1. INTRODUCTION:

Mangrove forests play a major role as a primary producer in the estuarine ecosystems (MacFarlane et al., 2007), and are an important habitat for a wide variety of species such as birds, insects, mammals and reptiles (Nagelkerken et al., 2008). In most of the developing countries, priority of necessity is given to food production and socio-economic development, which are often at the expense of biodiversity conservation (Adeel and Caroline, 2002). More than a half of the world’s population lives in coastal areas, while the existence of about half of the world’s coastal ecosystems are threatened with degradation due to human activities. An assessment of the world’s ecosystems reveals that about 35% of the mangroves and 20% of the coral reefs have been destroyed on a worldwide scale since 1960 (MEA, 2005). The insufficient purification of fertilizers usually contains several impurities and among them are heavy metals (Zarcinas et al., 2003). Mangrove sediments were extensively studied all around the world (India, Australia, Brazil, Malaysia, Arab, China, Thailand etc).

A study done by Kehrig et al. (2003) in Jequia mangrove forest, Brazil concluded that the mangrove forest has been polluted with heavy metals by the anthropogenic sources surrounding the estuary. While study done by Kamaruzzaman et al.(2007) concluded the concentration of heavy metals in Kerteh mangrove forest were generally belowthe levels found in polluted and unpolluted estuaries and mangroves. Sediments act as sinks and sources of contaminants in aquatic systems because of their variable physical and chemical properties Priju et al.(2007) , Pekey (2006), Marchand et al.(2006), Rainey et al.(2003), . And provide grouping of each variable. FA and CA are explanatory tools in multivariate statistical analysis to discover and interpret relationships between variables Yongming et al., (2006), Karbassi, et al.(2005) and Jonathan et al.(2004). . According to, Karbassi, et al.(2004), Facchinelli, et al.(2001), FA and CA are often used together to check the results and provide grouping of each variable. Yongming et al. (2006) explained that FA is widely used to reduce data and to extract a small number of factors depending on the correlation matrix, whereas CA is performed to further classify elements of different sources on the basis of their similarities chemical properties. Hierarchical cluster analysis using dendograms identifies relatively homogeneous groups of variables in similar properties and combines clusters until only one is left. The purpose of this study was: (1) to determine the physicochemical parameters (P^H, electrical conductivity and salinity), granulometric fractions, organic matter, heavy metals (Al, Cu, Fe, Pb and Zn) and base cations (Ca, Mg, Na and K) at high and low tides; (2) to identify the controlling factors by using factor analysis (FA) and cluster analysis (CA) at high and low tides and (3) to gauge the degree of anthropogenic influence on heavy metals concentration in mangrove sediment using geo-accumulation index (Igeo) Tidal current activity is mainly confined to mangrove channels. Outside the channels, mainly on the upper tidalflats, tidal current velocities decrease and sediment entrainment is frequently ascribed to wave action. The role of tidal processes on intertidal surface sediments is frequently stated but the differences at these stages have seldom been investigated, apparently because of methodological constraints Malvarez, et al. (2001). A multivariate statistical approach allows the researchers to manipulate more variables Davis, (1986). Factor Analysis (FA) and cluster analysis (CA) were the statistics methods used in the interpretation. FA and CA often used together to check the results.

2. MATERIALS AND METHODS:

Site description and characteristics.

This study was conducted in Bhavanapadu mangrove forest, Srikakulam District, North Coastal Andhra Pradesh. The total of study area spreads over from latitude (18⁰ 32’ N and 84⁰ 17’ E) (Figure 1). Bhavanapadu lagoon formerly earmarked as the Meghavaram Forest Reserve, but released by the Forestry Department to become a state land. The Meghavaram mangroves, Srikakulam District, experienced a 15% decrease from 1991 to 2000. In 1991 the mangroves covered 12.6 km²while in 2000 it was 10.7 km². Most of the mangroves have been lost due to the spread of rural development such as housing, aquaculture projects and surrounded by an industrial zone, Environmental Indicator Report, (2003).The southern spur of the estuary has been significantly degraded already and there is little left to protect. The northern spur is much larger and more irregular. There are still abundant and high quality mangroves remaining around the estuary Environmental Impact Assessment, (1992).

Soil Sampling and Analysis:

The sampling strategy was to study the spatial variability and tidal effects on a number of parameters. Mangrove sediments were sampled randomly and taken in triplicates with an auger at 33 stations from March 2005 to April 2008 (Figure 1) at low and high tide. The exact position of each sampling site was recorded using Global Positioning System (GPS). The sampling was done based to the accessibility to the mangrove forest. Mangrove surface sediments were chosen for this study as this layer controls the exchange of metals between sediments and water Malvarez, et al., (2001). Sampling bottles and the laboratory apparatus were acid soaked in diluted nitric acid before the analysis. After acid soaking, they were rinsed thoroughly first with tap water and then with distilled water to ensure any traces of cleaning reagent were removed. Finally, they were dried and stored in a clean place. The sediments were kept cool in an icebox during transportation to the laboratory. The physicochemical measurements of the surface sediment were made as soon as possible in the NEERI (National Environmental Engineering Research Institute), Laboratory. The physicochemical parameters (pH, electrical conductivity and salinity) were measured on 1:2 soils to water ratio extracts as soon as the samples reached the laboratory Muller, (1979). The pH, electrical conductivity and salinity electrodes were calibrated before the measurements were taken. For other analysis, the surface sediments were air-dried, and after homogenization using pestle and mortar, passed through a 2-mm mesh screen and stored in polyethylene bags. Organic matter was determined by using loss on ignition method while Granulometric analysis was done using pipette method Muller, (1979). For the determination of heavy metals, the samples were digested using aqua-regia. Approximately 2g of each sample was digested with 15 ml of aqua-regia (1:3 HCl: HNO₃) in a Teflon bomb for 2h at 120ºC. After cooling, the digested samples were filtered and kept in plastic bottles before the analysis. Radojevic and Bashkin Muller, (1979). Stated that aqua regia is adequate for extraction of total metals in soil sample and is widely used in most soil analysis. For base cations (Na, K, Ca and Mg), the method used in this study is the measurement of exchangeable cations using ammonium acetate. Heavy metals and base cations were analyzed using AAS with air/acetylene (Cu, Fe, Pb, Zn, Na, K, Ca and Mg) and nitrous oxide-acetylene (Al) at specific wavelengths (Atomic Absorption Spectrometer Perkin Elmer 4100).

Figure 1: Sampling locations of Bhavanapadu mangrove surface sediment sampling sites (n=33)

A to D: Freshwater drains

3. RESULTS AND DISCUSSION:

Descriptive Statistics:

The descriptive statistics of physico-chemical properties (pH, salinity, electrical conductivity) granulometric fraction, organic matter, heavy metals (Al, Cu, Fe, Pb and Zn) and base cations (Ca, Mg, Na and K) showed high value at high tide compared to low tide (Table 1).

Factor Analysis (FA):

Factor analysis (FA) was applied to discover and interpret relationships between variables at high and low tides. The results showed a different trend at high tide and at low tide. Tables 2 and 3 display the factor loadings with a Varimax rotation as well as the eigenvalues, percentile of variance and cumulative percentage at high and low tides. In reference to the eigenvalues, six factors at high tide and seven factors at low tide were extracted as they have eigenvalues greater than 1 (Tables 2 and 3). The bold values in Tables 2 and 3 are the factor loadings greater than 0.5 which taken in the determination of factors at high and low tide. At high tide (Table 2), factor one accounted for 22% of total variance and is mainly characterized by high levels of salinity, electrical conductivity and clay fraction. Factor two accounted for 13% of the total variance with sand loadings. Factor three consits of Cu and K with total variance of 11%. Factor four characterized by 9% of total variance with high loadings of Fe, Ca and Al. Factor five with 8% of total variance, contains high loading of Na and pH. Factor six with 7% of total variance is characterized by high loading of Pb.

Table 1: Physico-chemical properties, organic matter, granumetric fraction, Heavy metals and base cations (Sa=salinity; EC= electrical conductivity; OM=organic matter; HT=High Tide; LT= Low Tide)

Parameter		Min	Mean	Max	SD
pH	HT	5.4	6.5	7.6	0.4
	LT	4.5	6.1	7.2	0.6
Sa (%)	HT	1.5	4.5	7.4	2.2
	LT	0.1	0.5	1.5	0.4
EC (mS/cm)	HT	2.1	6.8	11.4	2.6
	LT	0.4	2.6	4.8	1.0
OM (%)	HT	6.4	9.0	11.7	1.3
	LT	1.4	2.5	5.2	0.8
Sand (%)	HT	91.9	93.6	95.1	0.8
	LT	91.7	95.0	97.3	1.7
Silt (%)	HT	2.5	3.6	5.2	0.8
	LT	0.4	2.7	6.4	1.8
Clay (%)	HT	2.2	2.8	4.2	0.4
	LT	1.3	2.4	2.9	0.4
Na (g kg ^-1)	HT	14.2	47.5	83.5	0.96
	LT	2.5	41.6	92.7	0.35
K (g kg ^-1)	HT	5.7	9.4	16.0	2.5
	LT	2.4	7.8	10.6	1.8
Mg (g kg ^-1)	HT	2.0	5.3	9.2	1.8
	LT	1.0	3.8	7.6	1.9
Ca (g kg ^-1)	HT	2.6	21.3	52.9	41.7
	LT	1.5	16.2	47.7	15.1
Fe (g kg ^-1)	HT	3.4	7.7	14.2	2.7
	LT	1.4	6.8	18.4	4.0
Cu (g kg ^-1)	HT	4.1	28.0	49.0	14.0
	LT	2.1	19.0	44.0	13.0
Zn (g kg ^-1)	HT	24.0	57.0	93.0	17.0
	LT	12.0	41.0	73.0	17.0
Pb (g kg ^-1)	HT	24.0	52.0	69.0	11.0
	LT	34.0	41.0	47.0	3.0
Al (g kg ^-1)	HT	4.4	14.8	3.5	8.2
	LT	2.4	9.5	2.4	6.0

Table 2: Rotated Component Matrix of Bhavanapadu mangrove forest at high tide

Variable Factor
	1	2	3	4	5	6
pH	-0.03	-0.12	-0.16	-0.11	0.86	-0.08
Sa	0.90	0.09	-0.09	0.08	0.01	-0.14
EC	0.87	0.16	-0.16	-0.04	-0.14	-0.09
OM	0.45	0.29	0.34	-0.40	-0.03	0.15
Clay	0.65	-0.19	0.24	-0.25	-0.35	0.20
Silt	-0.37	-0.84	-0.10	0.10	0.20	-0.07
Sand	0.04	0.96	-0.03	0.03	-0.02	-0.03
Al	-0.38	0.13	-0.57	0.64	0.04	0.17
Cu	0.01	0.09	0.56	0.02	0.19	0.38
Fe	0.01	-0.20	0.26	0.65	-0.14	0.30
Pb	-0.08	0.04	-0.07	0.09	0.01	0.88
Zn	-0.54	-0.24	-0.44	0.05	-0.46	-0.13
Mg	-0.29	0.45	0.37	0.05	-0.26	0.11
Ca	-0.02	0.09	-0.04	0.81	-0.06	-0.05
Na	-0.15	-0.11	0.09	-0.12	0.64	0.11
K	-0.21	0.06	0.75	0.20	-0.17	-0.23
Initial Eigenvalue	3.52	2.11	1.71	1.48	1.31	1.06
Percent of Variance	31.98	13.20	10.67	9.26	8.21	6.64
Cumulative Percent	21.98	35.18	45.84	55.11	63.32	69.96

Table 3: Rotated Component Matrix of Bhavanapadu mangrove forest at low tide

Variable Factor
	1	2	3	4	5	6	7
pH	-0.09	0.32	0.01	0.03	0.80	-0.16	0.16
Sa	-0.17	0.01	-0.03	0.73	0.04	-0.37	0.04
EC	0.28	0.24	0.01	-0.12	-0.75	-0.14	0.13
OM	-0.04	0.45	-0.42	0.33	-0.15	0.33	-0.11
Clay	-0.52	-0.06	0.61	0.11	0.10	-0.32	-0.08
Silt	0.91	-0.18	-0.08	0.06	-0.26	0.04	0.03
Sand	-0.89	0.21	-0.05	-0.09	0.26	0.03	-0.02
Al	0.78	0.20	-0.18	-0.29	0.22	0.11	-0.12
Cu	-0.07	0.06	0.84	0.10	-0.09	0.17	0.01
Fe	0.05	0.03	0.05	-0.03	-0.01	0.87	0.06
Pb	0.33	0.58	0.23	-0.34	0.13	0.15	-0.10
Zn	0.57	-0.12	-0.45	0.25	-0.26	0.03	-0.22
Mg	-0.16	0.83	0.13	0.09	0.02	-0.23	0.05
Ca	-0.20	0.63	-0.19	0.04	0.20	0.21	-0.03
Na	-0.05	-0.01	0.02	0.06	0.02	0.06	0.97
K	0.24	0.07	0.19	0.81	0.11	0.32	0.05
Initial Eigenvalue	3.89	2.04	1.63	1.49	1.27	1.11	1
Percent of Variance	24.26	12.73	10.21	9.34	7.92	7	6.28
Cumulative Percent	24.26	36.99	47.20	56.53	64.45	71.45	77.72

(Sa=salinity; EC= electrical conductivity; OM=organic matter)

At low tide (Table 3), factor one accounted for 24% of total variance and is mainly characterized by high levels of silt, Al and Zn. Factor two accounted for 13% of the total variance with Mg, Ca and Pb loadings. Factor three consist of Cu and clay with a total variance of 10%. Factor four is characterized by 9% of total variance with high loadings of salinity and K. Factor five, with 8% of total variance, contains high loading of pH while factor six, with 7% of total variance, is characterized by high loading Fe. Factor seven has 6% of total variance with Na loading.

Figure 2: Dendrogram showing hierarchical cluster analysis at high tide SA=salinity; EC= electrical conductivity; OM=organic matter)

Cluster Analysis (CA):

Cluster analysis (CA) was performed on the data set using average linkage between groups (Rescaled Distance Cluster). Although not substantially different from FA, CA can be used as a substitute method to confirm the results of FA. The results are illustrated in the dendrograms on Hierarchical Cluster Analysis (Figures 2 and 3). At high tide (Figure 2), two district clusters can be identified. Cluster one contains salinity, electrical conductivity, clay, organic matter, Cu, Pb and K. Cluster two contains pH, silt, sand, Na, Zn, Al, Fe, Mg and Ca. At low tide (Figure 3), two district clusters were observed, Cluster one contains silt, Al, Fe, K, Zn, electrical conductivity and salinity; Cluster two contains Ca, Mg, organic matter, clay, sand, pH, Pb, Cu and Na.

Geo-accumulation index (Igeo):

The geoaccumulation index (Igeo) introduced by Muller [19] was also used to assess metal pollution in sediments. Geoaccumulation index is expressed as follows:

Igeo = Log2 (Cn/1.5Bn) (Eq. 1)

Figure 3: Dendrogram showing hierarchical cluster analysis at low tide (Sa=salinity; EC= electrical conductivity; OM=organic matter)

Where Cn = measured concentration of heavy metal in the mangrove sediment, Bn = geochemical background value in average shale of element n, 1.5 is the background matrix correction in factor due to lithogenic effects.

4. DISCUSSION:

At high tide, FA results (Table 2) contain salinity, electrical conductivity and clay fraction (factor one). This match with the associations in cluster one results at high tide. Salinity and electrical conductivity clusters at the distance of one (Figure 2), shows that the contributions of seawater during high tide in Bhavanapadu. According to Church (1989), seawater contains 3.5% of salinity of which 90% is fully ionized ions and high salinity explained the high load of salinity and electrical conductivity at high tide. The salinity and electrical conductivity then form another cluster with organic matter and clay fraction. These associations then formed another cluster with FA results (factor one, factor six and factor three). While factor three consists of Cu and K, with total variance of 11% in agreement with CA results (cluster one) with the association of Cu, Pb, Mg, K and sand. Tidal flooding can bring additional ions such as K, Mg and Na into the system and allows ion exchange to occur, such as between K and Cu Preda, et al., (2000). Grande et al. (2003) obtained the similar findings between K and Cu, who observed a negative correlation with marine indicators such as K. The new condition induced by tidal clash causes precipitation of metals such as Cu. These associations are mainly related to anthropogenic inputs and reflect the complexing nature of clay. Liu et al (2003) explained that Pb most probably arises from indirect sources for instance atmospheric deposition. Based on FA (factor four, factor five and factor six) and CA (cluster 2), Al, Ca, Fe and Zn were correlated in FA and CA results. The CA results showed the relationship between Al, Ca, Fe and Zn at the distance of 12, 15 and 19. pH value in estuarine sediments is one of the important factor that regulates the concentration of dissolved metals in water and sediment Grande et al. (2003). The CA results showed associations between pH, Na silt and Mg. According to Hsue and Chen (2000), seawater plays an important role in buffering the pH change. The process of tidal clash occurs in the Meghavaram mangrove forest where the influx of seawater from the high tide resulted in major inputs of selected cations which were then adsorbed by the sediment (clay, silt and sand). The association between Mg and sand is strongly controlled by biogenic carbonates and plays an important role as a dilutant material of the heavy metals in the samples Rubio et al (2000). Biogenic carbonates are the dominant source of Ca, an abundant and important component in shallow marine biota sands as well as plays vital a role in the marine biogeochemical cycle. Morad (1998), Zhou et al (2004). Silt and Al clusters at the distance of 1 (Figure 2) and this association clusters with Fe (factor six in FA) at low tide. Salinity and electrical conductivity clusters at the distance of 14 at low tide. This distance is far higher compared to the association of electrical conductivity and salinity at high tide (Figures 2 and 3) which elaborates the lower contributions of seawater at low tide compared to high tide. FA results (Table 3) at low tide shows factor one accounted for 24% of total variance and is mainly characterized by high levels of silt, Al, Z n and EC. Cluster one (Figure 3) shows the silt and Al is as associated at the distance of 1. Besides, there is also an association of Zn of factor one in FA with Fe (factor six in FA), K and salinity (factor four in FA). Zhou et al. (2004) stated that besides anthropogenic enrichment, heavy metals occur naturally in silt and clay-bearing minerals of terrestrial and marine geological deposits. The natural occurrence of heavy metals complicates the assessment of potentially contaminated estuarine sediments. The measureable concentrations of metals do not automatically infer anthropogenic enrichment in the estuary. Therefore, heavy metal enrichment assessment is conducting in detail. While association of K is explained as a function of ionic strength. According to Hussien and Rabenhurst (2001), K is a monovalent cation with low replacing power compared with divalent cations such as Fe, Zn etc. Cluster 2 at low tide (Figure 3) shows the groups of Ca, Mg, organic matter, clay fraction, sand fraction, pH, Pb, Cu and Na. These associations are consistent with FA results with the loading factor of factor two, factor three, factor five and factor seven. In coastal environments such as in mangroves, the relationship between granulometric fractions, organic matter, base cations and heavy metals are become as functions of ionic strength of sediment solution and surface cation complexation Krishna Mohan, (2008). The concept of cation exchange capacity at low tide implies that ions will be exchanged between wetlands, colloid surface and the surrounding water. Sediment organic matter has higher ion exchange capacity than sediment colloids and plays an important role in cation exchange capacity Matagi, et al (1998).The replacing power of the cation exchange complex depends on its valence, ionic strength, and its diameter in hydrated form and its concentration in water. This clarifies the loadings of marine indicator divalent cations (Mg, Ca) together with organic matter and Pb due to the ionic strength. The role in buffering the pH change at low tide was also observed Hussien and Rabenhurst (2001).The Igeo showed that all the heavy metals are in Class 0 and Class 1 (Table 4) at high and low tide. This suggests that the mangrove sediment of Bhavanapadu Mangroves is having background concentrations for Al, Cu, Fe, and Zn and unpolluted for Pb Ashokkumar et al. (2009), Karbassi et al. (2006) detailed that Igeo values can be used effectively and more meaningful in explaining the sediment quality. In addition, Kathiresan (2010) explained that the input of metals into sediment that are located seawards to be low in the total concentration of most of the elements and this could be due to the mixing of enriched particulate material with relatively clean marine sediments.

Table 4: Geoaccumulation indexes (Muller 1979) of heavy metals concentration in sediment of Bhavanapadu Mangrove Forest

Geoaccu mulation index	Pollution Intensity	Heavy Metals (I_geoclass for Godavari Estuary Sediment
0	Back ground Concentration	Al. Cu, Fe, Zn, Pb
0-1	Unpolluted
1-2	Moderately to Unpolluted
2-3	Moderately Polluted
3-4	Moderately to highly Polluted
4-5	Highly Polluted
>5	Very highly Polluted

5. CONCLUSION:

The studied parameters showed high values at high tide compared to low tide. The study showed that factor and cluster analysis are useful tool to indentify the controlling factors of sediment data at high and low tide. Factor analysis extracted six factors at high tide and seven factors at low tide. While cluster analysis results illustrated two clusters at high and low. The salinity and electrical conductivity clusters as well as component loadings at high and low tide explained the tidal process where there is high contribution of seawater to mangrove sediment. The tidal flooding brings additional ions such as K, Mg and Na into system become the governing factor that controls the sediment chemistry. The Igeo value of heavy metals showed that the Bhavanapadu mangrove sediment are having background concentrations for Al, Cu, Fe, and Zn and unpolluted for Pb.

6. ACKNOWLEDGEMENT:

This work was carried out with financial assistance provided by the New Delhi MOEF Project (No. 22/26/2004-CSC (M) dtd. 24-2-2005). We are grateful to the commission for the grant-in-aid. Facilities at the Department of Environmental Sciences, Andhra University and Andhra Pradesh Forest Department, local village peoples of Bhavanapadu were utilized and we are thankful to the authorities’ concerned.

7. REFERENCES:

1. Adeel, Z. and Caroline, K. (Ed) (2002) Conserving Our Coastal Environment: A Summary of Unu’s Research on Sustainable Management of the Coastal Hydrosphere in the Adia Pacific Region. United Nations University, Tokyo, Japan.

2. Ashokkumar, S., P. Mayavu, P. Sampathkumar, P. Manivasagam and G. Rajaram., 2009. Seasonal Distribution of Heavy Metals in the Mullipallam Creek of Muthupettai Mangroves (Southeast Coast of India). American-Eurasian Journal of Scientific Research. 4 (4): 308-312.

3. Church, A. H., 1989. The ionic of the sea. The Phytologist. 68, 239-247.conductivity of the mangrove soils in Norten Taiwan. Proceeding Natural Science Council. 24,143-150.

4. Davis, J. C., 1986. Statistics and data analysis in geology. 2nd Edition. John Wiley, New York.

5. Environmental Impact Assessment, 1992. Proposed mangrove paradise resort complex on LA 91040377 Tuaran, Sabah. Perunding Sekitar, Kota Kinabalu.

6. Environmental Indicator Report, 2003. The Environment Protection Department (EPD) Sabah, Jalan Tunku Abdul Rahman, 88999 Kota Kinabalu, Sabah, Malaysia.Evolution. Blackwell Science Limited, London.

7. Facchinelli, A., Sacchi, E. and Mallen, L., 2001. Multivariate statistical and GIs -based approach to identify heavy metal sources in soils. Environmental Pollution. 114, 313–324.

8. Grande, J. A., Borrego, J., Morales, J. A. and Torre, M. L., 2003. A description of how metal pollution occurs in the Tinto-Odiel Rias (Huelva-Spain) through the application of cluster analysis. Marine Pollution Bulletin. 46, 475-480.

9. Hsue, Z. Y. and Chen, Z. S., 2000. Monitoring the changes of redox potential, ph and electrical.

10. Hussein, A. H. and Rabenhorst, M. C., 2001. Tidal Inundation of Transgressive Coastal Areas: International Journal of Environmental Research. 4, 280-289.

11. Jonathan, M. P., Ram-Mohan, V. and Srinivasalu., 2004. Geochemical variations of major and trace elements in recent sediments, off the Gulf of Mannar, the Southeast coast of India. Environmental Geology. 45, 466-80.

12. Kamaruzzaman, B. Y., Antotina, A., Airiza, Z., Syalindran, S. and Ong, M. C., 2007. The Geochemical Profile of Mn, Co, Cu, and Fe in Kerteh Mangrove Forest, Terengganu. The Malaysian Journal of Analytical Sciences. 11(2), 336-339.

13. Karbassi, A. R. and Amirnezhad, R., 2004. Geochemistry of heavy metals and sedimentation rate in a bay adjecent to the Caspian Sea. International Journal of Environmental Science and Technology. 1, 191-198.

14. Karbassi, A. R. and Shankar, R., 2005. Geochemistry of two sediment cores from the west coast of India. International Journal of Environmental Science and Technology. 3, 35-42.

15. Karbassi, A. R., Bayati, I. and Moatta, F., 2006. Origin and chemical partitioning of heavy metals in riverbed sediments. International Journal of Environmental Science and Technology. 3, 35-42.

16. Kathiresan, K., 2010. Mangrove Ecosystems. Distribution of Mangroves. In: Kathiresan, K. and Ajmal Khan, S. (Ed.) “International Training Course on Coastal Biodiversity in Mangrove Ecosystems”, Annamalai University, Parangipettai, India.

17. Kehrig, H. A., Pinto, F. N., Moreira, I. and Malm, O., 2003. Heavy Metals and Methylmercury in a Tropical Coastal Estuatry and Mangrove in Brazil. Organic Geochemistry. 34, 661-669.

18. Krishna Mohan, G.V. 2008. “Studies on the Ecorestoration of Bhavanapadu Mangroves of Srikakulam District of Andhra Pradesh”. Ph.D. Thesis, Andhra University, Visakhapatnam.

19. Liu, W, X., Li, X, D., Shen, Z, G., Wang, D. C., Wai, O. W. H. and Li, Y. S., 2003. Multivariate Statistical Study of Heavy Metal Enrichment in Sediments of the Pearl River Estuary. Environmental Pollution. 121, 377-388.

20. MacFarlane GR, Koller CE, Blomberg SP. Accumulation and partitioning of heavy metals in mangroves: A synthesis of field-based studies. Chemosphere 2007; In press.

21. Malvarez, G. C., Cooper, J.A.G. and Jackson, D.W.T., 2001. Relationships between Wave-Induced Currents and Sediment Grain Size on a Sandy Tidal-Flat. Journal of Sedimentary Research. 71, 705-712.

22. Marchand, C., Lalliet, V. E., Baltzer, F., Alberic, P., Cossa, D. and Baillif, P., 2006. Heavy Metals Distribution in Mangrove Sediments along the mobile coastline of French Guiana. Marine Chemistry. 98,1- 17.

23. Matagi, S. V., Swai, D. and Mugabe, R., 1998. A Review of Heavy Metals Mechanism in Wetlands. African Journal of Tropical Hydrobiology and Fisheries. 8, 23-35.

24. MEA (2005) “Our Human Planet: Summary for Decission Makers”, Millennium Ecosystem Assessment, Island Press, Washington DC, USA.

25. Morad, S., 1998. Carbonate Cementation in Sandstones: Distribution Patterns and Geochemical.

26. Muller, G., 1979. Schwermetalle in den sediments des Rheins-Veranderungen seitt 1971. Umschan. 79, 778–783. In Chen, C., Kao, C., Chen, C. and Dong, C., 2007. Distribution and accumulation of heavy metals in the sediments of Kaohsiung Harbor, Taiwan. Chemosphere. 66, 1431-1440.

27. Nagelkerken I, Blaber SJM, Bouillon S, Green P, Haywood M, Kirton LG, Meynecke JO, Pawlik J, Penrose HM, Sasekumar A, Somerfield PJ. 2008, The habitat function of mangroves for terrestrial and marine fauna: A review. Aquatic Botany , 89: 155-85.

28. Pekey, H., 2006. Heavy Metal Pollution Assessment in Sediments of the Izmit Bay, Turkey. Environmental Monitoring and Assessment. 123, 219-231.

29. Preda, M. and Cox, M. E., 2000. Sediment-water interaction, acidity and other water quality parameters in a subtropical Setting, Pimpama River, Southeast Queensland. Environmental Geology. 39, 319-329.

30. Priju, C. P. and Narayana, A. C., 2007. Heavy and Trace Metals in Vembanad Lake Sediments.

31. Radojevic, M. and Bashkin, V. N., 1979. Practical Environmental Analysis. Royal Society of Chemistry, Cambridge.

32. Rainey, M. P., Tyler, A. N., Gilvear, D. J., Bryant, R. G. and Mcdonald, P., 2003. Mapping Intertidal Estuarine Sediment Grain Size Distributions through Airborne Remote Sensing. Remote Sensing of Environment. 86, 480-490.

33. Rubio, B., Nombelia, M. A. and Vilas, F., 2000. Geochemistry of Major and Trace Elements in Sediments of the Ria De Vigo (NW Spain): An Assessment of Metal Pollution. Marine Pollution Bulletin. 40, 968- 980.

34. Yongming, H., Peixuan, D., Junji, C. and Posmentier, E. S., 2006. Multivariate Analysis of Heavy Metals Contamination in urban dusts of Xi’an, Central China. Science of Total Environment. 355, 176-186.

35. Zarcinas S, Fauziah CI, Mclaughlin MJ, Cozens G. Heavy metals in soils and crops in Southeast Asia (Peninsular Malaysia). Environmental Geochemistry and Health 2003; 26: 343-57.

36. Zhou, H., Peng, X. and Pan, J., 2004. Distribution, Source and Enrichment of Some Chemical Elements in Sediments of the Pearl River Estuary, China. Continental Shelf and Research. 24, 1857-1875.

Received on 28.03.2011 Modified on 05.04.2011

Asian J. Research Chem. 4(7): July, 2011; Page 1067-1072