Heavy metals in the riverbed surface sediment of the Yellow River, China

Abstract

One hundred and eleven riverbed surface sediment (RSS) samples were collected to determine the heavy metal concentration throughout the Inner Mongolia reach of the Yellow River (IMYR), which has been subjected to rapid economic and industrial development over the past several decades. Comprehensive analysis of heavy metal contamination, including the enrichment factor, geo-accumulation index, contamination factor, pollution load index, risk index, principal component analysis (PCA), hierarchical cluster analysis (HCA), and Pearson correlation analysis, was performed. The results demonstrated that a low ecological risk with a moderate level of heavy metal contamination was present in the IMYR due to the risk index (RI) being less than 150 and the pollution load index (PLI) being above 1, and the averaged concentrations of Co, Cr, Cu, Mn, Ni, Ti, V, and Zn in the RSS, with standard deviations, were 144 ± 69, 77.91 ± 39.28, 22.95 ± 7.67, 596 ± 151, 28.50 ± 8.01, 3793 ± 487, 69.11 ± 18.44, and 50.19 ± 19.26 mg kg⁻¹, respectively. PCA, HCA, and Pearson correlation analysis revealed that most of the RSS was heavily contaminated with Zn, Ni, and Cu, due to the influence of anthropogenic activities; moderately contaminated with Ti, Mn, V and Cr because of the dual influence of anthropogenic activities and nature; and slightly to not contaminated with Co because it occurs mainly in the bordering desert areas.
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Full text

RESEARCH ARTICLE
Heavy metals in the riverbed surface sediment of the Yellow
River, China
Qingyu Guan
1
&Ao Cai
1
&Feifei Wang
1
&Lei Wang
1
&Tao Wu
1
&Baotian Pan
1
&
Na Song
1
&Fuchun Li
1
&Min Lu
1
Received: 28 May 2016 /Accepted: 14 September 2016
#Springer-Verlag Berlin Heidelberg 2016
Abstract One hundred and eleven riverbed surface sediment
(RSS) samples were collected to determine the heavy metal
concentration throughout the Inner Mongolia reach of the
Yellow River (IMYR), which has been subjected to rapid eco-
nomic and industrial development over the past several de-
cades. Comprehensive analysis of heavy metal contamination,
including the enrichment factor, geo-accumulation index, con-
tamination factor, pollution load index, risk index, principal
component analysis (PCA), hierarchical cluster analysis
(HCA), and Pearson correlation analysis, was performed.
The results demonstrated that a low ecological risk with a
moderate level of heavy metal contamination was present in
the IMYR due to the risk index (RI) being less than 150 and
the pollution load index (PLI) being above 1, and the averaged
concentrations of Co, Cr, Cu, Mn, Ni, Ti, V, and Zn in the
RSS, with standard deviations, were 144 ± 69, 77.91 ± 39.28,
22.95 ± 7.67, 596 ± 151, 28.50 ± 8.01, 3793 ± 487,
69.11 ± 18.44, and 50.19 ± 19.26 mg kg
−1
, respectively.
PCA, HCA, and Pearson correlation analysis revealed that
most of the RSS was heavily contaminated with Zn, Ni, and
Cu, due to the influence of anthropogenic activities; moder-
ately contaminated with Ti, Mn, V and Cr because of the dual
influence of anthropogenic activities and nature; and slightly
to not contaminated with Co because it occurs mainly in the
bordering desert areas.
Keywords The Yellow River .Surface sediment .Heavy
metal .Comprehensive analysis
Introduction
With the rapid development of the economy and industry,
heavy metal contamination in aquatic ecosystems is becoming
a worldwide environmental problem because a disproportion-
ate amount of wastewater is being discharged into surface
water bodies (Woitke et al. 2003; Singh et al. 2004;Sakan
et al. 2009;Songetal.2010; Hang et al. 2009;Yuetal.
2011; Zheng et al. 2008). Heavy metals tend to be trapped in
aquatic environments and to accumulate in sediments during
the process of adsorption, hydrolyzation, and precipitation
(Loska and Wiechuła2003). The contaminated sediments
can act as secondary sources of pollution to the overlying
water column in the river (Shipley et al. 2011; Varol 2011),
and mechanical disturbance of the sediments can increase the
risk of contaminant release when they are re-suspended (Chen
et al. 2007a). Heavy metals in aquatic environments are
becoming a source of grave concern (Owens et al. 2005;Liu
et al. 2009; Zhang et al. 2009; Varol 2011;Xuetal.2014)
because of their toxicity, persistence in the environment (Li
et al. 2013; Karlsson et al. 2010;Khanetal.2013), transport
through flowing water, and subsequent accumulation in the
bodies of aquatic microorganisms, flora, and fauna, which
may, in turn, enter the human food chain and cause a host of
health problems (Chen et al. 2007b;Guanetal.2016;Li
et al. 2013; Mendoza-Carranza et al. 2016).
Responsible editor: Philippe Garrigues
*Qingyu Guan
guanqy@lzu.edu.cn
*Lei Wang
wanglei2013@lzu.edu.cn
1
Key Laboratory of Western China’s Environmental Systems
(Ministry of Education) and Gansu Key Laboratory for
Environmental Pollution Prediction and Control, College of Earth
and Environmental Sciences, Lanzhou University, Lanzhou 730000,
China
Environ Sci Pollut Res
DOI 10.1007/s11356-016-7712-z

In China, historical information on heavy metal contami-
nation in riverbed surface sediments (RSS) mainly focused on
industrialized areas such as the deltas of the Yangtze River,
Yellow River, and Pearl River (Zhong et al. 2011; Zhang et al.
2009; Bai et al. 2012), with only a few studies dedicated to the
upper reaches of the Yellow River (Ma et al. 2016). The Inner
Mongolia reach of the Yellow River, located at the end of the
upper reach of the Yellow River, is in the central region of
Mongolia, and it supplies the water to its neighboring cities.
The districts bordering the Inner Mongolia reach of the Yellow
River (IMYR) are also important energy bases and major
grain producing areas in northwest China. With social and
economic development, the cities bordering the IMYR, such
as Bayan Nur and Baotou, have been rapidly developing in-
dustries such as electroplating, metallurgy, leather dyestuffs,
mining, chemical engineering, and power generation, giving
rise to contaminant sources and the continuous discharge of
heavy metals in the main channel of the Yellow River (Li and
Zhang 2010). Most of the heavy metals are absorbed by the
bed materials, and because of the fast flowing stream and
discontinuously inputted sewage, monitoring the quality of
the water body alone may result in misleading and
underestimated stream contamination levels (Varol 2011). In
addition, heavy metals in the sediments of Chinese rivers are
not included in the monitoring list (China’s Ministry of
Environmental Protection 2008). Hence, studying this aspect
would help establish pollutant loading reduction goals, protect
the ecological environment, and preserve public health in the
Yellow River basin. The main objectives of the study were the
following:
(1) To determine the magnitude and spatial variation of the
contemporary deposition and storage of the total
sediment-associated heavy metals on the riverbed of
the IMYR and to compare these values with estimates
of the contamination levels with different assessment
methods.
(2) To determine the sources of sediment-associated heavy
metals in the main channel systems of the IMYR and to
explain the metal contamination in the study area.
This study not only provides valuable information related
to the heavy metals in the RSS of the IMYR but also serves as
a resource for local and regional environmental management
and water resource planning authorities.
Materials and methods
Study area
The focus of this study is the Inner Mongolia reach of the
Yellow River, which loops south near the city of Bayan Nur,
flows east to the city of Togtoh, and stays in the Inner
Mongolia Autonomous Area for 500 km. It belongs to
the upper to middle reaches of the Yellow River in
terms of its geology (Fig. 1). The Yellow River flows
through the northern border of the Hobq Desert (Yang
2003), and along the southern bank of this section, 10
tributaries named the BTen Great Gullies^flow through
the Hobq Desert (Fig. 1).Thisbasinisanimportant
energy base and a primary grain producing area in north
China; it also plays a decisive role in both the industrial
and agricultural economies. The cities along the Yellow
River, such as Bayan Nur, Urad Front Banner, Baotou,
and Dalad Banner, are rapidly developing their metallur-
gy, leather dyestuffs, mining, chemical engineering, and
power generation industries; especially, Baotou is one of
the major aluminous manufacturing bases of China. In
the process, drainage from mines releases heavy metal
ions, including Cu, Mn, V, Zn, and Ni, into the Yellow
River from both active mines and abandoned sulfide
tailing dumps, which are likely to cause environmental
problems related to both water and sediment quality
(Huang et al. 2007;LiandZhang2010;Fuetal.
2014). The cities along the Yellow River, such as
Bayan Nur, Urad Front Banner, Baotou, and Dalad
Banner, are rapidly developing their metallurgy, leather
dyestuffs, mining, chemical engineering, and power gen-
eration industries. This reach of the Yellow River is on
the fringe of the East Asian monsoon belt, and it has a
continental climate and a low and unevenly distributed
annual precipitation (150–363 mm). The duration of
sunlight is long (≥10 °C accumulated temperature
3004~3515 °C), and evaporation is intense (the annual
mean evaporation of 1939–3482 mm is as much as 10–
12 times the precipitation) (Yang 2003).
Sediment sampling and analytical procedure
Thirty-seven sampling sites were set up in the riverbed along
the main stream of the Yellow River (Fig. 1). For each site, to
make the samples more representative, we randomly took
three parallel underwater samples approximately 15 m away
from the bank using a homemade cylindrical steel sampler
(approximately 10 cm in diameter), plastic valve bags, and
tying cordage, and 111 samples of the riverbed surface sedi-
ment were obtained. All samples were analyzed for heavy
metal concentration at the Key Laboratory of Western
China’s Environmental Systems (Ministry of Education),
Lanzhou University. The analysis procedures were identical
to those described by Guan et al. (2014) and Pan et al. (2015).
Sample preparation involved air drying the samples and grind-
ing them to yield grain sizes smaller than 75 μm. Up to 4 g of
sample was weighed and poured into the center of a column
apparatus with boric acid, and the apparatus was pressurized
Environ Sci Pollut Res

to 30 t m
−2
for 20 s. Contamination during analysis may dis-
tort the result. To avoid potential contamination, all collected
samples were sealed in clean plastic bags. The grinding con-
tainer was rinsed with distilled water and subsequently air
dried before use. The column apparatus for sample compac-
tion was precleaned using absorbent cotton and alcohol and
then air dried. The processed sample, measuring approximate-
ly 4 cm in diameter and 8 mm in thickness, was analyzed
using a Philips Panalytical Magix PW2403 X-ray fluores-
cence (XRF) spectrometer (Holland) at ambient temperatures
and pressures (approximately 20 °C, 85 kPa). A calibration
curve was developed using 16 Chinese National Standard soil
reference samples (GSS-1 to GSS-16), 12 fluvial sediment
reference samples (GSD-1 to GSD-12) and five rock reference
samples (GSR-1 to GSR-5). The reproducibility of the ele-
ment measurements was evaluated by repeat analysis using
the National Standard soil reference sample GSS-8, with ana-
lytical uncertainties of <3 % for major elements and <5 % for
trace elements. Detection limits (calculated on the basis of 10
determinations of the blanks as three times the standard devi-
ation of the blank) in the sediment tests were 0.01 mg kg
−1
for
Co, Cr, Cu, Mn, Ni, Ti, V, and Zn and 0.01 % for Al. The
analytical results are reported in oxide compound form, apart
from the trace elements which are given in elemental form.
Risk assessment methods of sediment contamination
In this paper, the background value of each metal comes from
the 15 surface sediment samples from the Hobq Desert at
preindustrial levels (Fig. 1; Table 3). The background values
utilized were 279 mg kg
−1
for Co, 36.19 mg kg
−1
for Cr,
1.90 mg kg
−1
for Cu, 330 mg kg
−1
for Mn, 3.80 mg kg
−1
for
Ni, 2277 mg kg
−1
for Ti, 37.41 mg kg
−1
for V, 7.51 mg kg
−1
for Zn, and 5.77 % for Al. Approaches such as enrichment
factor (EF), geo-accumulation index (I
geo
), and contamination
factor (CF) have been widely used to study heavy metal con-
tamination and sources in river, estuarine, and coastal environ-
ments (Santos Bermejo et al. 2003; Owens et al. 2005; Zhang
et al. 2009; Zahra et al. 2014). Pollution load index (PLI) and
potential ecological risk index (RI) represent the sensitivity of
various biological communities to toxic substances and illus-
trate the sediment’s environmental quality and potential eco-
logical risk caused by heavy metals (Fernandes et al. 1997;
Huang et al. 2016;Mohammadetal.2010;Yangetal.2009;
Va r o l 2011).
Enrichment factor
In our case, we used the metal EF as an index to evaluate
anthropogenic influence on the sediments (Baptista et al.
2000; Gao and Chen 2012; Han et al. 2006; Zhang et al.
2009; Varol 2011;Xuetal.2014; Resongles et al. 2014). EF
is commonly defined as the observed metal to aluminum (Al)
ratio in the sample of interest divided by the background
metal/Al ratio (Owens et al. 2005; Zhang et al. 2009;Tam
and Wong 2000; Zahra et al. 2014) because Al is one of the
most abundant elements on the earth and usually has no con-
tamination concerns (Zhang et al. 2009). In addition, the alu-
minous averaged concentration in the surface sediments of the
IMYR (5.66) is less than its background value (5.77), with a
small standard deviation (0.86) and coefficient of variance
(0.15; Table 3). Hence, Al has been identified as the
Fig. 1 Site location map of the IMYR. The black solid circles represent the sampling sites for the surface sediment of the riverbed (RSS, n= 37), and the
brown solid circles represent the sampling sites for the surface sediments of the deserts (DSS, n=15)
Environ Sci Pollut Res

normalized element in this paper (formula 1). Mathematically,
EF is expressed as:
EF ¼
CAl

Sample
C=AlðÞBackground ð1Þ
where (C/Al)
Sample
is the metal to Al ratio in the sample of
interest, and (C/Al)
Background
is the natural background value
of metal to Al ratio. EF provides a classification system for the
degree of pollution (Table 1).
Geo-accumulation index
The I
geo
is another classical assessment model that enables
evaluation of metal pollution in sediments by comparing cur-
rent concentrations with preindustrial levels (Chabukdhara
et al. 2012; Christophoridis et al. 2009; Zhang et al. 2009;
Varol 2011; Zahra et al. 2014). The I
geo
is defined by the
following formula:
Igeo ¼log2
CSample
kCBacground
 ð2Þ
where C
Sample
is the measured content of the metal, C
Background
is the corresponding background content of the metal, kis the
background matrix correction factor introduced to ac-
count for possible differences in the background values
due to lithospheric effectsis, and the constant is used due
to potential variations in the baseline date (k=1.5;Lu
et al. 2009; Bhuiyan et al. 2010;Varol2011). I
geo
pro-
vides a classification system for the degree of pollution
(Table 1).
Contamination factor
Sediment contamination was also assessed using the
contamination factor and degree. In the version sug-
gested by Hakanson (1980), they assess the sediment
contamination by using the concentrations in the surface
desert sediments at preindustrial levels as reference
points.
CF ¼CSample
CBackground
ð3Þ
where C
Sample
is the mean content of the metal from the
sampling site, and C
Background
is the preindustrial
concentration of the individual metal. Hakanson (1980)
defines four categories of CF, shown in Table 1.
Tabl e 1 Evaluation criteria of
different risk assessment methods Assessment methods Level Values Comprehensive assessment level
Enrichment factor (EF) 1 EF ≤2 Deficiency to minimal enrichment
22<EF≤5 Moderate enrichment
35<EF≤20 Significant enrichment
420<EF≤40 Very high enrichment
5 EF > 40 Extremely high enrichment
0I
geo
≤0 Practically uncontaminated
10<I
geo
≤1 Uncontaminated to moderately contaminated
21<I
geo
≤2 Moderately contaminated
Geo-accumulation index (I
geo
)3 2<I
geo
≤3 Moderately to strongly contaminated
43<I
geo
≤4 Strongly contaminated
54<I
geo
≤5 Strongly to very strongly contaminated
6I
geo
< 5 Very strongly contaminated
1 CF < 1 Low contamination factor
Contamination factor (CF) 2 1 < CF ≤3 Moderate contamination factor
33<CF≤6 Considerable contamination factor
4 CF < 6 Very high contamination factor
1RI≤150 Low ecological risk
Ecological risk index (RI) 2 150 < RI ≤300 Moderate ecological risk
3300<RI≤600 High ecological risk
4 RI > 600 Significantly high ecological risk
1PLI≤1 Uncontaminated by heavy meals
2 PLI < 1 Contaminated by heavy metals
Environ Sci Pollut Res

Pollution load index
The PLI was proposed by Tomlinson et al. (1980), and for the
entire sampling site, PLI has been determined as the nth root
of the product of the nC
i
f
:
PLI ¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
Ci
f1Ci
f2Ci
f3⋯Ci
fn
n
qð4Þ
where C
i
f
is the single element pollution factor, the values
were calculated using Formula 3. This empirical index pro-
vides a simple and comparative means for assessing the level
of metal pollution (Mohammad et al. 2010; Varol 2011). If the
PLI is larger than 1, the sediment is considered to be contam-
inated by heavy metals (Table 1). However, if the PLI is small-
er than 1, the sediment is considered to be uncontaminated by
heavy metals (Varol 2011;Fujitaetal.2014; Table 1).
Potential ecological risk index
The potential ecological risk method was developed by
Hakanson (1980). The RI was introduced to assess the degree
of contamination of trace metals in the sediments. The formu-
las for calculating the RI are as follows:
Ei
r¼Ti
rCi
f¼Ti
rCi
s=Ci
n
 ð5Þ
RI ¼X
n
i¼1
Ei
rð6Þ
where C
i
s
is the content of the element in the sample, C
i
n
is the
background value of the element, C
i
f
is the single element
pollution factor, E
i
r
is the RI of an individual element, and
T
i
r
is the biological toxicity factor of an individual element,
which are defined for Ti,Mn, Zn, V, Cr, Cu, Ni, and Co as 1, 1,
1, 2, 2, 5, 5, and 5, respectively (Yi et al., 2011; Zhang et al.
2014). RI is the sum of E
i
r
. Table 1shows the factor standard
for different levels.
Multivariate analysis
Multivariate analyses of principal component analysis (PCA)
and hierarchical cluster analysis (HCA) were used in this
study. All experimental data performed in multivariate analy-
sis was standardized through z-scale transformation to avoid
misclassification due to wide differences in data dimensional-
ity (Varol 2011; Gao and Chen 2012). PCA can convert vast
raw data into minority unrelated variables and explain the
original data’s variance without losing much of the primary
information. PCA with Varimax rotation of standardized com-
ponent loadings was conducted for the deriving factors, and
PCs with an eigenvalue above 1 were retained (Li and Zhang
2010; Liu et al. 2003; Huang et al. 2013). Kaiser–Meyer–
Olkin (KMO) and Bartlett’s sphericity tests were performed
to examine the suitability of the data for PCA; KMO was used
to determine the correlation of variables. A high value (close
to 1) indicates that the principal component can be useful;
KMO = 0.860 in this study. Bartlett’s test of sphericity deter-
mines whether a correlation matrix is an identity matrix,
which may indicate that the variables are unrelated, that is, a
significance level of <0.05 is fit for PCA (Varol 2011); the
significance level = 0 in this study.
The HCA is a classification process using different
categories or clusters of data sets; it is an unsupervised
classification procedure that involves measuring either
the distance or the similarity between variables (Gao
and Chen 2012), and it is a widely used sorting method
(Ikem et al. 2011;LiandZhang2010;Varol2011;Gao
and Chen 2012). HCA is a multivariate technique whose
primary purpose is to classify objects of a system into
categories or clusters based on their similarities, and the
objective is to find an optimal grouping where the obser-
vations or objects within each cluster are similar, but the
clusters are dissimilar from each other (Ikem et al. 2011;
Islam et al. 2015; Paramasivam et al. 2015). The dendro-
gram visually displays the order in which parameters or
variables combine to form clusters with similar proper-
ties. Q-model hierarchical agglomerative HCA was per-
formed in the normalized data set through squared
Euclidean distances as a measure of similarity and
War d ’s method to obtain dendrograms. Stations in the
same cluster have a similar contamination source
(Singh et al. 2004; Chen and Huang et al. 2007;Liand
Zhang 2010). All multivariate analyses were completed
in SPSS 20.0 for Windows.
Results and discussion
Sediment pollution survey and assessment
Descriptive statistical analysis
A preliminary statistical analysis of metal contamination in the
study area has been performed, and the results show that the
elements of maximum and minimum averaged concentration
were Ti and Cu, respectively (Table 2). The sequence of the
average metal contents was Ti > Mn > Co > Cr > V > Zn > Ni
> Cu in RSS, whichwas similar to that of the desert, Ti > Mn >
Co > V > Cr > Zn > Ni > Cu (Table 2), indicating that some of
the metal concentrations in the surface sediments of the study
area fluctuated due to anthropogenic activities and most of the
elements are naturally occurring. Compared with the back-
ground value, the sequence of the ratio was Cu > Ni > Zn >
6>Cr>2>V>Mn>Ti>1>Al>Co.CoandAlwerenot
contaminated by anthropogenic means because their averaged
concentrations were less than their BVs (Table 2), and the
Environ Sci Pollut Res

average contents of Cu, Ni, and Zn were 12.08, 7.50, and 6.68
times their BVs, respectively (Table 2), indicating contamina-
tion by humans. The average concentrations of Cu, Ni, and Zn
in the RSS of the IMYR were higher than those for the upper
continent (Wedepohl 1995) and even higher than those for the
sediments from the south of Tegger Desert located near the
study area (10.37, 14.99, and 42.66 mg/kg for Cu, Ni, and Zn;
Guan et al. 2014). However, the surface sediments from north-
ern Mexico in semi-arid environment contained higherNi, Cu,
and Zn (69.5 ± 15.5, 400.5 ± 15.8, and 78.8 ± 6.5 mg kg
−1
,
respectively) than the RSS of the IMYR, due to mining activ-
ities (Meza-Figueroa et al. 2009).
Assessment of the degree of heavy metal contamination
The results from this study in the IMYR showed that
significant enrichments (5 < EF ≤20) of Cu, Ni, and
Zn were found in 100, 97, and 92 % of the sampling
sites, respectively, suggesting that these metal contami-
nations were currently a major concern. It is evidenced
by Nriagu that industrial activities such as smelting of
steel and non-ferrous metals are important sources for
anthropogenic heavy metals (Nriagu et al. 1979). Inner
Mongolia is known for its industries including steel and
rare earth industries, which greatly promote the increase
of its gross domestic product (Liu and Liu, 2013). The
higher EF and I
geo
values for Cu, Ni, and Zn in sediment
samples from Inner Mongolia may be from steel indus-
tries and rare earth industries. Moreover, a significant
enrichment of Cr was found in site 3 (Fig. 2a), indicating
that Cr contamination could be correlated to local point
sources. The averaged enrichment factors of Cr
(2.25 ± 1.37), Mn (1.82 ± 0.21), V (1.86 ± 0.25), and
Ti (1.72 ± 0.24) were found to range from 1.5 to 2 in the
IMYR, suggesting that these metal contaminations ap-
peared to be moderate in some localized areas (the
number of sampling sites with EF values larger than 2,
which for Cr, Mn, V, and Ti are 14, 9, 9 and 3,
respectively; Fig. 2a). The contamination level of heavy
metals in the RSS of the IMYR was reflected by the EF
values in the following ranking: Cu > Ni > Zn> Cr > Mn
> V > Ti. Moreover, the percentages of the I
geo
indexes
that reached the moderate or contaminated level (I
geo
>1)
in all of the RSS sites were 97 % for Cu, 92.5 % for Ni,
89%forZn,2.7%forCr,2.7%forMn,2.7%forV,
and0%forTi(Fig.3a–c). Assessment of the sediment
integrated pollution degree showed that the EF evaluated
result is in concordance with the I
geo
index result (Figs. 2a
and 3a). Especially noteworthy is that the significant con-
tamination of Cu, Zn, Mn, and V was observed at the
downstream of site 20 (Baotou; EF
Mn
,V>2;I
geo
(Cu,
Zn) >2; Figs. 2b, c and 3b, c), suggesting that the contam-
ination at site 20 may be due to anthropogenic inputs that
consist of the metals mentioned above. In addition, the I
geo
indexes of Co and Al were all negative, indicating that
contamination by Al and Co has not occurred in the study
area (Fig. 3a), and this result is similar to that by Guan
et al. (2016).
The calculated CF values for all nine metals are shown in
Fig. 4. Overall, the CF for all metals shows the descending
order of Cu > Ni > Zn > Cr > Mn > V> Ti > Al > Co. The
average CF values of Cu, Ni, Zn, Cr, Mn, V, Ti, Al, and Co
were 12.08 ± 4.04, 7.50 ± 2.11, 6.68 ± 2.57, 2.15 ± 1.09,
1.81 ± 0.46, 1.85 ± 0.49, 1.67 ± 0.21, 0.98 ± 0.15, and
0.51 ± 0.25, respectively. The CF for Cu was in the range of
Bhigh contamination^for all sites except site 24. Risk levels
for all other metals are as follows. High contamination: Ni
89.19 %, Zn 51.35 %, and Cr 2.70 %. Significant contamina-
tion: Ni 10.81 %, Zn 43.24 %, Mn 2.70 %, and V 2.70 %.
Moderate contamination: Zn 5.41 %, Cr 97.30 %, Mn
97.30 %, V 94.60 %, Ti 97.30 %, and Al 35.14 % (Fig. 4).
A high value of CF
Co
was observed at site 1 despite low
contamination at all other sites (Fig. 4). Because the IMYR
is bordered by the Ulan Buh Desert and the Hobq Desert
(Fig. 1), the aeolian sand with high Co concentration from
the bordering deserts is transported into the riverbed during
the windy seasons (especially the spring and winter), and sand
dunes may encroach onto the floodplain or even into the
Tabl e 2 Descriptive statistical
analysis of nine elements in the
IMYR
Co Cr Cu Mn Ni Ti V Zn Al
Max 339 295.43 44.15 1035 51.95 4985 121.70 104.70 7.90
Min 34 42.53 10.40 335 13.20 2064 26.93 15.80 3.95
Mean 144 77.91 22.95 596 28.50 3793 69.11 50.19 5.66
SD 69 39.28 7.67 151 8.01 487 18.44 19.26 0.86
CV 0.48 0.50 0.33 0.25 0.28 0.13 0.27 0.38 0.15
BG 279 36.19 1.90 330 3.80 2277 37.41 7.51 5.77
Mean/BG 0.51 2.15 12.08 1.81 7.50 1.67 1.85 6.68 0.98
Co, Cr, Cu, Mn, Ni, Ti, V, and Zn are trace elements (mg/kg), and Al is the major element (%)
SD standard deviation, CV coefficient of variation, BG background
Environ Sci Pollut Res

riverbed, which increases the Co concentration of the RSS in
the IMYR (Pan et al. 2015;Guanetal.2016). High Cr con-
tamination was observed at site 3 due to industrial effluents
and solid wastes from heavy industries (such as the mining
and smelting of non-ferrous metals, chemical production, and
lead–acid battery production, as well as the leather and dyeing
industry) in Bayan Nur (Fig. 4). The highest concentrations of
Mn, Ni, V, Zn, and Cu occurred at site 23, and the metal
concentrations in the RSS of the sites 21, 22, 27, 29, 31, and
33 were higher than those measured on average (Fig. 4),
which were ascribed to the influence of industrial activities
(steel and machine manufacture, metallurgy, and production
of rare earth materials) in bordering cities such as Baotou and
Dalad Banner (Huang et al. 2007; Li and Zhang 2010; Fig. 1).
Assessment of the degree of ecological risk and pollution
loading
The PLI values ranged from 1.63 to 3.57, with an average of
2.65, and the RI values ranged from 29.75 to 104.63, with an
average of 58.34 (Fig. 5), indicating that a low ecological risk
with a moderate level of heavy metal contamination was
present in the IMYR. Liu et al. (2005) reported that the PLI
values for agricultural soils with sewage irrigation in Beijing,
China ranged from 2.39 to 3.43, with an average of 3.06. Yi
et al. (2011) have registered an average RI value for the Yangtze
River as 94.31 (range 36–222.1). Yin et al. (2011) have calcu-
lated RI values for Taihu Lake ranging from 41.5 to 655, with
an average of 188. The RI value, taking the geochemical back-
ground and national soil background as reference, ranged from
9.5 to 209.09, with an average of 46.71, and it ranged from
31.56 to 1583.92, with an average of 267.42, for Luan River,
China, respectively (Liu et al. 2009). The present PLI range is
lower than the values for Beijing, and the present RI range is
lower than the values for Luan River, Taihu Lake, and Yangtze
River. In the present study, sites 3, 17, 21–23, 31, and 33 have
high PLI and RI values due to the solid wastes and wastewater
from the cities bordering the IMYR, which may dominate the
enhanced polluted metal distribution (Figs. 1and 5).
Heavy metal sources of the IMYR
Because heavy metals in the RSS of the IMYR have now been
shown to have a low ecological risk with a moderate level of
Fig. 2 Enrichment factor (EF) value of heavy metals in the RSS of the IMYR, China (a), and the EF values of Mn (b), V (c), and Cr (d) in each sampling
site present in b,c,andd,respectively
Environ Sci Pollut Res

contamination, it is important to analyze and control the
sources of pollution. Heavy metals in sediments often exhibit
complex interrelationships due to the disturbance of the orig-
inal concentration of the parent materials and contamination
by human activities (Chen et al. 2007a; Li and Zhang 2010;
Islam et al. 2015; Paramasivam et al. 2015). PCA, HCA, and
Pearson correlation analysis were conducted in the present
study to determine the sources of heavy metals and the causes
of their heavy metal contamination (Huang et al. 2013;Liu
et al. 2003; Loska and Wiechuła2003;Quanetal.2014; Singh
et al. 2004; Zahra et al. 2014; Varol 2011; Zheng et al. 2008).
Source analysis based on PCA
To further examine the extent of metal contamination in the
study area, we performed PCA analysis on the metals. PCA is
performed on the correlation matrix between different param-
eters followed by Varimax rotation. It gives two PCs with
eigenvalues >1, explaining 87.51 % of the total variance
(Table 3). PC1 accounted for 73.49 % of the total variance
and is mainly characterized by high positive loadings of var-
iables such as Cu, Mn, V, Zn, Al, Ti, and Co. Moreover, a
moderately positive loading of variables, which is present in
PC2 (Ni), also presents a significant positive loading in PC1.
The second PC accounted for 14.02 % of the total variance,
which consists only of Cr with high positive loading (Fig. 6;
Tab le 3). In PC1, the loading coefficients of Cu, Mn, V, Zn,
Al, and Ni were larger than 0.85, implying perhaps a common
source (Han et al. 2006). In this case, the Ti loading (0.639) is
not as high as the loadings of the other elements of the group
and is separated from the other elements in PC1 (Fig. 6a;
Tab le 3), which may, therefore, imply quasi-independent be-
havior within the group (Han et al. 2006). Co is the only
element with negative loading in the PC loading plot, suggest-
ing that the source of Co in the RSS is different from the other
metals, and it can be classed into a separate group. Overall, the
heavy metals in the RSS of the IMYR were classed into four
groups by PCA as follows: group 1 consisted of Cu, Mn, V,
Zn, Al, and Ni; groups 2, 3, and 4 consisted only ofTi, Co, and
Cr, respectively.
Source analysis based on HCA
Based on the information derived from the PCA above, we
performed HCA on the data. Distance metrics are based on the
Wards method, using squared Euclidean distance. As shown
Fig. 3 Geoaccumulation index (I
geo
) value of f heavy metals in the RSS of the IMYR, China (a), and the I
geo
values of Cu (b) and Zn (c)ineach
sampling site present in band c,respectively
Environ Sci Pollut Res

in Fig. 6b, the variables taken for this analysis are the same as
for the PCA. In this dendrogram, nine elements in the RSS of
the IMYR are grouped into five significant clusters based on
the similarities between them. Cluster 1 consisted of Cu, Mn,
V, Zn, and Al. Cluster 2 consisted of Ni, Ti, Co, and Cr. As the
similar source of Ni and the elements in cluster 1 (Han et al.
2006) and the distance between Ni and the elements in cluster
1 were apparently less than the distance between Ni and Ti,
Co, and Cr, respectively, (Fig. 6b),Cu,Mn,V,Zn,Al,andNi
were classed into the same cluster. Clusters 3, 4, and 5
consisted only of Ti, Co, and Cr, respectively, due to the dif-
ferences of their sources (Han et al. 2006).
Source analysis based on Pearson analysis
High correlation coefficients between different metals mean
common sources, mutual dependence, and identical behavior
during transport (Liu et al. 2003; Mohammad et al. 2010;
Wang et al. 2016; Islam et al. 2015; Paramasivam et al.
2015; Zheng et al. 2008). The correlation coefficients between
Cu, Mn, V, Zn, Al, Ni, and Ti were larger than 0.5 at the
p< 0.01 level, indicating that these metals are associated with
each other and may have common sources in the sediments
(Table 3). Cu, Mn, V, Zn, and Al are significantly positively
Fig. 4 The spatial distribution of heavy metal concentrations in the RSS of the IMYR and their evaluated contamination factors (CF). The red and black
dashed lines reflect the average concentration and background value for each heavy metal, respectively
Fig. 5 Heavy metal potential ecological risk indexes and pollution
loading indexes of the IMYR
Environ Sci Pollut Res

correlated (the correlation coefficients are larger than 0.9),
which may suggest a common origin, while Ni formed the
same group based on the significantly positive correlation of
Ni, V, and Al (Ni vs. V for 0.889 and Ni vs. Al for 0.871).
Although the correlation coefficients between Ti, Cu, Mn, V,
Zn, Al, and Ni appeared significant (larger than 0.5), the
values of these correlation coefficients are not as high as the
other metals mentioned above, implying an independent
source within a single group. Co is negatively correlated with
the other metals, reflecting different sources of Co compared
with the other elements. The absence of correlation among the
metals suggests that the concentrations of these metals are not
controlled by a single factor but by a combination of geo-
chemical support phases and their mixed association (Islam
et al. 2015; Paramasivam et al. 2015). Hence, in the present
study area, Cr is moderately to slightly correlated with the
other metals, which is clustered for one group.
The results of cluster and principal component analyses
match well with the Pearson correlation analysis. From the
overall statistical analyses, nine elements in the RSS of the
IMYR were classed into four significant groups: group 1
consisted of Cu, Mn, V, Zn, and Al; and groups 2, 3, and 4
consisted only of Ti, Co ,and Cr, respectively.
Identification of heavy metal contamination
Because the calculated Igeo and CF values of Al (−1.13 to
−0.13 for I
geo
and0.68to1.37forCF)indicatenon-
contamination to low contamination in the RSS of the
IMYR, the aluminous abundance in the Earth’scrustis
Tabl e 3 Statistical results of PCA and Pearson correlation analysis
Pearson correlation matrix of metals in the 1N1YR:
Co Cr Ti Cu Mn Ni V Zn Al
Co 1
Cr 0.090 1
Ti −0.547** 0.199 1
Cu −0.702** 0.082 0.502** 1
Mn −0.694** 0.143 0.624** 0.948** 1
Ni −0.601** 0.420** 0.540** 0.927** 0.904** 1
V−0.773** 0.119 0.652** 0.929** 0.968** 0.889** 1
Zn −0.759** 0.045 0.564** 0.990** 0.957** 0.912** 0.950** 1
AI −0.798** 0.004 0.551** 0.957** 0.936** 0.87I** 0.950** 0.976** 1
Principal component analysis of metals in the ININR:
Latent roots:
1 23456789
6.698 1.179 0.653 0.324 0.0766 0.034 0.023 0.009 0.004
Rotated loading matrix:
12
Co −0.826 0.193
Cr 0.026 0.982
Ti 0.639 0.242
Cu 0.964 0.067
Mn 0.962 0.138
Ni 0.884 0.407
V0.975 0.099
Zn 0.986 0.027
Al 0.982 −0.030
Variance explained by rotated components:
12
6.614 1.262
Percent of total variance explained:
12
73.493 14.021
**Correlation is significant at the 0.01 level (two-tailed)
Environ Sci Pollut Res

relatively high (Pekey et al. 2004; Zhang et al. 2009;Liand
Zhang 2010), which may indicate, therefore, that the Al comes
predominantly from natural origins. For instance, the higher
EF, I
geo
, and CF values of Cu, Zn, and Ni indicate that these
metals are the major contributors to sediment pollution and
mainly controlled by anthropogenic activities such as non-
ferrous metal mining and refining, manufacture and applica-
tion of chemical phosphate fertilizers, organic fertilizers and
pesticides, and waste disposal (Han et al. 2006; Bai et al.
2012;Wangetal.2011). However, combined with rock
weathering and soil formation (Huang et al. 2007;Liand
Zhang 2010), the moderate contamination of Mn and V in
the RSS was due to the dual influences of anthropogenic ac-
tivities and natural origins. The Ti in the RSS of the IMYR
faces a similar situation. Although Ti is an element that is
widely applied in the petroleum, coal, metallurgy, and chem-
ical industry (Mohammad et al. 2010), the CF, EF, and I
geo
values of Ti showed only moderate contamination. The sig-
nificant high contamination of Cr, observed in the areas bor-
dering the Bayan Nur (sampling site 3; Figs. 2and 4), might
be due to the effects of mining, metal smelting and
manufacturing in this city. However, the residual sampling
sites only reach the moderate contamination level, which in-
dicates natural sources of contamination (Fig. 3).
Among the sites,the range of CF, EF, and I
geo
values for Co
were 0.12 to 2.21, 0.09 to 1.77, and −3.62 to −0.31, respec-
tively, indicating an unpolluted to moderately polluted status
of the RSS. Combined with the result of the descriptive statis-
tical analysis that the average concentration of Co was less
than its background value, the Co in the RSS of the IMYR was
not due to anthropogenic activities. Further evidence shows
that the surface sediments from the Ulan Buh Desert and the
Hobq Desert also had high concentrations of Co (280–
300 mg/kg; Guan et al. 2016; Pan et al. 2015), almost two
times that of the surface sediments in the riverbeds
(143.52 ± 68.58 mg/kg), and they can be transported into the
RSS by wind erosion and solifluction to increase the concen-
tration of Co in the RSS.
Conclusion
In the present investigation, high PLI and RI values of heavy
metals were presented in the urban areas bordering the Yellow
River, which suggests that the RSS of the IMYR is moderately
polluted (the average value of PLI is 2.65) by heavy metals
and might create a low risk (the average value of RI is 58.34)
to this riverine ecosystem. The concentrations of Zn, Ni, and
Cu in the RSS of the IMYR were higher than their background
values; heavy contamination of these metals was determined
by the values of EF, I
geo
, and CF, and this was due to the
influence of anthropogenic activities. The moderate contami-
nation of Ti, Mn, V, and Cr indicated by the values of I
geo
and
CF was partly ascribed to the influence of anthropogenic ac-
tivities. The slight to no contamination with Co (EF <1.5; I
geo
<0; CF <1) shows that it is mainly from the bordering deserts.
The Co and Al in the RSS were mainly from natural sources
because the I
geo
and CF values indicate non-contamination.
However, the concentrations of Co in the RSS bordering the
deserts were high due to the inputting of desert materials. The
spatial distributions of Mn, Ni, V, Zn, and Cu concentrations
were similar in that high concentrations occurred in the areas
bordering the cities of Baotou and Dalad Banner. In addition,
high concentrations of Cr and Ni also occurred in the area
bordering Bayan Nur city, which suggests that industrial ac-
tivities from these cities played a major role in the heavy metal
contamination of these areas.
Fig. 6 Principal component (PC) loadings of the first two PCs of the heavy metals in the RSS of the IMYR (a). HCA shows the relevant association
among the parameters (b). Distance metrics are based on the Ward’s distance single linkage method (b)
Environ Sci Pollut Res

Acknowledgment We would like to express our sincere gratitude to the
editor and reviewers who have put considerable time and effort into their
comments on this paper. We are grateful to the first reviewer and profes-
sional editing service (Elsevier Language Editing Services) for improving
the languageof our manuscript. This work was supported by the National
Basic Research Program of China (No. 2011CB403301) and the National
Natural Science Foundation of China (Grant No. 41671188).
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