Spatial distribution and pollution evaluation of heavy metals in Yangtze estuary sediment

MPB-07740; No of Pages 8 Marine Pollution Bulletin xxx (2016) xxx–xxx

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Spatial distribution and pollution evaluation of heavy metals in Yangtze estuary sediment Ruimin Liu ⁎, Cong Men, Yongyan Liu, Wenwen Yu, Fei Xu, Zhenyao Shen State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, No. 19, Xinjiekouwai Street, Beijing 100875, China

a r t i c l e

i n f o

Article history: Received 31 March 2016 Received in revised form 19 May 2016 Accepted 23 May 2016 Available online xxxx Keywords: Heavy metals Yangtze River estuary Spatial distribution Ecological risk assessment Geographic information system

a b s t r a c t To analyze the spatial distribution patterns and ecological risks of heavy metals, 30 sediment samples were taken in the Yangtze River Estuary (YRE) in May 2011. The content of Al, As, Cr, Cu, Fe, Mn, Ni and Pb increased as follows: inner-region b river mouth b adjacent sea. According to Igeo and RI, As, Cr and Cd were the main pollutants. What is more, the greatest contaminated area appeared at the river mouth of the south branch of YRE. In Tucker 3, considering the fractions of metals, Mn turned to be the severest pollutant and As did not contribute too much to the contamination of the YRE. That was most probably because that Mn was closely related to the carbonateassociated (CARB) and As was related to organic-associated (OM) which is more stable than CARB. The fractions played an important role in the contamination assessment of heavy metals. © 2016 Elsevier Ltd. All rights reserved.

The estuary which is the confluence area of surface runoff and seawater is not only the main watercourse to transport the terrestrial matter into the sea but also the primary settling area (Ralston and Geyer 2009). However, anthropogenic activities have released large amounts of toxic substances into estuaries, causing many problems such as the increasing amount of endangered species and ecological deterioration (Curtosi et al. 2010; Mathivanan and Rajaram 2014; Chassaing et al. 2015; Islam et al. 2015). Sediments are important components of the estuarine ecosystems and they are the major sources and sinks of the toxic substances in water environment (Monikh et al. 2013). Therefore, it is crucial to assess the sediment contamination in estuarine areas. Among toxic substances, heavy metals are priority environmental pollutants in estuaries (Alves et al. 2014). Most of heavy metals in water are adsorbed and keep accumulating in the sediment (FisherPower et al. 2016). As long as there are some changes with environmental condition, heavy metals adsorbed in the sediment will dissolve in the water, causing secondary contamination (Aleksander-Kwaterczak and Helios-Rybicka 2009; Kim et al. 2010; Gillan et al. 2012). The heavy metals have also shown obvious cytotoxicity and lasting harmfulness, probably causing serious harm to organisms including human beings (Järup 2003; Oyewale and Musa 2006). Because of their bioaccumulation capacity and environmental persistence, special attention should be paid on sedimentary heavy metals (Venkatesha Raju et al. 2012; Sayadi et al. 2015). To assess the contamination degree of heavy metals, many methods have been used. Among those methods, the index of geo-accumulation ⁎ Corresponding author. E-mail address: [email protected] (R. Liu).

(Igeo) was widely used since it takes the effects of the human activities into consideration. However, it ignores the toxicity differences among different heavy metals (Ali et al. 2013; Yan et al., 2015). Additional methods should be incorporated to assess the contamination of heavy metals. The potential ecological risk index (RI) proposed by Hakanson comprehensively considered issues such as the toxicity of heavy metals and comprehensive effect of multiple contaminants (Zhang et al. 2012). It is widely used in quality evaluation of sediments (Li C. et al., 2015; Zhang et al. 2015; Chen et al. 2016). The content of heavy metal is a useful indicator of contamination assessment (Mânzatu et al. 2015; Janadeleh et al. 2016; Karbassi et al. 2016). However, it does not provide enough information about toxicity of heavy metals (Vaezi et al. 2015). The mobility of heavy metals, as well as their toxicities, greatly depends on their fractions (Lin et al. 2003; Singh and Kalamdhad 2013). Tessier divided element into five fractions (Tessier et al. 1979). Exchangeable, carbonate-associated fractions are more unstable and bioavailable than other fractions (Singh and Kalamdhad 2012; Singh et al. 2015). Therefore, evaluation of the fractions of elements qualitatively and quantitatively is an important basis in assessing the toxicity of the elements and research on their migration and transformation (Elkhatib and Moharem 2015). Many scholars have considered the difference of fractions in their researches using methods such as the N-way principal component analysis (Wang et al. 2013; Shin and Kim 2015). In this study, based on the heavy metal sample data in the sediments of Yangtze River estuary in May 2011, the main objectives of the present study were to (1) estimating heavy metal concentrations and to evaluate their contamination level in sediments; (2) describing the distribution pattern of heavy metals in sediments; (3) evaluating heavy metal

http://dx.doi.org/10.1016/j.marpolbul.2016.05.060 0025-326X/© 2016 Elsevier Ltd. All rights reserved.

Please cite this article as: Liu, R., et al., Spatial distribution and pollution evaluation of heavy metals in Yangtze estuary sediment, Marine Pollution Bulletin (2016), http://dx.doi.org/10.1016/j.marpolbul.2016.05.060

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R. Liu et al. / Marine Pollution Bulletin xxx (2016) xxx–xxx

Fig. 1. Locations of the Yangtze River Estuary and 30 sampling sites.

pollution status and its potential ecological risk; and (4) exploring relationship among distribution, species and fraction of heavy metals. The Yangtze River Estuary (YRE), the boundary between the Yellow Sea and the East China Sea, is divided into two main branches by Chongming Island (Fig. 1). YRE is the coastal outfall of Yangtze River which ranks third in length (6300 km), and it is on the most important strategic position of economic and social development in Yangtze River Basin (Chen et al. 2013; Adeleye et al. 2015). The land-based runoff and the highly polluted Huangpu River are two main sources of the contamination of the YRE (Guo et al. 2014). A considerable degree of contamination exists in the YRE and its adjacent sea (Zhang et al. 2009; Yin et al. 2015).

Table 1 The classification of Igeo and RI. Index

Category

Description

Geoaccumulation index (Igeo)

Igeo ≤ 0 0 b Igeo ≤ 1 1 b Igeo ≤ 2 2 b Igeo ≤ 3 3 b Igeo ≤ 4 4 b Igeo ≤ 5 5 b Igeo RI ≤ 150 150 b RI ≤

Practically uncontaminated Uncontaminated to moderately contaminated Moderately contaminated Moderately to heavily contaminated Heavily contaminated Heavily to extremely contaminated Extremely contaminated Low risk Moderate risk

300 300 b RI ≤

Considerable risk

600 600 b RI

High risk

Ecological risk (RI)

In May 2011, 30 typical sampling sites were established covering practically the whole Yangtze estuary. For each site, sample was obtained by mixing three subsamples collected at that site. Then the samples were immediately cryopreserved in bags made of PTFE. After the removal of litter, stone particles and organisms, the refrigerated samples were dried, ground and shaken through nylon membrane sieve (0.284 mm) to obtain a fine homogeneous powder and stored at 4 °C for further analysis. The Tessier sequential extraction procedure was used to analyze the contents of different fractions (exchangeable (EXC), carbonate-associated (CARB), Fe–Mn oxides-associated (Fe/ Mn), organic-associated (OM) and residual fractions (RES)) of metals (Shao et al. 2013; Rosado et al. 2016). And 12 metals were estimated: aluminum (Al), arsenic (As), cadmium (Cd), chromium (Cr), copper (Cu), ferrum (Fe), hydrargyrum (Hg), manganese (Mn), nickel (Ni), plumbum (Pb), stibonium (Sb) and zinc (Zn). The content of each metal is the sum of five corresponding fraction contents. Quality assurance and quality control were assessed using duplicates, method blanks, and standard reference materials. The accuracy of the determination method was systematically and routinely examined with standard reference materials. Three replicates were conducted to determine the total contents of the metals and errors were controlled within allowed scope. The geo-accumulation index (Igeo), ecological risk index (RI) and N-way principal component analysis were used to assess metal contamination. Because of the shortage of geochemical background values of Al, Fe, Hg and Sb, only eight metals: As, Cd, Cr, Cu, Mn, Ni, Pb and Zn were assessed through Igeo and RI. The classification of each index was listed in Table 1. Igeo is the most popular method used to evaluate the pollution of single element which considers the background value (Yao 2008; Wang

Table 2 Concentrations of heavy metals (μg/g). Element

Al

As

Cd

Cr

Cu

Fe

Hg

Mn

Ni

Pb

Sb

Zn

May 2011 December 2007 August 2010 February 2011 Background value

65557.22 69670.59 – – –

9.96 13.54 9.10 9.00 1.50

0.15 2.82 0.19 0.20 0.10

87.17 98.32 79.10 80.90 35.00

25.51 48.61 24.70 23.90 25.00

36254.97 43644.83 – – –

0.05 0.16 – – –

680.49 – 772.70 715.50 600.00

32.24 41.49 31.90 32.00 20.00

24.18 50.77 23.80 23.40 20.00

0.66 – – – –

84.91 129.73 82.90 78.10 71.00

Please cite this article as: Liu, R., et al., Spatial distribution and pollution evaluation of heavy metals in Yangtze estuary sediment, Marine Pollution Bulletin (2016), http://dx.doi.org/10.1016/j.marpolbul.2016.05.060

R. Liu et al. / Marine Pollution Bulletin xxx (2016) xxx–xxx

2013; Yan et al., 2015; Hussain et al. 2015). The formula of Igeo is defined as:

Igeo ¼ log2 ðC n =1:5Bn Þ

ð1Þ

3

Where Cn is the examined concentration of metal n in sediments, 1.5 acts as a constant factor to neutralize variations due to lithogenic actions (Ferati et al. 2015), Bn is the geochemical background value of metal n. Metal background values in YRE surface sediment act as reference value in this research (Table 2).

Fig. 2. The spatial distribution of the content of heavy metals.

Please cite this article as: Liu, R., et al., Spatial distribution and pollution evaluation of heavy metals in Yangtze estuary sediment, Marine Pollution Bulletin (2016), http://dx.doi.org/10.1016/j.marpolbul.2016.05.060

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Fig. 2 (continued).

RI was widely applied on aggregate elemental contamination assessment. It quantitatively expresses the potential ecological risk of a given contamination (Dehghan Madiseh et al. 2009). RI can be determined through the following formula:

C if ¼ C i0−1 =C in

Tir RI ¼

n X

Eir

ð2Þ

i¼1

Eir ¼ T ir  C if

ð3Þ

ð4Þ

where Eir is the potential ecological risk factor for a given substances, is the toxic response factor (Muller 1969), Cif is the contamination fac-

tor, Ci0 − 1 is the average content for a given substances, C n i is the geochemical background reference value in sediments. N-way principal component analysis could be used to assess environmental risks from the angle of the macroscopic and it considers the effects of fraction difference. Tucker 3 is an advanced N-way method

Fig. 3. Assessment results: (a) the levels of Igeo; (b) the potential ecological risk.

Please cite this article as: Liu, R., et al., Spatial distribution and pollution evaluation of heavy metals in Yangtze estuary sediment, Marine Pollution Bulletin (2016), http://dx.doi.org/10.1016/j.marpolbul.2016.05.060

R. Liu et al. / Marine Pollution Bulletin xxx (2016) xxx–xxx

in analysis three-way data (Ceulemans and Mechelen, 2003; Dong et al., 2010). This model can be written in the following way: P

Q

R

xijk ¼ ∑ ∑ ∑ aip bjq ckr g pqr þ eijk (5) p¼1 q¼1 r¼1

where xijk is the element in original three-dimensional matrix X, X(i × j × k) presents the sampling point i, j – th heavy metal and k – th heavy metal fraction. aip, bjq and ckr are element of matrix A(i × p), B(j × m) and C(k × n), respectively. gpqr is the element of kernel matrixG(p × q × r). Eijk is the element of error matrix E(i × j × k)of X. In this study, five fractions of twelve metals at 30 sampling sites were determined, structuring a three-dimensional array (30 × 12 × 5). Due to large differences in the units of the parameters, the data were scaled to unit standard deviation over the mode containing the parameters to assign the parameters the same importance during later analysis (Stanimirova et al. 2006). Then substitute the scaled three-dimensional array (30 × 12 × 5) into Tucker 3. ArcGIS10.0 was used to analyze the spatial variations of heavy metal contamination and all of the indices. In ArcGIS10.0, Kriging was used to predict the values of attributes at unsampled locations. Of the twelve metals, Al and Hg were the metals with the highest and lowest mean concentrations respectively (Table 2). Except Al, Fe, Hg and Sb for which there were no criteria, the mean concentrations of metals were all higher than their corresponding background values. The mean concentration of Cr was 6.638 times of its background, which was the largest among the twelve metals. It can be indicated that anthropogenic activities had a direct impact on the concentrations of metals in sediments. Compared with sediments collected in December 2007, the mean concentrations of most metals has obviously decreased and that of Cd has decreased by 94.57% (Song et al. 2011). However, compared with samples collected in August 2010 and February 2011, the concentrations of As, Cr, Cu, Ni, Pb and Zn in sediments collected in May 2011 increased by 4.73% and 6.39% at average, respectively (Wang et al. 2014; Wang H. et al., 2015). Those metals were closely related to the discharge of wastewater and the automobile exhaust (Wang J. et al., 2015). And the increasing human activity would cause the increasing of the amount of pollutant (Li et al. 2008). As to the spatial distribution of these metals, the concentration of Al, As, Cr, Cu, Fe, Mn, Ni and Pb increased as follows: inner-region b river

Fig. 4. The distribution percentage of the fractions of heavy metal.

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mouth b adjacent sea (Fig. 2). However, the concentration of Cd was contrary to that of those eight heavy metals, higher concentration was mainly distributed in the inner region. For Hg, Sb and Zn, although the concentration did not show an obvious trend, higher concentrations were basically near the land, especially in areas near the southern land of the south branch of YRE. The distributions of metals were influenced by the comprehensive action of various factors such as the sources of metals, water velocity, water salinity, pH, hydrodynamic condition (Li et al. 2013; Cao et al. 2015; Li R. et al., 2015). It was reported that over 30 km3 sewage including considerable industrial effluents was discharged into the YRE each year (Gu et al. 2013). Water velocity decreases from inner-region to adjacent sea, which is beneficial to the deposition of suspended particles carrying metals (Behrens et al. 2015). Suitable salinity and pH attribute to organic flocculation and coagulation of iron and manganese oxides, which is beneficial to the deposition of heavy metals (Nędzarek et al. 2015; Kumar et al. 2015; Wu G. et al., 2015). What is more, the circulating current pattern of the YRE is the comprehensive effects of the three parts: Taiwan warm current, coastal current and Yangtze diluted water (Dong et al. 2012; Cao et al. 2013; Annibaldi et al. 2015). Taiwan warm current controlled the hydrodynamic conditions in the adjacent area (Yanao and Matsuno 2013). In the near-shore area, the hydrodynamic conditions are controlled by the coastal current (Liu et al. 2011). Generally, most Igeo values of Cu, Mn, Pb and Zn were no N 0, suggesting no pollution was caused by them in the YRE (Fig. 3a). Almost all the values of Igeo for Cd and Ni were lower than 1, indicating that the YRE was uncontaminated or uncontaminated to moderately contaminated by them. Among the eight heavy metals, the Igeo of As valued the highest at all the sampling sites and those of Cr were the second highest. Although the concentrations of Cr were not high, the mean concentrations of Cr were considerably high compared with the background value. Among the 30 sampling sites, the Igeo at site-12 was the largest. Considering the total potential ecological risk, the potential ecological risk of metals in YRE sediments was shown to be in low risk or moderate risk (Fig. 3b). In YRE, the highest value of RI appeared at site-3, which was of moderate potential ecological risk. Cd was the foremost potential ecological risk contribution factor at site-3. The potential ecological risk of sampling points in south branch of YRE was higher than that of the north one. According to Igeo and RI, the contamination level of the river mouth of the south branch was the greatest all over the study area. The phenomenon was most probably because of pollutant exhausted by Shanghai and pollutant from the polluted Huangpu River. Shanghai is the most developed city in China and its resident population was up to 23.02 million in 2010 (Adeleye et al. 2015; Guo and Yang 2016). The south branch of YRE supplied 80% of daily water of Shanghai and this urban river suffered from chronic anthropogenic pollution (Guo et al. 2014). What is more, The South Branch is the dominant pathway of Yangtze River discharge and the highly polluted Huangpu River has carried strong anthropogenic signals into the south branch of YRE (Zhang 2007; Guo et al. 2014). In the evaluation of I geo and RI, As was the foremost pollutant among the eight metals. The mean concentration of As was 6.64 times as its background value, which leaded to the high value of Igeo. What is more, its toxic response factor is the second largest among the eight metals, which contributed to its high value of RI. The high concentration of As were probably derived from mining and usage of fertilizers in the YRE (Duan et al. 2013; Cao et al. 2015). The fraction distribution patterns of Al, As, Cr, Cu, Fe and Sb in sediments were similar (Fig. 4). For those metals, the concentration of RES was 83.99% of their total concentration at average, while the concentration of EXC and CARB only covered 4.00% at average, respectively. Especially for As, the RES covered 93.68% of its total concentration and the EXC and CARB covered 1.15%. It could be indicated that Al, As, Cr, Cu, Fe and Sb were stable and they were of minor hazard because that the RES is the most stable among the five fractions and EXC and CARB are

Please cite this article as: Liu, R., et al., Spatial distribution and pollution evaluation of heavy metals in Yangtze estuary sediment, Marine Pollution Bulletin (2016), http://dx.doi.org/10.1016/j.marpolbul.2016.05.060

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R. Liu et al. / Marine Pollution Bulletin xxx (2016) xxx–xxx

more bioavailable (Elkhatib and Moharem 2015; Singh et al. 2015). The proportions of Fe/Mn of Pb, Mn and Zn were around 20% of their concentrations. They are of high stability because the metals in Fe/Mn are bound by strong ionic bonds (Wu S. et al., 2015; Ma et al. 2016). The proportion of OM of Hg was 48.27% of its concentration, which was the highest among the eight metals. The proportions of CARB of Mn and Cd were higher than other metals: 36.91% and 26.43% of their concentrations, respectively. The proportion sum of EXC and CARB of Cd was 54.19%, which was the largest among the twelve elements, and that of Mn was the second largest. Those of other metals were all lower than 10% except Pb valued 10.78%. Therefore, Cd and Mn in sediments were of higher secondary release potential. The transfer ability of metals in sediments could be valued by transfer coefficient which is determined by the percentage of EXC (Maiz et al. 2000; Pueyo et al. 2004). The heavy metal transfer coefficient were Cd N Mn N Sb N Cu N Zn N Ni N As N Pb N Hg N Cr N Al N Fe. Tucker 3 model calculated the percentages of variances that explained by all modelings from [111] to [333] (Fig. 5a). The modeling with complexity [122] was selected for detailed interpretation. It explained 81.93% of the total data variance.

Kernel element g111 stands for the correlation among modeling A1 (sampling site), modeling B1 (heavy metal) and modeling C1 (fraction) (Table 3). It turned out that the RES was related to all the metals to some extent. Kernel element g122 stands for the correlation among A2 (sampling site), modeling B2 (heavy metal) and modeling C2 (fraction). It was indicated that the site-12 and site-23 were strongly related to Hg, Pb, Zn and their OM (Fig. 5b). And those two sites also related to Mn, Cd, Sb and their CARB. Since heavy metals in OM would not be decomposed unless they are in strong oxidation condition, Hg, Pb and Zn could hardly produce secondary pollution (Wu S. et al., 2015). Since metals in the CARB are unstable, the potential risk of Mn and Cd was high and the secondary pollution most probably happened at the site-12 and site-23. To compare the results of Tucker 3 with Igeo and RI, Mn turns to be the severest pollutant and As did not contribute too much to the contamination of the YRE. That is most probably because that it considers the fractions of metals in Tucker 3 compared with Igeo and RI. Mn was closely related to the CARB and As was related to OM which is more stable than CARB. Therefore, it could be indicated that not only the concentration but the fraction of the heavy metal could influence the

Fig. 5. Tucker 3 results: (a) Variances in arrays; (b) Loads.

Please cite this article as: Liu, R., et al., Spatial distribution and pollution evaluation of heavy metals in Yangtze estuary sediment, Marine Pollution Bulletin (2016), http://dx.doi.org/10.1016/j.marpolbul.2016.05.060

R. Liu et al. / Marine Pollution Bulletin xxx (2016) xxx–xxx Table 3 Kernel matrix generated by modeling [1 2 2]. g111

g121

g112

g122

−36.42

0

0

12.19

contamination level a lot. The phenomenon that fractions of metals are of great importance was also shown at many other areas (Gao et al. 2013; Huang et al. 2013; Kennou et al. 2015). It is necessary to consider the fraction of heavy metals in analyzing their bioavailability (Abuchacra et al. 2015; Cao et al. 2015). The result indicated that the mean concentrations of metals were all higher than their corresponding background values, except Al, Fe, Hg and Sb for which there were no criteria. Compared with samples collected in August 2010 and February 2011, the concentrations of As, Cr, Cu, Ni, Pb and Zn in sediments collected in May 2011 has increased, while the concentrations of Cd and Mn has increased. Spatially, the concentration of Al, As, Cr, Cu, Fe, Mn, Ni and Pb increased as follows: inner-region b river mouth b adjacent sea, while the distribution of other metals varied. The distribution of the metals were influenced by both anthropogenic activities and natural factors such as hydrodynamic condition, sediment properties, average particle size, organic matters, adsorption-desorption characteristics and flocculation of fine particles and hydrodynamic conditions. According to Igeo and RI, the YRE was not under severe contamination. However, As, Cr and Cd were the main pollutants among the eight heavy metals. They caused higher contamination, needing more attention. The greatest RI appeared at site-3, while the greatest Igeo were at site-12. Cd was the foremost potential ecological risk contribution factor at site-3 because of the high toxic response factor. What is more, the greatest contaminated area appeared at the river mouth of the south branch of YRE. In Tucker 3, Mn turned to be the severest pollutant and As did not contribute too much to the contamination of the YRE. That was partly because that Mn was closely related to the CARB and As was related to OM which is more stable than CARB. The analysis results indicated that the fractions of metals played an important role in the contamination level. Acknowledgments The research was funded by the National Natural Science Foundation of China(41571486), the Ministry of education and Social Science Fund (14YJAZH048), CRSRI Open Research Program (CKWV2014223/KY) and the National Basic Research Program of China (973 Project, 2010CB429003). The authors would like to thank the editors and the anonymous reviewers for their valuable comments and suggestions on this paper. References Abuchacra, P., Aguiar, V., Abuchacra, R.C., Neto, J.B., Oliveira, A.S., 2015. Assessment of bioavailability and potential toxicity of Cu, Zn and Pb, a case study in Jurujuba sound, Rio de Janeiro, Brazil. Mar. Pollut. Bull. 100, 414–425. Adeleye, A.O., Jin, H., Di, Y., Li, D., Chen, J., Ye, Y., 2015. Distribution and ecological risk of organic pollutants in the sediments and seafood of Yangtze estuary and Hangzhou Bay, East China Sea. Sci. Total Environ. 541, 1540–1548. Aleksander-Kwaterczak, U., Helios-Rybicka, E., 2009. Contaminated sediments as a potential source of Zn, Pb, and Cd for a river system in the historical metalliferous ore mining and smelting industry area of South Poland. J. Soils Sediments 9, 13–22. Ali, Z., Malik, R.N., Shinwari, Z.K., Qadir, A., 2013. Enrichment, risk assessment, and statistical apportionment of heavy metals in tannery-affected areas. Int. J. Environ. Sci. Technol. 12, 537–550. Alves, R.I.S., Sampaio, C.F., Nadal, M., Schuhmacher, M., Domingo, J.L., Segura-Muñoz, S.I., 2014. Metal concentrations in surface water and sediments from Pardo River, Brazil: human health risks. Environ. Res. 133, 149–155. Annibaldi, A., Illuminati, S., Truzzi, C., Libani, G., Scarponi, G., 2015. Pb, Cu and Cd distribution in five estuary systems of Marche, Central Italy. Mar. Pollut. Bull. 96, 441–449.

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Please cite this article as: Liu, R., et al., Spatial distribution and pollution evaluation of heavy metals in Yangtze estuary sediment, Marine Pollution Bulletin (2016), http://dx.doi.org/10.1016/j.marpolbul.2016.05.060