International Journal of

Environmental Research
and Public Health

Article
Heavy Metals in Sediment from the Urban and Rural
Rivers in Harbin City, Northeast China
Song Cui 1, * , Fuxiang Zhang 1 , Peng Hu 2 , Rupert Hough 3 , Qiang Fu 1 , Zulin Zhang 3 ,
Lihui An 4 , Yi-Fan Li 5 , Kunyang Li 1 , Dong Liu 1 and Pengyu Chen 1
1 International Joint Research Center for Persistent Toxic Substances (IJRC-PTS), School of Water Conservancy
and Civil Engineering, Northeast Agricultural University, Harbin 150030, China;
ZhangFuxiang823@163.com (F.Z.); ijrc_pts_neau_paper@yahoo.com (Q.F.); kunyleee@163.com (K.L.);
glwonder@163.com (D.L.); 18645148351@163.com (P.C.)
2 State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water
Resources and Hydropower Research, Beijing 100038, China; hp5426@126.com
3 The James Hutton Institute, Craigiebuckler, Aberdeen AB15 8QH, UK; Rupert.Hough@hutton.ac.uk (R.H.);
zulin.zhang@hutton.ac.uk (Z.Z.)
4 State Environmental Protection Key Laboratory of Estuarine and Coastal Research, Chinese Research
Academy of Environmental Sciences, Beijing 100012, China; anlhui@163.com
5 IJRC-PTS, State Key Laboratory of Urban Water Resource and Environment, Harbin Institute of Technology,
Harbin 150090, China; ijrc_pts_hit06@yahoo.com
* Correspondence: cuisong-bq@neau.edu.cn; Tel.: +86-451-5519-0568; Fax: +86-451-5519-0568




Received: 12 October 2019; Accepted: 5 November 2019; Published: 6 November 2019


Abstract: The concentrations and ecological risk of six widespread heavy metals (Cu, Cr, Ni, Zn,
Cd and Pb) were investigated and evaluated in sediments from both urban and rural rivers in a
northeast city of China. The decreasing trend of the average concentration of heavy metals was
Zn > Cr > Cu > Pb > Ni > Cd in Majiagou River (urban) and was Zn > Cr > Pb > Cu > Ni > Cd in
Yunliang River (rural). The results showed that the concentrations of Cd and Zn were significantly
elevated compared to the environmental background value (p < 0.05). Half of all sampling locations
were deemed ‘contaminated’ as defined by the improved Nemerow pollution index (PN ’ > 1.0).
Applying the potential ecological risk index (RI) indicated a ‘high ecological risk’ for both rivers,
with Cd accounting for more than 80% in both cases. Source apportionment indicated a significant
correlation between Cd and Zn in sediments (R = 0.997, p < 0.01) in Yunliang River, suggesting that
agricultural activities could be the major sources. Conversely, industrial production, coal burning,
natural sources and traffic emissions are likely to be the main pollution sources for heavy metals in
Majiagou River. This study has improved our understanding of how human activities, industrial
production, and agricultural production influence heavy metal pollution in urban and rural rivers,
and it provides a further weight of evidence for the linkages between different pollutants and resulting
levels of heavy metals in riverine sediments.

Keywords: heavy metals; sediment; contamination characteristics; possible source; ecological risk

1. Introduction
Heavy metal pollution in the aquatic environment has attracted extensive concern due to its
environmental persistence, potential adverse effects on human health and accumulation in the food
chain [1,2]. Once heavy metals enter the river, depending on the physico-chemical characteristics of
the river, they may be adsorbed to suspended particulate matter and later deposited to the sediments
under the action of gravity [3]. Thus, riverine sediments often act as a sink for heavy metals, leading to
elevated concentrations in sediments compared to inputs into the riverine system. If hydrodynamic

Int. J. Environ. Res. Public Health 2019, 16, 4313; doi:10.3390/ijerph16224313 www.mdpi.com/journal/ijerph
Int. J. Environ. Res. Public Health 2019, 16, 4313 2 of 15

conditions change or if changes to physico-chemical equilibria occur, metals present in the sediments
can be re-released into the water, thus causing secondary pollution [4]. Therefore, where sediments
act as a “sink” or “secondary source” for heavy metals, there is potential to use the sediments as an
effective environmental medium to monitor and evaluate the magnitude and sources of heavy metal
pollution in the aquatic environment [5–7].
With the rapid development of industry, the regular/increasing use of pesticides and fertilizers,
and the increasing intensity of human activities, large volumes of wastewater containing heavy metals
are discharged into aquatic systems. The atmospheric deposition of heavy metals from aeolian sources
could also lead to high pollution levels in water and sediment [8]. Pollution with heavy metals also
has the potential to occur during the processing and use of fossil fuels [9]. Thus, water pollution has
become an important issue that influences ecological quality and the sustainable development of the
social economy.
In China, the contamination of heavy metals in sediment from Pearl River, Liao River, Yangtze River
and Songhua River has caused widespread concerns since the late 1980s [10–14]. The statistical
evaluation of Cao [15] indicated that there was an increasing trend of heavy metal pollution from
the north to south of China. Additionally, the concentrations of heavy metals in sediments have
generally been found to be elevated in urban rivers compared to suburban and rural rivers [16], but this
urban-rural/suburban spatial distribution pattern might be diffused with urbanization [17]. Due to
these concerns, various indices and tools have been established for identifying potential the ecological
risk from heavy metal pollution as well as to support subsequent management/mitigation—these
include the Nemerow pollution index [18], the geo-accumulation index (Igeo ) [19,20], and potential
ecological risk [21]. Though the traditional Nemerow pollution index has been widely used to assess
ecological risk, it has a tendency to over-estimate ecological risk because it adopts a very precautionary
approach to risk estimation.
Harbin, one of the most important equipment manufacture and food production bases in China,
straddles the Songhua River. More than ten rivers flow through the city of Harbin, of which
Majiagou and Yunliang River are two representative rivers flowing through urban and rural areas,
respectively. As a result of rapid industrialization (Majiagou catchment) and agricultural intensification
(Yunliang catchment), both rivers have a relatively long history of receiving inputs/discharges of a
large range of pollutants. However, there have been few comprehensive comparative studies on the
distribution and sources of heavy metals, as well as the associated ecological risks for urban vs. rural
rivers. Thus, the objectives of this study were: (1) to reveal contamination levels and spatial distribution
characteristics of heavy metals in the sediments of the Majiagou River and Yunliang River; (2) to
identify the possible sources of heavy metals by Pearson’s coefficient coupled principle component
analysis (PCA); and (3) to evaluate the ecological risk by using the improved Nemerow pollution index
and the potential ecological risk index.

2. Materials and Methods

2.1. Study Area

The study area has a temperate continental monsoon climate [22]; the average temperature
is −19 ◦ C in January and 23 ◦ C in July, and the annual average temperature is 3.5 ◦ C. Rainfall is
mainly concentrated in the period from June to August. The Majiagou River (126◦ 410 –126◦ 430 E and
45◦ 320 –45◦ 490 N) flows through the central area of the city, and its course can be divided into three
sections which include the urban section (mUR, M1–M5), industrial zone (mIZ, M6–M8) and suburban
section (mSU, M9–M12). By contrast, the catchment of the Yunliang River (126◦ 170 –126◦ 380 E and
45◦ 300 –45◦ 410 N) is dominated by agricultural production, and thus these sampling sites named rural
section (yRU, Y1–Y6). Both these rivers are important tributaries of the Songhua River, which is the
major source of drinking water for inhabitants of Harbin and irrigation water for one of the most
Int. J. Environ. Res. Public Health 2019, 16, 4313 3 of 15

important food production bases in China [23]. Detailed information on sampling sites is illustrated
in Figure
Int. 1. Res. Public Health 2019, 16, x
J. Environ. 3 of 15

Locations of
Figure 1. Locations of sampling
sampling sites
sites in Majiagou
Majiagou River
River (M1–M12)
(M1–M12) and
and Yunliang River (Y1–Y6) in
Harbin City.
City.

2.2. Sample
2.2. Sample Collection
Collection
A total
A total of
of 18
18 surface
surface sediment
sediment samples
samples (12 (12 samples
samples (M1–M12)
(M1–M12) in in Majiagou
Majiagou River
River and
and 66 samples
samples
(Y1–Y6) in Yunliang River) were collected in October 2017. Sediment was collected
(Y1–Y6) in Yunliang River) were collected in October 2017. Sediment was collected by grab sampling by grab sampling
(0–10
(0–10 cm
cm from
from the
the surface)
surface) and
and stored
stored inin brown
brown glass
glass bottles
bottles that
that had
had been
been pre-washed
pre-washed with
with nitric
nitric
acid. At
acid. At each
each sampling
samplinglocation,
location,three
threesamples
samples were taken
were taken30 m30apart,
metersmixed well,
apart, andwell,
mixed then pooled
and thento
produce one representative sample per site. All sediment samples were stored in
pooled to produce one representative sample per site. All sediment samples were stored in a cooleda cooled container
and transported
container to the International
and transported Joint Research
to the International JointCenter
Research for Center
Persistent
for Toxic Substances
Persistent (IJRC-PTS)
Toxic Substances
(IJRC-PTS) laboratory at Northeast Agricultural University (Harbin) as soon as possible, andstored
laboratory at Northeast Agricultural University (Harbin) as soon as possible, and they were then they
in a refrigerator
were then storedprior to digestion.prior to digestion.
in a refrigerator
2.3. Sample Processing and Analysis
2.3. Sample Processing and Analysis
The treatment of the sediment sample was similar to the procedures used for the determination
The treatment of the sediment sample was similar to the procedures used for the determination
of heavy metals in the certified reference material for the environmental quality standard for soils
of heavy metals in the certified reference material for the environmental quality standard for soils
(GB15618-1995) [24]. The sediment samples were lyophilized, and plant roots, gravel and other foreign
(GB15618-1995) [24]. The sediment samples were lyophilized, and plant roots, gravel and other
matter were removed prior to grinding. Approximately 0.5 g of ground sample was digested in a Teflon
foreign matter were removed prior to grinding. Approximately 0.5 g of ground sample was digested
crucible on a hot plate by wet digestion (HCL–HNO3 –HClO4 –HF) (guaranteed reagent, Tianjin Yaohua
in a Teflon crucible on a hot plate by wet digestion (HCL–HNO3–HClO4–HF) (guaranteed reagent,
Tianjin Yaohua Chemical Reagent Co., Ltd.), until there were no obvious solid particles in the
crucible and no white smoke escaped. At this point, the crucible was removed from the hot plate and
allowed to cool to room temperature. The digestate was then diluted to 50 mL using deionized
water, and it was mixed thoroughly before storage at 4 °C prior to instrumental analysis. The
Int. J. Environ. Res. Public Health 2019, 16, 4313 4 of 15

Chemical Reagent Co., Ltd.), until there were no obvious solid particles in the crucible and no white
smoke escaped. At this point, the crucible was removed from the hot plate and allowed to cool to
room temperature. The digestate was then diluted to 50 mL using deionized water, and it was mixed
thoroughly before storage at 4 ◦ C prior to instrumental analysis. The concentrations of heavy metals
in the pretreated samples were determined using the ICE 3500 (Thermo Fisher Scientific, Waltham,
MA, USA) atomic absorption spectrophotometer; Cu, Cr, Ni, Zn were measured using the flame
portion, and the graphite furnace portion was used for the detection of Cd and Pb.

2.4. Quality Assurance/Quality Control

All the crucibles and glass containers were soaked in 10% HNO3 for 24 h, washed with ultrapure
water, and dried before use. Blank and standard samples were used—one per each set of 6 sediment
samples. The equivalent acid was added into a Teflon crucible without any exogenous substances in it,
and the same digestion program was performed with the sediment sample to make the blank sample.
Standard reference materials (GBW07305) from the Chinese Academy of Measurement Sciences were
used to make the standard sample, and then they were digested and analyzed using the same procedure.
The average recovery rate was 93%, and the concentration of heavy metals in blank samples was
always below the minimum detection limits. The correlation coefficient of calibration curves of the
6 heavy metals was greater than 0.9995 (0.995 is the minimum permissible limit for instrument test),
and the standard deviation between parallel samples was less than 5%.

2.5. Pollution Assessment Methods

2.5.1. Single Factor Pollution Index and Improved Nemerow Pollution Index
The single factor pollution index (Pi ) can be used to assess the magnitude of pollution attributed
to single pollutants in sediment. Deriving Pi for each measured pollutant in turn can be useful for
highlighting the most important pollutant in the suite of pollutants investigated [6]. The single factor
pollution index for heavy metals is calculated as:

Pi = Ci /Cire f (1)

where Ci is the measured concentration of heavy metals and Ciref is the environmental background value
which represents the element content of environment medium in the case of without any influences by
exogenous substances. Here we chose the I standard value of the Environmental Quality Standard for
Soils (GB15618-1995) proposed by the State Environmental Protection Administration of China (SEPA)
(Cu: 35 mg/kg, Cr: 90 mg/kg, Zn: 100mg/kg, Pb: 35mg/kg, Ni: 26 mg/kg and Cd: 0.2 mg/kg) [24].
The Nemerow pollution index [18] has been widely applied in the evaluation of heavy metal
pollution. However, this method has a tendency to over-estimate the magnitude of heavy metal
pollution [25]. This is because the method neglects differences in the toxicological profiles of the
different metals as well as their relative importance. Thus, the Nemerow pollution index can be
modified using different weighting factors that act as proxy measures for the biological toxicity and
relative importance of the different heavy metals.
In this study, the weighting factors were derived using the method of Deng [25]. Briefly,
a comprehensive weight was derived from the relative importance of each heavy metal (Rr i = Cimax /Ciref )
and the relative toxic importance (Rt i = Timax /Tiref ); where Cimax and Timax are the maximum background
concentrations and maximum toxicity for each heavy metal, respectively, and Tiref refers to the toxicity
coefficient (Cd = 30, Cr = 2, Zn = 1, Cu = Pb = Ni = 5.) [21,26]. The comprehensive weight was
calculated by:
Rri Rti
wi = n + n (2)
2 Rri 2 Rti
P P
i=1 i=1
Int. J. Environ. Res. Public Health 2019, 16, 4313 5 of 15

The equations for calculating the traditional Nemerow pollution index (PN ) and improved the
Nemerow pollution index (PN ’) are as follows:
s
P2iave + Pi 2max
PN = (3)
2
s
P2iave + Pi 2wi max
P0N = (4)
2
where PN is the improved Nemerow pollution index; Piave and Pimax are the mean and maximum values
of the single pollution index, respectively; and Piwmax is the top pollution factors of comprehensive
weight in all the pollution factors.
On this basis, this study also determined the corresponding evaluation criteria according to
environmental quality standard for soils (GB15618-1995) [24] in order to better reflect the comprehensive
effect of heavy metal pollution objectively (Table S1 in Supplementary Information).

2.5.2. Potential Ecological Risk Index

The potential ecological risk index (RI) was employed to assess the potential risks of one or
multiple ecological factors [21]. The classification standard of RI proposed by Hakanson was based
on considering the toxicity of 8 parameters (polychlorinated biphenyls (PCBs), Hg, Cd, As, Pb, Cu,
Cr and Zn). In this study, only 6 of these parameters were included, so the classification was adjusted
using the method of Chen [27]. The potential ecological risk index (RI) was calculated as follows:
m
X
RI = Eir (5)
i=1

Eir = Tire f × P ji (6)

where RI is the potential ecological risk index, Er i is the single ecological risk index of each heavy
metal, Pji is the single pollution index, and Tiref is the toxicity coefficient of each heavy metal [21,26].
The classification of Pi , PN , Er and RI are presented in Supplementary Table S1.

2.5.3. Statistical Analysis

A Pearson correlation matrix (PCM) and principal component analysis (PCA) were performed in
order to elucidate any associations between the heavy metals. Prior to this, the Kolmogorov–Smirnov
test was used to confirm normality. One-sample and independent-samples t-tests were conducted to
identify within-river and between-river differences in concentrations of the heavy metals in sediments.
A Pearson correlation analysis was then applied to analyze the strength of association between the
detected contents of heavy metals, which was considered to be significant if p < 0.05.
A principal component analysis (PCA) was used to help identify the potential sources of
contamination. Results were refined by applying a varimax rotation in order to reduce the
number of heavy metals that have high loadings on each factor [28]. Prior to the PCA, through
Kolmogorov–Smirnov test identified that all the heavy metal data were fit for normal distribution
(p > 0.05), the suitability of the data for factor analysis was determined using the Kaiser–Meyer–Olkin
(KMO) test for sampling adequacy as well as the Bartlett sphericity test. Additionally, all the data
(a total of 72) were qualified in which the KMO > 0.5 and the significance level of the Bartlett sphericity
test p < 0.001. In this study, factors with a cumulative contribution of variance >90% were selected
for inclusion.
Int. J. Environ. Res. Public Health 2019, 16, 4313 6 of 15

3. Results

3.1. Concentrations
The concentrations of six heavy metals in the surface sediments of Majiagou River and Yunliang
River are presented in Figure 2. The concentrations of measured heavy metals in Majiagou River
were: Cu (4.00–82.54), Cr (75.12–203.15), Zn (128.17–1416.71), Pb (8.86–57.49), Ni (7.91–30.38), and
Cd (0.08–4.08) mg/kg dw (dry weight). The average concentrations of heavy metals decreased in the
following order: Zn (358.54) > Cr (107.37) > Cu (28.05)> Pb (26.98) > Ni (17.82) > Cd (0.76) mg/kg.
Overall, the concentration of Zn was significantly higher than the environmental background value
(p < 0.05), while the Ni concentration was much lower than background (p < 0.01). However, there
were no similar differences observed for the other four metals (p > 0.05), indicating that these were not
elevated above background concentrations. The average and concentration ranges of heavy metals in
the Yunliang River were: 19.46 (15.75–22.29) for Cu, 68.19 (53.65–81.92) for Cr, 861.63 (113.23–2474.05) for
Zn, 32.75 (9.31–114.42) for Pb, 8.16 (Below the detection limit (BDL)–13.11) for Ni, and 1.83 (BDL–4.29)
mg/kg for Cd, respectively. The average concentrations of Cd (1.83 mg/kg) and Zn (861.63 mg/kg)
were 9.15 and 8.62 times higher than their environmental background values (0.2 mg/kg for Cd and
100 mg/kg for Zn), respectively. The measured concentrations of heavy metals in sediments from the
Majiagou River and Yunliang River were compared with those found in other studies (Supplementary
Table S2). The mean concentrations of all heavy metals measured in this study (except for Zn) were
significantly lower than those in the Xiangjiang River (p < 0.01), which is one of the most polluted
rivers in China [29,30]. The concentrations of Pb and Ni measured in this study were lower than those
detected in the Louro River in Spain (p < 0.01) [31], the Gorges River in Australia (p < 0.05) [32] and
the Gironde Estuary in France (p < 0.01) [33], all of which are heavily polluted. The concentrations of
Cd and Zn in the Majiagou and Yuliang Rivers were found to be greater than many of the Chinese
rivers included in Supplementary Table S2 (p < 0.05). For example, the mean concentrations of Cd and
Zn measured in the studied rivers were about 4–20 times higher than those in the Yangtze River [34]
and Yellow River [6]. In addition, the average concentrations of Cr in the sediment from the Majiagou
River was similar to measurements reported from the Songhua River, which tends to have elevated
levels of Cr compared to other Chinese rivers [35]. Emissions from coal combustion, especially during
winter, could lead to a high concentration of Cr in sediments in the study area.
Int. J. Environ. Res. Public Health 2019, 16, 4313 7 of 15
Int. J. Environ. Res. Public Health 2019, 16, x 7 of 15

Figure 2. Concentrations
Figure 2. Concentrations of
of Cu
Cu (A),
(A), Cr
Cr (B),
(B), Pb
Pb (C),
(C), Ni
Ni (D),
(D), Cd
Cd (E)
(E) and
and Zn
Zn (F)
(F) in
in surface
surface sediments
sediments of
of
Majiagou
Majiagou River
River and
and Yunliang
YunliangRiver
RiverininHarbin
HarbinCity
City(mg/kg).
(mg/kg).
3.2. Spatial Distribution
3.2. Spatial Distribution
The concentrations of heavy metals in sediments of both rivers appear to be a function of land
The concentrations of heavy metals in sediments of both rivers appear to be a function of land
use and its spatial distribution (Figure 3). As expected, Cr and Ni measured in sediments from
use and its spatial distribution (Figure 3). As expected, Cr and Ni measured in sediments from
industrialized parts of the Majiagou River catchment were elevated compared to rural sections
industrialized parts of the Majiagou River catchment were elevated compared to rural sections (p <
(p < 0.05). The highest concentration of Cu and Pb occurred in sediments from the urbanized areas
0.05). The highest concentration of Cu and Pb occurred in sediments from the urbanized areas of the
of the Majiagou River catchment, while the maximum concentration of Zn was in Yunliang River.
Majiagou River catchment, while the maximum concentration of Zn was in Yunliang River. The
The average concentrations of heavy metals in the Yunliang River were higher than those in the
average concentrations of heavy metals in the Yunliang River were higher than those in the
suburban section of Majiagou River except for Cr and Ni. Figure 3 suggests that Ni has a relatively
suburban section of Majiagou River except for Cr and Ni. Figure 3 suggests that Ni has a relatively
lower degree of dispersion within this study area, which may indicate that the majority of the Ni is
Int. J. Environ. Res. Public Health 2019, 16, 4313 8 of 15

Int. J. Environ. Res. Public Health 2019, 16, x 8 of 15

lower degree of dispersion within this study area, which may indicate that the majority of the Ni is
derived from
derived fromgeogenic
geogenicsources
sourcesrather than
rather from
than human
from activities.
human Overall,
activities. the concentrations
Overall, of heavy
the concentrations of
metalsmetals
heavy were greatest in sediments
were greatest locatedlocated
in sediments withinwithin
the urban and industrial
the urban areas of
and industrial Majiagou
areas River
of Majiagou
compared
River to the to
compared suburban areas of
the suburban Majiagou
areas River and
of Majiagou the
River Yunliang
and River. River.
the Yunliang The only
Theexception to this
only exception
trend was Zn, which was more abundant in sediments from the Yunliang River, suggesting
to this trend was Zn, which was more abundant in sediments from the Yunliang River, suggesting that runoff
fromrunoff
that agricultural production production
from agricultural could be ancould
important
be ansource [36,37].
important source [36,37].

Figure 3. Average
Average concentration
concentration ofof Cu,
Cu, Cr,
Cr, Pb,
Pb, Ni,
Ni, Cd
Cd and
and Zn in sediment of Majiagou
Majiagou River
River and
Yunliang River at different
different functional
functional areas.
areas.

3.3. Possible
3.3. Possible sources
sources
Inferences regarding
Inferences regardingthe thepossible
possiblesources
sourcesofof heavy
heavy metals
metalsin sediments
in sediments of the Majiagou
of the River
Majiagou and
River
Yunliang
and RiverRiver
Yunliang wereweredeveloped
developedusingusing
the Pearson correlation
the Pearson coefficients
correlation within-
coefficients and between
within- heavy
and between
metals measured at all sampling sites. There was a significant correlation
heavy metals measured at all sampling sites. There was a significant correlation between Cd and between Cd and Zn in the
Zn
sediments
in of the of
the sediments Yunliang
the Yunliang (R = 0.997,
River River p < 0.01)
(R = 0.997, (Supplementary
p < 0.01) (Supplementary TableTable
S3), indicating that they
S3), indicating that
couldcould
they have similar sources.
have similar The coexistence
sources. The coexistenceof Cd and of Cd Zn and
withinZnan agricultural
within catchment
an agricultural suggests
catchment
agronomic sources such as the excessive use of phosphate fertilizer and
suggests agronomic sources such as the excessive use of phosphate fertilizer and pesticides, which pesticides, which can enter the
riverenter
can via soil
therunoff [38,39].
river via There was
soil runoff a significant
[38,39]. There was correlation
a significantbetween Ni andbetween
correlation Cr in theNi sediments
and Cr in of
the Majiagou River (R = 0.74, p < 0.01) (Supplementary Table S4). Additionally,
the sediments of the Majiagou River (R = 0.74, p < 0.01) (Supplementary Table S4). Additionally, Pb Pb has a significant
correlation
has with Zn
a significant (R = 0.79,
correlation withp <Zn 0.01)
(R and
= 0.79,Cd p(R< = 0.01) p <Cd
0.73,and 0.01),
(R =respectively
0.73, p < 0.01),(Supplementary
respectively
Table S4). These results indicate that the sediments of the Majiagou
(Supplementary Table S4). These results indicate that the sediments of the Majiagou River could River could be receiving multiple be
pollutants from the same emission sources or at least spatially-similar
receiving multiple pollutants from the same emission sources or at least spatially-similar sources. sources.
While aa Pearson
While Pearson correlation
correlation analysis
analysis (Supplementary
(Supplementary Table Table S4)
S4) can
can bebe used
used to to make
make inferences
inferences
about sources for the heavy metals, it is a relatively simplistic analysis
about sources for the heavy metals, it is a relatively simplistic analysis given the complexity of given the complexity of the
the
riverine environment. Therefore, a PCA was also applied to the
riverine environment. Therefore, a PCA was also applied to the data from Majiagou River areas, data from Majiagou River areas,
because its
because its flow
flow patterns,
patterns, and and hence
hence itsits dispersion
dispersion of of metals,
metals, are
are known
known to to be
be particularly
particularly complex.
complex.
As illustrated in Figure 4, the first principal component (PC1) accounted
As illustrated in Figure 4, the first principal component (PC1) accounted for 58.6% of the for 58.6% of the total variance
total
and was heavily associated with Zn, Cd and Pb (consistent with
variance and was heavily associated with Zn, Cd and Pb (consistent with the Pearson’s correlationthe Pearson’s correlation analysis;
Supplementary
analysis; Table S4). Table
Supplementary The PC1 S4).could
The PC1 originate
couldfrom industrial
originate fromactivities
industrial because Harbin
activities is an
because
important industrial base in Northeast China with a long history
Harbin is an important industrial base in Northeast China with a long history of equipment of equipment manufacturing. It has
been reported that
manufacturing. Zn in
It has beenurban settings
reported thatis mainly
Zn in urbanderived from the
settings sewagederived
is mainly discharge fromfromthechemical
sewage
discharge from chemical enterprises, the processing of Zn containing minerals, the manufactureand
enterprises, the processing of Zn containing minerals, the manufacture of metal machinery, of
the wear
metal and tearand
machinery, of automobile
the wear and tires [40].
tear Cd is likely tires
of automobile to be[40].
from Cdelectronics,
is likely toprinting
be from and dyeing,
electronics,
electroplating
printing and chemical
and dyeing, industryand
electroplating sources [41]. Pb
chemical tends to
industry originate
sources from
[41]. Pb the
tendsindustrial utilization
to originate from
of minerals containing lead and the combustion of fossil fuels. All
the industrial utilization of minerals containing lead and the combustion of fossil fuels. All these sources are therefore likely
theseto
be present within the industrialized areas of Harbin.
sources are therefore likely to be present within the industrialized areas of Harbin.
PC2 accounted for 22.8% of the total variance, is highly loaded with Cr and Ni, and corroborates
the Pearson’s correlation analysis between Cr and Ni (Supplementary Table S4). Mineral weathering
and atmospheric deposition from coal-burning dust could lead to the accumulation of Ni and Cr in
Int. J. Environ. Res. Public Health 2019, 16, 4313 9 of 15

PC2 accounted for 22.8% of the total variance, is highly loaded with Cr and Ni, and corroborates
the
Int. Pearson’s correlation
J. Environ. Res. Public Healthanalysis
2019, 16, xbetween Cr and Ni (Supplementary Table S4). Mineral weathering 9 of 15
and atmospheric deposition from coal-burning dust could lead to the accumulation of Ni and Cr in
sediments[39,42,43].
sediments [39,42,43].Thus,Thus,
oneone inference
inference is thatisPC2
thatmay
PC2 may represent
represent a combination
a combination of coal
of coal combustion
combustion
and and natural sources.
natural sources.
PC3accounted
PC3 accountedfor for14.2%
14.2%of ofthe
thetotal
totalvariance
varianceand andisishighly
highlyloaded
loadedwith
withCu.
Cu.This
Thismay
mayhave
havebeen
been
causedbybythethe
caused emissions
emissions of vehicle
of the the vehicle
exhaust exhaust
and brakeandpadbrake
wearpad wear
[44,45], [44,45],
while while
the high the high
enrichment
enrichment of Cu in the soil along the main street of Harbin City has been investigated
of Cu in the soil along the main street of Harbin City has been investigated and thought attributable and thought
to
attributable to traffic sources [46]. Thus, we infer that PC3
traffic sources [46]. Thus, we infer that PC3 originated from traffic sources. originated from traffic sources.

Figure4.4.Plot
Figure Plotof
ofloading
loadingof
ofthree
threeprinciple
principlecomponents.
components.

3.4.
3.4.Pollution
PollutionDegree
DegreeAssessment
Assessment
The
Thespatial
spatialdistribution
distribution of Pof
i inPthe sediments of the Majiagou River and Yunliang River is presented
i in the sediments of the Majiagou River and Yunliang River is
in Figure 5, with the values
presented in Figure 5, with the of P following
i values of a decreasing
Pi following trend of Cd > Zn
a decreasing > Crof>Cd
trend Cu>>Zn Pb>>CrNi.> Cu
Over one>
> Pb
third of sampling
Ni. Over sites
one third ofwere assigned
sampling sites‘high’
werepollution
assignedstatus
‘high’onpollution
the basis status
of theironcontents of Cd
the basis and
of their
Zn alone. The average
contents of Cd and Zn alone. P i value of Cr was 1.19, indicating that the levels of Cr pollution
The average Pi value of Cr was 1.19, indicating that the levels of Cr were ‘low.’
The coefficient
pollution wereof‘low.’
variation for Cr wasof
The coefficient 0.32, which for
variation corresponds
Cr was 0.32,to awhich
moderate variability,
corresponds to suggesting
a moderate
that the sources for Cr are more likely to be diffuse pollution associated with
variability, suggesting that the sources for Cr are more likely to be diffuse pollution associated withatmospheric deposition,
agricultural
atmosphericactivities,
deposition,and discharge
agricultural fromactivities,
industrial and
and domestic
dischargewastewater. However,
from industrial andthedomestic
average
Pwastewater.
i values of Cu, Pb and Ni
However, the were less than
average 1, indicating
Pi values thatand
of Cu, Pb these
Niareas
wereare lessrelatively less polluted.
than 1, indicating thatItthese
can
be seen from Figure 5 that the P values in sediments of Yunliang River tended
areas are relatively less polluted. It can be seen from Figure 5 that the Pi values in sediments of
i to be lower compared
to those from
Yunliang thetended
River Majiagouto beRiver.
lower The exceptions
compared to this
to those fromobservation
the Majiagou re Cd andThe
River. Zn, exceptions
which have to
average Pi valuesreofCd
this observation 6.11
andand Zn,8.61,
which respectively,
have average indicating
Pi values‘high’
of 6.11levels of pollution
and 8.61, (as defined
respectively, indicatingin
Supplementary Table S1). (as
‘high’ levels of pollution Levels
definedof Niinin both rivers could
Supplementary be S1).
Table considered
Levels of ‘clean.
Ni in both rivers could be
considered ‘clean.
 Int. J.J.Environ.
Int. Environ.Res.
Res.Public
PublicHealth
Health2019, 16,x4313
2019,16, 1010ofof15
15
Int. J. Environ. Res. Public Health 2019, 16, x 10 of 15

Figure 5. Spatial distribution of Cu (A), Cr (B), Pb (C), Ni (D), Cd (E) and Zn (F) in surface
Figure 5. Spatial
sediments
Figure 5. Spatial
by distribution
the distribution
single factor ofof
CuCu
(A),(A),
pollution Cr(PPb
index
Cr (B), (B), PbNi
i). (C), (C), NiCd(D),
(D), Cd (E)
(E) and andin Zn
Zn (F) (F) in
surface surface
sediments
sediments by factor
by the single the single factorindex
pollution (Pi ). index (Pi).
pollution
The improved PN’ estimated that 58% of all sampling sites in the sediment of the Majiagou River
wereThe improved
polluted PNN’’ estimated
by heavy metals that
estimated that 58%
58%6),
(Figure of
of all
all sampling
of which thesites
sampling M7in
sites the
the sediment
inand sediment
M8 of
of the
sampling Majiagou
thesites withinRiver
Majiagou River
the
were polluted
polluted
industrial by
areabywereheavy
heavy metals
metals (Figure
considered (Figure 6), of which
6), of‘moderate’
to have which the M7 the
andand M7 and
M8 sampling
‘high’ M8 sampling
levels of sites sites within
within(according
pollution the
the industrial
to
industrial
Supplementary area Table
area were considered were considered
to have
S1), to have
‘moderate’
respectively. ‘moderate’
Inand ‘high’ levels
addition, and of
about ‘high’
60% of levels
pollution of pollution
(according
the sampling (according as
to Supplementary
sites categorized to
Supplementary
Table S1), Table
respectively. S1),
In respectively.
addition, aboutIn addition,
60% of the about
sampling60% of
sites the sampling
categorized
having ‘moderate pollution’ were located within the urban area of the Majiagou River, while PN’ sites
as havingcategorized
‘moderateas
having
defined ‘moderate
pollution’ thewere pollution’
located
suburban aswere
within
area located
the urban
‘clean.’ within
area
Thus, of thethe
emissions urban
Majiagou area
River,
and discharges ofwhile
thefrom
Majiagou
defined River,
PN ’industrial the while PN’
suburban
production
defined
area as
need the suburban
‘clean.’
further attention area government,
Thus, from as ‘clean.’
emissions Thus,
and discharges emissions
public, from
and andstakeholders
discharges
industrial
other from need
production
because industrial
thisfurther
study production
attention
suggests
need
from further
government,attention from
public, government,
and other public,
stakeholders and other
because stakeholders
this study
that they might be the most important contributors to heavy metals in the riverine environment. because
suggests thatthis
theystudy suggests
might be the
that
mostthey
Compared might
important be the most
to thecontributors
Majiagou toimportant
River, heavy contributors
metals
the majority inof to heavy
thesampling
riverine sitesmetals
environment. in Compared
along the the riverine
Yunliang toenvironment.
the indicated
River Majiagou
Compared
River, the to the
majority Majiagou
of samplingRiver, the
sites majority
along the of sampling
Yunliang Riversites along
indicated
‘no pollution.’ The exception to this were the Y1 and Y3 sites, where PN’ values of 10.6 and 16.3 the
‘no Yunliang
pollution.’ River
The indicated
exception
‘no pollution.’
to this
(‘serious were theThe exception
Y1 and
pollution’), Y3 sites,
respectively, to where
this
werewere the Y1 of
PN ’ values
determined. and Y3
10.6
This andsites,
might 16.3 where PN’ pollution’),
(‘serious
indicate pointvalues
sources ofof10.6 and 16.3
respectively,
pollution at
(‘serious
twopollution’),
were determined.
these respectively,
locations.This might indicate werepoint
determined.
sources This might indicate
of pollution at thesepoint sources of pollution at
two locations.
these two locations.

Figure 6.
Figure TheNemerow
6. The Nemerowpollution
pollution index
index of
of each
each sampling
sampling site.
site.
Figure 6. The Nemerow pollution index of each sampling site.
Int. J. Environ. Res. Public Health 2019, 16, 4313 11 of 15
Int. J. Environ. Res. Public Health 2019, 16, x 11 of 15

3.5. Potential
3.5. Potential Ecological
Ecological Risk
Risk Assessment
Assessment

The RI was
The RI was employed
employed toto quantitatively
quantitatively evaluate
evaluate the
the ecological
ecological risk
risk level
level of
of heavy
heavy metals
metals in
in the
the
sediments of the Majiagou River and Yuliang River. The values of E i and RI of each sampling site
sediments of the Majiagou River and Yuliang River. The values of Err and RI of each sampling site
i

according to
according to Equations
Equations (5)
(5) and
and (6)
(6) are
are illustrated
illustrated in
in Figure
Figure 7.
7.

7. The
Figure 7.
Figure Thevalue of of
value the the
single ecological
single risk index
ecological risk (A) and(A)
index the and
comprehensive potential ecological
the comprehensive potential
risk index (B) at each sampling site.
ecological risk index (B) at each sampling site.

Due to
Due to its
its high
high relative
relative toxicity,
toxicity, about
about 80%
80% of of the
the potential
potential ecological
ecological riskrisk posed
posed byby heavy
heavy metal
metal
contamination in
contamination in sediments
sediments of of the
the two
two rivers
rivers could
could bebe attributed
attributed toto Cd
Cd (Supplementary
(SupplementaryFigure FigureS1).
S1).
According to the results of E r , about 50% of values for Cd were greater than 40
According to the results of Er, about 50% of values for Cd were greater than 40 (‘moderate’ ecological (‘moderate’ ecological risk,
or higher
risk, (Supplementary
or higher (Supplementary Table S1)). Table Ecological risks associated
S1)). Ecological with Cd were
risks associated withespecially
Cd werepronounced
especially
at M8 (E = 612.7), Y1 (E = 418.5) and Y3 (E = 644), which are defined
pronounced at M8 (Er = 612.7), Y1 (Er = 418.5) and Y3 (Er = 644), which are defined as ‘serious
r r r as ‘serious ecological risk’
according to Table S1. Considerably lower ecological risks were associated
ecological risk’ according to Table S1. Considerably lower ecological risks were associated with the with the other five heavy
metalsfive
other (Zn, Pb, Ni,
heavy Cr and
metals (Zn,Cu). Pb, Ni, Cr and Cu).
The mean values of RI
The mean values of RI were 130.41 were 130.41 andand 201.91
201.91 in in sediments
sediments of of the
the Majiagou
Majiagou RiverRiver andand Yunliang
Yunliang
River, respectively, which is indicative of ‘high ecological risk’ (Supplementary
River, respectively, which is indicative of ‘high ecological risk’ (Supplementary Table S1). The Table S1). The discharge
from industrial
discharge fromand domestic
industrial and wastewater
domestic might be the primary
wastewater might be driver of the ecological
the primary driver of riskthein ecological
sediments
of the Majiagou River. Despite its rural catchment, the elevated
risk in sediments of the Majiagou River. Despite its rural catchment, the elevated levels of Cdlevels of Cd in sediments of the
in
Yunliang River enhanced the RI value, especially at the Y1 and Y3 sites.
sediments of the Yunliang River enhanced the RI value, especially at the Y1 and Y3 sites. Given thatGiven that riverine sediments
can act as
riverine both a sink
sediments canand
act assourcebothof heavy
a sink metals,
and sourcesites such as
of heavy Y1 and
metals, Y3 such
sites have astheY1potential to be
and Y3 have
implicated in the future re-release of Cd into the aquatic environment and
the potential to be implicated in the future re-release of Cd into the aquatic environment and any any associated consequences.
This could involve
associated food-chain
consequences. This related
couldexposure
involveand potential human
food-chain related health
exposurerisksand
due to the exploitation
potential human
of the Yunliang for irrigation water for agricultural production.
health risks due to the exploitation of the Yunliang for irrigation water for agricultural production.

4. Discussion
4. Discussion
Though the single factor pollution index method has been widely used, it is only applicable to
Though the single factor pollution index method has been widely used, it is only applicable to a
a single pollutant and does not take into consideration the mixture of heavy metals often present in
single pollutant and does not take into consideration the mixture of heavy metals often present in
pollution situations. While, the improved P ’ as a multiple element index integrates the average value of
pollution situations. While, the improvedN PN’ as a multiple element index integrates the average
the pollution index (Piave ) for individual sites and the single pollution index (Piwmax ) (Equations (2)–(4)).
value of the pollution index (Piave) for individual sites and the single pollution index (Piwmax)
The improved values (PN ’) were lower than traditional PN ; this was especially apparent in the Yunliang
(Equations 2–4). The improved values (PN’) were lower than traditional PN; this was especially
River where values of PN were almost three times as PN ’ (except for Y1 and Y3), which therefore resulted
apparent in the Yunliang River where values of PN were almost three times as PN’ (except for Y1 and
in a different conclusion when determining the degree of pollution (Figure 6). These differences can be
Y3), which therefore resulted in a different conclusion when determining the degree of pollution
attributed to the influence of overemphasis on the maximum pollution factors on the final results in the
(Figure 6). These differences can be attributed to the influence of overemphasis on the maximum
derivation of PN . For the Y4, Y5 and Y6 sampling sites, the maximum pollution factors (Zn) were more
pollution factors on the final results in the derivation of PN. For the Y4, Y5 and Y6 sampling sites, the
than 2.6, 6.1, and 2.5 times greater than the other heavy metals, respectively, and, more importantly,
maximum pollution factors (Zn) were more than 2.6, 6.1, and 2.5 times greater than the other heavy
metals, respectively, and, more importantly, there was only one factor (Zn) that was considered to be
‘moderate’ pollution, while the others were considered ‘clean,’ including the top factor of weight
Int. J. Environ. Res. Public Health 2019, 16, 4313 12 of 15

there was only one factor (Zn) that was considered to be ‘moderate’ pollution, while the others were
considered ‘clean,’ including the top factor of weight (Cd). This phenomenon was also found, although
less pronounced, at the other sites. Comparatively, the improved Nemerow index provided a less
bias evaluation of the quality of sediments by taking full consideration of the relative importance and
biological toxicity of heavy metals.
Both of the Majiagou River and Yunliang River are important tributaries of the Songhua River,
and thus their water environment quality will affect the security of drinking and irrigation water
for inhabitants and agricultural production along the Songhua River. Thus, different regulatory
measures should be paid to the environment treatment in the future for these two rivers according to
the correspondent pollution characteristics. Optimization and control in agricultural management
might be the adapted scheme for reducing the input of pollution sources in Yunliang River, where the
most important sources appear to mainly be from agricultural activities. However, the industrial areas,
located in the middle and upper reaches of Majiagou River, appear to be the priority to control and
management for reducing the input of pollutants from the wastewater discharge and atmospheric
deposition, as well as avoiding the adverse influence on population density areas in the lower reach.

5. Conclusions
The concentrations, the possible sources and ecological risk of six heavy metals in sediments
from urban and rural rivers were investigated in Harbin. The results showed that the concentrations
of heavy metals in the urban and industrial areas of the Majiagou River were significantly elevated
compared to those measured in sediments from suburban and rural areas. The exception to this was
Zn, with the highest concentrations measured in sediments from the predominantly rural Yunliang
River. It is possible that the excessive use of fertilizers and pesticides could be responsible for the
elevated levels of Cd and Zn measured in the Yunliang River sediments, given the land use of this
catchment is dominated by crop production. The source apportionment by Pearson correlation coupled
with the PCA indicated diverse sources in the sediments of the Majiagou River, with Zn, Cd and Pb
being thought to originate from industrial activities, Ni and Cr thought to be mainly derived from
coal combustion and natural sources, and Cu thought to be mainly from traffic emissions. However,
it must be noted that this is not a formal source apportionment and is reliant on inferences drawn from
the available information. The improved Nemerow pollution index indicated a higher incidence and
magnitude of pollution in the Majiagou River compared to the Yunliang River, and this was most
acute in the urban and industrial parts of the catchment. The potential ecological risk assessment
indicated high ecological risks associated with the sediments of both rivers, of which the Er of Cd
was significantly higher than the other metals (Cd accounted for more than 80% of the RI; p < 0.01).
Given the fact that riverine sediments can act as both a sink and a source for heavy metals, there is
potential for Cd to be implicated in secondary pollution events that could have wide implications,
e.g., when river water is used to irrigate food crops.

Supplementary Materials: The following are available online at http://www.mdpi.com/1660-4601/16/22/4313/s1,

Table S1: Classifications of heavy metal pollution degree and potential ecological risk; Table S2: Comparison of
heavy metal concentrations in surface sediments; Table S3: Pearson correlation matrix for heavy metals in surface
sediments of the Yunliang River; Table S4: Pearson correlation matrix for heavy metals in surface sediments
of the Majiagou River; Figure S1: Contribution of different heavy metals to the ecological risk index (RI) in
riverine sediments.
Author Contributions: All authors made significant contribution to this article. Conceptualization, S.C., F.Z.,
and P.H.; methodology, Z.Z., and Q.F.; data curation, K.L.; investigation, P.C., K.L. and F.Z.; experiments, K.L.;
supervision and validation, Y.-F.L. and D.L.; writing—original draft, F.Z.; writing—review and editing, S.C., R.H.
and L.A.
Funding: This research was funded by the National Key Research and Development Program of China
(2017YFC0404503), the National Natural Science Foundation of China (51779047), the Academic Backbone
Project of Northeast Agricultural University (17XG04) and the Excellent Youth Science Foundation of Heilongjiang
Province (YQ2019E001).
Conflicts of Interest: The authors declare no conflict of interest.
Int. J. Environ. Res. Public Health 2019, 16, 4313 13 of 15

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