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Spatial Distribution and Ecological Risk Assessment of Heavy Metals in Surface Sediment of Songhua River, Northeast China

Baolin LIU Deming DONG Xiuyi HUA Weihua DONG Ming LI

LIU Baolin, DONG Deming, HUA Xiuyi, DONG Weihua, LI Ming, 2021. Spatial Distribution and Ecological Risk Assessment of Heavy Metals in Surface Sediment of Songhua River, Northeast China. Chinese Geographical Science, 31(2): 223−233 doi:  10.1007/s11769-021-1186-8
Citation: LIU Baolin, DONG Deming, HUA Xiuyi, DONG Weihua, LI Ming, 2021. Spatial Distribution and Ecological Risk Assessment of Heavy Metals in Surface Sediment of Songhua River, Northeast China. Chinese Geographical Science, 31(2): 223−233 doi:  10.1007/s11769-021-1186-8

doi: 10.1007/s11769-021-1186-8

Spatial Distribution and Ecological Risk Assessment of Heavy Metals in Surface Sediment of Songhua River, Northeast China

Funds: Under the auspices of the National Natural Science Foundation of China (No. 42077343), the Major Science and Technology Program for Water Pollution Control and Treatment in China (No. 2009ZX07207-001-03), the Science and Technology Development Program of Jilin Province (No. 20200403020SF), the Natural Science Foundation of Changchun Normal University (No. 2019009)
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  • Figure  1.  Map showing the sampling sites along the Songhua River

    Figure  2.  Spatial distributions of Cu, Zn, Cr, Ni, Cd, Pb in surface sediments of the Songhua River

    Figure  3.  Mean concentrations of Cu, Zn, Cd, Pb, Ni and Cr in sediments collected from China. The data for the Songhua River in Jilin Province were from this study. The data for the Huaihe River were from Yang et al. (2017). The data for the Haihe River were from Tang et al. (2013). The data for the Yangtze River Estuary were from Wang et al. (2014). The data for the Songhua River Harbin region were from Li et al. (2017). The data for the Bortala River were from Zhang et al. (2016). The data for the Liaohe River were from Ke et al. (2017). The data for the Jialu River were from Fu et al. (2014). The data for the Xiangjiang River were from Chai et al. (2017). The data for the Zijiang River were from Zhang et al. (2018)

    Figure  4.  Hierarchical clustering analysis of heavy metals in surface sediments of the Songhua River

    Figure  5.  Spatial distribution of the sum of the toxic units (a), composition of toxic units of all heavy metals (b) and potential ecological risk index (PERI) (c) in surface sediments of the Songhua River

    Figure  6.  Contributions of heavy metals to the sum of toxic units in surface sediments of the Songhua River

    Table  1.   Descriptive statistics of heavy metal concentrations in surface sediments of the Songhua River

    CdPbZnCuCrNi
    Minimum / (mg/kg)2.02.417.08.59.717.5
    Maximum / (mg/kg)11.086.498.549.439.690.8
    Mean / (mg/kg)4.039.059.324.018.556.0
    S.D. / (mg/kg)2.127.918.09.28.617.6
    CV / %52.371.630.438.346.631.5
    ERLa / (mg/kg)5.035.0120.070.080.030.0
    ERM / (mg/kg)9.0110.0270.0390.0145.050.0
    TEL / (mg/kg)0.618.0123.035.737.335.0
    PEL / (mg/kg)3.536.0315.0197.090.091.3
    Compared with TEL and PELthe ratio of samples to the total samples in each guideline
    < TEL / %035.910092.394.912.8
    ≥ TEL < PEL / %61.52.607.75.187.2
    ≥ PEL (%)38.561.50000
    Compared with ERM and ERLthe ratio of samples to the total samples in each guideline
    < ERL / %79.538.51001001007.7
    ≥ ERL < ERM (%)18.061.500023.1
    ≥ ERM / %2.5000069.2
    Background valueb (mg/kg)0.1424.071.017.717.322.0
    Notes: S.D., standard deviation; CV, coefficients of variation; TEL, threshold effect level; PEL, probable effect level; ERL: effects range low; ERM, effects range median. a Threshold effect level or probable effect level for freshwater ecosystem (MacDonald et al., 2000). b Background value of sediment in the Songhua River (Li and Zheng, 1989)
    下载: 导出CSV

    Table  2.   Total variance explained by principle component analysis of heavy metals in surface sediments of the Songhua River (two principal components are elected)

    ElementComponent matrixRotated component matrix
    PC1PC2PC1PC2
    Cd0.0810.7910.0540.793
    Pb0.9060.1020.9020.133
    Zn0.8420.0460.8400.074
    Cu0.0480.8790.0180.880
    Cr0.7840.0950.7800.121
    Ni0.825–0.3780.837–0.350
    Initial eigenvalue2.8341.5632.8321.565
    Proportion of total variance/%47.22526.05547.20126.079
    Proportion of cumulative variance /%47.22573.28026.07973.280
    下载: 导出CSV

    Table  3.   Correlation analysis of heavy metals in surface sediments of the Songhua River

    ElementCdPbZnCuCrNiFe
    Cd10.1410.1040.436**0.006–0.112–0.162
    Pb10.739**0.1090.628**0.644**–0.086
    Zn10.0600.464**0.605–0.242
    Cu10.202–0.3090.010
    Cr10.567**0.363*
    Ni10.216
    Fe1
    Notes: ** Correlation is significant at the 0.01 level (2-tailed); * significant at the 0.05 level
    下载: 导出CSV
  • [1] Amano A, Kuwae M, Agusa T et al., 2011. Spatial distribution and corresponding determining factors of metal concentrations in surface sediments of Beppu Bay, southwest Japan. Marine Environmental Research, 71(4): 247–256. doi:  10.1016/j.marenvres.2011.01.009
    [2] Bai J, Cui B, Chen B et al., 2011. Spatial distribution and ecological risk assessment of heavy metals in surface sediments from a typical plateau lake wetland, China. Ecological Modelling, 222(2): 301–306. doi:  10.1016/j.ecolmodel.2009.12.002
    [3] Barhoumi S, Messaoudi I, Deli T et al., 2009. Cadmium bioaccumulation in three benthic fish species, Salaria basilisca, Zosterisessor ophiocephalus and Solea vulgaris collected from the Gulf of Gabes in Tunisia. Journal of Environmental Sciences, 21: 980–984. doi:  10.1016/S1001-0742(08)62371-2
    [4] Bergbäck B, Johansson K, Mohlander U, 2001. Urban metal flow—a case study of Stockholm. Review and conclusions. Water, Air, and Soil Pollution: Focus, 1(3–4): 3–24. doi:  10.1023/A:1017531532576
    [5] Brady J P, Ayoko G A, Martens W N et al., 2014. Enrichment, distribution and sources of heavy metals in the sediments of Deception Bay, Queensland, Australia. Marine Pollution Bulletin, 81: 248–255. doi:  10.1016/j.marpolbul.2014.01.031
    [6] Caeiro S, Costa M N, Ramos T B et al., 2005. Assessing heavy metal contamination in Sado Estuary sediment: an index analysis approach. Ecological Indicators, 5(2): 151–169. doi:  10.1016/j.ecolind.2005.02.001
    [7] Chai L, Li H, Yang Z et al., 2017. Heavy metals and metalloids in the surface sediments of the Xiangjiang River, Hunan, China: distribution, contamination, and ecological risk assessment. Environmental Science and Pollution Research, 24(1): 874–885. doi:  10.1007/s11356-016-7872-x
    [8] Chouvelon T, Strady E, Harmelin-Vivien M et al., 2019. Patterns of trace metal bioaccumulation and trophic transfer in a phytoplankton–zooplankton–small pelagic fish marine food web. Marine Pollution Bulletin, 146: 1013–1030. doi:  10.1016/j.marpolbul.2019.07.047
    [9] Cox M E, Preda M, 2005. Trace metal distribution within marine and estuarine sediments of western Moreton Bay, Queensland, Australia: relation to land use and setting. Geographical Research, 43(2): 173–193. doi:  10.1111/j.1745-5871.2005.00312.x
    [10] Dong J, Yu M, Bian Z et al., 2011. Geostatistical analyses of heavy metal distribution in reclaimed mine land in Xuzhou, China. Environmental Earth Sciences, 62(1): 127–137. doi:  10.1007/s12665-010-0507-5
    [11] Dong W, Liu B, Song Y et al., 2018. Occurrence and partition of perfluorinated compounds (PFCs) in water and sediment from the Songhua River, China. Archives of Environmental Contamination and Toxicology, 74: 492–501. doi:  10.1007/s00244-017-0474-x
    [12] Förstner U, Heise S, Schwartz R et al., 2004. Historical contaminated sediments and soils at the river basin scale: examples from the Elbe River Catchment Area. Journal of Soils and Sediments, 4(4): 247–260. doi:  10.1007/BF02991121
    [13] Förstner U, Wittmann G T W, 1981. Metal pollution in the aquatic environment. Berlin: Springer–Verlag. doi: 10.1007/978-3-642-69385-4
    [14] Fu J, Zhao C, Luo Y et al., 2014. Heavy metals in surface sediments of the Jialu River, China: their relations to environmental factors. Journal of Hazardous Materials, 270: 102–109. doi:  10.1016/j.jhazmat.2014.01.044
    [15] Gao Y, Li W, Zhang D et al., 2010. Bio–enhanced activated carbon filter with immobilized microorganisms for removing organic pollutants in the Songhua River. Water Science and Technology, 62(12): 2819–2828. doi:  10.2166/wst.2010.666
    [16] Hakanson L, 1980. An ecological risk index for aquatic pollution control. A sedimentological approach. Water Research, 14(8): 975–1001. doi:  10.1016/0043-1354(80)90143-8
    [17] Hall L W Jr, Scott M C, Killen W D, 1997. A screening level probabilistic ecological risk assessment of copper and cadmium in the chesapeake bay watershed. Chesapeake Bay Program, Annapolis, MD: US EPA.
    [18] Hao L, Liu H, Lu J et al., 2009. Vertical distribution of 137Cs and 210Pb and sedimentation rate in Songhua Lake. Journal of Jilin University (Earth Science Edition), 39(3): 470–473. (in Chinese)
    [19] Hester R E, Harrison R M, 2006. Risk assessment of metals in the environment. Issues in Environmental Science & Technology, 22: 102–131. doi:  10.1039/9781847552440-00102
    [20] Hu B Q, Li J, Zhao J T et al., 2013. Heavy metal in surface sediments of the Liaodong Bay, Bohai Sea: distribution, contamination, and sources. Environmental Monitoring and Assessment, 185(6): 5071–5083. doi:  10.1007/s10661-012-2926-0
    [21] Islam M S, Hossain M B, Matin A et al., 2018. Assessment of heavy metal pollution, distribution and source apportionment in the sediment from Feni River estuary, Bangladesh. Chemosphere, 202: 25–32.
    [22] Ke X, Gui S F, Huang H et al., 2017. Ecological risk assessment and source identification for heavy metals in surface sediment from the Liaohe River protected area, China. Chemosphere, 175: 473–481. doi:  10.1016/j.chemosphere.2017.02.029
    [23] Li Jian, Zheng Chunjiang, 1988. Databook of environmental background value. Chinese Environmental Science Press, Beijing, China, 342–348. (in Chinese)
    [24] Li F, Huang J, Zeng G et al., 2013a. Spatial risk assessment and sources identification of heavy metals in surface sediments from the Dongting Lake, Middle China. Journal of Geochemical Exploration, 132: 75–83. doi:  10.1016/j.gexplo.2013.05.007
    [25] Li K Y, Cui S, Zhang F X et al., 2020. Concentrations, possible sources and health risk of heavy metals in multi–media environment of the Songhua River, China. International Journal of Environmental Research and Public Health, 17(5): 1766. doi:  10.3390/ijerph17051766
    [26] Li N, Tian Y, Zhang J et al., 2017. Heavy metal contamination status and source apportionment in sediments of Songhua River Harbin region, Northeast China. Environmental Science and Pollution Research, 24(4): 3214–3225. doi:  10.1007/s11356-016-7132-0
    [27] Li R Y, Li R L, Chai M W et al., 2015. Heavy metal contamination and ecological risk in Futian mangrove forest sediment in Shenzhen Bay, South China. Marine Pollution Bulletin, 101(1): 448–456. doi:  10.1016/j.marpolbul.2015.09.048
    [28] Li X, Wang Y, Li B et al., 2013b. Distribution and speciation of heavy metals in surface sediments from the Yangtze estuary and coastal areas. Environmental Earth Science, 69(5): 1537–1547. doi:  10.1007/s12665-012-1988-1
    [29] Li Y, Zhang H, Chen X et al., 2014a. Distribution of heavy metals in soils of the Yellow River Delta: concentrations in different soil horizons and source identification. Journal of Soils and Sediments, 14: 1158–1168. doi:  10.1007/s11368-014-0861-0
    [30] Li Z Y, Ma Z W, Van der Kuijp T J et al., 2014b. A review of soil heavy metal pollution from mines in China: pollution and health risk assessment. Science of the Total Environment, 468−469: 843–853. doi:  10.1016/j.scitotenv.2013.08.090
    [31] Liu B, Bi S, Dong D et al., 2014. Background values of certain heavy metal elements in the sediments of the upper reach of Songhua River. Journal of Jilin Agricultural University, 36(4): 454–459. (in Chinese)
    [32] Liu B, Diao G, Han X et al., 2015. Spatial distribution and ecological risk assessment of heavy metals in surface sediments from Songhua River. Science Technology and Engineering, 15(8): 140–145. (in Chinese)
    [33] MacDonald D D, Ingersoll C G, Berger T A, 2000. Development and evaluation of consensus–based sediment quality guidelines for freshwater ecosystems. Archives of Environmental Contamination and Toxicology, 39(1): 20–31. doi:  10.1007/s002440010075
    [34] Mustafa G, Komatsu S, 2016. Toxicity of heavy metals and metal–containing nanoparticles on plants. Biochimica et Biophysica Acta (BBA) – Proteins and Proteomics, 1864(8): 932–944. doi:  10.1016/j.bbapap.2016.02.020
    [35] Nriagu J O, 1996. A history of global metal pollution. Science, 272(5259): 223–224. doi:  10.1126/science.272.5259.223
    [36] Nriagu J O, Pacyna J M, 1988. Quantitative assessment of worldwide contamination of air, water, and soils by trace metals. Nature, 33(6169): 134–139.
    [37] Omwene P I, Öncel M S, Çelen M et al., 2018. Heavy metal pollution and spatial distribution in surface sediments of Mustafakemalpaşa stream locaOryza sativated in the world's largest borate basin (Turkey). Chemosphere, 208: 782–792.
    [38] Pedersen F, Bjørnestad E, Andersen H V et al., 1998. Characterization of sediments from Copenhagen Harbour by use of biotests. Water Science and Technology, 37(6–7): 233–240.
    [39] Sadiq R, Husain T, Bose N et al., 2003. Distribution of heavy metals in sediment pore water due to offshore discharges: an ecological risk assessment. Environmental Modelling & Software, 18(5): 451–461. doi:  10.1016/S1364-8152(03)00010-0
    [40] Saleem M, Iqbal J, Shah M H, 2015. Geochemical speciation, anthropogenic contamination, risk assessment and source identification of selected metals in freshwater sediments—a case study from Mangla Lake, Pakistan. Environmental Nanotechnology, Monitoring & Management, 4: 27–36. doi:  10.1016/j.enmm.2015.02.002
    [41] Satpathy D, Reddy M V, Dhal S P, 2014. Risk assessment of heavy metals contaminationin paddy soil, plants, and grains (Oryza sativa L.) at the East Coast of India. Biomed Research International, 1–11. doi:  10.1155/2014/545473
    [42] Siddiqui E, Pandey J, 2019. Assessment of heavy metal pollution in water and surface sediment and evaluation of ecological risks associated with sediment contamination in the Ganga River: a basin-scale study. Environmental Science and Pollution Research, 26(11): 10926–10940. doi:  10.1007/s11356-019-04495-6
    [43] Statistics Bureau of Jilin Province, 2016. Available at http://tjj.jl.gov.cn/tjsj/tjnj/2016/ml/indexc.htm.
    [44] Suresh G, Sutharsan P, Ramasamy V et al., 2012. Assessment of spatial distribution and potential ecological risk of the heavy metals in relation to granulometric contents of Veeranam lake sediments, India. Ecotoxicology and Environmental Safety, 84: 117–124. doi:  10.1016/j.ecoenv.2012.06.027
    [45] Tang W Z, Zhao Y, Wang C et al., 2013. Heavy metal contamination of overlying waters and bed sediments of Haihe Basin in China. Ecotoxicology and Environmental Safety, 98: 317–323. doi:  10.1016/j.ecoenv.2013.09.038
    [46] Wang F, Wang W X, Huang X P, 2012. Spatial distribution of gut juice extractable Cu, Pb and Zn in sediments from the Pearl River Estuary, Southern China. Marine Environmental Research, 77: 112–119. doi:  10.1016/j.marenvres.2012.03.002
    [47] Wang J W, Liu R M, Zhang P P et al., 2014. Spatial variation, environmental assessment and source identification of heavy metals in sediments of the Yangtze River Estuary. Marine Pollution Bulletin, 87(1–2): 364–373. doi:  10.1016/j.marpolbul.2014.07.048
    [48] Yang Y, Jin Q, Fang J M et al., 2017. Spatial distribution, ecological risk assessment, and potential sources of heavy metal (loid)s in surface sediments from the Huai River within the Bengbu section, China. Environmental Science and Pollution Research, 24(12): 11360–11370. doi:  10.1007/s11356-017-8732-z
    [49] Zhang Z S, Sun X J, Wang Q C et al., 2010. Recovery from mercury contamination in the Second Songhua River, China. Water Air and Soil Pollution, 211(1–4): 219–229. doi:  10.1007/s11270-009-0294-3
    [50] Zhang Z X, Lu Y, Li H P et al., 2018. Assessment of heavy metal contamination, distribution and source identification in the sediments from the Zijiang River, China. Science of the Total Environment, 645: 235–243. doi:  10.1016/j.scitotenv.2018.07.026
    [51] Zhang Z Y, Li J Y , Mamat Z et al., 2016. Sources identification and pollution evaluation of heavy metals in the surface sediments of Bortala River, Northwest China. Ecotoxicology and Environmental Safety, 126: 94–101. doi:  10.1016/j.ecoenv.2015.12.025
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Spatial Distribution and Ecological Risk Assessment of Heavy Metals in Surface Sediment of Songhua River, Northeast China

doi: 10.1007/s11769-021-1186-8
    基金项目:  Under the auspices of the National Natural Science Foundation of China (No. 42077343), the Major Science and Technology Program for Water Pollution Control and Treatment in China (No. 2009ZX07207-001-03), the Science and Technology Development Program of Jilin Province (No. 20200403020SF), the Natural Science Foundation of Changchun Normal University (No. 2019009)
    通讯作者: DONG Deming. E-mail: dmdong@jlu.edu.cn

English Abstract

LIU Baolin, DONG Deming, HUA Xiuyi, DONG Weihua, LI Ming, 2021. Spatial Distribution and Ecological Risk Assessment of Heavy Metals in Surface Sediment of Songhua River, Northeast China. Chinese Geographical Science, 31(2): 223−233 doi:  10.1007/s11769-021-1186-8
Citation: LIU Baolin, DONG Deming, HUA Xiuyi, DONG Weihua, LI Ming, 2021. Spatial Distribution and Ecological Risk Assessment of Heavy Metals in Surface Sediment of Songhua River, Northeast China. Chinese Geographical Science, 31(2): 223−233 doi:  10.1007/s11769-021-1186-8
    • Sediment serves as the carrier and reservoir of numerous pollutants migrating and transferring in aquatic environments (Förstner and Wittmann, 1981). Pollutants entering into rivers or lakes via various pathways can be adsorbed by suspended solid, and finally precipitate into sediment, which acts as an ultimate receptor of pollutants. However, sediment can re-suspend and migrate via water movements. In the meantime, the adsorbed pollutants could be released into water, resulting in secondary pollution due to the changes of sediments in granularity, properties and hydrological conditions (Nriagu, 1996).

      Heavy metal pollution has received a widespread attention because of their environmental persistence and biological toxicity (Dong et al., 2011; Li et al., 2014b). They have a great impact on biological availability and toxicity of aquatic organisms (Hester and Harrison, 2006). More seriously, some heavy metals could accumulate and transfer in various species in freshwater food webs, leading to an increasing risk of rivers and oceans (Barhoumi et al., 2009; Chouvelon et al., 2019). Heavy metals in environments come from natural sources and anthropogenic sources including industrial activities, burning of fossil fuels, application of pesticides and chemical fertilizers, automobile exhausts, etc. (Nriagu and Pacyna 1988; Bergbäck et al., 2001; Förstner et al., 2004). However, little information is available on the levels, sources and ecological risk of heavy metals in a basin-scale study in China.

      The Songhua River Basin is one of the regions with a dense population and rapid economic development in China. Its drainage area is 54 560 000 km2, accounting for 69.3% of the total area of Northeast China. In recent years, the occurrence, behavior and ecological effects of heavy metals have been studied in the sediment of the Songhua River (Zhang et al., 2010; Li et al., 2017; Li et al., 2020), which have provided supports for understanding the pollution of sediments in the Songhua River Basin. However, these studies considered a small reach only and can not meet the theoretical and practical requirements for future exploitation and utilization towards the vast Songhua River. There are a lot of manufacturing activities along the Songhua River, including mining, petroleum processing, pharmaceutical industries, etc., which are considered to be the predominant sources (Gao et al., 2010). Jilin Province is known for its large-scale agricultural activities, which produce a large amount of non-point wastewater entering into rivers. Moreover, a large amount of domestic sewage is discharged into the Songhua River, leading to a declining tendency of the water quality (Li et al., 2017). This investigation on heavy metal contamination of sediment in watershed-scale will provide a detailed database to policy planners for management and rejuvenation of the Songhua River. In addition, the methods used in this study were suitable for further analysis of the threats that human activities have posed on heavy metal pollution.

      The major purposes of this study are: 1) investigating the distribution characteristics of heavy metals in surface sediments of the Songhua River using a geostatistical method; 2) evaluating potential ecological risks of the heavy metals referring to the standards of sediment quality; 3) analyzing the sources of heavy metals using factor analysis and multivariate statistical analysis.

    • The Songhua River traverses the central and eastern part of Northeast China with an overall length of 1897 km. The Songhua River Basin has a temperate continental monsoon climate with annual precipitation of about 500–1000 mm concentrated in summer. The soil consists of sand, loam and clay. There are abundant mineral resources distributed in the basin (e.g., gold, nickel, iron, copper, silica, etc.) (Liu et al., 2015). The Songhua River in Jilin Province flows through Jilin, Yushu, Fuyu and Songyuan cities. The Songhua River Basin serves 21.77 million people, accounting for 79% of the total population of Jilin Province (Statistics Bureau of Jilin Province, 2016). Jilin is famous for its large-scale manufacturing activities, including petroleum processing, chemical industry, electroplating, etc. Yushu, Fuyu and Songyuan cities are known for the grain production and processing.

    • The present study was conducted in July 2012 at 39 sites considering 790 km river stretch in Jilin Province from Lake Tianchi in Changbai Mountain to Fuyu County covering over 40% of the river length (Fig. 1). There are 16 sites from the mainstream and 18 sites from the main tributaries, including 6 sites at the Gudong River, 6 sites at the Huifa River, 4 sites at the Yinma River, 1 site at the Yitong River and 1 site at the Lalin River. We also collected 5 sediment samples from the Hunjiang River located in the Songhua River Basin. Three sampling sites were chosen in the distance of 1/4, 1/2, 3/4 width of each site from the river for obtaining the mean concentrations of heavy metals. According to Hao et al. (2009), the average sedimentation rate in the Songhua River was determined to be 0.7 cm/yr. The surface sediment was sampled at the depth of 0–15 cm, which could be representative of 20 yr of sedimentary history in the Songhua River. The sediment samples were collected using a self-made grab sampler, and then enclosed in polythene bags and taken back to the laboratory from Jilin University. After the sediments were air-dried at room temperature, they were ground and then sieved by a 100-mesh nylon screen. The homogenized sediment was digested using the ternary acid mixture (HNO3-HClO4-HF). The process of digestion was as follows: 10 mL of nitric acid was added in a 50 mL polytetrafluoroethylene (PTEE) beaker in which approximate 0.5 g of dried sample was previously added. Each beaker was heated on a low temperature to resolve organic matter. When the mixture was viscous, 10 mL of hydrofluoric acid was added to remove the silicon. At last, the beaker was continued to heat until the white smoke ran out after 5 mL of perchloric acid was added. After the digestion, the beaker was washed by dilute nitric acid, and then the eluent was diluted to 50 mL. Heavy metals were measured by a Shimadzu atomic absorption spectrophotometer (AA6300, Shimadzu, Japan).

      Figure 1.  Map showing the sampling sites along the Songhua River

    • The sediment certified reference materials GBW07311 (GSD-11) and GBW07366 (GSD-23) were used to ensure the precision and accuracy. The limits of detection (LODs) were 0.9 mg/kg, 0.5 mg/kg, 0.2 mg/kg, 1.9 mg/kg, 4.8 mg/kg and 4.6 mg/kg for copper (Cu), zinc (Zn), cadmium (Cd), lead (Pb), nickel (Ni) and chromium (Cr), respectively. Recoveries of Cu, Zn, Cd, Pb, Ni and Cr were 95%–104%, 94%–105%, 92%–99%, 86%–102%, 96%–104% and 90%–105%, respectively. QC was conducted by reagent blank and sample blank. All the analyses were carried out in triplicate, and the standard deviations were within ±5% of the mean values.

    • MacDonald et al. (2000) has developed two kinds of sediment quality guidelines to evaluate the ecological risks from heavy metals in the sediments to freshwater ecosystem: 1) the effect range low (ERL) / effect range median (ERM) and 2) the threshold effect level (TEL) or probable effect level (PEL). Low range effects (i.e., ERLs or TELs) are neglected due to the extremely low impacts on zoobenthos. However, median range effects (i.e., ERM or PEL) referring to the concentration higher than the threshold, has the possibility of causing adverse effect on zoobenthos. Therefore, the ratios between detectable concentrations and ERM or PEL can be used to evaluate the toxic effects of heavy metals (Pedersen et al., 1998).

    • The estimation of the potential toxicity of heavy metal in sediment was performed using toxic unit (TU, Pedersen et al., 1998), which was calculated using Equ. 1.

      $$ TU = {C_i}/PEL $$ (1)

      where ${C_i}$ is the concentration of heavy metal i; PEL is the probable effect level. we use $ \displaystyle\sum $ TU to represent the ecological risk of all the studied heavy metals at each sampling site.

      The ecological risk of individual metal ($E_r^i$) and potential ecological risk index ($PERI$) was also employed to assess ecological risk of heavy metals in sediment (Hakanson, 1980) and could be defined as Equs. 2 and 3.

      $$E_r^i = T_r^i \times \left(\frac{{{C_i}}}{{{C_0}}}\right)$$ (2)
      $$PERI = \sum\limits_{i = 1}^n {T_r^i \times \left(\frac{{{C_i}}}{{{C_0}}}\right)} $$ (3)

      where n is the number of heavy metals, ${C_0}$ is the background value of heavy metal, $T_r^i$ is the biological toxicity factor r of individual metal i, which was defined as 5 for Cu, Pb and Ni, 1 for Zn, 2 for Cr, and 30 for Cd (Suresh et al., 2012). The evaluation standard was illustrated as follows (Li et al., 2016): $E_r^i$ < 40, low risk level; 40–80, moderate risk level; 80–160, considerable level; 160–320, high level; >320, very high risk level. $PERI$ <150, low risk level; 150–300, moderate risk level; 300–600, considerable level; and > 600, high risk level.

    • Pearson correlation analysis and factor analysis were applied to investigate the correlations and the common pollution sources among the heavy metals. The significant components and associate loadings were extracted by principal component analysis (PCA) in which the method of varimax was used. PCA leads to a reduction of initial dimension of data (Islam et al., 2018) and has been widely used to identify the sources of heavy metals (Amano et al., 2011; Bai et al., 2011; Wang et al., 2012; Hu et al., 2013; Li et al., 2013a;b). Hierarchical clustering analysis (HCA) and PCA are often employed to confirm results and provide grouping of variables (Li et al., 2015). In this study, HCA was used to understand the relationships among heavy metals on the same dataset as PCA. Analysis was performed by Excel and SPSS (version 20.0).

    • The concentrations of heavy metals in surface sediments of the Songhua River were summarized in Table 1. The mean concentrations of the heavy metals decrease with Zn (59.3 mg/kg) > Ni (56.0 mg/kg) > Pb (39.0 mg/kg) > Cu (24.0 mg/kg) > Cr (18.5 mg/kg) > Cd (4.0 mg/kg). The mean concentrations of metals were higher than background values (Table 1) except Zn, and higher than ERM except Pb, whereas Cd, Pb, and Ni were higher than PEL.

      Table 1.  Descriptive statistics of heavy metal concentrations in surface sediments of the Songhua River

      CdPbZnCuCrNi
      Minimum / (mg/kg)2.02.417.08.59.717.5
      Maximum / (mg/kg)11.086.498.549.439.690.8
      Mean / (mg/kg)4.039.059.324.018.556.0
      S.D. / (mg/kg)2.127.918.09.28.617.6
      CV / %52.371.630.438.346.631.5
      ERLa / (mg/kg)5.035.0120.070.080.030.0
      ERM / (mg/kg)9.0110.0270.0390.0145.050.0
      TEL / (mg/kg)0.618.0123.035.737.335.0
      PEL / (mg/kg)3.536.0315.0197.090.091.3
      Compared with TEL and PELthe ratio of samples to the total samples in each guideline
      < TEL / %035.910092.394.912.8
      ≥ TEL < PEL / %61.52.607.75.187.2
      ≥ PEL (%)38.561.50000
      Compared with ERM and ERLthe ratio of samples to the total samples in each guideline
      < ERL / %79.538.51001001007.7
      ≥ ERL < ERM (%)18.061.500023.1
      ≥ ERM / %2.5000069.2
      Background valueb (mg/kg)0.1424.071.017.717.322.0
      Notes: S.D., standard deviation; CV, coefficients of variation; TEL, threshold effect level; PEL, probable effect level; ERL: effects range low; ERM, effects range median. a Threshold effect level or probable effect level for freshwater ecosystem (MacDonald et al., 2000). b Background value of sediment in the Songhua River (Li and Zheng, 1989)

      Spatial distribution of Cd, Pb, Zn, Cu, Cr and Ni in surface sediment of the Songhua River was shown in Fig. 2. Cu and Zn, two micronutrients for aquatic organisms in natural water, are toxic when their concentrations exceed the limits (Hall et al., 1997). The concentrations of Cu ranged from 8.5 to 49.4 mg/kg, which were lower than TEL at most sampling sites. The concentrations of Cu in sediment samples from certain subareas (e. g., the Hunjiang River and the middle reach of the Songhua River) were much higher than those from other subareas (Fig. 2a). The concentrations of Zn ranged from 17.0 to 98.5 mg/kg, which were higher than TEL especially in the downstream and posed a toxic effect to aquatic organisms (Fig. 2b).

      Figure 2.  Spatial distributions of Cu, Zn, Cr, Ni, Cd, Pb in surface sediments of the Songhua River

      Ni and Cr are frequently associated with rocks. There are high concentrations of Ni and Cr in the earth’s crust. In this study, Cr concentrations ranged from 9.7 to 39.6 mg/kg, which were higher in the midstream sediment of the Songhua River (Fig. 2c). However, the concentrations of Cr did not exceed its TEL, which had little effect on aquatic organisms. Ni concentrations ranged from 17.5 to 90.8 mg/kg, exceeding TEL in most samples in middle and lower reaches of the Songhua River, which could probably be toxic to aquatic organisms (Fig. 2d). Cr and Ni had similar spatial distributions. High concentrations of Cr and Ni were found in the sediment samples located in the middle reach of the Songhua River, where there were a larger amount of mining industries distributed (Liu et al., 2014).

      The concentrations of Cd ranged from 2.0 to 11.0 mg/kg, which were higher than background value and TEL in all sampling sites. Furthermore, Cd concentrations in downstream sediments were extremely high (Fig. 2e), which had a great adverse effect on aquatic organisms. Comparing to other elements, Pb could threaten the survival of aquatic organisms even at a low concentration (Sadiq et al., 2003). The concentrations of Pb ranging from 2.4 to 86.4 mg/kg were found to be higher in middle and lower reaches (Fig. 2f). Pb concentrations exceeded TEL in all the downstream, resulting in high ecological risks. High levels of Cd and Pb were found in the sediments located in urban river of Jilin, which was recognized as well-developed manufacturing cities (Dong et al., 2018). High concentrations of Cd and Pb were likely to be related to the industrial wastewater discharge. Moreover, high concentrations of Pb were found in the sediments located in the Huifa River, which was likely to be related to local mining activities.

      A comparison was made on mean concentrations of six heavy metals in sediments of the rivers in China. As shown in Fig. 3, the highest mean concentrations of Cu, Zn, Cd, Pb and Ni were observed in samples collected from the Xiangjiang River at 101 mg/kg, 443 mg/kg 13.7 mg/kg, 215 mg/kg and 57.1 mg/kg, respectively (Chai et al., 2017). The concentration of Ni in the Xiangjiang River is comparable to that in the Songhua River in Jilin Province at 56 mg/kg (this study). The highest mean concentrations of Cr were found in samples collected from the Songhua River (Harbin region) at 121 mg/kg (Li et al., 2017), and the Xiangjiang River at 120 mg/kg (Chai et al., 2017). The Lowest concentrations of Cu and Ni were observed in samples collected from the Songhua River Harbin region at 13.3 mg/kg and 12.9 mg/kg, respectively (Li et al., 2017). Concentrations of Zn and Cr in the Songhua River in Jilin Province presented the lowest among the listed rivers in China. Moreover, Tang et al. (2013) reported the lowest concentration of Cd in samples collected from the Huaihe River at 0.1 mg/kg, and Ke et al. (2017) reported the lowest concentration of Pb in samples collected from the Liaohe River at 10.6 mg/kg. The results indicated that the relatively high concentrations of Cd and Ni were observed in the sediments from the Songhua River compared to other rivers in China, which was likely due to the increase in pollution attributable to rapid industrial development during the last few decades (Li et al., 2017).

      Figure 3.  Mean concentrations of Cu, Zn, Cd, Pb, Ni and Cr in sediments collected from China. The data for the Songhua River in Jilin Province were from this study. The data for the Huaihe River were from Yang et al. (2017). The data for the Haihe River were from Tang et al. (2013). The data for the Yangtze River Estuary were from Wang et al. (2014). The data for the Songhua River Harbin region were from Li et al. (2017). The data for the Bortala River were from Zhang et al. (2016). The data for the Liaohe River were from Ke et al. (2017). The data for the Jialu River were from Fu et al. (2014). The data for the Xiangjiang River were from Chai et al. (2017). The data for the Zijiang River were from Zhang et al. (2018)

    • The result of PCA for heavy metal contents was presented in Table 2. Heavy metals could be grouped into two principle components (PCs) accounting for 73.3% of all the data variation. PC1 was loaded with Pb, Zn, and Ni explaining 47.2% of the total variance, suggesting that they may have similar sources (Omwene et al. 2018; Siddiqui and Pandey, 2019). Considering that Pb, Zn and Ni concentrations being higher than background values were mainly distributed in middle and lower reaches, PC1 may be related to anthropogenic sources, for instance, the industries of metal smelting, automobile exhaust, coal, coating material, etc. PC2 explained 26.1% of the total variance and showed a strong loading of Cd and Cu, indicating that they may have common sources. The Songhua River is heavily polluted by Cd, with the concentration greatly exceeding background values in all sampling sites. Cd can be fixed and deposited into sediment in the form of carbonate or hydroxide complex at an alkaline condition (Li et al., 2014a). Cd was always considered as the marker of unreasonable agricultural management (Satpathy et al., 2014; Mustafa and Komatsu, 2016). PC2 indicated electroplating wastewater and agricultural non-point source sewage (Bai et al., 2011). Cr had relatively strong correlation with conservative element Fe (Table 3), suggesting that Cr in the sediment is preferred to bind to the Fe-Mn oxides, which could be related to a lithogenic contribution (Cox and Preda 2005; Hu et al., 2013; Brady et al. 2014; Saleem et al., 2015).

      Table 2.  Total variance explained by principle component analysis of heavy metals in surface sediments of the Songhua River (two principal components are elected)

      ElementComponent matrixRotated component matrix
      PC1PC2PC1PC2
      Cd0.0810.7910.0540.793
      Pb0.9060.1020.9020.133
      Zn0.8420.0460.8400.074
      Cu0.0480.8790.0180.880
      Cr0.7840.0950.7800.121
      Ni0.825–0.3780.837–0.350
      Initial eigenvalue2.8341.5632.8321.565
      Proportion of total variance/%47.22526.05547.20126.079
      Proportion of cumulative variance /%47.22573.28026.07973.280

      Table 3.  Correlation analysis of heavy metals in surface sediments of the Songhua River

      ElementCdPbZnCuCrNiFe
      Cd10.1410.1040.436**0.006–0.112–0.162
      Pb10.739**0.1090.628**0.644**–0.086
      Zn10.0600.464**0.605–0.242
      Cu10.202–0.3090.010
      Cr10.567**0.363*
      Ni10.216
      Fe1
      Notes: ** Correlation is significant at the 0.01 level (2-tailed); * significant at the 0.05 level

      A dendrogram of heavy metal contents was shown in Fig. 4. In this dendrogram, there are two completely different clusters, one consists of Pb, Zn and Ni, while the other includes Cd, Cu and Cr. Different from PCA result, Cr is classified into cluster-2 group, suggesting anthropogenic inputs. These two groups of metals come from different sources, confirming the PCA results.

      Figure 4.  Hierarchical clustering analysis of heavy metals in surface sediments of the Songhua River

    • Numerical sediment quality guidelines (SQGs) are usually used to evaluate the degree of adverse impacts from the sediment-associated chemical substances on aquatic organisms (MacDonald et al., 2000; Caeiro et al., 2005). The results of potential effects in sediment were shown in Table 1. Compared with TEL and PEL standards, the concentrations of Cd, Pb and Ni in most samples were higher than TEL. The concentrations of Cd and Pb higher than PEL were found in 38.5% and 61.5% of samples , respectively. However, compared with ERL and ERM standards, Cu, Zn and Cr concentrations in all the samples were lower than ERL. The concentrations of Cd, Pb and Ni in 18.0%, 61.5% and 23.1% of samples were in the range from ERL to ERM, respectively. The concentrations of Cd and Ni in 2.5% and 69.2% of samples were higher than ERM.

      Fig. 5a showed the $\displaystyle\sum $ TU distribution of heavy metals in the Songhua River. $\displaystyle\sum $ TU exceeded 5 at all the sampling sites, which was above the moderate toxicity level (Pedersen et al., 1998). Higher toxicity was observed in middle and lower reaches of the Songhua River. Based on the composition of heavy metals (Fig. 5b), Cd accounted for a very high percentage of $\displaystyle\sum $ TU in all the samples of the Songhua River. Pb and Ni were also the main components in middle and lower reaches due to the mining and discharge of chemical sewage. The average toxicity of heavy metals in sediments of the Songhua River appeared in the order as Cd (6.7) > Pb (2.2) > Ni (1.6) > Cu (0.7) > Cr (0.5) = Zn (0.5). The contributions of $\displaystyle\sum $ TU decreased in the order of Cd (55.0%), Pb (18.0%), Ni (13.1%), Cu (5.7%), Cr (4.1%) and Zn (4.1%) (Fig. 6).

      Figure 5.  Spatial distribution of the sum of the toxic units (a), composition of toxic units of all heavy metals (b) and potential ecological risk index (PERI) (c) in surface sediments of the Songhua River

      Figure 6.  Contributions of heavy metals to the sum of toxic units in surface sediments of the Songhua River

      The average ${E_r}$ of the heavy metals decreased in following sequence: Cd (849) >> Ni (12.7) > Pb (8.1) > Cu (6.8) > Cr (2.1) > Zn (0.8). The average ${E_r}$ of Ni, Pb, Cu, Cr and Zn was less than 40, indicating a low ecological risk. The ${E_r}$ of Cd was greater than 320 in all the sediment samples, suggesting a very high risk to aquatic organism. The result of PERI of heavy metals in surface sediment of the Songhua River was shown in Fig. 5c. PERI values of heavy metals indicated high-risk grades in 74% of sediments collected from middle and lower reaches and most of tributaries of the Songhua River, where Cd imposed a very high risk, probably due to the wastewater input from upstream and nearby urban and industrial discharge and agro–runoff. Further research on the remediation of Cd in surface sediments of the Songhua River should be conducted. The Gudong River, an important tributary in the upper reaches of the Songhua River, showed a considerable risk of heavy metals with PERIs less than 600. In addition, the PERI was found to be higher in mainstream than in tributaries of the Songhua River (P < 0.05), indicating that surface sediments from mainstream were seriously polluted by heavy metals.

    • The basin-scale study provides information on the distribution, sources, and ecological risks of six heavy metals in the surface sediment of the Songhua River. The mean concentrations of studied heavy metals exceeded their geochemical background levels in the Songhua River except Zn. The spatial distribution of heavy metals was in close relationship with the emission characteristics along the Songhua River. Lower pollution levels and ecological risks of heavy metals were observed in sediments from upper reaches of the Songhua River and the Gudong River. Higher concentrations of Zn, Ni, Cd and Pb were found in middle and lower reaches of the Songhua River, indicating a great adverse effect on aquatic organisms. Source apportionment found that industrial sewage and mineral processing dominated higher concentrations of Pb, Zn and Ni in sediments. High concentrations of Cd were observed in the whole basin, which can be associated with electroplating and agricultural non-point sewage. Cr may originate from geogenic sources, as the low concentrations are comparable to its background value. The ecological risk of an individual metal demonstrated that Cd was at an extremely high-risk level in surface sediment of the Songhua River, while the other studied metals were at low risk levels. PERI also revealed that Cd was the most serious ecological risk factor. The ubiquitous presence of heavy metals revealed their widespread distribution in the sediments of the Songhua River. This study could provide a large amount of detailed information to understand the contamination levels of heavy metals and establish rational ecological protection measures in the Songhua River.

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