Risk and toxicity assessments of heavy metals in sediments and fishes from the Yangtze River and Taihu Lake, China

Chemosphere 93 (2013) 1887–1895

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Risk and toxicity assessments of heavy metals in sediments and fishes from the Yangtze River and Taihu Lake, China Jie Fu a, Xin Hu b, Xiancong Tao b, Hongxia Yu a, Xiaowei Zhang a,⇑ a b

State Key Laboratory of Pollution Control & Resource Reuse, School of the Environment, Nanjing University, Nanjing 210046, China Key Lab of Analytical Chemistry for Life Science (Ministry of Education), Center of Mordern Analysis, Nanjing University, Nanjing 210093, China

h i g h l i g h t s  Heavy metals might pose transcriptional effects on stress responsive genes.  Heavy metals in fishes might pose risk of adverse health effects to human.  Heavy metals in sediments of the Yangtze River posed significant ecological risk.

a r t i c l e

i n f o

Article history: Received 31 March 2013 Received in revised form 10 June 2013 Accepted 16 June 2013 Available online 12 July 2013 Keywords: Stress responsive gene Adverse effect Ecological risk Human health risk

a b s t r a c t Heavy metal pollution is one of the most serous environmental issues globally. To evaluate the metal pollution in Jiangsu Province of China, the total concentrations of heavy metals in sediments and fishes from the Yangtze River and Taihu Lake were analyzed. Ecological risk of sediments and human health risk of fish consumption were assessed respectively. Furthermore, toxicity of samples on expression of the stress responsive genes was evaluated using microbial live cell-array method. The results showed that the heavy metals concentrations in sediments from the Yangtze River were much higher than those in sediments from the Taihu Lake. However, the fishes from the Taihu Lake had higher concentrations of heavy metals than fishes from the Yangtze River. Ecological risk evaluation showed that the heavy metal contaminants in sediments from the Yangtze River posed higher risk of adverse ecological effects, while sediments from the study areas of Taihu Lake were relatively safe. Health risk assessment suggested that the heavy metals in fishes of both Yangtze River and Taihu Lake might have risk of adverse health effects to human. The toxicity assessment indicated that the heavy metals in these sediments and fishes showed transcriptional effects on the selected 21 stress responsive genes, which were involved in the pathways of DNA damage response, chemical stress, and perturbations of electron transport. Together, this field investigation combined with chemical analysis, risk assessment and toxicity bioassay would provide useful information on the heavy metal pollution in Jiangsu Province. Ó 2013 Elsevier Ltd. All rights reserved.

1. Introduction With rapid economic growth, industrialization and urbanization, heavy metal pollution has become serious in China. Cadmium polluted farmland over 12 000 ha and high concentrations were detected in 11 irrigation regions in China (Cheng, 2003). The major rivers and lakes had been generally polluted by heavy metals at different levels, with the pollution rates of sediments being up to 80.1% (Wang et al., 2010). The Jiangsu Province, located in the east of China, is one of the most developed areas in China. In recent years, heavy metal pollution caused by intensive anthropogenic activities in this area has become an urgent problem (Zhong et al., 2011). ⇑ Corresponding author. Tel.: +86 25 83593649; fax: +86 25 83707304. E-mail address: [email protected] (X. Zhang). 0045-6535/$ - see front matter Ó 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.chemosphere.2013.06.061

To systematically evaluate the pollution level, and to assess the toxicity and risk in environment of Jiangsu Province, a series of work have been carried out by our group. The first study focused on the fresh water systems, the second study researched the coastal area, and the third part studied the heavy metals in avian species. Here is the first paper examining the heavy metal pollution in the two largest fresh water systems, Yangtze River and Taihu Lake. The Yangtze River and Taihu Lake are the most important fresh water resources in the southern part of Jiangsu Province. Rapid development has resulted in the deterioration of water quality, aggravation of water contamination, and fragility of aquatic ecosystem in this area. Many studies have investigated the heavy metal pollution in the Yangtze River (Shen et al., 2006; Zhong et al., 2007; Wu and Wu, 2008; Zhong et al., 2011; Yu et al., 2012) and Taihu Lake (Qu et al., 2001; Wang et al., 2002; Liu et al., 2004;

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Zhu et al., 2005; Chi et al., 2007; Sun et al., 2008; Yin et al., 2011b) in Jiangsu Province. However, most of these studies only focused on chemical analysis, and subsequent risk assessment and source tracing. Chemical analysis help in determining the concentrations of contaminants and the magnitude of anthropogenic contamination. However, they do not provide any information about the biological effect induced by contaminants. Toxicity bioassays, on the other hand, provide us with reasonable information concerning the biological impacts of contaminants, but provide no indication as to the causes of the observed toxic responses. To fully evaluate the environmental impact of heavy metal pollution, both chemical and biological analysis should be carried out (Fu et al., 2011a). Recently, our group has applied an genome-wide microbial live cell-array system to assess toxicity of different environmental chemicals (Zhang et al., 2011; Su et al., 2012). The microbial live cell-array system consists of genetically engineered microorganisms tailored to respond to the environmental stimuli. The fusion of stress promoters to reporter genes, such as green fluorescence protein (GFP) provides the basic concept of cellular signals detection (Elad et al., 2010). In two previous studies on naphthenic acids and polybrominated diphenyl ethers, this system had been successfully used to not only assess the toxicity, but also to disclose the underlying molecular mechanisms (Zhang et al., 2011; Su et al., 2012). In this study, we further apply the stress responsive pathway-focused live cell-array bioassay in toxicity assessment of environmental samples. In the present paper, chemical analysis and toxicity bioassay were integrated to investigate the risk and toxicity of heavy metals in sediments and fishes from the Yangtze River and Taihu Lake, China.

2. Materials and methods 2.1. Study area Jiangsu Province is located along the eastern coast of China between 116°180 -121°570 E, and between 30°450 -35°200 N, bordering

on the Yellow Sea, with An’hui Province to its west, Shandong Province to its north, Shanghai City and Zhejiang Province to its southeast. It covers an area of 102 600 km2, accounting for 1.05% of the national area. The province has a coastline of 954 km and its water surface area is 17 300 km2 (Wei et al., 2011). The Yangtze River is the longest river in China and Asia, and the third longest river in the world in terms of its length (6300 km) and flux (9.6  1011 m3 y1). The Yangtze River in Jiangsu Province is the downstream of the whole river, just upstream of the river’s estuary in Shanghai, with an average flux of 3.08  104 m3 s1 and a length up to 450 km. In Jiangsu Province, the river serves as a curial important drinking water source, and flows through eight municipal cities and 36 counties with an area of 3.87  104 km2 and a population of more than 3  107 (He et al., 2011). The Taihu Lake is the third largest fresh water lake in China and located between 30°050 -32°080 N and between 119°080 -121°550 E, downstream of the Yangtze River. It is 68.5 km long and 56 km wide, with an average depth of 2.0 m and an area of 2388 km2 (Qin et al., 2007). The drainage basin of the lake is about 36 500 km2, and more than 200 brooks, canals and rivers are connected with the lake (Li et al., 1994).

2.2. Field sampling The sampling took place in June–July, 2011. The sampling sties (S1–S21) are shown in Fig. 1. S1–S4 and S5–S10 are in the Yangtze River of Nanjing and Chuangshu sections, respectively, which represent the upstream and downstream sites of the Yangtze River in Jiangsu province. S11–S14 are in the Chemical Industrial Park of Changshu. S15–S21 are in the north parts of Taihu Lake. Sediment samples (0–10 cm depth, approximately 1 kg) were collected in duplicate from S1–S12 and S15–S21 by an Ekman dredge. Soil samples were collected in duplicate from S13 and S14 by a stainless steel grab. Ten kinds of fishes (fish number >3) were collected from the Yangtze River of Nanjing section (within S1–S4) with the help of local fishermen. Seven kinds of fishes were collected from Changshu section (within S5–S10). Seven kinds of

Fig. 1. Location of sampling sites in the Yangtze River and Taihu Lake, Jiangsu Province, China.

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fishes and one kind of shrimp were collected from the Taihu Lake (within S15–S21). Sediment (or soil) samples were put in polypropylene (PP) bottles. Fish (or shrimp) samples were put in sampling bag and immediately stored in an icebox. Care was taken to avoid any contamination during the transport to laboratory, where they were stored in a 80 °C refrigerator until further analysis. 2.3. Physicochemical analysis of sediments Grain size of sediments was determined using a BT-9300H Laser Particle Size Analyzer (Bettersize Instruments Ltd., Dandong, China). The results were expressed as median particle diameter (MPD) and specific surface area (SSA). Loss on ignition (LOI) of sediments was determined by measuring the weight change after heating in a Muffle furnace. The heating program was as follows: at 200 °C roasting for 0.5 h, then rising to 550 °C and roasting for 2 h. The analysis results are summarized in Table S1. 2.4. Heavy metal analysis The freeze-dried sediment samples for metal analysis were passed through a 250 mesh nylon sieve. The total concentrations of As, Cd, and Hg were measured by Atomic Fluorescence Spectrometer (AFS). Sediment samples were digested by aqua regia. For the As measurement, the digestion solution was reduced by thiourea–HCl mixture. The concentrations of Cr, Cu, Ni, Pb, Zn and other elements (Al, Ba, Ca, Co, Fe, K, Mg, Mn, P, Sr, Ti and V) were measured by Inductively Coupled Plasma Optical Emission Spectrometer (ICP-OES, PerkineElmer, Wellesley, MA). Sediment samples were digested by HCl-HNO3-HF-HClO4 mixture. The freeze-dried fish samples for metal analysis were finely pulverized and homogenized. The concentrations of As, Cd, and Hg were measured by AFS. Fish samples were digested by HNO3– H2SO4 mixture. Before As analysis, the digestion solution was reduced by thiourea–HCl mixture. The concentrations of the rest metals and elements were measured by ICP-OES, where fish samples were digested by HNO3–HClO4 mixture. The heavy metal concentrations in extraction of sediments and fish residue for toxicity bioassay were also measured directly by AFS and ICP-OES. All analytical data were subject to strict quality control. The instruments were calibrated daily with the calibration standards. Precision and accuracy were verified using standard reference materials from the National Research Center for Geoanalysis, China. Differences in metal concentrations between this study and certified values were generally <10%. Blanks for digestion and analysis methods, were evaluated in duplicate with each set of samples. The relative deviation of the duplicate samples was <5% in the batch treatments. 2.5. Risk assessment The ecological risks of heavy metals in sediments were assessed using three different methods including consensus-based Sediment Quality Guidelines (SQGs), Potential Ecological Risk Index (PERI) and Geo-accumulation Index (GAI). In Consensus-based SQGs method, Macdonald et al. have given two consensus-based values: threshold effect concentrations (TECs), below which adverse effects are not expected to occur; and probable effect concentrations (PECs), above which adverse effects are expected to occur more often than not (Macdonald et al., 2000), (Niu et al., 2009). The mean PEC quotient (Qm-PEC) was introduced to predict the toxicity of sediment samples. In PERI method, the potential ecological risk index (RI) was introduced to assess the degree of heavy metal pollution in sediments (Håkanson, 1980). In GAI method, the geoaccumulation index (Igeo) was calculated, providing a classification

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system for the degree of pollution (Müller, 1969). The calculation and evaluation criteria for Qm-PEC, RI and Igeo are shown in Supporting Information (SI). The health risks of heavy metals in fishes were assessed using the target hazard quotients (THQ) method based on the USEPA Region III risk-based concentration table (USEPA, 2000). The models for estimating THQ were developed by Chien et al. (Chien et al., 2002) and the calculation and evaluation criteria for mean THQ (THQm) are shown SI. 2.6. Toxicity bioassay The toxicity of extracts of sediments and fish residues on stress responsive genes were analyzed using microbial live cell-array method, which were described in detail in our previous publications (Zhang et al., 2011; Su et al., 2012). The sediment samples were extracted using two different solutions, 1 M NH4Ac and 0.1 M CaCl2 solution. The procedure for preparation of sediment extraction was as follows: 6.25 g (2.5 g) of freeze-dried and homogenized sediment sample (passed through a 250 mesh nylon sieve) and 18.75 mL (22.5 mL) NH4Ac (CaCl2) solution were added into a centrifugal tube. After 2 h shaking in an oscillator (180 rpm), the samples were centrifugalized to removal the sediments (3000 rpm, 20 min). Fish residue was obtained by digesting with HNO3–HClO4 mixture. After removal of the acids by heating for 2 h, the residue was extracted using 0.1 M CaCl2 solution (described above) to give fish residue extraction. A library of transcriptional fusions of GFP that include different promoters for 21 stress-related genes in E. coli K12, was employed in this study. The 21 stress responsive genes and their functions are summarized in Table S3. They were selected from our database due to their high sensitivity for heavy metals. In this library, each promoter fusion was expressed from a low-copy plasmid that contains a kanamycin resistance gene, therefore allowed for continuous and real time measurements of the promoter activities. Exposure was done with a slight modification of our previously described methods (Zhang et al., 2011; Su et al., 2012). Strains of E. coli were inoculated into a fresh 96-well plate from a 96-well stock plate by use of disposable replicators (Genetix, San Jose, CA, US). Cells were incubated at 37 °C for 3 h in 96-well plate and then transferred into 384-well plates. Finally, 8 lL of NH4Ac (CaCl2) solution (extractant control) or sediment (fish residue) extracts were added into individual wells on the 384-well plates. The extraction was in advance diluted with corresponding extractant. The final concentrations were expressed as dilution factor, 10, 20, and 40 respectively. Fluorescence intensity (485/528 nm) and optical density (600 nm) of each well were consecutively monitored every 10 min for 4 h by a Synergy H4 hybrid microplate reader (BioTek Instruments Inc., Winooski, VT, US). Data processing and analysis were referred to the previous reports (Gou and Gu, 2011; Zhang et al., 2011). The fold-change (Fc) of gene expression at 4 h and Transcriptional Effect Level index (TELI) were used to evaluate the toxicity of extraction on stress responsive gene (SI). 2.7. Statistical analysis Cluster analysis and correlation analysis (CA) were carried out using SPSS 13.0 for windows (IBM Corporation, Armonk, NY). Canonical correspondence analysis (CCA) was executed using Canoco 4.5 for windows (Biometris-Plant Research International, Wageningen, Netherlands). ToxClust (Zhang et al., 2009) in R 2.14.1 (www.R-project.org) was used to analysis the gene expression. Other statistical analysis were conducted using Microsoft Office Excel 2003 (Microsoft Corporation, Redmond, WA), and OriginPro 7.5 (OriginLab Corporation, Northampton, MA).

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3. Results and discussion 3.1. Concentrations of heavy metals The total concentrations of heavy metals were significant different in sediments from the Yangtze River and Taihu Lake (Table 1). The sediments from Nanjing section had the highest mean concentrations of As, Cd, Cr, Hg, Pb, and Zn, while the sediments from Taihu Lake had the lowest mean concentrations of Cd, Cr, Cu, Hg, Ni, Pb, and Zn. This might suggest that heavy metal pollution in the Yangtze River was more serious than in the Taihu Lake, which were different with Yin et al’s observation (Yi et al., 2011). In fact, a small branch river, Qinhuai River, flows across Nanjing City and into the Yangtze River with large amount of sewage at S1 site (Fig. 1), which might be a significant source on the metal pollution at site S1–S4. Lead concentrations in sediments form the Yangtze River in our observation were much higher than those reported in other publications (Table S4) (Woitke et al., 2003; Barlas et al., 2005; Niu et al., 2009; Yi et al., 2011; Yin et al., 2011a; Yin et al., 2011b), indicating that Pb pollution in the Yangtze River was relatively serous. Cadmium and Hg concentrations in sediments form the Yangtze River in our observation were slightly higher than those reported in

other publications (Woitke et al., 2003; Barlas et al., 2005; Niu et al., 2009; Yi et al., 2011; Yin et al., 2011a; Yin et al., 2011b). A hierarchical cluster analysis on the elements distributions in the sediments showed a cluster consisted of As, Ba, Cd, Co, Cr, Cu, Hg, Mn, Ni, P, Pb, Sr, Ti, V, and Zn, indicating these metals mainly originated from anthropogenic sources (SI). Very significant correlations were found among Cd, Cr, Cu, Ni, Pb and Zn (p < 0.01) by Pearson algorithm (Table S5), suggesting these metals have the similar pollution sources (Håkanson and Jansson, 1983). However, As and Hg showed low correlations with other metals, indicating that As and Hg had a different distribution profile from the other metals. High positive correlations were found between SSA and Cd, Cu, Ni, Pb (p < 0.01), indicating that higher SSA could increased the adsorption of these metals. The positive correlations between LOI and As, Cr, Cu, Ni, Zn, suggested that these metals were likely present in organic form in the sediments. The fishes from Taihu Lake had the highest mean concentrations of Cd, Cr, Cu, Pb, and Zn (Table 2), indicating a greater effect of anthropogenic metals input on fishes in Taihu Lake. Compared with other reported data (Table S4), Pb concentrations in fishes form the Taihu Lake in our observation were much higher than those reported in other publications (Chi et al., 2007; Yang et al., 2007; Xie et al., 2010; Yi et al., 2011; Petkovšek et al., 2012). Except

Table 1 The concentrations of main heavy metals in sediments (mg kg1 dry weight). Samples Nanjing (S1–S4) Changshu (S5–S10) Chemical Industrial Park (S11–S14a) Taihu Lake (S15–S21) a

Range Average Range Average Range Average Range Average

As

Cd

Cr

Cu

Hg

Ni

Pb

Zn

18.83–29.95 21.90 5.58–14.57 10.00 7.10–13.46 9.75 0.18–16.73 9.82

2.47–2.50 2.48 2.15–2.51 2.28 2.41–2.64 2.44 0.07–0.27 0.14

91.45–103.58 95.58 71.95–98.25 85.78 74.31–116.39 92.49 34.47–106.76 68.09

76.98–90.00 83.24 36.05–74.90 56.94 62.06–117.61 83.78 18.55–51.55 34.14

0.22–0.42 0.36 0.03–0.84 0.23 0.07–0.16 0.12 0.06–0.23 0.11

52.27–59.00 55.58 38.85–55.10 46.90 48.76–63.98 56.38 27.32–50.74 36.23

442.65–513.71 454.82 330.70–413.48 371.17 345.66–446.50 401.65 26.36–44.02 33.55

159.61–217.24 189.00 87.36–194.14 131.78 128.62–250.00 173.51 69.00–201.63 105.55

S13 and S14 were soil samples.

Table 2 The concentrations of main heavy metals (mg kg1 dry weight), habitat site and food habit for fishes.

a

Location

No.

Fish species

As

Cd

Cr

Cu

Hg

Ni

Zn

Habitat

Food

Nanjing

F1 F2 F3 F4 F5 F6 F7

Hypophthalmichthys molitrix Parabramis pekinensis Carassius auratus Aristichthys nobilis Cyprinus carpio Hemicculter leuciclus Parasilurus asotus

3.85 3.00 4.04 3.05 1.96 4.65 2.90

0.49 0.46 0.44 0.50 0.55 0.68 0.77

1.61 1.88 1.46 3.90 1.35 6.11 1.22

12.06 1.94 2.18 2.39 2.86 7.22 9.36

0.36 0.41 0.40 0.33 0.15 0.22 1.39

0.33 0.07 0.15 0.92 0.10 1.66 0.12

4.58 5.09 4.56 5.16 5.51 8.04 4.98

54.09 82.79 144.11 73.92 240.43 99.70 79.50

Middle-upper Middle-lower Bottom Middle-upper Bottom Upper Middle-lower

Herbivorous Omnivorous Omnivorous Omnivorous Omnivorous Low-level carnivorous High-Level carnivorous

Changshu

F8 F9 F10 F11 F12 F13 F14 F15 F16 F17

Ctenopharyngodon idellus Hypophthalmichthys molitrix Parabramis pekinensis Carassius auratus Hemicculter leuciclus Aristichthys nobilis Mugil cephalus Pelteobagrus fulvidraco Coilia ectenes Lateolabrax japonicus

2.19 3.17 3.63 1.57 4.07 2.78 5.85 3.97 7.85 3.35

0.55 0.53 0.57 0.52 0.50 0.31 0.34 0.82 0.44 0.64

2.75 5.06 2.52 1.42 1.33 1.01 2.26 2.26 1.04 1.68

5.42 3.72 3.62 3.83 3.11 3.84 10.83 4.38 1.83 2.27

0.18 0.22 0.21 0.43 0.23 0.11 0.13 0.43 0.29 0.28

2.29 1.45 2.56 0.21 0.28 0.71 0.77 2.07 0.09 0.38

5.04 6.03 4.17 4.87 3.35 4.38 4.14 5.35 4.39 4.20

91.52 58.81 66.02 145.59 82.65 54.45 61.38 86.17 49.36 61.44

Middle-lower Middle-upper Middle-lower Bottom Upper Middle-upper Middle-upper Bottom Middle-upper Middle-lower

Herbivorous Herbivorous Omnivorous Omnivorous Low-level carnivorous Low-level carnivorous Low-level carnivorous Low-level carnivorous Middle-level carnivorous High-Level carnivorous

Taihu Lake

F18 F19 F20a F21 F22 F23 F24 F25

Hypophthalmichthys molitrix Carassius auratus Macrobrachium nipponense Cyprinus carpio Hemibarbus maculatus Hemisalanx prognathus Pelteobagrus fulvidraco Coilia ectenes

2.42 2.10 1.04 3.31 3.78 3.99 3.58 3.77

1.36 1.58 1.76 1.24 1.06 0.47 1.12 1.23

2.96 3.94 3.29 3.21 1.74 1.30 2.86 2.09

2.34 3.72 83.88 1.92 4.06 1.06 6.47 3.32

0.20 0.14 0.24 0.22 0.27 0.31 0.32 0.29

5.18 5.41 6.35 5.04 2.71 1.89 4.60 2.95

20.12 19.38 27.18 20.13 9.68 6.63 23.88 15.98

63.33 270.96 147.18 367.39 69.67 67.83 82.71 93.78

Middle-upper Bottom Bottom Bottom Middle-lower Bottom Bottom Middle-upper

Herbivorous Omnivorous Omnivorous Omnivorous Low-level carnivorous Low-level carnivorous Low-level carnivorous Middle-level carnivorous

Shrimp sample.

Pb

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Fig. 2. Risk assessments for heavy metals: (A) and (B) Qm-PEC of sediment samples; (C) and (D) RI of sediment samples; (E) and (F) mean Igeo of sediment samples; (G) and (H) THQm of fish samples.

Ravi River (Jabeen et al., 2012), As and Cd concentrations in fishes from the Taihu Lake in our observation were higher than those re-

ported in other publications (Chi et al., 2007; Yang et al., 2007; Xie et al., 2010; Yi et al., 2011; Petkovšek et al., 2012).

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Fig. 3. TELIs of 21 stress-responsive genes in E. coli exposing to NH4Ac extractions (A) and (B) and CaCl2 extractions of sediments (C) and (D), and CaCl2 extractions of fish residues (E) and (F).

Among the species, the shrimp, Macrobrachium nipponense, had the highest concentrations of Cd, Cu, Ni and Pb. Especially for Cu, the concentration in M. nipponense was in an order of magnitude higher than the values in fish species. The mean concentrations of Cd, Cu, Ni, Pb, and Zn in demersal fishes were higher than in fishes that inhabit the upper water column (Fig. S2A). This was possibly related to their greater dermal exposure to polluted sediments and greater uptake of heavy metals from benthos (Yi et al., 2011). However, the concentrations of heavy metals in carnivorous fishes were not greater than in omnivorous or herbivorous fishes (Fig. S2B), which indicated that habitat site might pose greater influence than food habits on the distribution of heavy metals in fish species. 3.2. Risk assessment Based on the concentrations, we can assess ecological risk of heavy metals in sediments and health risk of heavy metal in fishes. The Qm-PEC of sediments from the Yangtze River were >0.5, indicating that these sediments were toxic and seriously polluted by hea-

vy metals (Fig. 2A and B). While Qm-PEC of sediments from Taihu Lake were <0.5 and these samples can be considered as less polluted. The relatively good quality of sediments from Taihu Lake was related with the recent dredging project in Taihu Lake. Considering each metal’s contribution, Pb take account the most contribution in the Yangtze River with an average percentage of 45%. While in Taihu Lake, Ni and Cr pose greater proportion of risk than other metals. The PEC quotient of each metal indicated that the risk of the main heavy metals was in the following sequence: Pb > Ni > Cr > Cu > As > Cd > Zn. Similar risk pattern among the four sampling regions were demonstrated by the PERI and GAI approaches (SI). The three ecological assessment methods gave similar profiles for the pollution level among different sample sites. Correlation analysis revealed significant positive correlations among Qm-PEC, RI and mean Igeo with correlation coefficients >0.95 (n = 21, P < 0.01), suggesting that the three methods were in high conformity for evaluation the polluted sites. In our observation, Qm-PEC, RI and mean Igeo could distinguish the different pollution level of the Yangtze River and Taihu Lake (Fig. S2). However, for the

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pollution characteristic of each metal, different method obtained quite different results due to different assessing mechanisms. SQGs method is based on sediment quality guidelines. PERI takes into accounts the different background values of geography and combines with biological toxicology. GAI emphasized on the geochemical behavior of heavy metals. Comprehensively considering the three methods, it can be concluded that Pb and Cd posed the most affect to the ecological risk of sediments in the studied area. All the Qm-PEC of fishes were >0.5 (Fig. 2G), suggesting that people would experience health risks from the intake of heavy metals through fish consumption. The anchovy, Coilia ectenes, from Changshu section showed the highest Qm-PEC. Considering each metal’s contribution, As was the major risk contributor and accounted for over 50% of the Qm-PEC. The Qm-PEC for individual metal decreased was in the following sequence: As > Pb > Hg > Cd > Zn > Cu > Ni > Cr. The THQm for fishermen through the consumption of fish was about twice that of the general population. The higher THQm value for these adults suggested that they might experience a certain degree of adverse health effects.

3.3. Toxicity on stress responsive gene The Fc of gene expression at 4 h was used as an endpoint for toxicity assessment. Of the 21 selected genes, dacB, dps, and ybgI were most sensitive to the NH4Ac extraction of sediments with average Fc being 1.36, 1.31, and 1.32, respectively. The fold changes caused by exposing to NH4Ac extraction of sediments were higher than by exposing to CaCl2 extraction of sediments (average Fc were 1.25 and 1.08, respectively). This was possible related with higher concentrations of heavy metals in the NH4Ac extraction (Tables S6 and S7). S6, S7, S9, S10 from Changshu showed higher toxicity to the selected genes with average Fc being 1.97, 2.12, 2.15, and 1.76, respectively (Fig. S4A). According to our survey, the wastewater outfall of the Chemical Industrial Park is near our sampling sites, which was a pollution source and might result in the high toxicity of these sediments. Compared with sediments extraction, fish residue extraction showed higher toxicity with an average Fc value of 1.32 (Fig. S4C). Also, the heavy metal concentrations in fish residue extractions were higher than in sediments extraction (Table S8). In addition, the average Fc for all the tests was concentration-dependent with the values being 1.08, 1.23 and 1.33 for 40, 20, and 10 dilution factor, respectively. The altered gene expression at 4 h provided sensitive measurement on the effect of environmental samples to the stress responsive genes. However, it is difficult to be applied in the ecological assessment. Furthermore, only one time point does not reflect the time-dependence of gene response. Therefore, Gou and Gu (Gou and Gu, 2011) first introduced TELI that incorporated both the number and magnitude of genes with altered expression as well as the temporal pattern of response for toxicity assessment. The mean TELItotal of NH4Ac extraction of sediments were generally higher than CaCl2 extraction of sediments (average values were 22.38 and 15.64, respectively) (Fig. 3A and C), which was in accordance with the Fc at 4 h. S6, S7, S9, and S10 from Changshu section showed highest toxicity, resulting in the higher TELItotal in this section. For genes, ftsK, fsr and cyoA were the most sensitive genes (Fig. 3B), which were different with the Fc results. By integrating the time-dependence of gene expression profile, TELIs provide richer information on the responsiveness of genes caused by chemical exposure. Considering the gene functions, SOS response/DNA repair, drug resistance/sensitivity, energy stress were the strongest function groups with higher gene alterations. This suggested that the exposure of the sediments extraction may induce DNA damage, chemical stress, and perturbations of electron transport, which were the main toxicity mechanisms.

Fig. 4. Diagram of axis one and two for the canonical correspondence analysis relating TELIgene and heavy metals concentrations in the NH4Ac extraction of sediments (A) and CaCl2 extraction of fish residues (B). The TELIgene data were obtained from the bioassay test with exposures of 10 dilution factor.

The mean TELItotal of fish residue was 86.56, which was higher than that of sediments extraction. The F4 and F7 from Nanjing section showed the highest toxicity, and the mean TELItotal of Nanjing section were higher than the other sections. For genes, ftsK, fsr and cyoA were also the most sensitive genes, which were accordant with the results of sediments extractions. This indicated that the toxicity of sediments and fish residue to these stress responsive genes were through the similar mechanism. The TELItotal values were in the order of Ca2Cl extraction of fish residues > NH4Ac extraction of sediments > Ca2Cl extraction of sediments, which were accordant with heavy metals concentrations in the extraction solutions (Tables S6–S8). This seemed that heavy metals in the extractions possibly related with the TELIs. Canonical correspondence analysis proposed by TerBraak was firstly used to investigate community structure and its underlying environmental

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basis with the principle of dualism method (Ter Braak, 1986). The author successfully applied it to study the environmental influences to the distribution of polycyclic aromatic hydrocarbons (PAHs) in sediments (Fu et al., 2011b). Here, CCA were utilized to investigate impact of heavy metals on the profiles of TELIgene distribution. As shown in Fig. 4, ftsK, and cyoA were strongly affected by Cu, Cr and Ni. The impacts of heavy metals to fsr were more or less in the same intensity. Generally, heavy metals showed similar affecting direction. However, Hg in the extraction of fish residue showed the opposite direction, indicating that the impact of Hg on the TELIgene was different with other heavy metals.

3.4. Correlation between concentration, risk assessment and toxicity The risk assessments are calculated based on the total concentrations of heavy metals in environmental samples. Therefore, there are significant correlations between the concentrations and the risk indexes. The heavy metal concentrations in the extractions of environmental samples also showed significant correlations with the total metal concentrations in environmental samples. However, the toxicity data from bioassay did not showed good correlations with single metal concentration or risk index. This indicated that the toxicities of samples were complex and could be affected by the synthetic action of heavy metals. In addition, although the toxicity was not always in accordance with risk assessment due to the relatively complex compositions of extracts, the toxicity data could be an important supplement to help making the assessment.

4. Conclusions Combined with concentration, risk and toxicity assessment, it can be concluded: (1) The heavy metal pollution of sediments in the Yangtze River was worse than in the Taihu Lake, and the sediments from Changshu section had higher stress responsive gene toxicities; Highest mean concentrations of As, Cd, Cr, Hg, Pb, and Zn were found in sediments from Nanjing section. Higher toxicities to the selected genes were observed in sediment extractions from Changshu section. (2) People would experience health risks from the intake of heavy metals through fish consumption in the studied area. All the Qm-PEC of fishes in our observation were >0.5. The mean TELItotal of fish residue was 86.56, even higher than that of sediments extraction.

Acknowledgements This work was supported by China Postdoctoral Science Foundation Funds (No. 2012M510131), Jiangsu Planned Projects for Postdoctoral Research Funds (No. 1102002C), and Scientific Research Projects of Jiangsu Environmental Protection (No. 201135). Prof. Xiaowei Zhang was supported by Program for New Century Excellent Talents in University (Ministry of Education).

Appendix A. Supplementary material The Supporting Information includes detail information about the physicochemical parameters of sediments, risk assessment methods, supplementary information for toxicity bioassay method, and results and discussion for chemical analysis, risk assessment and toxicity bioassay. Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/ 10.1016/j.chemosphere.2013.06.061.

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