Distribution characteristics and toxicity assessment of heavy metals in ...

Environ Monit Assess (2011) 179:431–442 DOI 10.1007/s10661-010-1746-3

Distribution characteristics and toxicity assessment of heavy metals in the sediments of Lake Chaohu, China Hongbin Yin · Jiancai Deng · Shiguang Shao · Feng Gao · Junfeng Gao · Chengxin Fan

Received: 24 February 2010 / Accepted: 4 October 2010 / Published online: 27 October 2010 © Springer Science+Business Media B.V. 2010

Abstract The concentrations of Cu, Pb, Zn, Cd, and Ni were measured in surface and core sediments to determine their distribution characteristics and toxicity in the sediments of Lake Chaohu. The results revealed that metal concentrations in the surface sediments had a tendency to increase from the estuarine mouth to the lake center. The distribution characteristics of the five target metals were similar along the sediment profiles at each site. Principal component analysis revealed that all of the measured variables were loaded in the same component, indicating that there was a strong relationship among these measured variables, which was confirmed by the correlation analysis. Two sets of sediment quality guidelines (SQGs): simultaneously extracted metal (SEM) and acid (AVS) models (includ volatile sulfides ing SEM/AVS, SEM–AVS, and SEM– AVS/ foc ) and threshold effect level and probable effect level values were used to predict the sediment toxicity. Comparison of the results obtained

H. Yin (B) · J. Deng · F. Gao · J. Gao · C. Fan State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China e-mail: [email protected] S. Shao Colloge of Water Resource and Hydrology, Hohai University, Nanjing 210008, China

using these two sets of SQGs revealed that only a small portion of the entire set was identical, while the majority of the results were different and sometimes completely contradictory. These contradictory results would cause a great deal of trouble for environment managers. More accurate and universal SQGs must be developed for environmental researchers and local environmental managers and regulators. Keywords Distribution characteristics · Sediment quality guidelines (SQGs) · Toxicity assessment · Lake Chaohu

Introduction Since the industrial revolution, a large amount of pollutants (including heavy metals and persistent organic pollutants have entered aquatic ecosystems through various pathways as a result of anthropogenic activity (Förstner and Wittman 1979; Frignani and Bellucci 2004; Binelli et al. 2008)). River transportation is likely the dominant pathway through which heavy metals enter lake ecosystems (Olivares-Rieumount et al. 2005; Radakovitch et al. 2008; Yang et al. 2009). Toxic metals such as Cu, Pb, Zn, Cd, and Ni then accumulate in the sediments, after which they exert adverse effects on the lake ecosystem over long periods of time (Ghrefat and Yusuf 2006;

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Farkas et al. 2007). Heavy metal pollution has become a problem worldwide and thus attracted the attention of many countries. Understanding the concentrations and distribution characteristics of heavy metals in sediments can aid environmental managers and facilitate the supervision of lake water quality, which is always based on the appraisal of sediment quality by sediment quality guides (SQGs; MacDonald et al. 2000). Accordingly, the development of objective and scientific SQGs will provide guidance on how to maintain healthy aquatic ecosystems (Ingersoll et al. 2001). Currently, there are a variety of established SQGs that are based on empirical values or mechanical models (MacDonald et al. 2000; Hinkey and Zaidi 2007). These established values or models have enabled individuals to obtain a full assessment of sediment quality and to determine whether the chemical substances in the sediments are toxic to aquatic organisms or require remediation. Hübner et al. (2009) compared 15 sets of common empirical SQGs and concluded that the threshold effect level (TEL)/probable effect level (PEL)-based SQGs (TEL and PEL concentrations) were the most appropriate approach because they offered a solid and justified scientific basis and extensive comparability. The use of different relationships between acid volatile sulfides (AVS) and simultaneously extracted metals (SEM) to establish mechanicalmodels such as the ratio of SEM and AVS ( SEM/AVS), the difference between the SEM and AVS ( SEM–AVS) or the organic carbon normalized difference between SEM and AVS ( SEM– AVS)/ foc ) to assess metal toxicity has been widely applied (Di Toro et al. 1990; Burton et al. 2005; Yin et al. 2008). AVS is operationally defined as the amount of sulfides released during cold acid extraction, while SEM is defined as the amount of metals (normally including Cu, Pb, Zn, Cd, and Ni) liberated during the extraction of AVS (Di Toro et al. 1990). The AVS–SEM models are generally based on the hypothesis that when there are enough sulfides in the sediment, metals will react with the sulfide to form insoluble compounds that are not bioavailable to benthic biota; however, if the levels of sulfide are too low, the metals will be toxic toward the benthic biota. Di Toro et al. (1990) first proposed that a SEM/AVS>1 indi-

Environ Monit Assess (2011) 179:431–442

cated toxicity, then on the contrary, the pore water concentrations would be low and no toxicity to sediment dwelling organisms would be observed under these conditions. However, this prediction was not always accurate when the molar sum of the SEM was greater than that of the AVS, which could be due to the different sensitivities of the benthic biota in the sediment to metals (Hare et al. 1994). Burton et al. (2005) conducted a long period field toxicity experiment and concluded that sediments with a SEM to AVS ratio of 8.32 or greater would result in high macroinvertebrate toxicity, that ratios between 2.0 to 8.32 are occasionally toxic, and that ratios less than 2.0 are not toxic. They also stated that sediments with an OC-normalized excess SEM of less than 147.5 μmol/g of OC were not chronically toxic, while sediments with concentrations ranging from 147.5 to 154.4 μmol/g showed variable toxicity. Conversely, no toxicity was observed when OCnormalized excess SEM was less than 100 μmol/g of OC (Burton et al. 2005). According to the USEPA, when SEM–AVS is greater than 5, metals at sampling sites cause adverse effects on aquatic life (USEPA 2004). Additionally, if SEM–AVS is between 0 and 5, the sampling sites will likely cause toxicity to benthic biota (USEPA 2004). Finally, if SEM–AVS is less than 0, no toxicity will be observed at the sampling these models site (USEPA 2004). Therefore, ( SEM/AVS, SEM–AVS, SEM–AVS)/ foc ) are all useful tools for the assessment of sediment quality. Both the AVS–SEM models and the TEL–PEL established empirical values are widely used to assess the sediment quality, and both methods have been validated in the field and laboratory (Binelli et al. 2008; Burton et al. 2005; Lee et al. 2000). However, it is unclear whether uniform objective and consistent results can be obtained when using these two methods simultaneously. Accordingly, more studies should be conducted to verify the consistency of these two methods. Lake Chaohu, which is the fifth largest freshwater lake in China, is located on the Yangtze Delta Plain. The lake has a mean depth of 2.7 m, a surface area of 770 km2 and a water storage capacity of 2.1 billion cubic meters. Lake Chaohu is the most important drinking water source for Hefei


City (capital city of Anhui province), Chaohu City and several other cities and towns, as well as being used for irrigation, fishery and tourism purposes. However, the rapid economic and urbanization that has occurred in the region over the past 30 years has resulted in the lake becoming seriously polluted and it is now hypereutrophic (Yan et al. 1999). Despite these changes, there is currently no complete database of sediment heavy metals available for the lake. Therefore, this study was conducted (1) to characterize the distribution of heavy metals in sediments of Lake Chaohu and (2) to evaluate the sediment quality of Lake Chaohu based on the AVS–SEM models and the TEL–PEL values.

Materials and methods Sediment sampling Surface sediments (0–10 cm) were collected from 27 sites in Lake Chaohu in August 2008 (Fig. 1). Additionally, samples were collected from sites

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CH11, CH18, and CH21 using a core sampler to characterize the depth profile of heavy metals in the Lake Chaohu sediments. Sediment samples were collected from depths of 0–3, 3–6, 6–9, 9– 12, 12–18, 18–24, 24–30, and 30–36 cm, placed in polyethylene bags and then kept in a cooler on ice, after which they were transported to the laboratory and preserved under nitrogen at less than 4◦ C. Subsamples were then freeze dried, crushed, passed through 0.063-mm mesh sieves and stored at 4◦ C in the dark until analysis. Chemical analysis Total heavy metals analysis The total heavy metals were measured using the microwave-assisted acid digestion procedure (USEPA 1996). The concentrations of Pb, Cd, Zn, Cu, and Ni in the digests were analyzed by inductively coupled plasma-atomic emission spectrometry (ICP-AES; Perkin-Elmer DV4300), while samples that contained no detectable levels of these compounds were subjected to analysis

Beijing

Fig. 1 Location of sampling sites

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using a graphite furnace (GFAAS, GBC932AA, Australia). Reagent blanks were monitored throughout the analysis and used to correct the analytical results. Certified reference materials (GBW07309 (GSD9)) were obtained from the General Administration of Quality Supervision, Inspection, and Quarantine of the People’s Republic of China (NCRMAC 2007) for quality assurance purposes. Recoveries of the measured metals varied but all fell within the range of 90–105%, and the precision was within 10% relative standard deviation. AVS and SEM analysis Acid volatile sulfide was extracted in triplicate from sediment samples that had been stored under nitrogen at less than 4◦ C for no longer than 48 h. The acid volatile sulfide was measured using Hsieh’s cold diffusion method with ascorbic acid to prevent interferences from Fe(III) (Hsieh et al. 2002). Briefly, approximately 5 g of wet sediment were added to an oxygen-free flask with a test tube inside containing 5 ml of 3% alkaline zinc solution. Next, 20 ml of 6 M HCl were poured onto the sediment, and the cap of the flask was tightened. After reacting for 18 h, the flask was opened and the sulfide traps were removed from the bottles. A homogeneous ZnS suspension was ensured by vigorously vortexing the traps, after which the samples were treated with a sonicating water bath for about 30 s (Glenn et al. 1997). The sulfide was then quantified in aliquots of this suspension using the methylene blue method (Cline 1969). The average recovery of AVS from ZnS in the control (ZnS only, no ferric oxides) using this method was 98.7% (Hsieh et al. 2002). The sediment suspension remaining in the flask was filtered though a 0.45−μm membrane, and the concentration of heavy metals was then determined by ICP-AES. The precision of the triplicate analysis of Cu, Pb, Zn, Cd, Ni, and AVS were within 10%. Analysis of other parameters The sediment water content was measured based on the weight loss after drying at 105◦ C for


24 h. Samples of sediment for total organic carbon (TOC) analysis were treated with 1 N HCl volatilize carbonates, and the remaining carbon was then analyzed by a dry combustion-infrared technique (Leco Instruments). The precision of duplicate analysis was within 10%. Statistical analysis Data analysis (e.g., average, standard deviation, coefficient of variation (CV), maximum, and minimum concentrations) using statistical methods was performed in this study. Principal component analysis, correlation, and cluster analysis among the measured variables were conducted using SPSS 13.0 package. Comparison of average heavy metal concentrations between sites were determined using one-way ANOVA analysis, where p < 0.05 indicated a significant difference in analyte concentrations between sites.

Results and discussion Distribution characteristics of heavy metals in surface sediments The concentrations of Cu, Pb, Zn, Cd, and Ni in the surface sediments of Lake Chaohu are presented in Table 1. The concentrations ranged from 9.23–40.97 mg/kg for Cu, 18.97–89.20 mg/kg for Pb, 33.20–360.29 mg/kg for Zn, 0.08–1.01 mg/kg for Cd, and 12.36–49.04 mg/kg for Ni. The average concentrations of heavy metals in the sediments of Lake Chaohu were 26.23 mg/kg for Cu, 49.82 mg/kg for Pb, 153.68 mg/kg for Zn, 0.43 mg/kg for Cd, and 33.08 mg/kg for Ni. These values were lower than the corresponding concentrations in surface sediments of Taihu Lake, which is the third-largest freshwater lake in China and is also a hypereutrophic lake (Qu et al. 2001). The concentrations of Cd and Zn in the Lake Chaohu sediments showed larger spatial variation than other heavy metals (Table 1). The maximum heavy metals concentrations were all present in sediments in the western portion of Lake Chaohu, indicating that this area has been impacted by intense pollution. In addition, with the exception of Ni, the average concentrations of Cu, Pb, Zn, and


435

Table 1 Heavy metals concentrations in surface sediments of Lake Chaohu Sites

Cu (mg/kg) Pb (mg/kg) Zn (mg/kg) Cd (mg/kg) Ni (mg/kg) Fe (g/kg) Mn (mg/kg)

Western Lake Chaohu CH01 30.7 CH02 21.4 CH03 9.66 CH04 36.2 CH05 19.2 CH06 32.1 CH07 18.8 CH08 38.5 CH09 41.0 CH10 31.2 CH11 38.3 CH12 27.3 CH13 18.4 CH14 28.3 CH15 18.3 CH16 16.6 Min 9.66 Max 41.0 Average 26.6 CV 0.35 Eastern Lake Chaohu CH17 24.9 CH18 25.1 CH19 24.6 CH20 27.1 CH21 30.5 CH22 9.23 CH23 32.0 CH24 30.5 CH25 33.6 CH26 11.8 CH27 32.9 Min 9.23 Max 33.6 Average 25.7 CV 0.32 Min Total 9.23 Max Total 41.0 Average Total 26.2 CV Total 0.33 TEL 35.7 PEL 197

51.3 41.4 19.0 69.8 29.5 70.5 37.9 83.4 89.2 65.8 87.3 58.7 37.4 56.7 35.4 35.9 19.0 89.2 54.3 0.40 46.7 46.9 43.9 46.8 50.9 20.5 53.5 49.3 51.7 19.4 46.5 19.4 53.5 43.3 0.27 19.0 89.2 49.8 0.38 35 91.3

353.6 216.7 47.8 260.9 50.6 211.8 93.0 299.9 360.3 292.1 306.0 151.5 81.7 157.8 82.9 82.4 47.8 360.3 190.6 0.58 121.3 119.5 100.5 115.4 115.8 39.4 116.7 113.4 117.2 33.2 108.1 33.2 121.3 100.0 0.32 33.2 360.3 153.7 0.64 123 350

0.39 0.46 0.13 0.74 0.08 0.60 0.31 0.92 1.01 0.66 0.88 0.43 0.24 0.48 0.29 0.28 0.083 1.01 0.49 0.57 0.35 0.39 0.34 0.36 0.39 0.12 0.49 0.36 0.44 0.10 0.48 0.10 0.49 0.35 0.37 0.08 1.01 0.43 0.55 0.596 3.53

24.2 18.4 12.4 43.6 26.3 43.7 27.5 46.3 48.2 33.3 49.0 37.5 25.6 39.1 25.4 24.8 12.4 49.0 32.8 0.34 34.6 35.1 33.1 36.1 38.9 15.5 40.8 39.6 40.7 16.4 37.0 15.5 40.8 33.4 0.27 12.4 49.0 33.1 0.31 18 36

25.7 22.0 13.3 43.0 26.6 44.8 32.1 45.7 46.9 32.9 48.0 39.9 34.9 41.5 30.8 29.7 13.3 48.0 34.9 0.29 38.0 37.0 35.3 38.8 42.0 16.3 43.1 43.6 43.8 18.1 40.0 16.3 43.8 36.0 0.27 13.3 48.0 35.3 0.28 – –

679.2 784.8 376.5 1277.8 430.3 1295.0 1076.9 1590.2 1622.3 1192.9 1374.3 1192.8 979.1 1235.7 1016.4 974.8 376.5 1622.3 1068.7 0.34 1068.5 1043.0 1049.5 1062.3 1154.0 470.3 1132.7 1109.2 1095.1 477.7 780.3 470.3 1154 949.3 0.27 376.5 1622.3 1020.1 0.32 – –

TEL threshold effect level, PEL probable effect level

Cd in western Lake Chaohu sediments were all higher than the sediments in eastern Lake Chaohu ( p < 0.05). This may have been due to the greater influx of pollutants from rivers located along the western portion of Lake Chaohu, particularly the Nanfei River, which has become a drainage ditch for Hefei City and is the greatest source of con-

tamination to Lake Chaohu (Shang and Shang 2007). Generally, the pollutant concentrations are high in estuary sediments due to river transportation (Li et al. 2007; Zhang et al. 2009). As shown in Fig. 1 and Table 1, the maximum concentrations of heavy metals were not observed in the estuarine mouth sediments of the lake inlets, but in areas

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located far from these estuaries. Overall, there was a tendency for the concentrations of metals to increase from the estuarine areas to the lake center. These findings may be explained by the long-distance transportation of metals resulting from the large flow of the inlet rivers into Lake Chaohu. These findings are similar to the results of a study Carried out on the Gulf of Lion continental shelf (Radakovitch et al. 2008). Distribution characteristics of heavy metals in sediment cores The depth profile of Cu, Pb, Zn, Cd, and Ni in the sediment cores of CH11, CH18, and CH21 are shown in Fig. 2. The three representative sites are all located in the center of the eastern, middle and western areas of Lake Chaohu, which is likely only slightly influenced by ship transportation and

other anthropogenic activities and therefore provide a good record of pollution in the lake. The concentrations of Cu, Pb, Zn, Cd, and Ni in the sediment cores showed large variations spatially and with depth, which is similar to the results of other studies and was likely due to a combination of human activity and natural factors (Zhang and Shan 2008). The maximum concentrations of Cu, Pb, Zn, Cd, and Ni, which were 37.87, 84.17, 313.14, 0.95, and 59.82 mg/kg, respectively, were all observed at site CH11. The intensive urbanization, high amounts of sewage drainage and industrial wastewater from the mainland of eastern Lake Chaohu are potential sources for the enrichment of these elements at site CH11. The average concentrations of Cu, Pb, Zn, Cd and Ni in the sediments of site CH11 were all much higher than at the two other sites ( p < 0.05), while the heavy metals concentrations in the sediments

Heavy metal concentration (mg/kg)

Depth (cm)

(a)

Depth (cm)

(b)

Depth (cm)

(c)

0 5 10 15 20 25 30 35 40 0 5 10 15 20 25 30 35 40 0 5 10 15 20 25 30 35 40

0

20

40

0

25

Cu 0

20

40

20

40

Cu

0

60

Pb 0

25

Cu 0

50

50

45

90

Pb

0.00

0.25

0

60

120

0.00

0.25

200

400

Zn

0

30

0.50

0

30

0.0

0.6

1.2

Cd

60

Ni 60

Ni

Cd

Zn 0

0.50

Cd

Zn

Pb 0

120

0

30

60

Ni

Fig. 2 Heavy metal concentrations in sediment cores (mg/kg) a, b, and c refer to site CH18, site CH21 and site CH11, respectively


at site CH21 were slightly higher than at site CH18 ( p < 0.05). The concentration profiles of Cu, Pb, Zn, Cd, and Ni were similar at each site (Fig. 2). At site CH18 and site CH21, the concentrations of the five metals increased slightly in the upper layers, and then decreased with depth, with a sharp decrease being observed at depths below 12– 18 cm. The concentrations of Cu, Pb, Zn, Cd, and Ni at site CH11 showed a similar trend, but a rapid decrease in concentration was observed in the 18– 24 cm sediment interval. This difference was likely due to the heavy pollution along eastern Lake Chaohu. The maximum concentrations of Cu, Pb, Zn, Cd, and Ni in the sediments at these sites were not observed at the sediment–water interface (0– 3-cm intervals), but below the sediment–water interface (3–6- or 6–9-cm intervals; Fig. 2). This enrichment pattern was likely caused by sediment resuspension. Indeed, frequent resuspension will cause the components of the surface sediments to remain in a dynamic situation, especially in shallow lakes. Other factors, such as sediment transportation and bioturbation will also dilute the total metal contents in the surface sediment (Yin et al. 2008). Multivariate statistical analysis of heavy metals Multivariate statistical analysis methods such as principal component analysis (PCA), correlation analysis, and cluster analysis (CA) are good tools for the identification of pollution source and correlation among heavy metals in the natural environment (Loska and Wiechula 2003; Kazi et al. 2009). Large amounts of data can be reduced to several variables by PCA, which will help us to explicitly and simply determine the nature of a target substance while retaining a large portion of the original information. The PCAs of heavy metals in the sediments of Lake Chaohu are presented in Fig. 3. Eight components were extracted, but only the first component was valid (eigenvalue>1; Qu et al. 2001; Reid and Spencer 2009). All variables were found in component 1 (PC1), which accounted for 81.02% of the total variance (Fig. 3). The extraction of only one component by the PCA method indicates that there was a strong relationship among the heavy metals and TOC, as

437

Fig. 3 Loading plot of heavy metals and TOC in the space defined by PC1 and PC2

demonstrated by the Pearson correlation matrix shown in Table 2. The strong correlation among Cu, Pb, Zn, Cd, and Ni indicates that they likely originate from similar pollution sources. As has been previously reported, the pollution in Lake Chaohu occurs as a result of the pollution of the waters flowing into the lake by industrial wastewater and domestic sewage (Yan et al. 1999; Shang and Shang 2007). In this study, Fe and Mn were shown to be highly positively correlated with heavy metals, with correlation coefficients of 0.441 (Fe–Zn) to 0.975 (Fe–Ni) and 0.606 (Mn–Zn) to 0.883 (Mn–Pb), respectively, being observed. These findings indicate that iron oxides or hydroxides, manganese oxide, or magnesium hydroxide are the primary binding forms of the measured metals. During sediment early diagenesis, heavy metals bound to the organic carbon could be released to interstitial water due to their decomposition (Loska and Wiechula 2003). The strong correlation between heavy metals and TOC in the sediments indicates that TOC is primarily associated with heavy metals in sediments in Lake Chaohu and that this binding phase may cause pollution to the lake during organic carbon decomposition. CA of the data obtained from the 27 sampling sites of Lake Chaohu was conducted. This approach was capable of classifying different objects into the same group based on the distance or similarity between the measured variables and

438 Table 2 Pearson’s correlation matrix for metal concentrations and TOC (n = 27)

TOC total organic carbon Levels of significance: ∗ p < 0.05;∗∗ p < 0.01


TOC Fe Mn Cu Pb Zn Cd Ni

TOC

Fe

Mn

Cu

Pb

Zn

Cd

Ni

1

0.626∗∗

0.568∗∗

0.807∗∗

0.701∗∗

0.692∗∗

0.695∗∗

0.626∗∗ 0.975∗∗ 0.873∗∗ 0.919∗∗ 0.884∗∗ 0.539∗∗ 0.801∗∗ 1

1

0.879∗∗ 1

as shown in the dendrogram in Fig. 4. CA revealed the presence of two main clusters, Clusters A and B. Cluster A included four sites that were characterized by low levels of heavy metals, CH03, CH05, CH22, and CH26 (Table 1), indicating that there was low pollution in these areas. Cluster B included the remaining 22 sites and consisted of two sub-clusters, B1 and B2. B1 included sites CH01 and CH02, which were characterized by moderate pollution. B2 contained two sub-clusters, B12 and B22, which represented relatively highly contaminated sites and highly contaminated sites (Table 1). Specifically, B12 included sites CH04, CH06, CH08, CH09, CH10, and CH11, which were all characterized by relatively higher concentrations of heavy metals when compared to the other sites and should be given priority when managing the sediment quality. Overall, these results indicate that this technique (CA) is a time-saving and efficient method of assessing large-scale sediment data sets.

0.816∗∗ 0.883∗∗ 0.923∗∗ 1

0.441∗ 0.606∗∗ 0.757∗∗ 0.846∗∗ 1

0.721∗∗ 0.844∗∗ 0.876∗∗ 0.965∗∗ 0.859∗∗ 1

Toxicity assessment of heavy metals in sediments based on AVS–SEM models and TEL–PEL values The sediment toxicity assessment results calculated from the AVS–SEM models and metals exceeding the TEL–PEL values are shown in Table 3. As shown in Tables 1 and 3, only three sites (including sites CH03, CH22, and CH26) having metal concentrations were below the TEL values and a total of 14 sites (including sites CH01, CH04, CH06, CH08, CH09, CH11, CH12, CH14, CH20, CH21, CH23, CH24, CH25, and CH27) having metal concentrations were above the PEL values when compared with the consensus-based TEL–PEL values. The concentrations of at least two and at most five metals exceeded the TEL values at each of the 24 sampling sites (Table 3), which indicates that occasional toxicity will occur at these sites. Frequently adverse effects will be expected when metals concentrations in sediments exceed the PEL values (Hübner et al. 2009).

85

90

B

A

B1

B2

95

B21

100

B22

CH01 CH02 CH09 CH08 CH11 CH10 CH04 CH06 CH07 CH16 CH13 CH15 CH27 CH19 CH20 CH17 CH18 CH12 CH14 CH21 CH24 CH23 CH25 CH05 CH03 CH22 CH26

Similarity

Fig. 4 Dendrogram showing the relationship of sites with heavy metals contamination among the 27 sampling sites (based on data from Table 1). Two main clusters are present: cluster A, which represents sites that have low heavy metals concentrations, and cluster B, which represents sites with moderate (B1) and higher (B2) metal concentrations

0.871∗∗ 0.794∗∗ 1


439

Table 3 Sediments toxicity assessment resulting from SEM–AVS models and TEL–PEL values Sites TOC AVS SEM SEM/ SEM–AVS SEM–AVS/foc Metals (%) (μmol/g) (μmol/g) AVS (μmol/g) (μmol/g) OC exceeding TEL value

Metals exceeding PEL value

CH01 CH02 CH03 CH04 CH05 CH06 CH07 CH08 CH09 CH10 CH11 CH12 CH13 CH14 CH15 CH16 CH17 CH18 CH19 CH20 CH21 CH22 CH23 CH24 CH25 CH26 CH27

Zn – – Ni – Ni – Ni Zn, Ni – Ni Ni – Ni – – – – – Ni Ni – Ni Ni Ni – Ni

1.76 1.49 1.04 1.73 1.15 1.54 1.27 1.61 1.61 1.59 1.51 1.43 1.47 1.59 1.46 1.09 1.20 1.36 1.20 1.28 1.51 1.09 1.67 1.42 1.53 0.99 1.74

6.22 1.24 0.47 5.69 0.21 5.32 2.36 1.12 2.92 1.79 5.18 0.99 0.77 2.56 1.93 1.81 1.71 1.88 0.54 1.37 0.84 0.21 1.84 1.24 1.26 0.01 3.29

6.52 3.16 0.66 3.52 0.63 2.61 4.31 7.70 5.01 4.43 4.22 6.14 0.84 2.23 0.85 0.96 1.44 1.48 1.18 1.26 1.24 1.41 1.40 1.23 1.52 0.47 1.42

1.05 2.55 1.42 0.62 3.01 0.49 1.83 6.85 1.72 2.47 0.81 6.21 1.09 0.87 0.44 0.53 0.84 0.79 2.20 0.92 1.48 6.71 0.76 0.99 1.21 60.90 0.43

0.30 1.92 0.20 −2.17 0.42 −2.71 1.96 6.58 2.09 2.63 −0.96 5.15 0.07 −0.32 −1.09 −0.85 −0.27 −0.40 0.64 −0.10 0.40 1.20 −0.44 −0.01 0.26 0.46 −1.87

Overall, 51.9% (14 out of 27) of the sampling sites were predicted to cause frequent toxicity to benthic biota based on the PEL values (Tables 1 and 3). With the exception of site CH01, the other sites with metal concentrations above the PEL values all had higher Ni concentrations than the PEL-Ni values, indicating that Ni requires special consideration and administration. When compared with the TEL–PEL empirical values, the results of the AVS–SEM mechanical models were almost completely different. For example, the SEM to AVS ratio was greater than 2.0 at site CH22 and greater than 8.32 at site CH26, indicating that metals in these areas will occasionally cause toxic and highly toxic effects on the benthic biota (Burton et al. 2005). However, no metal concentrations exceeded the TEL values or the PEL values, indicating that the metals will not cause toxicity at these two sites (Hübner et al. 2009). These findings are significantly different

16.8 128.9 19.0 −125.5 36.7 −176.6 153.9 408.2 129.6 165.2 −63.7 361.5 4.91 −20.3 −74.2 −77.9 −22.4 −29.2 53.7 −8.16 26.6 110.5 −26.1 −0.75 17.1 46.7 −107.8

Pb, Zn, Ni Pb, Zn, Ni – Cu, Pb, Zn, Cd, Ni Ni Pb, Zn, Cd, Ni Pb, Ni Cu, Pb, Zn, Cd, Ni Cu, Pb, Zn, Cd, Ni Pb, Zn, Cd, Ni Cu, Pb, Zn, Cd, Ni Pb, Zn, Ni Pb, Ni Pb, Zn, Ni Pb, Ni Pb, Ni Pb, Ni Pb, Ni Pb, Ni Pb, Ni Pb, Ni – Pb, Ni Pb, Ni Pb, Ni – Pb, Ni

from the results obtained using the AVS–SEM models. The prediction of SEM–AVS/ foc models also differed from those based on the PEL values at sites CH07 and CH10. However, identical results were also predicted based on the AVS–SEM models and the TEL–PEL values at some sampling sites. For instance, no toxicity was predicted based on TEL–PEL values and this was consistent with the results obtained from SEM/AVS and SEM–AVS/ foc models. Indeed, eight, five, and five results were consistent with the results predicted by the TEL–PEL values in the 27 samplingsites based on SEM–AVS, SEM/AVS, and SEM–AVS/ foc , respectively. SEM–AVS had the highest similarity of the three AVS–SEM models with the results obtained from the TEL–PEL values. The contradictory results obtained from AVS– SEM and TEL–PEL methods might primary be due to these SQGs having some limitations.

440

The AVS–SEM and TEL–PEL methods were all based on laboratory experiment while not field results (Hinkey and Zaidi 2007; Hübner et al. 2009; De Jonge et al. 2009). A lot of researches have found that even the molar of SEM far larger than that of the AVS in sediments, whereas it still did not cause toxicity to benthic biota (Allen et al. 1993; Ankely et al. 1996). This was due to other metal-binding phases such organic matter, various form of Fe and Mn largely decreasing the bioavailability in sediments (Ankely et al. 1996; Leonard et al. 1996). Furthermore, the levels of AVS were quite dynamic in sediment, which might cause the results of the AVS– SEM models varied greatly at different sampling time (Yin et al. 2008). The empirical SQGs such TEL–PEL were greatly influenced by geography, environment, derivation and resilience factors (MacDonald et al. 2000; Ingersoll et al. 2001; Hübner et al. 2009). These SQGs were generally developed for a specific region, and have not been validated for other areas with possible differences in sediment geochemistry or biological diversity (Hübner et al. 2009). Furthermore, the dynamics environmental factors such as grain size, pH value, population stress, or exposure time, which can affect the bioavailability, toxicity and susceptibility of the SQGs were not fully considered or included (Hübner et al. 2009). In addition, most of empirical SQGs were based on the effects of a specific biological species or main clearly named species (Hübner et al. 2009). At last, these SQGs such TEL–PEL were not tried and tested worldwide (MacDonald et al. 2000; Hübner et al. 2009). This may limit the use of these SQGs in different regions. The contradictory results obtained from the two sets of SQGs could cause many problems for local environmental managers and regulators overseeing sediment dredging and point or nonpoint pollution control. Therefore, these SQGs might not be used alone when we make decisions or take management actions to mitigate or remediate toxic effects from sediment heavy metals. Other works such as laboratory toxicity tests, benthic community, and metal binding phase analysis should be done as a plus. Finally, more accurate and universal SQGs should be developed for different locations worldwide.


Conclusions This study was conducted to provide a detailed inventory of Cu, Pb, Zn, Cd, and Ni in the sediments of Lake Chaohu, as well as to identify the distribution characteristics and provide a toxicity assessment. The results showed that the metal concentrations are higher in the center of the lake than in the mouths of the estuarines and the lake inlets. In addition, similar metal distribution patterns were observed in the sediment cores. Furthermore, the Pearson correlation matrix indicated strong interrelationships among the measured variables. Cluster analysis was used to distinguish the contamination extent among the sampling sites. The use of two widely adopted SQGs (AVS–SEM models and TEL–PEL values) to predict sediment quality resulted in some contradictory conclusions. Indeed, only eight, five, and five results were consistent with the results predicted by TEL–PEL values in the 27 sampling sites based on SEM–AVS, SEM/AVS, and SEM–AVS/ foc , respectively. Therefore, readymade SQGs should not be used to assess the sediment quality alone. Other measures such as biology toxicity tests and metal binding phase analysis should be conducted to enhance analyses made solely based on the SQGs. Finally, more accurate and universal SQGs must be developed for environmental researchers and local environmental managers and regulators. Acknowledgements This work was jointly supported by State major project of water pollution control and management (Grant No. 2008ZX07526-002-08 and 2008ZX07103-003), Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences (Grant No. NIGLAS2010KXJ01), and the National Natural Science Foundation of China (Grant No. 20907057). We are grateful to Dr. Mark McCarthy for his kind assistance in the manuscript revision.

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