Assessment of spatial distribution and potential

Ecotoxicology and Environmental Safety ] (]]]]) ]]]–]]]

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Assessment of spatial distribution and potential ecological risk of the heavy metals in relation to granulometric contents of Veeranam lake sediments, India G. Suresh a,n, P. Sutharsan b, V. Ramasamy b, R. Venkatachalapathy c a

Department of Physics, Arulmigu Meenakshi Amman College of Engineering, Vadamavandal 604410 (Near Kanchipuram), Tamilnadu, India Department of Physics, Annamalai University, Annamalainagar 608002, Tamilnadu, India c CAS in Marine Biology, Faculty of Marine Science, Annamalai University, Tamilnadu, India b

a r t i c l e i n f o

abstract

Article history: Received 7 April 2012 Received in revised form 28 June 2012 Accepted 29 June 2012

The contents and spatial distributions of heavy metals (Cd, Cr, Cu, Ni, Pb and Zn) have been studied in surface sediments of Veeranam lake, Tamilnadu, India. Heavy metal contents are higher in open water area (limnetic zone) (OWA) than other two regions such as inflow river mouth (littoral zone) (IFR) and outflow river mouth region (OFR). Present metal contents are compared with both background and toxicological reference values. The comparative results suggest that the present metals except Cd and Pb create an adverse effect on the aquatic ecosystems associated with this lake. The Pollution Load Index (PLI) and Potential Ecological Risk (PER) are calculated and calculated PLI values (range: 1.18– 4.09 with an average of 2.03) show that the present sediments are polluted significantly and these values are higher in OWA region. From the PER values, each single element has low potential ecological risk. However, Cd shows higher ecological risk. The comprehensive PER index of the sediments shows moderate degree. The magnetic susceptibility is higher in OWA region. Granulometric analysis confirms that the silt is major content. Multivariate Statistical analyses (Pearson Correlation, Cluster and Factor analysis) were carried out and obtained results suggested that the heavy metals in present lake have complicated contamination sources or controlling factors and the heavy metals such as Cr, Cu, Ni and Zn may be incorporated in magnetic minerals which are presented in silt grains. Also it shows that the role of silt is incorporating the cations on their surface and raising the level of magnetic susceptibility and heavy metal contents. The present study recommends that the heavy metal levels are unlikely to cause additional adverse health risks to the aquatic ecosystem associated with this lake. Published by Elsevier Inc.

Keywords: Lake sediments Heavy metals Magnetic susceptibility Multivariate statistical analysis

1. Introduction Heavy metals are among the most persistent of pollutants in the ecosystem such as water, sediments and biota because of their resistance to decomposition in natural condition. Toxicity appears after exceeding level of indispensability. Heavy metals become toxic when they are not metabolized by the body and accumulate in the soft tissues. Metals have low solubility in water, get adsorbed and accumulated on bottom sediments (Jain et al., 2008). Thus, the sediment could be a potential source of heavy metals that will be released into the overlying water via natural and anthropogenic processes, where they could have an adverse effect on the drinking water quality and human health. Moreover, benthic biota or other organisms can ingest metal particles or contaminated water, which

n

Corresponding author. E-mail address: [email protected] (G. Suresh).

results in metals accumulating in their tissues and ultimately entering the food chain (Yin et al., 2011). The lake sediments are basic components of our environments as they provide nutrients for living organism. Lake bottom sediments are sensitive indicators for monitoring contaminants as they can act as a sink and a carrier for pollutants in the aquatic environment (Bai et al., 2011). Thus, the lake sediment analysis plays an important role in evaluating pollution status in aquatic environment. The mass-specific magnetic susceptibility (w) indicates the amount of magnetic particles in materials like soil, sediments or rocks. Magnetite is the mineral with the greatest known susceptibility (volume-specific susceptibility is between 1200–19,200 103 SI units) and its widespread occurrence in nature. It can be found in many different kinds of rocks, modern soils and sediments. Mineral magnetic measurements have been widely used for delineating the environmental pollution during the recent decades, because they are fast, cost-effective, non-destructive and sensitive (Suresh et al., 2011a). The magnetic carriers tend to absorb and/or incorporate

0147-6513/$ - see front matter Published by Elsevier Inc. http://dx.doi.org/10.1016/j.ecoenv.2012.06.027

Please cite this article as: Suresh, G., et al., Assessment of spatial distribution and potential ecological risk of the heavy metals in relation to granulometric.... Ecotoxicol. Environ. Saf. (2012), http://dx.doi.org/10.1016/j.ecoenv.2012.06.027

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G. Suresh et al. / Ecotoxicology and Environmental Safety ] (]]]]) ]]]–]]]

heavy metals in their crystalline structure (Morton-Bermea et al., 2009). Correlation between the magnetic susceptibility and heavy metals is highly complex. Magnetic susceptibility is driven by many controls with the contents of heavy metals ranking among minor influences. A large number of authors (Petrovsky et al., 1998, 2001; Durza, 1999; Shu et al., 2001; Schmidt et al., 2005; Lu and Bai, 2006; Yang et al., 2007; Morton-Bermea et al., 2009; Chaparro et al., 2008, 2010; Rezvan Karimi et al., 2011 and Wang et al., 2012) have investigated the uses of magnetic susceptibility in environmental pollution studies (especially heavy metal pollution). Hence, the correlation between magnetic susceptibility and heavy metal contents in sediments is greater importance. Li et al. (2011), and Abrahim and Parker (2008) have pointed out that the influence of sediment grain size on the magnetic parameters and heavy metal contents are strong. Due to the weathering of rocks and anthropogenic activities, the elements are produced and transported by the rivers and redistributed to the adjacent lakes and its sediments. One of the most important processes affecting this migration of elements is interaction with geological materials. The accumulation and distribution of elements depend mostly on the characteristics of the geological material such as mineral species and grain sizes (Taylor, 2007). The interaction between the heavy metals and sediment characteristics is very important because sediment is the sink for heavy metals. Many researchers have pointed out a strong influence of sediment grain size on the magnetic susceptibility. Hence, the multivariate statistical analyses such as Pearson’s correlation, Cluster and Factor analysis have been carried out to find out the interrelation among the parameters obtained from heavy metal, magnetic susceptibility and granulometric analysis. Hence, the main aspects of the present work are to (i) determine the content and spatial distribution of heavy metals (Cd, Cr, Cu, Ni, Pb and Zn) in the Veeranam lake surface sediments. (ii) Calculate the pollution load index (PLI) and potential ecological risk (PER) in order to assess toxicity of the sediments. (iii) Determine the magnetic susceptibility of the lake sediments. (iv) Measure granulometric fractions of the sediments such as contents of sand, silt and clay. (v) Know existing relations among the parameters obtained from heavy metal, magnetic susceptibility and granulometric analysis using multivariate statistical analyses.

180 mld (million liters a day) was being drawn for city supply. Also the lake is a source of agriculture water distribution. The lake gets the water from river Kollidam. The lake also provides considerable amount of fish, prawn and crab for human consumption. Therefore, Veeranam lake is of great significance to the social, economic and daily life of the local residents. Unfortunately, overpopulation, rapid urbanization, and point and nonpoint pollution sources along the Kollidam river and in and around the lake have all greatly affected the water quality of present lake. Furthermore, large quantities of untreated wastewater were directly discharged into river, and these ultimately entered the lake. The sediments of the Veeranam lake have been subjected to the pressures of above human activities. These are the potential sources of heavy metal contamination. These sources have the potential to threaten public health and impact the balance of the lake ecosystem (Yin et al., 2011). Particular attentions must be paid to the quality of sediments of the lake. Therefore, it is necessary to investigate the spatial distributions and assess ecological risks caused by heavy metals in Veeranam lake sediments.

2.2. Sample collection Surface sediment samples were randomly collected from 28 different sampling grids distributed in the whole Veeranam Lake in April 2010 (Fig. 1). The sampling locations were recorded (Latitudinal and Longitudinal position) using hand-held Global Positioning System (GPS) (Model: GARMIN GPS-12) unit. The upper 0–5 cm depth of sediments was manually collected with the help of a plastic spoon. They were then placed into polyethylene bags, and returned to the laboratory. All these samples were air dried at room temperature and sieved through a 2-mm nylon sieve to remove coarse debris. Among the 28 sampling sites, 4 sites (S1, S2, S3 and S4) were chosen in IFR region, 20 sites (S5, S6yS24) were chosen in OWA region and remaining 4 sites (S25, S26,..S28) were chosen in OFR region.

2.3. Heavy metal analysis In order to determine the content of heavy metals, 0.5 g of sediment was digested by microwave in Teflon vessels using 6 ml of supra-pure concentrated HNO3, 2 ml of H2O2 30% and 2 ml of concentrated HF; HF was then removed by the addition of an excess of H3BO3 (US EPA Method 3052 1996). The solution was transferred into a polyethylene volumetric flask and diluted with milliQ water to 100 ml. One milliliter of the solution was then diluted to 10 ml by adding HNO3 (Suresh et al., 2011b). All glass wares and plastic containers were washed with 10% nitric acid solution and rinsed thoroughly with milliQ water. Heavy metal contents (Cd, Cr, Cu, Ni, Pb and Zn) were measured by Inductively Coupled Plasma Optical Emission Spectrometry (Perkin Elmer 2100 DV) (US EPA Method 6020 1996). The precision of the analytical procedure was checked by analyzing standard reference materials of commercially available standards (Merck KGCA, 64271 Damstadt, Germany, ICP-Multi element standard solution IV, 23 elements in nitric acid) in triplicates.

2.4. Magnetic susceptibility measurement 2. Materials and methods 2.1. Study area Veeranam lake is located 14 km from Chidambaram in Cuddalore district and 235 km from Chennai in the state of Tamilnadu in South India. The location of lake is between latitudes 111050 5600 –111260 1100 and Longitudes 791150 3000 –791320 1000 . It is situated nearer to the seashore i.e., about 30 km from Bay of Bengal. Because of it’s to the sea-shore, cyclones hit the seashore during North East monsoon, resulting in heavy rains. The average rainfall of lake area is more than the state average of 950 mm. But due to the vagaries of the monsoon, Veeranam has to depend on water from Mettur dam often. Water released from the Mettur dam through Kollidam and Lower Anicut would also bring in sufficient inflow into the Veeranam Lake. The lake almost got its storage capacity as it received inflow from the Cauvery tributaries. The excess water of the lake passes to the Vellar river during rainy season. Along the lake shore, state highway road exists. In order to understand the heavy metal pollution in the lake, three zones (regions) of the lake such as inflow river mouth region (littoral zone) (IFR), open water area (limnetic zone) (OWA) and outflow river mouth region (OFR) are considered. Veeranam Lake is a biggest lake of Tamilnadu, India. It is one of the sources of drinking water to Chennai city. Chennai is the fourth most populous metropolitan area and the sixth most populous city in India. As of 2011, Chennai had a population of 4.68 million within the area administered by the municipal corporation and an extended metropolitan population of nine million. It is the capital of the state Tamilnadu. Catchment area of the lake is 25 km2 (Maximum length: 11.2 km and maximum width: 4 km). Maximum depth of the present lake is 7 m. The lake has a capacity to store about 1465 mcft of water. The amount of

The dry sediment samples were sealed with cling film then packed with palaeomagnetic plastic boxes (8 cm3) and the net weight was determined before magnetic measurements. Such specimens were hardened using a sodium silicate solution. Magnetic susceptibility measurements were carried out using a magnetic susceptibility meter, Bartington Instruments Ltd., linked to MS2B dual frequency sensor (0.47 and 4.7 KHz). For the laboratory measurements, five readings were taken for each sample in two different frequencies (Low and High) and an average is calculated (Suresh et al., 2011a).

2.5. Granulometric analysis The samples were also examined to measure their granulometric fractions such as contents of sand, silt and clay. As per the procedure given in Stemmer et al. (1998), grain size separation analysis was carried out. The separation of the particle-size fractions was achieved by a combination of wet sieving and centrifugation. About 100 g of collected samples were placed in the beaker and dispersed in distilled water. Coarse sand (4200 mm) and sand (200 63 mm) contents were separated by manual wet sieving using cooled distilled water. In the present case, content of coarse sand is in negligible amount. To separate silt-sized particles from clay (o 2 mm) the remaining suspension was poured into centrifuge bottles and centrifuged. After resuspension of the pellets in a small amount of water, the suspension was transferred into a centrifuge bottle, filled up with cooled water and recentrifuged. This procedure was repeated twice to decrease the clay content in the silt size fraction. To obtain the clay sized fraction, all supernatants were collected and centrifuged. This centrifugal force led to sedimentation of clay size particles.



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Fig. 1. Location of experimental sites in Veeranam lake, Tamilnadu, India. Note: 1, 2, 3,....28 respectively represents S1, S2, yS28.

2.6. Method of calculating pollution load index and potential ecological risk

2.7. Statistical analysis

2.6.1. Pollution load index (PLI) To assess the sediment environmental quality, an integrated pollution load index of the six metals is calculated according to Suresh et al. (2011b). The PLI is defined as the nth root of the multiplications of the contents (CFmetals)

Descriptive statistics including average, maximum, minimum, standard deviation and coefficient of variation are performed after analysis. The standard deviation and coefficient of variations are incorporated to reflect the degree of dispersion distribution of different metals and indirectly indicate the activity of the selected elements in the examined environment. Multivariate statistical analyses such as Pearson correlation analysis, Cluster analysis (CA) and Factor analysis (FA) were performed. Pearson’s correlation analysis was carried out among the variables obtained from above said analyses using SPSS 16. Cluster analysis (CA) is a multivariate technique, whose primary purpose is to classify the objects of the system into categories or clusters based on their similarities, and the objective is to find an optimal grouping for which the observations or objects within each cluster are similar, but the clusters are dissimilar from each other. CA can be readily carried out in most of the standard statistical packages. For this study, MINITAB (version 15) package is used for the analysis to classify the variables that are similar in groups as per the procedure given in Ramasamy et al. (2011). Factor analysis was carried out on the same data set (12 variables as in the above analyses) to assess the relationship using SPSS 16. It is actually performed on the correlation matrix between different parameters followed by varimax rotation.

PLI ¼ ðCF1 CF2 CF3 . . .CFn Þ1=n where CFmetals is the ratio between the content of each metal to the background values, CFmetals ¼CHmetal/CHback. The baseline values are determined from their corresponding minimum values, which can be considered as natural heavy metal values in Veeranam lake. The PLI gave an assessment of the overall toxicity status of the sample and also it is a result of the contribution of the six metals.

2.6.2. Potential ecological risk (PER) Potential ecological risk index (PER) is also introduced to assess the contamination degree of heavy metals in the present sediments. The equations for calculating the PER were proposed by Guo et al. (2010) and are as follows. C if ¼

Ci C in

,

Cd ¼

Eir ¼ T ir C if i ,

n X

C if

i¼1

PER ¼

3. Results and discussion m X

Eir

i¼1

C if

i

where is the single element pollution factor, C is the content of the element in samples and C in is the reference value of the element. The sum of C if for all metals examined represents the integrated pollution degree (Cd) of the environment. Eir is the potential ecological risk index of an individual element. T ir is the biological toxic factor of an individual element, which are defined for Cr¼2, Cu¼ Pb¼5, Zn ¼1, Cd ¼30 and Ni ¼ 6 (Guo et al., 2010 and Fu et al., 2009). PER is the comprehensive potential ecological index, which is the sum of Eir . It represents the sensitivity of the biological community to the toxic substance and illustrates the potential ecological risk caused by the overall contamination.

3.1. Spatial distribution of heavy metals and comparison of contents with those of background and toxicological reference values The spatial distribution of heavy metals (Cd, Cr, Cu, Ni, Pb and Zn) in Veeranam lake sediments are shown in Fig. 2. The total heavy metal contents in the sediments decrease in the order of Zn4Cu 4Cr4Ni4 Pb4Cd. Statistical summary and other comparisons of the metal contents are presented in Table 1. From the table, the measured heavy metal contents varied greatly as


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Fig. 2. Spatial distributions of Cd, Cr, Cu, Ni, Pb and Zn (mg/kg) in Veeranam lake.

Table 1 Statistical summary and other comparison of studied metals. Metal concentrations (mg/kg)

Mean Maximum Minimum Std. Dev CV (%) ERLa ERMa TRVb WSAc WCTMRL

Cd

Cr

Cu

Ni

Pb

Zn

0.81 3.90 0.20 0.72 88.67 5.0 9.0 0.60 0.30 0.1–1.5

88.20 150.41 39.84 24.50 27.78 80 145 26 97 20–190

94.12 125.64 65.44 19.83 21.07 70 390 16 32 20–90

63.61 95.60 34.32 17.92 28.17 30 50 16 49 30–250

30.06 41.00 20.11 5.49 18.26 35 110 31 20 10–100

180.08 598.80 68.80 119.51 66.37 120 270 110 129 50–250

Std. Dev: Standard deviation; CV: Coefficients of variation. a Effect range low and Effect range medium for freshwater ecosystem (Bai et al., 2011). b Toxicity Reference Value (Mohiuddin et al., 2010). c World surface rock average (Martin and Meybeck, 1979); WCTMRL: World common trace metal range in lake sediment (Forstner and Whitman, 1981).

follows: Cd, 0.2–3.9 mg/kg with an average of 0.81 mg/kg; Cr, 39.84–150.41 mg/kg with an average of 88.20 mg/kg; Cu, 65.44– 125.64 mg/kg with an average of 94.12 mg/kg; Ni, 34.32–95.60 mg/ kg with an average of 63.61 mg/kg; Pb, 20.11–41.0 mg/kg with an average of 30.06 mg/kg and Zn, 68.80–598.80 mg/kg with an average of 180.08 mg/kg. Studied heavy metal contents are higher in open water area (limnetic zone) (OWA) than the other two regions. Most of the studied metal contents are higher in S23. In order to predict the heavy metal pollution in the present sediments, comparative study made with both background (World surface rock average (WSA) and World common trace metal range in lake sediment (WCTMRL)) and toxicological reference values (Effect Range Low (ERL), Effect Range Medium (ERM) and Toxicity Reference Value (TRV)). Comparative results are presented in Table 1. It is evident that the average content of all measured heavy metals except Cr in the present sediments exceeded the corresponding values of WSA. Also, it is evident that the present sediment ranges of Cd, Cu and Zn are higher and others are lower than the corresponding values of world common trace metal range in lake sediment. In the case of comparison of



toxicological reference values, the average contents of Cd and Pb are lower than the ERL values. Average content of Cr, Cu and Zn is lies between corresponding ERL and ERM values. Average Ni content in present sediment exceeded both ERL and ERM values. However, Cr content in 1 site (3%), Ni content in 23 sites (82%) and Zn content in 7 sites (25%) are higher than corresponding ERM values. Average of the studied metals is higher than the TRV. According to Harikumar and Nasir (2010), the contents below ERL represent a minimal-effects range, which is intended to estimate conditions where biological effects are rarely observed. Contents are equal to or greater than ERL, but less than ERM represents a range within which biological effects occur occasionally. Contents at or above ERM values represents a probable effect range within which adverse biological effects frequently occur. In the present lake, there is no or rare biological effect may occur due to Cd and Pb. Occasional biological effect may occur due to Cr, Cu and Zn, since those contents are lies between the ERL and ERM. However, adverse biological effect may occur due to the Zn in 25% of sites. Finally, frequent adverse biological effect is possible due to the Ni content. Thus the level of heavy metals (Ni, Cr, Cu and Zn) found in the present sediments might create adverse effects on the aquatic ecosystems associated with this lake. The measured metal contents are compared with other lakes from different countries including India (Table 2). From this table, Texoma lake (USA), Manchar lake (Pakistan), Victoria lake (Africa) and Jannapura lake (India) have higher Cd content than present lake Cd value. All the lakes which are presented in the table have lower Cr and Cu contents when compared with present values. Content of Ni in Hussainsagar lake, India and Zn content in Vembanad lake, India have higher values and all the other lakes have lower values when compared with present corresponding values. Most of the lakes have higher values of Pb than present Pb value. Other comparisons are clearly presented in Table 2.

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3.2. Assessment of sediment contamination by calculating the pollution load index (PLI) and potential ecological risk (PER) 3.2.1. Pollution load index (PLI) The calculated PLI values of the present sediments are summarized in the Table 3. The PLI values are ranged from 1.18 to 4.09 with an average of 2.03. It shows much fluctuation. According to Suresh et al. (2011b), PLI values equal to zero indicates perfection, value of one indicate baseline levels of pollutants present and values above one indicate progressive deterioration. Extend of pollution increase with increase in the numerical PLI value. As per above grade, present sediments are polluted significantly, since PLI of all sites is higher than one. Higher PLI value is observed at S23 which may be due to the traffic effluents. PLI values are higher in OWA region than other regions. Prinju and Narayana (2006)have calculated PLI value for Vembanad lake, India. This is ranged from 0.49 to 3.70, which is lower than the present range. Chaparro et al. (2008) have registered average PLI value for Cauvery river (which is the source of Kollidam river and also the source of the present study area) as 4.24 (range; 2.16–6.76). The present range is lower than the values of Cauvery river. Also, Chaparro et al. (2011) have calculated the PLI value for another Indian river (Vellar-termination point of the lake) which is equal to 2.05. This average is matched with present values. Thus, progressive impact due to the heavy metals in present sediment is confirmed. Similarly, PLI for a zone is calculated by the following relation PLI for a zone ¼ ðPLI1 PLI2 PLI3 . . .PLIn Þ1=n where n is the number of sites. The calculated PLI zone value is 1.85, which is higher than 1. However, it is not much higher (Mohiuddin et al., 2010). The PLI can provide some understanding to the public about the quality of an environment.

Table 2 Mean concentration of measured elements in lake sediment samples from different countries including India along with present study. Sl. no

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

Name of the lakes

Metal concentrations (mg/kg)

Yilong lake, China East lake, Wuhan, China Texoma lake, USA Balaton lake, Hungary Songkhla lake, Southern Thailand Laguna lake (Philippines) Manchar lake Pakistan Taihu lake, China Hazar Lake, Turkey Chaohu lake, China Victoria lake, Tanzania, Africa Kariba lake, Zimbabwe Hussainsagar lake, India Jannapura lake, India Vembanad Lake, India Veeranam lake, India Range Mean

References

Cd

Cr

Cu

Ni

Pb

Zn

0.76 – 2.0 0.1–0.7 0.1–2.4 0.02–0.09 4.9–9.7 0.94 – 0.92 2.5 0.06 – 1.9 1–4 0.2–3.9 0.81

86.73 – 30 5.7–66 – – 14.7–26.8 56.2 17–79 80.1 11.0 29.3 40–60 – – 40–150 88.20

31.40 54.8 38 0.7–36 1.8–125 9.7–18.7 15.6–29.7 36.7 10–64 38.6 21.6 16.1 – 89.75 47 65–125 94.12

35.99 – – 4.4–55 2.5–21.9 9.7–18.7 16.1–26.6 – 38–130 44.7 – – 170–210 40.05 64 34–95 63.61

53.19 40.3 10 2.4–160 8.2–131 17–23 14.6–20.9 51.8 o DL 94.9 29.6 9.4 40–60 0.20 – 20–41 30.06

86.82 138 – 13–150 5.4–562 10.3–18.3 53.9–154 – 46–210 – 36.4 42.4 – 0.034 259 69–599 180.08

Bai et al. (2011) Yang et al. (2007) Yin et al. (2011) Nguyen et al. (2005) Pradit et al. (2010) Pradit et al. (2010) Arain et al. (2008) Yin et al. (2011) Ozmen et al. (2004) Zheng et al. (2010) Kishe and Machiwa (2003) Kishe and Machiwa (2003) Gurunadha Rao et al. (2008) Puttaiah and Kiran (2008) Prinju and Narayana (2006) Present study

Table 3 Basic statistics of pollution load index (PLI) and Potential Ecological Risk (PER) of sediments. CF

Average Maximum Minimum

PLI

Cd

Cr

Cu

Ni

Pb

Zn

4.05 19.50 1.00

2.21 3.78 1.00

1.44 1.92 1.00

1.85 2.79 1.00

1.49 2.04 1.00

2.62 8.70 1.00

2.03 4.09 1.18

Cd

12.23 36.14 5.99

Er

PER

Cd

Cr

Cu

Ni

Pb

Zn

121.61 585.00 30.00

4.43 7.55 2.00

7.19 9.60 5.00

7.41 11.14 4.00

7.47 10.19 5.00

2.62 8.70 1.00

150.93 628.86 50.49


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Table 4

Table 5 Distribution of magnetic susceptibility, PLI and PER in all sites.

Grade standards for Eir and PER. Potential ecological risk index Ecological risk index ðEi Þ of individual element

Potential ecological index (PER)

Grades of potential ecological risk

Site Number

o40 40–80 80–160 160–320

o 150 150–300 300–600 4600

Low-grade Moderate Severe Serious

Average Maximum Minimum

r

4320

Low Moderate Higher Much higher Serious

3.2.2. Potential ecological risk (PER) The Table 4 shows the factor standard of different levels (Guo et al., 2010). Calculated single element pollution factor ðC if Þ, degree of contamination, potential ecological risk of an individual element and comprehensive potential ecological index are presented in Table 3. The contamination degree of the heavy metals is of the order: Cd4Zn 4Cr 4Ni4Pb4Cu. The assessment of integrated pollution degree of sediments is based on the degree of contamination (Cd). The ranges of Cd is 5.99–36.14 with an average 12.13, indicating high contamination of the sediment environment (Fu et al., 2009). Combining the potential ecological risk index of individual metal ðEir Þ (Table 3) with its grade classifications (Table 4), each single element shows low potential ecological risk. However, Cd in the present sediments shows higher ecological risk. Generally, non-ferrous metal mining and refining, manufacture and application of phosphate fertilizers and waste disposal are the main anthropogenic sources of cadmium in the environment (ATSDR, 2008). Metal processing industries, lot of agriculture land and living residents are existed in and around the present lake and source river. These are the source of Cd in the present sediments. Also, according to ATSDR (2008), excess Cd accumulates in aquatic organism and agricultural crops. The major crops cultivated in Cuddalore district are paddy, sugarcane and groundnut. The Cd in present lake may be accumulated in the above major crops since the present lake is a source of agriculture water distribution. The consequence of the average Eir for heavy metals is Cd4Pb4Ni4Cu4Cr4Zn. The calculated PER values are ranged from 50.49 to 628.86 with an average of 150.93. The lower and higher PER values are observed at S5 and S23 respectively. This higher value may be due to the presence of higher heavy metal contents except Pb at S23. According to Table 4, the PER value (628.86) at S23 shows serious potential ecological risk. This may be due to the industrial and traffic activities. The sampling sites S12, S14–S16, S18, S20, S25 and S28 (8 sites) have PER value in the range of 150–300 shows moderate degree of ecological risk. About 68% (19 sites) of the sampling sites have low potential ecological risk, since those sites are having the PER value less than 150. On the whole, the comprehensive PER index due to the Cd, Cu, Cr, Pb, Zn and Ni of the present sediments shows moderate degree. 3.3. Magnetic susceptibility measurements Summary of the measured magnetic susceptibility values of present sediments are presented in Table 5. Low and high frequency magnetic susceptibility ranges from 21.30 10 8 m3/kg (S1) to 302.50 10 8 m3/kg (S23) and from 20.50 10 8m3/kg (S1) to 299.30 10 8 m3/kg (S23) respectively. Other sites are having intermediate values. Magnetic susceptibility values are higher in OWA region. Yang et al. (2007) suggested that the magnetic susceptibility of the sediment was controlled by anthropogenic sources like particles from vehicles, emission from industrials and fossil fuel

Magnetic susceptibility ( 10 8 m3/kg) Low frequency

High frequency

164.47 302.50 21.30

162.65 299.30 20.50

combustion, fertilizers, pesticides, etc. Elevated magnetic susceptibility values in the present sediments may be due to the traffic effluents and fertilizers. Yang et al. (2007) had reported the magnetic susceptibility range for Chinna lake sediments which is almost equal to the present range. The magnetic susceptibility of sediment is largely due to the presence of magnetic minerals. The spatial distribution of magnetic susceptibility in sediments is not only due to variable abundance of iron-bearing ferri and anti-ferromagnetic minerals, but also due to the presence of diamagnetic and paramagnetic minerals such as quartz, feldspar, carbonates and clays (Pattan et al., 2008). 3.4. Granulometric analysis Granulometric analysis has been carried out to determine the percentage of sand, silt and clay in the present sediments since these are having strong influence on both magnetic susceptibility and heavy metal contents (Li et al., 2011; Abrahim and Parker, 2008). Obtained results show that the range of sand, silt and clay in present sediment are 19.2–51.2%, 29.5–52.2% and 11.2–28.4% respectively. The grain size distribution in the samples indicated that the silt is the main component with mean value of 44.7%. Average of sand content is 33.1% which is the second most constituent in the samples. The least constituents is clay (average¼ 21.4%). The sediment samples in OWA region have more silt and clay contents. Since most of the sampling sites being located in the vicinity of the mouth of the inlet river, the percentage of sand in IFR region is higher when compared with other two regions. Grain size mainly reflects flow speed in the depositional site. 3.5. Pearson’s correlation analysis Obtained correlation coefficients are presented in Table 6 as the linear correlation matrix. From this table, the heavy metals Cr, Cu, Ni and Zn are positively correlated among themselves. Content of Pb is not correlated with none of the studied metals. No correlation is observed between Cd and studied metals except Zn. According to Suresh et al. (2011b), if the correlation coefficient between the metals is higher, metals have common sources, mutual dependence and identical behavior during the transport. The absence of correlation among the other metals suggests that the contents of these metals are not controlled by a single factor. However it is controlled by a combination of geochemical support phases and their mixed associations. In the present study, the metals Cr, Cu, Ni and Zn have common source whereas others don’t have common sources which may be due to the variations in the sources. All the studied heavy metals except Pb have positive correlation with PLI. The Cd content is well positively (R¼ 0.998) correlated and Zn content is positively correlated with PER. This shows that the Cd content (among the studied heavy metal contents) in the present sediment might create high ecological risk. Magnetic susceptibility is well positively correlated with Cr, Cu, Ni and Zn, and slightly correlated with Cd and Pb. According to Yang et al. (2007), Chaparro et al. (2006) and Lu et al. (2007), magnetic phases present in the sediments may adsorb heavy



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Table 6 Pearson correlation coefficients among the variables. Bold values in the table represent the positive correlation between the variables.

Cd Cr Cu Ni Pb Zn PLI PER MS Sand Silt Clay

Cd

Cr

Cu

Ni

Pb

Zn

PLI

PER

MS

Sand

Silt

Clay

1.000 0.321 0.184 0.354 0.113 0.600 0.686 0.998 0.366 0.501 0.211 0.482

1.000 0.903 0.945 0.229 0.920 0.896 0.469 0.807 0.707 0.693 0.502

1.000 0.909 0.271 0.852 0.815 0.240 0.799 0.591 0.646 0.330

1.000 0.284 0.874 0.897 0.406 0.876 0.746 0.748 0.500

1.000 0.245 0.323 0.077 0.316 0.069 0.169 0.086

1.000 0.958 0.643 0.806 0.678 0.665 0.494

1.000 0.727 0.839 0.773 0.751 0.567

1.000 0.410 0.530 0.444 0.497

1.000 0.778 0.803 0.504

1.000 0.906 0.803

1.000 0.479

1.000

Note: PLI—Pollution Load Index; PER—Potential Ecological Risk; MS—Magnetic susceptibility ( 10 8 kg/m3).

46.54 Cluster - 3 Similarity

64.36

82.18

Cluster - 2

Cluster - 1

Pb

Clay (%)

Silt(%)

Sand (%)

Variables

MS

Cu

PLI

Zn

Ni

Cr

PER

100.00 Cd

metals on their surface. In the present study, from the above well correlation, one can be assumed that the correlated elements may be adsorbed on the surface of the magnetic minerals. Less correlated metals have differences in nature and genetics when compared with other metals (Morton-Bermea et al., 2009). Also, magnetic susceptibility is well positively (R¼0.839) correlated with PLI. This suggests that the magnetic susceptibility can be used as a first approach to find out the heavy metal contamination due to the anthropogenic activities. Percentage of sand content is negatively correlated with all studied metals, PLI, PER and magnetic susceptibility except Pb. Both silt and clay contents are positively correlated with all studied metals, PLI, PER and magnetic susceptibility except Cd and Pb. However, percentage of silt having higher positive correlation coefficients than clay with above said parameters (Table 5). This shows that the heavy metals such as Cr, Cu, Ni and Zn may be adsorbed on the surface of the magnetic minerals which are presented in silt grains. Lake sediments are usually composed of fine grains of minerals with high cation exchange capacity (CEC). Cation-exchange capacity is defined as the degree to which sediment can adsorb and exchange cations. Sediment particles have negative charges on their surfaces. Therefore, fine particles have the ability to absorb the cations on their surface and raise the level of heavy metal contents.

Fig. 3. Dendrogram showing cluster of variables on the basis of similarity.

is incorporating the cations on their surface and raising both the level of magnetic susceptibility and heavy metal contents. Factor 2 accounted for 15.85% of the total variance, which mainly consists of Cd and PER. Here, the influence of Cd on PER is same as above. Percentage of clay is act as a second factor to increase the PLI in sediment. Results obtained from Cluster and Factor analysis are well matched with Pearson Correlation analysis.

3.6. Cluster analysis (CA) The variables taken for this analysis are same as Pearson’s correlation analysis. The derived dendrogram is shown in Fig. 3. In this dendrogram, all 12 parameters are grouped into three statistically significant clusters. Cluster 1 consists of the parameters such as Cd and PER. Cluster 2 consists of Cr, Cu, Ni, Zn, PLI, MS and % of silt and clay. Cluster 3 consists of Pb and % of sand. All the clusters are formed on the basis of existing similarities. Cluster 1 shows that the Cd content is highly significant for PER. From the cluster 2, the content of heavy metals such as Cr, Cu, Ni, Zn are responsible for PLI. Also it shows that the MS and PLI are having the higher similarity and these contents are highly incorporated in silt grains. Cluster 3 shows that the Pb has nonassociation with other metals and sand contents are not significant for the metal contents. 3.7. Factor analysis (FA) FA yielded two factors with eigen value 41, explaining 80.57% of the total variance. First factor accounted for 64.72% of total variance and have high positive loadings (0.718–0.959) of Cr, Cu, Ni, Zn, PLI, MS and % of silt. It shows that the heavy metals such as Cr, Cu, Ni and Zn are significant for PLI. The significant role of silt

4. Conclusion Content and spatial distribution of heavy metals (Cd, Cr, Cu, Ni, Pb and Zn), magnetic susceptibility level and granulometric contents were analyzed for Veeranam lake sediments. Heavy metal contents, magnetic susceptibility and calculated PLI values are higher in OWA region since this region has relatively static water movement which has more silt and clay contents. Comparative analysis shows that the Cd, Cu and Zn are higher than the corresponding values of world common trace metal range in lake sediment and the heavy metals (Ni, Cr, Cu and Zn) might create an adverse effects on the aquatic ecosystems associated with this lake. PLI values show present sediments are polluted significantly due to the studied heavy metals. From the PER values, each single element has low potential ecological risk and Cd shows higher ecological risk. Sources of the Cd in the present study area are metal processing industries, agriculture land and living residents. Serious potential ecological risk is observed at S23. The comprehensive PER index shows moderate degree. All the statistical analyses provided the same results. Overall statistical analyses suggested the followings (i) Heavy metals in present lake have complicated contamination sources or controlling factors and


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these analyses also confirm the seriousness of the Cd. (ii) The magnetic susceptibility can be used as a first approach to find out the heavy metal contamination due to the anthropogenic activities since it has strong relation with PLI. (iii) Existing relations among the studied variables show that the heavy metals Cr, Cu, Ni and Zn have same source and may be incorporated in magnetic minerals which are presented in silt grains. (iv) Significant role of silt is incorporating the cations on their surface and raising the level of magnetic susceptibility and heavy metal contents. In this study, the contents of some heavy metals are higher than background and toxicological reference values. These higher levels are unlikely to cause additional adverse health risks to the aquatic ecosystem associated with this lake.

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