Heavy Metal Pollution Assessment in the Sediment of Rao River,

China using the Geo-accumulation Vector

Bao Qian

1,*

, Zhe Wang

and Feng Yan

Bureau of Hydrology, Changjiang Water Resources Commission, CWRC, Wuhan 430010, China

Hydrology Bureau of Haihe River Water Conservancy Commission, the Ministry of Water Resources,

Tianjin 300170, China

School of Civil Engineering and Architecture, Nanchang University, Nanchang 330031, China

Keywords:

 Heavy metal, Geo-accumulation vector, Sediment, Rao River

Abstract: The heavy metal plays an significant role in the sediment pollution of the river. However, for the

heterogeneity of mineral composition, the background values of elements in sediment often contains

uncertainties, which is hard to be treated by the conventional geo-accumulation index. In the present work,

the geo-accumulation vector is introduced to deal with the uncertainty of background value and evaluate the

pollution of heavy metal in the sediment of Rao River, China. The results show that: the order of pollution

degree is: source < upper reaches < estuary < lower reaches < middle reaches. Dexing City, Poyang City

and Jingdezhen City are the most polluted area along Rao River, which respectively belong to “moderately

to heavily contaminated”, “moderately to heavily contaminated”, and “moderately contaminated” grades,

and respectively have risk probabilities of 28%, 8% and 40% to deteriorate. The mean values of the

elements in global shale should not be used as the background values of Rao River. Otherwise, the

evaluation results of Cu and Cd may be overoptimistic. Compared with the conventional method, the

geo-accumulation vector has apparent advantages in dealing with the uncertainty of background values and

the recognizing the cross-grade risk.

1 INTRODUCTION

Heavy metal is among the most common river

pollutants that are teratogenic and hard to degrade

(Xu et al., 2018; Peng et al., 2014). Heavy metal

adsorbs onto sediment particles, and its density is

greater than that of liquid (Peng et al., 2014; Yan et

al., 2018). Thus, the heavy metal load in water

environment is easily to be deposited into sediment

(Peng et al., 2014; Yan et al., 2018). When the

physicochemical environment in water–soil interface

is changed, heavy metal could be released into water

environment and result in secondary pollution (Yan

et al., 2018; Yan et al., 2019a; Yuan et al., 2015). In

addition, heavy metal can also be absorbed by

submerged macrophyte and benthos and then injure

human health by enrichment in food chain (Yan et al.,

2018; Yan et al., 2019b; Yuan et al., 2015). To sum

up, the heavy metal pollution assessment in the

sediment is among the constant research endeavors in

river water environment protection.

To assess the pollution of heavy metal in the

sediment, Muller (1969) put forward

geo-accumulation index model, which determined the

pollution degree of heavy metal quantization by

synthesizing measured and background value

information (Shi et al., 2009). Geo-accumulation

index is widely used globally to evaluate heavy metal

sediment. For example, Pathak et al. (2013) used the

geo-accumulation index to study the metal content of

surface sediment of an industrial area adjoining

Delhi, India. Zhang et al. applied geo-accumulation

index to qualify the heavy metal pollution in ediments

of Yangtze River (Zhang et al., 2009). Additionally,

Men et al. (2018) used geo-accumulation index to

assess the pollution of heavy metal in Beijing, China.

The conventional geo-accumulation index

considers the background value of the heavy metal to

be definite and unique, and often used the mean value

of the element in global shale as the background

information (Matschullat et al., 2000; Snežana et al.,

2017). However, recent study suggests that this

hypothesis does not seem to be reasonable.

140

Qian, B., Wang, Z. and Yan, F.

Heavy Metal Pollution Assessment in the Sediment of Rao River, China using the Geo-accumulation Vector.

In Proceedings of the 7th International Conference on Water Resource and Environment (WRE 2021), pages 140-150

ISBN: 978-989-758-560-9; ISSN: 1755-1315

Matschullat showed that the inhomogeneity of

sediment mineral distribution results in great

deference between local area heavy metal

background and whole global element averages

(Matschullat et al., 2000). Furthermore, Snežana

found that background investigation, such as core

acquisition or statistical distribution selection, is

always random (Snežana et al., 2017). This behavior

leads to a degree of uncertainty in the heavy metals

background value, which may significantly affect the

application of geo-accumulation index.

Based on statistics principle, Yan et al. improved

proposed geo-accumulation vector model to solve the

background value uncertainty (Yan et al., 2019a).

Heavy metal background is no longer treated as a

fixed value but as a random variable in

geo-accumulation vector (Yan et al., 2019c).

Accordingly, evaluation result is not a unique value,

but the probability of pollution status belongs to each

level. Geo-accumulation vector was preliminary

applied on heavy metal sediment evaluation in West

Dongting, which had apparent advantages in rank

evaluation and risk factor identification.

The Rao River flows through the Dexing Copper

Mine, which is the biggest copper mine in Asia (Ma

et al., 2015). Rao River is one of the most

heavy-metal polluted rivers in China (Zhang et al.,

1995). However, the background value uncertainty

greatly affected the results of heavy metal pollution

evaluation in Rao River. The objectives of this study

are: (i) assessing the pollution status of heavy metal

in the sediment of Rao River based on the

geo-accumulation vector model; (ii) identifying the

risk factor and pollution source of each segment of

Rao River; and (iii) further discussing the differences

between geo-accumulation index and

geo-accumulation vector in environmental

assessment.

2 MATERIALS AND METHODS

2.1 Study Area and Methods of

Chemical Analysis

As shown in Figure 1, Rao River is located in central

China. With an annual runoff of 10.7 billion m

, Rao

River covers a drainage area of 14,367 kilometers

(Ma et al., 2015; Zhang, 1995). Rao River has two

sources, the northern and southern of which are

Chang River and Le’an River, respectively. Rao

River flows into Poyang Lake, the largest freshwater

lake of China.

Figure 1: Location of the study area (Filled reverse triangles represent the sampling point).

The land types and economic structure of the cities

along the Rao River are quite different. In the upper

reaches of Rao River, which majorly contains Qimen

City and Wuyuan City, the mountainous proportion

exceeds 85%. As a result, the prime economic

structure is tourism. The middle reaches of Rao River

lies in the mountain-to-plain transitional zone, which

contains abundant mineral resources. The prime

economic types are therefore industry and mining.

For example, Jingdezhen City is famous for its

ceramic industry in the world; and Dexing City has

the largest opencast copper mine in Asia. The lower

reaches Rao River lies in the Poyang City, the land

type of which is plain, and the prime economic type is

agriculture.

According to Yan et al., the main pollutants in the

Rao river basin are copper (Cu), lead (Pb), and

cadmium (Cd) (Yan et al., 2018). Therefore, these

three indexes are selected for evaluation in this study.

To accurately reflect the heavy metal pollution of the

Heavy Metal Pollution Assessment in the Sediment of Rao River, China using the Geo-accumulation Vector

141

river, eight sampling sites were set up in Rao River,

as illustrated in Figure 1. Because of the

inhomogeneous geological condition of the sediment

in Rao River, the geochemical backgrounds of the

heavy metals are uncertain intervals instead of

concrete values. According to Zhang., the

geochemical background of Cu, Pb, and Cd are 14.16

mg/kg-41.97 mg/kg, 13.36 mg/kg-29.38mg/kg, and

0.065 mg/kg-0.257 mg/kg, respectively (Zhang et al.,

1995).

The sampling, pretreating, digesting, and

measuring methods refer to the Chinese Soil

Environmental Quality Risk Control Standard for

Soil Contamination of Agricultural Land (Ministry of

Ecological and Environment of the People’s Republic

of China, 2018). On 14 December 2019, three parallel

samples were collected from each site, which were

conserved in clean polyethylene bags and sent to the

Bureau of Hydrology, Changjiang Water Resources

Commission for further analysis. The sediment was

first screened through a 1 mm sieve and then

naturally air dried. The samples were ground in an

agate mortar (SP-40, Shanghai Shupei Corporation,

China) and then homogenized and sieved through a

100 μm mesh. After that, 0.5 g samples were digested

in a microwave oven (CEM MARS, PyNN

Corporation, USA) with an acid mixture (9 mL of

14.0 M HNO

, 3 mL of 11.7 M HCl, 2 mL of

23.0 M HF, and 2.5 mL of 8.8 H

). The samples

were then condensed to 1–2 mL for total metal

analysis.

There were two experimental instrument to make

environmental monitoring: the graphite furnace

atomic absorption spectrophotometry (ICE3500,

Thermofisher Corporation, USA), and the flame

atomic absorption spectrophotometry (SK-2003,

Persee Corporation, China). Compared with the flame

atomic absorption spectrophotometry, the graphite

furnace atomic absorption spectrophotometry had a

higher sensitivity, but a lower repeatability. As a

result, the graphite furnace atomic absorption

spectrophotometry was used to use to measure Cd,

the concentration of which was relatively lower;

while the flame atomic absorption spectrophotometry

was used to use to measure Cu and Pb, the

concentration of which were relatively higher.

The GSS-7 reference material from the Chinese

Environmental Monitoring Center was used to ensure

quality, where “GSS-7” was the number of the red

soil area in South China. The parallel errors were

controlled within 10%, and their average value of

three parallel samples was selected as the

concentration data to be evaluated.

2.2 Geo-accumulation Index

If M heavy metals are provided to participate in the

evaluation, the background and measured values of

the mth are b

and c

, respectively. Then the

geo-accumulation index of the mth heavy metals is

calculated as follows:

(1)

According to the value of I

, the pollution status of

heavy metal m can be classified into the following

categories: uncontaminated (I

≤0), uncontaminated

to moderately contaminated (0<I

≤1), moderately

contaminated (1<I

≤2), moderately to heavily

contaminated (2<I

≤3), heavily contaminated

(3<I

≤4), heavily to extremely contaminated

(4<I

≤5) and extremely contaminated (I

>5) (Ke et

al., 2017; Maanan et al., 2017).

In existing literature, the following methods are

used to select background values: (i) using the mean

value of the element in global shale as the background

information and (ii) using geochemistry investigation

of the deep core in the evaluation area as the

background information (Matschullat et al., 2000;

Snežana et al., 2017).

Although the background values determined by

method (i) are unique, certain differences in

background values exist between the global scale and

the evaluation area locally because of the

inhomogeneity of the continental geological structure

with the mineral composition (Matschullat et al.,

2000; Snežana et al., 2017). The results obtained

from the method (ii) can approximately reflect the

original status of heavy metals in the region.

However, because of the randomness of core

sampling and the selection of statistical distribution,

the background values of heavy metals in sediment

are generally not exact values but uncertain interval

∈[l

, s

] (Snežana et al., 2017). In addition, the

traditional geo-accumulation index experiences

difficulty dealing with the problem of heavy metal

pollution evaluation due to the uncertainty of

background values.

2.3 Geo-accumulation Vector

In contrast to the traditional geo-accumulation index

model, the ground accumulation vector model uses

vector P

={p

,…,p

} to reflect the pollution

condition, where p

is the probability of the

log

1.5





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142

pollution of the mth heavy metal belongs to grade j

(Yan et al., 2019b).

The universal calculation method can be derived

as follows (Yan et al., 2019c):

For grade 1:

(2)

for j =2,3... 6:

(2log 1) = ()

1.5 3 2 3 2

mmm

jm mmm

ccc

ppj j p b fbdb













  









(3)

and for grade 7:

(4)

In Eq. (2) to Eq. (4), f(b

) is the probability

density function of b

, which can be generally

calculated according to the statistical characteristics

of the measured values of the core elements (Yan et

al., 2019b). When investigation information is not

enough to determine the approximate distribution of

,Yan et al. proved that the uniform distribution

U(l

, s

) is the most likely distribution of f(b

) at this

time based on the maximum entropy principle (Yan

et al., 2019b).

In this case, the calculation method of p

is as

follows:

For grade 1:

(5)

for j =2,3... 6:





2/3

2/3 2/3

0Or















 























 





























(6)

and grade 7:

(7)

Using first-order moment principle for grade

recognition, the pollution feature value of

defined as follows:

(1.5)

mjm

Epj







(8)

When Eq. (9) is established,

belongs to grade

kEk





. (9)

The risk degree r

is defined as follows:

mjm







. (10)

Apparently,

quantifies the probability that P

belongs to the grates worse than grade

k considering

the uncertainties in background values.

As mentioned previously, the geo-accumulation

vector

={p

,…,p

} reflects the probability

that the pollution status of the

mth heavy metal

belongs to each grade (Yan et al., 2019b). To

quantify the comprehensive contamination status of

heavy metals in the study area, Yan et al. further

constructed a comprehensive geo-accumulation

vector

Q={q

,…,q

}, where q

reflects the

probability that the comprehensive contamination of

heavy metals in sediments belongs to grade

j (Yan et

al., 2019a). The formula is calculated as follows:

1.5

(log 0) ( ) ( )

1.5 1.5

mmmm

ppbfbdb











/48

(log 5) ( 2 ) ( ) ( )

1.5 1.5 48

mm m

mmmm

cc c

pp p pb fbdb











1.5

2/3

1.5

mmm

























/48

mm m

mmm

cl c

pls



























Heavy Metal Pollution Assessment in the Sediment of Rao River, China using the Geo-accumulation Vector

143



1, 2,....,7

jjjm

qwpj



 



, (11)

where w

is the weight of the mth heavy metal.

The grade recognition method of Q is similar to

that of P

. The coefficient p

in Eq. (8) is just

replaced with q

Geo-accumulation vector is not a denial to

geo-accumulation index. It expands and deepens the

traditional geo-accumulation index to uncertainty

analysis essentially. Furthermore, to make the

discussion more intuitive, p

, r

, and q

can also be

represented in forms of percentages (Yan et al.,

2019b).

3 RESULT AND DISCUSSION

3.1 Concentrations of Pollutants

The concentrations of the pollutants in the sediment

are illustrated in Figure 2.

Figure 2: Concentrations of the heavy metals in the sediment along Rao River.

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144

As is shown in Figure 2, the general trends of the

contents of Cu and Zn are as follows: source < upper

reaches< estuary< lower reaches< middle reaches.

Contrary, the general trend of the content of Cd

increases as following: source <upper reaches

<middle reaches<estuary<lower reaches. The reason

for this phenomenon is the difference in the

distribution of pollution sources. According to the

research of Yan et al, the pollution loads of Cu and Zn

majorly come from the industrial activity and mining

in Dexing City and Jingdezhen City, which lie along

the middle reaches of Rao River; while the pollution

loads of Cd majorly comes from the leaching from the

red soil of the farmland in the lower reaches of Rao

River

(Yan et al., 2018).

The mean contents of Cu, Pb and Cd in source

region are 15.81mg/kg, 16.70mg/kg and 0.077mg/kg,

respectively. Such behavior is close to the lower limit

of background values of heavy metals in the

sediments of Rao River’s water system.

The mean contents of Cu, Pb and Cd in upper

reaches region are 20.17 mg/kg, 18.90 mg/kg and

0.109 mg/kg, respectively. Among which, the heavy

metals contents in the sediments of Qimen City is

about 5% higher than that in Wuyuan City. In

comparison with source region, the heavy metals

contents of the sediments upper reaches increasingly

appear in different degrees. The increase range of Pb

and Cu is approximately 20%, whereas that of Cd is

over 40%.

The difference of heavy metals contents in middle

reaches is so large that the contents of Cu in Dexing

City reach up to 414.74 mg/kg, which is around 3.5

times that of Jingdezhen City. The Pb content in

Jingdezhen City is 250.67 mg/kg, which is 1.6 times

that of Dexing City. Generally, the contents of Cu, Pb

and Cd in the middle reaches are about 13, 10 and 7

times of that in the upper reaches and are far beyond

the upper limit of background value in Rao River.

The contents of Cu, Pb and Cd in the sediments of

lower reaches are 168.16 mg/kg,122.4 2mg/kg, and

1.388 mg/kg, respectively. In contrast to the middle

reaches, the contents of Cu and Pb in the lower

reaches decline by about 40%, whereas the content of

Cd increases by about 60%. In addition, the contents

of Cu, Pb and Cd in estuary region are 134.26 mg/kg,

98.52 mg/kg and 1.204 mg/kg, respectively. In

comparison with lower reaches, the contents of heavy

metals in sediments of estuary region decreases by

about 20%.

3.2 The Geo-accumulation Vectors of

Pollutants

According to Section 2.3, the geo-accumulation

vectors of pollutants are calculated and summarized

in Table 1.

Table 1: Summary of calculated geo-accumulation vectors of pollutants.

Sampling

Sites

Geo-accumulation Vector

Feature

value

Grade

Risk

degree

North Source {1.00,0.00,0.00,0.00,0.00,0.00,0.00} -0.50 uncontaminated 0.00

South Source {1.00,0.00,0.00,0.00,0.00,0.00,0.00} -0.50 uncontaminated 0.00

Qimen {1.00,0.00,0.00,0.00,0.00,0.00,0.00} -0.50 uncontaminated 0.00

Wuyuan {1.00,0.00,0.00,0.00,0.00,0.00,0.00} -0.50 uncontaminated 0.00

Jingdezhen {0.00,0.37,0.57,0.06,0.00,0.00,0.00} 1.19 moderately contaminated 0.06

Dexing {0.00,0.00,0.00,0.27,0.62,0.11,0.00} 3.34 heavily contaminated 0.11

Poyang {0.00,0.00,0.51,0.49,0.00,0.00,0.00} 1.99 moderately contaminated 0.49

Estuary {0.00,0.00,0.70,0.30,0.00,0.00,0.00} 1.80 moderately contaminated 0.30

North Source {1.00,0.00,0.00,0.00,0.00,0.00,0.00} -0.50 uncontaminated 0.00

South Source {1.00,0.00,0.00,0.00,0.00,0.00,0.00} -0.50 uncontaminated 0.00

Qimen {1.00,0.00,0.00,0.00,0.00,0.00,0.00} -0.50 uncontaminated 0.00

Wuyuan {1.00,0.00,0.00,0.00,0.00,0.00,0.00} -0.50 uncontaminated 0.00

Heavy Metal Pollution Assessment in the Sediment of Rao River, China using the Geo-accumulation Vector

145

Sampling

Sites

Geo-accumulation Vector

Feature

value

Grade

Risk

degree

Jingdezhen {0.00,0.00,0.00,0.53,0.47,0.00,0.00} 2.97 moderately to heavily contaminated 0.47

Dexing {0.00,0.00,0.25,0.75,0.00,0.00,0.00} 2.25 moderately to heavily contaminated 0.00

Poyang {0.00,0.00,0.56,0.44,0.00,0.00,0.00} 1.94 moderately contaminated 0.44

Estuary {0.00,0.00,0.81,0.19,0.00,0.00,0.00} 1.69 moderately contaminated 0.19

North Source {1.00,0.00,0.00,0.00,0.00,0.00,0.00} -0.50 uncontaminated 0.00

South Source {1.00,0.00,0.00,0.00,0.00,0.00,0.00} -0.50 uncontaminated 0.00

Qimen {0.96,0.04,0.00,0.00,0.00,0.00,0.00} -0.46 uncontaminated 0.04

Wuyuan {0.96,0.04,0.00,0.00,0.00,0.00,0.00} -0.46 uncontaminated 0.04

Jingdezhen {0.00,0.14,0.60,0.26,0.00,0.00,0.00} 1.62 moderately contaminated 0.26

Dexing {0.00,0.00,0.46,0.54,0.00,0.00,0.00} 2.04 moderately to heavily contaminated 0.00

Poyang {0.00,0.00,0.14,0.60,0.26,0.00,0.00} 2.62 moderately to heavily contaminated 0.26

Estuary {0.00,0.00,0.30,0.52,0.18,0.00,0.00} 2.38 moderately to heavily contaminated 0.18

As shown in the Table 1, in the source region, all

the metals certainly belong to the “uncontaminated”

grade. In the upper reaches, Cu and Pb are still

certainly belong to “uncontaminated”. Although Cd

is also classified “uncontaminated,” a 4% risk of

being in the “uncontaminated to moderately

contaminated” grade exists.

At two sampling cities in the middle reaches, the

distinction among each heavy metals’

geo-accumulation vectors is substantial. In

Jingdezhen City, the pollution sequence is

Pb>Cd>Cu. Pb belongs to the “moderately to heavily

contaminated” level, and a 47% risk of worsening

toward “heavily contaminated” is present. Cu and Cd

are classified “moderately contaminated,” and the

probability of being classified “moderately to heavily

contaminated” is 6% and 26%, respectively. In

Dexing City, the pollution sequence is Cu>Cd>Pb.

Cu belongs to the “heavily contaminated” level, and a

11% risk of worsening toward “heavily to extremely

contaminated” exists. Pb and Cd are classified as

“moderately to heavily contaminated”, and the

probabilities of being classified “heavily

contaminated” are 44% and 19%, respectively.

In the lower reaches and estuary regions, the

sorting of the pollution is Cd>Cu>Pb. The Cd of these

two regions belongs to the “moderately to heavily

contaminated” level, and the risks of being classified

“heavily contaminated” are 26% and 18%,

respectively. Likewise, Cu belongs to the

“moderately contaminated” level, and the probability

of being classified “moderately to heavily

contaminated” are 49% and 30%, respectively.

Similarly, Pb also belongs to “moderately

contaminated” grade, and 44% and 19% chances of

worsening to “moderately to heavily contaminated”,

respectively.

3.3 Comprehensive Geo-Accumulation

Vectors Results

Based on the entropy weighting model, the weighted

vector {0.39, 0.30, 0.31} is generated for {Cu, Pb,

Cd}

(Yan et al., 2019c; Yi et al., 2018). Then,

according to the Eq. (11), the comprehensive

geo-accumulation vectors are calculated and

summarized in Table 2.

Table 2: Summary of calculated comprehensive geo-accumulation vectors.

Sampling

Sites

Geo-accumulation Vector

Feature

value

Grade

Risk

degree

North Source {1.00,0.00,0.00,0.00,0.00,0.00,0.00} -0.50 uncontaminated 0.00

South Source {1.00,0.00,0.00,0.00,0.00,0.00,0.00} -0.50 uncontaminated 0.00

Qimen {0.99,0.01,0.00,0.00,0.00,0.00,0.00} -0.49 uncontaminated 0.01

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146

Wuyuan {0.99,0.01,0.00,0.00,0.00,0.00,0.00} -0.49 uncontaminated 0.01

Jingdezhen {0.00,0.19,0.41,0.26,0.14,0.00,0.00} 1.85 moderately contaminated 0.40

Dexing {0.00,0.00,0.22,0.50,0.24,0.04,0.00} 2.60 moderately to heavily contaminated 0.28

Poyang {0.00,0.00,0.41,0.51,0.08,0.00,0.00} 2.17 moderately to heavily contaminated 0.08

Estuary {0.00,0.00,0.60,0.34,0.06,0.00,0.00} 1.96 moderately contaminated 0.40

As shown in Table 2, the order of heavy metals

pollution in Rao River’s sediment is: source < upper

reaches < estuary < lower reaches < middle reaches.

The source region certainly belongs to

“uncontaminated.” Although the upper reaches

region is also classified “uncontaminated,” a 1% risk

of worsening to “uncontaminated to moderately

contaminated” exists.

In the middle reaches, the pollution level of

sediment in Dexing City is “moderately to heavily

contaminated,” and the risk of worsening toward

worse level is 28%. Similarly, the pollution level of

sediment in Jingdezhen City is “moderately

contaminated,” and a 40% risk of worsening to a

terrible grade exists. Combined with the discussion in

Section 3.2, Cu and Pb cause the deterioration of

heavy metals pollution of sediment in Dexing City

and Jingdezhen City, respectively.

In the lower reaches and estuary region, the

pollution level of heavy metals in sediment is

“moderately to heavily contaminated” and

“moderately contaminated,” respectively. The risks

of worsening are 8% and 40%, respectively.

Combined with the discussion of Section 3.2, the risk

of deterioration is mainly due to Cd.

As mentioned in Section 2.1, it is easily to find that

the economic structure and land use type become the

major influencing factors of heavy metals pollution in

Rao River.

The source and upper reaches regions of Rao River

located in the mountainous areas with high forest

coverage, where the economic structure is dominated

by the less polluting tourism industry. It is therefore

suggested that the pollution levels are pretty low in

source and upper reaches areas belonging to

“uncontaminated.”

According to the research of Yan et al, the

industrial structure of the middle reaches in Rao River

is dominated by industrial activity and mining, whose

pollution load is great

(Yan et al., 2018). Thus, the

pollution condition of heavy metals in sediment of

middle reaches is the most serious. Dexing Copper

Mine is the largest open copper mine, and Cu in slag

is easily leached by rain, which can confluence into

the river network with the slope. As a consequence,

the main risk factor of sediment in Dexing City is Cu.

The industrial structure of Jingdezhen is dominated

by ceramic production. Because of the Pb element in

the paint of ceramics, the Pb load in industrial

wastewater is quite prominent. Based on these

findings, the main controlling factor of sediment in

Jingdezhen City would be Pb.

The lower reaches of Rao River are located in a

plain area, and the soil is mainly red soil with weak

acidity, which is conducive to the release of Cd.

Besides, the crops in the lower reaches region of Rao

River are Indica Rice, which can absorb cadmium

well. Farmers are used to returning stalks to their

fields after harvest. As rice stalks rot, Cd can easily

enter the river network along with farmland runoff.

Hence, the main controlling factor in the lower

reaches of Rao River seems to be Cd.

3.4 Comparison between

Geo-accumulation Index and

Geo-accumulation Vector

The mean values of the Cu, Pb and Cd in global shale

are 45 mg/kg, 20 mg/kg and 0.3 mg/kg, respectively

(

Matschullat et al., 2000; Snežana et al., 2017).

According to the Eq. (1), the geo-accumulation

indices are calculated and summarized in Table 3.

Table 3: Summary of calculated geo-accumulation indices of Rao River.

Sampling

Sites

Geo-accumulation

index

Grade

North Source -2.06 uncontaminated

South Source -2.13 uncontaminated

Qimen -1.70 uncontaminated

Heavy Metal Pollution Assessment in the Sediment of Rao River, China using the Geo-accumulation Vector

147

Wuyuan -1.78 uncontaminated

Jingdezhen 0.83 uncontaminated to moderately contaminated

Dexing 2.62 moderately to heavily contaminated

Poyang 1.32 moderately contaminated

Estuary 0.99 uncontaminated to moderately contaminated

North Source -0.80 uncontaminated

South Source -0.89 uncontaminated

Qimen -0.63 uncontaminated

Wuyuan -0.71 uncontaminated

Jingdezhen 3.06 heavily contaminated

Dexing 2.34 moderately to heavily contaminated

Poyang 2.03 moderately to heavily contaminated

Estuary 1.72 moderately contaminated

North Source -2.51 uncontaminated

South Source -2.60 uncontaminated

Qimen -2.01 uncontaminated

Wuyuan -2.10 uncontaminated

Jingdezhen 0.62 uncontaminated to moderately contaminated

Dexing 1.02 moderately contaminated

Poyang 1.63 moderately contaminated

Estuary 1.42 moderately contaminated

Compared between Table 2 and Table 3, it is easily

to find that there are two differences between the

geo-accumulation index and geo-accumulation vector.

(i) In the evaluation of Cu and Cd, the evaluation

results of the geo-accumulation index are looser than

the geo-accumulation vector.

In the middle reaches, lower reaches and estuary

region, the pollution grades of Cu and Cd in Table 3

are about one category lower than those in Table 1.

The reason for this phenomenon is that the local

background values is not identified with their mean

values in global shale. For example, the background

values of Cu and Cd in the sediment of Rao River are

14.16 mg/kg-41.97 mg/kg, and 0.065 mg/kg-0.257

mg/kg, respectively. While their mean values in

global shale are 45 mg/kg and 0.3 mg/kg. Obviously,

compared with global shale, the natural content of Cu

in the sediment of Rao River is much lower. As the

result, using the global average value as the local

background may lead to the distortion that some

anthropogenic heavy metals are regarded as the

natural background, which makes the evaluation

overoptimistic.

(ii) Compared with the geo-accumulation vector, it

is hard for the geo-accumulation index to identify

risks.

As shown in Table 3, the geo-accumulation index

of Cu in the estuary region is 0.99, which is nearly to

the “moderately contaminated” grade. However, the

geo-accumulation index cannot recognize this

cross-grade risk, rather would be considered that it

certainly seems to be “uncontaminated to moderately

contaminated”.

By contrary, the geo-accumulation vector solves

this problem through introducing the risk degree. For

example, as indicated in Table 1, the pollution feature

value of Cu in Poyang City is 1.99, which belongs to

the “moderately contaminated” grade. Considering its

risk degree is 0.49, we can further deduce that the

pollution of Cu in Poyang City has a potential

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possibility of 49% to worsen to the “moderately to

heavily contaminated” grade.

4 CONCLUSIONS

In the sediment of Rao River, the pollution degrees of

heavy metals have significant regional differences,

and the main causes for these differences are the

economic structure and land use type. The order of

pollution degree is: source < upper reaches < estuary

< lower reaches < middle reaches. Dexing City,

Poyang City and Jingdezhen City are the most

polluted area along Rao River, which belong to

“moderately to heavily contaminated”, “moderately

to heavily contaminated” and “moderately

contaminated” grades, respectively, and have risk

probabilities of 28%, 8% and 40% to deteriorate,

respectively. The critical controlling heavy metals of

these 3 cities are Cu, Pb and Cd, respectively. The

fundamental causes would be their ceramic industry,

copper mining, and the red soil.

For the heterogeneity of mineral composition, the

mean values of the elements in global shale should

not be used as the background values of Rao River.

Otherwise, the evaluation results of Cu and Cd may

be overoptimistic.

Compared with the conventional geo-accumulation

index, the geo-accumulation vector has apparent

advantages in dealing with the uncertainty of

background values and the recognizing the

cross-grade risk.

FUNDING

This research was funded by Strategic Priority

Research Program Project of the Chinese Academy of

Sciences (XDA23040100), Water conservancy fund

project of Hunan Province (XSKJ2019081-30;

XSKJ2019081-32), the National Natural Science

Foundation of China, grant number 52069012.

CONFLICTS OF INTEREST

The authors declare no conflict of interest.

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