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


Abstract and Figures

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. Graphic abstractᅟ
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Heavy metals in the riverbed surface sediment of the Yellow
River, China
Qingyu Guan
&Ao Cai
&Feifei Wang
&Lei Wang
&Tao Wu
&Baotian Pan
Na Song
&Fuchun Li
&Min Lu
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
, 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
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
*Lei Wang
Key Laboratory of Western Chinas 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,
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 (Chinas 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
(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
(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 (150363 mm). The duration of
sunlight is long (10 °C accumulated temperature
3004~3515 °C), and evaporation is intense (the annual
mean evaporation of 19393482 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
Chinas 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
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
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
for Co, 36.19 mg kg
for Cr,
1.90 mg kg
for Cu, 330 mg kg
for Mn, 3.80 mg kg
Ni, 2277 mg kg
for Ti, 37.41 mg kg
for V, 7.51 mg kg
for Zn, and 5.77 % for Al. Approaches such as enrichment
factor (EF), geo-accumulation index (I
), 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 sediments 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=AlðÞBackground ð1Þ
where (C/Al)
is the metal to Al ratio in the sample of
interest, and (C/Al)
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
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
is defined by the
following formula:
Igeo ¼log2
 ð2Þ
where C
is the measured content of the metal, C
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
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
CF ¼CSample
where C
is the mean content of the metal from the
sampling site, and C
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<EF5 Moderate enrichment
35<EF20 Significant enrichment
420<EF40 Very high enrichment
5 EF > 40 Extremely high enrichment
0 Practically uncontaminated
1 Uncontaminated to moderately contaminated
2 Moderately contaminated
Geo-accumulation index (I
)3 2<I
3 Moderately to strongly contaminated
4 Strongly contaminated
5 Strongly to very strongly contaminated
< 5 Very strongly contaminated
1 CF < 1 Low contamination factor
Contamination factor (CF) 2 1 < CF 3 Moderate contamination factor
33<CF6 Considerable contamination factor
4 CF < 6 Very high contamination factor
1RI150 Low ecological risk
Ecological risk index (RI) 2 150 < RI 300 Moderate ecological risk
3300<RI600 High ecological risk
4 RI > 600 Significantly high ecological risk
1PLI1 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
PLI ¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
where C
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:
 ð5Þ
where C
is the content of the element in the sample, C
is the
background value of the element, C
is the single element
pollution factor, E
is the RI of an individual element, and
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
. 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 datas 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). KaiserMeyer
Olkin (KMO) and Bartletts 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. Bartletts 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 >
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
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
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
that reached the moderate or contaminated level (I
in all of the RSS sites were 97 % for Cu, 92.5 % for Ni,
and0%forTi(Fig.3ac). Assessment of the sediment
integrated pollution degree showed that the EF evaluated
result is in concordance with the I
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
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
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
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
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
leadacid 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
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 36222.1). 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, 2123, 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
) value of f heavy metals in the RSS of the IMYR, China (a), and the I
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
contamination to low contamination in the RSS of the
IMYR, the aluminous abundance in the Earthscrustis
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
V0.773** 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:
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:
6.614 1.262
Percent of total variance explained:
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
, 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
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
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.
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
, 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
CF was partly ascribed to the influence of anthropogenic ac-
tivities. The slight to no contamination with Co (EF <1.5; I
<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
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 Wards 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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... 2 When these pollutants are released into the environment, they enter the atmosphere, undergo hydrological circulation, and are finally deposited in riverbeds, reservoirs, and river deltas. 3 Naturally, sediments absorb a wide variety of pollutants, such as heavy metals, in their structure and surface. High levels of heavy metals in sediments may represent an anthropogenic source of pollution. ...
... The metals contained in this fraction have high mobility and bioavailability, which depend on changes in the pH and/or redox conditions of the sediment. 193.3.3 | Fraction III: Fraction linked to organic matter (oxidizable fraction)In the Bacanuchi River, the minimum and maximum metal concentrations were (mg/kg): Cd and Pb (BDL), Cr (BDL-2), Cu(1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13), Fe (127-245), Mn(12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22), Ni (BDL-7), and Zn(3)(4)(5)(6)(7)(8)(9)(10)(11)(12). Metal mobility was as follows: Ni > Zn > Cr = Cu. ...
... The metals contained in this fraction have high mobility and bioavailability, which depend on changes in the pH and/or redox conditions of the sediment. 193.3.3 | Fraction III: Fraction linked to organic matter (oxidizable fraction)In the Bacanuchi River, the minimum and maximum metal concentrations were (mg/kg): Cd and Pb (BDL), Cr (BDL-2), Cu(1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13), Fe (127-245), Mn(12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22), Ni (BDL-7), and Zn(3)(4)(5)(6)(7)(8)(9)(10)(11)(12). Metal mobility was as follows: Ni > Zn > Cr = Cu. ...
Full-text available
The objective of this research is to assess of heavy metal pollution and its fractionation in sediments of the Sonora and Bacanuchi rivers located in arid and semi‐arid climates. Sediment quality guidelines (U.S. EPA; SQGs), enrichment factor (EF) and geoaccumulation index (Igeo) were used to evaluate the extent of heavy metal pollution. Total heavy metals concentrations for both rivers were as follows (mg/kg): Cd (< BDL), Cu (2 ‐ 103), Cr (5 ‐ 27), Fe (9156 ‐ 34343), Mn (114 ‐ 573), Ni (22 ‐ 38), Pb (12 ‐ 59), and Zn (27 ‐ 111). According to sediment quality guidelines Cr, Ni, Pb, and Zn were classified as moderately polluted, while Cu and Fe were strongly polluted. Metals predominated in geochemical fractions in the following order: Residual> Fe/Mn oxides> interchangeable> organic matter. A significant proportion of heavy metals (Cu, Mn, Ni, Pb, Zn) was associated to the nonresidual fraction, proving that those metals have greater mobility and may be bioavailable to living beings. EF values indicated that Cu, Cr, Mn, Ni, Pb, and Zn come from enriched sources due to anthropogenic activities. In both rivers, Igeo values indicated non contaminated or moderately contaminated levels in the sediments. The present study confirms an anthropogenic source of heavy metals, which could be a danger for biota in the surrounding areas, due to its mobility and bioavailability. Therefore, it is recommended to monitor water and sediment quality periodically.
... EFs are used for the evaluation of trace element contamination in sediments (Resongles et al. 2014;Xu et al. 2014) and their calculation implies the use of a normalising element, which is very often Al (Guan et al. 2016;Zahra et al. 2014) as it is one of the most abundant elements on the earth and usually has no contamination concerns (Zhang et al. 2009). EFs are calculated using the following equation: ...
... where C TE is TE concentration measured in the sample, C BG is the corresponding TE background concentration and q is the background matrix correction factor, accounting for the possible differences in the background values owing to lithospheric effects (Bhuiyan et al. 2010;Lu et al. 2009;Varol 2011). EF and Igeo results were interpreted following the guidelines presented in Table 3 ( Guan et al. 2016). ...
... To facilitate the interpretation of the results and to highlight common patterns, a Pearson's correlation test was performed on the results. High correlation coefficients between different metals, which are considered as significantly different from zero at a 95-% confidence interval, means common sources, mutual dependence and identical behaviour during their transport (Guan et al. 2016). ...
Environmental contextTrace elements in coastal environments represent an environmental concern and their monitoring in sediment cores provides insight into their historical sources. A well-dated core from Kiel Bay, western Baltic Sea, provided trace element data, including lead, cadmium, rare earth elements, mercury and methyl mercury. Lead and mercury isotope ratios were useful for the apportionment of pollution sources, indicating that coal burning was a major contributor. AbstractWe present a comprehensive study on the variation of trace elements (TEs) and rare earth elements (REEs) in a well-dated sediment core from Kiel Bay, western Baltic Sea. Mass fractions of 34 elements (major and trace) together with other relevant parameters, such as organic carbon and grain size, were determined in a 20-cm core that covers the last century. Enrichment factors and geoaccumulation indices were determined to assess the possible influence of anthropogenic inputs on element distribution. The obtained results show that the highest enrichment of TEs occurred in the period 1917–1970 especially for the priority elements as Hg, Cd and Pb. Determination of methylmercury (MeHg) was also performed, as it showed the highest content in surface samples. The MeHg percentages ranged from 0.02 to 1.2% of the total Hg. REEs, which are nowadays considered as new emerging contaminants, did not reveal high enrichment attributable to anthropogenic influences, but provided useful baseline information for future monitoring of the area. The study of the Pb isotopic composition proved to be a valuable tool in determining the Pb pollution source, and revealed Pb in the layers that showed the highest enrichment came mainly from coal burning. Mercury isotopic signatures in the sediment core were used as a tool to identify the sources of Hg pollution. An isotope mixing model based on mass-dependent (MDF) and mass-independent fractionations (MIF) identified coal burning as the most probable dominant source for Hg anthropogenic contamination in the area.
... Guan Qingyu has studied the heavy metal pollution in the soil of southern Tengger Desert, analyzed the distribution and difference between city soil and natural soil, and found that CE, La, and Nd are widely distributed in various areas regardless of human activities, and other metal elements are closely related to human activities and natural processes (Guan et al. 2014). At the same time, he has also studied heavy metal pollution in the oasis desert region of Northwest China and the riverbed surface sediment in the Inner Mongolia section of the Yellow River, and determined the spatial distribution, pollution degree, and health risk of metals (Guan et al. 2016a(Guan et al. , 2018(Guan et al. , 2016b(Guan et al. , 2016c. As for the other authors, the number of published papers is less than 5, and most of the authors are in scattered distribution, indicating that less scholars in the international community study heavy metal pollution in desert, scientific research teams lack academic communication, and their research is relatively independent and lacks cooperation. ...
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In this paper, Web of Science (a database) is used to retrieve related literature in the field of heavy metal pollution in desert. CiteSpace is used to make a quantitative and qualitative evaluation on the literature in the field on the basis of a brief analysis on the research status, research focus, and evolution process in the field. Through CiteSpace visual analysis, a comparative analysis is given on related literature in terms of annual number of published papers, author groups, and their countries and regions, journals, publishing institutions, highly cited papers, research focuses, and burst terms, so as to explore the research status and future development trend of the field on a global scale. The results are shown as follows: (1) The literature in the field was originally published in 2000; the number of published papers increased steadily. The literature was mostly published on high-quality journals, the USA topped in terms of the number of published papers, and the research results achieved by developed countries had a greater influence. Chinese Acad Sci topped with the highest centrality and most published papers, which have made outstanding contributions to the field and occupy a leading position in the field. However, the fact is that there lacks communication and cooperation among research institutions. The most influential journal is Science of the Total Environment. (2) The hot research words in the field are as follows: heavy metal, soil, pollution, lead, desert, cadmium, and microelement. (3) In the field, burst terms have transformed from atmospheric deposition, biomonitoring, and phytoremediation to trace element, stream sediment, street dust, and water quality, and finally transformed to river and sediment. New words keep emerging in the research, and more and more attention is paid to the issue of heavy metal pollution in river sediment, which will be a future research hotspot in the field.
... Similarly, Igeo values ranged from moderately to heavily contaminated, where the main contributors were Cu, Mn, Li, Pb, and Zn. Commonly, contamination by trace elements is associated with industrial activities and urban development [52,[73][74][75][76]. However, in some sediments from areas with high population density and significant industrial activity, pollution by heavy metals was not detected [69]. ...
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The water management initiatives in freshwater systems focus on water availability to preserve this resource for human uses and the health of aquatic ecosystems. This work presents an assessment of the potential pollution risk caused by the metal availability in suspended sediments. The objective of this study was to determine the partitioning, association, and geochemical fractionation of metals in suspended sediments from a surface water body. Additionally, the environmental assessment for this reservoir was estimated using geoaccumulation, enrichment, and pollution indices of metals and the related potential risk by their elemental availability (RAC). Chemical, mineralogical, and morphological characterizations were obtained by inductively coupled plasma spectrometry, alpha spectroscopy, X-ray crystallography, and scanning electron microscopy. Clay, quartz, montmorillonite, and calcite were the main minerals of suspended sediments. Chemical fractionation was the parameter affecting the concentrations of metals in suspended sediments. The sediment composition is of natural origin; however, these finer particles can promote the scavenging of toxic metals. It contributes to obtaining moderate to high levels for enrichment/contamination indices. Although Ca, Mg, Sr, and U were the most accessible metals for aquatic biota, Pb and Mn in the exchangeable phase of suspended sediments are the potentially toxic elements in this aquatic ecosystem.
... The reference material (Average shale) used is recognized worldwide as the reference background values of unpolluted areas (Turekian & Wedepohl, 1961;Haynes, 2016 metal is entirely crystallized in the sediment while the higher FE values to 1.5 or 2 suggest anthropogenic sources (Garcia et al., 2008;Abreu et al., 2016). According to the EF of an element, Loska et al. (2004) and Guan et al. (2016) classified the enrichment factor (EF) into five levels: no or slight enrichment (EF < 2), moderate enrichment (2 ≤ EF < 5), significant enrichment (5 ≤ EF < 20), high enrichment (20 ≤ EF < 40) and extremely high enrichment (EF > 40). ...
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Seven sediment cores were collected from De Montigny Lake in order to determine concentrations, and contamination assessment of heavy metals such as Cr, Zn, Ni, Pb, Cu, Co and Cd. The mean concentrations of heavy metals are as follows: 48.3 mg/kg for Cr, 36.4 mg/kg for Zn, 20.6 mg/kg for Ni, 14.7 mg/kg for Pb, 10.2 mg/kg for Cu, 6.7 mg/kg for Co and 0.1 mg/kg for Cd. Based on the sediment quality guidelines, the mean concentration metals such as Cr, Cu and Ni exceeded the US Environmental Protection Agency (USEPA) guideline. However, the concentration of Cr was more than the Canadian Water Quality Guidelines for Protection of Aquatic Life (CCME), and Threshold Effect Level (TEL) guidelines. The metal contamination in the sediments was also evaluated using Enrichment Factor (EF) and geoaccumulation index (Igeo) to assess natural and anthropogenic factors. The results of enrichment factor methods demonstrated that sediments from De Montigny Lake were moderately to high enriched, mainly controlled by through anthropogenic activities. According to Sediment Quality Guidelines (SQGs), the concentrations metals from the core sediment of De Montigny Lake are classified as having moderate impacts with potential adverse biotoxic effects.
... PCA is a data reduction technique that reduces number of variables to a smaller set of scores called components without losing much of primary data [15]. Varimax rotation was employed in current analysis since variables were presumed to be uncorrelated [33]. ...
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Ewaso Nyiro basin covers an area of about 210,226 km ² , 36.3%, of Kenya drainage area and bears 5.8% of Kenya water potential with an annual yield of 1469 million m ³ . The river is the principal source of domestic and irrigation water to the arid north of Kenya. To determine metal and nutrient concentration of Ewaso Nyiro River surface water, a total of 30 water samples, 15 samples each for dry (February) and wet (August) seasons of 2019, were collected. Chromium, lead, iron, manganese, cobalt, cadmium, mercury, selenium, molybdenum, boron, copper, zinc, arsenic, nickel, aluminum, total phosphorus and nitrate were analyzed in the two seasons. Ecological risk assessment was determined by calculating contamination factor, pollution load index and ecological risk index. Multivariate statistical analysis was used to infer pollutants association and identify their potential sources. Cadmium, arsenic, lead, molybdenum, mercury, selenium and nickel were not detected in both seasons, while manganese, iron and aluminum were the main pollutants identified. Ewaso Nyiro irrigation water had a manganese contamination factor of 9.17, implying it was very contaminated. Twenty-seven and 40% of sampled sites in dry and wet seasons, respectively, had more than 0.3 mg/L of iron that is recommended by USEPA in drinking water. Herbicides, leached fertilizer and fuel leaking into the river water were the primary sources of anthropogenic pollution.
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The paper presents the results of research into toxic metal concentrations in the surface layer of bottom sediments in Lake Gopło. The research objectives were to identify the levels and spatial variability of Cu, Pb, Cd, Zn, Ni, Cr, As and Hg concentrations, their potential sources and the determinants of pollution levels. Metal contamination of the sediments was assessed using the geoaccumulation index (Igeo), pollution load index (PLI) and ecological risk index (RI). Chemometric methods (Pearson correlation, principal component analysis (PCA) and cluster analysis (CA) were used to determine the relationship between sampling sites and concentrations of toxic metals, thereby identifying the sources of contamination. The research found that grain-size composition, carbonate content and organic matter content in the bottom surface sediments of Lake Gopło are all characterised by low diversity. Therefore, the lithological features of the sediments are not a major factor in the concentrations and spatial variability of the metals. It was found that the metal concentrations in the great majority of samples were above regional geochemical background levels. The geochemical indices (Igeo, PLI, RI) indicate that the degree of toxic metal pollution in the sediments is slight in the central and southern parts of the lake and high in the northern part. The chemical analysis results showed that the samples in the central and southern parts of the lake differ little in their shares and concentrations of individual metals. This provides evidence that, as well as geogenic sources, their presence in sediments can be associated with non-point sources related to agricultural activities and with atmospheric sources (mainly the products of fossil fuel combustion). The higher concentrations of metals (especially Ni, Cd, Cr and Hg) in the northern part of the lake are influenced by the supply of industrial and communal pollutants from the lakeside town of Kruszwica. A factor limiting the migration of pollutants from the northern part of the lake towards the south is the lake's morphology of the lake, which hinders water exchange between the northern part and the rest of the lake.
Accompany with many chemical industries and mineral smelting plants, heavy metal pollution in the Yangtze River Economic Belt (YREB) has threatened public health and ecological safety, especially that in water environments. Based on bibliometric analysis and the time weight vector, we reviewed eight heavy metals pollutants in the surface water and sediments. Under good uncertainty control, we analyzed the fuzzy health risk and ecological risk, and the priority control metals and regions were established. Results show that the heavy metal pollution in surface water was light and mainly distributed downstream. Conversely, concentrated in the upstream areas, pollution in surface sediments exceeded the soil background values by more than 60%. The fuzzy total average carcinogenic risk was: As (6.12 × 10⁻⁵) > Cr (5.42 × 10⁻⁵) > Cd (3.81 × 10⁻⁵). The potential ecological risk in Yunnan and Guizhou was over 20 times the Grade IV (600) level, with the highest contribution being from Cd and Hg. Finally, five provinces were established as the priority control regions, and the heavy metal pollution sources were identified as combined pollution, mainly released due to the development and smelting of mineral resources, especially the smelting of nonferrous metals and the discharge of industrial wastewater.
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Serious pollution of multiple chemicals in irregulated e-waste recycling sites (IR-sites) were extensively investigated. However, little is known about the pollution in regulated sites. This study investigated the occurrence of 21 polybrominated diphenyl ethers (PBDEs) and 10 metals in a regulated site, in Eastern China. The concentrations of PBDEs and Cd, Cu, Pb, Sb, and Zn in soils and sediments were 1–4 and 1–3 orders of magnitude lower than those reported in the IR-sites, respectively. However, these were generally comparable to those in the urban and industrial areas. In general, a moderate pollution of PBDEs and metals was present in the vegetables in this area. A health risk assessment model was used to calculate human exposure to metals in soils. The summed non-carcinogenic risks of metals and PBDEs in the investigated soils were 1.59–3.27 and 0.25–0.51 for children and adults, respectively. Arsenic contributed to 47% of the total risks and As risks in 71.4% of the total soil samples exceeded the acceptable level. These results suggested that the pollution from e-waste recycling could be substantially decreased by the regulated activities, relative to poorly controlled operations, but arsenic pollution from the regulated cycling should be further controlled.
Previous assessments on rivers in SE China with highly developed economy and enormous population indicate diverse and relatively low particulate heavy metal pollution levels. However, the controlling mechanisms for heavy metal enrichment and transport remain enigmatic. Here, we target a mesoscale mountainous river, the Minjiang River, and obtain grain size, mineralogical and heavy metal concentration (Pb, Cd, Cr, Mn, Mo, Zn, V, Co, Ni, Cu) data from seasonal suspended particulate matter (SPM) near the river mouth, riverbed sediments and SPM samples from mainstream and major tributaries of the river. The results indicate that SPM samples have higher particulate heavy metal concentrations than riverbed sediments collected in pairs. Heavy metal concentrations of Cd, Zn, Cr, V, Co, Ni and Cu are higher in upstream SPM samples than those in downstream regions, whereas Pb, Mn and Mo concentrations don't show this spatial variation. Most heavy metals (e.g., Pb and Zn) show high concentrations in flood seasons and relatively low concentrations in dry seasons, revealing a hydrologic control. However, Cr and Mn show high concentrations in some dry season samples, suggesting incidental anthropogenic input events. The SPM-based pollution assessments using enrichment factor, geoaccumulation index and potential ecological risk index demonstrate that the Minjiang River is moderately to strongly polluted by particulate Pb, Cd, Mo and Zn contaminations and most particulate heavy metals have moderate to considerable potential ecological risks. We contend that transport and discharge of particulate heavy metals by the Minjiang River are controlled by both natural and anthropogenic forcings and the pollution levels are worse than previously known. Our findings suggest that particulate heavy metal discharge by subtropical mountainous rivers is related to sediment types and hydrologic characteristics. Therefore, high-spatiotemporal-resolution investigations on river SPM samples are highly recommended to better evaluate particulate heavy metal pollution levels and aquatic environmental conditions.
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Despite the increasing impact of heavy metal pollution in southern Mexico due to urban growth and agricultural and petroleum activities, few studies have focused on the behavior and relationships of these pollutants in the biotic and abiotic components of aquatic environments. Here, we studied the bioaccumulation of heavy metals (Cd, Cr, Ni, Pb, V, Zn) in suspended load, sediment, primary producers, mollusks, crustaceans, and fish, in a deltaic lagoon habitat in the Tabasco coast, with the aim to assess the potential ecological risk in that important wetland. Zn showed the highest concentrations, e.g., in suspended load (mean of 159.58 mg/kg) and aquatic consumers (15.43-171.71 mg/kg), particularly Brachyura larvae and ichthyoplankton (112.22-171.71 mg/kg), followed by omnivore Callinectes sp. crabs (113.81-128.07 mg/kg). The highest bioconcentration factors (BCF) of Zn were observed for planktivore and omnivore crustaceans (3.06e3.08). Zn showed a pattern of distribution in the food web through two pathways: the pelagic (where the higher concentrations were found), and the benthic (marsh plants, sediment, mollusk, fish). The other heavy metals had lower occurrences in the food web. Nevertheless, high concentrations of Ni and Cr were found in phytoplankton and sediment (37.62-119.97 mg/kg), and V in epiphytes (68.64 mg/kg). Ni, Cr, and Cd concentrations in sediments surpassed international and national threshold values, and Cd entailed a “considerable” potential risk. These heavy metals are most likely transferred into the food web up to fishes through the benthic pathway. Most of the collected fishes are residents in this type of habitat and have commercial importance. Our results show that the total potential ecological risk in the area can be considered as “moderate”. Nevertheless, heavy metal values were similar or surpassed the values from other highly industrialized tropical coastal regions.
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It is very important to strengthen the research about the heavy metal pollution of soil in vulnerable ecological regions of the south-central arid area of Northwest China for regulating and guiding local industrial and municipal activities and for protecting the environment. In this study, 48 surface soil samples were collected in the desert–loess transitional zone in the south of the Tengger Desert. The distributions of elements (heavy metal based) and the differences between urban and natural soils were analyzed. We observed that As, Pb, Cu, Zn and S were clearly enriched in the Baiyin area, and Ni and Cr were mainly enriched in the Zhongwei area. V, Mn, Ti, Bi, Co and W were enriched in the southeast margin of the Tengger Desert, where there is relatively little human activity. Over the entire study area, Ce, La and Nd were widely distributed across regions whether with strong or weak human activity. Based on the distributions of elements, we suggest that in the desert–loess transitional zone in the south of the Tengger Desert, the distribution and abundances of element As, Pb, Cu, Zn, S, Ni and Cr are strongly related to the human activities in the area, but the elements V, Mn, Ti, Bi, Co, W, Ce, La and Nd are derived mainly from natural sources.
The spatial distribution, bioavailability, potential risks and emission sources of 12 heavy metals in sediments from an acid leaching site of e-waste were investigated. The results showed that the sediments from the acid leaching site were significantly contaminated with Cu, Zn, Cd, Sn, Sb and Pb, especially in the middle sediments (30–50 cm), with average concentrations of 4820, 1260, 10.7, 2660, 5690 and 2570 mg/kg, respectively. Cu, Cd and Pb were mainly present in the non-residual fractions, suggesting that the sediments from the acid leaching site may exert considerable risks. Mn, Ni, Zn, Sn and Sb were predominantly associated with the residual fraction. Despite their low reactivity and bioavailability, uncommon pollutants, such as Sn and Sb, may exert environmental risks due to their extremely elevated total concentrations. All of these results indicate that there is an urgent need to control the sources of heavy metal emission and to remediate contaminated sediments. Capture abstract In addition to Ni, Cu, Zn, Cd and Pb, the sediments from an acid leaching site in Guiyu were heavily polluted with uncommon heavy metal pollutants, such as Sn and Sb.