1280 Journal of Food, Agriculture & Environment, Vol.8 (3&4), July-October 2010

www Journal of Food, Agriculture & Environment Vol.8 (3&4): 1280-1284. 2010 .world-food.net Meri-Rastilantie 3 B, FI-00980

WFL Publisher Science and Technology

Helsinki, Finland e-mail: info@world-food.net

Received 18 July 2010, accepted 30 October 2010.

Heavy metal contamination of sediments in mangrove forests of the Persian Gulf

Ali Davari *, Afshin Danehkar, Nematolah Khorasani and Hadi Poorbagher Department of Fisheries and Environmental Sciences, Faculty of Natural Resources, University of Tehran,

P.O. Box 4111, Sh Chamran Ave, Karaj, Tehran, Iran. ∗e-mail: adavarei@gmail.com

Abstract Mangroves are sensitive ecosystems with prominent ecological value that have unfortunately lost much of their areas across the world, partly because of pollution. The present study investigated heavy metal contaminations in three mangrove forests of southern Iran. Surface sediments (0-10 cm) were collected from the mangroves in northern Persian Gulf in April 2009. Also, biometric features of trees were measured. Metal concentration ranges were as follows: Al 1795-30240, Fe 6425-51530, Cd 0.6-3.45, Cu 14.1-98.28, Ni 14.1-204.54, Pb 34.15-191.6, V 57.38-825.26 and Zn 44.91- 306.15 µg g-1 dry weight. Principal component analysis on heavy metal concentration of sediments suggested three sources of pollution, industry, natural mineralogy of soils and oil, and industrial activities were the most important source. There was no significant difference among the three mangrove forests in heavy metal concentrations of sediments indicating that the pollution source was the same. There was little correlation between biometric characteristics of mangrove trees and heavy metal concentrations of sediment suggesting that pollution had no effect on mangrove trees. In conclusion, the overall average concentrations of above metals exceed the primary standard criteria but meet the secondary standard criteria of the Chinese National Standard of Marine Sediment Quality and also United State Environmental Protection Agency (USEPA) Standard.

Key words: Heavy metals, contamination, Persian Gulf, mangrove, sediment.

Introduction Mangrove forests, the intertidal wetlands of the tropics and subtropics, are key ecological habitats and sensitive ecosystems that link terrestrial and marine environments. Heavy metals can be delivered to intertidal zones from the catchment via fluvial transport, atmospheric deposition and local wastewater discharge 8, 25, 35. Pollution by heavy metals in natural environments has become a global problem 15. Mangrove forests and adjacent mudflats are increasingly being impacted by pollution originated from multiple sources including municipal waste, mariculture, shipping, industries and run-off from urban centers 5, 14. Sediments that accumulate in mangroves are potential repositories of anthropogenic pollution because of high total organic carbon content, anaerobic properties and rapid turnover and burial 19. Elevated concentrations of heavy metals have been recorded in mangrove sediments all over the world, which often reflects the long-term pollution caused by human activities 11, 33.

The Persian Gulf is one of the oldest water passages in the world rich in biodiversity with mangrove forests and coral reefs, particularly, along the Iranian coast. About 57-66% of the known oil and 45% of natural gas reserves of the world lie in the vicinity or beneath the sea bed of the Persian Gulf 21. Consequently, oil transport, urbanization and other industrial activities increase the environmental threats to coastal ecosystems. Also the Persian Gulf experienced three major wars: The Iraq-Iran war and the first and second Gulf wars. During these wars many oil tankers were attacked and sank, or oil fields were set on fire by Iraqi forces (during the first Gulf war) causing massive oil spills into the Gulf21.

In addition, the Persian Gulf is a major point of oil industry. As a result, the marine life of the Persian Gulf has experienced overwhelming stress during the past three decades.

There are many studies on heavy metals in mangrove sediments19, 24, 27, 28, 33, however, little information is available for mangroves of the subtropical regions, especially, northern Persian Gulf and the Iranian coasts 17. Presence of highly-pollutant human activities in the Gulf region, such as the oil industry and occurrence of environmental disasters like sinking of oil tankers, make it necessary to evaluate valuable coastal ecosystems in terms of heavy metal contamination. The necessity of such a study becomes more obvious with regard to the fact that mangrove forests are used to provide animals feedstuff and also leaves of mangrove trees are used as medicine for humans in southern Iran (personal observation). Consequently, pollution can be absorbed by trees and finally transferred to humans. This study thus aimed to investigate heavy metals concentrations in intertidal sediments of mangroves of the Gulf (Bushehr province), in particular, the sources and the extent of contamination providing an update of information that can be useful for environmental managers.

Materials and Methods The present study was conducted in April 2009 in three mangrove swamps of northern Persian Gulf, i.e. Bidekhun, Basatin and Melegonze (Fig. 1). Bidekhun mangrove swamp has an area of 80 ha, which is heavily disturbed by human activities resulted from discharges of industrial wastewaters. Basatin swamp has an area

Journal of Food, Agriculture & Environment, Vol.8 (3&4), July-October 2010 1281

of 30 ha, located in adjacent to the Bidekhun. Mangrove swamp in Melgonze is small with an area 10 ha, which is away from human activities and, therefore, less disturbed than the two other swamps.

Twenty two quadrats (10 m×10 m) were chosen in all sampling sites. Wooden stakes were placed at the corner of each quadrate and measuring tape was used to delineate the boundaries. Within each quadrate, three surface sediment samples (0-10 cm) were collected using a clean, acid-washed plastic scoop. Samples were stored in clean acid-washed plastic containers and transported to the laboratory. In the laboratory, first sediment samples were dried at room temperature (20±2°C [Mean ± SD]) and then oven dried at 105°C for 24 h. Dry samples were ground using a pestle and mortar and sieved (2 mm mesh size) to remove coarse particles. The sediment samples were stored in polyethylene bags for further analysis.

Dry samples were analyzed using the USEPA Methods 3050 20 for measuring Al, Fe, Cd, Cu, Ni, Pb, V and Zn. In this method heavy metals are extracted by digestion of samples using a boiling water bath for 2 h and a mixture of hot concentrated nitric and hydrochloric acids 20. The concentrations of metals were determined by inductively coupled plasma-atomic emission spectroscopy 20: . Total organic carbon (TOC) concentration was determined by digestion of 1 g dry sample using dichromate according to the method of Walkley and Black 23. Sediment texture was determined using a hydrometer and the percentages of sand, silt and clay was calculated following Carter 1993 (50 g per litre of suspension at 68°F (= 20°C)) 22. Three 1 m2 plots were established in all 22 quadrates (for each sampling site) and within each micro plot, several parameters, including seedling density, tree density, pneumatophore density, crab hole density, tree height and tree projective foliage cover, were recorded with counting and measuring tape 1.

Statistical analysis: Principal component analysis (PCA) was used to find relationship among sediment features and also differences in contamination of sediments among the three sampling sites. Variables had very different variance, hence, a correlation matrix was used 26 and cases with missing values were omitted. The variables included in PCA were TOC (%) and Al, Fe, Cd, Cu, Ni, Pb, V and Zn concentrations. Prior to the analysis, normality of

data was examined using one-sample Kolmogorov-Smirnov test. Reid and Spencer 27 showed that reducing effects of grain size had significant influence on PCA outputs, and effects of other factors affecting metal variability were identified more clearly. Therefore, we normalized the TOC percentage and metal concentrations through dividing them by percentage of particles <0.63 µm. The principal components (PC) with eigenvalues over one were selected 7. Preliminary PCA indicated that TOC had a low communality and was thus dropped from the analysis. Some variables had large loadings on more than one component and hence a Varimax rotation was applied. The PC loadings were considered significant when they were >0.6 27.

ANOVA was used to find a significant difference in PC scores among the three sampling sites 26, followed by a Duncan test when ANOVA detected a significant difference. ANOVA assumptions of normality and homogeneity of variance were examined by the Shapiro-Wilk and the Levene tests, respectively. Data were transformed using the function (x+3)3 to meet the homogeneity assumption.

To find correlation between the mangrove plants specifications (i.e. seedling density, tree density, pneumatorphore density, mean tree height and percent of foliage cover) with TOC percentage and metal concentration, a Pearson’s correlation coefficient was used. All statistical analyses were performed using SPSS 15.0 (SPSS Inc.).

Results Samples had a high percent of grains with a diameter less than 0.63 µm ranging from 21.00-85.00% (Fig. 2). In contrast, the percent of TOC was fairly low (0.29-3.01%). Fe and Al had the highest concentration among the measured metals (Fe 6425.00-51530.00, Al 1795.00-30240.00 µg g-1 [range]). Cd had the lowest concentration among the metals (range 0.6-3.45 µg g-1), after that, Ni, Pb, Zn and V had a higher concentration, respectively.

The three sampling sites had a similar tree density. The Melegonze sampling site had the highest density of seedlings and pneumatophore and tallest trees with the greatest percent of foliage cover (Fig. 3).

PC1, PC2 and PC3 explained 49.82, 24.24 and 10.74% of variance, respectively. Cd, Cu, Pb and Zn had the greatest loading with PC1, Al and Fe with PC2 and Ni and V with PC3 (Table 1). The same pattern was obtained for PC score coefficients (Table 2). Thus, higher scores for PC1 indicate greater concentrations of Cd, Cu, Pb and Zn. Al and Fe had their highest coefficients with PC2 and hence their higher scores with PC2 show their higher concentration. Similarly, Ni and V had their highest coefficient with PC3 and their higher scores with PC3 indicate their greater concentrations. No inverse relationship between any of two variables could be inferred based on PC loadings or coefficients. Since, negative loading or coefficients were quite small and did not thus contrast strongly with higher positive coefficients.

ANOVA indicated that there was only a significant difference among sites in PC2 scores (df = 2, F = 4.664, P = 0.022). A Duncan test showed that the samples from the site Bidekhon had significantly a greater mean PC2 scores than those from Basatin and Melegonze indicating that sediments from Bidekhon tended to have greater concentration of Al and Fe.

Concentrations of Al and Fe were positively correlated with percent of clay-silt fraction (particles <63 µm), TOC and crab hole

Persian Gulf

N 28°15’

N 27°45’

N 27°15’

N 26°45’

N 51°45’ N 52°45’ N 53°45’

Figure 1. Three mangrove forests in the northern Persian Gulf: Bidekhun (1), Basatin (2) and Melegonze (3).

2

3

1

1282 Journal of Food, Agriculture & Environment, Vol.8 (3&4), July-October 2010

PC Component loadings Variables

1 2 3

Al -0.055 0.957 0.178

Fe 0.121 0.964 -0.004

Cd 0.840 -0.005 -0.019

Cu 0.931 0.064 -0.091

Ni -0.037 0.299 0.912

Pb 0.955 -0.047 -0.162

V -0.215 -0.087 0.936

Zn 0.953 0.099 -0.135

Eigenvalue 3.672 2.196 1.340

% of variance 45.906 27.453 16.754

Table 1. The PC loadings obtained from a PCA performed on TOC and metal concentrations divided by percentage of particles < 0.63 µm. A Varimax rotation was applied. The values greater than 0.6 shown in bold.

700

600

500

400

300

200

100

0

Bidekhun

Basatin

Melegonze

Cla

y an

d si

lt (%

)

TO

C (%

)

Al (

x100

0 µ

g g-1

)

Fe (x

1000

µg

g-1)

Cd

(x10

00 µ

g g-1

)

Cu

(µg

g-1)

Ni (µ

g g-1

)

Pb (µ

g g-1

)

V (µ

g g-1

)

Zn

(µg

g-1)

Figure 2. Percent of clay and silt, TOC and heavy metal concentrations measured of sediments from three mangrove forests (Bidekhun, Basatin and Melegonze) of the northern Persian Gulf.

450

400

350

300

250

200

150

100

50

0

Seed

ling

dens

ity (#

x10-2

m-2)

Tre

e de

nsity

(#x

10-2 m

-2)

Pneu

mat

opho

n de

nsity

(#

m-2)

Cra

b ho

le d

ensi

ty (

# m

-2)

Mea

n tr

ee h

eigh

t (cm

)

Folia

ge c

over

(%)

Bidekhun

Basatin

Melegonze

Figure 3. The crab-hole density and charactersitics of mangrove trees measured in mangrove forests (Bidekhun, Basatin and Melegonze) of the northern Persian Gulf .

PC Component scores Variables

1 2 3

Al -0.043 0.495 -0.012

Fe -0.010 0.512 -0.105

Cd 0.261 -0.042 0.089

Cu 0.278 -0.001 0.047

Ni 0.083 0.052 0.528

Pb 0.282 -0.053 0.019

V 0.049 -0.149 0.568

Zn 0.278 0.022 0.018

Table 2. The PC scores obtained from a PCA performed on TOC and metal concentrations divided by percentage of particles < 0.63 µm. A Varimax rotation was applied. For each variable, the largest values among the three components shown in bold.

Journal of Food, Agriculture & Environment, Vol.8 (3&4), July-October 2010 1283

density, Ni with clay and silt fraction of sediment and Zn and Pb with mean tree height, V with clay and silt fraction and TOC and Zn with clay and silt fraction, crab hole density and mean tree height (Table 3).

Discussion This study investigated the heavy metal contamination in three mangrove forests (Bidekhun, Basatin and Melegonze) of the Persian Gulf. Principal component analysis suggested that three factors shaped the distribution of heavy metal concentrations in the coasts of the Persian Gulf. The first principal component (PC1) loaded strongly with Pb, Zn, Cu and Cd suggesting that this PC represents the influence of industrial activities, which are the main source of these metals. PC2 loaded mainly with Al and Fe indicating that this PC represents the mineralogy of sediments as the second source of pollution. PC3 loaded strongly with Ni and V suggesting that this PC represents oil pollution.

There was a significant difference in Al and Fe concentrations among the three sampling sites with the sampling site Bidekhun showing the highest concentrations. No difference was detected between the sampling sites in the rest of the metals. While sediments of Bidekhun had a higher concentration of Fe and Al, it is unlikely that vicinity of Bidekhun to the major petrochemical plant of Iran is the responsible factor for higher Al and Fe loads in sediments. Since Al and Fe are terrestrial indices 18, 30, 32, their concentrations are related to the soil properties rather than anthropogenic activities. Also, in the present study, lack of a significant difference between all studied sampling sites in heavy metal concentrations may indicate that the studied creeks have a common source of pollution: The Persian Gulf. The Persian Gulf has turned into the biggest water way for oil transport and, consequently, oil is a probable source of pollution. In addition, many industries have been raised in the region that introduced a high concentration of heavy metals into the Gulf due to lack of purification plants and control of air pollution 16.

There were significant correlations between percent of particles (<63 µm) and concentrations of Al, Fe, Ni, V and Zn. Fe and Al are most important of terrestrial silicates that are found at higher

levels in clay and silt than other soil components 3. Our results agree with Seidemann 31 who obtained a strong correlation between percent of organic materials and heavy metal contamination. Such a correlation may be related to the high specific surface area that fine grain sediments have and thus absorb a higher concentration of heavy metals 33. Fe and Al are the most important terrestrial metals that are found at higher levels in clay and silt than other soil components 3. The correlation of fine grain sediment with total organic carbon may also indicate the high specific surface area of fine grains. TOC had a significant correlation with Fe, Al, Ni and V that can be due to the high complexity of heavy metals with organic materials.

The positive correlation of crab-hole density with percent of fine particles (<0.63 µm) was in agreement with other studies 12, 13, 36. Such correlation may indicate that soils with finer texture may provide a higher food and thus increase the biomass of macro benthos. The crab-hole density was positively correlated with Fe and Al concentrations. This was due to absorption of those metals by fine grain sediment rather than a direct effect on crabs.

Seedling and tree densities were negatively correlated with Cd concentration. This result was expected as it is well documented that Cd interferes with nutrient absorption 29, nitrogen-related mechanisms 2 and function of chlorophyll leading to cessation of growth 9.

The present study indicated that there are three sources of heavy metal pollution in mangrove forest of the Persian Gulf, i.e. industry, natural mineralogy of soils and oil, and of them, industrial activities have the main effect on mangrove forests. The Cd, Cu, Pb, Ni and Zn concentrations in sediments, in the present study, were higher than those of most previous studies5,6, 10, 28 and also higher than the primary and secondary standards in the Chinese National Standard of Marine Sediment Quality 4, although lower than the secondary standard of the United State Environmental Protection Agency 34 indicating that mangroves in northern coasts of the Persian Gulf are in serious status. Such high pollution levels in the region, in spite of continuous efforts of organizations like ROMPE and the Iranian Department of Environment and occurrence of comprehensive

laws regarding wastewater control, indicate the large extent of pollution sources in the region. The industrial plants of the countries around the Persian Gulf are mainly related to oil industry, on which their economy is based. Therefore, efforts to decrease pollution level will not lead to adequate results without international cooperation.

Acknowledgements This investigation is a part of a project funded by National Iranian Oil Products Distribution Company (NIOPDC). The authors would like to thank Dr. Habibi soil lab for analysis of samples for heavy metals and also all researchers of Environment and Fishery Department, University of Tehran, for the valuable and contractive collaboration and support for this study.

Variables

Particles

< 0.63

mm (%)

TOC

(%)

Seedling

density

(# m-2)

Tree

density

(# m-2)

Pneuma-

tophore

density

(# m-2)

Crab

hole

density

(# m-2)

Mean

Tree

Height

(m)

Foliage

cover

(%)

r 0.848 0.559 -0.108 0.025 -0.132 0.480 -0.340 -0.303 Al

P < 0.001 0.007 0.631 0.911 0.557 0.024 0.122 0.170

r 0.862 0.580 -0.123 0.060 -0.186 0.440 -0.341 -0.347 Fe

P < 0.001 0.005 0.586 0.789 0.407 0.040 0.120 0.113

r 0.199 -0.041 -0.155 0.344 -0.018 0.074 -0.416 -0.099 Cd

P 0.375 0.856 0.490 0.117 0.938 0.743 0.054 0.660

r 0.333 0.350 -0.371 -0.042 -0.323 0.288 -0.305 -0.347 Cu

P 0.130 0.110 0.090 0.851 0.142 0.194 0.167 0.113

r 0.605 0.533 -0.102 0.014 -0.086 0.323 -0.383 -0.163 Ni

P 0.003 0.011 0.650 0.951 0.705 0.143 0.079 0.468

r 0.363 0.136 -0.420 0.133 -0.400 0.369 -0.451 -0.392 Pb

P 0.097 0.545 0.051 0.556 0.065 0.091 0.035 0.071

r 0.571 0.466 0.086 0.038 0.001 0.204 -0.207 0.021 V

P 0.005 0.029 0.705 0.867 0.996 0.364 0.355 0.926

r 0.442 0.237 -0.287 0.157 -0.339 0.425 -0.517 -0.294 Zn

P 0.040 0.287 0.195 0.487 0.123 0.049 0.014 0.183

Table 3. Pearson’s correlations between TOC and mangrove trees features, and concentration of heavy metals (µg g-1) in sedimens. Significant P-values are shown in bold.

1284 Journal of Food, Agriculture & Environment, Vol.8 (3&4), July-October 2010

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