Distribution and pollution, toxicity and risk assessment of heavy metals ...

3 downloads 0 Views 448KB Size Report
Oct 18, 2013 - Distribution and pollution, toxicity and risk assessment of heavy metals in sediments from urban and rural rivers of the Pearl. River delta in ...
Ecotoxicology (2013) 22:1564–1575 DOI 10.1007/s10646-013-1142-1

Distribution and pollution, toxicity and risk assessment of heavy metals in sediments from urban and rural rivers of the Pearl River delta in southern China Rong Xiao • Junhong Bai • Laibin Huang • Honggang Zhang • Baoshan Cui • Xinhui Liu

Accepted: 7 October 2013 / Published online: 18 October 2013 Ó Springer Science+Business Media New York 2013

Abstract Sediments were collected from the upper, middle and lower reaches of both urban and rural rivers in a typical urbanization zone of the Pearl River delta. Six heavy metals (Cd, Cr, Cu, Ni, Pb and Zn) were analyzed in all sediment samples, and their spatial distribution, pollution levels, toxicity and ecological risk levels were evaluated to compare the characteristics of heavy metal pollution between the two rivers. Our results indicated that the total contents of the six metals in all samples exceeded the soil background value in Guangdong province. Based on the soil quality thresholds of the China SEPA, Cd levels at all sites exceeded class III criteria, and other metals exhibited pollution levels exceeding class II or III criteria at both river sites. According to the sediment quality guidelines of the US EPA, all samples were moderately to heavily polluted by Cr, Cu, Ni, Pb and Zn. Compared to rural river sites, urban river sites exhibited heavier pollution. Almost all sediment samples from both rivers exhibited moderate to serious toxicity to the environment, with higher contributions from Cr and Ni. A ‘‘hot area’’ of heavy metal pollution being observed in the upper and middle reaches of the urban river area, whereas a ‘‘hot spot’’ was identified at a specific site in the middle reach of the rural river. Contrary metal distribution patterns were also observed along typical sediment profiles from urban and rural rivers.

R. Xiao  J. Bai (&)  L. Huang  H. Zhang  B. Cui  X. Liu State Key Laboratory of Water Environment Stimulation, School of Environment, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, People’s Republic of China e-mail: [email protected] R. Xiao College of Nature Conservation, Beijing Forestry University, Beijing 100083, People’s Republic of China

123

However, the potential ecological risk indices of rural river sediments in this study were equal to those of urban river sediments, implying that the ecological health issues of the rivers in the undeveloped rural area should also be addressed. Sediment organic matter and grain size might be important factors influencing the distribution profiles of these heavy metals. Keywords Heavy metals  River sediment  Pollution index  Top enrichment factor  Toxic unit  Ecological risk index

Introduction As a pioneer of industrialization and urbanization of the Pearl River delta, the Panyu district has attracted increasing levels of attention due to its rapid economic development and growing environmental pollution problems (Yu et al. 2011). Because coastal regions are among the most rapidly urbanized places worldwide, case studies are an effective means of raising widespread international interest in local sustainable development and pollution control issues (Zhang and Xue 2013). Therefore, an improved understanding of pollution patterns in specific sites with rapid urbanization can provide valuable feedback for local responses, such as regional ecological functional zoning, and pollution and waste management in the future. Heavy metals have been widely used as environmental monitoring factors, and their toxicity in humans, animals and plants is receiving increased attention (Grossman and Krueger 1995). Heavy metals, which may result from chemical leaching of bedrock, water drainage and runoff from banks and the discharge of urban industrial and rural agricultural wastewaters are widely present in rivers and

Distribution and pollution, toxicity and risk assessment of heavy metals

serve as important indicators of water environmental quality (Gupta et al. 1996; Bai et al. 2012). Sediments have a high capacity for accumulating extremely low and undetectable concentrations of heavy metals from overlying waters; therefore, the enrichment rate of heavy metals in river sediments is often a preferred indicator of the river’s contamination status (Soares et al. 1999). Moreover, several researchers have demonstrated that heavy metals could be released to water bodies from sediments in dry seasons, which would increase the potential ecological risk and toxicity to aquatic beings (Kumar et al. 2013). Therefore, it is necessary to investigate the potential risks of heavy metals in sediments in dry seasons compared to wet seasons. In recent decades, different metal assessment indices for sediment quality have been developed. These indices can be classified into four types: background enrichment indices, pollution indices, toxicity indices and ecological risk indices (Caeiro et al. 2005). The top enrichment factor (TEF) relative to the less-disturbed deeper sediment can be used to represent the relative accumulation level in a given soil profile, which can largely eliminate the impact of soil texture on assessment results (Ha˚kanson and Janson 1983). The pollution index (Pi) compares measured metal concentrations with different baselines, which is a quick and practical method for assessing heavy metal enrichment (Yang et al. 2009). The toxic unit (TU), which is defined as the ratio of the measured concentration to the probable effect level (PEL), is usually employed to evaluate the toxicities of various metals because heavy metals might pose adverse effects to environment if their concentrations exceed the PEL thresholds (Pedersen et al. 1998; MacDonald et al. 2000). The potential ecological risk index, which introduces a toxic-response factor for a given substance, provides a simple and quantitative value for the combined contamination risk to an ecological system (Ha˚kanson 1980). Additionally, sediment quality guidelines (SQGs) developed by the US EPA and soil quality thresholds (SQTs) from the China SEPA are usually adopted to evaluate the pollution levels or potential ecological risks of heavy metals in sediments (Bai et al. 2011; Ma et al. 2013). As continuum ecosystems, rivers clearly reflect the current status of environmental pollution from their upper to lower reaches in highly industrialized regions (Forstner and Muller 1973; Xu et al. 2011). Heavy metal concentrations in the upper reaches of rivers were usually lower than those downstream due to accumulation effects in the lower reach (Roline 1988; Bai et al. 2009). However, these concentrations are not always uniform throughout the watershed and may vary from site to site because of the different sources of anthropogenic inputs (Zheng et al. 2008; Liu et al. 2009; Lai et al. 2010). River sediment

1565

becomes the primary sink for storing these heavy metals from external sources; hence, sediment properties have significant effects on heavy metal distribution. Weng et al. (2001) and Frouz et al. (2007) stated that organic matter in soil/sediment (SOM) was the most important sorbent that controls the activities of heavy metals. Zhao and Li (2013) reported that grain size was the key factor influencing the contamination levels of heavy metals. And Zhao et al. (2013) found salinity increased the mobility of Cd, Cu, Mn and Pb in the sediments of Yangtze Estuary. Therefore, organic matter, grain size and salinity in sediment should be considered to more fully analyze the spatial distributions and risk levels of heavy metals in river sediments along an urban–rural river gradient. This study concentrates on the sediments from two tributaries of the Pearl River watershed flowing through the Panyu district. One tributary (named the Shiqiao River, flowing through the most developed urban area) has been polluted by domestic sewage, industrial effluent and waste leachate from primary electroplating, dyeing and leather production since the 1980s. The other tributary (named the Shawan River, flowing through a less-developed rural area) is contaminated by agricultural effluent and wastes from such locations as food plants, grease and meat plants, chemical plants, heavy machinery manufacturers, hardware plants and shipyards. The watershed of the Shiqiao River, which has a total river length of 29 km, supports the largest number of inhabitants (approximately 1.2 million) in the south corner of the Pearl River Estuary, receiving over 100 million tons of urban wastewaters and effluents from both banks (Yu et al. 2009). However, the Shawan River has been used as a primary source of drinking water in the Panyu district for a long time due to its cleaner water quality (Chen et al. 2009). To survey the sediment quality of rivers under the intense impacts of human activities, most previous researchers have investigated the metallic contents in the sediments from different rivers, such as inland/freshwater rivers (Zhou and Xia 2010), estuary/saltwater rivers (Bai et al. 2011) and urban/country rivers (Liu et al. 2006). However, scarce information is available concerning the different characteristics of heavy metal contamination in urban and rural river sediments with different local development levels within the same estuarine region. The primary objectives of this study are as follows: (1) to investigate the spatial distribution of heavy metals in sediments from the upper, middle and lower reaches of both urban and rural rivers; (2) to compare the profile distribution pattern of metals between urban and rural sediment profiles with consideration of SOM, grain size and salinity; and (3) to assess the enrichment, pollution, toxicity and risk levels of heavy metals in both urban and rural river sediments along an upper-middle-lower reach gradient.

123

1566

Materials and Methods Study area The city of Panyu is located in the center of the Pearl River Delta, south of Guangdong Province. Approximately 30 % of Panyu’s farmland has been converted to urban land during the period from 1979 to 2006 due to rapid economic growth and urbanization in this region (Yu and Ng 2006). Meanwhile, the increasing development of the dyeing and electroplating industries, combined with the heavy applications of agrochemicals, led to the pollution of 60 % of the rivers in this region (Sun et al. 2009). Although environmental protection policies and actions have been enforced to reduce water pollution since the 1990s, these rivers still present high pollution levels in both their water columns and sediments (Liu 2008). Sample collection and analysis Sediment samples from 0 to 60 cm depth were collected at low tide at each sampling site and sectioned into six transects at 10 cm intervals in March 2009 (during dry season). In total, 16 sampling sites were strategically distributed along the two rivers, including 9 sites (U1–U9) in the Shiqiao River and 7 sites (R1–R7) in the Shawan River, with an average distance of 3 km between neighboring sites. Sites U1–U2 and R1–R2 are located in the upper reaches, U3–U6 and R3–R5 are located in the middle reaches, and U7–U9 and R6–R7 are located in the lower reaches of the two rivers. In total, 96 sediment samples were collected in the above sampling sites, using plastic sampling utensils and latex gloves to avoid sample contamination with metals. All sediment samples were placed in polyethylene bags, and then brought to the laboratory. Some samples were used for determining sediment particle size; all other samples were air-dried at room temperature for three weeks and sieved through a 2 mm nylon sieve to remove coarse debris and stones. The subsamples were then ground with a mortar and pestle until all particles passed a 0.149 mm nylon sieve, for the determination of sediment chemical properties. For analysis of the total contents of Al, Cd, Cr, Cu, Ni, Pb and Zn, sediment samples were digested by a HClO4– HNO3–HF mixture in Teflon tubes. The digested sample solutions were analyzed by inductively coupled plasma atomic emission spectrometry (ICP/AES). Air-dried sediment (0.1 g) was transferred to an extraction vessel with 3 ml HNO3, 1 ml HClO4 and 1 ml HF and digested at a temperature of 160 °C for 6 h. The residue was then dissolved in 1 ml of 4 M HCl, diluted to 10 ml with deionized water and analyzed for the appointed trace metals (Thompson and Walsh 1989; Li et al. 1995). Quality

123

R. Xiao et al.

assurance and quality control were assessed for each batch of samples using duplicates, method blanks and standard reference materials (GBW07401) from the Chinese Academy of Measurement Sciences (1 blank and 1 standard for every 10 samples). Recovery rates of 95–105 % for samples spiked with standards certified the results as satisfactory. Sediment organic matter (SOM) was measured using dichromate oxidation (Nelson and Sommers 1982). Grain size analysis was conducted on a Laser Particle Size Analyzer (Microtrac S3500, America). Salinity was determined in the supernatant of 1:5 sediment-water mixtures using a salinity meter (VWR Scientific, West Chester, Pennsylvania, USA).

Results and Discussion Heavy metals distribution in sediments of both urban and rural rivers Horizontal distribution of heavy metals along both rivers Table 1 shows the mean contents of the total Cd, Cr, Cu, Ni, Pb and Zn in surface sediments at three sections (upper reach, middle reach, and lower reach) along both rivers. Generally, Zn showed the highest mean level, followed by Cr, Pb, Cu, Ni and Cd. In the urban river, Cd content was much higher in sediments of the upper reach, while Cu and Zn showed higher contents in sediments of the middle reach (P \ 0.05). This suggests that the Cd primarily originated from the upper reach of the river, whereas the majority of the Cu and Zn derived from the middle reach. The accumulation of Cd in the upper reach of the urban river is likely related to the wide distribution of small electroplating and leather factories in this region (Wei et al. 2002). Meanwhile, the high Cu and Zn deposition in the middle reach may be associated with surrounding industry such as color printing, machinery and equipment manufacture and repair firms (Perkins et al. 1993). Furthermore, the intensive traffic downtown could also increase the metal content related to atmospheric deposition in sediment, with decreasing distance from the roadway (Modlingerova et al. 2012; Zhao and Li 2013). However, no significant differences in the mean contents of Cr, Ni and Pb were observed among the three sections (P [ 0.05). For the rural river, Cd, Cu, and Pb showed significant enrichment in the middle reach (P \ 0.05), while Cr, Ni and Zn did not show significant differences among the three sections, despite slightly higher Cr and Zn contents in the middle reach than in the upper and lower reaches (P [ 0.05). This finding implies that the great amount of sewage and leachate drained off from households, livestock, foodstuffs factories and plastic/electronic/hardware

Distribution and pollution, toxicity and risk assessment of heavy metals

1567

Table 1 Concentrations of six tested heavy metals in sediments from the upper, middle, and lower reach of Shiqiao (urban) River and Shawan (rural) River at Panyu city (mg kg-1) River section

Cd

Cr

Cu

Ni

Pb

Zn

Urban river Upper Middle

4.36 ± 0.57*a

120 ± 5a

ab

147 ± 33

b

a

2.91 ± 1.16

81 ± 0.5*a a

b

133 ± 56* 69 ± 3.2

c

52 ± 3a 80 ± 33 56 ± 3

112 ± 9a a

a

102 ± 39

318 ± 0.4ab a

381 ± 61*a

a

76. ± 5

262 ± 65b

Lower

1.57 ± 0.14*

124 ± 7

Average

2.79 ± 1.31

133 ± 26*

100 ± 47*

66 ± 25

96 ± 30

327 ± 76*

Upper Middle

2.46 ± 1.33*1 3.39 ± 0.822

103 ± 101 117 ± 141

63 ± 18*1 80 ± 17*2

46 ± 21 55 ± 61

89 ± 321 96 ± 131

224 ± 931 267 ± 52*1

Lower

2.92 ± 0.09*12

105 ± 331

80 ± 312

56 ± 121

68 ± 12

259 ± 451

Average

2.99 ± 0.97

109 ± 22*

75 ± 23*

53 ± 9

86 ± 22

253 ± 68*

0.056

50.5

17

18.2

36

47.3

3.53

90

197

36

91.3

315

Rural river

Background§ PEL

 

The different letters (a,b,c) represent significant differences (P \ 0.05) between sections in the urban river.The different numbers (1,2,3) represent significant differences (P \ 0.05) between sections in the country river §  

Background values of Guangdong Province (Li et al. 2007) Probable effect level (Long et al. 1998)

* Represents significant differences (P \ 0.05) between the urban and country rivers

processing plants in this densely populated district could favor Cd, Cu and Pb accumulation in these river sediments, which are rich in Fe and Mn (Kong et al. 2008; Xiao et al. 2011). Generally, urban river sediments had higher Cr, Cu and Zn compared to rural river sediments (P \ 0.05). No significant differences in Ni and Pb contents were observed between urban and rural river sediments (P [ 0.05). Interestingly, the average Cd contents in sediments from the two rivers were almost the same. However, a decreasing trend of Cd content was observed along the urban river, while the rural river exhibited first an increase, then a decrease, indicating the differences in sources and sinks of pollutants between the two rivers. For the urban river, not only the middle reach but also the upper reach was pumped by heavy metal contaminants, which imply a dire situation for environmental restoration. Vertical distribution of heavy metals along typical sediment profiles Sediment profiles at Site MT in the Shiqiao River and Site HU in the Shawan River were selected to compare the vertical distributions of heavy metals in sediment profiles from both urban and rural rivers (Fig. 1). The contents of six tested metals in all sediment layers at both Sites MT and HU were much higher than background values for these heavy metals recorded in Guangdong Province (Table 1). At Site MT, these metals decreased sharply from surface to subsurface sediments, with the maximum and minimum values appearing at the surface (0–10 cm) and subsurface (10–20 cm) layer, respectively (Fig. 1). Higher

heavy metal contents in surface sediments suggested serious anthropogenic contamination in recent years. Interestingly, these heavy metal contents increased gradually from subsurface (10–20 cm) to deep layer (50–60 cm) along the sediment profiles. Because some studies reported that heavy metal contents decreased downward in the soil/ sediment profile (Li and Shuman 1996; Cabrera et al. 1999), the unusual ‘‘regular increasing’’ contents from the subsurface to deep sediments in this urban river might be caused by the river dredging works several years ago, in which the soil shifter rolled over the sediment cores, thus reversing the vertical distribution patterns from ‘‘downward decreasing’’ to ‘‘downward increasing’’. Most of the surface sediments were also observed polluted in the rural river (Fig. 1), implying that the present level of contamination of the rural river could not be neglected. However, contrary to the urban river’s increasing trend from the subsurface to bottom layer along its sediment profile, the heavy metal contents in rural river sediments exhibited a significant decline from surface sediments to deeper sediments at 40–50 cm, and then showed a slight increase at the bottom sediments, due to the leaching of the soluble fraction of heavy metals (Beesley et al. 2010). The decreasing trend along the sediment profile from the rural river indicates the vertical distribution characteristics of heavy metals along the sediment profile under relatively natural conditions. As shown in Fig. 1, the changing trends of SOM along the two typical profiles were consistent with those of heavy metals. Karimi et al. (2011) showed that heavy metals in sediment were significantly positively correlated with

123

1568

R. Xiao et al. -1

Cd (mg.kg ) 0.6 0.9 1.2 1.5 1.8

-1

Cr (mg.kg ) 80 120 160 200 0

-1

-1

Cu (mg.kg )

-1

Ni (mg.kg )

50 100 150 200 250 30

60

Pb (mg.kg )

90 120 150 40

50

60

70

80

-1

-1

Zn (mg.kg ) 100 200 300 400

MV ( µm)

SOM (g.kg ) 40

45

50

200

300

Salinity (‰) 400

0.50

0.52

0.54

0.56

0-10

Depth (cm)

10-20 20-30 30-40 40-50 50-60

-1

Cd (mg.kg ) 1

2

3

4

-1

Cr (mg.kg )

5 90 100 110 120 130 40

-1

Cu (mg.kg ) 60

80

100

-1

-1

Pb (mg.kg )

Ni (mg.kg ) 40

50

60

40

60

80

-1

Zn (mg.kg )

100 120 140

210

280

350 20

-1

MV(µ m)

SOM (g.kg ) 25

30

50

75

Salinity (‰)

100 125 0.48 0.54 0.60 0.66

0-10

Depth (cm)

10-20

20-30

30-40

40-50

50-60

Fig. 1 Vertical distributions of six heavy metals, SOM, grain size and salinity along two typical sediment profiles from Site MT(U3) (urban river) and Site HU(R1) (rural river). MV, mean volume diameter (lm)

organic matter, because organic matter could act as a major sink for heavy metals due to its strong complexing capacity for metallic contaminants (Bai et al. 2010). However, the grain size showed an inversely varying trend to those of organic matter and heavy metal contents along both sediment profiles following order: 0–10 cm (44.60 lm) \ 1020 cm (57.96 lm) \ 20–30 cm (79.17 lm) \ 50–60 cm (80.00 lm) \ 30–40 cm (101.40 lm) \ 40–50 cm (124.16 lm) (Fig. 1); this was in agreement with the result published by Moore et al. (2011), who showed that vertical grain size tends to be finer at the top and coarser toward the bottom, with a traction carpet in the bottom layer. Because the fine-grained fraction of sediments is associated with metal-rich aluminosilicate, metal contents decreased accordingly with increasing grain size along the sediment profile of the rural river (Lin et al. 2002). Meanwhile, the larger sediment grains, composed of quartz and carbonate sand (i.e., silica and carbonate), contained less organic matter, which discouraged heavy metal enrichment (Zhao et al. 2013). Furthermore, the salinity of the sediment decreased from 0.65 % to 0.46 % from top to bottom along the sediment profile of the rural river, suggesting a decreasing salt content with depth, which may contribute to the slight accumulation of metals in the bottom layer because lower salinity hinders metal mobility and bioavailability (Li et al. 2011). However, the total heavy metal contents were still highest in the high-salinity surface sediments, confirming that the percentage of heavy metals released upon salinization decreased as heavy metals were

123

strongly bound to organic matter and small aggregates (Acosta et al. 2011; Campana et al. 2013). The dredging works in the urban river also affected the vertical distribution of salinity, with the lowest salinity (0.49 %) at the 20–30 cm depth, although salt showed a downward leaching trend in other layers (Fig. 1). Although most literature reported that salinity was an important component controlling the partitioning of metals between sediments and overlying (as well as interstitial) water (Tessier et al. 1989; Duc et al. 2013), no significant correlations (P [ 0.05) were observed between salinity and heavy metal contents in this study, which was consistent with the result of Roca et al. (2012). Therefore, the effects of salinity on heavy metals in river sediments needs to be further clarified by additional research and consensus because of the limited data describing the partitioning of metals between sediments and overlying water in this study. In summary, SOM, grain size and recent anthropogenic influences rather than salinity are major factors affecting the horizontal and vertical variations of heavy metals in the urban and rural rivers of this study. Pollution assessment of heavy metals in sediment Assessment by SQGs and SQTs A general assessment of metal pollution was conducted by comparing the determined metal contents with the soil quality thresholds (SQTs) of China (Chen et al. 2001) and

Distribution and pollution, toxicity and risk assessment of heavy metals

1569

Table 2 General assessment of heavy metal pollutions in this study according to SQTs of China and SQGs of USEPA Cd

Cr

Cu

Ni

Pb

Zn

Urban river

1.40–4.94

114–202

65–229

49–137

69–171

192–429

Rural river

1.14–4.54

71–138

45–112

43–68

56–122

130–339

Range

SQTs of China SEPA

a

Class I

B0.2

B90

B35

B40

B35

B100

Class II

B0.6

B250

B100

B60

B350

B300

Class III

B1.0

B300

B400

B200

B500

B500

Class I (%)

0

0

0

0

0

0

Class II (%)

0

100

66

55

100

33

Class III (%) Exceeding III (%)

0 100

0 0

34 0

45 0

0 0

67 0

Class I (%)

0

14

0

0

0

0

Class II (%)

0

86

71

71

100

57

Class III (%)

0

0

29

29

0

43

Exceeding III (%)

100

0

0

0

0

0

Non-polluted



\25

\25

\20

\40

\90

Urban

Rural

SQGs of US EPAb Moderate-polluted



25–75

25–50

20–50

40–60

90–200

Heavily-polluted

[6

[75

[50

[50

[60

[200

Non-polluted (%)



0

0

0

0

0

Moderate-polluted (%)



0

0

22

0

11

Heavily-polluted (%)

0

100

100

78

100

89

Non-polluted (%) Moderate-polluted (%)

– –

0 14

0 29

0 57

0 14

0 14

Heavily-polluted (%)

0

86

71

43

86

86

Urban

Rural

a

Soil quality threshold (State Environmental Protection Administration (SEPA) 1995)

b

Sediment quality guideline (Pekey et al. 2004)

sediment quality guidelines (SQGs) of the US EPA (Pekey et al. 2004) (Table 2). The assessment based on SQTs indicated that Cr and Pb contents exceeded Class I levels in all urban river sediments and approximately 86 % of rural river sediments. All sediment samples in both urban and rural rivers had Cd levels exceeding class III criteria, whereas Cu and Ni exceeded class III criteria in less than 45 % of sediment samples in both rivers. Zn levels were generally higher in urban river sediments than rural river sediments. Approximately 67 % of sediment samples in the urban river exhibited Zn levels above the class III criteria. The degrees of pollution of the six metals followed the order Cd (exceeding class III) [ Zn [ Ni & Cu (class II and III) [ Pb & Cr (class I and II) in both river sediments, of which urban river sediments generally showed higher degrees of pollution than rural river sediments. This was consistent with previous studies, which showed that Cd

was the metal with the highest level of contamination in the Pearl River delta; among these heavy metals, Cu, Zn and Ni also showed high levels of enrichment (Li et al. 2007; Bai et al. 2011; Zhang and Shao 2013). According to the SQGs of the US EPA, all sediment samples were heavily polluted by Cr, Pb and Cu, and approximately 78–89 % of sediment samples were heavily polluted by Ni and Zn in the urban river. Except for Ni (moderate pollution in 57 % of samples), Cr, Cu, Pb and Zn showed heavy pollution levels in most samples ([70 %) from the rural river. This indicates that the recent rapid urban growth and industrial development along the route of the Pearl River has created serious pollution issues in rivers, especially in urban rivers. Similar phenomena could also be found in other countries. Suthar et al. (2009) stated that intensive urban and industrial activities, such as manufacturers of diesel engines, electroplating, paint and

123

1570

R. Xiao et al.

(b)

100

80 60 40

Urban river U-upper reach U-middle reach U-lower reach

8

60 40

Rural river R-upper reach R-middle reach R-lower reach

7 6 5 4 3

20 12 10 8 6

2

4

1

2

0

Urban river Rural river

80

Pollution index

Mean of pollution index

(a)

Cd

Cr

Cu

Ni

Pb

Zn

0

Cd

Cr

Cu

Ni

Pb

Zn

Sample sites

Fig. 2 a Comparison of the mean pollution indices of heavy metals in soils from the upper reach, middle reach, and lower reach of the urban river and the country river; b pollution index values of heavy metals in each sampling site. The dot line (Pi = 1) is the boundary

line between unpolluted (metal concentration lower than or equal to the background value of Guangdong Province) and polluted status (metal concentration higher than the background value of Guangdong Province)

varnish, heavy chains and automobile pistons in the city of Ghaziabad, India have contributed very strong Cd pollution and moderate pollution of Cr, Cu, Pb and Zn to the adjacent Hindon River. Mohiuddin et al. (2010) also found that strong anthropogenic influences in most populated urban areas and industrial establishments elevated the contents of these heavy metals in the water and sediments of the Tsurumi River in the city of Yokohama, Japan.

were heavily polluted by industrial waste. The mean Pi values for Cr, Cu, Ni, Pb and Zn along the whole urban river were higher than those of the rural river, suggesting a more serious pollution status in the urban river. The Pi values for all metals at all sites exceeded 1 (Fig. 2b), indicating that all sampling sites were polluted by heavy metals. Moreover, serious pollution levels of Cd, Zn and Cu, especially Cd, were observed among the six tested metals, as their Pi values approached or exceeded 5 (Song et al. 2011).

Pollution index Fig. 2 shows the ratios of heavy metal contents in sediment to their background values in Guangdong Province. This ratio is defined as the pollution index (Pi), with which the pollution levels of specific heavy metals can be easily and quickly found in contrast to background levels. As shown in Fig. 2, Cd yielded the highest pollution indices compared to other heavy metals (Fig. 2a). Pi values for Cd at all sites were [20 (Fig. 2b) in both rivers. The mean Pi values for Cd in the upper, middle, and lower reaches are 78, 52, and 28 in the urban river, and 44, 61, and 52 for the three sections of the rural river, respectively. Although the mean Pi value for Cd in both rivers was similar, the urban river showed an obvious decline in Cd pollution from the upper to the lower reach, suggesting that Cd in the urban river might originate from the upper reach, while the highest Pi value for Cd in the rural river appeared in the middle reach. Similar to Cd, the other five metals (Cr, Cu, Ni, Pb and Zn) also showed enrichment trends in the middle reach of the rural river, which indicates that the pollution sources of the rural river might mainly derive from the middle reach. However, the highest Pi values for Cd and Pb in the upper and middle reaches of the urban river illustrated that both the upper and middle reaches

123

Enrichment factor Facchinelli et al. (2001) showed that the upper layer contains most of the anthropic input and pedogenetic enrichment, while the underlying layer represents lithogenic input with minor anthropogenic pollution. The ratios of element contents in the upper layer to those in the underlying layer can be defined as the top enrichment factor (TEF). In this study, TEF was modified to be the ‘‘ratio of the ratio’’ between the surface sediment (0–10 cm) and the subsurface sediment (10–60 cm). It is expressed mathematically by (C/Al) 0-10/(C/Al) 10-60, where (C/Al) 0-10 and (C/Al) 1060 are the respective ratios of determined metal contents to Al contents in the surface and subsurface sediments in a given sediment profile. TEFs demonstrate the current difference in metal enrichment between upper and bottom sediments, which can model the variation in pollutant inputs along sediment profiles better than the enrichment factor (EF) (Wang et al. 2004). A natural pedogenetic enrichment is unlikely to produce TEF values exceeding 2, whereas higher values point to an important anthropogenic input from the top (Facchinelli et al. 2001). In this study, one hot spot (R4) in the middle

Distribution and pollution, toxicity and risk assessment of heavy metals

1571 5

4

4

3

3

Cd Cr Cu Ni Pb Zn

TEF

TEF

5

2

2

1

1

0

0 U1

U2

U3

U4

U5

U6

U7

U8

U9

Sampling sites

R1

R2

R3

R4

R5

R6

R7

Sampling sites

Fig. 3 Top enrichment factor (TEF) values of six tested heavy metals in each sampling site from the urban river (left) and rural river (right). The dot line (TEF = 1) is the boundary line between unenrichment

and enrichment of heavy metals in the top layer (0–10 cm) relative to the lower layer (10–60 cm)

reach of the rural river and one hot area (U1–U5) around the upper and middle reaches of the urban river were observed (Fig. 3), with the average TEF value exceeding 1 and even approaching 2 for most metals. This implies different types of pollution in the two rivers: point source pollution for the rural river and non-point source pollution for the urban river. Generally, the TEF values for all the tested heavy metals exceeded 1 at some sites (especially at U1–U4) of the urban river. Higher TEFs for Cu and Zn were observed at most sites along the urban river, except for U7 and U9 in the lower reach, indicating prevalent Cu and Zn enrichment in surface sediments of the urban river due to pollution sources located in the upper and middle reaches. Similarly, some sites had TEF values that exceeded 1 for certain metals, of which both sites R4 and R2 showed the highest TEFs ([1) for all tested metals. Additionally, R7 had TEF values exceeding 1 for all metals except Cd. In the rural river, three sampling sites produced TEF values exceeding 1 for Cd; similarly high values were observed at four sampling sites for Cr, Ni, Pb and Zn and five sampling sites for Cu. Among these sampling sites in the rural river, R1 showed lower TEF values (less than 1) for all heavy metals, indicating that the upper reach of the rural river was unpolluted. In the urban river, TEF values for Cd exceeded 1 at five sampling sites, six sampling sites for Ni and Pb, seven sampling sites for Cu, and eight sampling sites for Zn. However, U9 showed lower TEF values (less than 1) for all heavy metals, suggesting that the lower reach of the urban river was unpolluted. TEF values for most metals were maximized in the middle reach in both the urban and rural rivers. However, high values in the urban river were mainly observed in upper and middle reaches. Comparatively, the moderate TEF values for these heavy metals in rural river sediments were relatively low and evenly distributed. This was most likely related to the dispersed distribution of facilities in the rural area (Massas et al. 2009).

Toxicity assessment of heavy metals in sediment Fig. 4 shows the mean toxic unit values (TU) and the sum P of toxic units ( TU) of six heavy metals in sediments from the upper, middle and lower reaches of both urban and rural rivers. TU is defined as the ratio of the deterP mined content of each metal to the PEL value, and TU can be used to estimate the potential acute toxicity of multiple metals combined in sediment samples (Pedersen et al. 1998). The average toxic unit values of Cd, Cr, Cu, Ni, Pb and Zn in urban river sediments were 0.79, 1.48, 0.51, 1.85, 1.05 and 1.04, respectively, whereas they were 0.85, 1.22, 0.38, 1.48, 0.95 and 0.80 for the rural river. P Thus the average TU of these heavy metals in the sediments of both rivers exhibited moderate toxicity (Pedersen et al. 1998). Ni and Cr showed higher contributions to P TU values in both rivers, indicating that the sediment toxicity was mainly ascribed to Cr and Ni. However, Cu P contributed the least (7–8 %) to TU in both rivers’ P sediments. The highest TU value (9.94) was observed at Site U3 in the middle reach of the urban river, whereas the P lowest TU value (3.81) appeared at Site R1 in the upper reach of the rural river. Except for Site R1, all the other P sampling sites in both rivers had TUs greater than 4, indicating a moderate to serious toxicity of heavy metals to sediment-dwelling fauna in the study area (Wang et al. 2011; Xiao et al. 2012).

Potential risk assessment of heavy metals in sediment To assess the combined pollution of multiple metals in the sediments from the upper, middle, and lower reaches of both urban and rural rivers, the quantitative approach developed by Ha˚kanson (1980) was used in this study. The potential ecological risk index (RI) is given as follows:

123

1572

RI ¼

R. Xiao et al. 6 X

Eri

i¼1

¼

6 X

Tri



Cfi

i¼1

¼

6  X

Tri

i¼1

 Ci  i ; Cn

where Eri is the potential ecological risk parameter of heavy metal i; Tri is the toxic index of heavy metal i (e.g., Cd = 30, Cr = 2, Cu = Ni = Pb = 5, and Zn = 1); Cfi is the pollution parameter of heavy metal i; Ci is the determined content of heavy metal i in the samples, and Cni is the background value of heavy metal i in the study area. The RI degrees are listed in Table 3, and the RI values of sediment samples from the two rivers are presented in Table 4. As shown in Table 4, lower RI values (\100) were observed for the lower reach of the urban river and the upper reach of the rural river. This result implies moderate risks in these sections. However, the RI values for the upper and middle reaches of the urban river, and the middle and lower reaches of the rural river ranged from 100 to 200, suggesting that a large part of the watershed in the Panyu district exhibited high ecological risks because of heavy metal pollution. Two sampling sites in the urban river had much higher RI values (172.2 and 172.5) than others. The former was located in the upper reach and the

P Fig. 4 Toxic unit (TU) of each metal and sum of toxic units ( TU) of six heavy metals in each sampling site fromPthe urban river (U1– U9) and rural river (R1–R7). The solid line ( TU = 4) represents the threshold above which the toxic degree of heavy metals is considered moderate to serious

Table 3 Degrees of contamination and potential ecological risk corresponding to the values of Cfi , Cd, Eri and RI Cfi

Eri

Cd

RI

Range

Degree

Range

Degree

Range

Degree

Range

Degree

Cfi \ 1

Low

Cd \ 6

Low

Eri \ 30

Low

RI \ 50

Low

Moderate

50 B RI \ 100

Moderate

Considerable

100 B RI \ 200

High

High

RI C 200

Very high

1B

Cfi

\3

3 B Cfi \ 6 Cfi

C6

Eri

Moderate

6 B Cd \ 12

Moderate

30 B

High

12 B Cd \ 24

High

60 B Eri \ 120

Very high

Cd C 24

Very high

120 B Eri

\ 60

Eri

\ 240

C 240

Very high

Cfi

is the contamination parameter of heavy metal i, Cd can reflect the total contamination degree of samples; Eri is the potential ecological risk parameter of heavy metal i, RI represents the potential ecological risk index (Ha˚kanson 1980)

Table 4 Mean values of ecological risk factor (Eri ) and potential ecological risk index (RI) of heavy metals in surface sediments from the Shiqiao River (urban river) and Shawan River (rural river) along upper-middle-lower reach gradient River section

Eri Cd

RI Cr

Cu

Ni

Pb

Zn

3.61 ± 0.24

8.06 ± 0.66

1.82 ± 0.00

155 ± 16 118 ± 32

Shiqiao River (urban) Upper

130 ± 17

2.67 ± 0.13

8.18 ± 0.06

Middle

87 ± 34

3.28 ± 0.74

13.33 ± 5.62

5.52 ± 2.31

7.29 ± 2.83

2.18 ± 0.35

Lower

47 ± 4

2.75 ± 0.15

6.89 ± 0.33

3.89 ± 0.24

5.49 ± 0.41

1.50 ± 0.37

67 ± 5

Average

83 ± 39

2.97 ± 0.58

10.04 ± 4.79

4.55 ± 1.78

6.86 ± 2.18

1.87 ± 0.44

109 ± 40

Shawan River (rural) Upper

73 ± 39

2.30 ± 0.24

6.30 ± 1.83

3.17 ± 0.21

6.38 ± 2.34

1.29 ± 0.54

93 ± 44

Middle

101 ± 24

2.60 ± 0.31

8.03 ± 1.75

3.82 ± 0.44

6.90 ± 0.96

1.53 ± 0.30

124 ± 27

Lower

87 ± 2

2.33 ± 0.75

8.01 ± 3.17

3.88 ± 0.84

4.87 ± 0.08

1.48 ± 0.26

108 ± 2

Average

89 ± 29

2.43 ± 0.49

7.53 ± 2.40

3.65 ± 0.62

6.17 ± 1.64

1.45 ± 0.39

111 ± 32

Note: Take preindustrial reference values as Cni (Cd: 1.0; Cr: 90; Cu: 50; Ni: 73; Pb: 70; Zn: 175 mg kg-1) (Ha˚kanson 1980)

123

Distribution and pollution, toxicity and risk assessment of heavy metals

latter was located in the middle reach of the urban river. However, only one sampling site in the rural river had a very high RI value (164); this site was located in the middle reach. This confirmed the above result that the urban river had a ‘‘hot area’’ in the upper and middle reaches where drain outlets for domestic and industrial sewage were frequently observed on both banks of the river, whereas the rural river had a ‘‘hot spot’’ in the middle reach that might be caused by a large quantity of leather dyeing discharge. Among the six tested metals, Cd showed the highest contribution to RI values: only the Eri values for Cd ranged from moderate to high degrees, while all the other metals showed lower RI ranges. Interestingly, the mean RI value in surface sediments of the whole rural river was found to equal that of the urban river, and both reached a high risk level, which indicates severe ecological stress in the developed urban area as well as in the developing rural area. These heavy metals accumulated in sediments reached moderate toxicity levels throughout almost the whole river channels; moreover, they exceeded the moderately and heavily polluted thresholds of the US EPA. Several studies focused on heavy metal pollution in the Pearl River delta have reported that the maximum accumulation of metals was observed in the summer (wet season), while in the dry season, the heavy metals in sediment presented relatively low levels due to release from sediments (Yu et al. 2004; Zhang et al. 2013). Therefore, the heavy metal pollution grade in sediments might be slightly lower in the studied period (dry season) than in the wet season. Hence, the water and sediments in both rivers of the Panyu district need to be remediated to maintain clean water resources and aquatic ecosystem health. Remedial actions, such as constructing new wastewater treatment facilities, practicing area-source pollution control plans, implementing various spill-prevention projects and additional remediation measures should be taken in the future (Loar et al. 2011). The remedial actions require long-term monitoring because the recovery of aquatic communities after remediation can only be expected in the long term (Arini et al. 2012; Wu et al. 2012). Meanwhile, further studies are also needed to identify the most appropriate and cost-effective remedial action(s), such as chemical/physical remediation, animal remediation, phytoremediation and microremediation, to control the polluted river ecosystems efficiently.

Conclusions The heavy metal contents (Cd, Cr, Cu, Ni, Pb and Zn) in sediments from the upper, middle and lower reaches of an urban river (Shiqiao River) and a rural river (Shawan River) were investigated in this study. Generally, of these

1573

heavy metals, Cd pollution was the most serious, especially in sediments from the upper reach of the urban river and the middle reach of the rural river. Both Cu and Zn exhibited higher levels of enrichment, whereas higher toxicity of Cr and Ni were observed in surface sediments. Moreover, the typical vertical distributions of heavy metals, SOM and grain size along typical sediment profiles from the urban and rural rivers suggested that dredging projects, SOM content and grain size have important impacts on the distribution of heavy metals in sediments. The moderate to serious pollution levels, eco-toxicity, and ecological risks of these heavy metals in both rivers suggested the presence of widely dispersed pollution sources within and around the city of Panyu. The ‘‘hot area’’ with higher TEF and RI values in the upper and middle reaches of the urban river may demonstrate nonpoint source pollution in this densely populated area. However, a specific ‘‘hot spot’’ in the middle stream of the rural river may reflect point source pollution in the central rural area. This study suggests the high-efficiency remedial measures which aimed at pollution reduction and ecological recovery in river ecosystems should be identified and taken to meet the requirement of enviro-friendly development in the Pearl River delta. Acknowledgments This work was financially supported by the National Basic Research Program (No. 2013CB430406), the National Natural Science Foundation (No. 51379012), the Program for New Century Excellent Talents in University (NCET-10-0235), the Fok Ying Tung Education Foundation (132009), and the Fundamental Research Funds for the Central Universities (No.2013351). Conflict of interest of interest.

The authors declare that they have no conflict

References Acosta JA, Jansena B, Kalbitz K, Faz A, Martı´nez-Martı´nez S (2011) Salinity increases mobility of heavy metals in soils. Chemosphere 85(8):1318–1324 Arini A, Feurtet-Mazel A, Morin S, Maury-Brachet R, Coste M, Delmas F (2012) Remediation of a watershed contaminated by heavy metals: a 2-year field biomonitoring of periphytic biofilms. Sci Total Environ 425:242–253 Bai JH, Cui BS, Xu XF, Ding QY, Gao HF (2009) Heavy metal contamination in riverine soils upstream and downstream of a hydroelectric dam on the Lancang River China. Environ Eng Sci 26:941–946 Bai JH, Yang ZF, Cui BS, Gao HF, Ding QY (2010) Some heavy metals distribution in wetland soils under different land use types along a typical plateau lake China. Soil Till Res 106(2): 344–348 Bai JH, Xiao R, Cui BS, Zhang KJ, Wang QG, Liu XH, Gao HF, Huang LB (2011) Assessment of heavy metal pollution in wetland soils from the young and old reclaimed regions in the Pear River Estuary South China. Environ Pollut 159:817–824 Bai JH, Xiao R, Zhang KJ, Gao HF (2012) Arsenic and heavy metal pollution in wetland soils from tidal freshwater and salt marshes

123

1574 before and after the flow-sediment regulation regime in the Yellow River Delta China. J Hydrol 450–451:244–253 Beesley L, Jime´nez EM, Clemente R, Lepp N, Dickinson N (2010) Mobility of arsenic, cadmium and zinc in a multi-element contaminated soil profile assessed by in situ soil pore water sampling, column leaching and sequential extraction. Environ Pollut 158:155–160 Cabrera F, Clemente L, Barrientos ED, Lo´pez R, Murillo JM (1999) Heavy metal pollution of soils affected by the Guadiamar toxic flood. Sci Total Environ 242:117–129 Caeiro S, Costa MH, Ramos TB, Fernandes F, Silveira N, Coimbra A, Medeiros G, Painho M (2005) Assessing heavy metal contamination in Sado estuary sediment: an index analysis approach. Ecol Indic 5:151–169 Campana O, Blasco J, Simpson SL (2013) Demonstrating the appropriateness of developing sediment quality guidelines based on sediment geochemical properties. Environ Sci Technol 47(13):7483–7489 Chen Z, Kostaschuk R, Yang M (2001) Heavy metals on tidal flats in the Yangtze Estuary,China. Environ Geol 40:479–742 Chen SS, Fang LG, Zhang LX, Huang WR (2009) Remote sensing of turbidity in seawater intrusion reaches of Pearl River Estuary––a case study in Modaomen water way, China. Estuar, Coast Shelf Sci 82:119–127 Duc TA, Loi VD, Thao TT (2013) Partition of heavy metals in a tropical river system impacted by municipal waste. Environ Monit Assess 185:1907–1925 Facchinelli A, Sacchi E, Mallen L (2001) Multivariate statistical and GIS-based approach to identify heavy metal sources in soils. Environ Pollut 114:313–324 Forstner U, Muller G (1973) Heavy metal accumulation in river sediments, a response to environmental pollution. Geoforum 14:53–62 Frouz J, Elhottova D, Pizl V, Tajousky K, Sourkova M, Picek T, Maly S (2007) The effect of litter quality and soil faunal composition on organic matter dynamics in post-mining soil: a laboratory study. Appl Soil Ecol 37:72–80 Grossman GM, Krueger AB (1995) Economic growth and the environment. Quart J Econ 110:353–377 Gupta SK, Vollmer MK, Krebs R (1996) The importance of mobile, mobilisable and pseudo total heavy metal fractions in soil for three-level risk assessment and risk management. Sci Total Environ 178:11–20 Ha˚kanson L (1980) An ecological risk index for aquatic pollution control: a sedimentological approach. Water Res 14:975–1001 Ha˚kanson L, Janson M (1983) Principles of lake sedimentology. Springer, Berlin, p 316 Karimi R, Ayoubi S, Jalalian A, Sheikh-Hosseini AR, Afyuni M (2011) Relationships between magnetic susceptibility and heavy metals in urban top soils in the arid region of Isfahan, central Iran. J Appl Geophy 74(1):1–7 Kong GT, Meng HY, Li GH, Xiong T, Liu YX (2008) Fractionations of topsoil Cd and Pb and interrelationships among their own fractions in some rural/peri-urban vegetable fields in Pearl River Delta. Chin J Soil Sci 3:38–40 (in Chinese with English abstract) Kumar RN, Solanki R, Kumar JIN (2013) Seasonal variation in heavy metal contamination in water and sediments of river Sabarmati and Kharicut canal at Ahmedabad,Gujarat. Environ Monit Assess 185(1):359–368 Lai DS, Lin JB, Liu WS, Pan LK, Chu KH, Chen CY, Lin DB (2010) Metal concentrations in sediments of the Tamsui River, flows through central metropolitan Taipei. Bull Environ Contamination Toxicol 84:628–634 Li ZB, Shuman LM (1996) Heavy metal movement in metalcontaminated soil profiles. Soil Sci 161:656–666

123

R. Xiao et al. Li XD, Coles BJ, Ramsey MH, Thornton I (1995) Sequential extraction of soils for multi-element analysis by ICP-AES. Chem Geol 124:109–123 Li QS, Wu Z, Chu B, Zhang N, Cai S, Fang J (2007) Heavy metals in coastal wetland sediments of the Pearl River Estuary, China. Environ Pollut 149:158–164 Li QS, Liu YN, Du YF, Cui ZH, Shi L, Wang LL, Li HJ (2011) The behavior of heavy metals in tidal flat sediments during fresh water leaching. Chemosphere 82(6):834–838 Lin SW, Hsieh IJ, Huang KM, Wang CH (2002) Influence of the Yangtze River and grain size on the spatial variations of heavy metals and organic carbon in the East China Sea continental shelf sediments. Chem Geol 182:377–394 Liu Y (2008) Taking government as the main body of water pollution decreasing system. Ecol Economy 1:68–79 (in Chinese with English abstract) Liu L, Li FS, Xiong DQ, Song CY (2006) Heavy metal contamination and their distribution in different size fractions of the surficial sediment of Haihe River, China. Environ Geol 50:431–438 Liu JL, Li YL, Zhang B, Cao JL, Cao ZG, Domagalski J (2009) Ecological risk of heavy metals in sediments of the Luan River source water. Ecotoxicol 18:748–758 Loar JM, Stewart AJ, Smith JG (2011) Twenty-Five years of ecological recovery of east Fork Poplar Creek: review of environmental problems and remedial actions. Environ Manage 47(6):1010–1020 Ma ZW, Chen K, Yuan ZW, Bi J, Huang L (2013) Ecological risk assessment of heavy metals in surface sediments of six major Chinese freshwater lakes. J Environ Qual 42(2):341–350 MacDonald DD, Ingersoll CG, Berger TA (2000) Development and evaluation of consensus-based sediment quality guidelines for freshwater ecosystems. Arch Environ Contam Toxicol 39:20–31 Massas I, Ehaliotis C, Gerontidis S, Sarris E (2009) Elevated heavy metal concentrations in top soils of an Aegean island town (Greece): total and available forms, origin and distribution. Environ Monit Assess 151:105–116 Modlingerova V, Szakova J, Sysalova J, Tlustos P (2012) The effect of intensive traffic on soil and vegetation risk element contents as affected by the distance from a highway. Plant, Soil Environ 58(8):379–384 Mohiuddin KM, Zakir HM, Otomo K, Sharmin S, Shikazono N (2010) Geochemical distribution of trace metal pollutants in water and sediments of downstream of an urban river. Int J Environ Sci Technol 7(1):17–28 Moore A, Goff J, McAdoo BG, Fritz HM, Gusman A, Kalligeris N, Kalsum K, Susanto A, Suteja D, Synolakis CE (2011) Sedimentary deposits from the 17 July 2006 western Java Tsunami, Indonesia: use of grain size analyses to assess Tsunami flow depth, speed, and traction carpet characteristics. Pure appl geophy 168(11):1951–1961 Nelson DW, Sommers LE (1982) Total carbon, organic carbon, and organic matter. In: Page AL, Miller RH, Keeney DR (eds) Methods of soil analysis. American society of agronomy, Wisconsin, pp 539–579 Pedersen F, Bjurnestad E, Andersen HV, Kjholt J, Poll C (1998) Characterization of sediments from Copenhagen Harbour by use of biotests. Water Sci Technol 37:233–240 Pekey H, Karaka D, Ayberk S, Tolun L, Lu MB (2004) Ecological risk assessment using trace elements from surface sediments of Izmit Bay (Northeastern Marmara Sea) Turkey. Mar Pollut Bull 48:946–953 Perkins WS, Benefield L, Hill EW, Walsh WK (1993) Source reduction of pollutants from textile processing waste water. Natl Text Cent Quart Rep 31:13

Distribution and pollution, toxicity and risk assessment of heavy metals Roca N, Pazos MS, Bech J (2012) Background levels of potentially toxic elements in soils: a case study in Catamarca (a semiarid region in Argentina). Catena 92:55–66 Roline RA (1988) The effects of heavy metal pollution of the upper Arkansas River on the distribution of aquatic macroinvertebrates. Hydrobiologia 160:3–8 Soares HMVM, Boaventura RAR, Machado AASC, da Esteves Silva JGG (1999) Sediments as monitors of heavy metal contamination in the Ave river basin (Portugal): multivariate analysis of data. Environ Pollut 105:311–323 Song YX, Ji JF, Yang ZF, Yuan XY, Mao CP, Frost RL, Ayoko GA (2011) Geochemical behavior assessment and apportionment of heavy metal contaminants in the bottom sediments of lower reach of Changjiang River. Catena 85:73–81 State Environmental Protection Administration (SEPA) (1995) Environmental Quality Standard for Soils. State Environmental Protection Administration, China.GB15618-1995 (in Chinese) Sun YJ, Wang SG, Hu YC, Zhang C, Fang HY (2009) Discussions on the comprehensive improvement of heavily polluted municipal rivers and the emission reduction methods:a case study of the Shiqiao River in Panyu district of Guangzhou. Tropical Geography 29:207–212 (in Chinese with English abstract) Suthar S, Nema AK, Chabukdhara M, Gupta SK (2009) Assessment of metals in water and sediments of Hindon River, India: impact of industrial and urban discharges. J Hazard Mater 171:1088–1095 Tessier A, Carignan R, Dubreuil B, Rapin F (1989) Partitioning of zinc between the water column and the oxic sediments in lakes. Geochim Cosmochim Acta 53(7):1511–1522 Thompson M, Walsh JN (1989) Handbook of inductively coupled plasma spectrophotometry. Blackie, Glasgow, p 273 Wang GP, Liu JS, Tang J (2004) Assessment of heavy metal pollution of wetlands at downstream of an inland river. Rural Eco-Environ 20:50–54 (in Chinese) Wang SL, Lin CY, Cao XZ (2011) Heavy metals content and distribution in the surface sediments of the Guangzhou section of the Pearl River, Southern China. Environ Earth Sci 64: 1593–1605 Wei XG, He JH, Wang SY, Chen JJ, Du YQ, He WB, Yang XQ (2002) Concentration and evaluation on pollution of Cd in vegetable farm soils and vegetables of Guangzhou. Soil Environ Sci 11:129–132 (in Chinese) Weng LP, Temminghoff JM, Riemsdijk HV (2001) Contribution of individual sorbents to the control of heavy metal activity in sandy soil. Environ Sci Technol 35(22):4436–4443 Wu G, Wu JY, Shao HB (2012) Hazardous heavy metal distribution in Dahuofang catchment Fushun, Liaoning, an important industry city in China: a case study. CLEAN- Soil, Air, Water 40(12):1372–1375 Xiao R, Bai JH, Zhang HG, Gao HF, Liu XH, Wilkes A (2011) Changes of P, Ca, Al and Fe contents in fringe marshes along a pedogenic chronosequence in the Pearl River estuary, South China. Cont Shelf Res 31:739–747

1575 Xiao R, Bai JH, Gao HF, Wang JJ, Huang LB, Liu PP (2012) Distribution and contamination assessment of heavy metals in water and soils from the college town in the Pearl River Delta, China. CLEAN-Soil, Air, Water 40(10):1167–1173 Xu G, Sun JN, Xu RF, Lv YC, Shao HB, Yan K, Zhang LH, Blackwell MSA (2011) Effects of air-drying and freezing on phosphorus fractions in soils with different organic matter contents. Plant, Soil Environ 57(5):228–234 Yang ZF, Wang Y, Shen ZY, Niu JF, Tang ZW (2009) Distribution and speciation of heavy metals in sediments from the mainstream, tributaries, and lakes of the Yangtze River catchment of Wuhan, China. J Hazard Mater 166:1186–1194 Yu X, Ng C (2006) An integrated evaluation of landscape change using remote sensing and landscape metrics: a case study of Panyu, Guangzhou. International J Remote Sens 27:1075–1092 Yu ZH, Lin Q, Li CH, Huang HH, Yang ML, Han JL, Ca WG (2004) Variation features and ecological assessment of heavy metals from Pearl River estuary. J Fishery Sci China 11(3):214–219 (in Chinese with English abstract) Yu T, Fang HY, Zeng FT (2009) An assessment of water quality in the shiqiao river basin in Panyu, Guangzhou. China Rural Water Hydropower 12:23–30 (in Chinese with English abstract) Yu D, Jiang Y, Kang M, Tian Y, Duan J (2011) Integrated urban landuse planning based on improving ecosystem service: panyu case, in a typical developed area of China. J Urban Plan Development 137(4):448–458 Zhang LW, Shao HB (2013) Heavy Metal Pollution in Sediments from Aquatic Ecosystems in China. CLEAN-Soil, Air, Water, doi:10.1002/clen.201200565 Zhang XY, Xue XZ (2013) Analysis of marine environmental problems in a rapidly urbanising coastal area using the DPSIR framework: a case study in Xiamen, China. J Environ Plan Manag 56(5):720–742 Zhang DW, Zhang X, Tian L, Ye F, Huang XP, Zeng YY, Fan ML (2013) Seasonal and spatial dynamics of trace elements in water and sediment from Pearl River Estuary, South China. Environ Earth Sci 68(4):1053–1063 Zhao HT, Li XY (2013) Risk assessment of metals in road-deposited sediment along an urban–rural gradient. Environ Pollut 174:297–304 Zhao S, Feng C, Wang D, Liu Y, Shen Z (2013) Salinity increases the mobility of Cd, Cu, Mn, and Pb in the sediments of Yangtze Estuary: relative role of sediments’ properties and metal speciation. Chemosphere 91(7):977–984 Zheng N, Wang QC, Liang ZZ, Zheng DM (2008) Characterization of heavy metal concentrations in the sediments of three freshwater rivers in Huludao City Northeast China. Environ Pollut 154:135–142 Zhou X, Xia BC (2010) Defining and modeling the soil geochemical background of heavy metals from the Hengshi River watershed (southern China): integrating EDA, stochastic simulation and magnetic parameters. J Hazard Mater 180:542–551

123