Skip to main content

Risk assessment of heavy metals in the surface sediment at the drinking water source of the Xiangjiang River in South China



The Xiangjiang River is an important drinking water resource for the Hunan Province of China. It is crucial to ascertain the pollution status, influencing factors, ecological risks, and possible sources of heavy metals in the sediments of the Xiangjiang River. Sediment is both a source and a sink of heavy metals in aquatic ecosystems. In this study, surface sediment was collected from the Zhuzhou Reach of the Xiangjiang River and eight heavy metals were investigated.


In all sediment samples, all eight heavy metals were detected and their average concentration fell in the order of Zn > Pb > As > Cu > Cr > Ni > Cd > Co. Assessment shows extremely serious Cd pollution and a very high potential ecological risk from Cd. According to correlation analysis and principal component analysis (PCA), As, Cu, Ni, Pb, and Zn originate from industrial wastewater and mineral smelting activities, whereas Co, Cr, and Ni come from natural sources. Redundancy analysis (RDA) reveals that the organic matter content and the particle size of the sediment have a certain influence on the enrichment of heavy metals.


Among all eight examined heavy metals in the surveyed area, the content of Zn, Pb, and As is the highest, and that of Cd and Co is the lowest. Despite a low level of absolute content, the Cd in sediment already renders a high ecological risk and thus calls for urgent attention. Anthropogenic activities are the main source of heavy metals in the sediment. The distribution of heavy metals may also be influenced by sediment properties. The results provide guidance for controlling heavy metal pollution and protecting drinking water sources in the Xiangjiang River.


Heavy metals are a pressing concern in terms of their pollution in aquatic ecosystems because of their persistence, environmental toxicity, bioaccumulation, etc. [1,2,3,4]. Aquatic environments (e.g., lakes [5], rivers [6], reservoirs [7], and wetlands [8]) receive heavy metals in untreated or inadequately treated wastewater from domestic, industrial, and agricultural sources. Pollution from heavy metals is inflicting rivers worldwide [9, 10], especially in developing countries [11, 12].

As an important component in riverine ecosystems, sediment serves as both a sink and a source of heavy metals [13, 14]. Most heavy metals quickly deposit into the sediment after entering rivers, and are much more concentrated in the sediment than in the water body of riverine systems [15, 16]. Conversely, when the physicochemical or hydrological conditions change, heavy metals in the sediment may desorb or resuspend to cause secondary pollution in the water body [17, 18]. The accumulation of heavy metals in the sediment directly affects benthic organisms and also influences many other organisms through the food web [19, 20], and endangers the wellbeing of the aquatic ecosystem. Therefore, it is of great importance to assess and understand the distribution and accumulation of heavy metals in sediment.

The Xiangjiang River is a major tributary of the Yangtze River and a key river in South China. It flows through the Hunan Province and supports many densely populated cities, providing services to both industry and agriculture [21, 22]. Unfortunately, the Xiangjiang River has become one of the most seriously heavy metal-polluted rivers in China over the past few decades because of the wastewater discharge from mining and metallurgical industries carrying heavy metals [23]. Although many researchers have studied heavy metal pollution in the Xiangjiang River [24,25,26], a systematic study is still lacking to associate the sediment with the distribution, characteristics, risk assessment, possible sources, and impact of heavy metals.

The Zhuzhou Reach of the Xiangjiang River is a typical area polluted by heavy metals [27]. In this work, we collected 30 surface sediment samples from the Zhuzhou Reach of the Xiangjiang River in order to (1) characterize the occurrence and distribution of eight heavy metals (Cd, As, Pb, Zn, Cu, Ni, Cr, and Co) in the sediment; (2) assess the pollution status and the ecological risk of the heavy metals; (3) identify possible sources of heavy metal pollution from principal component analysis (PCA) and Pearson’s correlation analysis, and (4) use redundancy analysis (RDA) to analyze the sediment characteristics that influence heavy metal distribution. The results illuminate on the environmental behavior of heavy metals and provide a reference for controlling pollution and ecological remediation in nearby areas and similar riverine systems.

Materials and methods

Study area and sample collection

The Xiangjiang River is the second largest tributary of the Yangtze River and an important drinking water source in South China. The main channel of the river is 856 km long, and its basin covers 94,721 km2 and supports > 30 million residents of the Hunan Province [28]. It flows from south to north en route 6 major cities in Hunan, i.e., Yongzhou, Hengyang, Zhuzhou, Xiangtan, Changsha, and Yueyang, and finally joins the Yangtze River via the Dongting Lake. The Hunan Province is known as the “Nonferrous Metal Village” because of its abundant mineral resources (e.g., Cd, Zn, Pb, Cu, etc. [29]). However, the mining and smelting of nonferrous metals over the past years caused severe heavy metal pollution to the Xiangjiang River. The city of Zhuzhou at the east of the Hunan Province is at the lower reaches of the Xiangjiang River. The area is rich in mineral resources and active in mining, ore smelting, and other major industrial practices. Therefore, we chose the Zhuzhou Reach of the Xiangjiang River for pollution evaluation and risk assessment. At the upstream of the surveyed area, there are many companies producing Zn, Pb, As, and alloys as well as other companies that carry out comprehensive recovery of Cu, Au, Ag, Bi, Te, Cd, In, and other rare metals. In this work, eight target heavy metals (Cd, As, Pb, Zn, Cu, Ni, Cr, and Co) were chosen for evaluation after examining the actual situation of the study area and consulting literatures on heavy metals in the Xiangjiang River [21, 25, 28].

Ten sampling transects (S1–S10) were selected from upstream to downstream in the investigated area (Fig. 1). Surface sediment samples (1 kg) were collected in August, 2011 with a core sampler at the north side, in the middle, and at the south side of the river from the top 5 cm layer of the riverbed. Each sample consists of a mixture of three samples from one sampling site. The collections from each transect were stored in acid-rinsed polyethylene plastic bags and transported immediately to the laboratory for storage at − 20 °C until further analysis, all sediment samples were analyzed within a week.

Fig. 1

Map of the Xiangjiang River Basin and the sampling sites

Sample treatment and analysis

Heavy metals in the sediment samples were determined at the Chinese Academy of Environmental Sciences according to published protocols [30, 31]. Briefly, crude sediment samples were freeze-dried, finely ground, homogenized, and sieved (100 mesh, 150 μm). The prepared sample (0.05 g) was digested with mixed acid (5 mL HCl, 3 mL HNO3, 7 mL HF, 0.25 mL HClO4) in a Teflon beaker. After the digestion, the dissolved sample was diluted with 2% HNO3, and the concentrations of heavy metals were analyzed using inductively coupled plasma mass spectrometry (ICP-MS, Agilent 7500 series, USA). The recoveries of standard reference metals were 90–110%. For more details, refer to Lin et al. [32]. The content of organic matters (OM) in the sediment was determined according to the standard methods for soil analysis [33]. Grain size was measured by a laser scattering particle size distribution analyzer (LA-300, Horiba, Kyoto, Japan) to determine the median sediment size (D50) and the fine particle (< 63 μm) content.

All reagents and solvents used in the sediment analysis were of analytical grade. The analytical procedure conformed to the certified standards of GBW07309 (China Stream Sediment Reference Materials). The results were within the acceptable level of uncertainty given by certified values.

Pollution assessment of heavy metals


The geoaccumulation index (Igeo) formulated in 1969 is now one of the most widely used measures to evaluate heavy metal pollution in sediment because it can directly reflect the enrichment of exogenous heavy metals and provides consistent and comparable data [34, 35]. It is calculated as follows:

$$ I_{\text{geo}} = { \log }_{2} \left( {\frac{{C_{\text{sample}} }}{{1.5 C_{\text{backgroud}} }}} \right), $$

where Csample is the concentration of the heavy metal in the sediment sample and Cbackground is the geochemical background concentration (mg kg−1) of the heavy metal. A background matrix correction factor of 1.5 is adopted as a coefficient to compensate for weathering and lithogenic effects. Table 2 lists the background concentration of the investigated heavy metals in the Dongting Lake sediments [36]. Pollution can be classified into seven classes based on the Igeo value: Class 0 (Igeo ≤ 0), practically unpolluted; Class 1 (0 < Igeo ≤ 1), unpolluted to moderately polluted; Class 2 (1 < Igeo ≤ 2), moderately polluted; Class 3 (2 < Igeo ≤ 3), moderately to heavily polluted; Class 4 (3 < Igeo ≤ 4), heavily polluted; Class 5 (4 < Igeo ≤ 5), heavily to extremely polluted, and Class 6 (Igeo > 5), extremely polluted [37].

Assessment of potential ecological risk

The potential ecological risk index (RI) formulated by Hakanson in 1980 was used here to quantify the level of ecological risk of heavy metals in sediment [38]. The RI assesses the combined ecological and environmental toxicity to provide an overall evaluation of the potential risks of heavy metal pollution, and is calculated as follows:

$$ E_{r}^{i} = T_{r}^{i} \frac{{C_{\text{sample}} }}{{C_{\text{background}} }}, $$
$$ {\text{RI}} = \sum E_{r}^{i} , $$

where \( E_{r}^{i} \) and \( T_{r}^{i} \) are the potential ecological risk factor and the toxic response factor of the heavy metal, respectively, and Csample and Cbackground are the measured and background concentration. The \( T_{r}^{i} \) values for Co, Cd, Ni, Cr, Cu, As, Pb, and Zn, all taken from literature, are 5, 30, 5, 2, 5, 10, 5, and 1, respectively [39]. Table 1 lists the criteria for \( E_{r}^{i} \) and RI.

Table 1 Levels of potential ecological risk for heavy metal in sediments

Statistical analysis

PCA and RDA were performed using Canoco 5.0 (Biometris, Netherlands). Pearson’s correlation analysis was conducted using SPSS 20.0 (SPSS Inc., Chicago, IL, USA). Data analyses and statistical tests were performed using Microsoft Excel 2010.

Results and discussion

Occurrence and abundance of heavy metals in surface sediment

Table 2 summarizes the minimum, maximum, and average concentrations (represented by dry weight) of the eight heavy metals in the surface sediment samples from the Zhuzhou Reach of the Xiangjiang River. All eight heavy metals were detected in all sediment samples. The concentration of Cr, Co, Ni, Cu, Zn, As, Cd, and Pb ranged in 23.11–94.84, 5.26–25.24, 16.58–53.58, 20.54–159.3, 58.24–629.60, 34.74–186.80, 2.10–62.59, and 38.19–246.20 mg kg−1, respectively. The average concentration ranked in the order of Zn (257.17 mg kg−1) > Pb (102.52 mg kg−1) > As (98.38 mg kg−1) > Cu (71.29 mg kg−1) > Cr (59.71 mg kg−1) > Ni (36.29 mg kg−1) > Cd (23.31 mg kg−1) > Co (16.97 mg kg−1). With the sediment of the Dongting Lake taken as the background and a reference point, the concentration of the heavy metals is 1.65–70.64 times higher in the sediment of the Zhuzhou Reach, thus indicating remarkable pollution. In particular, Zn and Pb have higher concentration than other heavy metals, which is consistent with the Pb–Zn smelting activities around the Zhuzhou City. The lower concentration of other heavy metals is probably related to a less amount of discharge.

Table 2 Concentration of heavy metals (mg kg−1) in the sediment of the Xiangjiang River and of other rivers in South China

Table 2 also summarizes the concentration of the examined heavy metals in other reaches of the Xiangjiang River and in other rivers in South China [28, 36, 40,41,42]. The distribution of heavy metals is clearly different among different reaches of the Xiangjiang River (including Yongzhou, Hengyang, Zhuzhou, and Changsha). The Yongzhou Reach is the upstream and has the lowest concentration of heavy metals (except for Cr) in sediment [28]. The Hengyang Reach is located between Yongzhou and Zhuzhou and has the highest concentration of Cu, Zn, As, and Pb in sediment [40]. For the Zhuzhou Reach, the concentration of Co, Ni in the sediment is generally lower, but the concentration of Cd is the highest. The Changsha Reach is the downstream and does not have a heavy metal with particularly high or low concentration [28]. The distinct distribution of different heavy metals may be associated with specific local mining activities. However, the sediment of the Xiangjiang River is clearly more polluted by heavy metals than the sediment of other rivers in South China, including the Jinjiang River, Hanjiang River, and Yangtze River [41, 42]. The concentration of Zn seems to be the highest among the examined heavy metals for all rivers, possibly due to regional Zn production. This high level of Zn is not specific to Hunan. In fact, for the past 20 years in China, Zn has an annual production higher than all seven other heavy metals except Cu [43].

Spatial distribution of heavy metals

Figure 2 illustrates the concentration of the heavy metals at S1–S10. The total concentration of heavy metals gradually increases as the water flows through the Zhuzhou Reach and peaks at S6 (1042.00 mg kg−1), then suddenly decreases sharply at S7 to only 280.33 mg kg−1, but rises again to about 650 mg kg−1 at the downstream sites S8–S10. Zn has the highest concentration at S6 (435.84 mg kg−1) and then the next highest at S5 (127.04 mg kg−1). There is no significant difference in the concentrations of heavy metals within the upstream (S1–S4) and within the downstream (S8–S10), but there is a significant difference within the midstream (S5–S7).

Fig. 2

Spatial distribution of heavy metals in the sediment of the Zhuzhou Reach

The concentration of heavy metals is the highest at S6, possibly due to the discharge of local pollution sources. Other factors such as the grain size and the OM content of sediment can also affect the concentration of heavy metals. For instance, sediment with a higher content of OM adsorbs more heavy metals [44]. Indeed, Additional file 1: Table S1 shows that the OM content of S6 is the highest among all sites. In fact, S6 is located in the middle of the surveyed area and there is a sandbar between S6 and S7. This geographic feature may increase the deposition of particles carrying heavy metals into the sediment of S6.

Most heavy metals have slightly higher concentration in the middle of the river than on the south side, and all heavy metals have significantly higher concentrations on the north side (Fig. 3). In particular, the concentration of Zn and Pb in the north is nearly twice as much as in the middle and the south. The asymmetrical concentration can be associated with the nonferrous metal mining and smelting plants in the north that produce Zn, Pb, and alloys and discharge substantial amount of wastewater.

Fig. 3

Heavy metal concentration in the sediment collected at the south side, the north side, and in the middle of the river

Assessment of heavy metal pollution

Many measures have been used in literature to assess heavy metal pollution in sediment, e.g., the geoaccumulation index, the potential ecological risk index, the enrichment factor, etc. [45,46,47]. In this study, we used Igeo and RI to assess heavy metal pollution in the sediment of the Zhuzhou Reach with reference to the concentration of Dongting Lake sediments as the background.

Assessment of Igeo

Figure 4a shows a boxplot of the average Igeo values for the eighty heavy metals, which can be ranked in descending order as follows: Cd (5.01) > As (1.94) > Pb (1.40) > Zn (0.74) > Ni (0.11) > Co (0.04) > Cu (0.43) > Cr (− 0.26). The pollution levels of the metals are thus segmented into 4 classes according to the Igeo values. That is, the Zhuzhou Reach is extremely polluted by Cd (Class 6), moderately to heavily polluted by As and Pb (Class 2), unpolluted to moderately polluted by Zn, Ni, Co, and Cu (Class 1), and practically unpolluted by Cr (Class 0).

Fig. 4

Box diagram of Igeo values of heavy metals (a), and pollution classes in different sites (b) in the sediment of the Zhuzhou Reach

The different colors in Fig. 4b denote the class of Igeo for each heavy metal at S1–S10. The pollution of Cd is highly variable geographically, from Class 3 (S7) up to Class 6 (S3–S6, S8, S10). The pollution of As and Pb varies from Class 1 to Class 3, and the pollution of all other metals stay within two neighboring classes. The pollution of Cr, Co, and Ni appears to be mild (Class 0 Cr for 60%, Class 1 Co for 70%, Class 1 Ni for 80% of the 10 sites). Pollution is more severe midstream (S5–S7) and downstream (S8–S10) than upstream (S1–S4).

Assessment of potential ecological risk

Additional file 1: Table S2 lists the calculated \( E_{r}^{i} \) values of the heavy metals, from which the RI values were derived (Fig. 5). The RI value of the whole surveyed area ranged from 422.31 to 4854.41, with an average of 2246.64, thus indicating high ecological risk (Table 1) from heavy metal pollution. The sediment of S7 has RI > 300, and all others even have RI > 600 (Fig. 5). Among the tested metals, Cd creates very high ecological risk to the sediment at all sites (\( E_{r}^{i} \) > 320). The level of single metal ecological risk of As is the lowest at S7 (\( E_{r}^{i} \) = 28.84), considerable at S5, S6, and S10, and moderate at all other sites. Other heavy metals (Pb, Cu, Ni, Co, Zn, and Cr) have low ecological risk in all sediments (\( E_{r}^{i} \) < 40).

Fig. 5

RI values of heavy metals in the sediment (derived from the \( E_{r}^{i} \) values)

Identification of the sources of heavy metals

Analyzing the sources of heavy metals can help understand their distribution. Principal components analysis (PCA) and Pearson’s correlation analysis were used to analyze the relationship and source of the heavy metals [28, 48].

In this study, PCA was performed based on the determined concentrations of heavy metals with varimax rotation. The Kaiser–Meyer–Olkin (KMO) test gives 0.76 (> 0.7) and Bartlett test gives 397.58 (df = 28, p < 0.001), both indicating strong correlation among variables and confirming that PCA can be used to reduce the dimensionality of variables. In the PCA results (Fig. 6 and Additional file 1: Figure S1), PC1 accounts for 51.30% of the variation, and its representative congeners include Cd, Zn, As, Cu, and Pb, implying a common source that is possibly exogenous discharge [24, 49]. This speculation is further supported by the Pearson’s correlation analysis (Fig. 7), in which all heavy metals are significantly positively correlated with each other (r > 0.60, p < 0.01). Note that Pb and Zn, the main pollutants of the current Pb–Zn mining and smelting industry in China [50, 51], have a particularly high correlation (r = 0.95, p < 0.01). The PC2 accounts for 39.85% of the variation, and its representative congeners include Co, Ni, and Cr. The PCA results also indicate that at sites 5, the sediment at north side is affected by Cd, Zn, As, Cu, Pb (Additional file 1: Figure S1), while the sediment at the middle of river is mainly affected by Co, Ni, and Cr. It may be due to the effects of hydrodynamics or other sources, in the pollution assessment, Cr, Ni, and Co all have lower Igeo and lower \( E_{r}^{i} \). Therefore, they may have been derived from natural processes such as soil erosion and rock weathering. This hypothesis is also supported by previous study [28, 46].

Fig. 6

Principal component profile of heavy metals in the sediment of different sampling sites

Fig. 7

Pearson’s correlation coefficients (r) of heavy metals and environmental factors in the sediment of the Zhuzhou Reach

Effects of environmental variables on the heavy metals

Many environmental factors affect the distribution of heavy metals in sediment. The redundancy analysis (RDA) was used here to further investigate the environmental variables that affect the distribution of heavy metal. The considered environmental variables include the organic matter content (OM), median size (D50), and fine particle content (grain size < 63 μm) of the sediment samples (Additional file 1: Table S1). In the RDA result (Fig. 8), the angle between the environmental variable and the heavy metal reflects their relevance. The concentrations of heavy metal are positively correlated to OM, i.e., sediment with relatively high OM tends to be more polluted by heavy metals. It was previously suggested that OM in sediment can adsorb heavy metals from the environment [52]. The grain size distribution of sediment is also an important factor that affects the enrichment of heavy metals [53]. Sediment particles can adsorb heavy metals by both physical and chemical processes. Physical adsorption is mainly related to the specific surface area of the sediment, and a smaller particle size of the sediment gives larger surface area and thus stronger adsorption capacity. Chemical adsorption is related to the active components contained in sediment particles, and the sediment tends to adsorb a greater amount of active components when it has smaller particle size [44, 54, 55]. Therefore, heavy metals are generally negatively correlated with D50. The fine particle content (grain size < 63 μm) has influences on the heavy metal concentration. Figure 7 also shows the correlation between the environmental variables and the heavy metals.

Fig. 8

Redundancy analysis diagram between heavy metals and sediment variables


Heavy metal pollutants in river sediment have been a serious environmental concern in aquatic ecosystems. This study examines eight heavy metals in the surface sediment of the Zhuzhou Reach of the Xiangjiang River. All eight heavy metals were detected at all sampling sites, with a concentration higher than the background value. Their average concentration fell in the order of Zn > Pb > As > Cu > Cr > Ni > Cd > Co. Pollution appeared more serious in the midstream and on the northern side of the Xiangjiang River, possibly due to point source pollution nearby. Among the selected heavy metals, Cd presents very high ecological risk and should be carefully monitored and remediated. Most other heavy metals incur limited, if any, pollution according to the risk assessment. It was found from PCA and correlation analysis that Pb, Zn, As, Cu, and Cd are mainly originated from anthropogenic activities (industrial wastewater and mineral exploitation), whereas Cr, Co, and Ni can be attributed to the release from natural sources. Organic matter and grain size are the main factors affecting the distribution of heavy metals in the sediment, and heavy metals become enriched in the sediment when its organic matter is higher and particle size is smaller.

Availability of data and materials

The datasets obtained and analyzed in the current study are available from the corresponding author on reasonable request.



Principal component analysis


Redundancy analysis

I geo :

Geoaccumulation index


Potential ecological risk index


Organic matters

D 50 :

Median size


  1. 1.

    Zhang C, Yu Z-G, Zeng G-M, Jiang M, Yang Z-Z, Cui F, Zhu M-Y, Shen L-Q, Hu L (2014) Effects of sediment geochemical properties on heavy metal bioavailability. Environ Int 73:270–281

    CAS  Article  Google Scholar 

  2. 2.

    Bryan GW, Langston WJ (1992) Bioavailability, accumulation and effects of heavy metals in sediments with special reference to United Kingdom estuaries: a review. Environ Pollut 76:89–131

    CAS  Article  Google Scholar 

  3. 3.

    Redwan M, Elhaddad E (2017) Heavy metals seasonal variability and distribution in Lake Qaroun sediments, El-Fayoum, Egypt. J Afr Earth Sci 134:48–55

    CAS  Article  Google Scholar 

  4. 4.

    Jordanova M, Hristovski S, Musai M, Boskovska V, Rebok K, Dinevska-Kovkarovska S, Melovski L (2018) Accumulation of heavy metals in some organs in barbel and chub from Crn Drim River in the Republic of Macedonia. Bull Environ Contam Toxicol 101:392–397

    CAS  Article  Google Scholar 

  5. 5.

    Chen R, Chen H, Song L, Yao Z, Meng F, Teng Y (2019) Characterization and source apportionment of heavy metals in the sediments of Lake Tai (China) and its surrounding soils. Sci Total Environ 694:133819

    CAS  Article  Google Scholar 

  6. 6.

    Li Z, Liu J, Chen H, Li Q, Yu C, Huang X, Guo H (2019) Water environment in the Tibetan Plateau: heavy metal distribution analysis of surface sediments in the Yarlung Tsangpo River Basin. Environ Geochem Health 17:1–9

    Google Scholar 

  7. 7.

    Nguyen CC, Hugie CN, Kile ML, Navab-Daneshmand T (2019) Association between heavy metals and antibiotic-resistant human pathogens in environmental reservoirs: a review. Front Environ Sci Eng 13:46

    Article  CAS  Google Scholar 

  8. 8.

    Jia L, Liu H, Kong Q, Li M, Wu S, Wu H (2019) Interactions of high-rate nitrate reduction and heavy metal mitigation in iron-carbon-based constructed wetlands for purifying contaminated groundwater. Water Res 169:115285

    Article  CAS  Google Scholar 

  9. 9.

    Akcay H, Oguz A, Karapire C (2003) Study of heavy metal pollution and speciation in Buyak Menderes and Gediz river sediments. Water Res 37(4):813–822

    CAS  Article  Google Scholar 

  10. 10.

    Olivares-Rieumont S, de la Rosa D, Lima L, Graham DW, D’Alessandro K, Borroto J, Martinez F, Sanchez J (2005) Assessment of heavy metal levels in Almendares River sediments—Havana City, Cuba. Water Res 39:3945–3953

    CAS  Article  Google Scholar 

  11. 11.

    Wang Y, Yang Z, Shen Z, Tang Z, Niu J, Gao F (2011) Assessment of heavy metals in sediments from a typical catchment of the Yangtze River, China. Environ Monit Assess 172:407–417

    CAS  Article  Google Scholar 

  12. 12.

    Li X, Wang Y, Li B, Feng C, Chen Y, Shen Z (2013) Distribution and speciation of heavy metals in surface sediments from the Yangtze estuary and coastal areas. Environ Earth Sci 69:1537–1547

    CAS  Article  Google Scholar 

  13. 13.

    Pejman A, Bidhendi GN, Ardestani M, Saeedi M, Baghvand A (2015) A new index for assessing heavy metals contamination in sediments: a case study. Ecol Ind 58:365–373

    CAS  Article  Google Scholar 

  14. 14.

    Huang Z, Zhao W, Xu T, Zheng B, Yin D (2019) Occurrence and distribution of antibiotic resistance genes in the water and sediments of Qingcaosha Reservoir, Shanghai. China. Environ Sci Eur 31:1–9

    CAS  Article  Google Scholar 

  15. 15.

    Shyleshchandran MN, Mohan M, Ramasamy EV (2018) Risk assessment of heavy metals in Vembanad Lake sediments (south-west coast of India), based on acid-volatile sulfide (AVS)-simultaneously extracted metal (SEM) approach. Environ Sci Pollut Res 25:7333–7345

    CAS  Article  Google Scholar 

  16. 16.

    Liu M, Zhong J, Zheng X, Yu J, Liu D, Fan C (2018) Fraction distribution and leaching behavior of heavy metals in dredged sediment disposal sites around Meiliang Bay, Lake Taihu (China). Environ Sci Pollut Res 25:9737–9744

    CAS  Article  Google Scholar 

  17. 17.

    Kouidri M, Youcef ND, Benabdellah I, Ghoubali R, Bernoussi A, Lagha A (2016) Enrichment and geoaccumulation of heavy metals and risk assessment of sediments from coast of Ain Temouchent (Algeria). Arab J Geosci 9:354

    Article  CAS  Google Scholar 

  18. 18.

    Liang A, Wang Y, Guo H, Bo L, Zhang S, Bai Y (2015) Assessment of pollution and identification of sources of heavy metals in the sediments of Changshou Lake in a branch of the Three Gorges Reservoir. Environ Sci Pollut Res 22:16067–16076

    CAS  Article  Google Scholar 

  19. 19.

    Krasnici N, Dragun Z, Erk M, Raspor B (2013) Distribution of selected essential (Co, Cu, Fe, Mn, Mo, Se, and Zn) and nonessential (Cd, Pb) trace elements among protein fractions from hepatic cytosol of European chub (Squalius cephalus L.). Environ Sci Pollut Res 20:2340–2351

    CAS  Article  Google Scholar 

  20. 20.

    Fu J, Zhao C, Luo Y, Liu C, Kyzas GZ, Luo Y, Zhao D, An S, Zhu H (2014) Heavy metals in surface sediments of the Jialu River, China: their relations to environmental factors. J Hazard Mater 270:102–109

    CAS  Article  Google Scholar 

  21. 21.

    Chai L, Wang Z, Wang Y, Yang Z, Wang H, Wu X (2010) Ingestion risks of metals in groundwater based on TIN model and dose-response assessment—a case study in the Xiangjiang watershed, central-south China. Sci Total Environ 408:3118–3124

    CAS  Article  Google Scholar 

  22. 22.

    Hu H, Jin Q, Kavan P (2014) A study of heavy metal pollution in china: current status, pollution-control policies and countermeasures. Sustainability 6:5820–5838

    CAS  Article  Google Scholar 

  23. 23.

    Chen J-Q, Wang Z-X, Wu X, Zhu J-J, Zhou W-B (2011) Source and hazard identification of heavy metals in soils of Changsha based on TIN model and direct exposure method. Trans Nonferr Metals Soc China 21:642–651

    CAS  Article  Google Scholar 

  24. 24.

    Mao L, Mo D, Guo Y, Fu Q, Yang J, Jia Y (2013) Multivariate analysis of heavy metals in surface sediments from lower reaches of the Xiangjiang River, southern China. Environ Earth Sci 69:765–771

    CAS  Article  Google Scholar 

  25. 25.

    Fang X, Peng B, Wang X, Song Z, Zhou D, Wang Q, Qin Z, Tan C (2019) Distribution, contamination and source identification of heavy metals in bed sediments from the lower reaches of the Xiangjiang River in Hunan province, China. Sci Total Environ 689:557–570

    CAS  Article  Google Scholar 

  26. 26.

    Li H, Yang J, Ye B, Jiang D (2019) Pollution characteristics and ecological risk assessment of 11 unheeded metals in sediments of the Chinese Xiangjiang River. Environ Geochem Health 41:1459–1472

    CAS  Article  Google Scholar 

  27. 27.

    Jiang M, Zeng G, Zhang C, Ma X, Chen M, Zhang J, Lu L, Yu Q, Hu L, Liu L (2013) Assessment of heavy metal contamination in the surrounding soils and surface sediments in Xiawangang River, Qingshuitang District. PloS ONE 8:e71176

    CAS  Article  Google Scholar 

  28. 28.

    Chai L, Li H, Yang Z, Min X, Liao Q, Liu Y, Men S, Yan Y, Xu J (2017) Heavy metals and metalloids in the surface sediments of the Xiangjiang River, Hunan, China: distribution, contamination, and ecological risk assessment. Environ Sci Pollut Res 24:874–885

    CAS  Article  Google Scholar 

  29. 29.

    Peng B, Tang X, Yu C, Tan C, Yin C, Yang G, Liu Q, Yang K, Tu X (2011) Geochemistry of trace metals and Pb isotopes of sediments from the lowermost Xiangjiang River, Hunan Province (P. R. China): implications on sources of trace metals. Environ Earth Sci 64:1455–1473

    CAS  Article  Google Scholar 

  30. 30.

    Wang H, Wang J, Liu R, Yu W, Shen Z (2015) Spatial variation, environmental risk and biological hazard assessment of heavy metals in surface sediments of the Yangtze River estuary. Mar Pollut Bull 93:250–258

    CAS  Article  Google Scholar 

  31. 31.

    Liao Y, Min X, Yang Z, Chai L, Zhang S, Wang Y (2014) Physicochemical and biological quality of soil in hexavalent chromium-contaminated soils as affected by chemical and microbial remediation. Environ Sci Pollut Res 21:379–388

    CAS  Article  Google Scholar 

  32. 32.

    Lin C, He M, Liu X, Guo W, Liu S (2013) Contamination and ecological risk assessment of toxic trace elements in the Xi River, an urban river of Shenyang city, China. Environ Monit Assess 185:4321–4332

    CAS  Article  Google Scholar 

  33. 33.

    Huang Y, Fu C, Li Z, Fang F, Ouyang W, Guo J (2019) Effect of dissolved organic matters on adsorption and desorption behavior of heavy metals in a water-level-fluctuation zone of the Three Gorges Reservoir, China. Ecotoxicol Environ Saf 185:109695

    CAS  Article  Google Scholar 

  34. 34.

    Audry S, Schafer J, Blanc G, Jouanneau JM (2004) Fifty-year sedimentary record of heavy metal pollution (Cd, Zn, Cu, Pb) in the Lot River reservoirs (France). Environ Pollut 132:413–426

    CAS  Article  Google Scholar 

  35. 35.

    Tran Thi Thu D, Cappuyns V, Swennen R, Nguyen Ky P (2013) From geochemical background determination to pollution assessment of heavy metals in sediments and soils. Rev Environ Sci Bio-Technol 12:335–353

    Article  CAS  Google Scholar 

  36. 36.

    Qian Y, Zheng MH, Gao L, Zhang B, Liu W, Jiao W, Zhao X, Xiao K (2005) Heavy metal contamination and its environmental risk assessment in surface sediments from Lake Dongting, People’s Republic of China. Bull Environ Contam Toxicol 75(1):204–210

    CAS  Article  Google Scholar 

  37. 37.

    Chowdhury R, Favas PJC, Pratas J, Jonathan MP, Ganesh PS, Sarkar SK (2015) Accumulation of trace metals by mangrove plants in Indian Sundarban Wetland: prospects for phytoremediation. Int J Phytorem 17:885–894

    CAS  Article  Google Scholar 

  38. 38.

    Hakanson L (1980) An ecological risk index for aquatic pollution-control—a sedimentological approach. Water Res 14:975–1001

    Article  Google Scholar 

  39. 39.

    Teng Y, Wu J, Lu S, Wang Y, Jiao X, Song L (2014) Soil and soil environmental quality monitoring in China: a review. Environ Int 69:177–199

    CAS  Article  Google Scholar 

  40. 40.

    Tang J, Chai L, Li H, Yang Z, Yang W (2018) A 10-year statistical analysis of heavy metals in river and sediment in Hengyang segment, Xiangjiang River Basin, China. Sustainability 10:1057

    Article  CAS  Google Scholar 

  41. 41.

    Zhuang Q, Li G, Liu Z (2018) Distribution, source and pollution level of heavy metals in river sediments from South China. CATENA 170:386–396

    CAS  Article  Google Scholar 

  42. 42.

    He Z, Li F, Dominech S, Wen X, Yang S (2019) Heavy metals of surface sediments in the Changjiang (Yangtze River) Estuary: distribution, speciation and environmental risks. J Geochem Explor 198:18–28

    CAS  Article  Google Scholar 

  43. 43.

    Fu Z, Wu F, Chen L, Xu B, Feng C, Bai Y, Liao H, Sun S, Giesy JP, Guo W (2016) Copper and zinc, but not other priority toxic metals, pose risks to native aquatic species in a large urban lake in Eastern China. Environ Pollut 219:1069–1076

    CAS  Article  Google Scholar 

  44. 44.

    Liang J, Liu J, Xu G, Chen B (2019) Distribution and transport of heavy metals in surface sediments of the Zhejiang nearshore area, East China Sea: sedimentary environmental effects. Mar Pollut Bull 146:542–551

    CAS  Article  Google Scholar 

  45. 45.

    Jiang Y, Chao S, Liu J, Yang Y, Chen Y, Zhang A, Cao H (2017) Source apportionment and health risk assessment of heavy metals in soil for a township in Jiangsu Province, China. Chemosphere 168:1658–1668

    CAS  Article  Google Scholar 

  46. 46.

    Chen Y, Jiang X, Wang Y, Zhuang D (2018) Spatial characteristics of heavy metal pollution and the potential ecological risk of a typical mining area: a case study in China. Process Saf Environ Prot 113:204–219

    CAS  Article  Google Scholar 

  47. 47.

    Grygar TM, Popelka J (2016) Revisiting geochemical methods of distinguishing natural concentrations and pollution by risk elements in fluvial sediments. J Geochem Explor 170:39–57

    Article  CAS  Google Scholar 

  48. 48.

    Jiang D, Wang Y, Zhou S, Long Z, Liao Q, Yang J, Fan J (2019) Multivariate analyses and human health assessments of heavy metals for surface water quality in the Xiangjiang River Basin, China. Environ Toxicol Chem 38:1645–1657

    CAS  Google Scholar 

  49. 49.

    Zhu J, Zhang J, Li Q, Han T, Xie J, Hu Y, Chai L (2013) Phylogenetic analysis of bacterial community composition in sediment contaminated with multiple heavy metals from the Xiangjiang River in China. Mar Pollut Bull 70:134–139

    CAS  Article  Google Scholar 

  50. 50.

    Qi J, Zhang H, Li X, Lu J, Zhang G (2016) Concentrations, spatial distribution, and risk assessment of soil heavy metals in a Zn–Pb mine district in southern China. Environ Monit Assess 188:413

    Article  CAS  Google Scholar 

  51. 51.

    Dong Y, Zhong N, Yang X (2002) Current progress in evaluation indexes for heavy metals pollution in soils. Trace Elem Sci 9:11–17

    Google Scholar 

  52. 52.

    Damak M, Fourati R, Ellech B, Kallel M (2019) Assessment of organic and metallic contamination in the surface sediment of Monastir Bay (Eastern Tunisia): spatial distribution, potential sources, and ecological risk assessment. Mar Pollut Bull 149:110500

    Article  CAS  Google Scholar 

  53. 53.

    Zonta R, Cassin D, Pini R, Dominik J (2019) Assessment of heavy metal and As contamination in the surface sediments of Po delta lagoons (Italy). Estuar Coast Shelf Sci 225:106235

    CAS  Article  Google Scholar 

  54. 54.

    Ugochukwu N, Mohamed I, Ali M, Iqbal J, Fu Q, Zhu J, Jiang G, Hu H (2013) Impacts of inorganic ions and temperature on lead adsorption onto variable charge soils. CATENA 109:103–109

    CAS  Article  Google Scholar 

  55. 55.

    Hegedusova A, Hegedus O, Toth T, Vollmannova A, Andrejiova A, Slosar M, Mezeyova I, Pernyeszi T (2016) Adsorption processes of lead ions on the mixture surface of bentonite and bottom sediments. Bull Environ Contam Toxicol 97:876–880

    CAS  Article  Google Scholar 

Download references


Not applicable.


This work was supported by the Water Pollution Control and Management (No. 2018ZX07502-001-005).

Author information




ZH and XZ were involved in the experiments and manuscript writing. ZH was responsible for the data analysis. CL and JD collected samples. ZH and BZ designed the study. XZ and BZ contributed to correction of the manuscript. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Xingru Zhao or Binghui Zheng.

Ethics declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Huang, Z., Liu, C., Zhao, X. et al. Risk assessment of heavy metals in the surface sediment at the drinking water source of the Xiangjiang River in South China. Environ Sci Eur 32, 23 (2020).

Download citation


  • Heavy metal
  • Risk assessment
  • Sediment
  • Distribution
  • The Xiangjiang River