Marine Pollution Bulletin 84 (2014) 115–124

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Heavy metal contamination and ecological risk in Spartina alterniflora marsh in intertidal sediments of Bohai Bay, China Minwei Chai a,b, Fuchen Shi b,⇑, Ruili Li a,⇑, Xiaoxue Shen a a b

Key Laboratory for Heavy Metal Pollution Control and Reutilization, School of Environment and Energy, Shenzhen Graduate School of Peking University, Shenzhen 518055, PR China College of Life Sciences, Nankai University, Tianjin 300071, PR China

a r t i c l e

i n f o

Article history: Available online 13 June 2014 Keywords: Bohai Bay Chemical speciation Ecological risk Heavy metal Spartina alterniflora

a b s t r a c t To investigate the effects of Spartina alterniflora on heavy metals pollution of intertidal sediments, sediment cores of a S. alterniflora salt marsh and a mudflat in Bohai Bay, China were analyzed. The results showed that S. alterniflora caused higher total C and P, but lower bulk density and electrical conductivity. The levels of Cd, Cu and Pb were higher in S. alterniflora sediment. Both Cd and Zn were higher than the probable effect level at both sites, indicating their toxicological importance. The geo-accumulation and potential ecological risk indexes revealed higher metal contamination in S. alterniflora sediment. Multivariate analysis implied that anthropogenic activities altered mobility and bioavailability of heavy metals. The percentage of mobile heavy metals was higher in S. alterniflora sediment, indicating improvement of conversion from the immobilized fraction to the mobilized fraction. These findings indicate that S. alterniflora may facilitate accumulation of heavy metals and increase their bioavailability and mobility. Ó 2014 Elsevier Ltd. All rights reserved.

1. Introduction The ubiquitous use of anthropogenic heavy metals associated with rapid economic development has significantly changed their original distribution patterns in the natural environment, enabling their delivery to intertidal zones from river catchments via fluvial transport, atmospheric deposition, and local wastewater discharge (Qiao et al., 2007; Li et al., 2013; Golestaninasab et al., 2014). Unlike biodegradable organic pollutants, heavy metals have the potential for bioaccumulation and biomagnification, resulting in potential long-term effects on human health and ecosystems (Pan and Wang, 2012). Bohai Bay, which is the second largest bay in the Bohai Sea, a semi-closed shallow sea in North China, receives industrial and domestic sewage from Beijing and Tianjin. Owing to the large amount of contaminant inputs and poor physical self-cleaning capacity, Bohai Bay has become one of the most degraded marine systems in China. Thus, the impact of urbanization and economic development on sediment quality of the intertidal zone of Bohai Bay is of concern. Accordingly, many recent studies have been conducted to investigate the heavy metal pollution status of river outlets and northwestern coastal areas in Bohai Bay (Li et al., 2011; Zeng et al., 2013; Gao et al., 2014). ⇑ Corresponding authors. Tel./fax: +86 22 23502477 (F. Shi). Tel.: +86 755 26033141; fax: +86 755 26032078 (R. Li). E-mail addresses: [email protected] (F. Shi), [email protected] (R. Li). http://dx.doi.org/10.1016/j.marpolbul.2014.05.028 0025-326X/Ó 2014 Elsevier Ltd. All rights reserved.

When environmental conditions change (pH, cationic exchange capacity, nutrient status, redox potential, etc.), some of the sediment-bound heavy metals may be remobilized and released back into the water, where they can have adverse effects on living organism (Morillo et al., 2002; Peng et al., 2009). In fact, the mobility of metals in the environment depends strongly on their chemical forms or types of the binding of the element (Cuong and Obbard, 2006; Yu et al., 2010). Numerous analytical techniques have been used to identify the key factors that control distribution and speciation of heavy metals in the coastal and estuarine sediment in order to understand their mobility and potential ecological risks (Tessier et al., 1979; Kersten and Förstner, 1986; Cuong and Obbard, 2006). One of the most common methods is the threestage sequential extraction procedure proposed by the European Community Bureau of Reference (BCR) (Cappuyns et al., 2007). This method, which illustrates the acid extractable, reducible, oxidizable and residual fractions of metals in sediment, might provide a great deal of useful information regarding the chemical nature or potential mobility and bioavailability of a particular element, thereby offering a more realistic estimate of actual environmental impact (Cuong and Obbard, 2006; Chakraborty et al., 2014). As for total heavy metal accumulation in sediment, a number of indices have also been developed in the last decade to assess heavy metals contamination and its ecological effects, including the threshold effect level (TEL) and probable effect level (PEL) guidelines (MacDonald et al., 2000), geo-accumulation index (Igeo)

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(Müller, 1981), and potential ecological risk index (Hakanson, 1980; Shi et al., 2010). Spartina alterniflora, an invasive halophyte from North America, was intentionally introduced to the coastal region of China in 1979 (Wan et al., 2009). This species now occupies the naked mudflat and has formed dense monospecific stands in the intertidal zone in Bohai Bay, China. The negative effects of S. alterniflora on the native ecosystems have become increasingly obvious in the coastal wetlands of South China, such as landscape change, impact on endangered species, decrease in abundance of native species, degradation of native ecosystems and considerable economic loss (Tian et al., 2008; Li et al., 2009). However, some studies have shown positive effects of S. alterniflora in preventing erosion and promoting sediment accretion, absorbing nutrients, improving the growth of mollusks and attracting many regional waterfowl (Zhang, 2007; Wan et al., 2009; Li et al., 2010). Previous studies have shown that S. alterniflora can facilitate organic carbon and nitrogen storage (Zhang et al., 2010; Yang et al., 2013) and influence cycles of sulfur (Li et al., 2009; Nie et al., 2009) in salt marsh sediment. Furthermore, this species may have significant effects on the biogeochemical redox processes and bacterial sulfate reduction, thereby enabling it to control the chemical speciation, bioavailability, toxicity and mobility of many heavy metals in salt marshes (Wang et al., 2013). Many studies have suggested that S. alterniflora can greatly influence the mobility of heavy metals in sediment and has the potential for use in heavy metals remediation (Hempel et al., 2008; Salla et al., 2011; Nalla et al., 2012; Chai et al., 2013). Currently available data on heavy metals in Bohai Bay are not sufficient for evaluation of their total environmental impact because the chemical state of heavy metals in sediment needs to be known to evaluate their mobility, bio-availability and toxicity. Furthermore, little data is available regarding the effects of S. alterniflora on fractionation and bioavailability of heavy metals in the intertidal zone of Bohai Bay. Based on the above discussion, it was hypothesized that S. alterniflora may alter the sediment properties, and improve accumulation of heavy metals, thereby affecting heavy metal pollution. Consequently, this study was conducted (1) to quantify the influence of S. alterniflora on sediment properties and heavy metals accumulation in intertidal sediment in Bohai Bay; (2) to assess the potential ecological risk and sources of heavy metals and (3) to identify the speciation of heavy metals.

2. Materials and methods 2.1. Sediment sampling and analysis Sediment cores were collected in August 2012 from the coastal wetland of Bohai Bay (39°00 N, 117°460 E) (Fig. 1). The climate in this study area belongs to warm and humid subtropical monsoon climate. The tidal regime is semidiurnal, with a maximum range of 2.92 m. Annual mean rainfall is around 622 mm and annual mean evaporation is around 1800 mm; annual mean temperature is 11.7 °C and mean temperatures of the coldest (January) and hottest (July) months are 3.5 and 26.2 °C, respectively (Yang, 2005). Six sample locations were selected along the coastline. In each sample location, three sediment cores in mudflats with and without S. alterniflora were collected (acid-washed PVC pipes, 100 cm length, 7.5 cm internal diameter), respectively. The unvegetated mudflat was considered as a control. Then, 18 sediment cores were immediately sliced at 0–10 cm, 10–20 cm, and 20–30 cm using a plastic cutter, after which the subsamples were immediately sealed with plastic bags, and transported back to the laboratory on the same day.

The sediment samples were air-dried for the analysis of the physicochemical parameters. Bulk density was determined by drying the sediment at 70 °C for 24 h. pH was determined in deionized water using mass ratios of 1:2.5 (sediment to water). Electrical conductivity (EC) was determined in deionized water using mass ratios of 1:5 (sediment to water). Total carbon (TC), total nitrogen (TN) and total phosphorus (TP) were measured using an elemental analyzer Vario EL Cube (Elementar, Germany). 2.2. Determination of heavy metals To determine the total heavy metal concentrations, sediment samples were subjected to microwave digestion in a mixture of 9 ml HNO3, 3 ml HF and 1 ml HCl. All reagents were of analytical grade or better. The concentrations of Cd, Zn, Pb and Cu in sediments were determined by inductively coupled plasma mass-spectrometry (ICP-MS). Pollution levels of heavy metals could also be characterized by the geo-accumulation index (Igeo) put forward by Müller (1969). Igeo has commonly been cited by researchers in environmental studies (Abrahim and Parker, 2008; Shi et al., 2010), and could be defined by the following equation: Igeo = log2 (Cn/1.5Bn), where, Cn is the measured content of the metal n and Bn is the background or pristine value of the metal. The constant factor 1.5 was introduced to analyze natural fluctuations in the contents of a given substance in the environment and very small anthropogenic influences (Loska et al., 2004). As shown in Table 1, seven classes of Igeo were proposed (Müller, 1981). Ecological risks associated with heavy metals were assessed using the potential ecological risk index (RI) developed by Hakanson (1980). RI could be used to comprehensively evaluate the ecological risks posed by heavy metals, because it covers a variety of research domains, including biological toxicology, environmental chemistry, and ecology (Shi et al., 2010).

Eir ¼ T ir  cif ¼ T ir  cis =cin RI ¼

n X i¼1

Eir ¼

n X T ir  cis =cin i¼1

cif

where is the contamination factor, cis is the concentration of heavy metals in the sediment, and cin is a reference value for heavy metals. The cin values for Cd, Zn, Pb and Cu are 0.5, 80, 25 and 30, respectively (Hilton et al., 1985). T ir is a toxic-response factor for a given substance, which accounts for toxicity and sensitivity requirements. The T ir values for Cd, Zn, Pb and Cu are 30, 1, 5 and 5, respectively (Hilton et al., 1985). Eir is the monomial potential ecological risk factor, and RI is calculated as the sum of all risk factors for heavy metals in sediment, which represents the sensitivity of the biological community to the toxic substances and illustrates the potential ecological risk caused by the overall contamination. Hakanson (1980) defines five categories of Eir and four categories of RI values as shown in Table 2. Multivariate analysis, such as principle component analysis (PCA) and hierarchical cluster analysis (HCA), has been shown to be an effective tool for understanding the significant groupings and dominant pathways. At both sites, PCA was performed to investigate sediment properties (bulk density, pH and EC), nutrient elements (TC, TN and TP), and heavy metals (Cd, Zn, Pb and Cu) with varimax rotation. 2.3. Sequential extraction of heavy metals The sequential extraction procedure (SEP) used to analyze heavy metal speciation was the improved BCR three-step scheme (Guillén et al., 2012). The various fractions of heavy metals were determined by inductively coupled plasma-mass spectrometry (ICP-MS).

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Study Site

100 m Fig. 1. Location of the sampling site in intertidal zone of Bohai Bay. In each sample location, three sediment samples at top sediment (0–30 cm) are random collected.

Table 1 Pollution grades of geo-accumulation index of the metals. Igeo class

Igeo value

Pollution quality

0 1 2 3 4 5 6

Igeo 6 0 0 < Igeo < 1 1 < Igeo < 2 2 < Igeo < 3 3 < Igeo < 4 4 < Igeo < 5 5 < Igeo

Uncontaminated Uncontaminated to moderately contaminated Moderately contaminated Moderately to heavily contaminated Heavily contaminated Heavily to extremely contaminated Extremely contaminated

Note: Igeo was classified by Müller (1981). Igeo can be defined as Igeo = log2(Cn/1.5Bn). Cn is the measured content of the metal n; Bn is the background or pristine value of the metal. The constant factor 1.5 is introduced to analyze natural fluctuations in the contents of a given substance in the environment and very small anthropogenic influences (Loska et al., 2004).

2.4. Statistical analysis Each treatment was replicated three times and all data are expressed as the means ± S.D. Student t-test was performed to determine if the differences were significant (P < 0.05). PCA was used to investigate the potential pollution sources (natural or anthropogenic) and characteristics. HCA of the normalized data set was conducted using Ward’s method with Euclidean distances as a measure of similarity. The classification is based on visual observation of the dendrogram. Pearson coefficient analysis was

conducted to identify the relationship among heavy metals and nutrient elements, as well as support the results obtained by multivariate analysis.

3. Results and discussion 3.1. Physicochemical properties and metal levels Plant invasions can affect salt marsh sediment processes and change many components of the C, N, water, and other cycles in the ecosystems (Ehrenfeld, 2003; Li et al., 2009; Peng et al., 2011). In the surface sediment (0–10 cm), the value of EC in S. alterniflora sediment was lower than in the mudflat (P < 0.05) (Fig. 2), suggesting that S. alterniflora led to reduced salinity of the sediment. The higher levels of TC, and TP in S. alterniflora sediment compared to the mudflat (P < 0.05), may be derived from the decomposition of roots and leaf litter (Hibbard et al., 2001; Cheng et al., 2006), which is consistent with previous studies (Zhou et al., 2008; Wang et al., 2013). The pH in S. alterniflora sediment at 0–10 cm was lower than in the mudflat (P < 0.05), suggesting that S. alterniflora leads to acidification of sediment, which is in agreement with the results of previous studies of this species (Zhou et al., 2008) and other marine plants such as mangroves (Tam and Wong, 2000; Zhou et al., 2010). The C/N ratio indicates a close relationship between the transfer of TC and TN during biological

Table 2 Standards of the potential ecological risk according to Eir and RI. Eir

Grade of ecological risk of single metal

RI

Grade of potential ecological risk of environment Low

Eir < 40

Low

RI < 150

40  Eir < 80

Moderate

150 6 RI < 300

Moderate

80  Eir < 160

Considerable

300 6 RI < 600

Considerable

160  Eir < 320

High

RI = 600

Very high

Eir = 320

Very high

Note: Eir was classified by Hakanson (1980). Eir is the monomial potential ecological risk factor. RI is calculated as the sum of all risk factors for heavy metals in sediment, which represents the sensitivity of the biological community to the toxic substance and illustrates the potential ecological risk caused by the overall contamination.

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Fig. 2. Vertical profiles of selected physicochemical properties in mudflat (N) and S. alterniflora sediment (h) in Bohai Bay, China. Values are mean ± standard deviation (n = 3). Values at the same depth followed by different letters are significantly different (P < 0.05). BD, bulk density; EC, electrical conductivity; TP, total phosphorus content; TC, total carbon content; TN, total nitrogen content.

decomposition in soil (Wang et al., 2005). In both sites, the C/N ratios ranged between 16.88 and 21.15, implying that sediment organisms could obtain balanced nutrition (Liu et al., 2012). Overall, S. alterniflora improved the sediment fertility and promoted the physicochemical properties of the intertidal habitat, which would influence the biogeochemical processes of other elements (including heavy metals) in the sediment. Vertical profiles of the total metals at the two sites are shown in Fig. 3. Except for stable levels of Cd and Zn in the mudflat, the contents of all heavy metals in the top layer were generally higher than those in deeper layers at both sites, which suggests an increase in anthropogenic heavy metals released into the Bohai Bay water system and sediment in the past few decades (Gao et al., 2014). Compared to mudflat, the higher levels of Cd, Pb and Cu were detected in S. alterniflora sediment. This may be

related to the decomposition of litter and roots from S. alterniflora and subsequent accumulation of organic matter, which improves the absorption of sediment onto heavy metals (Marchand et al., 2011). The mean concentrations of heavy metals in sediments of intertidal Bohai Bay are shown in Table 3. The background values and related values reported for surface sediments from some of the other coastal areas of China are also summarized for comparison. When compared to the mudflat, higher levels of measured metals except Zn were detected in S. alterniflora sediment. In the mudflat, the mean Cu, Pb, Zn and Cd concentrations were as high as 1.61, 2.06, 12.49 and 66.18 times the background values in Bohai Bay. Correspondingly, relatively higher levels of 1.99, 2.94, 12.14 and 74.64 were detected in S. alterniflora sediment than in the background sediment. These data were consistent with the findings of previous studies in the Yangtze and Pearl River Estuaries,

Fig. 3. Vertical profiles of total metal concentrations (lg g1) in mudflat (N) and S. alterniflora sediment (h) in Bohai Bay, China. Values are mean ± standard deviation (n = 3). Values at the same depth followed by different letters are significantly different (P < 0.05).

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M. Chai et al. / Marine Pollution Bulletin 84 (2014) 115–124 Table 3 Heavy metal concentrations (lg g1) in surface sediments from coastal and estuary wetlands in China. Study site

Cd

Zn

Pb

Cu

References

Mudflat, Bohai Bay (±S.D.) S. alterniflora, Bohai Bay (±S.D.) Bohai Bay Bohai Bay Bohai Bay

7.28 0.35 8.21 1.32 0.36 0.45 0.20

919.37 26.60 893.63 71.61 121.75 73.68 102.50

33.65 11.20 48.12 3.82 25.45 21.94 21.20

40.57 5.03 50.06 3.70 35.56 14.92 28.00

This study

An et al. (2010) Zhang et al. (2011) Zhan et al. (2010)

Mudflat, Dongtan, Shanghai S. alterniflora, Dongtan, Shanghai Yangtze River Estuary Yangtze River Estuary

0.70 0.82 0.06 0.26

80.00 100.00 69.00 94.30

20.00 28.00 22.20 27.30

20.00 35.00 13.00 30.70

Quan et al. (2006) Quan et al. (2006) Zhao et al. (2008) Zhang et al. (2009)

Mangrove, Zhanjiang, Guangdong Mangrove, Shenzhen, Guangdong Mangrove, Shenzhen, Guangdong Mangrove, Shenzhen, Guangdong Pearl River Estuary Pearl River Estuary Pearl River Estuary

– – 0.15 2.96 0.96 0.20 0.62

69.00 252.00 125.00 252.00 139.00 130.00 30.40

36.00 77.00 48.7 77.0 43.00 53.30 53.30

37.00 93.00 48.30 93.00 56.00 39.00 39.40

Vane et al. (2009) Vane et al. (2009) Xie et al. (2010) He et al. (2013) Ma et al. (2011) Huang et al. (2006) Huang et al. (2006)

Background values in Bohai Bay

0.11

73.59

16.37

25.21

Li et al. (1994)

This study

Note: S.D., standard deviation.

where S. alterniflora and mangrove wetlands adsorbed heavy metals at greater levels than the mudflats (Quan et al., 2006; Vane et al., 2009; Xie et al., 2010). Furthermore, the levels of heavy metals in this study were similar to those of mangroves in Guangdong (Vane et al., 2009; Xie et al., 2010; He et al., 2013), but obviously higher than those from the Yangtze River Estuary (Huang et al., 2006; Zhao et al., 2008; Zhang et al., 2009; Ma et al., 2011). 3.2. Assessment of potential ecological risk Heavy metals are natural components of the earth’s crust and many serve as important trace elements for plant growth; however, excessive levels could become contaminants and affect a range of biochemical and physiological processes in plant species (Kopittke et al., 2009). Many sediment quality guidelines (SQGs) have been used to assess the environmental concerns associated with both freshwater and marine sediments (MacDonald et al., 1996, 2000). In the present study, two sets of sediment quality guidelines were applied to assess heavy metal contamination. All metals investigated in this study are indicators of marine sediment quality classification in the National Standard of China GB 186682002 (Table 4; SEPA, 2002). Class I sediment is suitable for mariculture, nature reserves, endangered species reserves, and leisure activities such as swimming, while Class II can be used for industry and tourism sites and Class III is only suitable for harbors. When compared with the Chinese government’s target values for marine sediment, Pb levels at both sites were under Class I standard for Marine Sediment Quality, while the levels of Cu were above those of Class I, but lower than those of Class II. The levels of Cd and Zn

Table 4 Heavy metal guideline values of some different criteria used to distinguish marine quality (lg g1).

Class I Class II Class III TEL PEL

Cd

Zn

Pb

Cu

References

0.50 1.50 5.00 0.99 4.98

150.00 350.00 600.00 121.00 459.00

60.00 130.00 250.00 35.80 128.00

35.00 100.00 200.00 31.60 149.00

SEPA (2002) SEPA (2002) SEPA (2002) MacDonald et al. (2000) MacDonald et al. (2000)

Note: TEL, threshold effect level, indicates concentrations below which adverse effects on biota are rarely observed. PEL, probable effects level, indicate concentrations above which adverse effects on biota are frequently observed.

exceeded the Class III standard, indicating that they had some influence on the marine sediment quality at both sites. In general, concentrations of the four studied heavy metals suggested that the overall sediment quality in the intertidal zone of Bohai Bay had been affected by heavy metal to a certain extent. The threshold effect level (TEL) and probable effect level (PEL) for some substances with potential environmental risks were applied to facilitate the interpretation of sediment quality (MacDonald et al., 1996). The TEL represents chemical concentrations below which adverse biological effects rarely occur, while the PEL is used to identify concentrations above which adverse effects on biota are frequently observed (Long et al., 1998; Yang et al., 2012). According to this criterion, heavy metals in Beihai mangrove sediment in China are below the TEL and PEL, indicating that they are toxicologically unimportant (Vane et al., 2009), while the Cr and Ni levels in Hainan mangrove sediment exceed the PEC benchmarks (MacDonald et al., 2000; Vane et al., 2009). In the present study, Cd and Zn concentrations at both sites were higher than their corresponding PEL values, indicating that adverse biological effects may occur frequently (Tables 3 and 4). Cu levels in both sites were higher than the TEL value, but far below its PEL benchmarks, suggesting that adverse biological effects caused by Cu may be observed occasionally. In the mudflat, the Pb concentration was higher than the background value in Bohai Bay, but did not reach the corresponding TEL value, while the level of Pb was higher than the TEL and lower than the PEL in S. alterniflora sediment. In other words, Cd and Zn could be regarded as relatively contaminated areas that pose a risk of toxicity to biota in both mudflats and S. alterniflora sediment. In fact, heavy metals always occur in sediments as complex mixtures. To determine the possible biological effects of combined metals, mean PEL quotients (m-P-Q) for the four heavy metals were calculated using the following formula:

m-P-Q ¼ RC x =PELx Þ=n where Cx is the sediment concentration of component X, PELx is the PEL for compound X and n is the number of components. Three classes of toxicity probability for biota were defined as follows (Long et al., 1998): m-P-Q < 0.1 (8% probability of being toxic); 0.11–1.5 (21% probability of being toxic); 1.51–2.3 (49% probability of being toxic); >2.3 (73% probability of being toxic). In the present study, the mean PEL quotients were 1.00 and 1.08 in mudflat and

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S. alterniflora sediment, respectively, indicating that the combination of the four studied metals has a 21% probability of being toxic. Analogous results were observed for surface sediments of the intertidal Bohai Bay and the coastal Shandong Peninsula (Yellow Sea), where the combination of studied heavy metals have a 21% probability of being toxic (Gao and Li, 2012; Li et al., 2013). In the present study, sediment background values were selected as the reference for assessment of heavy metal pollution (Hilton et al., 1985). The Igeo values of the four metals decreased with depth in S. alterniflora sediment, with similar trends being observed for Cu and Cd in the mudflat (Table 5). The average Igeo values in S. alterniflora sediment occurred in the increasing order of Cu (0.15) < Pb (0.35) < Zn (2.89) < Cd (3.44), with relatively lower Igeo values of Pb (0.21) < Cu (0.16) < Zn (2.94) < Cd (3.28) being observed for the mudflat. Additionally, the samples were heavily contaminated by Cd and moderately to heavily contaminated by Zn at both sites. In S. alterniflora sediment, the mean Igeo values of Cu and Pb ranged from 0 to 1, suggesting none-moderate pollution, while the mean Igeo values of Cu and Pb in the mudflat were lower than 0, indicating no contamination. Overall, the Igeo values in S. alterniflora sediment were higher than in the mudflat, demonstrating greater heavy metal contamination in S. alterniflora sediment. In the present study, the monomial potential ecological risk factor (Eir ) and its grades (Tables 2 and 6) indicated that Cd contamination at both sites posed a very high risk, while Cu, Zn and Pb posed low risks. When compared to the mudflat, the Eir values of Cd at 0–20 cm in S. alterniflora sediment were higher, with lower Eir values of Cd being observed at 20–30 cm. Overall, the average Eir values for heavy metals at both sites were in the order of Cd > Zn > Pb > Cu. Potential ecological risks of heavy metals have previously been assessed to better understand the heavy metal pollution (Hakanson, 1980; Shi et al., 2010; Yu et al., 2010). Based on assessment of Eir , Yu et al. (2010) reported moderate contamination of Zn, Ni, and Cr, as well as lower contamination of Cu and Pb in Quanzhou Bay, China. Shi et al. (2010) also noted considerable ecological risk of heavy metal contamination in urban and suburban sediment deposited in the streets of Shanghai, China.

To quantify the overall potential ecological risks of observed metals at both sites, comprehensive potential ecological risk RI is calculated as the sum of all four risk factors. Furthermore, RI is intended to characterize the sensitivity of local ecosystems to toxic metals and represent ecological risk caused by the overall contamination (Shi et al., 2010). The grades of RI and their values are shown in Tables 2 and 6, respectively. At both sites, the values of RI changed from 300 to 600, which are classified as considerable risk, with higher values occurring at 0–10 cm than 10–30 cm. RI values in S. alterniflora sediment were higher than in the mudflat, and decreased with increasing depth. Furthermore, Cd accounted for >90% of the total risk at both sites, which was higher than the 52% reported for Shanghai, China (Shi et al., 2010) and 32% for Huelva Peninsula, Spain (Guillén et al., 2012), indicating that Cd was the only metal posing a very high risk to the environment.

3.3. Multivariate statistical analysis Multivariate analysis was performed for sediment properties, nutrient elements and heavy metals (Table 7). Two main components explained 80.69% of the total variance in the mudflat, with three main components explaining 81.48% of the total variance in S. alterniflora sediment. In the mudflat, factor 1 (42.40% of the total variance) portrayed positive loading on TC, Cu and Pb, confirming the heavy metal contamination and nutrition element accumulation; factor 2 (38.29% of the total variance) showed a high positive loading on TN and EC. Similarly, in S. alterniflora sediment, factor 1 accounted for 34.21% of the total variance and was strongly and positively loaded relative to TC and TN. Factor 2 (30.54% of total variance) indicated high positive loading on Cu and negative loading on EC. Factor 3 (16.73% of total variance) showed only positive loading on TP. Overall, nutrients may play a major role in heavy metals distribution at both sites due to the association of TC (factor 1) and TN (factor 2) with metals such as Cu and Pb (factor) in the mudflat, as well as similar associations of TC, TN (factor 1) and TP (factor 1) with Cu in S. alterniflora sediment.

Table 5 The geo-accumulation index of heavy metals in mudflat and S. alterniflora sediment in Bohai Bay. Study sites

Depth (cm)

Mudflat

0–10 10–20 20–30 0–10 10–20 20–30

S. alterniflora

Geo-accumulation index (Igeo) Cd

Zn

3.35 ± 0.03 3.21 ± 0.07 3.27 ± 0.13 3.66 ± 0.19 3.44 ± 0.08 3.20 ± 0.07

2.98 2.89 2.94 3.02 2.84 2.82

Pb ± 0.05 ± 0.12 ± 0.12 ± 0.19 ± 0.04 ± 0.06

0.24 0.16 0.74 0.44 0.40 0.22

Cu ± 0.25 ± 0.11 ± 0.10 ± 0.08 ± 0.13 ± 0.05

0.04 ± 0.05 0.23 ± 0.13 0.28 ± 0.10 0.25 ± 0.09 0.16 ± 0.02 0.04 ± 0.05

Note: Data are shown as mean ± standard deviation. Igeo can be defined as Igeo = log2 (Cn/1.5Bn). Cn is the measured content of the metal n; Bn is the background or pristine value of the metal. The constant factor 1.5 is introduced to analyze natural fluctuations in the contents of a given substance in the environment and very small anthropogenic influences (Loska et al., 2004).

Table 6 Ecological risk index of heavy metals in mudflat and S. alterniflora sediment in Bohai Bay. Ecological risk index

Depth (cm)

Eir (Cd)

Eir (Zn)

Eir (Pd)

Eir (Cu)

RI

Mudflat

0–10 10–20 20–30 0–10 10–20 20–30

459.38 ± 8.04 417.61 ± 20.92 433.66 ± 39.70 573.82 ± 76.45 490.39 ± 28.38 414.36 ± 18.70

11.82 ± 0.42 11.16 ± 0.88 11.50 ± 0.94 12.20 ± 1.57 10.73 ± 0.32 10.58 ± 0.45

8.97 ± 1.48 6.73 ± 0.54 4.49 ± 0.30 10.19 ± 0.53 9.93 ± 0.93 8.75 ± 0.28

7.72 ± 0.25 6.37 ± 0.56 6.19 ± 0.43 8.94 ± 0.58 8.38 ± 0.14 7.71 ± 0.29

487.89 ± 7.98 441.87 ± 21.15 455.84 ± 38.28 605.15 ± 76.65 519.43 ± 28.87 441.40 ± 19.42

S. alterniflora

Note: Data were shown as mean ± standard deviation. Eir is the monomial potential ecological risk factor. RI is calculated as the sum of all risk factors for heavy metals in sediment, which represents the sensitivity of the biological community to the toxic substance and illustrates the potential ecological risk caused by the overall contamination.

M. Chai et al. / Marine Pollution Bulletin 84 (2014) 115–124 Table 7 Total variance explained and rotated component matrix of principal components analysis. Variable

Bulk density pH EC TC TN TP Cu Cd Zn Pb Initial eigenvalue Percentage of variance Cumulative percentage

Mudflat

S. alterniflora

Factor 1

Factor 2

Factor 1

Factor 2

Factor 3

0.81 0.09 0.06 0.94 0.37 0.83 0.90 0.47 0.20 0.90 4.24 42.40 42.40

0.18 0.96 0.93 0.12 0.79 0.45 0.07 0.61 0.88 0.18 3.83 38.29 80.68

0.83 0.05 0.10 0.98 0.88 0.21 0.59 0.51 0.40 0.44 3.42 34.21 34.21

0.04 0.92 0.90 0.03 0.11 0.13 0.73 0.59 0.43 0.55 3.05 30.54 64.75

0.26 0.08 0.11 0.03 0.22 0.83 0.30 0.32 0.66 0.48 1.67 16.73 81.48

Note: Extract method: principal component analysis. Rotation converges in three iterations. Bold values indicate strong loadings.

In addition to PCA, HCA was used to identify relationships among metals and provide grouping of variables, after which a dendrogram with single linkage Euclidean distances was generated. The HCA results for the heavy metals in mudflat and S. altenriflora sediments are shown in Fig. 4. The distance axis represented the degree of association between groups of variations, with a lower value on the axis being associated with more significant clusters. In the dedrogram of the mudflat, all ten parameters were grouped into three clusters. In the mudflat, cluster 1 consisted of TC, Pb and Cu, while cluster 2 consisted of EC, TN and Cd and cluster 3 consisted of pH, Zn, bulk density and TP. It should be noted that S. alterniflora affected the relevant association among variables, with the following three clusters being obtained: cluster 1:TC, TN, Cd and Zn; cluster 2: Cu, Pb and pH; cluster 3: bulk density, TP and EC. Cu and Pb in both sites may originate from similar sources due to their being within the same cluster. Cu and Zn in the mudflats were relatively independent, while in S. alterniflora

Fig. 4. Hierarchical clustering analysis of the varriables in mudflat and S. alterniflora sediment in Bohai Bay, China. TC, total carbon content; TN, total nitrogen content; TP, total phosphorus content; BD, bulk density; EC, electrical conductivity.

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sediment they were included in the same cluster, implying their similar anthropogenic origion, such as vehicle fumes and leather manufacture. Correlation analysis of nutrient elements and heavy metals was carried out to provide more information about the correlations among nutrient elements and heavy metals. TC and TP were positively related in the mudflat (r = 0.999, P = 0.021 < 0.05) and S. alterniflora (r = 0.999, P = 0.029 < 0.05) sediments, indicating that these nutrients might, at least in part, have similar sources. In the mudflat, the Pb level was well correlated with TP (r = 0.998, P = 0.045 < 0.05). In S. alterniflora sediment, Cu level was positively related to N (r = 0.999, P = 0.023 < 0.05) and Cd (r = 0.997, P = 0.047 < 0.05). The results of PCA, HCA, and simple correlation analysis suggested that the investigated metals may have originated from different sources, and that anthropogenic sources may have affected and polluted the sediment at both sites. Although heavy metals mainly occurred in silicate or basic mineral associated forms with limited mobility, anthropogenic influence altered their mobility and bioavailability to a certain extent (Asa et al., 2013). The toxicity and mobility of heavy metals in sediment depended not only on their concentrations, but also their chemical properties and surrounding conditions such as pH, redox potential, contents of clays and organic matter, (Thomson and Frederick, 2002; Guillén et al., 2012). To obtain a complete overview of the heavy metal pollution state, chemical speciation of heavy metals was also investigated in the present study. 3.4. Heavy metals speciation The percentages of heavy metals from each extraction step are shown in Fig. 5. An internal check of the results of the sequential extraction procedure was conducted by comparing the sum of the four steps (water/acid-soluble + reducible + oxidizable + residual) with the total metal content. In the present study, the recoveries of the four heavy metals ranged from 93.75% to 105.11%, indicating satisfactory results and validating the findings. The heavy metal contents in the fractions were evaluated based on comparison to the sum of the metal contents in the sequential extraction steps, which represented 100%. The results revealed that most of the heavy metals were highest (above 90%) in the residual fraction bound in mineral lattice, which is generally regarded as relatively stable or inert under normal conditions; therefore, minerals in this fraction are less likely to harm the environment (Carral et al., 1995; Yu et al., 2010). The residual fractions of Cd and Zn were higher than those of Cu and Pb at both sites. Additionally, Cu and Pb showed similar oxidizable fraction distributions, with recovery percentages of 5.95–8.95%, which were higher than the residual fractions of Cd and Zn (0.55–0.98%). The potentially mobile fraction is considered the sum of the first three steps from the sequential extraction procedure, i.e. the acid-soluble fraction, the reducible fraction, and the oxidizable fraction (Pérez et al., 2008; Guillén et al., 2012). In the present study, the percentage of mobile heavy metal fractions in the mudflat increased in the order of Zn (0.50–1.28%, mean 0.90%) < Cd (1.78–2.03%, mean 1.92%) < Pb (4.89–6.48%, mean 5.87%) < Cu (4.95–8.88%, mean 7.36%), with relatively higher percentages of Zn (1.34–1.69%, mean 1.47%) < Cd (1.95–2.03%, mean 1.92%) < Pb (5.51–10.73%, mean 7.45%) < Cu (8.02–10.13%, mean 9.24%) in S. alterniflora sediment. The results indicated that growth of S. alterniflora may improve conversion from the immobilized fraction to the mobilized fraction. Generally, residual heavy metals reflect background geochemical conditions, while anthropogenically sourced heavy metals preferentially partition to the non-residual fraction (Bird et al., 2005; Liu et al., 2011). At both sites, the percentages of Cd and Zn in various fractions remained relatively constant along the profile

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(Cd)

(Cd)

Mudflat

100%

100%

97%

97%

94%

94%

91%

91%

88%

88%

85%

Spartina alterniflora

85% 0-10 cm

(Zn)

10-20 cm

20-30 cm

0-10 cm (Zn)

Mudflat

100%

100%

97%

97%

94%

94%

91%

91%

88%

88%

10-20 cm

20-30 cm

Spartina alterniflora

85%

85% 0-10 cm (Pb)

10-20 cm

0-10 cm

20-30 cm (Pb)

Mudflat

100%

100%

97%

97%

94%

94%

91%

91%

88%

88%

10-20 cm

20-30 cm

Spartina alterniflora

85%

85% 0-10 cm (Cu)

10-20 cm

0-10 cm

20-30 cm (Cu)

Mudflat

100%

100%

97%

97%

94%

94%

91%

91%

88%

88%

85%

10-20 cm

20-30 cm

Spartina alterniflora

water/acid-soluble fraction reducible fraction oxidizable fraction residual fraction

85% 0-10 cm

10-20 cm

20-30 cm

Depth (cm)

0-10 cm

10-20 cm

20-30 cm

Depth (cm)

Fig. 5. Percentage of individual heavy metal extracted in each step of the sequential extraction procedure BCR-modified in mudflat and S. alterniflora sediment in Bohai Bay, China.

(Fig. 5), which may reflect the background levels of the metals in the sediment. These findings also indicate that the effects of S. alterniflora on speciation of Cd and Zn were limited. There were no significant differences in residual Pb between S. alterniflora sediment and that of the mudflat at below 10 cm, which may be related to its natural origin. In the upper 10 cm, the lower residual fraction of Pb and increased oxidative fraction in S. alterniflora sediment were probably due to the mobilization effect of S. alterniflora on sediment Pb. However, the oxidative fraction of Cu decreased from the top to bottom layer in the mudflat, in contrast to the increasing trend detected in S. alterniflora sediment. Therefore, other mechanisms such as leaching, post-depositional remobilization and bioturbation may also occur in Cu speciation in sediment. Accordingly, further investigation of these processes is warranted. 4. Conclusion In summary, S. alterniflora substantially modified sediment properties of Bohai Bay, China, resulting in increased total C, N, and P, as well as reduced bulk density and electrical conductivity.

Furthermore, S. alterniflora led to the accumulation of Cd, Cu and Pb in the intertidal sediment. Cd and Zn were toxicologically important at both sites due to their concentrations being higher than the TEL and PEL values. The mudflat and S. alterniflora sediments had a 21% probability of toxicity based on the mean PEL quotient. Both the geo-accumulation index and potential ecological risk index demonstrated higher metal contamination in S. alterniflora sediment. At both sites, the geo-accumulation indices of Cd were highest, with a very high risk estimation based on the potential ecological risk index. Multivariate analysis (PCA and HCA) suggested that heavy metals were influenced by anthropogenic inputs. All heavy metals presented the highest percentages in the reducible fraction, with the percentages of non-residual fraction occurring in the order Zn < Cd < Pb < Cu. S. alterniflora improved the conversion from the immobilized (residual) fraction to the mobilized (non-residual) fraction. The results presented herein represent the current state of sediment quality in mudflats and S. alterniflora sites in the intertidal zone of Bohai Bay and will help managers of the marine environments evaluate the use of S. alterniflora for remediation of heavy metal pollutions.

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