Environ Sci Pollut Res DOI 10.1007/s11356-014-3770-2

RESEARCH ARTICLE

Ecological risk assessment and sources of heavy metals in sediment from Daling River basin Lei Zhao & Dong Mi & Yifu Chen & Luo Wang & Yeqing Sun

Received: 19 August 2014 / Accepted: 23 October 2014 # Springer-Verlag Berlin Heidelberg 2014

Abstract To investigate the distribution, source, and ecological risk of heavy metals in Daling River basin, 28 surface sediments collected in this region were analyzed by experimental and theoretical methods. Seven heavy metals, including Pb, Cr, Hg, Cu, As, Cd, and Zn, were detected in all samples. Monte Carlo simulation was used to assess the ecological risks of these heavy metals. It was found that the pollution of Cd was the most serious; the ecological risks in Daling River and Bohai Bay were significantly higher than those in estuary, Bohai Sea, and wetland, but overall, the ecological risks of these heavy metals were low to aquatic organisms in Daling River basin at present. Correlation analysis, principal component analysis, and cluster analysis showed that these heavy metals might originate from the same pollution sources located near Daling River and Bohai Bay.

Keywords Heavy metal . Sediment . Ecological risk . Monte Carlo . Source . Daling River basin

Responsible editor: Philippe Garrigues Electronic supplementary material The online version of this article (doi:10.1007/s11356-014-3770-2) contains supplementary material, which is available to authorized users. L. Zhao : Y. Chen : L. Wang : Y. Sun College of Environmental Science and Engineering, Dalian Maritime University, Dalian 116026, Liaoning, People’s Republic of China L. Zhao : Y. Chen : Y. Sun (*) Institute of Environmental Systems Biology, Dalian Maritime University, Dalian 116026, Liaoning, People’s Republic of China e-mail: [email protected] D. Mi (*) Department of Physics, Dalian Maritime University, Dalian 116026, Liaoning, People’s Republic of China e-mail: [email protected]

Introduction With the rapid development of industry and agriculture, heavy metal pollution is becoming increasingly serious in the whole word (Heikens et al. 2001; Uluturhan and Kucuksezgin 2007). Large amounts of heavy metal are emitted into the water and accumulated in aquatic food chain easily, resulting in sublethal effects or death on aquatic organism (Almeida et al. 2002; Jones et al. 2001; McGeer et al. 2000). Sediments have the capacity for accumulating heavy metals from overlying waters; therefore, the enrichment of heavy metals in sediments is often a preferred indicator of the contamination status (Soares et al. 1999; Xiao et al. 2013). Sediments also provide habitat and a food source for benthic fauna (Yi et al. 2011). They have been used to assess the pollution of water bodies and reflect the source of pollution extensively, which can provide the information of historical deposition of pollutants (Fox et al. 2001). Furthermore, sediments could also be a secondary contamination source because pollutants may be directly and indirectly toxic to the aquatic biota and even other organisms throughout the marine food web (Karageorgis et al. 2001; Pekey et al. 2004). The distribution of heavy metals in sediments could be used to study anthropogenic impacts on ecosystems as well as evaluate the ecological risk posed by human waste discharges (Kwon and Lee 2001; Zheng et al. 2008). Ecological risk assessment is to evaluate the likelihood that adverse ecological effects may occur or are occurring as a result of exposure to one or more stressors (Posthuma et al. 2010). The most traditional methods used to evaluate the ecological risk of heavy metals in sediments include the potential ecological risk index (Håkanson 1980) and the index of geo-accumulation (Chen et al. 2007). In recent years, a new method called probabilistic risk assessment is described for ecological risk assessment (Hope 2006; Solomon et al. 1996). In contrast with conventional

Environ Sci Pollut Res

deterministic methods, probabilistic risk assessment is generated by multiple iterations using Monte Carlo simulation based on the distribution of exposure concentrations and toxicity data, which takes the uncertainty of data into consideration (Burmaster and Anderson 1994; Chow et al. 2005; Guo et al. 2012; Kooistra et al. 2005). This method has been employed in different ecological risk assessments, such as to assess the ecological risk of phenols and polycyclic aromatic hydrocarbons to river and lake (Guo et al. 2012; Yang et al. 2006; Zhong et al. 2010) and to evaluate the risk of mercury and copper to aquatic ecosystems (Duvall and Barron 2000; Schuler et al. 2008). There usually exist complex relationships among different heavy metals in sediments (Sun et al. 2010). In fact, there are a large variety of reasons affecting the relative abundance of heavy metals, such as accumulation effects in the lower reach (Bai et al. 2009). So far, the main reasons include the original heavy metal contents of rock and parent materials, various processes of soil formation, salinity, landfill, land use, and organic matter (Bai et al. 2012; Lado et al. 2008; Li et al. 2009; Xiao et al. 2011; Zhao et al. 2013). However, these concentrations are not always uniform throughout the drainage basin and may vary from site to site due to different sources of anthropogenic inputs (Lai et al. 2010; Liu et al. 2009). This makes it necessary to assess the ecological risk of heavy metals and to identify and control pollution sources in environments. Daling River is located in northeastern China and has a length of 397 km and a drainage area of 2.35×103 km2, which is an important water supply and irrigation resource in Liaohe River delta (Wang et al. 2013). As a consequence of anthropogenic activities, enormous quantities of pollutants have been discharged into the Daling River basin. Heavy metals in wastewater and sewage have been discharged into the Daling River basin and accumulated in sediments, where the aquatic ecosystem may be threatened. There has been little monitoring for heavy metals in this important area, and the ecological risk assessment and pollution sources of heavy metals in this area have not been reported previously. Therefore, to protect the water resources of this area, it is of great importance to evaluate the heavy metals pollution status in Daling River basin. In the present work, we investigate the influence of heavy metal pollution on Daling River basin by evaluating the ecological risk of these heavy metals in sediment, ranking the risk of these compounds by Monte Carlo simulation, and identifying further the possible pollution sources by Pearson correlation analysis, principal component analysis (PCA), and cluster analysis. To our knowledge, this is the first report evaluating the ecological risk of heavy metals in Daling River basin by statistical analysis and Monte Carlo simulation.

Materials and methods Data sources The exposure concentrations of heavy metals came from 28 sediment samples, which were collected from five regions in Daling River basin (see Fig. 1). Sample sites can be classified into different types by geography in this study, including Daling River (R01-06), wetland (W01-05), estuary (E0104), Bohai Bay (B01-04), and Bohai Sea (S01-09). The wetlands of Daling River basin are dominated by extensive reed (Phragmites australis). Sediment samples were collected using a bucket grab and then packed in solvent-rinsed glass bottles with Teflon-lined caps. After collection, they were stored at −20 °C until extraction. Sediment samples were freeze-dried and passed through a 1-mm clean plastic sieve to remove shell fragments. Sieved sediments were ground in an agate mortar. The powdered sediments were then transferred to a clean nylon membrane sieve (0.071 mm) and shaken to obtain a perfect homogeneous powder. Samples were microwave-digested for determining contents of heavy metals. Further details of sample digestion can be found in the article of Yi et al. (2011). The concentrations of Pb, Cr, As, Cd, Cu, and Zn were determined by an inductively coupled plasma mass spectrometer (ICP-MS 7700X), using the US EPA Method 6020 (US EPA 2007). Hg was measured using a Hydra-C DMA (Teledyne Leeman Labs). Each treatment included at least one reagent blank and a representative reference standard and, typically, a sample replicate to avoid any possible contamination. Satisfactory recoveries were obtained for Pb (94– 106 %), Cr (98–103 %), As (97–102 %), Cd (93–98 %), Cu (97–106 %), Zn (96–103 %), and Hg (97–105 %). Risk analysis Håkanson’s method could be used to evaluate the potential ecological risk of metal contaminants in sediments (Håkanson 1980). The potential ecological risk index (RI) may reflect the sensitivity of various biological communities to toxic substances and show the potential ecological risk caused by various pollutants in the environment (Yi et al. 2011). According to Håkanson’s method, the RI of metal contaminants in sediments can be calculated using the following equation: RI ¼

m X

Eri

ð1Þ

i¼1

where, Eri ¼ T ri C if

ð2Þ

Environ Sci Pollut Res Fig. 1 Location of sample sites in study area

Statistical analysis

and C if ¼ C i =C in

ð3Þ

RI is calculated as the sum of all risk factors for heavy metals in sediments, Eir is the potential ecological risk for single factor, Tri is the toxic response factor for a given metal, Cif is the contamination factor, Ci is the measure concentration of metals in sediment, and Cin is the reference value for metals. See Hilton et al. (1985) for more details. In the present study, instead of Håkanson’s RI, the probabilistic distribution of RI was calculated using Monte Carlo simulation to randomly sample values from the distribution of exposure concentrations. A certain amount of iterations in a particular Monte Carlo simulation was needed to calculate the ecological risk, where the exposure concentrations were derived stochastically from the corresponding log-normal distributions to generate the distributions of risk index for the 5,000 iterations, which incorporated both uncertainty and variability.

SPSS 17.0 was used for the statistical analysis to obtain the features of the datasets. Changes were considered as statistically significant (*) if the p value 0.05), respectively. According to the degree standards used to describe the single risk factor (Eir) as suggested by Håkanson (1980), the risk probabilities of single heavy metal from Daling River basin in different risk degrees were shown in Table 1. The results showed that Cd and Hg posed relatively high ecological risk in these areas. Monte Carlo simulations indicated that the moderate risk probability of Cd (14.81 %) was 6.09 times higher than that of Hg (2.43 %). And what is more, the considerable risk probability of Cd (1.76 %) was 4.51 times higher than that of Hg (0.39 %). Furthermore, the high risk probability of Cd was (0.07±0.04) %. The results also indicated that the ecological risk of Pb, Cr, As, Cu, and Zn in Daling River basin was low. Consequently, it was possible to rank the risk of these seven heavy metals to the ecosystem as Cd > Hg > (Pb ≈ Cr ≈ As ≈ Cu ≈ Zn), suggesting that Cd is the most important risk factor to Daling River basin. This result is also consistent with many previous studies, where Cd is usually a serious pollution factor while the ecological risks of other metals are low (Fu et al. 2009; Huang et al. 2009; Kooistra et al. 2001; Lado et al. 2008; Liu et al. 2009; Sun et al. 2010; Sundaray et al. 2011).

were shown in Fig. 3. The results showed that the low, moderate, considerable, and very high risk probabilities of RI were 75.57±0.67, 16.17±0.60, 6.41±0.39, and 1.88± 0.22 %, respectively. It is suggested that the potential ecological risk of Pb, Cr, Hg, Cu, As, Cd, and Zn in sediments was relatively high in Daling River basin. And what is more, the low ecological risk of heavy metals in these areas was obviously threefold higher than the others. Therefore, it indicated that Daling River basin is classified as the low contamination, but still, it was a fact in the moderate, considerable, and very high ecological risk of heavy metals in sediments. From the view of this point, much more attention should be paid in analyzing, evaluating the ecological risk, and controlling the heavy metals discharged in Daling River basin. Table S2 showed the results of Shapiro-Wilk tests of lntransformed RI, which indicated that RI in different sample types were all fitted to a log-normal distribution (p>0.5). According to the degree standards by Håkanson (1980), one can obtain the risk probabilities of all heavy metals in Daling River, estuary, Bohai Sea, wetland, and Bohai Bay shown in Table 2. The risk probabilities in estuary, Bohai Sea, and wetland were very similar for all risk degrees. Nevertheless, the probabilities of low ecological risk in Daling River and Bohai Bay were significantly lower than those in estuary, Bohai Sea, and wetland while higher in moderate, considerable, and high ecological risk. Based on the results above, Daling River basin belongs to the low ecological risk area, while the ecological risks of Daling River and Bohai Bay are significantly higher than that of other areas, which may be related to different sources of anthropogenic inputs in these areas.

Probabilistic ecological risk assessment for all factors

Comparison with the Håkanson method

The Shapiro-Wilk normality test was used to determine the normal distribution of RI in Daling River basin, which indicated that RI was fitted to a log-normal distribution (W=0.94, p=0.10>0.05). Normal distribution and Q−Q plot of lntransformed RI were shown in Figs. S1(a) and S1(b), respectively. According to degree standards used to describe all risk factors (RI) as suggested by Håkanson (1980), the risk probabilities of RI in Daling River basin for different risk degrees

The potential risk of heavy metals in Daling River basin can also be evaluated by the conventional Håkanson’s method (1980). Table S3 showed the calculated results, which indicated that the ecological risks of all heavy metals were low, and relatively speaking, Cd was the highest risk factor to Daling River basin. This conclusion is consistent with the present Monte Carlo simulation. In addition, the results based on Håkanson’s method also indicated that the total ecological risk of these metals in most of the sampling sites were low,

Ecological risk assessment Probabilistic ecological risk assessment for a single factor

Table 1 Risk probabilities of heavy metals from Daling River basin in different risk degrees (%). Results were expressed as arithmetic mean±1×standard deviation (SD), which are generated by Monte Carlo simulation after 3,000 to 5,000 iterations

Risk degree

Pb

Cr

Hg

As

Cd

Cu

Zn

Low Moderate Considerable High

100 0 0 0

100 0 0 0

97.12±0.27 2.43±0.24 0.39±0.10 0

100 0 0 0

83.36±0.60 14.81±0.57 1.76±0.21 0.07±0.04

100 0 0 0

100 0 0 0

Environ Sci Pollut Res Fig. 3 Risk probabilities of RI in Daling River basin for different risk degrees. a The low risk. b The moderate risk. c The considerable risk. d The very high risk

except for some sites in Daling River (B04 and B06) and Bohai Bay (B01). The trends of RI based on Håkanson’s method were also consistent with those by the Monte Carlo simulation. However, the conventional Håkanson’s method could not estimate the probabilities of different ecological risk degrees, due to no consideration of the uncertainty and incompleteness of concentration measurements, while the present method based on Monte Carlo simulation was an experimental probabilistic method to solve this problem since computers could easily simulate a large number of experimental trials that have random outcomes (Papadopoulos and Yeung 2001). Such an analysis is closer with the underlying physics of actual measurement processes that are probabilistic in nature (Harwood 2000), and it facilitates the environmental protection departments to make the corresponding decisions effective.

Heavy metal pollution source analysis Correlation between heavy metals Correlations between different heavy metals were shown in Fig. 4, which the upper triangle described the correlation of raw data in different heavy metals and the lower triangle described the correlation coefficients. Significant correlations were found between Pb and Cr (r=0.58), Pb and Hg (r=0.57), Hg and Cr (r=0.71), Pb and As (r=0.75), As and Cr (r=0.76), As and Hg (r=0.60), Pb and Cd (r=0.48), Hg and Cd (r= 0.73), As and Cd (r=0.50), Pb and Cu (r=0.68), Cr and Cu (r =0.79), Hg and Cu (r=0.72), As and Cu (r=0.73), Cu and Cd (r=0.49), Zn and Cr (r=0.53), Hg and Zn (r=0.60), Zn and Cd (r=0.63), and Zn and Cu (r=0.79) at p

Ecological risk assessment and sources of heavy metals in sediment from Daling River basin.

To investigate the distribution, source, and ecological risk of heavy metals in Daling River basin, 28 surface sediments collected in this region were...
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