Environ Monit Assess (2014) 186:1219–1234 DOI 10.1007/s10661-013-3451-5
Spatial distribution and risk assessment of heavy metals in sediments from a hypertrophic plateau lake Dianchi, China Zhang Yuan & Shi Taoran & Zhang Yan & Yu Tao
Received: 30 May 2013 / Accepted: 14 September 2013 / Published online: 28 September 2013 # Springer Science+Business Media Dordrecht 2013
Abstract The sediment in Dianchi Lake, a hypereutrophic plateau lake in southwest China, was investigated and the concentration of heavy metals (Cu, Cr, Ni, Zn, Pb, Fe, Mn, and Cd) in the sediment and sediment properties were determined. Their spatial distribution and sources were analyzed using multivariate statistics. The result indicated that the studied metals exhibited three distinct spatial patterns; that is, Cu, Pb, Zn, and Ni had a similar distribution, with a concentration gradient from the north to the south part of the lake; Cd and Cr presented a similar distribution; Fe and Mn presented a quite different distribution than other metals, which indicated their different sources and geochemistry processes. Correlation and cluster analysis (CA) provided origin information on these metals and the CA result was observed corresponding to those three spatial patterns. Principal component analysis further displayed metal source and driving factors; that is, Cu, Pb, Zn, Ni, Cd, and Cr were mainly derived from anthropogenic sources, and Fe and Mn were mainly the result of natural processes. Sediment assessment was conducted using geoaccumulation index (Igeo), potential ecological risk indices, and USEPA guidelines. The result indicated that, generally, Cd was the most serious risk metal; Pb and Cu posed moderate potential ecological risk; Cr, Zn, and Ni had slight ecological risk; Fe and Mn had little risk. Comparison of the assessment tools showed that each of the methods had its Z. Yuan : S. Taoran : Z. Yan : Y. Tao (*) State Key Laboratory of Environment Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China e-mail:
[email protected]
limitation and could bias the result, and the combined use of the methodologies and local knowledge on lithology or metal background value of soil in the practice would give a more comprehensive understanding of the metal risk or pollution. Statistical analysis also indicated that nutrients had different impacts on Fe, Mn, and trace elements, which implied that in the assessment of metal risk, nutrients impact should be taken into consideration especially for eutrophic waters. Keywords Spatial distribution . Heavy metals . Risk assessment . Eutrophication . Sediments . Dianchi Lake
Introduction Contamination with heavy metals has become a great concern around the world, especially in developing countries including China (Memet 2011; Zheng et al. 2008). In aquatic environments, heavy metals in contaminated habitats may accumulate in organisms, which may enter into the food chain and finally result in human health problems (Krzysztof and Danuta 2003). Previous studies have shown that human exposure to a high concentration of heavy metals could lead to their accumulation in the human body and cause hazard to the internal organs (Waisberg et al. 2003; Bocca et al. 2004). However, the accumulation of heavy metals will depend on their species and existing media, such as water or sediments (Gaur et al. 2005). Sediments are ecologically important for aquatic habitats and also as a reservoir of heavy metals, which can significantly influence the behavior and bioavailability
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of metals (McCready et al. 2006). In addition, sediments not only act as a carrier of heavy metals but also a potential secondary source of heavy metals in aquatic systems; that is, sediments could be either a source or sink for metals in water environments (Fatoki and Mathabatha 2001). Therefore, sediments are usually used as an indicator to reflect the environment quality of aquatic systems (Unlu et al. 2008). Due to the complexity of the chemical behavior of metals in the sediment, there has been no widely accepted sediment quality standard so far (Andrea et al. 2008). Some methodologies to assess heavy metal pollution in sediments have been proposed, such as the geoaccumulation index, potential ecological risk index, pollution load index, sediment enrichment factor, ratio of secondary phase to primary phase, and equilibrium partitioning approach (Seralathan et al. 2008; Kwon and Lee 2001; Hakanson 1980). Of these methods, the geoaccumulation index, potential ecological risk index, and enrichment factor are more widely used in the risk or pollution assessment than others. These three methods have something in common as they all use heavy metal concentration in the relatively uncontaminated sediment as background value, through comparing the heavy metal concentration in studied samples with the background value to evaluate the pollution degree of the heavy metal in the sediment (Burgess and Pellertier 2002). However, it is difficult to accurately explain the heavy metal pollution in sediments with a single method because each method has its limitation. Therefore, it requires multiple evaluation methods as well as the particularity of local knowledge such as lithology or soil property to be integrated to obtain a more reasonable assessment. In this work, we use geoaccumulation index, potential ecological risk index, and the Sediment Quality Guidelines (SQGs) to evaluate sediment quality and potential risk of the sediment from the hypertrophic Dianchi Lake, and evaluate the possible impact of high nutrients on the assessment result. The Dianchi Lake is located in the subtropical monsoon zone, with an average temperature of 15 °C. It is the largest freshwater lake in the Yungui Plateau, southwest of China, and the sixth largest one in China, with an altitude of 1,886 m, water surface area of 298 km2, and an average depth of 4.7 m. It is a highly eutrophic lake located in Kunming, the capital city of Yunnan Province. This lake functions as an important water source for the 6.8 million people in Kunming city, and supplies water for the growing industrial and agricultural needs around
Environ Monit Assess (2014) 186:1219–1234
the city (Wan et al. 2011). This area is a remote and economically underdeveloped region compared to eastern China, and used to be regarded as a “pristine” area. However, with rapid population and economy growth, the Dianchi Lake has been faced with increasing heavy metal pollution as well as eutrophication since the 1970s, and this situation became more serious after 1990 (Liu et al. 2012). Currently, the serious environmental status has caused wide concerns from the public and government of different levels, and a great amount of fund has been invested in the research and recovery of the lake. The objectives of this study are: (1) to investigate the spatial distribution of the heavy metals (Cd, Cr, Cu, Pb, Zn, Mn, Fe, and Ni) and give a comprehensive picture of current situation of heavy metal pollution in the lake sediment; (2) to explore the sources of metal pollution using multiple statistical methods; and (3) to assess the potential risk posed by sediment metals and evaluate or compare the applicability of the assessing methods which could be influenced by high nutrients in this lakes. This work is expected to contribute to metal risk assessment with the nutrients impact and to provide a scientific basis for the lake management and restoration.
Materials and methods Sample collection The Dianchi Lake covers 298 km2 of water area and involves two major parts, Caohai and Waihai (Fig. 1). Caohai is adjacent to Kunming city, having only 3 % of the total lake area, and receiving a large amount of wastewater, while Waihai accounts for 97 % of the lake area. Surface sediments (0–10 cm) were collected at 10 sites with the grab sampler in Dianchi Lake in November 2010. As the refractory pollutants in sediments, such as metals, have little seasonal variation (Surindra et al. 2009), no seasonal sampling was conducted. However, the samples collected at each site were mixed by three subsamples in the vicinity of 10 m. This process minimized the sampling error. The sampling sites were distributed equally throughout the whole lake with GPS used for location. Figure 1 shows the locations of the sampling sites. Two sites were located in Caohai and eight in Waihai. At each site, three replicates were sampled and preserved in polythene bags and transported to the laboratory at 0– 4 °C for further analysis.
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Fig. 1 Dianchi location in China and sampling sites in Dianchi lake
Sample processing and measurement The sediment samples were firstly freeze-dried using vacuum freeze drier; then, the dried samples were ground and sieved through a 0.149-mm nylon sieve for measurement of different parameters. For total metal analysis, 0.2 g sediment were weighed and transferred in a 50 ml Teflon vessel, which was placed on a heating plate with 10 ml 37 % HCl. Then, a total of 15ml 68 % HNO3, 10 ml concentrated HF and 4 ml concentrated HClO4 were added in the vessel for digestion until the supernatant became clear, and brownish-colored fume was no longer generated. When the solution became nearly dry, 1 % HNO3 was added and the solution was filtered through a 0.45 μm filter membrane into 50-ml polyethylene bottle, and then this final solution was diluted to 50 ml with 1 % HNO 3 for instrumental analysis. This digested solution was analyzed for Cu, Cr, Fe, Mn, Ni, and Zn using a flame atomic absorption spectrometry (Perkin Elmer 603); Cd and Pb were measured
using a graphite furnace atomic absorption spectrometry (Perkin Elmer Z/3030). The analytical result for metals is shown in Table 1. For the measurement of pH, electricity conductivity (EC), total nitrogen (TN), total phosphorus (TP), total organic carbon (TOC), and organic matter (OM), the extracted solution was first prepared by adding Milli-Q water to the sediment sample (water/sediment=5:1). The mixture was then mechanically shaken at 250 r/min for 24 h. After shaking, the suspension was centrifuged at 8,000 r/min for 5 min, and then the overlaying solution was filtered through a 0.45 μm Millipore membrane for the analysis of physiochemical properties mentioned above. The pH sediment was measured using a digital pH meter (model pH-538, WTW, Germany). The EC was analyzed using Systronic Conductivity Meter (model 306). The TN determination was conducted with alkaline potassium persulfate digestion and UV spectrophotometric measurement following the National Standard Method (GB 11894-89). Determination of TP was conducted with ammonium molybdate
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Environ Monit Assess (2014) 186:1219–1234
Table 1 Concentration of metals in the sediment (mean ± standard deviation, in mg/kg, n=3) Sites
Cu
Cr
Ni
Zn
Pb
Fe (g/kg)
Mn
Cd
C1
345.54±23.32
122.63±35.78
174.29±31.33
548.04±102.30
347.03±62.02
20.30±1.34
1.47±0.40
1.85±0.12
C2
431.54±35.11
135.65±22.50
199.86±13.29
569.89±22.51
376.71±79.59
18.72±0.38
1.37±0.02
3.14±0.15
W1
89.44±4.06
115.54±16.53
128.76±9.07
220.84±76.39
61.26±11.72
20.76±7.12
1.70±0.25
1.13±0.35
W2
84.53±0.21
119.42±10.98
106.12±5.27
171.73±42.52
67.47±19.75
24.35±0.59
2.08±0.33
2.70±1.43
W3
82.44±12.82
87.88±15.64
111.17±17.61
100.06±6.24
54.27±5.35
24.43±0.30
2.01±0.08
0.50±0.20
W4
46.53±4.43
68.20±17.44
76.90±9.58
103.57±25.32
106.96±6.97
23.30±0.34
1.99±0.28
0.45±0.08
W5
71.67±5.75
82.95±12.05
94.03±7.45
111.53±18.96
60.45±35.16
23.20±0.78
1.76±0.37
0.64±0.17
W6
60.75±7.29
69.23±7.58
102.11±4.85
54.70±11.65
96.85±22.26
22.94±0.32
1.56±0.02
0.16±0.06
W7
45.16±9.90
64.06±4.75
102.32±16.30
75.36±34.54
57.33±33.38
21.15±1.13
1.38±0.11
0.82±0.14
W8
45.69±3.80
70.79±7.79
109.10±22.75
193.59±30.77
48.05±27.73
21.49±0.75
1.34±0.12
0.48±0.16
122.39±134.07
88.04±31.6
117.59±37.23
201.37±184.65
126.71±125.74
21.48±2.64
1.63±0.30
1.20±0.96
Average
spectrophotometric method following the National Standard Method (GB 11893-89). TOC test was done with TOC analyzer (Shimadzu-VCPH). The OM was determined with potassium dichromate oxidation method with the National Standard Method (GB 8834-1988). Quality assurance Analytical pure chemicals and deionized water were used in the sample treatment and analysis. All used glassware was washed with dilute nitric acid followed by rinsing with deionized water for three times. Three replicate digestions were made for each sample, and reagent blanks and the National sediment reference material (GBW07318) were synchronously analyzed with the samples. The relative error of three replicates of each group for all elements was less than 10 %, and the recovery of the reference material for all metals was between 85 and 110 %.
Geoaccumulation index The geoaccumulation index (Igeo) is defined as the following expression: Cn Igeo ¼ log2 1:5Bn
Potential ecological risk index The potential ecological risk index (RI) was introduced to assess the degree of heavy metal pollution in sediments, according to the toxicity of heavy metals and the organism response to the environment (Hakanson 1980). It is calculated as follows:
Assessment of sediment quality or risk
study, we used the metal concentrations in local soils as the background value as soils are as a result of various geochemistry processes in the area. The Bn value of Cd, Cr, Zn, Cu, Pb, Fe, Mn, and Ni is 0.218, 65.2, 89.7, 46.3, 40.6, 52,200, 626, and 42.5 mg/kg, respectively (China National Environmental Monitoring Centre 1990). Factor 1.5 is the background matrix correction factor due to lithospheric effects (Reddy et al. 2004). The geoaccumulation index consists of seven classes: class 0 (practically unpolluted), Igeo≤0; class 1 (unpolluted to moderately polluted), 0W3>W5>W4. Regarding the entire lake average, the overall pollution status in the sediment of the entire lake followed the order of Cd>Pb>Cu>Zn>Ni>Cr>Mn>Fe, and Cd and Pb were “moderately polluted”; Zn and Ni were “unpolluted to moderately polluted”; Mn and Fe were “practically unpolluted”. However, trace metals of Cu, Zn, Pb, and Cd were close to “heavily polluted” level in Caohai. The result of this methodology indicates that Pb and Cd were the most polluted metals while Fe and Mn posed no pollution. However, this result may be biased by the method itself and Fe is a typical example of this in our study as discussed below. Potential ecological RI
Extraction method—principal component analysis
influence of the nutrient (phosphorus) and its interaction with metals. PC 3 explained 8.6 % of the total variance and was significantly related to TN levels (Fig. 4(b1)). It may reflect anthropogenic sources mainly derived from agricultural activities such as intensive fertilizer application (Liu et al. 2012). The score plot (Fig. 4(a2, b2)) clearly classified the sampling sites into two groups: Caohai (C1 and C2) and Waihai (W1–W8), suggesting that their environments were significantly different as described above. However, in both Fig. 4(a2), (b2), C1 and C2 were vicinal along both principal components, while W1–W8 were vicinal along PC1 but scattered along PC 2 and PC 3. This indicates that sites in Waihai had a similarity in metal pollution whereas the impact from nutrients (PC2 for P and PC3 for N) varied greatly across Waihai. Potential risk and pollution assessment Geoaccumulation index The Igeo of individual metals at each site, as well as for the entire lake average, was calculated and listed in Table 8. The Igeo value of Caohai (C1 and C2) was significantly higher than that in Waihai (W1–W8) for all metals (pPb>Cu>Ni>Mn>Cr>Zn. For the individual elements, Cd, Pb, and Cu posed moderate potential ecological risk; Ni, Mn, Cr, and Zn posed slight potential ecological risk on the basis of the entire lake average. Spatially, the pollution at the ten sampling sites decreased in the following sequence: C2>C1>W2>W1>W6>W7>W5>W3>W4>W8. C2 had the highest ecological risk level (300≤RI≤600), C1 and W2 had a moderate ecological risk level (150≤RI≤300), and the other sites had a low potential ecological risk level (RI
