Spatial distribution, ecological risk assessment and source identification for heavy metals in surface sediments from Dongping Lake, Shandong, East China

Catena 125 (2015) 200–205

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Spatial distribution, ecological risk assessment and source identification for heavy metals in surface sediments from Dongping Lake, Shandong, East China Yunqian Wang a, Liyuan Yang a,⁎, Linghao Kong b, Enfeng Liu c, Longfeng Wang a, Jingru Zhu a a b c

Department of Resources and Environment, University of Jinan, Jinan 250022, China Department of Environment, Beijing Normal University, Beijing 100875, China Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China

a r t i c l e

i n f o

Article history: Received 12 May 2014 Received in revised form 24 September 2014 Accepted 23 October 2014 Available online 11 November 2014 Keywords: Dongping Lake Heavy metals Sediment Spatial distribution Potential ecological risk Source identification

a b s t r a c t Surface sediment samples collected from 18 sites in Dongping Lake were analyzed for selected heavy metals including As, Cd, Cr, Cu, Hg, Pb, and Zn to determine their spatial distribution, source, and potential ecological risks. The enrichment degree of the studied metals decreased in the order of Cd N Hg N As N Pb N Cu N Cr N Zn, and the average concentrations of Cd, Hg and As were 3.70, 3.69 and 3.37 times their background values. With the exception of Cd, the concentrations of heavy metals decreased progressively from the southeast to the north and west within the lake. Based on the enrichment factor (EF) and the potential ecological risk index (PERI), As, Cd and Hg were the heavy metal contaminants of most concern in surface sediments. Moreover, referencing to the results of multivariate statistical analyses, we deduced that anthropogenic As and Hg were mainly from industrial and mining sources within the Dawen River watershed, whereas, Cd originated from agricultural sources. © 2014 Elsevier B.V. All rights reserved.

1. Introduction Over the past two decades, considerable attention has been given to trace metals and metalloids in both terrestrial and aquatic ecosystems because they can be accumulated by biota and at high concentrations are potentially toxic (Horowitz and Elrick, 1988; Li et al., 2013; Yang et al., 2003, 2012). In aquatic systems, heavy metals have a high affinity for particulate matter and will therefore accumulate in surface sediments (Sundaray et al., 2011). Once deposited, however, chemical and biological processes may allow heavy metals to be desorbed from surface sediments upon which they are released into the water column (Li and Davis, 2008). Sediments, then, serve as both sinks and potential secondary sources of heavy metals, and the research on heavy metals in surface sediments provides significant insights into the metal pollution in aquatic systems. Dongping Lake is the main freshwater source for meeting the requirements of daily life and agricultural irrigation in Shandong Province. Villages and towns are the major inhabitation communities in the watershed of Dongping Lake. The area with agriculture land use is mainly distributed in the west and south plain in the watershed. There are some industries in the southeastern parts of catchment, including Dongping power plant, Zhoucheng mechanical manufacture factory, ⁎ Corresponding author. Tel.: +86 13953119697. E-mail address: [email protected] (L. Yang).

http://dx.doi.org/10.1016/j.catena.2014.10.023 0341-8162/© 2014 Elsevier B.V. All rights reserved.

Dongping printing house and various food manufacturing plants. With the rapid development of economy, Dongping Lake receives more and more agricultural runoff and industrial discharges, which cause many serious water environmental problems. In recent years, various published reports have focused attentions on the levels of nutrients in water bodies, the evaluation of water quality and study of aquatic organisms in Dongping Lake, which indicated that the lake has been polluted by anthropogenic activities and has affected the safety of drinking water, agricultural irrigation and aquaculture (Chen et al., 2011; He et al., 2010; Tian et al., 2013). Nevertheless, information concerning the pollution of heavy metals in the sediment and the potential ecological risk has been limited. The main goals of this study are (1) to describe the spatial distribution of heavy metals in surface sediments of Dongping Lake using contour maps produced by the ordinary kriging geostatistical method, (2) to assess the metal pollution degree and potential ecological risks by the methods of enrichment factor (EF) and potential ecological risk index (PERI), and, (3) to indentify the potential sources of anthropogenic heavy metals using multivariate statistical analysis. 2. Material and methods 2.1. Study area Dongping Lake (35°30′–36°20′N, 116°00′–116°30′E) lies in Dongping County, west of Shandong Province, China. As the second largest

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freshwater lake in Shandong Province, this lake covers 627 km2 and has a total storage volume of 4 × 109 m3. The annual mean depth of the lake is 2–4 m. Dongping Lake serves as an important flood control project in the lower reaches of Yellow River and is the last reservoir along the Eastern Route of China's South-to-North Water Diversion Project. Relying on its special geographical position and function, Dongping Lake acts as an important conservation area. Dawen River, the only major tributary to Dongping Lake (Fig. 1), flows for 209 km and through two cities. Previous comprehensive evaluations performed on the water quality of Dawen River revealed serious contamination, and mining, chemical, electric power, manufacturing and many other industries within the river basin have been proven to be the main responsible parties for the load of pollutants (Guo and Xu, 2007; Xu, 2003).

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with a HNO3–HF–HClO4 mixture, and then the sample solutions were filtered, adjusted to a suitable volume with double deionized water. The total concentrations of Cd, Cr, Cu, Pb and Zn, were analyzed using Inductively Coupled Plasma-Atomic Emission Spectrometry (ICP-AES, JY38S, Longumeau, France). Hg and As were analyzed by Atomic Fluorescence Spectrometry (AFS, AFS-230E, Beijing, China). The analytical data quality was guaranteed using quality assurance and quality control (QA/QC), including analysis of reagent blanks, duplicate samples and standard reference materials (GSS-1, GSS-8, GSS-10 and GSS-11) for each batch of samples. The analytical precision for replicate samples was within ±10% and the measurement errors between determined and certified values were less than 5%. 2.4. Geostatistical analysis methods

2.2. Sample collection In July 2012, eighteen surface sediment samples were collected in Dongping Lake (Fig. 1) at locations selected within the main body of the lake to provide good area coverage. All surface sediment samples were collected using a gravity corer; these were then packed in sealed plastic bags and transported to the laboratory. In the lab, air-drying reduced the water content in all the sediment samples; then the samples were sieved through a 10-mesh (2 mm) nylon sieve to remove gravel, organic debris and other dopants. Then, portions of all samples (about 50 g) were ground to pass through a 100-mesh nylon sieve and stored for further analyses. 2.3. Analytical methods Elemental analyses were performed in the Hubei Geological Research Laboratory, a subordinate research institute of the Ministry of Land and Resources, PR China. 0.25 g subsamples were digested

The ordinary kriging (OK) method was employed using ArcGis 9.3 to map the spatial distribution of heavy metals. OK analysis is based on the assumption that changes of data have normal or log-normal distributions and the expected values of regionalized variables are unknown. The interpolation process of OK is similar to the weighted moving average and the weights are derived from the kriging equations (Xie et al., 2011). The accuracy of OK is reliable when compared with other methods of spatial interpolation and can achieve good prediction effects (Bai et al., 2011; Wei et al., 2009). 2.5. Assessment methods The background values play an important role in evaluating polluted degree of heavy metals. Dongping Lake is located at the Yellow River flood-plain, and the original lake basin was filled by mud and sand from the Yellow River (Guo, 1990). For this reason, the mean concentrations of elements in sediments of the Yellow River (Zhao and Yan, 1994) are

Fig. 1. Location map of the study area and sampling sites.

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selected as the background values of heavy metals in surface sediments from Dongping Lake. 2.5.1. EF The EF was used to determine the enrichment degree of single metal contamination in surface sediment of Dongping Lake. To reduce the influence of the particle grain size effects on heavy metal contamination, concentration data should be normalized by a conservative element. Al is inert in migration process and mainly comes from natural lithogenic sources, so Al as a standardized element has already gained general recognition (Huang et al., 2014; Muñoz-Barbosa and Huerta-Diaz, 2013). The EF was calculated with respect to Al according to the following equation (Kartal et al., 2006): EF ¼

ðcx =cAl Þsediment ðcx =cAl Þbackground

ð1Þ

where (cx/cAl)sediment is the ratio of concentration of element (cx) to that of Al (cAl) in sediment samples and (cx/cAl)background is the ratio in reference background. The EF consists of five classes: EF b 2, minimal enrichment; EF = 2–5, moderate enrichment; EF = 5–20, significant enrichment; EF = 20–40, very high enrichment; and EF N 40, extreme enrichment. 2.5.2. PERI The EF can only reflect the influence of human activities on the enrichment of single heavy metal and do not consider the bioavailability or combined effects of heavy metals. Therefore, the PERI was applied to further assess the ecological risk posed by heavy metals in surface sediments. PERI, which was proposed by Hakanson (Hakanson, 1980), took into consideration the toxicology of heavy metals and illustrated the potential ecological risk caused by overall levels of contamination. The proposed Eq. (2) was used to calculate the risk index (RI) as follows: RI ¼

X

i

Er ¼

X

  i i i T r C s =C n

ð2Þ

where RI is the sum of individual potential ecological risk for all heavy metals, Eir is the PERI of an individual element, Tir is the toxic-response factor for a given heavy metal, Cis is the present concentrations of heavy metals in surface sediments, and Cin is the reference values of heavy metals. The toxic-response factors for As, Cd, Cu, Cr, Hg, Pb, and Zn are 10, 30, 5, 2, 40, 5, and 1, respectively. The background values (mean concentrations of elements in sediments of the Yellow River) of heavy metals were applied as reference values in this work. Table 1 provides the grading standards of potential ecological risk from heavy metals.

Lake. Correlation analysis is used to establish correlations among the various types of heavy metals. PCA is used to simplify the data and make it easier to identify the factors that explain most of the variance in the data (Li and Zhang, 2010). PCA has been proven to be an effective tool that can be used to identify potential sources of heavy metals and has been widely used in combination with correlation analysis (Micó et al., 2006; Wang et al., 2012a). 3. Results and discussion 3.1. Concentrations of heavy metals in surface sediments of Dongping Lake Table 2 summarized the basic statistics related to heavy metals in sediments of Dongping Lake as well as the background values and sediment quality guidelines (SQGs) used in this study. When comparing the average values of heavy metals with the background values, the results indicated that all heavy metals in surface sediments from Dongping Lake were higher than their background values; in particular, Cd, Hg, and As were 3.70, 3.69 and 3.37 times their background values. The enrichment degree of studied metals decreased in the order of Cd N Hg N As N Pb N Cu N Cr N Zn. Also, based on the coefficient of variance, the concentrations of heavy metals varied distinctly among sampling sites. This was particularly true for As and Cu, with the coefficients of variance of 22.9 and 26.5, respectively. SQGs include a threshold effect concentration (TEC) and a probable effect concentration (PEC); below the TEC, adverse biological effects are rarely expected to occur whereas the PEC is defined as the level above which adverse biological effects are expected to occur more often than not. When compared to the TEC–PEC SQGs, 89% (As), 44% (Pb), and 100% (Cu and Cr) for the samples from Dongping Lake fell into the range between TEC and PEC. Concentrations of As in 11% of the samples were higher than PEC. That is, adverse biological effects were probable due to the concentrations of these metals in the Dongping Lake sediments. Despite that the concentrations of Cd and Hg measured in this study were basically lower than TEC, they all far exceeded their background values. 3.2. Spatial distribution of heavy metals in surface sediments from Dongping Lake Fig. 2 showed the spatial distribution of Cd, As, Hg, Cu, Pb, Cr, and Zn in surface sediments from Dongping Lake as shown using contour maps. The concentration of Cd decreased from the margins of the lake toward the center of the lake (Fig. 2(a)). The zones with higher concentrations of Cd were confined to the south and northwest portions of the lake, where the concentrations of Cd were above 4.5 times higher than the background values.

2.6. Multivariate statistical methods Previous studies have proven that various natural and anthropogenic sources contribute to the concentrations of heavy metals found in sediments (Christophoridis et al., 2009; Varol and Şen, 2012). Multivariate analyses such as correlation analysis and principal component analysis (PCA) were performed, using SPSS 18.0, in this study to explore the sources of heavy metals in surface sediments found in Dongping Table 1 Indices and grades of potential ecological risk. Eir

Grades of ecological risk for a single metal

RI

Grades of potential ecological risk to the environment

Eir b 40 40 ≤ Eir b 80 80 ≤ Eir b 160 160 ≤ Eir b 320 Eir ≥ 320

Low risk Moderate risk Considerable risk High risk Very high risk

RI b 150 150 ≤ RI b 300 300 ≤ RI b 600 RI ≥ 600

Low risk Moderate risk Considerable risk High risk

Table 2 General characteristics of the heavy metal concentration in surface sediments from Dongping Lake and guidelines for maximum freshwater sediment concentrations (mg/kg).

Maximum Minimum Average CVa Backgroundb TECc PECd b TEC (%) ≥ TEC b PEC (%) ≥ PEC (%) a b c d

Cd

As

Hg

Cu

Pb

Cr

Zn

0.348 0.218 0.285 13.7 0.077 0.99 4.98 100 0 0

38.5 19.2 25.3 22.9 7.5 9.79 33 0 89 11

0.068 0.032 0.055 16.1 0.015 0.18 1.06 100 0 0

86.1 36.5 52.0 26.5 33.9 31.6 149 0 100 0

41.3 29.2 35.5 11.0 15 35.8 128 56 44 0

102.8 67.2 89.3 8.9 60 43.4 111 0 100 0

115.4 79.9 100.5 9.3 90 121 459 100 0 0

CV: coefficient of variance. Background: the mean concentrations of elements in sediments of the Yellow River. TEC: threshold effect concentration. PEC: probable effect concentration.

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order of the southeastern N the western ≈ the northern parts of Dongping Lake. The lowest concentrations for Cu, Zn and Cr appeared at S1 (Fig. 1). 3.3. Pollution assessment 3.3.1. EF The EF was calculated for each element relative to the background values after normalization by element Al (Table 3). The average EF values of Cd, Hg and As were 3.01, 2.98 and 2.71, respectively. They reached moderate enrichment and were considered to be contaminated. The average EF value of Pb was 1.90, showing that Pb was in minimal enrichment. The average EF values of Cu, Cr and Zn approximated 1, which indicated that the metals were primarily natural in origin. The degree of enrichment of heavy metals, in descending order, was: Cd N Hg N As N Pb N Cu N Cr N Zn. 3.3.2. Potential ecological risk assessment Potential ecological risks related to heavy metals in surface sediments from Dongping Lake were calculated based on Eq. (2). Table 4 presented both the PERI of individual elements and the comprehensive potential ecological index. The consequences of mean potential ecological risk degree was ranked as Hg N Cd N As N Pb N Cu N Cr N Zn. The sampling sites with the highest degree of potential risk caused by each heavy metal were S5 for As, S6 for Cu, Pb, and Zn, S8 for Hg and Cr, and S9 for Cd. Eir for Cu, Pb, Cr and Zn were less than 40, and they posed a low potential ecological risk. However, 22% of the sites suggested a moderate risk in view of As. Cd in all sampling sites posed a considerable risk and 33% of the sites showed a high risk for Hg. The PERIs for all sampling sites were described using a contour map (Fig. 3). When considering the entire lake, the metals may pose considerable risk in the southeastern portions of the Lake, and moderate in the other lake areas. The level of risk generally decreased from the southeast to the north and west progressively, which roughly matched the concentration tendencies for most heavy metals. The mean RI for the entire lake indicated the presence of considerable ecological risk. For the integrated metals, 39% of the sites were at moderate risk and 61% of those were at considerable ecological risk. 3.4. Identification of sources of heavy metals in surface sediments from Dongping Lake

Fig. 2. Spatial distributions of Cd (a), As (b), Hg (c), Cu (d), Pb (e), Cr (f) and Zn (g) in surface sediments of Dongping Lake.

The spatial distribution of Hg exhibited the following pattern in concentration: the southeastern N the western N the northern parts of Dongping Lake (Fig. 2(c)). High concentrations of Hg which were above 4.5 times higher than the background values were found in the southeast of the lake. The spatial distributions of As and Pb in surface sediments (Fig. 2(b), (e)) were similar to Hg, with the highest concentrations also appearing in the southeastern part of the lake. Cu, Cr and Zn had generally similar spatial distributions in the lake (Fig. 2(d), (f) and (g)). Their concentrations decreased in the

3.4.1. Correlation analysis Correlations between heavy metals may reflect the source(s) of pollution and the dispersal of sediment-associated heavy metals within the lake, the latter of which is related to the redistribution of the heavy metals within the sediment. Elements exhibiting high correlations may share common sources, analogous behaviors during transformation and migration under certain physiochemical circumstances (Wang et al., 2012b). To explore the correlations between heavy metals, Pearson's correlation analysis was employed (Table 5). Concentration of Cd was not significantly correlated with any of the studied heavy metals. However, a significantly positive correlation at P b 0.01 was found between several elemental pairs: Hg–As (0.647), Cu–As (0.66), Pb–As (0.759), Pb–Hg (0.689), Pb–Cu (0.858), Cr–Pb (0.648), Zn–Cu (0.779), Zn–Pb (0.809) and Zn–Cr (0.9). Three elemental pairs, Cu–Hg (0.562), Cr–Cu (0.57) and Zn–As (0.513), had a

Table 3 The EF value of each heavy metal in surface sediments from Dongping Lake. EF

Cd

As

Hg

Cu

Pb

Cr

Zn

Max Min Average

4.46 2.03 3.01

3.94 1.94 2.71

3.76 1.67 2.98

1.82 0.89 1.22

2.11 1.56 1.90

1.25 1.13 1.19

0.97 0.82 0.89

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Table 4 Results of potential ecological risk assessment for heavy metals in surface sediments from Dongping Lake. Sampling sites

S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 S12 S13 S14 S15 S16 S17 S18 Average Max Min

Er

Table 5 Pearson correlation coefficients for heavy metals in surface sediments from Dongping Lake.

RI

Cd

As

Hg

Cu

Pb

Cr

Zn

123.51 106.36 129.74 96.23 112.21 84.94 103.64 123.90 135.58 128.96 101.69 105.97 94.29 117.66 89.61 106.36 105.19 130.91 110.93 135.58 84.94

29.20 25.60 37.60 40.40 51.33 31.33 40.80 48.27 33.60 31.87 25.87 26.40 28.40 26.00 30.53 30.93 28.80 39.73 33.70 51.33 25.60

120.00 125.33 162.67 149.33 170.67 160.00 178.67 181.33 165.33 168.00 144.00 154.67 128.00 85.33 133.33 146.67 144.00 141.33 147.70 181.33 85.33

5.38 7.42 7.74 8.58 11.24 12.70 9.48 9.50 7.32 6.64 6.53 5.40 6.08 5.93 5.97 7.80 5.96 8.26 7.66 12.70 5.38

9.73 11.53 13.30 13.00 13.13 13.77 12.57 13.77 12.10 11.13 11.00 10.57 10.93 10.00 10.50 11.50 11.17 13.07 11.82 13.77 9.73

2.24 3.25 3.07 3.19 3.16 3.15 3.04 3.43 2.74 2.74 3.00 2.80 2.92 3.13 2.96 2.82 2.81 3.10 2.98 3.43 2.24

0.89 1.21 1.17 1.19 1.21 1.28 1.15 1.24 1.06 0.99 1.08 1.01 1.04 1.15 1.05 1.09 1.05 1.24 1.12 1.28 0.89

290.95 280.70 355.30 311.94 362.95 307.16 349.35 381.43 357.73 350.33 293.17 306.82 271.66 249.21 273.96 307.18 298.98 337.63 315.91 381.43 249.21

significantly positive correlation at P b 0.05. Further, Hg, As, Cu and Pb were positively correlated among each other and might have common sources. 3.4.2. PCA PCA was performed to further assist the identification and analysis of sources of heavy metals in surface sediments from Dongping Lake. The results of Kaiser–Meyer–Olkin (KMO = 0.701) and Bartlett's sphericity tests (P = 0) indicated that heavy metal concentrations in surface sediments from Dongping Lake were suitable for PCA. Table 6 showed the results of PCA by applying varimax rotation for heavy metals and Fig. 4 showed the variation diagram in rotated space. The results indicated that PCA reduced the number of variables to three principal components (PCs), which explain 91.63% of the data variance. The first PC, loaded heavily by Cr and Zn, and moderately by Cu, accounted for 40.10% of the total variance. Cu might imply quasi-independent behavior within the group (Cai et al., 2012). The second PC accounted for 36.05% of the total variance with high loading on As, Hg and Pb, and moderate loading on Cu. The third PC that correlated strongly with Cd (0.98) accounted for 15.48% of the total variance.

Cd As Hg Cu Pb Cr Zn

Cd

As

Hg

Cu

Pb

Cr

Zn

1 0.259 0.138 −0.170 0.076 −0.202 −0.126

1 0.647⁎⁎ 0.660⁎⁎ 0.759⁎⁎ 0.452 0.513⁎

1 0.562⁎ 0.689⁎⁎ 0.200 0.242

1 0.858⁎⁎ 0.570⁎ 0.779⁎⁎

1 0.648⁎⁎ 0.809⁎⁎

1 0.900⁎⁎

1

⁎ Correlation is significant at P b 0.05 (two-tailed). ⁎⁎ Correlation is significant at P b 0.01 (two-tailed).

3.4.3. Sources identification Three main sources could be identified according to correlation analysis, PCA and the EF values of metals, i.e. (1) Cr, Zn and, to a lesser extent Cu, mostly originated from natural sources; (2) As, Hg, Pb and, to a lesser extent Cu, represented industrial and mining sources; (3) Cd mainly originated from agriculture sources. One group of elements comprised Cr, Zn and, to a lesser extent Cu. The concentrations of Zn, Cr and Cu were slightly higher than the background values, and the mean EFs were 0.89, 1.12 and 1.22, respectively. An EF which approximates 1 would indicate that the concentration is identical to that in unpolluted samples (Kartal et al., 2006). In addition, the results of potential ecological risk assessment indicated that the concentrations of Cr, Zn and Cu presented a low potential ecological risk. These findings suggested that this group of elements might originate from natural sources. A second group including As, Hg, Pb and, to a lesser extent Cu, could be identified as an “anthropogenic factor”. The concentrations of As, Hg and Pb were much higher than their background values, and the mean EF values were 2.71, 2.98 and 1.90, respectively. The elements of As, Cu, Hg and Pb had similar spatial distribution trends with the highest concentration appearing in the southeastern part of Dongping Lake, near the inlets of the Dawen River (Fig. 2). It was reported that 80% of the Dawen River was heavily polluted and 400 million tons of wastewater flowed into Dongping Lake from the Dawen River in 2000 (Chen et al., 2007). The various industries and mining activities in the Dawen River basin may make great contributions to the concentrations of As, Cu, Hg and Pb (Guo and Xu, 2007; Xu, 2003). Heavy metals delivered by the Dawen River were first transferred into sediments through sorption processes and were delivered to the southeastern part of Dongping Lake. The sediment-associated heavy metals were then spread slowly in a north-northwesterly direction through the lake. These behaviors of heavy metals could explain the spatial distribution patterns of As, Cu, Hg and Pb, in that the concentrations of these metals decreased from the southeast to the north and west progressively. In consequence, As, Cu, Hg and Pb might mainly come from industry and mining activities.

Table 6 Rotated component matrix for principal component analysis loadings for heavy metals in surface sediments from Dongping Lake. Elements

Fig. 3. Spatial distribution of risk index (RI) of heavy metals in surface sediments from Dongping Lake.

Cd As Hg Pb Cu Cr Zn Eigenvalue % of variance % of cumulative

Component PC1

PC2

PC3

−0.116 0.384 0.021 0.655 0.651 0.932 0.962 2.807 40.10 40.10

0.098 0.766 0.951 0.714 0.667 0.114 0.234 2.523 36.05 76.15

0.980 0.297 0.024 0.077 −0.124 −0.105 −0.048 1.084 15.48 91.63

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References

Fig. 4. Principal component analysis loading plot of heavy metals.

The spatial distribution of Cd was different from that of the other heavy metals, with higher concentrations appearing in the southern and the northwestern parts of the lake. Cd is closely related to the intensive use of pesticides and chemical fertilizers, and is usually considered as a marker element of agricultural activities (Rodríguez Martín et al., 2006). In the Dongping Lake catchment, areas with agriculture land use were intensively distributed along the northwest and south bank of the Lake. Further, according to the official data, the amount of chemical fertilizers used merely in Dongping County was above 125 thousand tons every year. Consequently, Cd may mainly originate from agriculture sources. 4. Conclusions This study analyzed the spatial distribution, ecological risks, and sources of heavy metals including As, Cd, Cr, Cu, Hg, Pb and Zn in surface sediments from Dongping Lake. The average concentrations of Cd, Hg, and As were 3.70, 3.69 and 3.37 times their background values, respectively. The mean concentrations of Cr and Zn were similar to the background values. As, Cr, Cu, Hg, Pb, and Zn exhibited similar spatial distributions in concentrations decreasing from the southeast to the north and west of the lake progressively. In contrast, Cd concentrations decreased from the northwestern and the southern portions to the center of the lake. Based on the results of the EF and PERI, As, Cd, and Hg were identified as the major heavy metal pollutants in surface sediments. Pollution of As and Hg were mostly originated from industrial and mining sources, and mainly introduced into the lake via Dawen River, whereas, pollution of Cd originated predominately from agriculture activities. The metals in the surface sediment may pose moderate to considerable ecological risk. Important strategies should be implemented to reduce discharge of industrial wastewater within the Dawen River watershed. Also, controlling agricultural non-point source pollution is required to decrease ecological risks from Cd pollution. Acknowledgments This research was financially supported by the Natural Science Foundation of Shandong Province (no. ZR2012DL09) and the National Natural Science Foundation of China (no. 41271214, 40672076).

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