Assessment of metal contamination in coastal sediments of the Maluan Bay (China) using geochemical indices and multivariate statistical approaches

Marine Pollution Bulletin xxx (2015) xxx–xxx

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Assessment of metal contamination in coastal sediments of the Maluan Bay (China) using geochemical indices and multivariate statistical approaches Zaosheng Wang a,⇑, Yushao Wang b, Liuqin Chen a,c, Changzhou Yan a, Yijun Yan a, Qiaoqiao Chi a a b c

Key Laboratory of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, 1799 Jimei Boulevard, Xiamen City 361021, China Key Laboratory of Tropical Marine Environmental Dynamics, South China Sea Institute of Oceanology, Chinese Academy of Sciences, Guangzhou 510301, China School of Environment, Northeast Normal University, Changchun 130117, China

a r t i c l e

i n f o

Article history: Received 23 May 2015 Revised 24 July 2015 Accepted 25 July 2015 Available online xxxx Keywords: Heavy metals Coastal sediments Geochemical indices Multivariate statistics PCA/FA Pollution-risk gradients

a b s t r a c t Total concentrations and chemical forms of heavy metals in surface sediments of Maluan Bay were determined and multiple geochemical indices and guidelines were applied to assess potential contamination and environmental risks. Metal concentrations exhibited significant spatial variation and the speciation of Cr was presented dominantly in the residual fraction, while Cd was found mostly in the non-residual fraction and thus of high potential bioavailability. Cluster analysis separated four subgroups of sampling sites with different levels of contamination. Further, a multivariate method offered the specific interpretation of possible contaminant sources and/or pathways. Factor scores characterized the sampling locations and elucidated the pollution status, pointing out the impact of multiple ‘‘hidden hotspots’’ of contaminants and providing further evidence of the existence of clear pollution-risk gradients in lagoon areas. The study supports the integrative approach as powerful tool to diagnose the pollution status scientifically for management decisions in coastal sediment of complex environment. Ó 2015 Elsevier Ltd. All rights reserved.

1. Introduction With the intensive development of industrialization and rapid expansion of the urbanization economic boom induced in China, coastal and estuarine zones are often contaminated by anthropogenic-related processes, which have raised concerns especially for metals due to their toxicity, abundance and persistence in the environment, and subsequent accumulation in aquatic habitats where they are a potential threat to ecosystems (Wang et al., 2014). In aquatic environments, trace metals discharged into coastal waters rapidly become associated with marine particulate matter and incorporated in sediments as a result of adsorption, hydrolysis and co-precipitation, making coastal sediments the most important repository for metal contaminants (Hou et al., 2013). Correspondingly, changes in environmental conditions, such as pH, temperature and redox potential, can cause the metals to be released from the solid to the liquid phase, transforming sediments from the main sink of metals to the sources of them for the overlying waters. In this sense, the contents of heavy metals in sediments are constantly monitored to provide important basic ⇑ Corresponding author. E-mail address: [email protected] (Z. Wang).

information for environmental assessment in coastal environments rather than the measurements in the water and/or in the suspended materials, which were not conclusive because of water discharge fluctuations and low resident times. Further in sediments, trace metals interact with sediment matrix through different binding mechanisms, including adsorption to mineral surfaces, association with carbonates, Fe/Mn oxyhydroxides, organic matter, sulfides and the lattices of refractory crystalline minerals such as silicates, exhibiting different environmental behavior critically dependent on their chemical forms and/or interactions, and thus influencing mobility, bioavailability and toxicity to organisms (Castillo et al., 2013). Therefore, an effective scheme incorporating the determination of the geochemical fractionation of trace metals in sediments with the measurements of their total concentrations has been proposed, which can provide more detailed information with respect to the origin, mobilization, biological availability and potential toxicity to various species (Gao and Li, 2012; Huang et al., 2013). Over the past decades, various indices have been developed to assess heavy metal contamination of sediments and its ecological risk. These indices, such as the enrichment factor (EF) and the geoaccumulation index (Igeo) to assess the metal contamination, and sediment quality guidelines (SQGs) as well as the potential

http://dx.doi.org/10.1016/j.marpolbul.2015.07.064 0025-326X/Ó 2015 Elsevier Ltd. All rights reserved.

Please cite this article in press as: Wang, Z., et al. Assessment of metal contamination in coastal sediments of the Maluan Bay (China) using geochemical indices and multivariate statistical approaches. Mar. Pollut. Bull. (2015), http://dx.doi.org/10.1016/j.marpolbul.2015.07.064

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ecological risk index (ER) to evaluate the ecological risk posed by heavy metals in sediments, provide useful information that can be easily communicated to local managers and decision makers (Hou et al., 2013). However in complex coastal and estuarine regions, the complexity and the large variance of sediment data sets limit the use of common evaluation methods to comprehensively assess the real state of contamination and rank the level of contamination for different sites, which is essential to get information on the range of the anthropogenic influences and further identify the pollution in ‘‘hot spots’’ (i.e., sampling sites strongly influenced by human activity). On this point, the application of multivariate statistical methods that can help to simplify and organize large data sets and to make useful generalization to lead to meaningful insight is strongly recommended to offer a more informative and comprehensive assessment of the state of pollution (Simeonov et al., 2000; Sakan et al., 2009). Maluan Bay (24°320 4700 N, 118°000 3800 E), located in the northwest of Xiamen promontory of Fujian Province, is a large embayment lagoon along the southeastern coast of China (Fig. 1), and is also a biological productive system with significant role for aquatic flora and fauna. Over the past 30 years, Maluan Bay is affected by a wide variety of contamination sources as the receptor of discharges from industrial, urbanization and port activities of surrounding areas. Moreover, a seawall artificially created in the 1960s restricted the water exchange between Maluan Bay and West Sea that made the physical self-cleaning capacity of this lagoon very poor and further degraded the environmental quality in this zone. Earlier monitoring studies did not provide clear information on the real status of contamination in this lagoon, limiting the reaction of local authorities and the general public (Zhang et al., 2007; Wang et al., 2011). Thus, a more comprehensive assessment integrating

various geochemical indexes and using multivariate statistical methods was required. Specifically, the objectives of the present study were: (1) to quantify and explain the spatial distributions and chemical fractions of metal profiles (Cd, Cr, Cu, Zn, Pb and Ni) in surface sediments from Maluan Bay; (2) to explore the degree of heavy metal contamination using contamination indices; (3) to assess environmental risks of these metals by comparison with sediment quality guidelines (SQGs) and the potential ecological risk index (ER); and (4) to identify the possible sources of these metals and, more importantly, characterize the inherent contamination at different locations by using multivariate statistical methods, including cluster analysis (CA) and principal component analysis–factor analysis (PCA/FA).

2. Materials and methods 2.1. Study area and sampling design Maluan Bay (24°320 4700 N, 118°000 3800 E) is located in the northwest of Xiamen promontory, southeastern coast of China (Fig. 1). Due to the development of heavy industry, rapid urbanization and port activities, Maluan Bay has been affected by anthropogenic inputs, and is currently becoming a receptor of diverse sources of contaminants. Therefore in the present study, taking into account the various types of existing contamination sources and suspected contaminant gradients together with their differing coastal conditions, eight sampling locations covering the entire bay area were distributed along the banks of the lagoon extending from the inner to outer bay and forming four transects, which cover a wide range

Fig. 1. Schematic overview of the investigated area and sampling sites in Maluan Bay showing the main geographic features. The co-ordinates are given in the central table. Waterways or watercourses of tidal seawater from the West Sea are highlighted by arrows.

Please cite this article in press as: Wang, Z., et al. Assessment of metal contamination in coastal sediments of the Maluan Bay (China) using geochemical indices and multivariate statistical approaches. Mar. Pollut. Bull. (2015), http://dx.doi.org/10.1016/j.marpolbul.2015.07.064

Z. Wang et al. / Marine Pollution Bulletin xxx (2015) xxx–xxx

of degree and reflect most of the inherent characteristics of contamination in Maluan Bay. Corresponding to the first transect, ML1 and ML2 sites were situated in the inner Bay and characterized by low levels of contaminants because of being relatively far from the main contamination sources. On the contrary, ML3 and ML4 sites of the second transect in the middle Bay were subjected to intense industrial and urban contamination, receiving effluents from the heavily industrialized Xinlin complex of hundreds of industries such as basic and fabricated metal products, thermal power stations, metallurgical industries, a coking plant, and a plating factory. In contrast, although also in the lagoon center, ML5 and ML6 sites were located in mariculture areas where shrimp and shellfish aquaculture is well developed. Sites ML7 and ML8 were placed in the narrow passage between the lagoon and the West Sea, close to traditional harbor zones of Xiamen with variable degrees of heavy shipping traffic as a possible source of metal contaminants from more than a century of intense port activities. The fourth transect in the outer bay was also under the influence of the West Sea, considering that Maluan Bay daily receives tidal current from the West Sea, although the created embankment alters the geological and hydrological dynamics and restricts the seawater flow and renewal. Further detailed information about the coordinates, and the characteristics of each sampling location are described in Fig. 1. 2.2. Field sampling Surface (top 10 cm) sediment samples were collected during the ebb tide in November 2014, using stainless steel grab sampler and/or plastic spatula and sealed in acid–rinsed polyethylene bags with no head space and temporarily kept in a cooler box with ice packs at 4 °C to prevent changes in chemical composition among different phases. After being immediately transferred to the laboratory, the samples were stored below 20 °C in the dark until further processing and analysis. Samplings were at least triplicated at each location to ensure the representativeness of the samples. During sample collection, a hand-held global positioning system (GPS) was used to locate the sites. In addition, physicochemical parameters or characteristics including pH, conductivity, salinity, dissolved oxygen, and chlorophyll-a of overlying water were directly determined on-site using a portable YSI 6600V2 Sonde water quality monitoring systems (YSI, Yellow Spring, OH, USA). 2.3. Physicochemical analysis of sediments In the laboratory, samples were first sieved through a 2-mm nylon sieve to remove coarse debris and fragments of shells, and then ground using an agate mortar and pestle in a fume cupboard, followed by screening with a 0.5 mm sieve to remove large particles and homogenize the mixture. Before analysis, all of the samples were treated with 10% H2O2 and 0.5 M HCl to remove organic matter and carbonates. The distribution of grain sizes in sediment samples was determined using a Masterizer-2000 laser particle analyzer (Malvern Instruments Ltd., UK) allowing measurements ranging from 0.02 to 2000 lm and a repeatability error of <3%. Textural classification of the sediment samples was based on the relative percentages of clay (<4 lm), silt (4–63 lm), and sand (>63 lm). Due to the high specific surface of the smaller particles and strong association with metals (Idris et al., 2007), the analysis of total organic carbon (TOC), metal content and speciation were conducted for fine grain-size fractions (<63 lm) to describe the characteristics of sediment samples after further sieve using 63 lm nylon mesh. TOC was determined by a TOC analyzer

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(TOC-VCPH SSM 5000A, Shimadzu, Japan) through subtracting the inorganic carbon (IC) from the total carbon. The overall precision of measurements was within 5% based on replicate sediment analyses. 2.4. Analytical methods For the analysis of total heavy metals, approximately 0.25 g of dried and homogenized sediment samples were weighed accurately and placed into CEM Teflon digestion tubes, in which, a mixture of concentrated HF–HNO3–HClO4 (10 ml grade HNO3, 5.0 ml HClO4 and 5.0 ml HF (for digestion of metals bound to silicates)) were added, a Teflon watch cover was put in place, and the sample was left at room temperature overnight. On the following day, the sample was digested in a microwave digestion system (Mars Xpress, USA). After digestion, the Teflon tubes were cooled to ambient temperature, uncapped, and placed on a hot plate around 150 °C and kept under slight boiling state until the solid residue disappeared and the solution turned into white or light yellow-greenish pasta-like material and then evaporated to dryness. After cooling, 10.0 ml 5% grade HNO3 was added to completely dissolve it and then filtered through 0.45-lm filter membrane (Xinya membrane, Millipore). The filterable fractions were diluted to 25 ml with Mill-Q water and stored at 4 °C prior to analysis. The geochemical fraction of heavy metals was determined using the optimized BCR sequential extraction procedure (Rauret et al., 1999). This method provides the exchangeable (carbonates/exchangeable ions), reducible (oxides Fe/Mn), oxidizable (organic matter and sulfides) and residual (remaining, non-silicate bound metals) fractions of heavy metals in sediments. Briefly, all extractions were carried out for 16 h at room temperature using a mechanic shaker and following each extraction step, solid/liquid separation was achieved by twice de-ionized water washing and then centrifuging at 4000 rpm for 20 min, the resulting supernatant being decanted into polyethylene bottles and stored at 4 °C until analysis. The extraction of stage 1 for the exchangeable fraction was started with dried sediments of 1.20 g in 50 ml polypropylene centrifuge tubes with 40 ml of 0.11 M CH3COOH. For reducible fraction of step 2, 40 ml of 0.50 M NH2OHHCl (pH 2) was added into the residue of stage 1 with continuous agitation for 16 h. For the oxidizable fraction of step 3, the residue from step 2 was digested by using 10 ml of 8.8 M H2O2 for 1 h at room temperature, then the tube was covered and heated at 85 °C for 1 h in a water bath. Then 10 ml of 8.8 M H2O2 was added again and heated at 85 °C for 1 h; subsequently 40 ml of 1 M CH3COONH4 were added to the residue with continuous agitation for 16 h. The residual solid from step 3 was digested with a mixture of HF–HNO3–HClO4 as described above for the method of total metal concentration analysis. The sequential extraction procedure was performed with no interruption once started according to Gao et al. (2010). All of the above metal-containing solutions were subsequently analyzed by inductively coupled plasma mass spectrometry (ICP-MS, Agilent 7500) to determine the concentrations of Cd, Cr, Cu, Zn, Pb and Ni. Furthermore, the concentrations of Al were analyzed by inductively coupled plasma-optical emission spectroscopy (ICP-OES; Perkin-Elmer, Optima 7000 DV) to calculate the enrich factor for each element normalization. 2.5. Quality control and quality assurance (QC/QA) In order to guarantee the analytical data quality, laboratory quality control and quality assurance were implemented by utilizing standard operating procedures, calibration with standards, analysis of reagent blanks, recovery of spiked samples and analysis

Please cite this article in press as: Wang, Z., et al. Assessment of metal contamination in coastal sediments of the Maluan Bay (China) using geochemical indices and multivariate statistical approaches. Mar. Pollut. Bull. (2015), http://dx.doi.org/10.1016/j.marpolbul.2015.07.064

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of replicates. The precision and accuracy of the analytical procedures were checked by recovery tests on spiked sediment samples. Adopted analytical procedures indicated 92–106% recoveries of the spiked metals and standard sediment materials (GBW07401, National Institute of Metrology, China) were achieved from the tested samples, which agreed with certified values. The average differences between the certified and measured results were within 10% variability. Background correction and matrix interference were monitored through the analysis calibration checks and the performance of procedural blanks every twelve samples, which typically changed by 3–5% in each batch of samples (one blank and one standard for each 12 samples). Additionally, indium and rhenium were used as internal standards to correct for non-spectral interferences of matrix-induced signal variations and signal instabilities from high-dissolved solids in the different matrices. In-house control samples were regularly analyzed for further quality control. All analyses were duplicated to ensure the representativeness of the quantitative results, and the ultimate results were expressed as the mean and standard deviation. All reagents used in the experiment were analytical-reagent grade or better. Milli-Q water (MilliporeÒ, Bedford, MA, USA) was used to prepare standard solutions, dilutions and blanks throughout the experiments. All glassware and plastic were pre-cleaned by soaking in 10% HNO3 (v/v) for overnight and followed by soaking and rising with Milli-Q water before use. 2.6. Assessment of sediment contamination and ecological risks In this study, three different indices were used to assess the degree of heavy metal contamination and ecological risks in the surface sediments of Maluan Bay. For comparison purposes, the average upper continental crust (UCC) values were chosen as the reference background values in all of the following related indices. 2.6.1. Enrichment factor (EF) The enrichment factor (EF) is a useful tool as a contamination index in environmental media to assess the extent of sediment contamination with respect to heavy metals by differentiating their naturally occurring and anthropogenic sources. To calculate the EF values for a given metal, the concentration was normalized to the textural characteristics of the Earth’s crust. In this study, aluminum was used as the reference element for geochemical normalization, considering that it is one of the main components of the Earth’s crust and alumina-silicate is generally the predominant carrier phase for metals in coastal sediments, together with that the natural concentration of Al in marine sediments tends to be uniform (Lin et al., 2013). The EF values for heavy metals in sediments were calculated using the following formula:

EF ¼

ðM sed =Alsed Þ ðMc =Alc Þ

where Msed and Mc represent the contents of the examined heavy metal in the sediment and the Earth’s crust, respectively, and Alsed and Alc are the contents of Al in the sediment and the Earth’s crust, respectively. Typical concentrations (mg kg1) of heavy metals in the Earth’s crust were used to calculate the EF values in this study (Taylor and McLennan, 1995; McLennan, 2001): 80.40 for Al, 0.098 for Cd, 83.0 for Cr, 25.0 for Cu, 44.0 for Ni, 17.0 for Pb, and 71.0 for Zn. According to Sakan et al. (2009), seven tiers of contamination levels were categorized based on different EF values: EF < 1 indicates no enrichment; 1 < EF < 3 is minor enrichment; 3 < EF < 5 is moderate enrichment; 5 < EF < 10 is moderately severe

enrichment; 10 < EF < 25 is severe enrichment; 25 < EF < 50 is very severe enrichment; and EF > 50 is extremely severe enrichment. 2.6.2. Geoaccumulation index (Igeo) The geoaccumulation index (Igeo) is a contamination index which is defined by the following equation:

Igeo ¼ log2 ðC n =1:5Bn Þ where Cn is the concentration of metals measured in sediment samples and Bn is the geochemical background concentration of the metal (n) which is the same as those used in the aforementioned enrichment factor calculation. Factor 1.5 is the background matrix correction factor due to lithospheric effects (Müller, 1969). The geoaccumulation index consists of seven classes: Igeo 6 0 (Class 0, practically uncontaminated); 0 < Igeo 6 1 (Class 1, uncontaminated to moderately contaminated); 1 < Igeo 6 2 (Class 2, moderately contaminated); 2 < Igeo 6 3 (Class 3, moderately to heavily contaminated); 3 < Igeo 6 4 (Class 4, heavily contaminated); 4 < Igeo 6 5 (Class 5, heavily to extremely contaminated); Igeo > 5 (Class 6, extremely contaminated) (Varol, 2011). 2.6.3. Potential ecological risk factor (ER) ER is an index of ecological risk assessment developed by Hakanson (1980) and widely used to evaluate the degree of pollution of heavy metals in the sediments. The equations for calculating the ER are as follows:

ERif ¼ Tri  C if ¼ Tri  ðC is =C in Þ RI ¼

i X

ERif

f

where ERi is the potential ecological risk factor for a given element i; Tri is the biological toxicity factor for element i, which is defined as Cd = 30, Cr = 2, Cu = Pb = Ni = 5, Zn = 1 (Hakanson, 1980); Cif, Cis and Cin are the contamination factor, the concentration in the sediment, and the background reference value for element i, respectively. RI is the sum potential toxicity response index for various heavy metals in the sediments. According to Hakanson (1980), the ER index consists of five classes for ecological risk level of single-factor pollution as below: ER 6 40 (low risk); 40 < ER 6 80 (moderate risk); 80 < ER 6 160 (considerable risk); 160 < ER 6 320 (high risk); EF > 320 (very high risk). Corresponding to RI, four categories were defined for general level of potential ecological risk: RI 6 150 (low risk); 150 6 RI 6 300 (moderate risk); 300 < RI 6 600 (considerable risk); RI P 600 (very high risk). 2.7. Statistical analyses The significant spatial differences of total metal concentrations in sediments and geochemical indices of Igeo, EF and ER were statistically analyzed with one-way analysis of variance (ANOVA) (p < 0.05). When the ANOVA was significant, the Student–Newma n–Keuls (S–N–K) test was employed for the comparison. Data for all sites were tested for normality and homogeneity of variance and transformed when necessary. Interrelationships among the heavy metals, and between heavy metals and TOC content as well as grain size composition in heterogeneous sediments were tested using Pearson’s coefficient to determine if metrics were positively or negatively related with statistical significance set at ⁄p < 0.05 or ⁄⁄ p < 0.01. Hierarchical agglomerative cluster analysis (CA), which can reveal specific linkages between sampling sites because it provides an indication of similarities or dissimilarities between their trace metal contaminations (Simeonov et al., 2000), was performed on the normalized dataset (z-scores) using Ward’s method with

Please cite this article in press as: Wang, Z., et al. Assessment of metal contamination in coastal sediments of the Maluan Bay (China) using geochemical indices and multivariate statistical approaches. Mar. Pollut. Bull. (2015), http://dx.doi.org/10.1016/j.marpolbul.2015.07.064

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squared Euclidean distances as a measure of similarity to assess the interrelationships among the sampling sites. Thereafter, to reduce a large number of variables to summary orthogonal factors or principal components and further verify or quantify interrelationships among the original variables in a dataset, multivariate factor analysis (FA) employing principal component analysis (PCA) as the extraction procedure (PCA/FA) was performed to experimental data standardized through z-scale transformation to avoid misclassification due to wide differences in data dimensionality. The data set was rearranged in a correlation matrix, and principal components were then subjected to varimax rotation (raw) to generate varifactors (VFs) by the Varimax normalized method, then three factors (or new variables) were obtained considering Eigen values higher than 1.0 (Kaiser’s criteria). Furthermore, Kaiser–Meyer–Olkin (KMO) and Bartlett’s sphericity tests were performed to examine the suitability of the data for PCA/FA [24]. KMO is a measure of sampling adequacy that indicates the proportion of variance that is common, i.e., variance that may be caused by underlying factors. A high value (close to 1) generally indicates that principal component/factor analysis may be useful, as was the case in this study, where KMO = 0.76. Bartlett’s test of sphericity indicates whether a correlation matrix is an identity matrix, which would indicate that variables are unrelated. The significance level of 0 in this study (less than 0.05) indicated that there were significant relationships among the variables. For the factor analysis, the variables were auto scaled (Varimax normalization) to be treated with equal importance. Only variables whose coefficient was 0.3 or higher were considered components of the factor, a value which approximates Comrey’s cutoff for a reasonable association between an original variable and a factor (Comrey, 1973). Besides the analysis of the variables aggregated by PCA-factor, a representation of estimated factor scores from each sampling site was performed to provide concise descriptions and better interpretation and to characterize the studied sampling locations. All statistical data processing in the present study were carried out using commercial statistics software package SPSS (version 19.0 for Windows). 3. Results and discussion 3.1. General properties of sediments In this study, grain size and TOC concentration were determined to obtain the general characteristics of the surface sediment samples from Maluan Bay. The studied surface sediments were characterized by inhomogeneous concentrations of TOC that

Fig. 2. Spatial variation of TOC concentration and mean grain sizes in surface sediments from Maluan Bay.

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constituted 0.58–7.14% of the dry weight of the sediment with an average of 2.67% (Fig. 2). The lowest and anomalous highest TOC contents were recorded at site ML2 and ML3, respectively. The variation in TOC contents among the sediment samples was significant. The mineral fraction of particles varied from 28.5% to 39.1% for clay (less than 4 lm) with an average of 32.9%. The fraction of silt (4–63 lm) particles varied from 60.4% to 68.4% with an average of 64.3%. Sand content (greater than 63 lm) varied from 0.54% to 7.21% with an average of 2.82%. Therefore, the surface sediments were predominantly composed of silt and clay, especially for site of ML2 and ML6, and the percentages of fine fraction (clay + silt) are >90% for all of the samples. Furthermore, site ML8 close to the mouth of tidal inlet has relatively more sand than those of other sites, although the grain size distribution or the texture of sediments did not exhibit clear spatial difference among sampling sites. 3.2. Spatial distribution of heavy metals and their chemical fractionations During the study period, all heavy metals in surface sediments from sampling locations showed significant spatial variations (ANOVA, p < 0.05). Total metal concentrations varied from 0.23 to 0.8 lg g1 for Cd, 51.7 to 124.7 lg g1 for Cr, 17.8 to 102.8 lg g1 for Cu, 170.2 to 384.6 lg g1 for Zn, 36.6 to 61.4 lg g1 for Pb, and 19.2 to 59.1 lg g1 for Ni. The highest metal concentrations were found in Middle Bay due to metal-containing wastewater discharges form Xinlin industrial complex, showing the highest mean values of Cd, Zn, Pb and Ni at site ML3 and the highest mean values of Cr and Cu at site ML4. While the sediments from the inner bay had the lowest mean metal concentrations in sites of ML1 and ML2. The heavy metals concentrations can be divided into three classes. One class was Zn where the concentrations exceeded 100 lg g1. The second class was Cr, Cu, Pb and Ni where the concentrations were moderate generally ranged from 1 to 100 lg g1. The third class was Cd where the concentrations were lower than 1 lg g1. Metal measurements as total concentration is the most fundamental way for sediment quality assessment, but for further understanding of potential mobility, bioavailability and toxicity of metals in sediments, metal fractionation occurred in different geochemical forms is of crucial importance, which had distinct mobility, migration ability and chemical behavior. Therefore, the sequential extraction procedure was proposed to obtain the information about the strength and ways of metal associating with sediments. In this study, the distributions of different fractions of heavy metals in sediments of Maluan Bay were shown in Fig. 3 and presented as percentages of the sum of all fractions. The metals in the acid-soluble fraction are considered to be the weakest bound metals in sediments and defined as the ‘‘exchangeable fraction’’ which may equilibrate with the aqueous phase and thus become the most mobile portion. The more metals in this fraction, the more easily bioavailable they are, and the higher risk they could pose to the environment. Except for Cr at all sites and Cu at site ML1, the relative proportions of metals in the acid-soluble fraction were generally high, especially for Cd. The mean proportion of Cr in acid-soluble fraction was the lowest among the studied metals, and its value was only 1.87%, indicating its low mobility. In contrast, the contents of acid-soluble Cd on average were higher than other Cd fractions. The highest percentage of this fraction, up to 78.2% at site ML6, was observed for Cd with the mean of 63%, indicating its high mobility. The high proportion of Cd in this fraction may be owing to the fact that it is a typical anthropogenic element and mostly enters the aquatic environment through the discharge of industrial effluents. The mean proportions of acid-soluble Zn and Ni were 37.4% and 35.1%, respectively, indicating that they had some degree of mobility.

Please cite this article in press as: Wang, Z., et al. Assessment of metal contamination in coastal sediments of the Maluan Bay (China) using geochemical indices and multivariate statistical approaches. Mar. Pollut. Bull. (2015), http://dx.doi.org/10.1016/j.marpolbul.2015.07.064

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Fig. 3. The spatial variations of studied heavy metals in total concentrations and their distributions in different phases of sediments from Maluan Bay. For total metal concentrations, points sharing the same letter (a, b, c or d) are not significantly different (p < 0.05).

For Cu, although its mean proportion in the acid-soluble fraction was 21.3%, it presented a clear spatial variation with a range of 0.44–26.33%. On average, the percentage of Pb in the acid soluble fraction from different sampling sites was relatively constant, with the mean value of 6.4% and the range of 3.14–10.01%. The reduction was the most abundant fraction for Pb and Cu (except at site ML1) at all sites. The proportion of Pb in the reducible fraction was much higher than that of other metals in this fraction. 58.5% of Pb was measured in this fraction with the range of 45.2–71.1%. Except 8.1% of Cu at site ML1, 39.4% of Cu was measured in this fraction with the range of 34.7–43.8%. The percentages of Cd, Cr, Zn and Ni in reducible fraction were moderate,

with the mean values of 16.8%, 15.0%, 13.8% and 13.4%, respectively. The mean percentage of metals in the oxidizable fraction was 5.9% for Cd, 19.5% for Cr, 20.8% for Cu, 4.8% for Zn, 6.1% for Pb and 10.4% for Ni. The spatial variations of the percentages of Cd and Pb in the oxidizable fraction were more significant than the other metals, with the range of 3.1–11.0% and 2.1–13.4%, respectively. The results showed that most of the Cr in all the sediments was strongly retained in the residual fraction. Its average percentage in this fraction was 63.9% with the range of 52.8–92.3%. High proportions of this fraction were also observed for Cu, Zn and Ni in some

Please cite this article in press as: Wang, Z., et al. Assessment of metal contamination in coastal sediments of the Maluan Bay (China) using geochemical indices and multivariate statistical approaches. Mar. Pollut. Bull. (2015), http://dx.doi.org/10.1016/j.marpolbul.2015.07.064

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sites. These facts indicated that these metals are strongly bound in the crystal lattices of minerals and, consequently, have relatively low mobility, bioavailability and toxicity. Granulometry and organic matter content are two important factors that greatly influence the geochemical behaviors of heavy metals in sediments. In order to reveal the possible associations existing between these variables, Pearson’s correlation was performed to the whole datasets of the elements concentrations in surface sediments of the study area. The correlation coefficient matrix among heavy metals (Cd, Cr, Cu, Zn, Pb and Ni), TOC and grain size distribution are presented in Table 1. Significantly positive correlation was observed for Cd, Cu, Zn, Pb and Ni, indicating that these metals are associated with each other and may have similar natural or anthropogenic sources. However, Cd and Cr showed low correlation, indicating that they have different pollution sources. Except Cr, TOC had strong positive correlations with metals, especially Cd and Zn, which indicated that these metals are easily bound to the organic matter, and increase with the increase of organic matter in the sediments. 3.3. Risk assessment of heavy metals To assess the contamination extent of metals in surface sediments of Maluan Bay, total mean metal concentrations were compared against the National Standard of Marine Sediment Quality (GB 18668-2002, China) (Table S1). According to this criterion, The primary sediment standard criteria, which are the strictest, are applied to protecting the habitats for marine life including natural, rare and endangered species as well as the areas for leisure activity such as human recreation and swimming sports; the secondary standard criteria can be used for general industrial use and coastal tourism; and the third standard criteria is just suitable for harbor. Although there was significant spatial variation in the metal concentrations from Maluan Bay, the overall average concentrations of metals in surface sediments were above or close to the primary standard criteria. Among the eight sampling sites, we found that the cases of metal concentrations exceeding the primary standard criteria are 7 for Cd, Cr and Zn (except for site ML2), 6 for Cu (except for ML1and ML 2) and 2 for Pb (i.e., ML3 and ML4) respectively. In addition, we found Cu concentration at site ML4 and Zn concentration at site ML3 even exceeds the secondary standards. Because there are no Ni standards available in GB 18668-2002, we cannot evaluate the sediment quality with respect to Ni. Metal concentrations in surface sediments from sampling conducted for this study suggest that the overall sediment quality in Maluan Bay was considerably to moderately polluted for Cd, Cr, Pb, Ni, Cu and Zn in general. In order to better understand sediment quality and discern the metal contamination in the Maluan Bay, geochemical normalization of enrichment factor (EF) is widely employed to identify anomalous metal concentrations for environmental assessment. The results presented in Fig. 4 showed that the EF ranges were as follow: Cd, 0.7–2.7 (average 1.5); Cr, 0.4–1.4 (average 0.7); Cu,

0.2–1.2 (average 0.7); Zn, 0.5–1.9 (average 1.0); Pb, 0.4–1.7 (average 0.9); Ni, 0.3–1.2 (average 0.6). Based on the recommendation using EF = 1.5 as an assessment criterion (Zhang and Liu, 2002), EF values less than 1.5 suggest that the trace metals may be entirely from crustal materials or natural weathering processes, while EF values greater than 1.5 indicate that a significant portion of metal is delivered from non-crustal materials. Specifically in this study, EF values of Cr, Cu and Ni are generally less than 1.5, suggesting that these metal contaminations are currently not a major problem at present although moderate enrichment of these metals was found in a few localizations. In contrast, relatively high EF values of Cd (ML3, ML7 and ML8), Zn (ML8) and Pb (ML8) are found to be greater than 1.5, indicating the significant contamination of these metals in these sites. In fact, moderate to significant enrichment of these metals are found in several sites (Fig. 3). The contamination of these metals could be correlated to local point sources. For example, ML3 is directly affected by the discharge of Xinlin industrial complex and ML8 is near the harbor in West Sea. Therefore, the EF suggests the presence of contaminated sediments and does reflect the influence of local point sources of contaminants to the sediment quality. Another commonly used geochemical criterion to evaluate the heavy metal pollution in sediment is the geoaccumulation index (Igeo) determined and defined by comparing current concentrations with pre-industrial levels. As presented in Fig. 4, the results of the calculated Igeo values from this study were ranged from 1.09 to 2.44 showing much fluctuation of spatial variations. The Igeo values of Cd at all sites except ML2 were >1, suggesting that these sites were moderately polluted by Cd, and the site of ML3 had the highest Igeo values of Cd. While the Igeo values for Ni in sites of ML1 and ML2 were <0, indicated that sediments in inner bay were not polluted by Ni. The Igeo values of Ni in other sites were >0 and <1 indicating sediments from these sites had ‘‘uncontaminated to moderately contaminated’’ class with Ni. The Igeo values for Cu varied mostly, ranging from 1.09 to 1.46, most of sites fell in range of 0–1 (uncontaminated to moderately contaminated), except ML1–2 (far less than 0, uncontaminated) and ML3–4 (in range of 1–2, means moderately contaminated). For the Cr metal, ML5–7 were found to contain similar contamination level (far less than 1), ML2 and ML 4 fell in uncontaminated and ‘‘moderately contaminated’’ class, respectively, while the other sites were fell in uncontaminated to moderately contaminated. The Igeo values of Zn and Pb were similar, showing ML1–2 and ML5–6 had ‘‘unpolluted to moderately contaminated’’ class, and ML3–4 and ML7–8 had ‘‘moderately contaminated’’ class. In general terms, the variations of Igeo values in the studied sediments are well consistent with the EF, exhibiting similar trends. Toxic indices of heavy metals in sediments of the Maluan Bay were presented in Fig. 4. The results showed that Cd had the highest single potential ecological risk indices in the sediment, which was in a range of 70 and 245, indicating considerable or high risks of this metal. The single potential ecological risk indices of Cr, Cu, Zn, Pb and Ni were less than 20 or lower, indicating relatively low

Table 1 Pearson’s correlation coefficients between the measured metals and sediment properties (n = 8).

Cr Cd Cu Pb Zn Ni * **

Cd

Cu

Pb

Zn

Ni

%TOC

%Clay

%Silt

%Sand

0.522

0.756* 0.834*

0.718* 0.795* 0.852**

0.600 0.945** 0.908** 0.873**

0.753* 0.916** 0.948** 0.865* 0.897**

0.257 0.890** 0.744* 0.765* 0.902** 0.744*

0.688 0.355 0.574 0.669 0.598 0.426

0.467 0.351 0.579 0.449 0.557 0.366

0542 0.131 0.197 0.536 0.267 0.232

Correlation is significant at the 0.05 level (2-tailed). Correlation is significant at the 0.01 level (2-tailed).

Please cite this article in press as: Wang, Z., et al. Assessment of metal contamination in coastal sediments of the Maluan Bay (China) using geochemical indices and multivariate statistical approaches. Mar. Pollut. Bull. (2015), http://dx.doi.org/10.1016/j.marpolbul.2015.07.064

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Z. Wang et al. / Marine Pollution Bulletin xxx (2015) xxx–xxx

Fig. 4. The distribution of enrichment factor (EF), geo-accumulation index (Igeo) and potential ecological risk index (ER) in sediment. For each indice, bars sharing the same letter (a, b, c, or d) are not significantly different (p < 0.05). The horizontal dash-dot line (Zero value) was used for judging the Igeo.

risks. The RI of the heavy metals in the surface sediments decreased in an order of ML3 (307.43) > ML4 (237.45) > ML8 (213.52) > ML6 (198.25) > ML7 (185.88) > ML5 (175.35) > ML1 (134.49) > ML2 (95.52). ML3 had a considerable potential ecological risk, whereas ML1 and ML2 in the inner bay had low risk. Other sites had moderate risk. 3.4. Pollution characteristics through multivariate statistical analyses 3.4.1. Cluster analysis (CA) Cluster analysis was applied to the coastal sediment quality data set to group the similar sampling sites (spatial variability) as objects. Spatial CA rendered a dendrogram (Fig. 5) where all eight sampling sites on the Maluan Bay were grouped into four

statistically significantly clusters divided into two bigger subgroups. The subsamples in these clusters had similar pollution levels and/or sources. The first contains heavily polluted sites from Middle Bay located near discharges of Xinlin industrial complex and site ML8 near to tidal inlet of West Sea. The second one indicates a moderately polluted buffer zone consisting of sites on inner bay and other sites. In both big clusters, two subgroups could be found. In the first one they represent the most severely polluted area (ML3 from middle bay) and the less contaminated area (ML8 from outer bay and ML4 from middle bay). In the second one, they reflect the separation between one (site ML1 and ML2 of inner bay, lower contents for metals) and another part (ML5, ML6 and ML7) of the buffer zone moderately affected by pollutants.

Please cite this article in press as: Wang, Z., et al. Assessment of metal contamination in coastal sediments of the Maluan Bay (China) using geochemical indices and multivariate statistical approaches. Mar. Pollut. Bull. (2015), http://dx.doi.org/10.1016/j.marpolbul.2015.07.064

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Z. Wang et al. / Marine Pollution Bulletin xxx (2015) xxx–xxx

Table 2 Sorted rotated factor loadings (pattern) of 28variables after varimax rotation for the three significant principal factors obtained in the multivariate principal component analysis.

Fig. 5. Dendogram showing hierarchical clustering of sampling sites from Maluan Bay. Distance metrics are based on the Euclidean distance single linkage method (nearest neighbor).

3.4.2. Principal component analysis/factor analysis (PCA/FA) To compare the compositional pattern between the sediment samples and examine the extent of metal contamination as well as to further identify the factors influencing each one, principal component analysis/factor analysis (PCA/FA) were performed on the entire data set of 28 variables included: total metal concentrations, grain size distribution, TOC concentrations, enrichment factors, geoaccumulation indexes and potential ecological risk indices in sediments. Varimax-rotation was used to maximaze the sum of the variance of the factor coefficients. As shown in Table 2, the three factors with eigenvalues greater than 1.0 explain about 92.19% of the total variance in the sediment quality data set. Therefore, these three factors play a critical in explaining metal contamination in the study area. Individually, Factor 1, which has the highest loadings of TOC, Cd, Cd-Igeo and Cd-ER, and medium loading of Cu, Cu-Igeo, Cu-ER, Zn, Zn-Igeo, Zn-ER, Ni, Ni-Igeo, Ni-ER, and accounts for 46.96% of variance, can be considered mainly as Cd factor associated with Cu, Zn and Ni. The results from this study show that these metals (especially Cd) are bound closely with the organic matter. This is consistent with the classical sediment geochemical and environmental studies that organic carbon is commonly used to explain the enrichment in order to interpret heavy metal concentrations in sediments. Factor 2, which has the high loading of sand%, Cr-EF and Pb-EF and intermediate loading of Cd-EF, Zn-EF and Ni-EF, and accounts for 27.14% of variance, can be considered as grain size and enrichment factor and suggest that metal contamination (except for Cu) is associated with this factor. Factor 3, which has medium loading of silt% and Cr, Cr-Igeo, Cr-ER, can be considered as a Cr contamination factor and suggests that Cr contamination may be associated with silt% as well. It accounts for 18.09% of variance. In order to confirm the descriptions of these new factors, a graphical representation of the estimated factor scores corresponding to each sampling sites is presented in Fig. 6, indicating the most relationship among variables in each sampling site. For factor 1, had strong positive loadings (>0.90) on Cd, and a moderate positive loading (>0.70) on Cu, Zn, Pb and Ni, and thus portrayed the anthropogenic inputs and geoaccumulation of these metals. This factor exhibited the maximal value of positive score in site ML3. Indeed, sampling sites of ML3 are characterized by high metal concentrations of Cd, Cu, Zn and Ni in sediments. Factor 2 had strong positive loadings on EF of Cr and Pb and moderate positive loadings

Factor 1

Factor 2

Factor 3

% of Variance % of Cumulative Initial Eigen value TOC %Clay %Silt %Sand

46.96 46.96 13.15 0.915 – – –

27.14 74.10 7.60 – 0.467 – 0.914

18.09 92.19 5.06 – 0.732 0.751 –

Cd Total EF Igeo ER

0.959 0.487 0.953 0.960

– 0.860 – –

– – – –

Cr Total EF Igeo ER

0.350 – 0.401 0.350

0.377 0.934 0.434 0.377

0.797 0.336 0.748 0.797

Cu Total EF Igeo ER

0.802 0.560 0.850 0.801

– 0.693 – –

0.524 0.363 0.329 0.524

Zn Total EF Igeo ER

0.884 0.411 0.877 0.886

– 0.883 0.318 –

0.322 – 0.318 0.313

Pb Total EF Igeo ER

0.721 – 0.714 0.721

0.541 0.982 0.541 0.541

0.363 – 0.360 0.363

Ni Total EF Igeo ER

0.871 0.414 0.867 0.871

– 0.878 – –

0.379 – – 0.380

The utilized variables were TOC concentrations, grain size distribution, total metal concentrations, enrichment factors, geoaccumulation indexes and potential ecological risk indices in sediments. Factors are numbered consecutively from left to right in order of decreasing variance.

Fig. 6. Representation of estimated factors scores determined by the FA/PCA for each sampling sites integrating metal concentrations and geochemical indexes after multivariate analysis.

Please cite this article in press as: Wang, Z., et al. Assessment of metal contamination in coastal sediments of the Maluan Bay (China) using geochemical indices and multivariate statistical approaches. Mar. Pollut. Bull. (2015), http://dx.doi.org/10.1016/j.marpolbul.2015.07.064

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Z. Wang et al. / Marine Pollution Bulletin xxx (2015) xxx–xxx

on EF of Cd, Zn and Ni, respectively. This factor showed maxima value of positive score in site of ML8. Finally, factor 3 values are related to moderate positive loadings of Cr, and maximal positive score for this factor were found in site ML4. On the whole, an almost complete agreement between the results from these two approaches of CA and PCA/FA was observed in both cases. 3.5. Strengths and limitations of various assessment methods The present investigation assessed the degree of metal pollution in sediment samples from Maluan Bay through various contamination indices of SQGs, EF, Igeo, and ER, as well as by multivariate statistical techniques. Although each evaluation method has its own unique strengths and limitations, use of these tools can provide much information to elucidate the characterizations of pollution status from different aspects. In current study, chemical-specific, numerically based National Standard of Marine Sediment Quality (GB 18668-2002, China) (SEPA, 2002) was applied to assess the sediment contamination by comparing it with the bulk chemical concentrations of individual metals. Although the SQGs method provided little insight into the potential ecological impact of sediment contaminant in Maluan Bay, it did provide a base to the regulators and decision makers due to its ease and simplicity. For example, if the chemical concentration exceeds the criteria, then the sample is contaminated and exceeds the regulatory limit, then prompting regulatory action. Therefore, the prevalent SQGs method was recommended to be used firstly in a ‘‘screening’’ manner or in a ‘‘weight of evidence’’ approach (Burton, 2002). Further, geochemical indices of EF, Igeo and ER can be used to infer whether the metals are from natural weathering processes of rocks or from anthropogenic sources and reflect the status of environmental contamination. These methods are particularly important with metals, which may occur naturally in high concentrations in some areas of the world. In the present study, the metal contamination sampled in Maluan Bay by the simple examination can be more correctly evaluated if the complementary information based on metal baseline values is considered using the geochemical index that provides a more suitable appraisal of the pollution process caused by anthropogenic inputs. Nonetheless, the complexity and the high variability of sediment datasets require the application of multivariate statistical methods to further characterize the site-specific state of pollution (Einax and Soldt, 1999). In general term, the results from CA and PCA/FA are well consistent in the present study. Specifically, CA uncovered similarities in the data set and could help to locate existing pollution pattern for detecting main emission sources in Maluan Bay. PCA/FA allowed a semi-quantitative assessment of the polluted status by factor scores, which identified ML3, ML4 and ML8 as the highly impacted sites and ‘‘hidden hot spots’’ of contamination based on their high values of positive factor scores and seemed to be the most threatened sites. Furthermore, the PCA/FA corroborated the primitive hypothesis of metal contamination in sediments, which were in concordance with the realistic scenarios, because sites of ML3 and ML4 were the nearest to the outfall of wastewater discharge from Xinlin industrial complex and site ML8 was affected by contaminants from West Sea subjected to metal contaminants as a result of more than a century of intense harbor activity in this area. Therefore, the results of PCA/FA represented a more realistic picture of the pollution status and demonstrated the existence of two complex contamination systems separately, occurring as a result of multiple metal-input point sources in Maluan Bay. In addition, perhaps special geological situation between the tidal inlet and the central part of Maluan Bay, sites of ML5, ML6 and ML7 were less impacted because of being progressively dilution by comparison with their factor scores. In this respect, special attention is also required to inner

sites of ML1and ML2 in the innermost area, where both sites showed negative values of factor 1 and 2 scores, indicating insufficient hydrodynamic patterns to free environment of contamination in this area based on the presence of industrial outfalls nearby and uncover the complex mechanisms under investigation to strengthen the existence of clear pollution and risk gradients in Maluan Bay through further apparent evidence (Fig. 6). These findings also suggested the difficulty with modifying or preventing metal pollution in this area by straightforward strategies, e.g., those that only consider a single source or single pathway of metal emissions, potentially requiring comprehensive consideration to develop effective and efficient management policies to control metal discharged into the lagoon areas. To sum up, the multivariate statistical approaches, especially for PCA/FA, not only reduce natural variability, improving the statistical power in data inter-comparison among locations, but deliver more substantial information on links between sampling sites, latent factors responsible for the data set structure and pollutant sources apportioning. Finally, it seems very recommendable to combine various indices and statistical approaches instead only on one of them in order to gain better information of pollution patterns, especially in complex scenarios of coastal environments. Studies utilizing the geochemical indices and multivariate statistical approach to assess the sediment quality in this coastal lagoon of Maluan Bay represented a recent concern and are at a start point stage. Continuous monitoring and additional more extensive researches through realistic in situ short or long-term exposures are required to ascertain the presupposed risks and definitely pinpoint the uncovered mechanisms of presented gradients in future studies.

4. Conclusions Different useful tools, guidelines, indices and approaches (sequential extraction, geochemical normalization, and methods of multivariate statistical analysis) have been employed for evaluation of sediment contamination in Maluan Bay, China. The obtained results indicated that total concentrations of Cd, Cr, Cu, Zn, Pb and Ni showed significant spatial variation and sediments in studied region were considerably contaminated by Cd, Cr and Pb, and moderately polluted by Cu and Zn when compared with sediment quality criteria. More than 50% of Cr in the sediments was strongly retained in the residual fraction with low mobility and thus of low bioavailability and toxicity to organisms, while Cd was found mostly in the non-residual fractions with high bioavailability, signifying the presence of anthropogenic sources for this metal. The high variability of chemical fractions for Cu, Zn, Pb and Ni at various sampling sites and various environmental phases suggested that metal species and/or sources were influenced by a wide range of complex and different contamination mechanisms. The metal assessment using geochemical indices showed preliminary but still relevant information regarding contamination or background enrichment indices (EF and Igeo) and ecological risk indices (ER). Significant positive correlation matrixes were observed among Cd, Cu, Zn, Pb and Ni, indicating that these metals were derived from similar sources and also moving together. The CA classified all the sampling sites into four subgroups of spatial similarities with different levels of pollution. Nonetheless, the PCA/FA applied on the entire dataset identified three varifactors and allowed more site-specific and accurate information on pollution levels in each sampling site, demonstrating the presence of ‘‘hidden hot spots’’ of contaminant sources and/or pathways, and providing further evidence to the existence of clear pollution and risk gradients in Maluan Bay. These findings potentially illustrated the difficulty with modifying or preventing metal

Please cite this article in press as: Wang, Z., et al. Assessment of metal contamination in coastal sediments of the Maluan Bay (China) using geochemical indices and multivariate statistical approaches. Mar. Pollut. Bull. (2015), http://dx.doi.org/10.1016/j.marpolbul.2015.07.064

Z. Wang et al. / Marine Pollution Bulletin xxx (2015) xxx–xxx

pollution in the lagoon area by straightforward strategies. Although the application of SQGs or geochemical indices could be a general measurement of sediment quality, the results from this study suggest that it be not sufficient and reasonable to use this kind of method only to evaluate the environmental status and sediment contamination. Therefore, this study suggests the need for integrative use of geochemical indices and multivariate statistical methods especially with PCA/FA as an effective tool to provide complementary information in a broader sense for diagnosing the pollution status in complex environment, thus contributing to a more comprehensive assessment of human-induced environmental risk and better management decisions in future coastal sediments. Acknowledgments Research presented in this project was mainly supported by the National Nature Science Foundation of China (NSFC) under grant reference 21377125/B070403, partly by the open fund of key laboratory of global change and marine-atmospheric chemistry, SOA (GCMAC 1302) and by State Key Laboratory of Tropical Oceanography, South China Sea Institute of Oceanology Chinese Academy of Sciences (Project No. LTO1203). The authors would like to express their sincere appreciation for these financial supports to accomplish the study. Appendix A. Supplementary material Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.marpolbul.2015. 07.064. References Burton, G.A.J., 2002. Sediment quality criteria in use around the world. Limnology 3, 65–75. Castillo, M.L.A., Trujillo, I.S., Alonso, E.V., Torres, A.G.D., Pavón, J.M.C., 2013. Bioavailability of heavy metals in water and sediments from a typical Mediterranean Bay (Málaga Bay, Region of Andalucía, Southern Spain). Mar. Pollut. Bull. 76, 427–434. Comrey, A.L., 1973. A first Course in Factor Analysis. Academic, New York. Einax, J.W., Soldt, U., 1999. Geostatistical and multivariate statistical methods for the assessment of polluted soils-merits and limitations. Chemom. Intell. Lab. Syst. 46, 79–91. Gao, X.L., Li, P.M., 2012. Concentration and fractionation of trace metals in surface sediments of intertidal Bohai Bay, China. Mar. Pollut. Bull. 64, 1529–1536.

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Please cite this article in press as: Wang, Z., et al. Assessment of metal contamination in coastal sediments of the Maluan Bay (China) using geochemical indices and multivariate statistical approaches. Mar. Pollut. Bull. (2015), http://dx.doi.org/10.1016/j.marpolbul.2015.07.064