mean metal concentration of these metal except Zn were highest in shell tissues. Maximum metal concentrations of Cr were observed in muscle while mean concentration for this metal were highest in shell tissues. The
highest mean and the maximum concentration of Ni were
determined in shell tissue. The maximum metal concentration for Cd was observed in shell tissues while the mean
concentration of Cd was highest in gill tissues.
The accumulation of investigated heavy metals in muscle tissue was in order as; Fe>Mn>Ni>Zn>Cu>Cr>Pb>
Cd>Co. This order was as; Fe>Mn>Zn>Ni>Cu>Cr>Pb>
Co>Cd in gill tissue while it was Fe>Mn>Ni>Zn>Cu>
Cr>Pb>Co>Cd in shell tissues. The significant differences
were not found for Cd between gill and muscle (p<0.05).
The differences in Fe, Cr and Mn were not statistically significant between gill and shell (p<0.05) but significant differences were observed for other metals. Results show that the
concentrations of Fe, Mn, Zn and Ni have shown high levels when their mean concentrations were compared with
the mean concentration levels of other metals. The highest
Co, Cr, Cu, Fe, Mn and Ni concentrations were found in shell
tissues. General evaluation of the heavy metal accumulation
in tissues could be ordered as shell>gill> muscle. The highest
Co, Cr, Cu, Fe, Mn and Ni concentrations were found in
shell tissues whereas the least metal concentrations were
found in muscle tissues. These results are similar to those
reported by Yilmaz [25] who indicated the heavy metal accumulations on economically important fish inhabiting
nearly to Tersakan River. According to the results of Yilmaz
[25], the lowest metal contents were determined in edible
parts (muscle) of all species.
Yap et al. [12] investigation regarding P. viridis at the
west coast of Peninsular Malaysia revealed that P. viridis
contained metal concentrations in the soft tissues which
ranged from 0.68 to 1.25 µg/g d.w. for Cd, 7.76 to 20.1
d.w. µg/g for Cu, 2.51 to 8.76 d.w. µg/g for Pb and 75.1 to
129 d.w. µg/g for Zn. These metal concentrations were
relatively higher for Cd, Cu, but less for Pb and Zn com-
211
pared to the concentrations in Unio sp. in the Tersakan
River. Yarsan and Bilgili [26] measured heavy metal
concentrations in tissues of Unio stevenianus of Van lake
1.43±0.81 µg/g d.w. for Pb, 0.09±0.02 µg/g d.w. for Cd,
5.83±0.73 µg/g d.w. for Cu, 15.93±3.26 µg/g d.w. for Zn.
Our results were generally higher for all metals compared
to their findings. Kır and Tuncay [27] investigated the accumulation of some heavy metals in muscle, liver and carapace tissues of crayfish (Astacus leptodactylus) inhabiting
Kovada Lake. They showed that the metal concentrations
found in the liver and carapace were higher than those in
the muscle. These results were relatively similar with our
results, but our results showed that mussels accumulate the
heavy metals at a higher level than their surroundings. The
studies on Cu and Zn in Crongon crongon [28] and Humarus
americanus [29] present a marked ability on the part of crustaceans to regulate internal copper and zinc concentrations.
In the present study, it has been shown that essential metals
such as Zn, Cu, Mn and Fe were the most abundant metals
in different tissues analyzed throughout the year, whereas Co
was the least abundant in Unio sp. inhabiting Tersakan
River. Zn and Fe play a role in the enzymatic and respiratory
processes [30] and also Cu and Zn are apparently essential
metals for crustaceans [31, 32]. The gill was also the main
target organs for Pb and Zn accumulation which is in accordance with the findings of Manly and George [33] and VanBalogh [34]. Adams et al. [35] showed Zn was predominantly accumulated by the gill and the mantle in Amblema
perplicata. Undoubtedly, the unionid gills play a distinct
role in metal uptake and storage. As the gills serve also as
breeding chambers, its involvement in metal storage may
have harmful effects on the reproductive success of freshwater unionids [36].
Our result showed that some metals were found in
higher concentration on shell tissue (Fig. 1). But as there is
a lack of available information on shell metals for the adjacent regions of southwestern Turkey, concentration data
related to other geographical zones were taken from literature and were compared with the data reported in the present study [37-40]. Yap [37] analyzed distributions of Cu
and Zn in the shell lipped part periostracum and soft tissues
of Perna viridis sampled from 17 geographical sites. The
concentrations of Cu in the periostracum and the soft tissues
of P. viridis were 7.41- 42.63 µg/g d.w. and 3.49-31.1 µg/g
d.w., respectively. Meanwhile, the concentrations of Zn in
the periostracum and soft tissues of P. viridis were 4.9039.79 µg/g d.w. and 65.75-144.9 µg/g d.w., respectively.
General accumulations of Cu and Zn in the periostrcum in
Perna viridis were higher than in shell tissue in our study.
Yap et al. [38] measured distributions of Cu, Zn, and Pb
concentrations in the selected soft tissues (foot, cephalic
tentacle, mantle, muscle, gill, digestive caecum, and remaining soft tissues) and shells of the mud-flat snail Telescopium telescopium were determined in snails from eight
geographical sites in the south-western intertidal area of
Peninsular Malaysia. The shell demonstrated ten times
higher concentrations of Pb (41.23± 1.20 digestive caecum)
when compared to our result. Yap et al. [39] determined
trace metals (Fe, Cu, and Ni) in the Mangrove Area of Peninsular Malaysia using different soft tissues of flat tree oyster
Isognomon alatus. The mean concentrations of Fe, Cu and Ni
in muscle were 797.43 µg/g d.w., 4.31 µg/g d.w. and 0.90
µg/g d.w., respectively. Their study show that highest Fe
and Cu concentrations were found in the muscle but the
concentrations of Ni in our study were higher than their
findings. They also determined Fe, Cu and Ni (1129.71
µg/g d.w., 7.83 µg/g d.w. and 1.21 µg/g d.w., respectively)
concentrations in mantle plus gill and these results were
relatively similar with Fe concentrations but our study
showed higher concentrations of Cu and Ni. Kraak et al.
[40] made evident that the Cu regulation capacity of U. pictorum is organ-specific: gill, digestive gland and shell
showed higher accumulation capacity for Cu than gonads
and kidney. Bourgoin and Risk [41] found a considerably
higher concentration of Pb in the recent shells of a bivalve
Mya truncata than that in fossilized shells. This indicated
a temporal increased concentration of Pb in the environment. They suggested that the bivalve shell could be used as
a ‘benchmark’ to monitor the anthropogenic inputs of Pb to
the environment. High degrees of heavy metal variability in
the total shells of Unio sp. suggested it to be generally a
more sensitive and precise biomonitoring material for heavy
metals than the total soft tissues of Unio sp. Our study suggests that mussel shells can be used as a biomonitoring material as the metals directly absorb onto the shell surfaces of
the mussels. This obviously could be due to the outer layer
of the shell which is directly contacted with water.
Levels of heavy metal accumulation found in all tissues of the analyzed mussel varied greatly. The distribution
of total metal load IMBI values varied from 0.01 to 0.44,
0.00 to 0.47, and 0.00 to 0.43 in muscle, gill, and shell respectively. In summer, total metal load values in muscle
and gill calculated high concentration when compared with
other seasons. In contrast, the highest total metal loads
were determined on winter for shell. Generally, IMBI values calculated in muscle were higher than gill and shell.
IMBI concentrations were calculated in many articles
for European eel Anguilla anguilla as a good monitoring
tool [15, 42, 43]. However, the freshwater mussels are
more useful in heavy metal studies to monitor polluted
streams. Also, they are abundant and widely distributed
along the Aegean coastal streams. This reason increases the
importance of freshwater mussels. IMBI values were calculated for all tissues and were then compared with seasonally. It was shown that higher IMBI values were determined in muscle and summer. Our data showed that IMBI
values can be successfully calculated for bivalves.
Accumulation of heavy metals in mussels may be considered as an important warning signal for health and human consumption. Dietary standards and guidelines applicable for heavy metals in crustaceans are summarized by
Turkish Food Codes (TFC) [44] and EU [45]. The limit values
for human consumption of metals are shown Table 5. Consequently, it can be concluded that the levels of heavy metals
in muscle are not at acceptable levels for all of the studied
212
TABLE 5 - The limit values for human consumption of metals for Crustaceans (TFC [44] and EU [45])
Cadmium (Cd)
EU
(Max.limit)
(µg/g)
T.F.C (Max.limit)
(µg/g)
0.50
6
Periodic Average
(µg/g)
0.047±0.00
0.197±0.04
0.150±0.08
0.789±0.09
0.50
Tissue
5
muscle
gill
shell
Lead (Pb)
EU
(Max.limit)
(µg/g)
T.F.C
(Max.limit)
(µg/g)
0.50
Periodic Average
(µg/g)
0.151±0.08
1.031±0.32
0.149±0.06
2.734±0.73
0.50
Tissue
25
Tissue
muscle
gill
shell
muscle
gill
shell
5
4
20
3
15
3
Cr
Co
Cd
4
2
10
1
5
2
1
0
0
A
300
W
SP
0
SU
A
Tissue
2000
W
SP
SU
A
Tissue
800
SP
SU
W
SP
SU
W
SP
SU
Tissue
muscle
gill
shell
muscle
gill
shell
muscle
gill
shell
W
250
1500
600
150
Mn
Fe
Cu
200
1000
400
100
200
500
50
0
0
A
120
W
SP
0
A
SU
Tissue
20
muscle
gill
shell
W
SP
SU
A
Tissue
500
Tissue
muscle
gill
shell
muscle
gill
shell
100
400
15
80
Zn
Pb
Ni
300
60
10
200
40
5
100
20
0
0
A
W
SP
SU
0
A
W
SP
SU
A
FIGURE 1 - Average heavy metal accumulation in tissues on seasonally (A:Autumn; W:Winter; Sp:Spring; Su:Summer).
213
samples in this region. Mean concentration of Cd and Pb
levels in muscle were higher than the acceptable values for
human consumption designated by various health organizations [44, 45]. The concentration of Cd was higher in summer and Pb concentration was determined higher in winter
and summer. The means of heavy metal concentrations were
beneath Turkish legal standards, which are 1.0 µg/g for Cd,
20.0 µg/g for Cu and 50.0 µg/g for Zn (wet weight) in bivalve mollusks [46].
4. CONCLUSION
When the data were compared with the acceptable values for human consumption designated by various health
organizations, concentrations of some metals such as Cd
and Pb were higher than the designated limits, especially
in summer. In general, the measured heavy metals were
found in higher concentrations in winter and autumn than
the concentrations in spring and summer. No seasonal differences were found for the concentrations of Fe, Ni and
Zn, while the concentrations of other heavy metals had seasonal differences, generally as a result of the measured concentrations in summer (p <0.05). On the other side, considering the tissues together, the highest levels of heavy
metals were found in shell samples, while the lowest levels
in muscle. It has been also shown that IMBI were useful
for determining metal accumulations in Unio sp.
ACKNOWLEDGMENTS
The authors would like to thank financial support from
The Scientific and Technological Research Council of Turkey (TÜBİTAK), Ministry of Agriculture and Muğla City
Directorate for laboratory analysis.
[6]
Avelar, W.E.P., Mantelatto, F.L.M., Tomazelli, A.C., Silva,
D.M.L., Shuhama, T. and Lopes, J.L.C. (2000) The marine
mussel Perna perna (Mollusca, Bivalvia, Mytilidae) as an indicator of contamination by heavy metals in the Ubatuba bay,
Sao Paula, Brazil. Water Air and Soil Pollution, 118, 65-72.
[7]
Goldberg, E. D. (1975) The Mussel Watch a first step in global
marine monitoring. Marine Pollution Bulletin, 6, 111.
[8]
Kavun, V.Y., Shulkin, V. M. and Khristoforova, N. K. (2002)
Metal accumulation in mussels of the Kuril Islands, north-west
Pacific Ocean. Marine Environmental Research, 53, 219–226.
[9]
Larsson, A., Haux, C. and Sjöbeck, M. (1985) Fish pshysiology and metal pollution: results and experiences from laboratory and field studies. Ecotoxicology and Environmental
Safety, 9, 250-281.
[10] Kalay, M. and Canlı, M. (2000) Elimination of essential (Cu,
Zn) and nonessential (Cd, Pb) metals from tissue of a freshwater fish Tilapia zilli. Turkish Journal of Zoology, 24, 429-436.
[11] Nemesok, J.G. and Hughes, G.M. (1988) The effects of copper
sulphate on some biochemical parameters of rainbow trout.
Environmental Pollution, 49, 77-85.
[12] Yap, C.K., Ismail A. and Tan, S.G. (2004) Heavy metal (Cd,
Cu, Pb and Zn) concentrations in the green-lipped mussel
Perna viridis (Linnaeus) collected from some wild and aquacultural sites in the west coast of Peninsular Malaysia. Food
Chemistry, 84, 569–575.
[13] Barlas, M., Dirican, S. and Özdemir, N. (2001), Fish fauna of
Tersakan River. Proceedings of the National of XI. National
Symposium on Fisheries-Hatay, Turkey, 309–318.
[14] Önsoy, B., Filiz, H., Tarkan, A.S., Bilge, G. and Tarkan, A.N.
(2011) Occurrence of non-native fishes in a small man-made
lake (Lake Ula, Muğla): past, present, future perspectives. Turkish Journal of Fisheries and Aquatic Sciences, 11, 209–215.
[15] Maes, J., Belpaire, C. and Goemans, G. (2008) Spatial variations and temporal trends between 1994 and 2005 in polychlorinated biphenyls, organochlorine pesticides and heavy metal
in European eel (Anguilla anguilla) in Flanders-Belgium. Environmental Pollution. 153, 223-237
[16] Broman, D., Lindqvist, L. and Lundbergh, I. (1991) Cadmium
and zinc in Mytilus edulis L. from the Bothnian Sea and the
northern Baltic proper. Environmental Pollution, 74, 227–244
[17] Canlı, M. and Atlı, G. (2003) The relationships between heavy
metal (Cd, Cr, Cu, Fe, Pb, Zn) levels and the size of six mediterranean fish species. Environmental Pollution, 121, 129–136.
[18] Barghigiani, C. and Ranieri D,S. (1992) Mercury content in
different size of important edible species of the northern Tyrrhenian sea. Marine Pollution Bulletin, 24, 114-116.
REFERENCES
[1]
Malik, A. (2004) Metal bioremediation through growing cells.
Environment International, 30, 261–278.
[2]
Leland, H.V., Luoma, S.N. and Wilkes, D.J. (1978) Heavy
metals and related trace elements. Water Pollution Control
Federation, 50, 1469-1514.
[3]
Corbett, R.G. (1977) Effects of coal mining on ground and surface water quality Monongalia County-West Virginia. Science
of the Total Environment, 8, 21.
[4]
Mance, G. (1987) Pollution threat of heavy metals in aquatic
environment. Elsevier- London, 363.
[5]
Langston, W.J. (1990) Toxic effects of metals and the incidance of marine ecosystems In: Furness, R.W. and Rainbow,
P.S. (ed). Heavy Metals in the Marine Environment, CRC
Press, New York, 256.
214
[19] Zyadah, M.A. (1999) Accumulation of some heavy metals in
Tilapia zilli organs from lake Manzalah, Egypt. Turkish Journal of Zoology, 23, 365-372.
[20] Usero, J., Izquierdo, C., Morillo, J. and Gracia, I. (2003)
Heavy metals in fish (Solea vulgaris, Anguilla anguilla and
Liza aurata) from salt marshes on the southern Atlantic Coast
of Spain. Environmental International, 1069, 1–8.
[21] Cabrera, F., Vlemente, L., Diaz Barrientos, E., Lŏpez, R. and
Murillo, H. M. (1999) Heavy metal pollution of soil affected
by the Guadiamar toxic Hood. Science of the Total Environment, 242, 117–129.
[22] Amiard, J.C., Amiard-Triquet, C., Berhet, B. and Metayer, C.
(1987) Comparative study of the patterns of bioaccumulation
of essential (Cu-Zn) and non-essential (Cd-Pb) trace metals in
various estuarine coastal organisms. Journal of Experimental
Marine Biology and Ecology, 106, 73-79.
[23] Heath A.G. (1987) Water Pollution and Fish Physiology. CRC
Press, Florida USA, 245.
[24] Bryan G.W. and Langston, W.I. (1992) Bioavailability, accumulation and effects of heavy metals in sediments with special
reference to United Kingdom estuaries, review. Environmental Pollution, 76, 89-131.
[25] Yilmaz, F. (2009) The comparison of heavy metal concentrations (Cd, Cu, Mn, Pb, and Zn) in tissues of three economically
important fish (Anguilla anguilla, Mugil cephalus and Oreochromis niloticus) Inhabiting Köycegiz Lake-Mugla (Turkey).
Turkish Journal of Science & Technology, 4 (1), 7-15.
[26] Yarsan, E. and Bilgili, A. (2000) Heavy Metal Levels in Mussels (Unio stevenianus Krynicki) Obtained from Van Lake.
Turkish Journal of Veterinary & Animal Science, 24, 93-96.
[27] Kır, İ. And Tuncay, Y. (2010) The Investigation of some
Heavy Metals in Crayfish (Astacus leptodactylus) Inhabiting
Kovada Lake, Sdu Journal of Science, 5 (2), 179-186.
[28] Amiard J.C., Amiard-Triquet, C. and Metayer, C. (1985) Experimental study of bioaccumulation, toxicity and regulation
of some trace metals in various estuarine and coastal organism.
Symposia Biologica Hungarica, 29, 313-323.
[29] Chou, C.C., Uthe, J.F., Castell, J.D., Kean, J.C. and Lores,
E.M. (1987) Effect of dietary on growth survival and tissue
concentration of cadmium, zinc, copper and silver injuvenile
American lobester (Homarus americanus). Canadian Journal
Of Fisheries & Aquatic Sciences, 44, 1443-1450.
[30] Bryan, G.W. (1968) Concentration of zinc and copper in tissues of decapod crustacean. Journal of the Marine Biological
Association of the United Kingdom, 48, 303-321.
[40] Kraak, M.H.S., Toussaint, M., Bleeker, E.A.J. and Lavy, D.
(1993) Metal regulation in two species of freshwater bivalves,
Ecotoxicology of Metals in Invertebrates (SETAC Special
Publications). Lewis, Chelsea, MI, 175-186.
[41] Bourgoin, B. P. and Risk, M. J. (1987) Historical changes in
lead in the eastern Canadian Arctic, determined from fossil and
modern Mya truncata shells. Science of the Total Environment. 67, 287-291.
[42] Esteve, C., Alcaide, E. and Urena, R. (2012) The effect of metals on condition and pathologies of European eel (Anguilla anguilla): In situ and laboratory experiments. Aquatic Toxicology, 109, 176-184.
[43] Maes, G.E., Raeymaekers, J.A.M., Pampoulie, C., Seynaeve,
A., Goemans, G., Belpaire, C. and Volckaert, F.A.M. (2005)
The catadromous European eel Anguilla anguilla (L) as a
model for fresh water evolutionary ecotoxicology: relationship
between heavy metal bioaccumulation, condition and genetic
variability. Aquatic Toxicology, 73, 99-114
[44] TFC. (2008) Turkish Food Codes, Official Gazette, Communique on Maximum Limits for Contaminants of Food Ingredients (Communique Number: 2008/26).
[45] EU. (2006) Commission Regulation Directive, (EC) No
1881/2006 of 19 December.
[46] Republic of Turkey, Ministry of Food, Agriculture and Livestock (1996) Guidebook of fisheries quality control services.
General Directorate for Protection and Control, Ankara (in
Turkish).
[31] Voutsinou-Taliadouri, F. (1982) Monitoring of some metals in
some marine organisms from the Saronikos Gulf. J. Etud. Pollut., 6, 329-333.
[32] Rainbow, P.S. and White, S.L. (1989) Comparative strategies
of heavy metal accumulation by crustaceans: zinc, copper and
cadmium in a decapod, an amphipod and a barnacle. Hydrobiologia, 174, 245-262.
[33] Manly, R. and George, W.O. (1977) the occurrence of some
heavy metals in populations of the freshwater mussel Anodonta anatina (L.) from the River Thames. Environmental
Pollution, 14, 139-154.
[34] Van-Balogh, K. (1988) Heavy metal pollution from a point
source demonstrated by mussel (Unio pictorum L.) at Lake Balaton, Hungary. Bulletin of Environmental Contamination and
Toxicology, 41, 910-914.
[35] Adams, T.G., Atchinson, G.J. and Vetter, R.J. (1981) the use
of the three ridge clam (Amblema perplicata) to monitor trace
metal contamination. Hydrobiologia, 69, 187-194.
[36] Gundacker, C. (2000) Comparison of heavy metal bioaccumulation in freshwater molluscs of urban river habitats in Vienna.
Environmental Pollution, 110, 61-71.
[37] Yap, C.K. (2012) Distribution of Cu and Zn in the shell lipped
part periostracum and soft tissues of Perna viridis: The potential
of periostracum as a biomonitoring material for Cu. Pertanika
Journal of Tropical Agricultural Science, 35 (3), 413–426.
[38] Yap, C.K., Noorhaidah, A., Azlan, A., Nor Azwady, A.A., Ismail, A., Ismail, A.R., Siraj, S.S. and Tan, S.G. (2009) Telescopium telescopium as potential biomonitors of Cu, Zn, and
Pb for the tropical intertidal area. Ecotoxicology and Environmental Safety, 72, 496–506.
[39] Yap, C.K., Azmizan, A.R. and Hanif, M. S. (2011) Biomonitoring of trace metals (Fe, Cu and Ni) in the mangrove area of
Peninsular Malaysia, using different soft tissues of flat-tree
oyster Isognomon alatus. Water, Air and Soil Pollution, 218,
19-36.
215
Revised: July 11, 2014; August 27, 2014
Accepted: September 09, 2014
Tuncer Okan Genç
Muğla Sıtkı Koçman University
Faculty of Science
Department of Biology
48000 Kötekli
TURKEY
FEB/ Vol 24/ No 1a/ 2015 – pages 208 - 215
HEAVY METAL ACCUMULATION IN BIOINDICATORS OF
POLLUTION IN URBAN AREAS OF NORTHEASTERN POLAND
Zbigniew Mazur1, Maja Radziemska2, Joanna Fronczyk2 and Jerzy Jeznach2,*
1
University of Warmia and Mazury in Olsztyn, Faculty of Environmental Management and Agriculture, Pl. Łódzki 4, 10-718 Olsztyn, Poland
2
Warsaw University of Life Sciences – SGGW, Faculty of Civil and
Environmental Engineering, ul. Nowoursynowska 159, 02-776 Warszawa, Poland
ABSTRACT
The aim of the study was to assess the contamination
of the environment within the city of Olsztyn (northeastern
Poland) by determining the level of lead, cadmium and zinc
in Scots pine (Pinus silvestris L.) needles, the roots and
leaves of broadleaf plantain (Plantago major L.), and the
caps of fly agaric mushrooms (Amanita muscaria). The total content of: Zn, Pb, and Cd was assessed by means of the
flame atomic absorption spectroscopy (FAAS) method.
Plant samples for analysis were taken one month prior to the
beginning of the heating season at ten different locations,
including along busy transportation routes and residential
neighborhoods. Significant differences in the content of
cadmium, lead and zinc were found based on the place
from which samples were collected as well as the plant species. The highest concentrations of cadmium, lead and zinc
were noted in broadleaf plantain leaves, broadleaf plantain
roots and fly agaric mushrooms, respectively, near a large
subdivision of single-family houses. Low heavy metal contents in plants were noted in the City Forest, which is located
far from sources of contamination. A significant correlation
was confirmed between the amount of Pb in the leaves and
roots of broadleaf plantain, whereas the correlation between
the content of this element in the cap of the fly agaric mushroom and leaves as well as roots of broadleaf plantain was
found to be high.
KEYWORDS: heavy metals, Pinus sylvestris L., Plantago major L.,
Amanita muscaria, urban areas
1. INTRODUCTION
One of the main kinds of environmental pollution in
urban areas is heavy metal contamination [1-5]. The main
sources of these elements in air and soil are, above all,
industry, transport and the burning of fuels for energy [69]. Soil as well as many species of plants (e.g., mosses, lichens, mushrooms, grasses and herbs, as well as tree bark,
leaves and needles) are used to identify these con-taminants [3, 10-14]. Excessive soil and air contamination can
pose a risk to human and animal health, and have a toxic
effect on plants [15]. The effect of individual metals on living organisms is, however, varied [14, 16]. Lead is easily
taken up by plants from contaminated soils and accumulated particularly in their mass [17]. Cadmium is an extremely significant pollutant due to its large solubility in
water and high toxicity to most organisms, being the fourth
most toxic element to vascular plants [18]. Zinc is characterized by a high mobility in soil and high uptake by plants,
owing to the fast solubility of compounds in which it appears, especially in an acidic environment [19].
In many countries, hard coal is the cheapest and most
common fuel used for heating houses. The burning of coal
during heating season results in a considerable emission of
gases, mainly ashes, into the atmosphere [20]. These ashes
are a mixture of toxic substances, including aromatic hydrocarbons (AHC), heavy metals and dioxins [21-23]. In Poland, there are approximately 1 million furnaces (most often
manufactured in the last century) in use, which run on hard
coal. In 2010, the emission of ashes from the burning of
hard coal in Poland amounted to 445.3 thousand Mg, including 142.7 thousand Mg released by households [24].
The percentage of heavy metal emissions connected with the
burning of this fuel in individual household furnaces ranges
from 10% in the case of Hg to 30% in the case of Pb, and
as much as 40% for Cd.
The aim of the study was to asses environmental pollution by determining lead, cadmium and zinc concentration in Scots pine needles, broadleaf plantain (Plantago
major L.) roots and leaves and fly agaric mushrooms (Amanita muscaria) at 10 sampling points in Olsztyn (northeastern Poland) which varied in terms of their degree of
urbanization.
216
2.1 Study area and Sample collection
Olsztyn (Poland) and the areas around the city include
mainly wooded, agricultural and tourist areas. There is no
heavy industry which could influence the analyzed areas in
terms of the emission of heavy metal contaminants. The
city is home to 180,000 residents.
In the northwestern part of the city, there is an automatic station for measuring the current levels of air pollution. Among others, the temperature and humidity of the
air, air pressure, wind speed and direction, precipitation,
sulfur dioxide, nitrogen oxides, particulate matter, and Pb,
As, Ni, and Cd content in air are measured here. The average contents of lead, arsenic, nickel and cadmium for the
year 2011 were found to be 0.006 ng·m-3, 0.82 ng·m-3, 0.75
ng·m-3, and 0.16 ng·m-3 respectively. These values did not
exceed the permissible levels of these substances in air.
The heating season in this region of Poland usually begins in the middle of September, thus the period of dry deposition (lack of rainfall at this time), which usually occurs
near the source of pollution, lasted a few weeks.
Plant samples and soils for analysis were taken from
ten places located in various parts of the city, in the middle
of October 2012 (Tab. 1, Fig. 1). Four samples of each
plant were taken at each of these locations; samples from
along roads were gathered at the same distance from the road
and no further than 30 m apart from one another, with those
collected from other places being no further than 15 m apart.
The occurrence of all of the analyzed plant species in close
proximity to each other determined the locations from which
samples were collected. Heavy metals were analyzed in the
2-year-old pine needles. Only the caps of ripe mushroom
fruiting bodies were analyzed. At each of the sampling
points, 30 individual soil samples were taken using Egner's
soil sampler from the 0-20 cm soil layer. These samples were
combined into composite samples.
2.2 Laboratory and Statistical analysis
Scots pine (Pinus silvestris L.) needles, caps of fly agaric mushrooms (Amanita muscaria) and the roots and
leaves of broadleaf plantain (Plantago major L.), were
washed with distilled water, cut into pieces and dried at
60oC to a constant weight. Soil samples were prepared for
analysis by drying at room temperature and sifting them
FIGURE 1 - Localization of sampling points, Olsztyn, Poland.
217
TABLE 1 - Characteristics of sampling points.
Sampling points
1
(53°44'20''N; 20°27'48''E)
2
(53°45'14'' N; 20°26'58''E)
3
(53°45'24'' N; 20°27'22''E)
4
(53°45'39'' N; 20°26'08''E)
5
(53°44'56'' N; 20°28'40''E)
6
(53°44'52'' N; 20°30'29''E)
7
(53°43'57'' N; 20°29'34''E)
8
(53°46'27'' N; 20°28'45''E)
9
(53°48'10'' N; 20°27'27''E)
10
(53°46'12'' N; 20°24'03''E)
Characteristics
National Road No. 51. Samples collected 5 m from the edge of the road. Traffic intensity of approx. 15,000
vehicles per 24-hour period.
Northern part of the "Słoneczny Stok" subdivision (approx. 200 single-family houses), built mainly in the 60s80s of the past century. Many houses equipped with coal-burning furnaces. Very low traffic intensity.
Park on campus of the University of Warmia and Mazury. 500 m radius characterized by low traffic intensity.
Southeastern part of large subdivision - "Dajtki" (approx. 1,000 single-family houses) built mainly in the 70s80s of the past century. Many houses equipped with coal-burning furnaces. Very low traffic intensity.
Middle of "Brzeziny" subdivision (approx. 200 single-family houses). Mostly newly built houses. Coal burnt
sporadically. Low traffic intensity.
Local road (samples collected 8 m from the edge of the road). Traffic intensity of a few thousand vehicles per
24-hour period (mainly passenger cars and buses).
Road No. 598 (samples collected 15 m from the edge of the road). Traffic intensity of a few thousand vehicles
per 24-hour period.
Road in city center (samples collected 60 m from the edge of the road). Traffic intensity of a few tens of thousands of vehicles per 24-hour period.
City Forest. Located 700 m away from the nearest buildings and 1,200 m away from the nearest busy road.
National Road No. 16 (samples collected 10 m from the edge of the road). Traffic intensity of 15,000 vehicles
per 24-hour period.
through a 0.3-mm nylonscreen. The samples were kept at
an ambient temperature until analysis. The total contents of
lead (Pb), zinc (Zn) and cadmium (Cd) were determined by
flame atomic absorption spectrophotometry in an air–acetylene flame on a SpectrAA 240FS spectrophotometer
(VARIAN, Australia), using a Sample Introduction Pump
System. The analysis was performed on extracts obtained after “wet” soil mineralization in nitric acid (analytically pure
HNO3 at a concentration of 1.40 g cm−3), poured into Teflon™ vessels HP500 and placed in a MARS 5 microwave
oven (Microwave Accelerated Reaction System, manufactured by CEM Corporation, USA). All reagents used in
the research contained analytical high purity water (of 0.055
µS.cm-1) prepared by the double distillation of deionized
water in a Crystal system (Adrona Laboratory Systems).
All glass- and polyethylene flaskware had previously
been treated for 24 hours in 5 mol . l-1 HNO3, and then
rinsed with ultrapure water. Chemicals, purchased from
Merck (Germany), were of analytical grade. The solutions
of metals were prepared by the dilution of certified standard solutions (Fluka).
The research results were subjected to statistical analyses, calculating average values, standard deviations and coefficients of variability. Pearson's simple correlation coefficients (r) were also calculated between the contents of the
analyzed heavy metals in plant samples.
The contents of selected heavy metals at the individual
analyzed locations within the city of Olsztyn (Poland) have
been presented in Tables 2 and 3. Significant differences
occurred in the concentration of cadmium (Cd), lead (Pb) an
zinc (Zn), depending on the place from which the samples
were collected as well as the plant species. The chemical
composition of pine needles is a reflection of the abun-
dance of nutrients in trees as well as environmental pollution, although climate conditions, such as the direction and
strength of wind, temperature and amount of rainfall also
play an important role [25]. In the presented studies, the concentration of cadmium (Cd) in pine needles (Pinus sylvestris
L.) ranged from 0.08 mg·kg-1 in samples collected in the City
Forest (sampling point 9), with the nearest buildings 700 m
away and busy road 1200 m away from the sample collection point, to 1.10 mg·kg-1 in samples collected 5 m from
the edge of National Road No. 51, where the road traffic
intensity was approx. 15,000 vehicles per 24-hour period
(point 1). Gworek et al. [26] found the maximum cadmium
content in pine needles to be at a level of 0.62 mg·kg-1 dry
mass at 5-10 m from a busy road. A high dependency of the
content of microelements in pine needles on air pollution
from different sources was confirmed by studies conducted
by Lehndorff and Schwark [27]. Molski and Dmuchowski
[28], on the other hand, recorded a maximum concentration
of 0.17 mg·kg-1 dry mass in needles from pines near the
northern border of Finland. Mushrooms, which are characterized by the ability to absorb nutrients from the substrate
and gather numerous elements in their fruiting bodies at
concentrations that are much higher than in the actual substrates on which they grew, can also be an indicator of environmental contamination with heavy metals [29-31].
Das [32] reported a high variability in the heavy metal content of various mushroom species depending on the level
of environmental pollution. Similarity to Scots pine needles, the lowest content of cadmium in the cap of the fly
agaric mushroom, i.e., 0.09 mg·kg-1 dry mass, was noted in
samples derived from the City Forest (sampling point 9),
with the highest content of 1.89 mg·kg-1 dry mass near a subdivision of single-family houses, where inhabitants burn
hard coal to heat their homes. Broadleaf plantain Plantago
major L., which is considered an herb, is a plant that is commonly used in environmental biomonitoring; analyses makes
use of its above-ground (leaves, stems) as well as below-
218
TABLE 2 - Cadmium (Cd), lead (Pb) and zinc (Zn) contents in plants (mg kg-1 d.m.).
Sampling
points
1
Type of sample
Cd
Pb
Zn
Needles of Pinus sylvestris L.
Amanita muscaria
Leaves of Plantago major L.
Roots of Plantago major L.
1.10
1.11
2.22
1.23
8.07
2.82
15.1
23.4
85.2
37.3
45.5
75.8
2
Amanita muscaria
0.81
1.89
2.68
2.01
5.13
5.38
21.7
27.1
75.2
126.2
98.7
90.4
3
Amanita muscaria
0.69
0.19
0.31
0.31
5.96
0.92
4.95
7.43
29.9
19.6
51.1
56.4
4
Amanita muscaria
0.10
1.12
1.88
1.99
1.04
4.67
18.9
17.8
41.9
92.7
68.7
86.1
5
Amanita muscaria
0.40
0.49
0.60
0.52
1.87
1.04
5.09
4.67
90.1
24.7
33.0
39.1
6
Amanita muscaria
0.09
0.51
1.22
2.41
2.88
0.60
8.11
10.5
61.3
29.6
39.2
38.8
7
Amanita muscaria
0.91
0.43
1.10
1.05
2.53
0.82
5.78
5.49
28.9
22.5
32.6
28.9
8
Amanita muscaria
0.59
1.12
1.51
1.73
4.02
1.48
8.31
8.12
53.3
34.6
50.1
52.9
9
Amanita muscaria
0.08
0.09
0.19
0.37
0.49
0.52
3.78
4.89
22.8
19.6
38.4
31.3
10
Amanita muscaria
0.32
1.73
1.72
1.61
4.81
2.61
22.2
24.9
59.5
46.0
68.1
102.4
TABLE 3 - Cadmium (Cd), lead (Pb) and zinc (Zn) contents in soils (mg kg-1 d.m.).
Sampling points
Cd
Pb
Zn
1
2
3
4
5
6
7
8
9
10
1.04
1.29
0.35
0.99
0.25
0.59
0.38
0.29
0.16
0.83
40.1
69.8
5.8
41.2
17.7
5.7
35.2
22.6
5.97
62.3
110.2
119.5
39.8
99.4
17.6
40.2
65.8
45.1
29.7
141.2
219
ground (roots) parts [14, 33, 34]. In the above-ground parts
of broadleaf plantain Plantago major L., the lowest concentration of cadmium, equal to 0.19 mg·kg-1 dry mass, occurred in samples taken from the City Forest (point 9),
whereas roots of plant samples collected on the premises
of the University of Warmia and Mazury in Olsztyn (sampling point 3) contained the lowest amount of this element,
i.e., 0.31 mg·kg-1 dry mass. The highest concentration of
this metal (2.68 mg·kg-1 dry mass) in leaves of the broadleaf plantain (Plantago major L.) were present in samples
from the northern part of a subdivision of single-family
houses, where most homes are equipped with coal-burning
furnaces and traffic is minimal (point 2). In terms of the
below-ground parts of the tested plant, the highest Cd content, equal to 2.41 mg·kg-1 dry mass, was present in samples
collected 8 m from the edge of a local road with a traffic
intensity of a few thousand vehicles in a 24-hour period,
mainly passenger cars and buses (sampling point 6). Much
higher cadmium contents in the leaves and roots of broadleaf
plantain (Plantago major L.) were reported by FilipovićTrajković et al. [34] who analyzed contaminated soils of
Serbia.
Pearson's simple correlation coefficients (r) between
the content of cadmium (Cd) in the analyzed plants and soil
(Table 4) revealed a high correlation between the content
of this element in the caps of Amanita muscaria and the
above-ground parts of Plantago major L. A moderate correlation occurred between the cadmium contents of Plantago major L. roots and leaves, and in Amanita muscaria
caps.
The content of lead (Pb) in pine needles (Pinus sylvestris L.) was highest (8.07 mg·kg-1 dry mass) in samples
collected 5 m from the edge of National Road No. 51,
which is characterized by high traffic intensity (sampling
point 1), and lowest in those samples derived from the City
Forest (point 9). Similar dependencies were observed in
research carried out by Gworek et al. [26], whereas
Buszewski et al. [10] reported average Pb levels of 5.811.5 mg·kg-1 dry mass when analyzing its content in Pinus
sylvestris L. needles in Toruń (Poland). An increased
content of lead in Pinus Eldarica Medw. needles was
confirmed near a highway in Teheran by Kord et al. [35].
As of 2000, lead concentration in Scots pine needles was
40 mg·kg-1 dry mass in the urbanized areas of Erzurum of
northeastern Turkey, and 19 mg·kg-1 dry mass in the suburban areas of this region of the above-mentioned country
[36].
Near subdivisions of single-family houses (sampling
points 2 and 4), a high concentration of lead amounting to
5.38 and 4.67 mg·kg-1 dry mass respectively, was noted in
Amanita muscaria caps; the reason behind this may be the
burning of hard coal in this area. When analyzing mushrooms of the Amanita species in the province of Reggio
Emilia (Italy), Cocchi et al. [37] determined lead content to
be in the range of 0.32 - 4.43 mg·kg-1 dry mass. Lead (Pb)
content in broadleaf plantain leaves (Plantago major L.)
oscillated between the values of 3.78 in samples from the
City Forest, where the nearest busy road was 1,200 m away
from the point of sample collection (sampling point 9), to
22.2 mg·kg-1 dry mass in sampling point 10 - National Road
No. 16, where samples were collected just 10 m from the
edge of the road and traffic intensity was approx. 15,000 vehicles in a 24-hour period. In the roots of broadleaf plantain
(Plantago major L.), the contents were slightly higher and
ranged from 4.67 mg·kg-1 dry mass in samples taken from
the middle of a subdivision of single-family houses where
hard coal is burned only sporadically (sampling point 5), to
27.1 mg·kg-1 dry mass in sampling point no. 2. Malizia et
al. [14], when analyzing the content of trace elements in
samples of broadleaf plantain leaves and roots collected in
the fall and grown on soils characterized by various levels
of contamination, revealed the content of lead to be 0-8.0
mg·kg-1 dry mass in the leaves and 4.0-28.0 mg·kg-1 dry mass
in the roots. Kurteva [38], when analyzing environmental
pollution in various regions of Bulgaria in 2006-2008, determined the content of lead in the above ground parts of broadleaf plantain (Plantago major L.) to fall in the range of 2.22
to 7.09 mg·kg-1 dry mass.
Table 5 presents the dependencies between the concentrations of lead in plants and soil. A very high correlation was noted between the amount of Pb in the leaves and
roots of broadleaf plantain (Plantago major L.), with a high
correlation between the content of this element in the cap
of the fly agaric mushroom and the leaves as well as roots
of broadleaf plantain. A high correlation between the
amount of lead in the leaves and roots of Plantago major
L. was confirmed in the research of Malizia et al. [14]. A
moderate correlation was noted between the content of lead
in Plantago major L. needles and roots.
The concentration of zinc (Zn) in pine needles (Pinus sylvestris L.) ranged from 22.8 mg·kg-1 dry mass at sampling
point 9 (in samples from the City Forest) to 90.1 mg·kg-1 dry
mass in samples taken from the middle of a subdivision
of single-family houses where hard coal is burned sporad-
TABLE 4 - Correlation between cadmium (Cd) contents in analyzed samples.
Type of sample
Needles of Pinus
Amanita muscaria
Leaves of P. L.
Roots of P. L.
0.90***
0.62*
0.80**
0.73**
0.89***
0.62**
Amanita muscaria
0.19*
Leaves of P. L.
0.33*
Roots of P. L.
-0.18*
Soil
0,26*
Significant at: *** p ≤ 0.001, ** p ≤ 0.01, * p ≤ 0.05.
220
TABLE 5 - Correlations between lead (Pb) contents in analyzed samples
Type of sample
Needles of Pinus
Amanita muscaria
0.22*
Leaves of P. L.
0.33*
Roots of P. L.
0.54*
Soil
0.33*
Amanita muscaria
Leaves of P. L.
Roots of P. L.
0.88***
0.83***
0.81**
0.95***
0.89***
0.85***
TABLE 6 - Correlation between zinc (Zn) contents in analyzed samples.
Type of sample
Needles of Pinus
Amanita muscaria
0.47*
Leaves of P. L.
0.53*
Roots of P. L.
0.62*
Soil
0.25*
Amanita muscaria
Leaves of P. L.
Roots of P. L.
0.89***
0.70**
0.64**
0.83***
0.72**
0.89***
TABLE 7 - Average cadmium (Cd), lead (Pb) and zinc (Zn) content in analyzed plants (mg kg-1 d.m.).
Type of sample
Caps of Amanita muscaria
Cd
0.51 ± 0.37
CV = 72.54%
0.87 ± 0.62
CV = 72.26%
1.34 ± 0.81
CV = 60.44%
1.32 ± 0.75
CV = 56.82%
Pb
3.68 ± 2.36
CV = 64.13%
2.09 ± 1.83
CV = 87.56%
11.4 ± 7.6
CV = 66.67%
13.4 ± 8.9
CV = 66.42%
Zn
54.8 ± 23.9
CV = 43.61%
45.3 ± 35.7
CV = 78.80%
52.8 ±21.8
CV = 41.29%
60.2 ± 26.7
CV = 44.35%
± standard deviation (SD) and coefficients of variation (CV)
ically (sampling point 5). Similar values (20.1-94.9 mg·kg-1
dry mass) were reported by Gworek et al. [26] when analyzing lead concentration in pine needles near a busy road.
Similar amounts of zinc, i.e., 48.72-79.60 mg·kg-1 dry mass
in Pinus sylvestris L. needles were reported by Buszewski
et al. [10]. The lowest content of zinc in the fly agaric
mushroom (Amanita muscaria) was determined in sampling points 3 and 9, that is in the University park and the
City Forest (19.6 mg·kg-1 dry mass), with the highest in
the area of a large subdivision of single-family houses 126.2 dry mass (sampling point 2). Comparable Zn contents in mushrooms were reported by Das (2005). The concentration of this element in the above-ground parts of
broadleaf plantain Plantago major L was found to be between 32.6 and 98.7 mg·kg-1 dry mass in samples collected
in the proximity of road no. 598, where traffic intensity is
equal to a few thousand vehicles in a 24-hour period, as well
as in the area of a large subdivision of single-family houses
(sampling points 7 and 2). The roots, on the other hand, were
characterized by zinc contents of 31.3-102.4 mg·kg-1 dry
mass in samples collected in the City Forest and 10 m from
the edge of National Road No. 16 (sampling points 9 and
10).
Greater differences between the concentration of zinc
in the leaves and roots of broadleaf plantain were obtained by Kurteva [38], Malizia [14], and FilipovićTrajković et al. [34].
The dependencies between zinc contents in the analyzed plants and soil are presented in Table 6. A high correlation (r = 0.89) was noted between Zn content in the
leaves and roots of broadleaf plantain and, similarity to
cadmium, between the contents of this element in Amanita
muscaria caps and Plantago major L. leaves. A moderate
correlation occurred between lead content in Pinus sylvestris L. needles and Plantago major L. roots. A high dependency between the concentrations of Cd, Pb and Zn in
broadleaf plantain leaves and roots was found in the research of Filipović-Trajković et al. [34].
The average cadmium (Cd), lead (Pb), and zinc (Zn)
contents in Scots pine (Pinus sylvestris L.) needles, fly agaric mushroom (Amanita muscaria) caps, and broadleaf
plantain (Plantago major L.) leaves and roots collected
from 10 sampling location in Olsztyn (Poland) are presented in Table 7. The largest quantities of cadmium and
lead accumulated in the leaves and roots of broadleaf plantain Plantago major L. The ability of broadleaf plantain
leaves and roots to exhibit high concentrations of heavy
metals was confirmed in the works of Siromly [39] and Filipović-Trajković et al. [34]. The lowest cadmium and lead
contents were found in Pinus sylvestris L. needles and Amanita muscaria caps, respectively. Small differences between plants, not exceeding 33%, were noted in the case of
zinc content. The highest variability was present in the lead
content of fly agaric mushroom (Amanita muscaria) caps,
221
with the lowest in the zinc content of broadleaf plantain
(Plantago major L.) leaves.
4. CONCLUSIONS
Significant differences were found in the contents of
cadmium, lead and zinc depending on the place of sample
collection as well as plant species. The leaves and roots of
broadleaf plantain Plantago major L. accumulated the
most cadmium and lead. Small differences between plants
were noted in the case of zinc contents. The highest Cd and
Pb content in Scots pine (Pinus sylvestris L.) needles were
found in samples collected 5 m from a very busy road. The
amount of analyzed metals was highest in fly agaric mushroom (Amanita muscaria) caps and leaves of broadleaf
plantain samples collected near a large subdivision of single-family houses, where hard coal is burned. The highest
Cd, Pb and Zn contents in broadleaf plantain roots were
observe near busy transportation routes. Low heavy metal
content was noted in the City Forest, which is located far
away from roads and residential neighborhoods. Cadmium
content in the fly agaric mushroom was significantly correlated with the amount of this metal present in broadleaf
plantain leaves. A very high correlation was established between the amount of Pb in the leaves and roots of broadleaf
plantain, whereas the correlations between the content of
this element in fly agaric mushroom caps and the leaves
and roots of broadleaf plantain were found to be high. Zinc
content in the leaves of broadleaf plantain was significantly
correlated with the content of this element in the roots of
this plant and in the fly agaric mushroom.
REFERENCES
[1]
Grzebisz W., L. Cieśla L., Komisarek J., Potarzycki J. (2002).
Geochemical assessment of heavy metals pollution of urban
soils. Polish Journal of Environmental Studies, 11, 5, 493-499.
[2]
Yoon J., Cao X., Zhou Q., Ma L.Q. (2006). Accumulation of
Pb, Cu, and Zn in native plants growing on a contaminated
Florida site. Science of the Total Environment, 368, 456-464.
[3]
Uyar G., Ören M., Yildirim Y., Ince M. (2007). Mosses as indicators of atmospheric heavy metal deposition around a coalfired power plant in turkey. Fresenius Environmental Bulletin,
16, 182-192.
[4]
Taghipour H., Mosaferi M., Armanfar F., Gaemmagami S.J.
(2013). Heavy metals pollution in the soils of suburban areas
in big cities: a case study. International Journal of Environmental Science and Technology, 10, 243–250.
[5]
Sharma R.K., Agrawal M. (2013). Heavy metals pollution in
the soils of suburban areas in big cities: a case study. International Journal of Environmental Science and Technology,10,
243-250.
[6]
Grigalavičienė I., Rutkovienė V., Marozas V. (2005). The Accumulation of Heavy Metals Pb, Cu and Cd at Roadside Forest
Soil. Polish Journal of Environmental Studies, 14, 1, 109-115.
222
[7]
Lammel G., Ghim Y.S., Broekaert J.A.C., Gao H.W. (2006).
Heavy metals in air of an eastern china coastal urban area and
the yellow sea. Fresenius Environmental Bulletin, 15, 15391548.
[8]
Kasassi A., Rakimbei P., Karagiannidis A., Zabaniotou A.,
Tsiouvaras K., Nastis A., Tzafeiropoulou K. (2008). Soil contamination by heavy metals: measurements from a closed unlined landfill. Bioresource Technology, 99, 8578–8584.
[9]
Kabala C., Chodak T., Szerszen L., Karczewska A., Szopka
K., Fratczak U. (2009). Factors influencing the concentration
of heavy metals in soils of allotment gardens in the city of
Wroclaw, Poland. Fresenius Environmental Bulletin, 18,
1118-1124.
[10] Buszewski B., Jastrzębska A., Kowalkowski T., Górna-Binkul
A. (2000). Monitoring of selected heavy metals uptake by
plants and soils in the area of Toruń, Poland. Polish Journal of
Environmental Studies, 9, 6, 511-515.
[11] Krawczyk J., Klink A., Wislocka M. (2009). Influence of tree
canopies on concentration of some metals in throughfall, soil
and moss Pleurozium schreberi (Bird.) Mitt. Fresenius Environmental Bulletin, 18, 1186-1191.
[12] Yener S.H., Yarci C. (2010). Alcea pallida Waldst. et Kit.
(Malvaceae) as a heavy metal biomonitor in Istanbul (Turkey).
Fresenius Environmental Bulletin, 19, 1024-1030.
[13] Sawidis T., Breuste J., Mitrovic M., Pavlovic P., Tsigaridas K.
(2011). Trees as bioindicator of heavy metal pollution in three
European cities. Environmental Pollution, 159, 12, 3560-70.
[14] Malizia D., Giuliano A., Ortaggi G., Masotti A. (2010). Common plants as alternative analytical tools to monitor heavy metals in soil. Chemistry Central Journal, 6, (Suppl 2), S6.
[15] Singh A, Sharma R.K., Agrawal M., Marshall F.M. (2010).
Health risk assessment of heavy metals via dietary intake of
foodstuffs from the wastewater irrigated site of a dry tropical
area of India. Food and Chemical Toxicology, 48, 611-619.
[16] Desplanque C., Rolland C., Schweingruber F.H. (1999). Influence of species and abiotic factors on extreme tree ring modulation: Picea abies and Abies alba in Tarentaise and Maurienne (French Alps). Trees, Structures and Function, 13, 218227.
[17] Patra M., Bhowmik N., Bandopadhyay B., Sharma A. (2004).
Comparison of mercury, lead and arsenic with respect to genotoxic effects on plant systems and the development of genetic
tolerance. Environmental and Experimental Botany, 52, 199–
223. DOI: 10.1016/j.envexpbot.2004.02.009
[18] Pedro C.A, Santos M.S.S, Ferreira S.M.F, Gonçalves S.C.
(2013). The influence of cadmium contamination and salinity
on the survival, growth and phytoremediation capacity of the
saltmarsh plant Salicornia ramosissima. Marine Environmental Research, 92, 197-205.
[19] MacBride M.B. (1994). Environmental chemistry of soils. Oxford University Press, New York, Oxford, 406 pp.
[20] Bond T.C., Bhardwaj E., Dong R., Jogani R., Jung S., Roden
C., Streets D.G., Trautmann N.M. (2007). Historical emissions
of black and organic carbon aerosol from energy-related combustion, 1850–2000. Global Biogeochemical Cycles, 21,
GB2018.
[21] Smołka-Danielowska D. (2006). Heavy metals in fly ash from
a coal-fired power station in Poland. Polish Journal of Environmental Studies, 15, 6, 943-446.
[22] Seshadri1 B., Bolan N.S., Naidu R., Brodie K. (2010). The role
of coal combustion products in managing the bioavailability of
nutrients and heavy metals in soils. Journal of Plant Nutrition
and Soil Science, 10, 3, 378-398.
[23] Sitarz-Palczak E., Kalembkiewicz J. 2012. Study of Remediation of Soil Contamined with Heavy Metals by Coal Fly Ash.
Journal of Environmental Protection, 3, 1373-1383.
[39] Siromlya T. I. (2011). Influence of Traffic Pollution on Ecological State of Plantago major L. Contemporary Problems of
Ecology, 4, 499-507.
[24] Poland’s Informative Inventory Report. Submission under UN
ECE Convention on Long-range Transboundary Air Pollution.
(2012). Polish Ministry of the Environment – KOBiZE, Warsaw, Poland.
[25] Pöykiö R., Perämäki P., Niemelä M. (2005). The use of Scots
pine (Pinus sylvestris L.) bark as a bioindicator for environmental pollution monitoring along two industrial gradients in
the Kemi-Tornio area, northern Finland. International Journal
of Environmental and Analytical Chemistry, 85, 2, 127-139.
[26] Gworek B., Dećkowska A., Pierścieniak M. (2011). Traffic
pollutant indicators: Common Dandelion (Teraxacum officinale), Scots Pine (Pinus Silvestris), Small Leaved Lime (Tilia
cordata). Polish Journal of Environmental Studies, 20, 1, 8792.
[27] Lehndorff E., Schwark L. (2008). Accumulation histories of
major and trace elements on pine needles in the Cologne Conurbation as function of air quality. Atmospheric Environment,
42, 833-845.
[28] Molski B., Dmuchowski W. (1990). The comparison of environmental pollution in Northern Finland near Kevo and in Poland with the use of Pinus silvestris L. as bioindicator, Reports
from the Kevo Subarctic Research Station, 21, 27–30.
[29] Barcan V. SH., Kovnatsky E.F., Smetannikova M.S. (1998).
Absorption of heavy metals in wild berries and edible mushrooms in an area affected by smelter emissions. Water Air and
Soil Pollution, 103, 173-195.
[30] Gast C.H., Jansen E., Bierling J. (1988). Heavy metals in
mushrooms and their relationships with soil characteristics.
Chemosphere, 17, 789-799.
[31] Svoboda L., Zimmermannová K., Kalac P. (2000). Concentrations of mercury, cadmium, lead and copper in fruiting bodies
of edible mushrooms in an emission area of a copper smelter
and mercury smelter. Science of the Total Environment, 246,
61-67.
[32] Das N. (2005). Heavy metals biosorption by mushrooms. Natural Product Radiance, 4, 6, 454-459.
[33] Sowa I., Wójciak-Kosior M., Kocjan R. (2012). The content
of some trace elements in selected medicinal plants collected
in the province of Lublin. Acta Scientiarum Polonorum Horticulture, 11, 6, 15-22.
[34] Filipović-Trajković R., Ilić Z.S., Šunić L., Andjelković S.
(2012). The potential of different plant species for heavy metals accumulation and distribution. Journal of Food, Agriculture and Environment, 10, 1, 959-964.
Accepted: May 14, 2014
[35] Kord, B, Mataji, A., Babaie, S. (2010). Pine (Pinus Eldarica
Medw.) needles as indicator for heavy metals pollution. International Journal of Environmental Science and Technology, 7,
1, 79-84.
[36] Yilmaz S., Zengin M. (2004). Monitoring environmental pollution in Erzurum by chemical analysis of Scots pine (Pinus
sylvestris L.) needles, Environment International, 29, 1041–
1047.
[37] Cocchi L., Vescovi L., Petrini L.E. Petrini O. (2006). Heavy
metals in edible mushrooms in Italy. Food Chemistry, 98, 277284.
[38] Kurteva M.K. (2009). Comparative study on Plantago major
and P. lanceolata (Plantaginaceae) as bioindicators of the pollution in the region of the Asarel Copper Dressing Works. Phytologia Balcanica, 15, 2, 261-271.
223
Jerzy Jeznach
Warsaw University of Life Sciences – SGGW
Faculty of Civil and Environmental Engineering
Department of Environmental Improvement
ul. Nowoursynowska 159
02-776 Warszawa
POLAND
FEB/ Vol 24/ No 1a/ 2015 – pages 216 – 223
HEAVY METAL CONTAMINATION OF
PLANT RAW MATERIAL INTENDED FOR FOOD
Elvyra Jariene1,*, Honorata Danilcenko1 and Maria Jeznach2
1
Aleksandras Stulginskis University, Agronomy faculty, Agriculture and Food Sciences Institute, Kaunas-Akademija, Lithuania
2
Warsaw University of Life Sciences – SGGW, Faculty of Human Nutrition and
Consumer Sciences, Department of Organization and Consumption Economics, Warsaw, Poland;
ABSTRACT
Plants best absorb heavy metals by exchange. Most
heavy metals get into plant roots and tubers from soil and
into leaves from the atmosphere. The aim of this research
was to determine the accumulation levels of heavy metals
in organically grown pumpkin peel and flesh and in the tubers of Jerusalem artichoke. The following pumpkin and Jerusalem artichoke cultivars were selected for the investigations: Cucurbita maxima cv ‘Rouge vif d’Etampes’, Cucurbita moschata cv ‘Muscade de Provence’ and Helianthus
tuberosus L. cvs ‘Rubik’, ‘Albik’, and ‘Sauliai’ and grown
in a certified organic field. Heavy metal residues in all samples did not exceed the maximum permissible concentrations. Tubers of the investigated Jerusalem artichoke cvs are
more sensitive to the effect of heavy metals than the over
ground pumpkin fruits.
KEYWORDS:
Pumpkins, Jerusalem artichoke, Heavy metals.
1. INTRODUCTION
In developing economy and social welfare, people are
constantly concerned about food safety and quality. Food
is routinely analyzed for a variety of elements to assess the
possible nutritional or toxicological implications and to ensure its compliance with governmental regulations or product quality [1-3]. In order to eat safe food, firstly there is a
need to grow high quality raw material. Consumers are exposed to a diversity of chemicals in all areas of life. Air,
water, soil and food are unavoidable components of the human environment. Each of these elements influences the
quality of human life and each of them may be contaminated [4-8].
Pumpkins and Jerusalem artichoke are known as vegetables that are nutritious and dietary. They contain a lot
of biologically active substances and are well valuable for
their healing properties and simple cultivation technology.
Recently, both plants are intensively grown in Lithuania.
The aim of this research was to determine the accumulation
levels of heavy metals (Cd, Pb, Cr, Ni, Cu, Zn) in organically grown pumpkin peel and flesh and in the tubers of
Jerusalem artichoke.
The following pumpkins and Jerusalem artichoke cultivars were selected for the investigation: Cucurbita maxima cv ‘Rouge vif d’Etampes’, Cucurbita moschata cv
‘Muscade de Provence’ and Helianthus tuberosus L. cvs
‘Rubik’, ‘Albik’, and ‘Sauliai’.
The experiments were carried out on limnoglacial
loam on boulder clays, carbonate deeper gleyic luvisol
(Calearie Luvisol) in the Sakiai region (Lithuania), on a
certified organic field, whose soil richness was known and
adequate for plant needs. Due to this, the plants were not
fertilized additionally and pesticides were not used. The
soil agrochemical characteristics were as follows: slightly
neutral and neutral, medium humus content, phosphorusrich and potassium-rich. In the experimental area the soil
was drained by drainage and the relief was artificially leveled. Heavy metal concentrations were determined in the
soil [9, 10], their amounts were as follows: Cd – 0.23 mg
kg-1, Cr – 11.76 mg kg-1, Ni – 9.27 mg kg-1, Pb – 11.67 mg
kg-1, Zn – 153.67 mg kg-1, Cu - 24.50 mg kg-1 and did not exceed the maximum permissible concentrations (MPC) [11].
The experimental plants were grown in the following
manner: pumpkins – interlinear – 2 m, with distance between the plants – 2 m, and Jerusalem artichokes – interlinear – 0.7 m, with distance between the plants 0.3 m in
4 repetitions. The repetition variants were distributed at
random. Pumpkin fruits were harvested in the last decade
of September and the Jerusalem artichoke tubers – in the
first decade of November. For analysis of the heavy metal
content, 5 kg of vegetables were collected from every rep-
224
etition. Jerusalem artichoke tubers and pumpkin fruit samples were washed with deionized water. The peel and flesh
from pumpkin fruits were treated separately. Samples were
weighed and air-dried for a day to reduce the water content.
All samples were oven-dried at 70-80°C for 24 h. The dry
samples were ground to fine powder using a stainless steel
mill.
Heavy metals (Cd, Pb, Cr, Ni, Cu, Zn) were determined
using a mass spectrometer (Thermo Finnigan MAT, Bremen, Germany). Powdered samples of Jerusalem artichoke
tubers and the peel and flesh of pumpkin fruits were transformed into solutions and analyzed. All analyzes were carried out in the Laboratory of Agricultural and Food Sciences, Institute of Aleksandras Stulginskis University and
in the Agrochemical Laboratory of the Lithuanian Research Centre for Agriculture and Forestry. All analyses
were conducted in three repetitions.
The data was statistically treated using Anova data
analysis and management module of the integrated system
STATISTICA. For the evaluation of the analyses, one factor
analysis of variance was carried out. Averages of separate
treatments were calculated, the standard deviation and the
least significant difference at a 95% probability level were
estimated using the Fisher’s LSD test (P<0.05).
Table 1 shows the concentrations of heavy metals determined in the pumpkin fruits. The highest values of Cd
and especially high Zn were determined in cv ‘Muscade
de Provence’ pumpkin flesh at 0.011 mg kg-1 and 2.976 mg
kg-1, respectively. The lowest value of Cd was determined in
the peel of cv ‘Muscade de Provence’ and in the peel and flesh
of cv ‘Rouge vif d’Etampes’ pumpkin at 0.002 mg kg-1, while
the values of Zn in the flesh of cv ‘Rouge vif d’Etampes’
pumpkin reached 0.702 mg kg-1 (Table 1). Cadmium is
known to be a principal toxic element, since it inhibits many
life processes [12]. Some studies have found out close links
between the low amount of zinc and increased risk of cancer.
Zinc is an important element in numerous proteins and plays
an essential role in several cell functions, e.g. the functioning
of the immune system [6].
The results of the analysis have shown that Pb was determined only in cv ‘Muscade de Provence’ pumpkin flesh
at 0.003 mg kg-1 and there were no traces of this metal in
other pumpkin genotypes. The Pb content in the pumpkin
flesh varied between 0.4 and 1.9 mg kg-1 as reported by
other studies [13].
The highest values of Cr were determined in cv ‘Muscade de Provence’ and cv ‘Rouge vif d’Etampes’ pumpkin
flesh at the level of 0.101 and 0.109 mg kg-1, respectively,
while the lowest values were identified in peels of the
above mentioned cvs. at 0.042 and 0.032 mg kg-1, respectively. There are no reports related to the accumulation of
Cr in pumpkin peel and flesh, while in pumpkin seeds the
Cr levels vary from 0.01 mg kg-1 to 0.28 mg kg-1 depending
on the cultivars [14].
Nickel plays a role in body functions including enzymatic functions. It occurs naturally more commonly in
plants rather than in animal flesh. It activates some enzyme
systems in trace amounts but its toxicity at higher levels is
more significant [15]. The highest amount of Ni was determined in cv ‘Muscade de Provence’ pumpkin flesh and the
lowest one  in cv ‘Rouge vif d’Etampes’ pumpkin peel at
0.221 mg kg-1 and 0.035 mg kg-1, respectively.
Copper is an essential micronutrient which functions
as a biocatalyst, in addition to iron is required for body pigmentation, maintains a healthy central nervous system, prevents anemia and is interrelated with the function of Zn and
Fe in the body [16]. Cu concentrations in all tested samples
were at very similar amounts and varied between 0.563 and
0.792 mg kg-1, with cv ‘Rouge vif d’Etampes’ pumpkin
peel having the lowest and cv ‘Muscade de Provence’
pumpkin flesh having the highest values. The results obtained here for pumpkin flesh were similar compared to
other published results [13].
Generally, the highest values of Cd, Cr, Ni, Cu and Zn
in pumpkin fruits were detected in flesh of cv ‘Muscade de
Provence’ and cv ‘Rouge vif d’Etampes’ (Table 1). They,
however, do not exceed the maximum allowable concentrations recommended for vegetables by the EU [17].
Jerusalem artichoke tubers had a higher heavy metal
content compared to the investigated pumpkin peels and
flesh. Table 2 show the concentrations of heavy metals determined in Jerusalem artichoke cvs tubers. The results of the
analysis have shown that the levels of the same heavy metals in samples from different cultivars showed no significant
differences. Higher amounts of Cd, Pb, and Zn were deter-
TABLE 1 - Heavy metals residue (mg kg-1 d.w.) in the peels and flesh of various pumpkin genotypes)
Pumpkins genotype
Cucurbita moschata
Cucurbita maxima
cv ‘Muscade de Provence’ pumpkin
cv ‘Rouge vif d’Etampes’ pumpkin
peel
flesh
peel
flesh
0.002±0.00a
0.011±0.01b
0.002±0.00a
0.002±0.00a
Cd
a
a
a
0.000±0.00
0.003±0.00
0.000±0.00
0.000±0.00a
Pb
0.042±0.02a
0.101±0.05b
0.032±0.01a
0.109±0.01b
Cr
0.053±0.02b
0.221±0.03c
0.035±0.01a
0.064±0.01b
Ni
0.605±0.19a
0.792±0.25a
0.563±0.01a
0.607±0.15a
Cu
0.753±0.23a
2.976±0.21b
0.709±0.20a
0.702±0.15a
Zn
Note:  The average values marked by the same letter in a row have no significant differences at P≤0.05
Heavy metals
225
TABLE 2 - Heavy metals residue (mg kg-1 d.w.) in Jerusalem artichoke tubers (mean ± SD, n=12)
Heavy metals
Jerusalem artichoke cultivars
‘Albik’
‘Rubik’
0.04±0.01a
0.03±0.01a
Cd
0.06±0.03a
0.04±0.00a
Pb
3.27±0.35a
3.59±0.33a
Cr
0.64±0.19a
0.70±0.02a
Ni
3.29±0.35a
3.60±0.33a
Cu
a
9.27±2.93
8.60±1.09a
Zn
Note:  The average values marked by the same letter in a row have no significant differences at P≤0.05
mined in cv ‘Albik’ tubers at 0.04 mg kg-1, 0.06 mg kg-1
and 9.27 mg kg-1, respectively; Ni – in cv ‘Rubik’ tubers
at 0.70 mg kg-1; Cr and Cu – in cv ‘Sauliai’ tubers at 4.09
and 4.00 mg kg-1, respectively. Our results show that the
residue of these metals in Jerusalem artichoke tubers does
not exceed the MPC as recommended for vegetables by the
EU [17].
However, particularly high zinc values (9.27 mg kg-1)
have been detected in cv ‘Albik’ tubers (Table 2). High
concentrations of lead and other heavy metals (Cd) have
been occasionally reported [2].
Plants best absorb heavy metals by exchange. The concentration of heavy metals in plants increases in the following order: Cd>Pb>Zn>Cu, depending on their mobility and
amount in soil; however, a reliable relationship between
their amounts in soil and plants was not established. Most
heavy metals get into plant roots from the soil and into leaves
from the atmosphere. Roots are more sensitive to the effect
of heavy metals than the over ground fruits [18] and the reason for these differences in our study could be explained by
the different physiological activity of pumpkins and Jerusalem artichoke plants. Moreover, heavy metal bioavailability
depends on the plant species and its ability to absorb metals.
Up to a certain concentration, some higher plants may selfregulate the absorption of metals, e.g. in order to protect
from the effect of Cd, plants can change the medium pH in
the rhizosphere. Plants increase soil pH during the absorption of NO32- rather that NH4 + [19]. Plants commonly accumulate cadmium, to a lesser extent – zinc and copper, and
lastly – manganese, nickel, and chromium. It has also been
established that the content of heavy metals in Jerusalem artichoke tubers may increase the level of soil pollution with
heavy metals [1,3] and the plant may be a promising crop for
bioremediation in contaminated soils [19].
‘Sauliai’
0.03±0.02a
0.03±0.01a
4.09±0.97a
0.58±0.29a
4.00±0.97a
8.55±1.85a
ACKNOWLEDGEMENT
This publication is funded by European Social Fund
and the Budget of the Republic of Lithuania (project „Eureka“E! 6855 – ECORAW „Higher functionality food
products from organic vegetable raw materials“).
REFERENCES
[1]
Antonkiewicz, J. and Jasiewicz, C. (2003). Assessment of Jerusalem artichoke usability for phytoremediation of soils contaminated with Cd, Pb, Ni, Cu and Zn. Pol. Archiwum
Ochrony Środowiska, 29: 81-87.
[2]
Cieslik, E. and Baranowski, M. (1997). Minerals and lead content of Jerusalem artichoke new tubers. Brom. Chem. Toxicol.,
30: 66-67.
[3]
Jasiewicz, C. and Antonkiewicz, J. (2002). Heavy metals extraction by Jerusalem artichoke (Helianthus tuberosus L.)
from soils contaminated with heavy metals. Chemia i
Inżynieria Ekologiczna, 9 (4): 379-386.
[4]
Orisakwe O.E., Kanayochukwu N. J., Nwadiuto A.C., Daniel
D. and Onyinyechi O. (2012). Evaluation of Potential Dietary
Toxicity of Heavy Metals of Vegetables. J Environment Analytic Toxicol, 2: 136.
[5]
Melø R., Gellein K., Evjea L., and Syversen T. (2008). Minerals and trace elements in commercial infant food. Food and
Chemical Toxicology, 46:3339–3342.
[6]
Song Y., Leonard S.W., Traber M.G., and Ho E. (2009). Zinc
deficiency affects DNA damage, oxidative stress, antioxidant
defenses and DNA repair in rats. The Journal of Nutrition, 139
(9):1626-1631.
[7]
Jarienė, E.; Danilčenko, H.; Neverauskaitė, L.; Černiauskienė,
J.; Mažeika, R.; and Staugaitis, G. (2013). Chemical Composition and Pollution Research in Pumpkin Fruit of Various
Genotypes // Rural development 2013: proceedings. 6 (20):
115-118.
Heavy metal residues in the peels and flesh of the analyzed pumpkin fruits and Jerusalem artichoke tubers did
not exceed the maximum permissible concentrations (EU
Commission Regulation (EC) No. 1881/2006).
[8]
Murphy, S.P. (2002). Dietary reference intakes for the U.S.
and Canada: update on implications for nutrient databases, J.
Food Compos. Anal., 15: 411-417.
[9]
ISO 11466:95. Soil quality - Extraction of trace elements soluble in aqua regia.
Tubers of the investigated Jerusalem artichoke cultivars are more sensitive to the effect of heavy metals than
the over ground pumpkin fruits.
[10] ISO 11047:1998. Soil quality -- Determination of cadmium,
chromium, cobalt, copper, lead, manganese, nickel and zinc -Flame and electrothermal atomic absorption spectrometric
methods.
4. CONCLUSIONS
226
[11] Mazvila, J. (2001). Heavy metals in Lithuanian soils and
plants. Monograph, 343 p.
[12] Anonymous, (2000). International programme on chemical
safety (IPCS INCHEM). Joint FAO/WHO expert committee
on food additives (JECFA), Safety Evaluation of Certain Food
Additives and Contaminants, Report No. TRS 44-JECFA
53/273, FAS 46- JECFA 55/247, WHO, Geneva.
[13] Karanja, J. K., Mugendi, J. B.,. Fathiya, M. K. and. Muchugi,
A. N. Comparative Study on the Nutritional Value of the
Pumpkin, Cucurbita Maxima varieties from different Regions
in
Kenya
(http://elearning.jkuat.ac.ke/journals/ojs/index.php/jscp/article/view/907).
[14] Danilčenko, H.; Jarienė, E.; Gajewski, M.; Černiauskienė, J.;
Kulaitienė, J.; Sawicka, B.; and Aleknavičienė, P. (2011). Accumulation of elements in some organically grown alternative
horticultural crops in Lithuania // Acta Scientiarum Polonorum: Hortorum Cultus. Lublin, 10(2): 23-31.
[15] Divrikli, U., Horzum, N., Soylak, M., Elci, L. (2006). Trace
heavy metal contents of some spices and herbal plants from
western Anatolia, Turkey. Int. J. Food Sci. Technol., 41: 712716.
[16] Akinyele, I.O., Osibanjo, O. (1982). Levels of trace elements
in hospital diet. Food Chem., 8: 247-251.
[17] COMMISSION REGULATION (EC) No. 1881/2006. Setting
maximum levels for certain contaminants in foodstuffs.
[18] Öncel I., Keles, Y., Üstün, A. S. (2000). Interactive effects of
temperature and heavy metal stress on the growth and some
biochemical compounds in wheat seedlings. Environmental
pollution, 107: 315 – 320.
[19] Nyguist J., Greger M. (2009). Response of two wetland plant
species to Cd exposure at low and neutral pH. Environmental
and Experimental Botany, 65: 417 – 424.
Accepted: July 03, 2014
Elvyra Jariene
Aleksandras Stulginskis University
Agronomy Faculty
Agriculture and Food Sciences Institute
Kaunas – Akademija, LT-53361
LITHUANIA
Phone: +37037 752326
FEB/ Vol 24/ No 1a/ 2015 – pages 224 – 227
227
COPPER REMOVAL FROM CONTAMINATED
GROUNDWATER USING NATURAL AND ENGINEERED
LIMESTONE SAND IN PERMEABLE REACTIVE BARRIERS
Joanna Fronczyk1*, Maja Radziemska1 and Zbigniew Mazur2
1
Warsaw University of Life Sciences - SGGW, Faculty of Civil and Environmental Engineering, Nowoursynowska 159, 02-776 Warsaw, Poland
2
University of Warmia and Mazury in Olsztyn, Faculty of Environmental Management and Agriculture, Pl. Łódzki 4, 10-727, Olsztyn, Poland
ABSTRACT
NOMENCLATURE
This work investigates the performance of natural and
engineered limestone sand (thermally and chemically modified) with respect to copper removal from groundwater
protected by permeable reactive barriers. The engineered
sorbents (particle size 0.25 – 0.50 mm) were prepared using
high temperature at 850ºC and solutions of 0.25M Fe(III),
1M NaOH and 1M HNO3. Batch tests were carried out using a constant mass of the sorbent (0.5 g) and 50 mL of
numerous copper ion solutions at different pH values at a
desired shaking time in order to analyze the mechanism of
copper removal. At a final pH value above 6.3, natural and
engineered limestone sand removed over 94% of Cu(II).
Test data indicated that the mechanism of copper removal is
significantly affected by the contact time and the final pH
level. The main processes leading to the removal of copper
ions included precipitation with calcite or carbonate solids,
chemisorption and physisorption. The test data fitted well
with the pseudo-first-order, pseudo-second-order and intraparticle diffusion kinetic models (excluding data for Fe(III)modified limestone) as well as the linear Henry’s isotherm.
The most suitable for further application as a reactive material
in PRBs technology is NaOH-modified limestone, followed by thermally-modified, HNO3-modified, Fe(III)modified limestone, and limestone.
KEYWORDS: groundwater contamination, limestone sand, natural
and engineered sorbents, permeable reactive barriers
C0 is the initial concentration;
Ct is the concentration after time t;
KD is the partitioning coefficient of Henry’s law;
KID is the intraparticle diffusion rate constant;
KPFO is the equilibrium rate constant of the pseudo-first
order model;
KPSO is the equilibrium rate constant of the pseudo-second order model;
qeq is the equilibrium adsorption capacity of the copper
ions;
qt is the adsorption capacity determined after time t;
R is the removal ratio.
1. INTRODUCTION
The occurrence of toxic inorganic (especially heavy
metals, e.g. copper) and organic compounds in subsoil,
caused by the development of civilization, is one of the most
important environmental problems. Maintaining public
health and biodiversity preservation require the development of new methods of soil and groundwater remediation.
Permeable Reactive Barriers (PRBs) are an effective alternative to conventional remediation methods of contaminated groundwater such as pump and treat technology, soil
flushing by surfactant solutions [1], biodegradation [2] and
electrokinetic methods [3]. The technology is based on the
use of materials (natural and synthetic) that react with the
pollutants present in the subsoil system. The nature of the
processes (i.e. natural attenuation processes) depends on
the material used for PRB construction. The most common
processes used in this technology include reduction, sorption
and precipitation, while the most widely and successfully
used reactive material is zerovalent iron [4-6], followed by
activated carbon [7, 8], and natural sorbents e.g. zeolites
[9, 10]. There are also reports that natural calcium carbonate may successfully be used in the PRB technology.
228
The application of limestone for pH neutralization of acid
mine drainage has been reported by numerous researchers
[11-13]. Moreover, the removal of numerous metals (e.g.
Fe(II), Fe(III), Al(III), Mn(II)) [11, 14, 15], including heavy
metals (Zn(II), Ni(II), Cu(II), Cr(III), Cd(II) and Pb(II))
was observed in limestone-based treatment systems [13,
16, 17]. Mechanisms of toxic metals removal by natural calcium carbonates include ion exchange of calcium and magnesium, sorption by carbonates, as well as precipitation.
Recently, natural minerals and organic matter-based
materials that were thermally or chemically modified are
used in various water treatment technologies. As an example, the following materials may be mentioned: Fe(III)modified zeolite [18], Na-X zeolite [19], HCl-modified
clays [20], oxidized granular activated carbon [21], montmorillonite/Trametes versicolor [22]. Thus, the impact of
thermal and chemical pre-treatment of natural limestone on
its removal efficiency of copper ions seems a valuable
study project. In this regard, the presented studies have
been carried out to determine the effect of contact time, initial pH level and initial concentration of copper ions on the
sorption efficiency by natural limestone and its modified
forms using 0.25M Fe(III), 1M HNO3, 1M NaOH at 850ºC.
Additionally, the utility of natural and engineered limestone
sand application as reactive materials in groundwater protection has been discussed.
2.1 Material
Natural Polish limestone from Czatkowice and its thermally and chemically modified forms were investigated in
the research. The composition of natural limestone is very
close to the formula CaCO3, with the chemical composition
in oxide (wt.%) C2O – 1.60%, N2O5 – 0.78%, MgO –
0.05%, Al2O5 – 0.49%, SiO2 – 1.34%, K2O – 0.48%, CaO
– 87.4%, FeO3 – 7.80%. Moreover, studies on the sorption
of nitrogen revealed that the natural limestone has a specific surface area of 0.91 m2/g, an average pore size of
133.4 Å, and is characterized by a non-porous structure.
Figure 1 shows XRD spectra, SEM photographs and EDX
mapping of the material. Before the pre-treatment stage, the
natural limestone sand was sieved to obtain a uniform grain
size of 0.25 – 0.50 mm. In the next step, the limestone was
modified in four ways as follows:
(1) 100 g of limestone was placed in a muffle furnace
at 850ºC for 5 hours, then cooled down to room temperature;
(2) 100 g of limestone was mixed with 200 mL of
0.25 M Fe(III) (Aldrich) solution at pH of 10.0 (adjusted
with 1M NaOH in the beginning) for 7 days at room temperature;
(3) 50 g of limestone was shaken with 500 mL 1M
HNO3 solution for 7 days;
(4) 50 g of limestone was shaken with 500 mL 1M
NaOH solution for 7 days.
After the reaction processes, the chemically treated
samples were filtered and washed until the pH and the electrical conductivity values attained a constant level, dried at
60 ºC, and placed in plastic bags.
2.2 Sorption test
A series of experiments were conducted to evaluate the
effect of time, initial pH of the solution (pHi) and initial
Cu(II) concentration on the copper removal efficiency of
natural and engineered limestone sand. All sorption tests
were conducted in a batch system at room temperature of
21±2ºC. 0.5 g of the sorbent and 50 mL of the copper ions
solution were placed in a plastic bottle, then shaken for a
desired period and centrifuged at 3000 rpm for 5 min. At
the end of the experiments, the pH of each solution was
measured with a standard pH-meter (Schott, Germany) and
probe-calibrated with pH 4, 7, and 10 buffers. The concentration of copper (dissolved fraction) in both initial and
equilibrium solutions was determined by atomic absorption
spectroscopy – AAS (ICE-3000, Thermo Scientific, USA)
with an air-acetylene flame and wavelength of 324.8 nm.
The stock solutions for the sorption tests were made by dis-
FIGURE 1 – XRD spectra, SEM image and energy dispersive X-ray mapping (EDX) of natural limestone
229
solving CuSO4•5H2O salts of analytical grade (Merck) in
deionized water. The pH of the solution was adjusted using
1M HCl and 1M NaOH at the beginning of the tests (both
analytical grade).
Sorption kinetic tests were performed at an initial copper ion concentration of 50.0 mg/L and at an initial pH of
5.0. The desired shaking time was 10, 30 minutes and 1, 3,
5, 7, 24, 30, 50 and 75 hours. Sorption equilibrium tests
were carried out with the solution of initial copper concentrations from 5.0 to 50.0 mg/L (in increments of 5.0 mg/L).
The pH of the initial solutions was in the range of 5.0 – 5.5.
Additionally, experiments of the pH effect (from 3.0 to
11.0 in increments of 1.0) were also performed. All experiments were conducted in duplicates and the average values were used in further calculations.
The Cu(II) removal ratio R (%) was calculated from
the formula:
(1)
and the sorption capacity was determined as follows:
(2)
where C0 and Ct are initial copper ion concentration
and copper ion concentration after time t of the contact time
(mg/L), respectively; R is the removal ratio of the copper
ions by biosorbents (%); qt is the adsorption capacity of
copper ions determined after time t of the contact time
(mg/g); and V is the volume of the copper ions solution (L).
The results of the sorption kinetic tests were analyzed
using pseudo-first order, pseudo-second order and intraparticle diffusion kinetic models with application of the
following equations, respectively [23-25]:
(3)
3. RESULTS AND DISSCUSION
3.1 Sorption kinetics
In this study, the sorption processes are defined as adsorption, co-precipitation, and surface precipitation. Figure
2 shows the kinetics of copper ions removal as well as the
variation of the final pH of the solution (pHf) depending on
the shaking time. Analysis of the obtained test results allows
to conclude that only the contact time exceeding 24 hours
(for limestone, Fe(III)-modified limestone and NaOHmodified limestone) and 50 hours (for HNO3-modified
limestone) caused an increase of the pHf to the value higher
than 6.3, and thereby resulted in the precipitation of the
copper ions in form of metal hydrooxides and metal carbonates in alkaline conditions [26, 27]. By contrast, regardless of the shaking time, in the case of thermally-modified
limestone with a Cu(II) solution the measured pHf was
higher than 12.0, which may be related to the dissolution of
calcium carbonate. Because of the significant effect of the
solution pH on the type of copper removal processes, further calculations using common adsorption kinetic models
were performed for those samples, in which pHf was lower
than 6.3. The time, after which equilibrium was attained, was
determined as follows: less than 10 minutes for thermalmodified limestone, 3 hours for limestone, 7 hours for
HNO3-modified limestone, and 24 hours for NaOH-modified limestone. Longer equilibration time may be related to
physical adsorption, while a shorter  to chemisorption
[16, 17]. Based on the experimental results, the equilibrium
time for Fe(III)-modified limestone was difficult to determine. Table 1 shows the critical parameters of the sorption
kinetics. According to the coefficient of determination
(R2), the pseudo-second order kinetic model is the most appropriate for Cu(II) sorption by limestone and thermallymodified limestone, the pseudo-first order and intraparticle
diffusion models fit best the test data for NaOH-modified
and HNO3-modified limestone, while data for Fe(III)-modified limestone could not be described using common kinetic models. Moreover, cop-
(4)
(5)
where qeq is the equilibrium adsorption capacity of the
copper ions (mg/g); qt is the adsorption capacity determined after time t (mg/g); KPFO and KPSO are the equilibrium rate constants of the pseudo-first order model (1/min)
and pseudo-second order model (g/mg min); respectively;
KID is the intraparticle diffusion rate constant (mg/g
min1/2); C is the intercept (mg/g).
Data from the equilibrium sorption tests were described using the linear sorption isotherm (Henry’s isotherm), which implies constant sorption in a solid-liquid
system:
(6)
where KD is the Henry’s distribution coefficient (L/g).
TABLE 1 - Kinetic constants of Cu(II) adsorption at pHf below 6.3
Kinetic model
LS
Pseudo first order kinetic model
kPFO (1/min)
qeq-cal (mg/g)
R2
Pseudo second kinetic model
kPSO (g mg-1 min-1)
2.584
qeq-cal (mg/g)
2.337
R2
0.999
Intraparticle diffusion
kID (mg/g min1/2)
0.014
C (mg/g)
2.038
R2
0.354
T-LS
NaOH-LS
HNO3-LS
-
0.001
1.329
0.967
0.005
0.817
0.909
1.002
4.990
0.999
-
-
0.002
4.917
0.802
0.027
0.000
0.967
0.019
0.161
0.814
where LS is limestone; T-LS is thermally-modified limestone; NaOH-LS is
NaOH-modified limestone; HNO3-LS is HNO3-modified limestone; qeq is the
equilibrium adsorption capacity of the copper ions (mg/g); qt is the adsorption
capacity determined after time t (mg/g); KPFO and KPSO are the equilibrium rate
constants of the pseudo-first order model (1/min) and pseudo-second order
model (g/mg min), respectively; KID is the intraparticle diffusion rate constant
(mg/g min1/2); C is the intercept (mg/g).
230
13
6
13
12
5.5
12
5
11
5
11
4.5
10
8
7
3
6
2.5
5
2
4
9
3.5
8
1
1
0.5
0
3500
0
1000
1500
2000
Time (min)
2500
3000
3
2
Thermally-modified limestone
0
500
1000
1500
2000
Time (min)
2500
3000
1
0
3500
13
6
13
12
5.5
12
5
11
5
11
4.5
10
4
9
3.5
8
7
3
6
2.5
5
2
4
1.5
3
1
0.5
0
500
1000
1500
2000
Time (min)
2500
3000
10
4.5
4
9
3.5
8
7
3
6
2.5
5
2
4
1.5
3
2
1
2
1
0.5
0
3500
0
NaOH-modified limestone
0
Adsorption capacity (mg/g)
6
5.5
Final pH
500
4
1.5
2
0
5
2
1
0
6
2.5
3
Limestone
7
3
1.5
0.5
4
Final pH
3.5
10
4.5
6
13
5.5
12
5
11
Final pH
9
Final pH
4
6
5.5
HNO3-modified limestone
0
500
1000
1500
2000
Time (min)
2500
3000
1
0
3500
10
4.5
4
9
3.5
8
7
3
6
2.5
5
2
Final pH
4
1.5
3
1
2
0.5
Fe(III)-modified limestone
0
0
500
1000
1500
2000
Time (min)
2500
3000
1
0
3500
FIGURE 2 – Effect of the contact time on the adsorption capacity and final pH
per sorption by limestone did not follow the intraparticle
diffusion model. This may indicate that surface sorption to
a larger extent contributes to the rate-limiting step than intraparticle diffusion [28]. The agreement of experimental
observation of Cu(II) sorption by limestone and NaOH-modified limestone with intraparticle diffusion kinetic model indicates two steps of the processes: (1) the initial surface adsorption, and (2) the final equilibrium stage with slower intraparticle diffusion due to low adsorbate concentration in
the solution [29]. This is in agreement with other studies,
which demonstrate that surface adsorption and intraparticle
diffusion were involved in the adsorption of heavy metals
[30, 31]. Furthermore, the calculated values of the sorption
capacities at equilibrium (at pHf below 6.3) using the
pseudo-first order and pseudo-second order kinetic models
were similar to the experimental data. The calculated qeq for
the desired test conditions (initial copper concentration of
50.0 mg/L and solid-to-solution ratio of 0.01 g/L) were
2.337 mg/g for limestone, 1.329 mg/g for NaOH-modified
limestone and 0.817 mg/g for HNO3-modified limestone.
231
contaminated ground-water with the reactive material to ensure the occurrence of all the mechanisms of copper removal.
3.2 Equilibrium test
Figure 3 shows the relationship of the adsorption capacity and copper concentration in the equilibrium solution. By contrast to previous reports [16], the data from
the experimental equilibrium tests for Cu(II) did not fit the
common sorption isotherms (Freundlich, Langmuir and
Redlich-Peterson isotherms). The test results indicated that
Henry’s isotherm, which shows a linear relationship, fits
the data best with the values of the coefficient of determination above 0.8 for NaOH-modified (R2=0.808) and thermally-modified limestone (R2=0.831), and above 0.9 for
limestone (R2=0.949), HNO3-modified (R2=0.927) and
Fe(III)-modified limestone (R2=0.920). The slope of the
sorption isotherm represents the Henry’s distribution coefficient (KD), which is presented together with the determined
equations in Table 2. Comparing the calculated values of
KD, the materials can be ranked with regard to their copper
removal efficiency as follows: NaOH-modified, thermallymodified, HNO3-modified, Fe(III)-modified limestone, and
finally limestone with the lowest removal efficiency. The
measured sorption capacities of natural and engineered
limestone sand (from 4.948 to 4.963 mg/g) were comparable to the values from the kinetic tests. It should be noted
that apart from precipitation, a small amount of copper ions
may be removed through ion exchange with Ca and chemisorption (although the pHf was higher than 6.3) and at higher
pH levels through precipitation with calcite or carbonate
solids [26, 32-34]. Moreover, according to Aziz et al. [16],
both adsorption and precipitation mechanisms are expected
to occur at low concentrations of heavy metals.
TABLE 2 – Henry’s isotherm results
Henry’s
LS
T-LS
Fe(III)-LS NaOH-LS HNO3-LS
isotherm
KD (L/g)
8.954
11.074
10.123
11.476
10.867
Equaqeq=8.954 qeq=11.074 qeq=10.123 qeq=11.476 qeq=10.867
tions
Ceq
Ceq
Ceq
Ceq
Ceq
R2
0.949
0.831
0.920
0.808
0.814
3.3 Effect of initial pH
FIGURE 3 – Adsorption isotherms of Cu(II) for natural and engineered limestone sand
The sorption capacities determined for the contact time of
75 hours were similar for all materials (from 4.949 to 4.995
mg/g) and were the sum of the Cu(II) removed firstly due
to the precipitation of copper oxides and carbonates, and
secondly due to the sorption by mineral sorbents. The
measured and calculated qeq for the tested materials correspond well with the values reported by Sdiri et al. [17] and
Aziz et al. [16], who obtained sorption capacities for various limestones in the range from 0.784 to 1.845 mg/g and
2.915 mg/g, respectively. Finally, a PRB should be designed to provide a sufficiently long contact time of the
The effect of initial pH of the solution on the copper
removal ratio at a final pH of the solution is presented in
Fig. 4. For natural limestone and all its chemically modified forms, the pHf of solution was substantially constant
(from 7.4-7.9), irrespective of pHi in the range from 3.0 to
7.0. This may suggest the buffering capacity of the applied
materials. Further increase of pHi led to the increase of the
pHf to the maximum value of 10.7. By contrast, in the case
of thermally modified limestone, the pHf was independent
of the pHi values and reached 12.4. The increase of pHf
may result from the presence of dissolved calcium carbonate in the solution. For all materials tested, after the
shaking period the pH was above the solubility point of
heavy metals, which led to their precipitation, therefore the
232
calculated removal ratio was higher than 94.29%. Sdiri et
al. [17] reported that precipitation of metal carbonates occurs when the concentration product ([M2+1] [CO32-]) is
higher than the corresponding solubility product constant
(Ksp). Accordingly, the precipitation of copper as a metal hydroxide is expected at a pH of above 6.
physisorption (by HNO3-modified and NaOH-modified
limestone) and precipitation into insoluble hydroxide and
carbonate forms in an alkaline environment (all tested materials). On the basis of equilibrium tests and the calculated
Henry’s partitioning coefficients, the material with the
highest removal efficiency was NaOH-modified limestone,
followed by thermally-modified, HNO3-modified, Fe(III)modified limestone, and limestone. Finally, according to
the obtained copper removal efficiency exceeding 94.29%
it can be concluded that all materials may be recommended
as low-cost reactive materials for the removal of Cu(II)
from contaminated groundwater using PRBs technology.
However, verification tests should be performed prior to
the application of these materials in field conditions.
ACKNOWLEDGMENTS
This research was supported by Grant no. NN523
561638 obtained from the Ministry of Science and Higher
Education of the Republic of Poland.
REFERENCES
FIGURE 4 - Variation of final pH and removal ratio at different values of the initial solution pH
[1]
Mulligan, C.N., Yong, R.N., Gibbs, B.F., James, S. and Bennett, H.P.J. (1999) Metal removal from contaminated soil and
sediments by the biosurfactant surfactin. Environ. Sci. Technol. 33(21), 3812–3820.
[2]
Małachowska-Jutsz, A. and Kalka, J. (2010) Influence of mycorrhizal fungi on remediation of soil contaminated by petroleum hydrocarbons. Fresenius Environmental Bulletin
19(12b), 3217-3223.
[3]
Kim, S.H., Han, H.Y., Lee, Y.J., Kim, C.W. and Yang, J.W.
(2010) Effect of electrokinetic remediation on indigenous microbial activity and community within diesel contaminated
soil. Sci. Total Environ. 408, 3162–3168.
[4]
Permeable Reactive Barrier Technologies for Contaminant
Remediation, (1998) EPA (U.S. Environmental Protection
Agency). EPA/600/R-98/125.
[5]
Vogan, J.L., Focht, R.M., Clark, D.K. and Graham, S.L., 1999.
Performance evaluation of a permeable reactive barrier for remediation of dissolved chlorinated solvents in groundwater. J.
Hazard. Mater. 68, 97-108.
[6]
Farrell, J., Melitas, N., Kason, M. and Li, T. (2000) Electrochemical and column investigation of iron-mediated reductive
dechlorination of trichloroethylene and percholoroethylene.
Environ. Sci. Technol. 34, 2549-2556.
[7]
Lorbeer, H., Starke, S., Gozan, M., Tiehm, A. and Werner, P.,
2002. Bioremediation of chlorobenzene-contaminated
groundwater on granular activated carbon barriers. Water Air
Soil Poll. Focus. 2, 183-193.
[8]
Bortone, I., Di Nardo, A., Di Natale, A. Erto, M., Musmarra,
D., Santonastaso, G.F. (2013) Remediation of an aquifer polluted with dissolved tetrachloroethylene by an array of wells
filled with activated carbon. J. Hazard. Mater. 260, 914-920.
4. CONCLUSIONS
The results presented in this paper indicate that limestone as well as thermally and chemically modified limestone show high efficiency in copper ion removal. Moreover, the data signify that the contact time, pH, and initial
metal concentrations play major roles in Cu(II) removal.
The results (with the exception of data for Fe(III)-modified
limestone) are consistent with the commonly used kinetic
models such as the pseudo-first order, pseudo-second order
and intraparticle diffusion kinetic models. Copper ions
were removed mainly by chemisorption (by limestone),
233
[9]
Czurda, K.A. and Haus, R. (2002) Reactive barriers with fly
ash zeolites for in situ groundwater remediation. Appl. Clay
Sci. 1-2, 3-20
[10] Fronczyk, J. and Garbulewski, K. (2013) Evaluation of zeolitesand mixtures as reactive materials protecting groundwater at
waste disposal sites. J. Environ. Sci. 25(9) 1764-1772.
[11] Ziemkiewicz, P.F., Skousen, J.G., Brant, D.L., Sterner, P.L.
and Lovett, R.J. (1997) Acidic mine drainage treatment with
armored limestone in open limestone channels. J. Environ.
Qual. 26, 1017-1024.
[12] Watten, B.J., Sibrell, P.L. and Schwarz, M.F. (2004) Effect of
acidity and elevated PCO2 on acid neutralization within pulsed
limestone bed receiving coal mine drainage. Environ. Eng.
Sci. 21, 786-802.
[28] Georgescu, I., Mureşeanu, M., Cârjă, G., and Hulea, V. (2013)
Adsorptive removal of cadmium and copper from water by
mesoporous silica functionalized with N-(aminothioxomethyl)-2-thiophen carboxamide. J. Envirom. Eng.. 139(10),
1285-1296.
[29] Örnek, A., Özacar, M. and Şengil, A.İ. (2007) Adsorption of
lead onto formaldehyde or sulphuric acid treatment acorn
waste: equilibrium and kinetic studies. Biochem. Eng. J.,
37(2), 192-200.
[30] Unuabonah, E.I., Adebowale, K.O. and Olu-OWolabi, B.I.
(2007) Kinetic and thermodynamic studies of the adsorption
of lead(II) ions onto phosphate-modified kaolinite clays. J.
Hazard. Mater. 144(1-2), 386-395.
[13] Miller, A., Figueroa, L. And Wildeman, T. (2011) Zinc and
nickel removal in simulated limestone treatment of mining influenced water. Appl. Geochem. 26, 125-132.
[31] Li, X., Yang, L., Li, Y., Ye, Z. and He, A. (2012) Efficient
removal of Cd2+ from aqueous solutions by adsorption on PSEDTA resins: equilibrium, isotherms, and kinetic studies. J.
Environ. Eng. 138(9), 940-948.
[14] Aziz, H.A. and Smith, P.G. (1992) The influence of pH and
coarse media on manganese precipitation from water. Water
Res. 26, 853–855.
[32] Papadopoulos, P. and Rowell, D.L. (1989) The reactions of
copper and zinc with calcium carbonate surfaces. J. Soil Sci.
40, 39-48.
[15] Santomarino, S. and Webb, J.A. (2007) Estimating longevity
of limestone drains in treating acid mine drainage containing
high concentrations of iron. Appl. Geochem. 22, 1244-2361.
[33] Sdiri, A. and Higashi, T. (2013) Simultaneous removal of
heavy metals by natural limestones. Appl. Water Sci. 3, 29-39.
[16] Aziz, H.A., Adlan, M.N., Ariffin, K.S. (2008) Heavy metals
(Cd, Pb, Zn, Ni, Cu and Cr(III)) removal from water in Malaysia: Post treatment by high quality limestone. Bioresour. Technol. 99, 1578–1583.
[34] Sdiri, A., Higashi, T. and Jamoussi, F. (2014) Adsorption of
copper and zinc onto natural clay in single and binary systems.
Int. J. Environ. Sci. Tech. 11, 1081-1092.
[17] Sdiri, A., Higashi, T., Jamoussi, F. And Bouaziz, S. (2012) Effects of impurities on the removal of heavy metals by natural
limestones in aqueous systems. J. Environ. Manage. 93, 245253.
[18] Kragović, M., Daković, A., Sekulić, Ž., Trgo, M., Ugrina, M.,
Perić, J. and Gatta, G.D. (2012) Removal of lead from aqueous
solutions by using the natural and Fe(III)-modified Zeolite.
Appl. Surf. Sci. 258, 3667-3673.
[19] Derkowski, A., Franus, W. and Waniak-Nowicka, H. (2007)
Textural properties vs. CEC and EGME retention of Na-X zeolite prepared from fly ash at room temperature. Int. J. Miner.
Process. 82(2), 57-68.
[20] Vengris, T., Binkiene, R. and Sveikauskaite, A. (2001)
Nickel , copper and zinc removal from waste water by a modified clay sorbent. Appl. Clay Sci. 18, 183-190.
[21] Huanq, X., Gao, N.Y. and Zhanq Q.L. (2007) Thermodynamics and kinetics of cadmium adsorption onto oxidized granular
activated carbon. J.Environ. Sci. (China) 19(11), 1287-1292.
[22] Akar, S.T., Akar, T., Kaynak, Z., Anilan, B., Cabuk, A.,
Tabak, Ö., Demir, T.A. and Gedikbey, T. (2009) Removal of
copper(II) ions from synthetic solution and real wastewater by
the combined action of dried Trametes versicolor cells and
montmorillonite. Hydrometallurgy 97, 98-104.
[23] Lagergren, S. (1898) Zur theorie der sogenannten adsorption
geloster stoffe. Kungliga Svenska Vetanskapsakademines
Handlingar 24, 1-39.
[24] Ho, Y.S., and McKay, G. (1999) Pseudo-second order model
for sorption processes, Process. Biochem. 34, 451–465.
[25] Weber, W.J. and Morris, J.C. (1963) Kinetics of adsorption on
carbon from solution. J. Sanit. Eng. Div. Am. Soc. Civ. Eng.
89, 31-60.
[26] Petrovic, M., Macan, M.K. and Horvat, A.J.M. (1999) Interactive sorption of metal ions and humic acids onto mineral particles. Water Air Soil Poll. 34, 41-56.
[27] Yong, R.N. and Mulligan C.N. (2004) Natural attenuation of
contaminants in soils. Lewis Publication, Florida.
234
Accepted: May 15, 2014
Joanna Fronczyk
Warsaw University of Life Sciences – SGGW
Faculty of Civil and Environmental Engineering
Department of Geotechnical Engineering
ul. Nowoursynowska 166
02-776 Warsaw
POLAND
FEB/ Vol 24/ No 1a/ 2015 – pages 228 – 234
THE SALINITY PROBLEM AT YELKOMA LAGOON
(YUMURTALIK-ADANA) AND ITS RESTORATION BY
MIXING WITH FRESHWATER FROM CEYHAN RIVER
Harun Aydin1,*, Hüseyin Karakuş2 and Osman Erdem3
1
2
Yüzüncü Yıl University, Department of Environmental Engineering, Van, Turkey
Dumlupınar University, Department of Geological Engineering, Kütahya, Turkey
3
Nature Research Society, Ankara, Turkey
1. INTRODUCTION
ABSTRACT
The Yelkoma Lagoon located at Ceyhan River Delta
has been significantly deteriorated due to dam construction
activities that cause lower freshwater inputs. Compared to
the Mediterranean (~40 ppt), high salinity of the Yelkoma
Lagoon (up to 116.0 ppt), has adversely affected the ecological life in the lagoon as evidenced by the number of
fish deaths. In addition to less freshwater input, high evaporation rates and structure of the inlet channel between the
lagoon and the Mediterranean are determined as major
causes of high salinities. In this study, we applied EndMember Mixing Analysis to calculate the monthly amount of
freshwater that should be contributed from the Ceyhan
River to the Yelkoma Lagoon to decrease salinity and ensure
sustainability of ecological life and fisheries. As a first stage
of the study, a conceptual hydrological model of the
Yelkoma Lagoon has been developed based on water volume and its salinity. A bathymetric map of the lagoon was
produced allowing measurement of the area and volume of
each sector in the lagoon. Monthly salinities were obtained
from field surveys for all components of mixing equations.
The optimum salinity of the lagoon water has been foreseen
to be between the salinity of the Ceyhan River and the Mediterranean. Based on the conceptual model and using a mass
balance equation, contributions of saline and freshwater
end-members were calculated. As a result of mixing calculations, monthly required flow-rates of freshwater from the
Ceyhan River to the Yelkoma Lagoon were determined to
be in the range of 0.199 m3/s to 3.698 m3/s. The calculated
freshwater has been given to the lagoon since March 2009,
and positive developments have been observed in terms of
ecological life.
KEYWORDS: freshwater mixing, salinity, Yelkoma Lagoon, wetland restoration
Wetlands provide significantly essential environmental
functions and products to us and to our economies, such as
reed production, lagoon fishery, reduction of flooding, recharging of groundwater, treatment of wastewater, protection of shorelines, and support of a large biodiversity [1-7].
In general, the productivity and sustainability of wetlands
are mainly depending on their hydrology and hydrochemistry including the amount of nutrients, water level fluctuations, evaporation, salinity, etc. However, over the last several decades, productivity and sustainability of wet-lands are
threatened by various natural and artificial sources including
climate change, industrialization, agricultural activities,
uncontrolled construction, and unplanned hydrological activities in theirs watersheds.
Among the wetlands, lagoons are known to be more
sensitive to climate change and anthropogenic activities
because of their shallow depth, limited sea connection, low
freshwater input, etc. [6, 8-10]. One of the most important
physical characteristics of coastal lagoons is the salinity
[11]. According to the salinity, lagoons can be classified as
fresh water (<3 ppt), hyposaline (3-30 ppt), marine (3035 ppt), and hypersaline (>35 ppt) ones [12]. The salinity
of lagoons is mainly related to hydrological balance, freshwater input, and tidal effects [13]. Due to in-creasing evaporation rates, especially in dry summer seasons, hypersaline
conditions may dominate in the lagoon at semi-arid regions.
The negative impact of hypersaline con-ditions could be observed on productivity and diversity in the lagoons.
Turkey has a total of 135 international important wetlands of which 14 are listed under the Ramsar Convention
[14]. However, about 30% of the wetland area of Turkey
has been deteriorated and/or drained over the last 60 years
due to poor water management practices and water pollution. One of the Ramsar sites of Turkey is Yumurtalık Lagoons (Fig. 1), consisting of the whole of the alluvial delta
formed by the Ceyhan River in the eastern Mediterranean,
235
with a complex of several lagoons as well as salt and freshwater marshes.
The 2011 World Wildlife Fund report stated that dams
constructed for energy production, flood protection, irrigation, and drinking water supply on the Ceyhan River have
significantly affected freshwater input of the Yumurtalık
Lagoons [15]. Also Tekelioğlu et al. [16] have reported
that salinity is a vital problem at Yumurtalık Lagoons, especially in Yelkoma Lagoon. And previously published
data indicate that the existence of hypersaline conditions in
this lagoon could be related to lack of freshwater input and
poor flushing similar to Ria Formosa lagoon in Portugal [6,
8]. To determine the contributions of mixing components
in the hydrological budget [17-21], there is a widely used
hydrochemical-mixing model known as End-Member
Mixing Analysis (EMMA). As the first study related to rehabilitation of coastal wetlands in Turkey, we present the
EMMA results applied to determine the monthly amount
of fresh water that should be given to Yelkoma Lagoon
from the Ceyhan River to decrease the salinity of lagoon
waters, and to ensure the sustainability of fisheries and biodiversity.
2.1 Description of the Study Area
Yumurtalık Lagoons, consisting of a complex of lagoons, salt and freshwater marshes, are located at Yumurtalık district of Adana (Fig. 1a). These lagoons are situated
on an area of about 164.3 km2 between the downstream of
the Ceyhan River and İskenderun Bay on the shore of the
eastern Mediterranean. Yelkoma Lagoon, one of the important fishery locations in Turkey, is located along the
northeast-southwest direction at the southeast of Yumurtalık Lagoons with a 16.5 km2 lake area, and about 10.0 km
length and 1.7 km width (Fig. 1b). The bathymetric map of
the Yelkoma Lagoon was produced by a hydrographic survey carried out in August 2008, and the deepest point, average depth, and water volume were obtained as 70.0 cm
asl, 35.0 cm asl and 5.18×106 m3, respectively (Fig. 2). The
Yelkoma Lagoon is connected to the Mediterranean, at the
east of the lagoon by communication channel or inlet,
which has a narrow width (~10.0 m), shallow depth, and
4.0 km length.
FIGURE 1 - (a) Location map of the study area and (b) sampling locations. The stars represent salinity observation stations
236
evaporation for the lagoon have been calculated as 818.4 mm
and 1137.4 mm, respectively (Fig. 3). The highest and the
lowest monthly average temperatures vary between 27.8 °C
(August) and 10.2 °C (January), while long-term mean annual temperature was calculated to be 18.9 °C. According
to the Thornthwaite climate classification, semi-arid and less
humid climate conditions dominate the vicinity of the
Yelkoma Lagoon [22].
FIGURE 2 - Bathymetric map of Yelkoma Lagoon (August 2008).
2.2 Climate and Hydrological Conditions
The long-term (1975 to 2008) monthly meteorological
parameters, such as air temperature, precipitation, and panevaporation rates, were obtained from the nearest meteorological stations that are operated by the State Meteorological Organization (DMİ) of Turkey. In order to calculate evaporation losses from the Yelkoma Lagoon, observed pan-evaporation readings were multiplied by a pan
coefficient (0.7). The long-term average monthly meteorological parameters for Yelkoma Lagoon were generated by
using the Thiessen Polygon Method in GIS environment.
The long-term mean total annual precipitation and pan-
As a result of changes in the Ceyhan riverbed, many
lakes, lagoon, meanders, and marshes have been formed by
floods in the Yumurtalık Lagoons. But the current
stream-bed of the Ceyhan River along the Yumurtalık Lagoons was formed after a vast flood that occurred in 1932.
However, flooding in Yumurtalık Lagoons was completely
prevented after the construction of a series of dams on the
Ceyhan River between the years of 1971 and 2003 [23].
Thus, Yumurtalık Lagoons was disconnected from the
Ceyhan River and, therefore, receives freshwater input
only from rainfalls. On the other hand, the connection of
Yelkoma Lagoon with the Mediterranean Sea is provided
by a narrow inlet channel having approximately 4.0 km in
length, at the eastern part of the lagoon. We thought that
the narrow shape of the inlet channel might be the cause of
less mixing of lagoon waters with the Mediterranean sea
waters by tidal effects. The flushing is good around inlet
parts of the lagoon while remaining parts are poorly
flushed. The low freshwater input and the restricted sea
connection of the Yelkoma Lagoon result in increasing salinity during spring, summer, and autumn seasons. This situation is also supported by previously published studies for
Yelkoma Lagoon [16, 23].
2.3 Hydrochemistry
A sampling campaign took place on July 2008, to
measure hydrochemical parameters around the Yelkoma
Lagoon. All sampling locations are shown in Fig. 1b and
given in Table 1, along with field measurements of water
250
30
25
20
Temperature (C)
Evaporation, Precipitation (mm)
200
150
15
E
100
P
T
10
50
5
0
0
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Set
Oct
Nov
Dec
FIGURE 3 - Long-term mean meteorological parameters for Yelkoma Lagoon (E: evaporation, T: temperature, P: precipitation).
237
TABLE 1 - Some physicochemical parameters obtained from field work and literature for Yelkoma Lagoon.
Coordinate (UTM Zone 36)
T
EC
Sal
pH
Description
X
Y
(C°)
mS/cm (ppt)
1
738495
4065440
21.56
8.26
70.5
52.75 Station 1*
2
738495
4065440
22.25
8.26
81.4
61.12 Station 2*
3
738495
4065440
22.87
8.20
91.8
69.19 Station 3*
4
738495
4065440
22.27
8.16
94.0
71.16 Station 4*
5
738495
4065440
22.41
8.14
96.5
73.22 Station 5*
CEY
729840
4058851
28.66
7.99
0.7
0.29 Ceyhan River
DAL
739236
4065956
32.11
8.28
75.4
44.82 Fishery Shelter
KOP
730975
4060844
31.43
8.48
0.7
0.29 Bridge
MED
739098
4063584
32.68
8.29
69.4
40.29 Mediterranean
ORB
735431
4063846
30.45
8.27
5.0
2.39 Old River Bed-Sea Connection
SEA
735454
4063854
32.08
8.18
179.4 127.60 Sea-Old River Bed Connection
YEL
731951
4060021
33.42
8.17
161.5 108.49 Yelkoma Lagoon
* sign represents 12-month average values between 2007 and 2008 from [16]
Code
30
120
25
80
20
60
15
40
10
20
5
Salinity (ppt)
100
1
2
3
4
5
Min
Max
Temperature
0
Dec 07 Jan 08 Feb 08 Mar 08 Apr 08 May 08 Jun 08 Jul 08 Aug 08 Sep 08 Oct 08 Nov 08
120
Temperature (C)
a
0
0
b
20
100
Salinity (ppt)
80
60
80
60
Precipitation (mm)
40
100
40
120
20
140
Precipitation
1
2
3
4
5
Min
Max
0
Dec 07 Jan 08 Feb 08 Mar 08 Apr 08 May 08 Jun 08 Jul 08 Aug 08 Sep 08 Oct 08 Nov 08
160
FIGURE 4 - The temporal variation of salinity with (a) temperature and (b) precipitation. Min and Max represent average minimum and maximum
salinity of the Mediterranean, respectively [25]. The numbers represent salinity observation stations in Fig. 1b at Yelkoma Lagoon [16].
238
TABLE 2 - Some seasonal physicochemical parameters of Yelkoma Lagoon.
Year
Season
1998-1999 Spring
Summer
Autumn
Winter
2007-2008 Spring
Summer
Autumn
Winter
pH
T (°C)
EC (mS/cm)
S (ppt)
7.00
23.50
35.9
22.8
8.00
37.00
143.7
74.3
7.50
26.50
131.0
58.0
7.90
18.30
28.5
20.0
8.12
22.64
59.7
42.1
8.26
29.07
113.7
77.9
8.26
22.43
125.0
101.6
8.20
11.10
43.5
39.4
T: temperature; EC: electrical conductivity; S: salinity
References
[24]
[16]
FIGURE 5 - (a) Hydrological conceptual model of Yelkoma Lagoon and (b) divisions of the lagoon.
phase pH, temperature (T), electrical conductivity (EC), and
salinity (S) using a YSI 556 Multiprobe System with an accuracy of ±0.2 units, ±0.15 °C, ±1 µS/cm and ±1 ppt, respectively. Besides this, the same parameters were also obtained
from previously published studies [16, 24]. Tekelioğlu et al.
[16] also observed pH, T, EC, and S at five different stations
on a monthly basis, between December 2007 and November
2008, in the Yelkoma Lagoon.
The mean salinity of the Yelkoma Lagoon is calculated
as 65.5 ppt while the highest and the lowest salinity are
recorded as 25.6 ppt in February and 116.4 ppt in October,
respectively. It is clearly seen from field measurements
(Table 1) that salinity values at the entrance of the Yelkoma
Lagoon (DAL) and the Mediterranean shore (MED) were
measured around 40 ppt while this parameter was observed
to be about 108 ppt in the lagoon’s remote parts from the
sea. Salinity values that were observed at stations in the
Yelkoma Lagoon show a gradual increase from the sea toward the hinterland (Table 1). The temporal variation of salinity with temperature and precipitation are given in Fig. 4.
The salinity of Yelkoma Lagoon waters increases with rising temperatures, and also with the decrease of precipitation. We thought that this should be due to low freshwater
input, high evaporation rate, and restricted sea connection
that are all reducing flushing in the lagoon. Some previ-
239
ously published data on the seasonal variation of physicochemical parameters of the Yelkoma Lagoon are also given
in Table 2. It is clearly seen from Table 2 that lagoon waters become more saline in the years between 1998 and
2008. Especially, except for the summer season, salinity
values of lagoon water increased by about 85% as the rest
of the year at this time interval.
2.4 Conceptual Model and Mixing Calculations
In the scope of this study, based on water volume and
salinity, a conceptual hydrological model of the Yelkoma
Lagoon has been developed (Fig. 5a). This conceptual
model consists of the required freshwater volume of the
Ceyhan River, the volume of the Yelkoma Lagoon, and the
volume of precipitation, and their salinities.
Based on the conceptual model, monthly freshwater
contributions from the Ceyhan River to the Yelkoma Lagoon were calculated by using three-component EMMA.
Since the groundwater levels in close vicinity of the lagoon
are generally less than 0.5 m asl, hydraulic gradient is assumed as 0; thus, the groundwater inflow to the lagoon was
neglected from the conceptual model and also from the
mixing calculations. Then, the lagoon was divided into four
sectors that have known volume and salinity (Fig. 5b).
Area and volume of each sector were acquired from the bathymetry map. The amounts of monthly total effective precipitations were calculated from monthly precipitation, and
the corrected pan-evaporation data of the nearest meteorological stations (Karataş and Yumurtalık). The salinity of
precipitation was accepted as 0 [26]. The monthly average
salinity values of the Ceyhan River and the Yelkoma Lagoon were obtained from previously published data [16],
and from field surveys (July 2008) (Table 1).
We made the following assumptions: (1) final salinity
of lagoon waters should be between the Ceyhan River
(0.29 ppt) and the Mediterranean Sea (~38 ppt); (2) after
mixing of freshwater, the final salinity of the lagoon water
has been foreseen to be 20 ppt (in Jan, Feb, Dec), 25 ppt
(in Mar), 30 ppt (in Apr), 35 ppt (in May) and 38 ppt (in
Jun to Nov); (3) because of low tidal effect and flushing in
the lagoon, it was accepted that there is not any change in
the monthly salinity of each sector; (4) monthly average
salinity of each sector was accepted that represents a whole
month. Considering above statements and assumptions, the
amount of freshwater for each month can be calculated
from the following mass-balance equations:
(Cf  Vf ) = (Cc  Vc) + (Cy  Vy) + (Cp  Vp)
(1)
Vf = Vc + Vy + Vp
(2)
Vc = [Vy  (Cy - Cf) + Vp  (Cp - Cf)] / (Cf - Cc)
(3)
where, the subscripts f, c, y and p represent the salinity
(C) and volume of water (V) for final mixture, the Ceyhan
River, the Yelkoma Lagoon and precipitation, respectively.
The restorations of coastal wetlands have been conducted worldwide to protect critical habitats for marine life,
related freshwater aquatic life, stabilize beach erosion, etc.
However, nowadays, there are many negative pressures on
wetlands. One of the major stressors is the salinity that may
directly affect plant growth and diversity of species in
wetlands [27, 28]. The freshwater inflow to the lagoons occurs from rivers, runoff, groundwater, and precipitation.
Also, freshwater input is an important source in diluting the
salinity of these systems [29-32], and supplying nutrients
and organic matter from upstream sources [33]. As in the
Yelkoma Lagoon, lack of freshwater input to the wetlands
has been found to adversely affect productivity and growth
of wetland vegetation [34-36]. In addition, due to the
hypersaline condition in the Yelkoma Lagoon, salt formation and fish deaths were observed on the shores of the
lagoon at our field survey in July 2008 (Fig. 6a).
Table 3 shows results of three-component EMMA calculations to achieve the estimated salinity values. Monthly
amount of freshwater contributions were obtained as flowrates in the range of 0.199 m3/s to 3.698 m3/s with an average
of 1.967 m3/s. The estimated freshwater contributions should
be given from the Ceyhan River to the lagoon, increasing at
the end of the rainy period. In order to attain an effective
mixing, it has been recommended that the freshwater from
the Ceyhan River should be given from three different
points to the lagoon (Fig. 5b). Also, we suggested that the
old riverbed of the Ceyhan River, which is located at the
northwest of the lagoon, is a suitable flow path instead of
constructing a new one.
At the end of our study, a restoration plan aiming for
freshwater supply from the Ceyhan River to the Yelkoma
Lagoon was proposed on December 2008. With the collaboration of the Nature Research Society, General Directorate
of Nature Conservation and National Parks (DKMP), General Directorate of State Hydraulic Works (DSİ), and local
fishery cooperative, the improvement of the old riverbed was
started at the end of December 2008. After the reclamation
of the old riverbed, calculated freshwater has been conveyed
to the lagoon since March 2009 and hydrologic restoration
of the Yelkoma Lagoon wetland was successfully applied
according to the proposed plan. Two major consequences
have been observed after the implementation of the restoration plan. First, the reed growth has been noted around freshwater input points at the Yelkoma Lagoon (Fig. 6b). Second,
after the application of the proposed plan, salinity of the lagoon water was measured as ~28 ppt on April 2009, which
is a very close approximation to the estimated value (30 ppt)
for April (Table 3). Thus, these results were interpreted as
having achieved dilution of hypersaline lagoon waters.
The presence of nutrient supply to the lagoon by the freshwater from the Ceyhan River is also supported by the
reeds’ growth around the input points. Since then, the
Yelkoma Lagoon started to regain its wetland ecosystem functions, and
240
FIGURE 6 - (a) Before and (b) after freshwater input to Yelkoma Lagoon.
TABLE 3 - The observed and estimated salinity values at Yelkoma Lagoon and the amount of freshwater required for lowering salinity.
Jan
Observed salinity (ppt)
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
41.3 29.7 39.6 42.3 56.8 66.4 75.9 91.5 100.4 109.3 95.1 37.5
Estimated salinity (ppt)
20.0 20.0 25.0 30.0 35.0 38.0 38.0 38.0 38.0 38.0 38.0 20.0
3
Amount of freshwater (m /s) 1.431 0.199 0.858 0.729 1.419 2.140 3.042 3.411 3.555 3.698 2.519 0.605
positive developments have been observed in terms of
ecology and productivity.
4. CONCLUSION
This paper describes restoration of the Yelkoma Lagoon, at the eastern Mediterranean shore, which was disconnected from the Ceyhan River by a large flood in 1932
that formed the current stream-bed of the river. Also construction of a series of hydraulic structures has restricted
natural flow of the Ceyhan River since 1971. Both of these
natural and anthropogenic impacts led to alteration of natural
conditions, such as hypersaline waters, less nutrient input,
etc., which dominate the Yelkoma Lagoon. This is a first
study about restoration of a deteriorated coastal wetland by
freshwater supply in Turkey. Mixing calculations were
made by End-Member Mixing Analysis (EMMA) to achieve
natural salinity conditions in the Yelkoma Lagoon. The calculated freshwater was launched in March 2009, and hydrologic restoration of the Yelkoma Lagoon wetland was successfully applied. After supplying freshwater to the
Yelkoma Lagoon, reed growth has been observed around
freshwater input points and the salinity of the lagoon water
was measured as 28 ppt in April 2009. Thus, this situation
was interpreted as dilution of lagoon waters and nutrient
input, and the lagoon started to regain its wetland ecosystem functions. The overall results of this study indicate that
the Yelkoma Lagoon is a sensitive ecosystem to any artificial impact. Therefore, further hydrological studies and
long-term hydrochemical monitoring programs are suggested for the Yelkoma Lagoon.
241
ACKNOWLEDGEMENTS
This study is part of a project entitled “Management
Plan of Yumurtalık Lagoon" conducted by the Turkish Nature Research Society on behalf of the General Directorate
of Nature Conservation and National Parks (DKMP) and financially supported by Baku-Tbilisi-Ceyhan (BTC) Pipeline
Company. The authors also thank Mr. Nazmi Tekelioğlu
(Çukurova University) for providing the data. We also thank
anonymous reviewers for their comments and suggestions,
which improved the manuscript.
REFERENCES
Brinson, M., Lugo, A., and Brown, S. (1981) Primary productivity, decomposition and consumer activity in freshwater wetlands. Ann. Rev. Ecol. Evol. System, 12, 123–161.
[2]
Turner, K. (1991) Economics and wetland management. Ambio, 20, 59-63.
[3]
Whiting, G. and Chanton, J. (2001) Greenhouse carbon balance of wetlands: methane emission versus carbon sequestration, Tellus B, 53, 521–528.
[4]
LOICZ, 2005. Landeocean interactions in the coastal zone;
science plan and implementation strategy. In: Kremer, H.H.,
Le Tissier, M.D.A., Burbridge, P.R., Talaue-McManus, L.,
Rabalais, N.N., Parslow, J., Crossland, C.J., Young, W. (Eds.),
IGBP Report 51/IHDP Report 18, 68 pp.
[5]
Mitsch, W.J. and Gosselink, J.G. (2008) Wetlands, John Wiley
th
& Sons, 4 Edition, New York.
[6]
Mudge, S.M., Icely, J.D. and Newton, A. (2008) Residence
time in a hypersaline lagoon: Using salinity as a tracer. Estuarine, Coastal and Shelf Science, 77, 278-284.
[7]
[8]
[9]
[14] RAMSAR (2013) The annotated Ramsar list of wetlands of
international importance: Turkey. http://www.ramsar.org/cda/en/ramsar-pubs-annolist-annotated-ramsar15840/main/ramsar/1-30-168%5E15840_4000_0__ [last accessed December 4, 2013].
[15] World Wildlife Fund (2011) Protection of Wetlands of Turkey - Problems and Solutions. http://awsassets.wwftr.panda.org/downloads/2subat_bilginotu_1.pdf [in
Turkish].
[16] Tekelioğlu, N., Çevik, F., Gökçe, G., Erçen, Z., Derici, O. B.
and Erdoğan, E. (2009) Development of a modern fish barriers
based on some parameters determined using the water and
mud samples from Yelkoma Lagoon (KYF 2/9). UNDPBTC/KYF, Adana, 41 p [in Turkish].
[1]
[13] Miller, J.M., Pietrafesa, L.J. and Smith, N.P. (1990) Principles
of Hydraulic Management of Coastal Lagoons for Aquaculture
and Fisheries. FAO Fisheries Technical Papers no. 314.
Filho1, A.T.R., Furian, S., Victoria, R.L., Mascre, C., Valles,
V. and Barbiero, L. (2012) Hydrochemical variability at the
Upper Paraguay Basin and Pantanal wetland. Hydrol. Earth
Syst. Sci., 16, 2723–2737.
Newton, A. and Mudge, S.M. (2003). Temperature and salinity regimes in a shallow, mesotidal lagoon, the Ria Formosa,
Portugal. Estuarine, Coastal and Shelf Science, 57, 73-85.
Tett, P., Gilpin, L., Svendsen, H., Erlandsson, C.P., Larsson,
U., Kratzer, S., Fouilland, E., Janzen, C., Lee, J., Grenz, C.,
Newton, A., Ferreira, J.G., Fernandes, T. and Scory, S. (2003)
Eutrophication and some European waters of restricted exchange. Continental Shelf Research 23, 1635-1671.
[10] Diamantopouloua, E., Dassenakisa, M., Kastritisa, A.,
Tomarab, V., Paraskevopouloua, V. and Poulosb, S. (2008)
Seasonal fluctuations of nutrients in a hypersaline Mediterranean lagoon. Desalination, 224, 271–279.
[11] Saunders,K.M., Mcminn, A., Roberts, D., Hodgson, D.A. and
Heijnis, H. (2007) Recent human-induced salinity changes in
Ramsar-listed Orielton Lagoon, south-east Tasmania, Australia: a new approach for coastal lagoon conservation and
management. Aquatic Conserv: Mar. Freshw. Ecosyst., 17, 5170.
[12] Kjerfve, B. (1994) Coastal lagoons. In Coastal Lagoon Processes, Kjerfve, B (ed.). Oceanography Series no. 60, Elsevier
Science Publishers: Amsterdam; 1–8.
242
[17] Mulholland, P.J. (1993) Hydrometric and stream chemistry
evidence of three storm flowpaths in Walker Branch Watershed. Journal of Hydrolology, 151(2-4), 291-316.
[18] Petz, T.R. and Faure, G. (1997) Mixing of water in streams:
Big Walnut Creek and its tributaries, Ohio. Ohio Journal of
Science, 97(5), 113-115.
[19] Tardy, Y., Bustillo, V. and Boeglin, J.L. (2004). Geochemistry
applied to the watershed survey: hydrograph separation, erosion and soil dynamics. A case study: the basin of the Niger
River, Africa. Applied Geochemistry, 19(4), 469-518.
[20] Lieberman, J, Vulava, V, and Callahan, T. (2008) Application
of geochemical end-member mixing analysis to delineate water sources to a forested riparian wetland. Southeastern Section, 57th Annual Meeting, Paper No: 31-20, April 10-11,
2008.
[21] Vulava, V.M., Garrett, C.G., Ginn, C.L. and Callahan, T.J.
(2008) Application of geochemical end-member mixing analysis to delineate water sources in a lowland watershed. Proceedings of the 2008 South Carolina Water Resources Conference, October 14-15, 2008.
[22] Şensoy S, Demircan M, Ulupınar U. and Balta I. (2008) Climate of Turkey. State Meteorological Organization of Turkey
(DMI); http://www.dmi.gov.tr/files/en-US/climateofturkey.pdf [last accessed December 4, 2013].
[23] Erdem, O. and Saraç, B. (2007) Management Plan of Yumurtalık Lagoon. General Directorate of Nature Conservation and
National Parks (DKMP), Ankara, 208 p [in Turkish].
[24] ENCON (1999) Biological and ecological research project of
wetlands of international importance - Sub-project II. General
Directorate of Nature Conservation and National Parks
(DKMP), Ankara, 529 p [in Turkish].
[25] Pinardi, N., Allen, I., Demirov, E., DeMey, P., Korres, G., Lascaratos, A., Le Traon, P.Y., Maillard, C., Manzella, G. and
Tziavos, C. (2003) The Mediterranean Ocean forecasting system: first phase of implementation (1998–2001). Annales Geophysicae, 21, 3-20 pp.
[26] Apello, C.A.J. and Postma, D. (2005) Geochemistry, Groundwater and Pollution. Balkema Publishers, Amsterdam, 649 p.
[27] Greenway, H. and Munns, R. (1980) Mechanisms of salt tolerance in nonhalophytes. Ann. Rev. Plant Physiol., 31, 149190.
[28] Kozlowski, T.T. (1984) Plant responses to flooding of soil. BioSci., 34, 162-167.
[29] Gray, L.J. (1993) Response of insectivorous birds to emerging
aquatic insects in riparian habitats of a tallgrass prairie stream.
American Midland Naturalist, 129, 288-300.
[30] Shentsis, I. and Rosenthal, E. (2003) Recharge of aquifers by
flood events in an arid region. Hydrological Processes, 17,
695-712.
[31] Environmental Protection Agency (2013) Connectivity of
Streams and Wetlands to Downstream Waters: A Review and
Synthesis of the Scientific Evidence. EPA/600/R-11/098B.
[32] Day, J., Ibáñez, C., Scarton, F., Pont, D., Hensel, P., Day, J.
and Lane, R. (2011) Sustainability of Mediterranean deltaic
and lagoon wetlands with sea-level rise: the importance of
river input. Estuaries and Coasts, 34(3), 483-493.
[33] Konstantinos, A.K., Nicolaidou, A. and Reizopoulou, S.
(2001) Temporal variations of nutrients, chlorophyll a and particulate matter in three coastal lagoons of Amvrakikos Gulf
(Ionian Sea, Greece). Marine Ecology, 22, 201-213.
[34] Ingold, A. And Havill, D.C. (1984) The influence of sulphide
on the distribution of higher plants in salt marshes. Journal
Ecology, 72, 1043-1054.
[35] Flynn, K.M., McKee, K.L. and Mendelssohn, I.A. (1995) Recovery of freshwater marsh vegetation after a saltwater intrusion event. Oecologia, 103, 63-72.
[36] Baldwin, A.H. and Mendelssohn, I.A. (1998) Effects of salinity and water level on coastal marshes: an experimental test of
disturbance as a catalyst for vegetation change. Aquatic
Botany, 61, 255-268.
Revised: June 12, 2014
Harun AYDIN
Yüzüncü Yıl University
Department of Environmental Engineering
65080, Tuşba, Van
TURKEY
FEB/ Vol 24/ No 1a/ 2015 – pages 235 - 243
243
DETERMINING NATURAL AND CULTURAL CHANGES
AROUND WETLANDS BY MEANS OF REMOTE SENSING
TECHNIQUES: A CASE STUDY IN EGIRDIR LAKE
Namık Kemal Sonmez1,*, Işın Onur2 and Sevda Altunbaş3
2
1
Akdeniz University, Faculty of Science, Space Science and Technology Department; 07059 Antalya, Turkey
Akdeniz University, Faculty of Agriculture, Farm Structures and Irrigation Department; 07059 Antalya, Turkey
3
Akdeniz University, Remote Sensing Research and Application Center, 07059 Antalya, Turkey
ABSTRACT
In wetlands, the land use/cover is under pressure of
natural and human impacts and studies focus on changes of
these areas are important. Turkey’s wetlands are under
threat due to gain land for agricultural production. Not only
restrict the wildlife habitat, causes pollution due to usage
of herbicides, pesticides and fertilizers. Eğirdir Lake area
and its surrounding in particular has been an important settlement region in the northeast Mediterranean. After
1960’s, the distinctive usage of the lake for agricultural irrigation increased the negative impact on it. The aim of this
study is to examine the degree of the impact using Landsat
satellite data dated 1987, 2002 and 2006, which are acquired and processed with remote sensing science and technology. Supervised classification was used in the research
and land use/cover changes are presented. Class numbers
and character names of the classes in the study area follow
the CORINE methodology. According to the research results, the lake area decreased from 45,212 ha to 44,974 ha
between 1987 to 2002 and then increased to 45,226 in 2006.
With respect to specific cover types, the spread of Marshy
and Reed Bed cover type expands from 1,345 ha to 2,903
ha between 1987 and 2002 and decreased to 2,625 ha in
2006. The Forest areas in the region decreased from 48,391
ha to 37,966 ha over the approximately 20 year study period.
Furthermore scattered spread of Open Areas with Little or
No Vegetation increased from 10,677 to 14,927 ha between
1987 to 2006. Perennial production patterns determined as
Permanent Crops increased from 5,892 ha to 6,090 ha. Another land use class is annual agricultural production named
as Arable Lands according to CORINE methodology,
which expands from 4259 ha to 8585 ha. between 1987 to
2006. According to research findings, Urban Fabric land
use class around Eğirdir Lake is determined to increase
1,066 ha to 1,422 ha. According to research results, some
land use around Eğirdir Lake has
been detected as not appropriate, especially Forest land types
are decreased significantly and convert to agricultural lands
and urban structures. An increase in Marshy and Reed Bed
cover is detected in approximately 20 years.
KEYWORDS: Remote Sensing, Wetland, Supervised Classification, Monitoring, CORINE
1. INTRODUCTION
Wetlands are the most important ecosystems of the
world and like other ecosystems, they are subject to significant changes over time. These changes can be attributed to
either natural or human factors. The negative effects caused
by humans on wetlands mainly started during the last quarter of the twentieth century and continues until today. Xie
et al. [1] stated that with decreasing water resources and increasing population pressures, dam construction, pollution,
resource overuse, biologic influx, drainage, climate change
and some inappropriate policies of governments, wetlands
are decreasing in the world. Serious problems arise in and
around wetlands as they are for the purpose of agricultural
production. Other problems include being buried, burning
of organic matter, salinization, drought, and deficiency
of physical properties of the soil. Owing to these conditions increased the research on qualitative and quantitative
changes and made them become more important [2].
Eğirdir Lake, Turkey’s second biggest fresh water resource and its surrounding areas form one of the most important wetland areas in Anatolia for centuries. The lake
provides water for drinking by people living around the
lake and habitat for animals like fishes and crayfishes
which are used for commercial purposes [3]. Increase of
the urban land cover in the region, insufficiency of infrastructure, overuse of herbicides, pesticides and fertilizers,
as well as over hunting constitute some of the important negative changes in the lake ecology in recent years. These
244
changes and transformations occurred due to misuse of lands
also caused important problems for people living in the region. For this reason, it is important to determine the land
use types, as well as to detect their change over time.
Monitoring and mediating the negative consequences
of land use and land cover while sustaining the production
of essential resources has therefore become a major priority of researchers and policymakers around the world [4].
In natural sciences, to examine land cover’s qualitative and
quantitative changes, remote sensing science and technology
has been used for many years [5]. Recently, land use/ cover
change detection has become one of the most popular topics in remote sensing research and development [6].
Remote Sensing (RS) technologies have been used effectively in many different applications concerning land- use
and land-cover change. This system is used for more detailed analysis of collected land-use information. This technology allows relatively accurate, fast and easy acquisition,
analysis, and production of data on land cover and land use
[7]. Studies carried out by using remote sensing science
and technology; make it possible to monitor land use and
land cover changes for long periods. Traditionally, Landsat
MSS, Landsat TM, and SPOT satellite systems have been
used to study wetlands [8]. As noted by researchers, the
selection of an appropriate change detection algorithm is
critical [9]. Supervised and unsupervised classification
methods are the most commonly used digital classification
to map wetlands. Rani et al. [10] carried out Wetland Assessment and Monitoring Using Image Processing Techniques for Ranchi city of Jharkhand state for the years
1996-2004. They reported that, the signatures of wetlands
and associated land features were identified in an unsupervised classification approach based on their DN value using satellite data. They also found that there were drastic
changes between 1996 and 2004.
These studies carried out by means of satellite data, increases the effect of examining the change in water science
and wetlands via remote sensing [11, 12].
In this study, land use class levels were determined according to the Coordination of Information on the Environment (CORINE) methodology [13]. One of the main purposes of this methodology is to standardize studies about
environment and environmental change and it has been
used over the years at different levels (international, within
the European Union, national and regional) [14]. The
CORINE system has a hierarchical level system ranging
from one to three. It was established by the European Union
as a land cover/use definition hierarchy. Sonmez and Sarı
[15] carried out a study with remote sensing and GIS integration with visual analyses techniques. Researchers used
land classification process with CORINE (Coordination of
Information on the Environment) methodology [13].
This study is carried out by using remote sensing techniques on Landsat TM images of 1987, 2002 and 2006 for
Eğirdir Lake wetland. Land cover and land use conditions
were achieved by supervised classification technique and
comparative analysis were made for each study period.
2.1 Data
The study aims to detect land cover and land use
changes with Landsat satellite images by Maximum likelihood supervised classification in Eğirdir Lake district.
In addition high resolution IKONOS satellite images,
land use plans and 1:25 000 scaled topographic maps were
used as ancillary data.
2.2 Study Area
Eğirdir Lake and its surrounding area lies between the
Mediterranean and terrestrial climate passage zone. The
lake, which is 917 m higher than sea level, has an average
depth of 10-12 m. and provides benefit for fisheries, agricultural irrigation, and for energy production. Besides
sporting activities like climbing, wind surfing, cycling, it is
also used for health tourism [16].
In the lake-basin, mountains are partly covered with
forests, horticulture and other agricultural activities occur
densely on plains. Lands surrounding the lake belong to the
government, individuals and the municipality. Eğirdir
Lake is designated on the stands in Wetlands List which
has an international importance.
This study, carried out in lake-district of Isparta, encompasses Eğirdir Lake and an adjacent 5 km surrounding
it. The region is located between 37008’58” and 36041’08”
north latitudes and 30018’31” and 30058’20” east longitudes
and encompasses an area of 117000 ha. (Figure 1).
2.3 Methods
2.3.1 Determining Land Use Types and Levels
Determining Land Use Types and Levels were conducted in several consecutive phases. Initially, satellite data
were pre-processed. The pre-processing methods performed
in this study include image enhancement and georeferencing. In this phase, histogram equalization, standard deviation
stretch and necessary brightness/contrast adjustments were
performed on each image in order to facilitate image interpretation. Furthermore, satellite images and aerial photographs georeferenced using topographic maps and resampled
according to the second order polynomial transformation
method and the frames of the data were mosaicked.
In the second stages, studies were carried out in a region encompassesing important wetlands in the lake district. In this scope CORINE methodology was used as a
base to determine types of land cover and land use.
This methodology relies on the principle of generating
land use classes hierarchically (from one to three) [13].
CORINE legend, which allows a minimum mapping
unit of 1.56 ha at 1/25000 scale to be determined, was used
as a base [14]. Assessments made in the region and resolution of Landsat satellite image is taken into consideration
and according to CORINE hierarchical structure, distinguishable land use classes are determined and presented in
Table 1.
245
FIGURE 1 - Study Area
Change detection and monitoring involve the use of
multi-date images to evaluate differences in land use/ cover
due to environmental conditions and human actions between
the acquisition dates of images. Several authors have attempted to observe better and define change features using
remotely sensed data. As noted by researchers, the selection
of an appropriate change detection algorithm is critical.
TABLE 1 - Land Cover Classes
Level 1
Level 2
1. Artificial surfaces
1.1. Urban fabric
2. Agricultural areas
2.1. Permanent crops
2.2. Arable land
3. Forests and
semi-natural areas
3.1. Forests
4. Water bodies
4.1. Inland waters
4.2. Marshy and reed bed cover
3.2. Open spaces with little or no vegetation
3. RESULTS AND DISCUSSIONS
2.3.2 Classification of the Study Area
In the supervised classification process, first, pixel values are analyzed and statistical properties of the classes are
determined. Then, samples of the classes are prepared and
with the help of their digital properties, all images are classified. Sample pixels are collected from homogenous areas
for every thematic class to form control pixels. Statistical
information (mean, standard deviation, covariance matrix
etc.) is formed primarily for these sets. Then, all pixels are
examined statistically and every pixel is registered to the
nearest control area that shows nearest statistical properties. Success of the supervised classification is mostly relies on the quality of the control set.
Minimum Distance and Maximum Likelihood are most
used supervised classification methods. Maximum likelihood method is used in this study. In this method, statistical
concepts like mean vector, standard deviation, and covariance are used. When chosen bands are assessed, we detect
which control class is similar to spectral model in which ratio. Similarity is mostly determined as percentage. Pixel is
registered to a class that shows maximum similarity [17].
In the scope of remote sensing science and technology,
classification results of remote sensing images are given in
Figure 2. As can be seen in the figure, significant changes
have occurred around wetlands.
Supervised classification analyze results of three different years are presented in Figure 2.
According to the classification results, urban fabric around
the lake has spread from 1066 ha to 1422 ha between 1987
and 2006. In recent years, researchers have reported an increase in settlements around the lake [18]. Data obtained
from Turkish Statistical Institute supports this finding. In
addition to the regional enlargement, there has been a significant decrease in population density according to the
results of census. Researchers reported that, due to the areal
enlargement, there has been a decrease in population originating from migration [19].
According to the result of classification, lands having
qualification of Permanent Crops which mainly has orchards were enlarged from 5892 ha to 6090 ha. Annual arable lands are detected between 1987 and 2006 increased
from 4259 ha to 8585 ha. Hence there has been no industry
246
FIGURE 2 - Land use classes for years 1987, 2002 and 2006
and decrease in the fishery sector in years, the tendency of
local people is on orchard and vegetable cultivation. In recent years, irrigated agriculture practices lead to the transformation of the fallow lands to agricultural production
lands and there has been serious areal increase in vegetable
cultivation. According to Balik et al. [20] dramatic changes
have occurred in the lake in the past 50 years.
Majority of the cover types in the region is Forested
Areas. According to the results, these areas are decreased
48391 ha to 37966 ha between 1987 to 2006. In addition,
Open Areas with Little or No Vegetation is enlarged from
10677 to 14927 ha between 1987 to 2006. Thus, forested
areas, which have ecologically high ecosystem sensitivity,
decreased due to human activities or natural pressure. In
some regions, it is damaged and the land become barren
and that can also be clearly seen from classified images. As
a matter of fact, while Forested Lands are decreased Open
Areas with Little or No Vegetation Lands are enlarged. In
addition, the surrounding of the study area is mountainous
and having high slope, has importantly rise the erosion risk.
Erol [21] reported that Forest ecosystems are being rapidly
and directly transformed by the land uses of the expanding
human populations and economies. According to researcher, some forest sites in the watershed of Egirdir Lake
have degraded.
Eğirdir lake is one of the very important wetlands in
Turkey and sheltering extremely important living things.
Between 1987-2002 it has decreased from 45212 ha to
44974 ha and in 2006 it increased back to 45226 ha. Increase and decrease in the lake area also affected reed beds.
Thus, Marshy and reed bed cover around the lake increased
from 1345 ha to 2903 ha between 1987 to 2002 with in-
verse ratio with the lake’s area decreased to 2625 ha in
2006 (Table 2).
TABLE 2 - Land use results and areal extends
Land Cover Classes
Open spaces with
little or no vegetation
Forests
Permanent Crops
Arable land
Urban fabric
Marsh and reed bed cover
Inland waters
Total
1987
(ha)
2002
(ha)
2006
(ha)
10677
11153
14928
48391
5892
4260
1066
1346
45213
116845
42535
5970
7990
1319
2903
44975
116845
37966
6091
8585
1423
2625
45227
116845
4. CONCLUSIONS
According to the study results, an important changes
around Eğirdir Lake has been detected. The Lake that covers 39% of the study area, has important wetland property
and has no significant change between 1987 to 2006. In
addition, several researchers have stated that the lake water is used for energy production and agricultural irrigation.
Thus, the lake water levels decreased in years and thereby
negatively affected the lake’s ecological balance. Nowadays
despite the water usage increases, lake water level remains
stable due to the expansion valve.
Researchers conducted by local and national agencies
in study area shows that lake water is polluted with chemical herbicides, pesticides and fertilizers and eutrophication
has started to develop. This phenomenon is also detected with
247
1987(%)
2002(%)
2006(%)
OpSp : Open spaces with little or no vegetation, For: Forests, PerC: Permanent Crops,
ArbL: Arable land, UrbF: Urban fabric, MrsR: Marsh and reed bed cover, InlW: Inland waters
FIGURE 3 - Land cover changes
classification process. An important determined in the duration of 1987 to 2006 in Arable Land in this study. These
areas correspond to 4% slice of the research area in 1987
and doubled in 2006 with 7%. The study area includes an
important wetland, and an increase in Arable land has a
negative effect in region’s economy (Figure 3).
Another land cover change in the region is the decrease
in forest cover (41% in 1987 decreasing to 32% in 2006).
This finding shows that forests were transformed to Open
Areas with Little or No Vegetation. According to research
results, increase in Open Spaces with Little or No Vegetation is described as decrease in Forests. As a result, changes
and transformations occurred in wetlands and its surrounding has to be done with plans which are susceptible to environment and ecology. Otherwise this area’s value will be
lost non-recyclable and there will arise important environmental problems.
ACKNOWLEDGEMENTS
Authors would like to thank to the Scientific Studies
Management Unit of Akdeniz University in Antalya, Turkey.
[2]
Baktır, I. and Sarı, M. (2002) Lake Avlan and Its Influences
on Ecological Balance and Socio-Economic Status of Elmalı
Country. Environmental Problems of The mediterranean
Regıon EPMR-2002 International Conferance. 12-15 April
2002 NEU Nicosia-Northern Cyprus.
[3]
Altunbas, S. (2008) Eğirdir Gölü Yönetim Planı (2008-2012).
Lake Management Plan Series: 1. Isparta. ISBN 978-975-585956-9.
[4]. Ellis, E. and Pontius, R. (2007) Land-use and land-cover
change. In Encyclopedia of Earth. http://www.eoearth.org/article/land-use_and_land-cover_change (14.09.2010).)
[5]. Chou, T.Y., Lei, T.C., Wan, S., Yang, L.S. (2005) Spatial
knowledge databases as applied to the detection of changes in
urban land use. International Journal of Remote Sensing, 26,
3047–3068.
[6]. Zhang, Y., Burbridge S. (2003) A neural network based approach to detecting urban land cover changes using Land- sat
TM and IKONOS. Proceedings of the 2nd IEEE GRSS/ ISPRS
Joint Workshop on Data Fusion and Remote Sensing over Urban Areas Imagery, Berlin, Germany. 157-161.
[7]. Thongmanivong, S. (1999) Land use changes in the upper ca
river basin, xiengkhuang provinces, Lao PDR 1995-97. Application of Resource Information Technologies (GIS/ GPS/
RS) in Forest Land & Resources Management Workshop. October 18-20, Hanoi, Vietnam.
[8]. Shepherd, I., Wilkinson, G., Thompson, J. (1999) Monitoring
surface water storage in the north Kent Marshes using Landsat
TM Images, International Journal of Remote Sensing, 21:9,
1843-1865.
[9]
REFERENCES
[1]
Xie, Z., Xuegong, X., Lei, Y. (2010) Analzing Qalitative and
Quantitative Changes in Coastal Wetland Associated to the Effects of Natural and Anthropogenic Factors in a part of Tianjin,
China. Estuarine, Coastal and Shelf Science.86, 379-386.
248
Sonmez, N.K. and Onur, I. (2012) Monitoring of Land Use and
land cover changes by using fuzzy supervised classification
method: A case study of Antalya, Turkey.Journal of Food, agriculture &Environment. 10, 963-967.
[10]. Rani, M., Kumar, P., Yadav, M., Hooda, R.S. (2011) Wetland
Assessment and Monitoring Using Image Processing Techniques: A Case Study of Ranchi, India. Journal of Geographic
Information System, 3, 345-350
[11] Seto, K.C., Woodcock, C.E., Song, C., Huang, X. (2002) Monitoring Land-Use Change in The Pearl River Delta Using
Landsat TM. International Jornal of Remote Sensing 23, 19852004.
[12] Vela, A., Pascualini, V., Djelouli, A., Langar,H., Pergent, G.,
Pergent_Martini, C., Ferrat, L., Ridha, M., Djabou, H. (2008)
Use of Spot 5 and Ikonos Imagery for mapping Biocenoses In
a Tunisian Coastal Lagoon (Mediterranean Sea). Estuarine,
Coastal and Shelf Science 79, 591-598.
[13] Ecsc-Eec-Eaec (1993) CORINE land cover technical guide
ISBN 92-826-2578-8 ©ECSC - EEC - EAEC, Brussels, Belgium
[14] Heymann, Y., Steenmans, C., Crosille, G. And Bossard, M.
(1994) Corine Land Cover Technical Guide (Luxembourg: Office for Offical Publications of the European Communities).
[15] Sonmez, N.K. and Sarı, M. (2007) Monitoring Of Land Use
Changes In The West Mediterranean Region Of Turkey: A
Case Study On Antalya-Turkey Coast. Fresenius Environmental Bulletin. 16, 1325-1330.
[16] Basayigit, L., Dinc, U., (2010) Prediction of Soil Loss in Lake
Watershed Using GIS: A Case Study of Egirdir Lake, Turkey.
Natural&Environmetal Sciences. 1, 1-11.
[17] Onur, I., (2007) Monitoring and Analysing of Land Use and
Land Cover Change by Means of Remote Sensing and Geographic Information Systems: A Case Study of Antalya-Kemer. MSc Thesis, Istanbul Technical University, Turkey.
[18] Bulut, C., Atay, R., Uysal, K. (2009) Seasonal Variation of
Physico-Chemical Parameters in Egirdir Lake and It’s Limnological Evaluations. Anadolu University Journal of Science
and Technology. 10:2, 447-454.
[19] Alkan, H., Korkmaz, M., Altunbaş, S. (2009) Interactions Between Local People and Lakes: An Example From Turkey.
Journal of Environmental Engineering and Lanscape Management, 3, 17.
[20] Balık, I., Cubuk, H., Cınar, S. (2008) Spatial and Seasonal
Variations in Catch of Silver Crucian, Carp Carassius gibelio
(Bloch, 1782) in Lake Eğirdir, Turkey. Turkish Journal of
Fisheries and Aquatic Sciences. 8:347-353.
[21] Erol, A. (2012) Climate Change Assessments for Lakes Region of Turkey. Ecologia Balkanica. 4:1, 87-93.
Namık Kemal SONMEZ
Akdeniz University
Faculty of Science
Space Science and Technology Department
07059 Antalya
TURKEY
Phone: +90 242 3102285
Fax: +90 242 2276581
FEB/ Vol 24/ No 1a/ 2015 – pages 244 – 249
249
ECOTOURISM, PROTECTED AREAS
AND NATURE CONSERVATION
Nihan Yenilmez Arpa1,* and Yusuf Ceran2
1
Ministry of Forestry and Water Affairs General Directorate of
Nature Conservation and National Parks, National Parks Department-Division Director, Ankara, Turkey
2
Ministry of Forestry and Water Affairs General Directorate of Nature
Conservation and National Parks, Nature Conservation Department-Department Director Ankara, Turkey
ABSTRACT
1. INTRODUCTION
The authors of this paper have conducted a series of
interviews with local administrative staff through questionnaires, one to one meetings with visitors and national parks
managers, and field studies to assess the general state of
ecotourism in Turkey’s national parks. The existing official data on national parks, ecotourism and visitor management were also used in the assessment process. Results indicate that national parks of Turkey have unique values to
support ecotourism. However, the authors believe that the
willingness for ecotourism is high yet the know-how is low
amongst the park managers and local people. These stakeholders have minimal experience on ecotourism and the infrastructure to support development of ecotourism is also
limited.
Currently, the visitors of the protected areas are mainly
domestic, and they are interested more in recreational activities in the national parks rather than in their natural-cultural values. Approximately only 5 percent of the visitors
are naturalists and interested in the nature and ecotourism
activities such as trekking, bird watching, biodiversity observation and conservation, and camping.
This study is focusing if ecotourism could be supported by Plan, Policy and Engagement of the Target
Stakeholders to certainly perform as an important tool for
biodiversity conservation. Besides conservation of the biodiversity ecotourism could contribute to the economic development and strengthens the institutional capacity of local people and preserves the local culture and traditional
lifestyle in the protected areas.
KEYWORDS: ecotourism, protected areas, assessment, biodiversity conservation, national parks
“The concept of Ecological Tourism or popularly
known as Ecotourism is a new dimension of the global
tourism industry in which selected sites and locations and
sometimes entire geographical zones are preserved for people to visit and appreciate them in their pristine natural
beauty” [1].
Ecotourism is defined as "responsible travel to natural
areas that conserves the environment and improves the
well-being of local people" [2]. It supports biological conservation, increases the local development and offers interpretation opportunities for both visitors and local community.
Ecotourism could provide the economic resources necessary for the preservation of the protected areas; however successful case studies are still rare in Turkey.
Nature based tourism and ecotourism in protected areas is becoming a particularly important component of
government policy in many countries. It is one of the most
powerful tools for protecting the environment and natural resources and generating income for local communities in
and around the protected areas [3].
The development of the ecotourism in Turkey started
in 2000s, and over the years it has become a buzzword. The
explosion of interest in ecotourism led to the emergence of
a lively debate among academics, governmental institutions, non-governmental organizations and tourism industry regarding its merits and importance. Many ecotourism
workshops, meetings and training activities were held during the past few years and many successful case studies of
the existing ecotourism sites were presented. The Ministry
of Culture and Tourism (MoCT) prepared a tourism strategy and ecotourism has been incorporated as an important
segment of the strategy [4]. The Ministry of Forestry and
Water Affairs (MoFWA) also identified eco-tourism as an
important tool and focused to develop it in the protected areas [5]. In addition, a planning disposition has been developed by General Directorate of Nature Conservation and
National Parks (GDoNCNP) to identify eco-tourism plan-
250
ning stages and planning approach [6]. Although there is
some progress on ecotourism in the protected areas, the authors observe that there are still no definite results emerged
from these efforts. On the other hand, the potential value of
the protected areas in relation to ecotourism is still not
known adequately in Turkey.
In this study the existing attractions and activities in
the national parks were reviewed to understand the current
practice and the requirements for managing ecotourism
successfully and increasing its positive effects. In the process, the attractions for ecotourism in the protected areas
were identified as core (natural) and supporting (manmade). Core attractions include natural and/or cultural and
heritage attractions, while supporting attractions are manmade infrastructures and materials [7].
Results indicate that most of the activities are spontaneous without specific planned and developed products
and targeting the visitors. There has been not enough investment in advertising, researching the market and technologies serving ecotourism. On the other hand all kind of
nature based activities that have been implemented in the
protected areas are labeled as ecotourism. The major differences between ecotourism and nature-based tourism are not
known by ecotourism business operators, protected areas
manager and other stakeholders.
The main source of data for this study is gathered from
“40 national parks” in Turkey which were declared under
article 2873 of National Parks Law and managed by
GDNCNP under the MoFWA.
A list of Turkey’s national parks is given in Table 1.
TABLE 1 - National Parks of Turkey
No
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
Name
YozgatÇamlığı
Karatepe-Aslantaş
Soğuksu
ManyasKuşcenneti
Uludağ
Yedigöller
DilekYarımadası-Büyük Menderes Deltası
SpilDağı
Kızıldağ
GüllükDağı-Termessos
KovadaGölü
MunzurVadisi
Beydağları
GeliboluYarımadası Historical NP
Köprülü Canyon
Ilgaz Mountain
Başkomutan Historical
Göreme Historical
Altındere Valley
Boğazköy-Alacahöyük
Nemrut Mountain
Beyşehir Lake
Kazdağı
Altınbeşik Cave
Hatilla Valley
Kaçkar Mountains
Karagöl-Sahara
Aladağlar
Marmaris
Saklıkent
Troya Historical NP.
Honazdağı
Küre Mountains
Sarıkamış-Allahuekber Mountains
Ağrı Mountain
Gala Lake
Sultan Sazlığı
TekTek Mountains
İğneadaLongos Forest
Nenehatun Historical NP
Space (ha)
267
4.145
1.187
17.058
13.024
1.623
27.598
6.867
55.106
6.700
6.551
42.674
31.018
33.439
35.719
1.118
40.948
9.614
4.648
2.600
13.827
86.855
20.935
1.147
16.944
52.970
3.251
55.064
29.206
1.643
13.517
9.429
37.753
22.519
88.014
6.087
24.357
19.335
3.155
387
848.119
http://www.milliparklar.gov.tr/mp/index.htm 2013 [8]
251
Declaration time
05.02.1958
29.05.1958
19.02.1959
22.07.1959
20.09.1961
29.04.1965
19.05.1966
22.04.1968
09.05.1966
03.11.1970
03.11.1970
21.12.1971
16.03.1971
22.11.1973
12.12.1973
02.06.1975
08.11.1981
25.11.1986
09.09.1987
21.09.1988
07.12.1988
11.01.1993
17.04.1994
31.08.1994
31.08.1994
31.08.1994
31.08.1994
21.04.1995
08.03.1996
06.06.1996
07.11.1996
21.04.1998
07.07.2000
19.10.2004
17.11.2004
05.03.2005
17.03.2006
29.05.2007
13.11.2007
06.06.2009
Province
Yozgat
Osmaniye
Ankara
Balıkesir
Bursa
Bolu
Aydın
Manisa
Isparta
Antalya
Isparta
Tunceli
Antalya
Çanakkale
Antalya
Kastamonu-Çankırı
Afyonkarahisar-Kütahya-Uşak
Nevşehir
Trabzon
Çorum
Adıyaman-Malatya
Konya
Balıkesir
Antalya
Artvin
Rize-Artvin
Artvin
Niğde-Adana-Kayseri
Muğla
Muğla-Antalya
Çanakkale
Denizli
Kastamonu-Bartın
Kars-Erzurum
Ağrı-Iğdır
Edirne
Kayseri
Şanlıurfa
Kırklareli
Erzurum
TABLE 2 - Inventory sheet of ecotourism attractions in national parks.
Name of the
National Park
Natural attractions
Cultural Heritage Attractions
Geological/ geomorphological features (valleys,
plains, canyons, cliffs, caves, beaches, bays, etc.)
Hydrological resources (rivers, lakes, springs, waterfalls etc.)
Biological resources (flora, fauna)
Detailed inventory of ecotourism attractions is crucial
to understanding of ecotourism values and to managing
visitors in the protected areas. Therefore, n inventory sheet
and a questionnaire were used to survey visitors and park
managers and note their opinions The inventory sheet has
been adapted from Ceballos-Lascurain study [8] on inventory of ecotourism attraction. The sample inventory sheet
is given in Table 2.
The questionnaire included open ended questions that
were used to identify visitor profile, ecotourism activities
in the parks and to gauge visitor expectation on ecotourism.
The main headlines were related to the profile of visitors,
joining activities, needs and requirements.
The assessment of ecotourism attractions, activities
and types of the visitors was focused on park managers and
visitors. The results indicate that there are six types of visitors. The first group is domestic and picnic group who live
in and around the protected areas. This group generally is
not interested in the protected area and its values. They
prefer to spend their time outside their living areas for pic-
Archeological features
Support Attractions
and Physical Facilities
Interpretive facilities and
services
Local folklore, traditional lifestyle
Visitor center
Historical and architectural landmarks
Nature trails
nic and recreation. The second group includes students and
short time training groups who come from high schools,
universities, the army and other organized groups. They
visit the protected areas with their teachers, guides and
leaders within their study tour program and they spend
maximum a day in the protected areas. The third group
consists of daily international visitors. They visit the protected areas as a part of their vacation. Their main program
is in the hotels. They spend generally a day to join organized
activities such as rafting, trekking, canoeing, jeep safari, etc.
The fourth group includes domestic visitors who are naturalists and adventures. They enjoy being in nature, prefer
trekking, camping, bird watching, wildlife observation,
climbing and they enjoy consuming local foods and meals.
They move with a small group by professional guides and
they spend 3-5 days depending on the activities and program. The last group is international visitors who want to
get benefit and enjoy of the nature and protected areas. The
common properties of the visitors in the last two groups are
experience, higher education, higher income bracket, middle age to elderly, ask and tell their friends and colleagues
about trips and they enjoy spend time not closed areas but
in nature. They would like to get familiar with local culture
and traditional lifestyle. They are real ecotourists.
TABLE 3 - Visitor profile for national parks of Turkey
1
Type of Visitors
Domestic picnic group-family
group
Main interest
Recreation and picnic
To see main attractions of the national
park. They join short term training on national park’s values.
They are interested in adventure activities
such as rafting, safari, canoeing,
2
Students and short
time training group
3
International daily group
4
Domestic naturalist
and adventure-ecotourists
To join many of the natural activities such
as trekking, camping, bird watching, wildlife observation, climbing, botanic tours
5
International visitors-trekkers
and naturalists-ecotourists
To join many of the natural activities such
as trekking, camping, bird watching, wildlife observation, climbing, botanic tours
6
Volunteer and individual visitors
Doing holiday with small budget and get
experience with learning by doing.
252
General specifications
They spend limited time in the national park. They live
around the areas.
Generally more than 20 students and/or staff are joining the
activities. This travel is a part of the study visit for the
group.
They travel with their family and/or friends with a small
group.
They travel with a group who is interested in nature based
activities. They are responsible travelers. They support
conservation of natural values and social-cultural values
together. They learn and they teach others. Many of the activities have been done with local people. Local knowledge
and traditional lifestyle is important for them. They can
spend time and money for the areas and local people.
They travel with a group who is interested in nature based
activities. They are responsible travelers. They support
conservation of natural values and social-cultural values
together. They learn and they teach others. Many of the activities have been done with local people. Local knowledge
and traditional lifestyle is important for them. They can
spend time and money for the areas and local people.
They visit with local transports. One or two people trip together. They spend limited budget for their travel.
Protected areas visitor numbers
10.145.972
11.053.158
5.958.963
2.773.544
2009
2.203.229
2010
2011
2012
2013
FIGURE 2 - Visitor numbers of protected areas between 2009-2013 [8].
National Park visitor numbers
5.342.785
4.209.685
2.983.963
1.182.364
2009
1.067.743
2010
2011
2012
2013
FIGURE 3 - Visitor numbers of national parks between 2009-2013 [9].
The assessment process shows that there is another
group that they generally want to live with the family, local
houses and in the camping tents; and they would like to join
daily activities in the village and farms. They are volunteers and travel alone and/or sometimes with two friends
together. However, they do not prefer spending money for
conservation of protected areas or for the local people. But
this group is not the main interested group for protected
areas. The visitor profile is given Table 3.
The number of visitors of the protected areas (national
parks, nature parks, natural monuments and nature reserve)
between 2009 and 2013 are given in Figure 2 and 3.
The other output of our analyses is the connection between ecotourism values and attractions. The attractions in
the protected areas were divided into two categories: core
attractions and supporting attractions [8]. At the same time
main ecotourism activities were identified for each national
park. The results related with ecotourism values and attractions for each national park are given in Table 4.
The visitors who come to understand and spend valuable time in the national parks mainly enjoyed and were impressed by the unique and natural habitat, monumental
mountains, amazing landscapes, charming waterfalls, rivers and natural beauties and living culture and living in the
intact nature. Besides their enjoyment they want to see nature-friendly facilities such as visitor and interpretation
centers, path network and boards, guiding services, cycle
paths and renting cycle, information points and local level
small business to eat, buy and accommodate. Good prepared maps, books, booklets and other informative publications are preferred by visitors. On the other hand they
mentioned that they do not want to see too much roads
and motor cars, buildings everywhere and recreational facilities and waste in the nature.
253
TABLE 4 - Values and activities of national parks of Turkey [9].
No
Name
Values
Core attractions/values
(Natural-Cultural heritage)
Black pine forests and rare species of
steppe ecosystem which are the latest remainders of natural old forests in Central
Anatolia steppes
Archeological features, The arts of Late
Hittite, Roman and Byzantine periods, calabrian pine forests, oak forests, maquis
groves and flora and fauna species of
Mediterranean Region
Pine forests, steppe ecosystems, black
Walter, wildlife The fossils of trees
known as stoned forest or silicified tree
fossils and belonged to miocene age that
is 15 million years ago
1
YozgatÇamlığı
2
Karatepe-Aslantaş
3
Soğuksu
4
Manyas Kuşcenneti
Wetland, water birds, avifauna
5
Uludağ
6
Yedigöller
Forest species especially abies species,
Uluadağ,one of 6 sacred mountains called
as Olympus in Anatolia, has a very rich
habitat such as forest lands, maquis areas,
peatlands, sub-alpine mors, alpine steeps
and open areas
Wetland, lakes (7 landslide lakes), mixed
deciduous forest, aquaculture, wildlife
7
DilekYarımadası-Büyük
Menderes Deltası
8
Spil Dağı
9
Kızıldağ
10
GüllükDağı-Termessos
11
Kovada Gölü
12
MunzurVadisi
Scrub (natural and unweathered Mediterranean habitats), forests, sea mammals,
aqua species, wildlife, flora-fauna, wetland-fish trap, sand dunes, hills, valleys,
bays and canyons within its morphological structure,Panionion, the sacred meeting center of Ion city in 9th century BC,
antique Thebai city, Ayayorgi Monastery
and Karine, HagiosAntonios Monasteries
and Zeus Cave, historical Doğanbey Village (Domatia) many endemic flora and
fauna species.
Pine forest, mountain, plant species, geomorphological structure, rich flora and
fauna, endemic plant species, historical
and mythological ruins
Cedar and pine forests (oxygen-rich forests), mountain, flora-fauna species, Mediterranean Forest belt is located in the
transition zone to the steppes of Central
Anatolia Region, geological and morphological characteristics consisted of interesting calcereous and karstic geographical
formations
Archeological ad historical values, Mediterranean forest and shrub species, wild
life (mountain goat), rich biological diversity, epic and geomorphological formation (Mecene Canyon) and Termessos
antique city
Wetland, lake, forest, Mediterranean forest species, bird species
Wetland, lake, grassland, forest, river, endemic plant species and fauna species,
rich vegetation wildlife
254
Activities
Supporting attractions/values
(man-made)
Recreational facilities such as table, WC, car park,etc.
Camping, trekking, photography/photo safari
-
Trekking,
photo safari,
Visitor center, trails, wildlife observation cottage, information
and sign boards, recreational facilities such as table, WC, car
park, etc. restaurants, ways, path
network
Visitor center, trails, bird watching tower information and sign
boards
Camping, trekking, photography/ photo safari, black
walter observation
Tourism infrastructures as hotels
and bungalows,
New visitor center, entrance
point, ski center, recreational facilities such as table, WC, car
park, etc.
Bungalows, entrance points,
guest houses, bungalows, recreational facilities such as table, WC,
car park, etc. trails, boards
Visitor centers, old ancient settlement (Eski Doğan Bey Köyü),
natural marine, small fish houses,
local pensions, visitor centers, entrance points, recreational facilities (especially for swimming),
boards , trails
photography/
Camping, trekking, photography/ photo safari, bird
watching, other avifauna activities as bird banding,
Camping, trekking, photography/
photo
safari,
kayaking, skiing, wildlife observation
Camping, trekking, photography/ photo safari (autumn
colors), handline fishing,
wildlife observation
Camping, trekking,
Birdwatching, photography/
photo safari, fishing, trekking, camping, diving, cycling, riding boat-cano, seal
observation
Recreation facilities, visitor center (still construction step),
boards
Camping, trekking, photography/ photo safari
Trails, recreational
bungalow,
facilities,
Camping, trekking, photography /photo safari,
Visitor and training center, information and entrance point,
boards, trails,
Camping, trekking, photography/photo safari, wildlife
observation
Trails, viewpoints, recreational
facilities such as table, WC, car
park, etc.
-
Camping, trekking, bird
watching,
photography/photo safari
Trekking,
photography/photo safari
13
Beydağları
Mountains, dunes and sea side, forest,
Mediterranean forest species, wildlife,
cultural values as Çıralı. Olympus,
Phaselis, Idyros antique cities , Beldibi
Cave used 9 thousand years ago, rich biological diversity, epic and geomorphological formation, Göynük Canyon, defile
folds of Kesmeboğazı and, step walls of
Beldibi Channel and Tahtalı Mountain
History on Çanakkale War.
14
Gelibolu Yarımadası Historical NP
15
Köprülü Canyon
16
Ilgaz Mountain
17
Başkomutan Historical
18
Göreme Historical
Geological and geomorphological features. natural and cultural values
19
Altındere Valley
20
Boğazköy-Alacahöyük
21
Nemrut Mountain
22
Beyşehir Lake
23
Kazdağı
24
Altınbeşik Cave
25
Hatilla Valley
26
Kaçkar Mountains
Rich vegetation, geological and geomorphological structures, Sümela Monastery
and small monasteries in the region, European-Siberian phyto-geographical region
origin plant species planted in humid
warm and humid cold weather and unique
samples of forest and alpine ecosystems,
wildlife
various historical arts in Hattuşaş
(Boğazköy), the capital city of Hittites,
one of the most oldest civilization of Anatolia, and surrounding of Hattuşaş, step
vegetation
Cultural and archeological ruins, Kommagene archeological ruins, Antiochos tumulus and huge monuments on Mount
Nemrut and Eskikale, Yenikale, KarakuşTepe and Cendere Bridges, step vegetation and species, sunrise
wetland areas, islands, forests, different
ecosystems, cultural resource values, sundown
oxygen store, endemic kazdağı abies, endemic species, waterfalls, valleys, sociacultural values and traditional festival
Curtain travertines, stalactites, dickites
and columns, Damlataş pools and cave
pearls due to the hydrological conditions,
Mediterranean forest ecosystem
rich plant and wildlife diversity and variable landscaping values, Hatilla Valley and
small valleys
Rich biodiversity, flora and fauna species,
glaciers of Pleistocene epoch, actual glaciation, glacial lakes, glacial valleys,
cirque glaciers, moraines and rich fauna
due to the land characteristics and variety
Mediterranian species and maquis species, specific and unique cypress forest
purely spread in the region, Selge Antique
city and terrace fields, landscape values,
historical bridge and settlements, valley
formed with Köprü River, the geological
structure formed of clay, sandstone and
conglomerate rocks and karstic geographical formations, historical, archeological
and cultural values,
Pine forest, mountain, topographical structure, rich forest and vegetation, high wildlife potential, unique scenery, rich recreation resources, winter tourism opportunities
and atmosphere useful for human health
History on Independence War
255
Trails, viewpoints,
Entrance points, Recreational facilities such as table, WC, car
park, etc. tent camping,
Camping, hiking, trekking,
photography/ photo safari,
canyoning
Visitor centers, roads, cemeteries
and martyrdoms, ınformation and
simulation centres, recreational
park, etc.
-
Camping, trekking, photography/photo safari, visitation
of historical places
Ski facilities such as hotels, teleferic, accommodation units, entrance gate, ways, path network,
boards, recreational facilities
such as table, WC, car park, etc.
Visitor centers, roads, cemeteries
and martyrdoms, ınformation and
simulation centres
Paths, museum, visitor cente
Camping, hiking, trekking
canyoning
Trekking,
photography/
photo safari, visitation of historical places
Trekking,
photography/
photo safari
Entrance gate, recreational facilities, restaurants and buffets
photography/photo
safari,
visitation to the historical
places
Entrance gate, paths and way,
boards
Trekking,
photography/
photo safari, visitation to the
historical places
Entrance gate, paths and way,
boards
Trekking,
raphy/photo safari,
Way, bird watching tower
Camping, trekking,
bird watching, photography/
photo safari, boat tours,
Visitor centre, local guide unit,
entrance gate, boards, path network
path
Trekking,
photo safari
photog-
photography/
Road, path
Camping, hiking, trekking
Entrance gate, visitor center, recreational facilities such as table,
WC, car park, etc.
Camping, climbing hiking,
trekking, photography/photo
safari
27
Karagöl-Sahara
lake, forest, plateau, rich landscape values
and scenery integration, pin and abies
mixed forests
geological and geomorphological structures
such as deep valleys, unique hills, step glacial rocks, caves and great canyons high
plateaus; the unique characteristic examples
of natural landscape, natural living spaces,
plateaus and rich biological diversity
unique landscape and natural, cultural and
recreational values
1000-1100 m height with very high valley
slopes
cultural treasuries of east and west meet,
feed with antique epics and reach at truth
natural and cultural values
28
Aladağlar
29
Marmaris
30
Saklıkent
31
Troya Historical NP.
32
Honazdağı
33
Küre Mountains
Canyons, channels, caves and dolines included in West Black Sea Karst Belt
34
S History on Sarıkamış Operation
35
Sarıkamış-AllahuekberMountains
Ağrı Mountain
36
Gala Lake
37
Sultan Sazlığı
38
Tek Tek Mountains
39
İğneada Longos Forest
40
Nenehatun Historical NP
Highest volcanic mountain of Turkey and
Europe and the second highest volcanic
mountain of the world and it also includes
the greatest glacier of Turkey, step ecosystem and step species.
Migrant birds visit between Europe and
Africa, wetland, flora and fauna
unique fresh water and salt water ecosystems in steppe ecosystem, rich biological
diversity and two main migration routes
used by migrant birds between Africa and
Europe are intersected here and it is also
one of the most important wetlands of the
country
Şuayb City (Özkent) ruins and caves, rock
tombs of Assyria and Roma periods in
Soğmatar (Yağmurlu) historical city, The
castle ruins from Assyria period and
Senem Caves abdstepe ecosystem species
aka Lake, Mert Lake and marshy lands,
Deniz Lake, Hamam Lake, Pedina Lake,
deep spot forests and deciduous forests,
Erikli Lake and surrounding marshy lands
and deep spot forests in the north
History bastions and historical and cultural values
But other groups who are coming for outdoor activities
such as recreation, picnic and adventure they want to see
many of the facilities such as outdoor sport arenas, restaurants and barbeque facilities, good design roads and car
parks, local product selling units, bungalows, other accommodation units in the national parks.
4. CONCLUSIONS
For effective site management, visitor management
and development ecotourism in the parks, the park systems
require a sustainable tourism policy and strategy. It needs
strong links between private tourism businesses, and protected area systems. Representatives from all sectors need
Accommodation unit, recreational facilities such as table, WC,
car park, etc.
Entrance building, recreational
park, etc. paths, trails,
camping
Entrance gate, bards, trails
Trekking, , photography/
photo safari
Trekking,
photography/
photo safari
Camping, hiking, photography/ photo safari
Entrance building, path network
Entrance building, path network
Entrance building, recreational
park, etc. paths, trails,
Symbolic entrance gates, path
network, accommodation opportunity, boards
-
recreational facilities such as table, WC, car park, etc. paths,
trails,
Visitor centre, boards
-
Camping, hiking, climbing,
safari
Camping, hiking, photography/photo safari, canyoning,
Trekking,
photography/
photo safari
safari
Trekking, bird watching,
photography/photo
safari,
boat tours,
Trekking, bird watching,
photography/photo
safari,
boat tours,
Trekking,
/photo safari
photography
boards
Camping, bird watching,
boat tours,
Fence around the park, historical
Photography, visit of the historical places
to work together to develop sustainable forms of tourism
for protected areas. It should integrate environmental concerns into national and regional tourism policies. Furthermore, sustainable nature-based tourism needs to be made a
fundamental part of government policies relating to tourism. The strategy should be based on careful consultation
and be approved and understood by local stakeholders.
The quality of the natural environment is increasingly
regarded as a major attraction by visitors. Protected areas
in Turkey provide an amazingly rich resource for visitors.
It is a strong force for conservation and sustainable development, generating income for parks and their local communities and awareness and support from visitors and
within a wider public arena.
256
Ecotourism is experiencing rapid growth. There is a
high level of expectation among ecotourism managers
about what ecotourism can produce, but there is also a great
deal of concern about the challenges it creates. It is clear
that without planning and management, ecotourism will
not succeed. It should be well managed and well understand by protected areas managers, staff and other related
parts.
[10] Anonymous (2013). Official Archives, Dossiers and Files of
General Directorate of Nature Conservation and National
Parks under the Ministry of Forestry and Water Affair, Ankara, Turkey
Activities related with ecotourism, needs to consider
visitor infrastructures and services based on existing values
and attractions, because, protected areas offer rich opportunities for environmental education. They are an outdoor
classroom for visitors and young generation. Interpretive
trails, visitor centers, and other interpretive facilities
should design and implement based on nature-friendly and
core management objectives of the protected areas.
Current ecotourism activities in Turkey may provide
benefits for conservation and local people, but there is no
regular and systematic structure to get benefits and how to
manage it. The benefit sharing methodology and financial
mechanism should be identified and through the process a
systematic benefit structures should be established.
REFERENCES
[1]
Eagles, P.F.J., Browman, M.E. and Tao, T. (2001) Guidelines
for tourism in parks and protected areas of East Asia, IUCN –
The World Conservation Union 2001, pp. 24-34
[2]
The Ecotourism Society-TIES (1990). http://www.ecotourism.org/what-is-ecotourism, March 2013.
[3]
Lindberg, K., Furze, B., Staff, M. and Black, R. (2010). Ecotourism and other services derived from forests in the AsiaPacific Region: Outlook. Forestry Policy and Planning Division, Rome, Regional Office for Asia and the Pacific, Bangkok, pp. 1-12
[4]
Ministry of Culture and Tourism (MoCT) Turkey Tourism
Strategy 2023, Action Plan 2007-2013, Ankara
[5] Ministry of Forestry and Water Affairs (MoFWA) 1st Forest
and Water Council, Council Decisions, 21-23 March 2013,
Ankara p
[6]
General Directorate of Nature Conservation and National
Parks (GDoNCNP), Technical Specification on Ecotourism
Planning in the Protected Areas Case on Beyşehir Lake National Park and Ilgaz Mountains National Park 2013, 2014 Ankara
[7]
Ceballos-Lascuráin, H. (1996). Tourism, ecotourism, and protected areas. The state of nature-based tourism around the
world and guidelines for its development. IUCN, Gland, Switzerland, and Cambridge, UK , pp. 157-164
[8] Ministry of Forestry and Water Affairs, General Directorate of
Nature Conservation and National Parks (MoFWA,
GDNCNP), List of Protected Areas of Turkey,
http://www.milliparklar.gov.tr/mp/index.htm 2013
[9]
Anonymous (2013). Visitor Monitoring Sheets of General Directorate of Nature Conservation and National Parks under the
Ministry of Forestry and Water Affair, Ankara, Turkey
257
Revised: July 31, 2014
Nihan YENİLMEZ ARPA
Ministry of Forestry and Water Affairs
General Directorate of Nature Conservation and National Parks
Ankara
TURKEY
Phone: + 90 312 207 6008
Mobile: 90 533 399 90 98
Fax: + 90 312 207 6710
[email protected]
FEB/ Vol 24/ No 1a/ 2015 – pages 250 - 257
SUBJECT INDEX
A
L
Amanita muscaria
assessment
216
250
195
163
188
208
250
188
163
163
228
modelling
monitoring program
monitoring
multi-metal bioaccumulation index
172
163
244
208
national parks
natural and engineered sorbents
nutrients
250
228
203
permeable reactive barriers
Pinus sylvestris L.
Plantago major L.
protected areas
pumpkins
228
216
216
250
224
Remote Sensing
244
salinity
soil accumulation
Supervised Classification
235
172
244
threats
trophic state
203
203
Unio sp.
urban areas
208
216
Water Quality
water quality
wetland restoration
Wetland
wind energy
wind turbines
188
203
235
244
163
163
Yelkoma Lagoon
235
Zinav Lake Basin
180
M
B
basin planning
bat deaths
benthic macroinvertebrates
bioaccumulation
biodiversity conservation
biotic indices
bird deaths
bird migration
limestone sand
N
P
C
CORINE
244
ecosystem
ecotourism
EIONET-SOIL
erosion
erosion
Esen River
180
250
172
172
195
188
E
R
S
F
freshwater mixing
235
Geographic Information Systems
GIS-Remote sensing
groundwater contamination
172
195
228
G
T
U
H
heavy metal
heavy metals
heavy metals
heavy metals
hierarchical clustering analysis
180
208
216
224
180
Jerusalem artichoke
224
K-factor
Küçük Menderes River Basin
172
195
LANDSAT-7 ETM+
172
W
J
K
L
Y
Z
258
AUTHOR INDEX
Z
A
Zinav Lake
Zinav Lake
172
203
Altunbaş, Sevda
Aydin, Harun
244
235
Barlas, Murat
Bektaş, Nihal
Buhan, Ekrem
Buhan, Ekrem
Buhan, Ekrem
188
203
172
180
203
Çelik, Mehmet Ali
Ceran, Yusuf
195
250
Danilcenk, Honorata
Dirim Buhan, Saliha
Dirim Buhan, Saliha
Dirim Buhan, Saliha
224
172
180
203
Erdem, Osman
Erdoğan, Ali
Evren İnanan, Burak
235
163
208
Fronczyk, Joanna
Fronczyk, Joanna
216
228
Gülersoy, Ali Ekber
195
Jariene, Elvyra
Jeznach, Jerzy
Jeznach, Maria
224
216
224
Karakuş, Hüseyin
Kiziroğlu, İlhami
Koçer, Mehmet Ali T.
235
163
203
Mazur, Zbigniew
Mazur, Zbigniew
Mete Doğan, Hakan
Mete Doğan, Hakan
Mete Doğan, Hakan
Mete Kılıç, Hakan
216
228
172
180
203
172
B
C
D
E
F
G
J
K
M
259
M
Mete Kılıç, Orhan
180
Okan Genç, Tuncer
Onur, Işın
208
244
Polat, Fatih
Polat, Fatih
Polat, Fatih
172
180
203
Radziemska, Maja
Radziemska, Maja
216
228
Sencer Yılmaz, Doğaç
Sencer Yılmaz, Doğaç
Sonmez, Namık Kemal
Sukatar, Atakan
172
180
244
188
Ütük, Gökhan
208
Yenilmez Arpa, Nihan
Yilmaz, Fevzi
Yorulmaz, Bülent
Yorulmaz, Bülent
250
208
188
208
O
P
R
S
U
Y
260

FEB – Fresenius Environmental Bulletin founded jointly by F. Korte

Transcription

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