Journal of Coastal Zone Management

Heavy Metal Pollution Assessment in Sediment of Mussel Habitats UsingGeochemical Indices

Hossein Saberi Kouchesfehani^1* and Seyed Naser Azizi^{2 1}Department of Ecology, Inland Waters Aquaculture Research Center, Iranian Fisheries Sciences Research Institute Agricultural Research, Education and Extension Organization (, Bandar Anzali, Iran
²Department of Chemistry, University of Mazandaran, Babolsar, Iran
^*Correspondence: Hossein Saberi Kouchesfehani, Department of Ecology, Inland Waters Aquaculture Research Center, Iranian Fisheries Sciences Research Institute Agricultural Research, Education and Extension Organization (, Bandar Anzali, Iran, Tel: +981344426400, Email:

Received: 17-Feb-2019 Published: 08-Mar-2019

Introduction

Heavy metals are dramatically important for their bioaccumulation potential into the flora and fauna organisms in a wetland ecosystem where may act as a sink for metal pollution [1-3].

Geochemical indices and bioaccumulation data have been found necessary for risk assessment of contaminants, including trace metals in the aquatic ecosystem [4].

The Anzali wetland, because of its role in the preservation of unique species, such as swam mussel was enrolled in the Ramsar convention as an international wetland [5]. This wetland was influenced by a large amount of urban, industrial, wastewater, and aquaculture waste, which may lead to an expansion in the level of metals.

Various geochemical indices have been developed to assess heavy metal contamination in sediment [6], which sediment quality guidelines (SQGs) [7], enrichment factor (EF) [8], contamination factor (CF) [9], pollution load index (PLI) [10], geoaccumulation index (Igeo) [11] and potential ecological risk Index (RI) [12] have pulled in more consideration lately. The objective of the present work was to ponder the distribution and contamination level and potential risks of heavy metals in vital natural locales utilizing the Igeo, EF, CF, PLI, RI, SQGs indices. The statistical analysis as a capable tool also applied to acquire the pollution conditions in this coastal wetland [13] and to answer the inquiry whether these indices can predict the contaminant level in soft tissue of mussel in five particular imperative environmental areas.

Materials and Methods

Study regions

The open surface area of Anzali wetland is estimated at 58 km² with latitude and longitude coordinates of 37˚28 ́N and 49˚25 ́W respectively. This wetland with the maximum depth of 2 meters and an average depth of lower than 1 meter gets its water mostly by direct and surface spillover from encompassing area uses. This intricate biological community incorporates an extensive variety of inland, beachfront and marine living space. For this study, twenty-five sampling stations from this area were selected at five Study regions (Figure 1).

Figure 1. Coordinates of the study points in the five regions of the mussel habitats in Anzali wetland: Abkenar (AB) in the west, Bahambar (BH), Mahrouzeh (MA) and Hendekhaleh (HE) in the center and also Sheijan (SH) in the east which was shown in Figure 1.

Sample collecting and analysis

In the wake of acquiring authorization from the Environmental Protection Agency of Bandar Anzali, Mussels (10 from each region) were collected from 5 regions and also, the sediments in the depth of almost 10-20 cm from where the mussels were taken in a once-off testing trip in October and November 2016. The sediment samples were dried totally to a consistent weight in an oven at 45°C for 48 hrs. The dried sediments were then ground and passed through a <63 μ nylon sieve.0.5 g of prepared sample was digested with 8ml of concentrated HCl,HNO3 in proportion to 1HCl/3HNO₃ and 3 ml of concentrated HClO₄ for 6 hrs at 90°C. The processed samples were then diluted to 25 ml with deionized water [14].

The soft tissue of Mussels were dissected and put away at −80°C until the examination. Digestion of soft tissues was done by nitric acid using the methodology depicted for analysis [15]. In this Analysis technique, one gram of the sample and 10 ml of concentrated nitric acid was placed in a 250 ml digestion container. The mixture became slowly boiled for 30-45 minutes to oxidize the effortlessly oxidized mixes and cooling this mixture, 5ml of 70% HClO₃ was added and become slowly boiled till a thick white smoke appeared. It changed into then cooled again, 20 ml of distilled water was delivered, and the mixture changed into boiled so that no additional smoke wound up noticeable.

The solution transformed into then cooled and filtered in Whitman paper. Water added into conveying to the degree to achieve the check in a 25 ml Erlenmeyer. A blank digest was additionally arranged using the same procedure. The concentrations of Pb, Cd, Cu, Zn, and Ni inside the digestion solution and the blank were obtained with a flame atomic absorption spectrometer (Analytic Jena version NOVA 400P) using standard reference materials (AccuStandard, Inc., USA). The metal concentration in the sediment and mussel is expressed as mg kg⁻¹ dry weight. All statistical calculations were performed by Excel 2010 (p<0.05) and XL stat software (version: 2016.02.28451). The correlations among the concentrations of heavy metal between the random variable were also explored using Pearson correlation test (p<0.05)

Results and Discussion

In sediment

Results of statistic tests, including maximum, minimum, and standard deviation of surface sediments and mussels are displayed in Table 1.Comparing the greatest mean concentrations of heavy metals in sediments, Zn (117.90 mgkg^-1) in Sheijan, was significantly different from those in Abkenar, Hendekhaleh, and Mahrouzeh areas. The highest mean concentrations of Ni (25.28 mg kg^-1) and Cd (1.58 mg kg^-1) also recorded in Mahrouzeh and appeared significantly different comparing with all other regions. The highest mean value of Pb (31.20 mg kg^-1) watched in Sheijan, which significantly different with the other regions. The highest mean value of Cu (42.12 mg kg^-1) was also estimated in Bahambar and revealed no significant differences with all other regions. The data also showed that the values of Zn, Cu, Pb, and Cd were more than the background values (Table 1) [9]. Which may be associated with anthropogenic activities, such as industrial and municipal wastewater and ship transportation [16].

Table 1: Element concentration of surface sediments and soft tissue of mussels from sampling regions (mg kg-1).

The metal concentrations in sediment were near the range of those found previously by Jamshidi [17]. Who measured Zn in the range of 90.77-203.61 mg kg^-1, Cu in the range of 31.60^-111.45 mg kg^-1, Ni in the range of 60.34^-105.15 mg kg^-1, Pb in the range of 13.01-93.00 mg kg ^-1, and Cd in the range of 0.05^-1.4 mg kg^-1. In this study, the extent of heavy metal pollution in Sheijan and Hendekhaleh and also the distribution of metal patterns (Zn>Cu>Pb>Ni>Cd) were similar to those recorded in previous research, except for Ni which was higher than Pb in previous assessment

The enrichment factor

The enrichment factor (EF) can be utilized to determine the contamination and risk stage for surveying the level of contamination in the sediments [18] which calculated using the subsequent equation [19].

EF=(CMe/CFe)sample/(CMe/CFe)

Background Where (CMe/CFe) in the sample is the ratio of heavy metal concentration (CMe) to the concentration of Fe (CFe) in the sediment sample and (CMe/CFe)in the background is the same reference ratio in the background sample. The estimations of around 1 recommend that metals originated from natural sources [20]and also EF>1.5, 1.5–3, 3–5, 5–10, and>10 were showed the beginning of human impact, minor, moderate, intense, and really excessive level of enrichment factor respectively [21]. The EF of sampling sites is shown in Figure 2.

Figure 2. Mean value of EF geochemical indices in regions.

The mean EF values for all the metals aside from Zn and Ni were discovered more than 1, showing enrichment of metals. Furthermore, the highest of EF for Pb and Cd appeared in the range of moderate and severe enrichment level which may derive from anthropogenic impacts (Figure 3). The Low EF values of Ni affirmed that it originated from the natural processes. Comparatively, among Abkenar, Hendekhaleh, Bahambar, Mahrouzeh, and Sheijan significant differences of EF were also observed for all metals with the exception of Cu. Geoaccumulation index (Igeo) has been broadly utilized for the assessment of soil and sediment To depict the level of metals contaminations[22]. Which calculated using the equation.

Igeo=log 2/Cn1.5Bn

Where Cn is the concentration of metal n in the sediment and Bn is the geochemical background value of element n in the background sample [23-25]. The values of 1.5 might be demonstrated the most reduced human effect level and can be characterized in seven levels: practically uncontaminated, Igeo ≤0—uncontaminated to moderately contaminated, 0 ≤ Igeo ≤ 1—moderately contaminated, 1 ≤ Igeo ≤ 2— moderately to heavily contaminated, Igeo ≤ 3 heavily contaminated, 3≤Igeo ≤4—heavily to extremely contaminated, 4 ≤ Igeo ≤ 5, and extremely contaminated, 5< Igeo.

The Igeo data in this study have increased the order of Cd (+0.72) in Mahrouzeh< Pb (+0.12) in Sheijan < Cu (−0.27) in Bahambar < Zn (−0.28) in Sheijan < Ni (−1.83) in Mahrouzeh (Figure 3).

Figure 3. Mean value of Igeo geochemical indices in regions.

The Igeo values of Cd and Pb also indicated un-contamination to moderately level contamination in sediments. Additionally, Igeo mean qualities for Ni, Zn, and Cu showed unpolluted status.

Contamination factor

The CF was obtained by dividing the concentration of each metal by the foundation estimations(reference value) of metal which CF<1, 1 ≤ CF<3, 3 ≤ CF<6, and CF ≥ 6 represent low degree, moderate degree, considerable degree, and a very high degree of contamination respectively [26,27]. According to the data, the highest mean estimations of CF in the study regions for Cd (5.8) in Mahrouzeh demonstrated a considerable degree of contamination and also, Pb (1.86) in Sheijan, Cu (1.14) in Hendekhaleh, and Zn (1.24) in Sheijan, showed a moderate degree of contamination, while Ni (0.49) in Mahrouzeh revealed the low degree of contamination (Figure 4).

Figure 4. Mean value of CF geochemical indices in regions.

Pollution load index

To identify the metal contamination as a total scale in sediment, Tomlinson recommended an equation for calculating the pollution load index (PLI).

PLI=(CF1 × CF2 × CF3 ×…× CFn) 1/n

Where n is the number of metals. PLI>1 indicates pollution and demonstrates dynamic deterioration of the quality, whereas PLI<1 shows no pollution condition. PLI value of zero indicates the starting point of becoming infected [28]. PLI also can provide some understanding about the quality of wildlife inhabit and information to the decision-makers on the pollution of the regions [29]. The calculated PLI values of metals in sediment are shown in Table 4 which ranged from 1.04 to 0.86, 1.02 to 0.70, 0.99 to 0.73, 0.88 to 0.72, and 0.78 to 0.53 for Sheijan, Bahambar, Hendekhaleh, Mahrouzeh, and Abkenar, respectively. The data confirmed that the sediment in Sheijan and Bahambar were more contaminated than other areas (PLI >1) (Table2).

Table 2: Pollution load index and risk index value of heavy metals in sediment regions (mean ± SD).

The risk index

RI is used for better understanding the integration of metals pollution and ecological risk potential which can be calculated by the following equation. The Tri were valued: Zn= 1, Ni=5, Cu=5, Pb=5, and Cd=30.

Eri=Tri X Cfi =Tri X (Ci /Cn)

RI=Σ Ni

RI is characterized into four gatherings: RI<150, 150 ≤ RI<300, 300 ≤ RI<600, and RI ≥ 600 which indicated low, moderate, high, and very high ecological risk level [12]. Due to the high concentrations of cd in Mahrouzeh, Hendekhaleh, Abkenar, and Bahambar and also Tri value of Cd, RI index was higher in these areas comparing to Sheijan and placed in the range of moderate ecological risk level (Table 2).

Sediment quality guidelines

SQGs are used to comprehend the distinction in contamination in marine biological communities [30] applying the many potential contaminants (e.g. polynuclear aromatic hydrocarbons, chlorinated organic, and metals) which are defined on tree levels, Effect range low (ERL), effect range median (ERM) and high impact level (PEL). The low ERL level shows the low effects of metal contaminants, while ERMs and PELs represent probably unpleasant effects of heavy metal pollution (Table 3) [31].

Table 3: Sediment quality guidelines from NOAA (Long et al., 1995) and environment of Canada cISQG=Environment of Canada and, a ERL=Effect range low (NOAA),b ERM=Effect range medium (NOAA), DPEL=Probable effects level (Environment of Canada).

Comparing the values of heavy metals data in present assessment with the ISOGs table values, Zn (12%), Cu (80%), Ni (52%), Cd (72%), and Pb (12%) were above the levels of the ISOGs which Cd and Cu showing adverse effects on the sedimentary habitat. Concentrations of all metals in sampling sites were also lower than the corresponding values of ERM and PEL suggesting no adverse effects. In addition, Cu (48%), Ni (32%), and Cd (36%) were higher than the ERL amounts, showing portable effect level (Table 3).

Correlation analysis

Pearson correlation analysis (CA) was connected to consider the quality of the connection between random variable [32]. In this research, the significant correlation between the variable in sediment revealed a negative relationship between Pb and pH (-0.502), negative relationship between Pb and TOM (-0.677) positive correlation between Cd and EC (0.600) and negative relationship between Ni and pH (-0.785). The results of -0.503 and -0.599 were also shown a inversely relationship between Zn with EC, and also between Zn with TOM respectively. The significantly positive correlation between the variables indicates that metals have a higher content in higher values of EC, TOM, and EC. On the other hand the correlation between Pb and Ni was 0.572 which may be indicated their common origin in the sediment.

Principal component analysis

Principal component analysis (PCA) is connected to distinguish fewer uncorrelated factors from an expansive arrangement of information by focusing on axes with maximum variance. The PCA data also gives as briefly as possible conceivable valuable data [33] with the most significant variable alongside the base loss of unique information [34].

In this examination, we standardize the information independently by the PCA method for the five distinctive testing areas, comparing the compositional patterns among the sediment samples and distinguishing the factors that correlate with one another.

The first component (F1) recoded 38.55% total variance and positively correlation with Ni, showing different sources of Ni in field study.

Component F2 showed 26.74% total variance and positively correlated with Pb, Cd, and Zn displaying the similar pollution sources such as road transportation, farming and urban wastewater [17,35,]. The fourth component (F4) also, displayed just 8.05 % total variance with positive correlation with Cu, showing the different source of copper in this investigation (Table 4).

Table 4: Eigen values on the correlation matrices of heavy metals and square of factor loadings.

Hierarchical cluster analysis

Hierarchical cluster analysis (HCA) was applied to place sediment data in a group with similar characteristics. The resulting dendogram (Figure 5) grouped all the 25 testing stations into 5 significant clusters. There were 2 sites in the cluster I, 2 sites in cluster II, 5 sites in cluster III, 4 sites in cluster IV, and 11 sites in cluster V.

Figure 5. Dendogram of cluster analysis between the sampling sites at Anzali wetland.

The mean concentrations of heavy metals were low in cluster I (S7, S9 in Bahambar). Cluster II consists of S10, S6 in Bahambar that was characterized by the high metal contamination of Pb and Cd. Cluster III that incorporated S21–S25 in Sheijan, showing the higher heavy metal concentrations. Cluster IV consists of S1, S2, S3, and S4 in Abkenar, with a PLI of 0.53, recorded lower contamination than in other clusters. Cluster V including S11–S20 and S8 in Mahrouzeh and Hendekhaleh were characterized by the highest mean concentrations of Pb and Cd which relates to the anthropogenic activities.

For the heavy metals in the tissue of A. cygnea, the highest concentration of Cd(0.093 mg kg⁻¹) and Zn (49.16 mg kg⁻¹) were recorded in Abkenar and Sheijan respectively, and the largest of Pb(0.52 mg kg⁻¹), Ni(1.09 mg kg⁻¹), and Cu(3.69 mg kg⁻¹) were estimated in Mahrouzeh.

To respond to the inquiry whether these indices can predict the contaminant level in the group of swan mussel, the data revealed that Pb concentration in tissues of A. cygnea showed the maximum in Bahambar, but at the same time, the geochemical data displayed Sheijan as the most polluted region. Cd concentration in tissue also was the highest in Abkenar, but the geochemical data showed Hendekhaleh and Mahrouzeh that were more infected. The highest Cu concentration in the soft tissue of mussel measured in Mahrouzeh and unexpectedly, geochemical indices showed the highest Cu concentration in Bahambar. Furthermore a maximum concentration of Zn and Ni in mussel recorded in Hendekhaleh and according to geochemical indices, the most exceedingly bad conditions were watched for these metals in Sheijan and Mahrouzeh respectively. Such inconsistencies are also observed on the values of PLI and RI indices for estimating of heavy metals into the mussels (Table 2). Finally, the results indicated that the geochemical indices can't always predict heavy metal contaminants in mussels which may be because of complicated relationships among vital tissue and its environment. Therefore, simultaneous use of surroundings information, such as geochemical and bioaccumulation data of their vital tissues shows a more comprehensive illustration of the pollutant situation and prevents from going astray in environmental assessment.

Conclusion

In the present investigation, concentrations of Zn, Cu, Pb, and Cd were higher than mean crust values, which suggested that several sampling stations are polluted with these metals and may be a threat to the sedimentary habitat of swan mussel. The EF, Igeo and CF also revealed that sediments in this study were impressively polluted with Cd and Pb.Moreover , Mahrouzeh, Hendekhaleh, Abkenar, and Bahambar also have a moderate ecological risk level according to RI index.

The PLI values of metals in the sediments confirmed that the sediment of Sheijan and Bahambar were more contaminated than other areas (PLI>1).

The multivariate analysis represents that Ni comes from parent rocks, whereas Cd and Pb (most likely) and Cu and Zn (less likely) originate from anthropogenic sources. Comparing geochemical data with metal concentrations in the soft issue of the mussels revealed that the geochemical indices can't always predict heavy metal contaminants in mussels. Therefore, simultaneous use of surrounding information and the bioaccumulation data on organisms present a comprehensive illustration of the pollution condition and prevents from going astray in environmental assessment.

Acknowledgment

Authors are thankful to the analytical Division, Faculty of Chemistry, and university of Mazandaran for providing financial support (No. 20591) and Inland Waters Aquaculture Research Centre, Iranian Fisheries Sciences Research Institute for providing laboratory facilities to complete this study.

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