ORIGINAL ARTICLE

Geochemical characteristics of heavy metals in coastal sedimentsfrom the northern Beibu Gulf (SW China): the background levelsand recent contamination

Peng Xia • Xianwei Meng • Aiping Feng •

Ping Yin • Jun Zhang • Xiangqin Wang

Received: 22 June 2010 / Accepted: 31 August 2011 / Published online: 29 September 2011

� Springer-Verlag 2011

Abstract Four sediment cores and one hundred surface

sediments were collected from the intertidal zone of the

northern Beibu Gulf (SW China). In order to detect the

intensity of metal contamination recently, the background

levels were successfully established for Pb, Zn, Cd and Cr,

based on the linear regression of deeper sediments (pre-

industrial). Aluminum is a better geochemical normalizer

than Fe and it is commonly used to describe the natural

metal variability of the coastal sediments. The evident

enrichment of Zn and Cd is recorded in the surface sedi-

ments of the eastern side of the Guangxi coast and the

central part of the Qinzhou Bay, but it does not exceed the

effects range-low values, due to a low percentage of fine-

grained sediments in the region. Although the Pb and Cr

concentrations are mainly of natural origin, 3 and 6% sites

exceed the effects range-low values, respectively; indicat-

ing the potential for adverse ecological effects of metals on

the benthic communities.

Keywords Heavy metals � Coastal sediments �Background levels � Linear regression � Contamination �Beibu Gulf

Introduction

Heavy metals, originated from both natural and anthropo-

genic sources, are continuously introduced to the estuarine

and the coastal sediments, commonly acting as sinks of

river borne metals, which may cause significant and per-

manent disturbances in the coastal systems (Angelidis and

Aloupi 1997; Dassenakis et al. 1996; Jha et al. 2003).

Based on the metal concentrations alone, it is therefore

difficult to distinguish the natural from the anthropogenic

sources due to the differences of the grain-size distribution

and the mineralogical composition in these sediments

(Loring 1991; Roussiez et al. 2005). It is desirable, there-

fore, to establish both background metal concentrations and

their natural variability prior to assessing human impacts

(Veinott et al. 2001).

For these reasons, linear regression based on deep sed-

iments (e.g., pre-industrial) can provide a more meaningful

basis for the regional background levels (Aloupi and

Angelidis 2001a; Covelli and Fontolan 1997; Doherty et al.

2000; Loring 1991; Roussiez et al. 2005; Schropp et al.

1990; Summers et al. 1996; Veinott et al. 2001), assuming

that early diagenetic processes did not alter the vertical

distribution of the considered element (Blaser et al. 2000;

Szefer et al. 1995; Veinott et al. 2001). If surface sediments

have concentrations of metals that are statistically higher

than those of deeper sediments, the metal enrichment is

suggested (Alexander et al. 1993; Baptista Neto et al. 2000;

Summers et al. 1996).

The coastal zone of Guangxi province is not only an

important Mangrove/Wetland Reserve but also a shellfish

culture area. On its northern shores are located the cities of

Beihai, Qinzhou and Fangcheng, where the quantity of coal

and petroleum consumption increased rapidly from

4.2 9 106 to 2.8 9 107 ton and from 1.8 9 105 to

P. Xia (&) � X. Meng � A. Feng � J. Zhang � X. Wang

First Institute of Oceanography,

State Oceanic Administration, Qingdao 266061, China

e-mail: pengxia@fio.org.cn

X. Meng

e-mail: mxw@fio.org.cn

A. Feng

e-mail: fengap@fio.org.cn

P. Yin

Qingdao Institute of Marine Geology,

China Geological Survey, Qingdao 266071, China

123

Environ Earth Sci (2012) 66:1337–1344

DOI 10.1007/s12665-011-1343-y

1.4 9 106 ton (Chen et al. 1997) during 1990–2006,

respectively. Therefore, the metals that derived from the

fossil fuel combustion, the urban/industrial sewage and

runoff have markedly increased and are still continuing,

which may be causing significant and permanent distur-

bances in the coastal systems. However, except for

research about ecological, chemical, sedimentological

characteristics (Li 2004; Liang and Li 2002; Wen et al.

2002; Xia et al. 2008; Zheng et al. 1995), few other studies

on this field have been carried out, and even fewer on the

metal concentrations and their distribution in the surface

sediments along the coastal zone. The principal aim of this

paper is (1) to choose the most suitable normalizer for

establishing the regional background levels based on the

deeper sediments, (2) and to determine if there has been

any anthropogenic input of metals to the surface sediments.

Materials and methods

Sampling sites

The study area is located in the Guangxi coast, facing

Beibu Gulf and bordering Vietnam on the southwest. It has

a humid subtropical climate, with the average temperature

ranging between 17� and 22�. The coastline of Guangxi

runs for 1,628 km, along which the beach area is 1,005,

and 1,438 km2 in shallow waters within the depth of 5 m

(Song 2009). There are 56.54 km2 of mangrove habitat

(40% of China’s total) and large deposits of titanium,

quartz sands and xenotime. In the shallow sea, there are

more than 50 economic fish species and 10-odd commer-

cial shrimp species.

One hundred surface sediment samples (0–2 cm) and

four sediment cores (80–86 cm long) were collected during

2006–2007 from the intertidal zone of Guangxi province

(SW China; Fig. 1). To minimize any disturbance during

sampling, the sediment cores were carefully obtained using

10 cm diameter PVC tubes. The cores were extruded using

a manually driven piston, and were sliced using stainless

steel cutters at 2 cm intervals. After collection, the subs-

amples were sealed with polyethylene bags and kept in 4�C

until analysis.

Laboratory analysis

The grain-size distribution was measured by wet sieving

and the following fractions were determined: silt ? clay

(\63 lm), sand (63 lm [ x [ 2 mm) and gravel

([2 mm). The major and trace element contents were all

determined in a fraction \2 mm. The total organic carbon

(TOC) concentrations were obtained by digesting *0.5 g

of dried sediment with potassium dichromate in a sulfuric

medium and titration of the excess of dichromate with

ferrous sulfate (Nelson and Sommers 1996).

Samples for element analysis were wind-dried at room

temperature, and then finely powdered in an agate mortar.

Approximately 0.2 g of the dried ground sediment was

totally digested in a mixture of 5:4:1 HNO3 ? HCl ? HF

using a microwave oven (Loring and Rantala 1992). The

residue was solubilized by HNO3 and diluted to volume.

The major (Al, Fe) and trace elements (Pb, Zn, Cd and Cr)

were analyzed by ICP-OES and ICP-MS, respectively. The

detection limits (lg g-1) were found to be 1,000 for Al and

Fe; 5 for Cr; 2 for Pb and Zn; and 0.03 for Cd. The quality

assurance of the analytical results was controlled with the

use of the standard reference materials (GSD9 Stream

Sediment, GBW07313 Marine Sediment), and the recov-

eries obtained from the above reference materials are

shown in Table 1.

Statistical methods

Based on the assumption that the natural metal concen-

trations should depict a high degree of correlation with the

normalizer element, the regional background levels can be

estimated by a linear regression from the data of the deeper

sediments (Roussiez et al. 2005).

The values for each metal were tested to determine if

they were normally distributed using a Kolmogorov–

Smirnov test (Roussiez et al. 2005; Veinott et al. 2001). In

the paper, all metals met the normality test (p \ 0.05), and

no-transformation (log- or inverse-) was performed. A 95%

prediction interval has been drawn for each regression

using Systat Sigmaplot 10. Then, all samples that were

standing outside the upper limit were eliminated and con-

sidered as anomalous or enhanced (Doherty et al. 2000).

After each elimination step, the least-squares regression

was recalculated. This process was repeated until no

sample fell outside the upper limit, after that the solid line

represents the regional background levels with the slope-

intercept form. Finally, data from the recent sediments

were compared to the old sediments and examined for

evidence of metal enrichment by anthropogenic inputs.

Results and discussion

Regional background levels

The contents of Al, Fe, TOC and silt ? clay (S ? C) in the

cores are presented in Fig. 2a–d, and the fine-grained

fraction (S ? C) is more abundant in core Q32 and F14

(65.99 ± 22.85 and 59.92 ± 15.15%, respectively) than in

cores Q24 and O18 (45.97 ± 24.44 and 39.94 ± 13.09%,

respectively). They also have a great granulometric

1338 Environ Earth Sci (2012) 66:1337–1344

123

variability, varying from fine-grained material (1.67%

S ? C, 51 cm of core Q24) to sandy sediments (97.98%

S ? C, 75 cm of core F14). Similar patterns in the distri-

bution of Al, Fe, TOC and metals are also found in the

cores. Based on 210Pb dating using the CIC model, the

sedimentation rates of the cores ranged from 0.25 to

1.68 cm year-1 (as yet unpublished). Then, the deeper

sediments below 50 cm would be at least 30 years old and

probably much older (e.g., 1679–1807 AD in core Q24,

corresponding to the pre-industrial), when the anthropo-

genic activities in the Guangxi shore and its adjacent area

were very scarce (Song 2009).

The down-core variations of metals are showed in

Fig. 2e–h, and its concentrations along the cores are in the

range 3.97–108.12 lg g-1 for Pb, 8.93–88.83 lg g-1 for

Zn, 0.02–0.17 lg g-1 for Cd and 10.71–70.67 lg g-1 for

Cr. Maximal concentrations of Pb, Zn, Cd and Cr were

generally obtained in the surface layers (\10 cm) of cores

Q24 and F14, then, showing a decreasing trend with depth.

This general pattern is not observed for the bottom layers

(\40 cm) in core Q32, where metal concentrations are at

least two times higher than on the surface. The differences

in the metal concentrations, particularly in core Q32, are

greatly due to the lithological differences. Without some

normalization procedure, it could be interpreted as an

enrichment or contamination in the deeper sediments of

core Q32.

The raw data are not sufficient to quantify the intensity

of contamination in the sediment layers. Normalization to a

conservative element is a common procedure used for the

detection and the quantification of anomalous metal con-

centrations, based on the main assumption that the exis-

tence of a linear relationship between the normalizers (e.g.,

Al, Fe, Li, Cs, Sc and TOC) and the fine-grained fractions

in the ‘‘natural’’ sediments. Among these potential nor-

malizers, Al and Fe have been the most frequently used in

the estuarine and coastal studies (Roussiez et al. 2005;

Schiff and Weisberg 1999), while Li, Cs, Sc and TOC are

more unusual and were used to a lesser extent.

Table 2 presents the affinities of the potential normalizers

with the studied metals and the fine-grained fractions. A subset

of 61 deeper sediment samples was used, including the core

F14 (50–80 cm, N = 15), O18 (50–84 cm, N = 17), Q24

(60–82 cm, N = 11) and Q32 (50–86 cm, N = 18). A strong

positive correlation between elements and the S ? C was

found (0.76 \ r\0.82, P \ 0.001; Table 2), implying that

the major and trace elements are associated with the fine-

grained fractions in the study area. Furthermore, the strong

positive correlation between metals and Al, Fe (0.90 \ r \0.99, P \ 0.001) suggest either their common accumulation

in the fine-grained aluminosilicate materials, and/or as a

geochemical substrate for metals. Nevertheless, non-correla-

tion of TOC with the S ? C and all the metals (-0.15\ r\0.12, P [ 0.05) indicate that the distributions of TOC

(1.28 ± 0.23%) do not significantly affect the pattern of the

metal distributions in the sediments.

Compared to the other candidates, Al depicts the higher

correlation both with the S ? C (r = 0.86) and with the

metals (r [ 0.93) investigated. Moreover, Al is the major

constituent of the fine-grained aluminosilicates with which

the bulk of heavy metals is associated and insensitive to the

anthropogenic sources. These indicate that Al is the most

suitable element for the normalization procedure in the

study area. Also, Fe may be an alternative choice, since this

element has a similar behaviour as Al in our samples.

108.0° 108.2° 108.4° 108.6° 108.8° 109.0° 109.2° 109.4° 109.6°

21.2°

21.4°

21.6°

21.8°

22.0°

22.2°

Nanliu River

Dafeng Rive

r

Qin

jiang

Rive

r

Maoling River

Fangcheng River

Beihai

Qinzhou

Fangcheng

E

N

F14

O18

Q24

Q32

Beibu Gulf

Guangxi province

(b)

China

(c)

(a)Cores

Surface

intertidal zone

depth contours

10 m

20 m

Fig. 1 Location of a the samples collected from the intertidal zone of b Guangxi, c China

Table 1 Metal concentration recoveries (%) from reference materials

Element Al Fe Pb Zn Cd Cr

GSD9 102.6 99.9 97.9 99.0 101.9 99.3

GBW07313 97.0 98.2 99.3 95.2 –a 99.0

a Not certified

Environ Earth Sci (2012) 66:1337–1344 1339

123

The Metal/Al ratios show a net increase in the upper

sediments, indicating an evidence of human disturbance,

which can be detected up to the depth of 10 cm for Pb (core

F14) and to the depth of 30 cm for Zn, Cd and Cr (core

Q32). A similar but weaker anthropogenic impact is also

apparent in the core O18 and Q24, suggesting that the

excess metal concentrations in the upper layers should be

attributed to anthropogenic inputs. On the other hand, all

the metals (Fig. 2i–l) showed relatively stable Metal/Al

ratios below 50 cm depth, with the exception of core Q24

(a)

(e)

(b) (c) (d)

(f) (g) (h)

(i) (j) (k) (l)

Fig. 2 Vertical profiles of Al, Fe, TOC, silt ? clay, metals (Pb, Zn, Cd and Cr) and Metal/Al ratios in the sediment cores

Table 2 Pearson correlation matrix of potential normalizers and

heavy metals in the deeper sediments (N = 61)

Normalizer S ? C Pb Zn Cd Cr

Al 0.82** 0.99** 0.99** 0.93** 0.95**

Fe 0.76** 0.98** 0.98** 0.90** 0.92**

TOC 0.12* -0.06* -0.02* 0.08* -0.15*

S ? C 1.00 0.79** 0.81** 0.78** 0.76**

* Non-significant (P [ 0.05)

** Correlation is all significant at the 0.01 level (two-tailed)

1340 Environ Earth Sci (2012) 66:1337–1344

123

at the depth of 49–59 cm, which showed an evident dis-

turbance similar to that of the S ? C at the same depth. It

should be attributed to the deposition of coarse-grained

resuspended sediments, which diluted the S ? C content to

1.66–7.90%, related to the excessive quarrying of sand on

the Qinjiang riverbed (Lang et al. 2007).

With the exception of the disturbance (Fig. 2i–l), any

difference in metal concentrations of deeper sediments

between cores could be a result of natural variability (Loring

1991). It has been a successfully met the assumption,

because the deeper sediments (below 50 cm) were free of

any metal inputs derived from human activities in the region.

The Scatter plots of linear regression models between Al

and the metals are presented in Fig. 3. A few anomalous

points of Pb, Cr, Hg and As (5, 3, 8 and 4, respectively) fall

outside the 95% prediction intervals, but they are all near

the prediction lines. An examination of the raw data indi-

cated nothing unusual for these points other than natural

variability. For the samples from the model suite, the

coefficient of determination (R2) of metals with Al is

greater than 0.93 and is linear for all metals. The param-

eters and characteristics of the regional background levels

(solid lines, Fig. 3) are showed in Table 3.

Surface metal enrichment

A wide range of values for metal concentrations is

observed in the surface sediments of the Guangxi coast

(Table 4). In order to distinguish natural and anthropogenic

inputs, it was more useful to calculate the non-dimensional

enrichment factor (EF) by normalizing the metal concen-

trations to Al (Balachandran et al. 2005; Covelli and

Fontolan 1997).

For a given heavy metal, EF is calculated as follows

(Rule 1986):

EF ¼ Me/Alð Þsample= Me/Alð Þbackground;

where (Me/Al)Sample is the metal to Al ratio in the samples

of interest, (Me/Al)Background is the natural background

value of the metal to Al ratio. This general method has

been used in a number of research works (Audry et al.

2004; Grant and Middleton 1990; Ruiz-Fernandez et al.

2007; Rule 1986; Xia et al. 2011), although based on dif-

ferent normalizing elements and methods of background

determination. If an EF value is between 0.5 and 1.5 (i.e.,

0.5 B EF B 1.5), it is suggesting that the metal may be

entirely from crustal materials or natural weathering pro-

cesses (Zhang and Liu 2002). However, if an EF value is

greater than 1.5 (i.e., 1.5 \ EF), it is suggesting that a

significant portion of the metal is closely associated with

anthropogenic inputs.

The scatter plots of Pb, Zn, Cd and Cr to Al for the

surface sediments are presented in Fig. 4, and each dia-

gram shows the regression line (background levels,

Table 3) with the 95% prediction interval. According to the

EF values (Table 4), two main groups have been identified:

(1) Pb and Cr. Their data points are mostly located below

the dotted line of EF = 1.5 (Fig. 4a, d), indicating that they

(b)

(d)(c)

(a)Fig. 3 Determination of

regional background levels for

Pb, Zn, Cd and Cr based on their

correlation with Al (n = 61).

The dashed lines represent

prediction interval at the 95%

confidence level

Table 3 Characteristics of regression equations from the deeper

sediments (n = 61, Al %)

Metals

(mg/kg)

R2 N SE Regression equation

Pb 0.98 56 (5) 1.51 [Pb] = 3.55 [Al] ? 3.16

Zn 0.96 58 (3) 4.17 [Zn] = 6.78 [Al] ? 7.84

Cd 0.93 53 (8) 0.009 [Cd] = 0.0095 [Al] ? 0.026

Cr 0.98 57 (4) 1.97 [Cr] = 4.72 [Al] ? 12.83

(x) Number of anomalous samples removed from the regressions

Environ Earth Sci (2012) 66:1337–1344 1341

123

are mainly caused by the land-based natural inputs from the

erosion products. A few anomalous points of Pb (16) and

Cr (6) fall outside the 95% prediction intervals with the

maximum EF values of 4.66 and 3.40, respectively.

However, most of them are close to the dotted line,

showing that they are mainly of natural origin; (2) Zn and

Cd. Since 40 and 57 samples are enhanced (EF [ 1.5,

Fig. 4b, c) with maximum EF values of 7.45 for Zn and

21.08 for Cd, implying that both metals are mostly of

anthropogenic inputs.

The spatial distributions of the enhanced (EF [ 1.5)

metal concentrations are predominantly located on the

eastern side of Guangxi coast and in the central part of the

Qinzhou Bay (Fig. 5). It is not surprising that Zn and Cd

concentrations are markedly enhanced at these stations of

the eastern coast, especially near the mouth of Nanliu

River, due to some anthropogenic (domestic ? industrial)

inputs from the city of Beihai (1.5 million population) to

the intertidal zone by the river (Xia et al. 2008). Despite the

large number of enhanced stations existed, the enhanced

metal concentrations (Zn and Cd) do not exceed the effects

range-low (ERL) values at any station (Fig. 4b, c), indi-

cating that its potential risk of toxicity is still lower (Long

et al. 1995). The reason for this is that the metal-rich clay

minerals (Al, 2.05 ± 2.02%) will be carried away from the

shore into the deeper-shelf environments by strong tidal

currents and wave action (Doherty et al. 2000).

The three and six sites of Pb and Cr (respectively) is

slightly higher than ERL values (Fig. 4a, d), indicating

the potential for adverse ecological effects of metals on

the local benthic communities (Long et al. 1995). The

maximum Pb concentration (65.98 lg g-1) is found in

the central part of the Qinzhou Bay, only 4 km far from

the Qinzhou Port. The atmospheric transport of Pb from

the combustion of leaded gasoline (fishing boats and

cargo ships) is one of the main pollution pathways to the

adjacent surface sediments ( Aloupi and Angelidis

2001b). Furthermore, the abundance of fine-grained

fractions in the semi-closed harbor region (Fig. 5a) is apt

to absorb the metals in the water column (Loring 1991;

Summers et al. 1996). The enhanced Cr concentrations

(Fig. 5d) are mainly located in the mouths of Maoling

River and Dafeng River, indicating that river inputs are

an important local source of chromium pollution. This

may be derived from either mining or industrial activities

in the catchment areas.

Table 4 Metal concentrations (lg g-1) and enrichment factor (EF) in the surface sediments; ERL and ERM guideline values (lg g-1) and the

percent incidence of biological effects in the concentration ranges defined by the two values (Long et al. 1995)

Element Concentrations EF Guidelines Percent incidence of effects

Mean ± SD Range Mean ± SD Range ERL ERM \ERL ERL–ERM [ERM

Pb 18.34 ± 12.97 1.90–65.98 1.22 ± 0.50 0.53–4.66 46.7 218 97 3 0

Zn 52.63 ± 24.33 5.86–98.91 2.23 ± 1.71 0.68–7.45 150 410 100 0 0

Cd 0.14 ± 0.11 0.01–0.59 2.71 ± 2.89 0.41–21.08 1.2 9.6 100 0 0

Cr 33.23 ± 23.94 3.79–129.71 1.10 ± 0.53 0.28–3.40 81 370 94 6 0

(b)

(d)(c)

(a)Fig. 4 Metal Al scatter plot for

the surface sediments of

Guangxi coast. Solid linerepresents the regression line

based on deeper sediments

(background), dashed linesdefine the 95% prediction

interval, dotted line delineates

the boundary between the

natural and the enhanced

concentrations

1342 Environ Earth Sci (2012) 66:1337–1344

123

Conclusions

The paper provides key information to establish accurate

background levels for Pb, Zn, Cd and Cr in the coastal

sediments of the northern Beibu Gulf (SW China), based

on the linear regression of deeper sediments (pre-indus-

trial). It has successfully met the assumptions that the

deeper sediments were free of regional metal inputs

derived from human activities. Aluminum was better

suited than Fe for such normalization to accurately

describe the natural metal variability of the coastal sedi-

ments. Any difference in the metal concentrations of the

deeper sediments could be a result of natural variability

(similar metal/Al ratios), with the exception of core Q24

at the depth of 49–59 cm. It should be attributed to the

deposition of more coarse-grained sediments, related to

the excessive quarrying of sand on the Qinjiang riverbed

in the past.

In the surface sediments, Zn and Cd are mostly associ-

ated with the presence of anthropogenic inputs (EF [ 1.5),

but do not exceed the effects range-low values at any sta-

tions, due to a low percentage of fine-grained sediments in

the region. Although the Pb and Cr concentrations are

mainly derived from natural inputs (EF \ 1.5), 3 and 6%

of the sites (respectively) exceed effects range-low values,

indicating the potential for adverse ecological effects of

metals on the benthic communities. The enhanced stations

are mainly located on the eastern side of Guangxi coast and

in the central part of the Qinzhou Bay, which should be

attributed to differential discharge of untreated effluents

originating from the industrial and urban sources as well as

from combustion of leaded gasoline along with the fishing

and cargo activities.

Acknowledgments The authors thank Drs L. J. Liu, M. Z. Fu (First

Institute of Oceanography, SOA) and X. M. Dong for their great help

in the core sampling and laboratory analysis, and Ms. Lenka Sche-

uterova (Czech Republic) for her help in improving the English of the

manuscript. Thanks to two anonymous reviewers for their detailed

and constructive comments. This work was supported by the National

Basic Research Program of China (973 Program) under grant

No.2010CB951203; National 908 Program under grant No.GX908

(supplement).

References

Alexander CR, Smith RG, Calder FD, Schropp SJ, Windom HL

(1993) The historical record of metal enrichment in two Florida

estuaries. Estuaries 16:627–637

108.0° 108.2° 108.4° 108.6° 108.8° 109.0° 109.2° 109.4° 109.6°

21.2°

21.4°

21.6°

21.8°

22.0°

22.2°

NanliuRiver

Dafeng River

Qin

jiang

River

Maoling River

Fangcheng River

Beihai

Qinzhou

Fangcheng

E

N

Beibu Gulf

10 m

20 m

108.0° 108.2° 108.4° 108.6° 108.8° 109.0° 109.2° 109.4° 109.6°

21.2°

21.4°

21.6°

21.8°

22.0°

22.2°

NanliuRiver

Dafeng River

Qin

jiang

River

Maoling River

Fangcheng River

Beihai

Qinzhou

Fangcheng

E

N

Beibu Gulf

10 m

20 m

108.0° 108.2° 108.4° 108.6° 108.8° 109.0° 109.2° 109.4° 109.6°

21.2°

21.4°

21.6°

21.8°

22.0°

22.2°

NanliuRiver

Dafeng River

Qin

jiang

River

Maoling River

Fangcheng River

Beihai

Qinzhou

Fangcheng

E

N

Beibu Gulf

10 m

20 m

108.0° 108.2° 108.4° 108.6° 108.8° 109.0° 109.2° 109.4° 109.6°

21.2°

21.4°

21.6°

21.8°

22.0°

22.2°

NanliuRiver

Dafeng River

Qin

jiang

River

Maoling River

Fangcheng River

Beihai

Qinzhou

Fangcheng

E

N

Beibu Gulf

10 m

20 m

(b)

(c) (d)

QinzhouBay

QinzhouBay

QinzhouBay

QinzhouBay

(a)

(0, 1.5)EFSymbol

[1.5, 5)[5, 20)[20, 40)

EFSymbol

Fig. 5 Spatial distributions of enhanced metal concentrations in the surface sediments: a Pb; b Zn; c Cd and d Cr

Environ Earth Sci (2012) 66:1337–1344 1343

123

Aloupi M, Angelidis MO (2001a) Geochemistry of natural and

anthropogenic metals in the coastal sediments of the island of

Lesvos, Aegean Sea. Environ Pollut 113:211–219

Aloupi M, Angelidis MO (2001b) Normalization to lithium for the

assessment of metal contamination in coastal sediment cores

from the Aegean Sea, Greece. Mar Environ Res 52:1–12

Angelidis MO, Aloupi M (1997) Assessment of metal contamination

in shallow coastal sediments around Mytilene Greece. Int J

Environ Anal Chem 68(2):281–293

Audry S, Schafer J, Blanc G, Jouanneau JM (2004) Fifty-year

sedimentary record of heavy metal pollution (Cd, Zn, Cu, Pb) in

the Lot River reservoirs (France). Environ Pollut 132:413–426

Balachandran KK, Lalu Raj CM, Nair M, Joseph T, Sheeba P, Venugopal

P (2005) Heavy metal accumulation in a flow restricted, tropical

estuary. Estuar Coast Shelf Sci 65(1–2):361–370

Baptista Neto JA, Smith BJ, McAllister JJ (2000) Heavy metal

concentrations in surface sediments in a nearshore environment,

Jurujuba Sound, Southeast Brazil. Environ Pollut 109:1–9

Blaser P, Zimmermann S, Luster J, Shotyk W (2000) Critical

examination of trace element enrichments and depletions in

soils: As, Cr, Cu, Ni, Pb, and Zn in Swiss forest soils. Sci Total

Environ 249:257–280

Chen RQ, Cao CC, Ruan GH (1997) Invariable element geochemistry

of coals in Guangxi. Coal Geol China 9(2):40–44 (in Chinese

with English abstract)

Covelli S, Fontolan G (1997) Application of a normalization

procedure in determining regional geochemical baselines. Envi-

ron Geol 30(1/2):34–45

Dassenakis MI, Kloukiniotou MA, Pavlidou AS (1996) The influence

of long existing pollution on trace metal levels in a small tidal

Mediterranean Bay. Mar Pollut Bull 32(3):275–282

Doherty GB, Brunskill GJ, Riddm J (2000) Natural and enhanced

concentrations of trace metals in sediments of Cleveland Bay, Great

Barrier Reef Lagoon, Australia. Mar Pollut Bull 41:337–344

Grant A, Middleton R (1990) An assessment of metal contamination

of sediments in the Humber Estuary, UK. Estuar Coast Shelf Sci

31:71–85

Jha SK, Chavan SB, Pandit GG, Sadasivan S (2003) Geochronology

of Pb and Hg pollution in a coastal marine environment using

global fallout 137Cs. J Environ Radioact 69:145–157

Lang YS, Li JW, Deng XD, Zhang W, Yan DR, Chen L (2007)

Mineralogy and geochemistry of supergene manganese ore

deposits in Qinzhou-Fangcheng area, southern Guangxi, with

implications for ore genesis. Miner Depos 26(5):527–540 (in

Chinese with English abstract)

Li C (2004) Quantitative distribution of mangroves in Guangxi

Zhuang Autonomous Region. J Beijing For Univ 26:47–52 (in

Chinese with English abstract)

Liang W, Li G (2002) Discussion on the modern sedimentary of

mangrove coasts in Guangxi. J Guangxi Acad Sci 18(3):131–134

(in Chinese with English abstract)

Long ER, Macdonald DD, Smith SL, Calder FD (1995) Incidence of

adverse biological effects within ranges of chemical concentra-

tions in marine and estuarine sediments. Environ Manag

19:81–97

Loring DH (1991) Normalization of heavy-metal data from estuarine

and coastal sediments. ICES J Mar Sci 48:101–115

Loring DH, Rantala RTT (1992) Geochemical analyses of marine

sediments and suspended particulate matter. Technical Report,

Fisheries and Marine Service 700, p 58

Nelson DW, Sommers LE (1996) Total carbon, organic carbon, and

organic matter. In: Sparks DL et al (eds) Methods of soil

analysis. Part 3-Chemical methods. Soil Sci Soc Am, Madison,

pp 961–1010

Roussiez V, Ludwig W, Probst JL, Monaco A (2005) Background

levels of heavy metals in surficial sediments of the Gulf of Lions

(NW Mediterranean): an approach based on 133Cs normalization

and lead isotope measurements. Environ Pollut 138:167–177

Ruiz-Fernandez AC, Hillaire-Marcel C, Paez-Osuna F (2007) 210Pb

chronology and trace metal geochemistry at Los Tuxtlas,

Mexico, as evidenced by a sedimentary record from the Lago

Verde Crater Lake. Quart Res 67:181–192

Rule JH (1986) Assessment of trace element geochemistry of

Hampton Roads Harbor and Lower Chesapeake Bay area

sediments. Environ Geol Water Sci 8:209–219

Schiff KC, Weisberg SB (1999) Iron as a reference element for

determining trace metal enrichment in Southern California

coastal shelf sediments. Mar Environ Res 48:161–176

Schropp SJ, Lewis FG, Windom HL, Ryand JD, Calder FD, Burney

LC (1990) Interpretation of metal concentrations in estuarine

sediments of Florida using aluminium as a reference element.

Estuaries 13:227–235

Song JF (2009) Estimate the rationality of spatial structure of Beibu

Gulf (Guangxi) economic zone and analyze the agglomeration

trend. Int J Bus Manag 4(1):132–137 (in Chinese with English

abstract)

Summers JK, Wade TL, Engle VD, Malaeb ZA (1996) Normalization

of metal concent rations in estuarine sediments from the Gulf of

Mexico. Estuaries 19(3):581–594

Szefer P, Kusak A, Szefer K, Jankowska H, Wolowicz M, Ali AA

(1995) Distribution of selected metals in sediments cores of Puck

Bay, Baltic Sea. Mar Pollut Bull 40:615–618

Veinott G, Perron-cashman S, Anderson MR (2001) Baseline metal

concentrations in coastal Labrador sediments. Mar Pollut Bull

42(3):182–192

Wen Y, Liu S, Yuan C (2002) The population distribution of

mangrove at Yingluogang of Guangxi, China. Acta Ecol Sin

22(7):1160–1165 (in Chinese with English abstract)

Xia P, Meng XW, Yin P, Liu LJ (2008) Heavy metal pollution and its

potential ecological risk in the sediments from the Beihai

intertidal zone of Guangxi province. Adv Mar Sci 26(4):471–477

(in Chinese with English abstract)

Xia P, Meng XW, Yin P, Cao ZM, Wang XQ (2011) Eighty-year

sedimentary record of heavy metal inputs in the intertidal

sediments from the Nanliu River estuary, Beibu Gulf of South

China Sea. Environ Pollut 159(1):92–99

Zhang J, Liu CL (2002) Riverine composition and estuarine

geochemistry of particulate metals in China-weathering features,

anthropogenic impact and chemical fluxes. Estuar Coast Shelf

Sci 54:1051–1070

Zheng W, Lin P, Xue X, Lu C, Zheng F, Yin Y (1995) Dynamics of

carbon, hydrogen and nitrogen elements in Rhizophora stylosamangrove community at Yinluo Bay of Guangxi, China. Chin J

Appl Ecol 6(1):17–22

1344 Environ Earth Sci (2012) 66:1337–1344

123