Environ Earth Sci (2015) 73:1837–1848 DOI 10.1007/s12665-014-3538-5
ORIGINAL ARTICLE
Preliminary assessment of heavy metal contamination in surface sediments from a river in Bangladesh Md. Saiful Islam • Md. Kawser Ahmed • Md. Habibullah-Al-Mamun • Md. Fazlul Hoque
Received: 3 February 2014 / Accepted: 8 July 2014 / Published online: 24 July 2014 Ó Springer-Verlag Berlin Heidelberg 2014
Abstract Contamination of heavy metals in sediment is regarded as a major crisis globally, with a large SHARE in developing countries, such as Bangladesh. The objective of this study is to assess the contamination level and seasonal variation of heavy metals and their ecological risk in sediments. Heavy metals (Cr, Ni, Cu, As, Cd, and Pb) in sediments were investigated from eight different sites of Paira River situated at the southern part of Bangladesh and metals were measured by using inductively coupled plasma mass spectrometer. The mean concentration of Cr, Ni, Cu, As, Cd, and Pb in sediments was 45, 34, 30, 12, 0.72 and 25 mg/kg dw, respectively. Metals in sediment during winter were higher than summer season. The concentrations of metals in sediment were compared with both background and toxicological reference values. The comparative results suggested that the selected metals created an adverse effect on the aquatic ecosystems of the studied river. The pollution load index for three sites were higher than baseline values indicating progressive deterioration of sediments by heavy metal. Potential ecological risks of metals in sediment indicated moderate to considerable risk. This study suggested that more attention should be directed
Md. S. Islam (&) Md. F. Hoque Department of Soil Science, Patuakhali Science and Technology University, Dumki, Patuakhali 8602, Bangladesh e-mail:
[email protected] Md. S. Islam Md. Habibullah-Al-Mamun Graduate School of Environment and Information Sciences, Yokohama National University, Yokohama, Japan Md. K. Ahmed Md. Habibullah-Al-Mamun Department of Fisheries, Dhaka University, Dhaka 1000, Bangladesh
to the comprehensive risk assessment of heavy metals of this riverine aquatic environment. Keywords Heavy metals Sediments Ecological risk River Bangladesh
Introduction In Bangladesh, a huge amount of untreated industrial wastes have been discharged into low lands and water bodies every day. Besides, the riverine aquatic environment is receiving a considerable amount of suspended materials contaminated with heavy metals from the neighboring country, such as India through the Teesta and the Brahmaputra Rivers (Mohiuddin et al. 2011; Ahmad et al. 2010; Haque et al. 2007). Consequently, it poses severe threat to fish and other aquatic biota. The river Paira is the main river flowing beside the Patuakhali district located at the southern part of Bangladesh. The area of Patuakhali district is about 3,204.58 square kilometer; total population is about 1,444,340 persons; and population density is 451 persons per square kilometer (BBS 2011). As a pioneer of industrialization and urbanization in Bangladesh, the study river has raised attention due to its environmental pollution. The Paira River has been considered as the main flow of polluted water from the peripheral rivers of the capital Dhaka City, Bangladesh to the Bay of Bengal. The inhabitants of Patuakhali district largely depend on this river for drinking and other household purposes, irrigation for agriculture and aquaculture, and for carrying merchandise. The unpleasant odor of the polluted water of Paira River can be sensed from a short distance. As a result of insensible human activities on the one hand, and failure by the concerned authority to enforce
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the rules and regulations to save the river on the other hand, the Paira is dying biologically. Sediment is an essential, integral, and dynamic part of the river basin, with the variety of habitats and environments. Recently, the pollution of aquatic ecosystems has been considered as a topic of much discussion; and the issue of metal pollution in sediments has received more attention due to the toxic and persistent characters (Jiang et al. 2014; Varol and Sen 2012; Gao and Chen 2012; Yuan et al. 2011; Zhan et al. 2010; Armitage et al. 2007; Sin et al. 2001). Sediments usually provide useful information for environmental and geochemical pollution status (Uluturhan et al. 2011). More than 90 % of the total heavy metal load in aquatic environment is bound to the suspended particulate matter and sediments (Varol and Sen 2012; Calmano et al. 1993). Therefore, sediments can be polluted with various kinds of hazardous and toxic substances including heavy metals, which can be accumulated in sediments via several pathways: disposal of liquid effluents, terrestrial runoff, traffic emissions, brick kilns and leachates carrying chemicals originating from numerous urban, industrial, and agricultural activities, as well as atmospheric deposition (Chen et al. 2012; Shikazono et al. 2012; Varol and Sen 2012; Bai et al. 2011; Ahmad et al. 2010; Liu et al. 2003; Mucha et al. 2003). Depending on the hydrodynamics and environmental conditions, heavy metals tend to adsorb from water column on to surfaces of fine particles and usually move to the sediments and can affect the benthic organisms and food chain (Lourin˜oCabana et al. 2011; Saha and Zaman 2013). Surface sediments may serve as a metal pool that can release heavy metals to the overlaying water via natural and anthropogenic processes (industrial discharges, municipal waste water, household garbage and city runoff), causing potential adverse health effects to the aquatic ecosystems (Varol and Sen 2012; Mohiuddin et al. 2011; McCready et al. 2006). Various indices have been developed to assess the environmental risk of heavy metals in surface sediment based on their total content, bioavailability, and toxicity (Yang et al. 2009; Yu et al. 2011). For example, geoaccumulation index, enrichment factor, and contamination factor of individual heavy metal in sediment are calculated using its total content and sediment quality guideline value (Hou et al. 2013; Huang et al. 2013; Zhang et al. 2013; Yang et al. 2009). To evaluate the combined risk of multiple heavy metals in sediment, the pollution load index (PLI) and potential ecological risk index (PER) have also been developed (Huang et al. 2013; Yang et al. 2009). The pollution load index compares the metal concentrations with baseline values, which helps in assessing the enrichment of heavy metals in sediment (Yang et al. 2009). The potential ecological risk index introduces a toxic–response
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factor for a given substance that provides a simple and quantitative value for ecological risk assessment system (Ha˚kanson 1980). Thus, it is important to determine the intensity of metal pollution by inventorying the concentrations and their distribution in a riverine ecosystem. To our knowledge, there has been no study reported on the pollution of heavy metals in the Paira River and metal toxicity data severely insufficient to accomplish the ecological risk assessment. Therefore, the objective of this study is to evaluate the metal concentrations in surface sediments in two different seasons and to assess the screening level of ecological risk of heavy metals.
Materials and methods Study area and sampling locations This study focused on an important river located at the southern part of Bangladesh (Fig. 1). The length of the study river is about 25 km long and makes a junction between the Patuakhali district and the capital city through river transportation. Agriculture, aquaculture, and fishing are the primary activities of the people living beside this river. This river receives domestic raw sewage, household waste, and industrial waste from surrounding habitation. During the last decades, natural and human activities have caused a complete deterioration of the river ecosystems. Sample collection and preparation Forty-eight pairs of sediment samples were collected in March 2012 (winter) and in September 2012 (summer), respectively. In Bangladesh, two seasons distinctly observed and the water level and industrial activities also varied accordingly. During winter season there is almost no rainfall, consequently, the water level in the river goes down and the river remains stagnant. On the other hand, heavy rainfall in summer season causes higher water flow in the river. As a result, metal accumulation in sediment may differ during two distinct seasons. The samples were collected from eight stations P1 (22°270 03.8800 N, 908260 50.1100 E) P2 (228250 48.2600 N, 908260 58.4800 E), P3 (228240 42.5300 N, 908260 37.7800 E), P4 (228250 29.2300 N, 908250 06.0300 E), P5 (228240 25.6200 N, 908230 31.1500 E), P6 (228230 02.9400 N, 908220 36.8400 E), P7 (228210 29.6700 N, 908210 20.4000 E) and P8 (228200 51.4900 N, 908220 41.3900 E). At each point, composite sediment samples were collected using standard protocol (US EPA 2001). The river bed sediment samples were taken at a depth of 0–5 cm using a portable Ekman grab sampler. Three composite samples of mass approximately 200 g were collected at each station. The upper 2 cm of each sample was taken from the center
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Fig. 1 Map of the sampling area of the Paira River in Bangladesh
of the catcher with an acid-washed plastic spatula to avoid any contamination from the metallic parts of the sampler. Composite sediment samples were collected into polyethylene air tight bags in the field and transported to the laboratory of Patuakhali Science and Technology University, Bangladesh, for pretreatment. The samples were dried in oven at 45 °C for 48 h to gain constant weight. The dried samples were then ground using mortar and pestle and sieved through 106 lm aperture. The lower particle size fraction was homogenized by grinding in an agate mortar and stored in labeled glass bottles until chemical analyses were carried out. The pretreated samples were stored in plastic bags under freezing condition until chemical analysis was carried out. Sample digestion and metal extraction All chemicals were analytical grade reagents and Milli-Q (Elix UV5 and MilliQ, Millipore, USA) water was used for solution preparation. The Teflon vessel and polypropylene containers were cleaned, soaked in 5 % HNO3 for more than 24 h, then rinsed with Milli-Q water and dried. For metal analysis, 0.5 g of sediment sample was treated with
5 mL 69 % HNO3 acid (Kanto Chemical Co., Japan) and 2 mL 30 % H2O2 (Wako Chemical Co., Japan) in a closed Teflon vessel and was digested in a Microwave Digestion System (Berghof speedwaveÒ, Germany). Three stepdigestion procedures were followed: (1) temperature and power were maintained at 180 °C and 85 %, respectively, for 15 min; (2) temperature was kept steady at 200 °C for 15 min together with 90 % of the power; and (3) reduced temperature (100 °C) and power (40 %) were used for 10 min to cool down the Teflon vessels (maximum microwave power is 1,000 W, when power is 100 %). The digested samples were then transferred into a Teflon beaker and total volume was made up to 50 mL with Milli-Q water. The digested solution was then filtered using syringe filter (DISMICÒ—25HP PTFE, pore size = 0.45 lm) Toyo Roshi Kaisha, Ltd., Japan and stored in 50 mL polypropylene tubes (Nalgene, New York). Afterwards, the vessels were cleaned by Milli-Q water and dried with air. Instrumental analysis and quality assurance For heavy metals, samples were analyzed using inductively coupled plasma mass spectrometry (ICP-MS, Agilent
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7700). Multielement Standard XSTC-13 (Spex CertiPrepÒ, USA) solutions were used to prepare calibration curve. The calibration curves with R2 [ 0.999 were accepted for concentration calculation. Internal calibration standard solutions containing 1.0 mg/L of indium (In), yttrium (Y), beryllium (Be), tellurium (Te), cobalt (Co), and thallium (TI) were purchased from Spex Certi PrepÒ, USA. Multielement solution (Agilent Technologies, USA) 1.0 lg/L was used as tuning solution covering a wide range of masses of elements. A blank also carried out in the sequential extraction experiment. All test batches were evaluated using an internal quality approach and validated if they satisfied the defined internal quality controls (IQCs). For each experiment, a run included blank, certified reference materials (CRM) and samples were analyzed in duplicate to eliminate any batch-specific error. Before starting the analysis sequence, relative standard deviation (RSD \5 %) was checked using a tuning solution purchased from the Agilent Technologies.
PLI ¼ ðCF1 CF2 CF3 CFn Þ1=n
Analytical methods for physicochemical parameters
where, Cfi is the single element pollution factor, C i is the content of the element in samples and Cni is the reference value of the element. The reference value of Cr, Ni, Cu, As, Cd and Pb in sediments were 60, 68, 45, 13, 0.3 and 20 mg/kg (Yi et al. 2011; Hilton et al. 1985). The sum of Cfi for all metals examined represents the integrated pollution degree (Cd ) of the environment. Eri is the potential ecological risk index of an individual element. Tri is the biological toxic factor of an individual element. The toxic-response factors for Cr, Ni, Cu, As, Cd and Pb were 2, 6, 5, 10, 30 and 5, respectively (Ha˚kanson 1980; Xu et al. 2008; Guo et al. 2010; Fu et al. 2009). PER is the comprehensive potential ecological index, which is the sum of Eri . It represents the sensitivity of the biological community to the toxic substance and illustrates the potential ecological risk caused by the overall contamination.
The pH of sediments was measured in 1:2.5 of sediment to water ratio. The suspension was allowed to stand overnight prior to pH determination. The pH was measured using a pH meter with the calibration of pH 4, 7, and 9 standards. For electrical conductivity (EC) determination, 5.0 g of sediment was taken in 50 mL polypropylene tubes. Then, 30 mL of distilled water was added to the tube. The lid was closed properly and was shaken for 5 min. After that EC was measured using an EC meter (Horiba D-52). Percent N and C of sediment was measured using elemental analyzer (model type: vario EL III, Elenemtar, Germany). For N and C determination, sediment samples were weighed in tin or silver vessels and loaded in the integrated carousel. In a fully automatic process, the transfer of the sample through the ball valve into the combustion tube was performed. Each sample was individually flushed with carrier gas to remove atmospheric nitrogen, resulting in a zero blank sampling process. The catalytic combustion was carried out at a permanent temperature of up to 1,200 °C. The element concentration from the detector signal, and the sample weight on the basis of stored calibration curves were measured. Risk calculation
where CFmetals is the ratio between the content of each metal to the background values in sediment, CFmetals = CHmetal/CHback. The PLI gave an assessment of the overall toxicity status of the sample and also it is a result of the contribution of the six metals. Potential ecological risk (PER) Potential ecological risk index (PER) is also introduced to assess the contamination degree of heavy metals in sediments. The equations for calculating the PER were proposed by Guo et al. (2010) and are as follows. n X Ci Cfi ¼ i ; Cd ¼ Cfi ð2Þ Cn i¼1 Eri ¼ Tri Cfi ; PER ¼
m X
Eri
ð3Þ
i¼1
Statistical analysis Data were statistically analyzed using the statistical package, SPSS 16.0 (SPSS, USA). The means and standard deviations of the metal concentrations in sediments were calculated. A Pearson’s bivariate correlation was used to evaluate the inter-element relationship in sediments. Other calculations were performed by Microsoft Excel 2010.
Pollution load index (PLI)
Results and discussion
To assess the sediment quality, an integrated approach of pollution load index of the six metals such as: Cr, Ni, Cu, As, Cd and Pb is calculated according to Suresh et al. (2011). The PLI is defined as the nth root of the multiplications of the contamination factor (CF) of metals.
Physicochemical properties and heavy metals in sediment
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ð1Þ
The physicochemical parameters and seasonal distribution of heavy metals viz. Cr, Ni, Cu, As, Cd, and Pb in sediment
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Table 1 Physicochemical properties in sediment samples of Paira River, Bangladesh Sites
pH
EC (mS/m)
%N
%C
C/N ratio
wi
su
wi
su
wi
su
wi
su
wi
su
P1
7.02
8.62
28.5
40.4
0.19
0.12
1.18
1.82
6
15
P2
6.91
7.12
29.8
15.4
0.17
0.13
1.79
1.51
11
12
P3
6.62
6.62
29.8
26.5
0.13
0.13
1.13
1.22
9
9
P4
6.93
6.88
42.8
25.7
0.17
0.22
1.21
1.55
7
7
P5
6.13
6.52
27.5
23.3
0.11
0.12
0.74
1.22
7
10
P6
6.17
6.68
42.5
24.3
0.21
0.15
2.31
1.51
11
10
P7
5.43
6.94
19.7
16.2
0.13
0.12
1.84
1.59
14
13
P8
6.88
7.91
26.7
20.4
0.12
0.11
0.89
1.11
7
10
wi winter, su summer season, respectively
samples are presented in Tables 1 and 2. The sites generally have pH ranging from 5.43 to 8.62 which was neutral or slightly acidic except at P1 site (pH was 8.62) indicating slightly alkaline (Table 1). The composition of the organic carbon in sediment samples was varied among the sites due to its origin in the aquatic environment. The organic carbon in sediments ranged from 0.74 to 2.31 % (Table 1). The highest percentage of organic carbon might be attributed to the high amount of drainage water at P6 site. A wide range of values for metal concentrations were observed among the sampling sites. As shown in Table 2, concentrations of heavy metals at sites P7 and P8 were much higher than others sites. This is due to the downstream sites (P7 and P8) of the Patuakhali district urban area and the effects from the urbanization. Elevated concentrations of heavy metals in surface sediment were found at two sites (P7 and P8) close to the urban area of Patuakhali district indicated that urbanization drove metal contamination in surface sediment (Li et al. 2012; Yang et al. 2009). The urban activities (industrial discharges, municipal waste water, household garbage, and urban runoff) of Patuakhali district urban area are the main reasons of higher metal input at P7 and P8 sites. Higher heavy metal contaminations in Yuandang Lagoon due to the municipal sewage discharge or other unknown pollution sources from Xiamen City were observed by Yan et al. (2010) which are in line with our findings. Metals content in sediment during winter season were higher than summer which was due to the lower flow of river water during winter season that facilitated to accumulate heavy metals in sediment (Islam et al. 2014; Mohiuddin et al. 2011). Metal concentrations in samples followed the descending order of site-P7 [ site-P8 [ site-P3 [ site-P5 [ site-P2 [ siteP6 [ site-P1 [ site-P4 (Table 2). Interestingly, this descending trend of metals among sites does not follow a downstream pattern which is due to the metal input in
sediments from site specific characteristics, e.g., flow of the rivers, location of pollution sources and their waste disposal system (Ahmad et al. 2010; Alam et al. 2003). For example, P3 site is located at the upstream but the levels of metals were higher than some downstream sites. This is due to the effects from heavy boating activity at the ferry ghat and coal burning brick fields at this site (Mohiuddin et al. 2011; Dı´az-Somoano et al. 2009). The average concentration of heavy metals in sediments were in the following decreasing order of Cr [ Ni [ Cu [ Pb [ As [ Cd. The chromium enrichment of sediment can have been caused by two reasons: (1) natural: concentration of Crbearing minerals; and (2) anthropogenic: industrial activities such as tanneries and textile factories which are discharging Cr based oxidants (chromate, dichromate, etc.) (Facetti et al. 1998). Consequently, the waste discharged from such industries is responsible for elevated Cr level in the exposed sediment (Islam et al. 2014; Mohiuddin et al. 2011). Higher level of Cu indicates its higher input in the sites (P7 and P8), which is originated from anthropogenic activities such as vehicle and coal combustion emissions (Li et al. 2012; Zhu et al. 2011), car lubricants (Fu et al. 2014; Al-Khashman 2007), and natural activities such as metal contents of rocks and parent materials, processes of soil formation (Yi et al. 2011; Yang et al. 2009; Li et al. 2008; Liu et al. 2003). In the present investigation, the As concentration in sediment observed during winter was higher than summer for all sites. Recently, the anthropogenic activities such as treatment of agricultural land with arsenical pesticides (Fu et al. 2014; Mandal and Suzuki 2002), treating of wood using chromated copper arsenate, burning of coal in thermal plants power stations, and sediment excavation that alters the hydraulic regime and/or arsenic source material increased the rate of discharge into freshwater habitat (Pravin et al. 2012; Baeyens et al. 2007). Among the sampling sites, the highest value of Cd was observed at P7 site (1.6 and 0.86 mg/kg) during winter and summer season, respectively. Slightly higher Cd levels during winter may be attributed to the variation in water capacity of the river; where low water flow in winter resulted the precipitation of the metals in sediment; thereby increasing its concentration (Islam et al. 2014). Domestic and industrial effluents, municipal runoffs and atmospheric deposition (Shikazono et al. 2012; Varol and Sen 2012) are the major sources of the observed elevated level of Pb at P7 and P8 sites. In sediment, Pb was notably higher in winter compared to summer which indicates that there is a significant change in organic profile by resuspension and/or deposition (Islam et al. 2014) or by changes in redox and pH conditions (Bastami et al. 2012; ElNemr et al. 2007; Liang and Wong 2003). Statistical analyses were performed to elucidate the associations among heavy metals in sediment and to
123
40 (28–49) 36 (32–39)
15 (8.3–20) 24 (21–26)
58 (53–60) 40 (32–49) 0.86 (0.56–1.4) 0.85 (0.46–1.3)
0.15 (0.06–0.24)
17 (14–22) 26 (23–27) 21 (17–23) 29 (26–31)
1.6 (0.75–3.1) 1.4 (0.45–2.6)
5.2 (3.9–7.3) 6.8 (4.0–10)
0.61 (0.31–1.0)
9.1 (6.3–13)
12 (9.4–13) 17 (14–18)
14 (11–19) 0.58 (0.26–0.95)
0.49 (0.24–0.87) 0.42 (0.24–0.69)
6.7 (4.9–11)
0.81 (0.66–1.1)
8.1 (6.2–10)
3.5 (2.1–4.4)
15 (14–18)
17 (14–19)
32 (31–36) 44 (40–46) 0.72 (0.41–1.2) 9.3 (7.5–10) 17 (14–18)
1.1 (0.52–1.5)
15 (14–18)
21 (19–22) 0.29 (0.12–0.52)
0.26 (0.09–0.51) 0.79 (0.45–1.1)
0.53 (0.41–0.89)
2.6 (2.1–3.3)
4.8 (4.0–5.8)
5.4 (4.0–6.3)
6.8 (4.4–12)
Winter Summer Winter
Winter
Summer
Pb Cd As
identify the important factors involved in controlling the transport and distribution of metal contaminants (Chen et al. 2012; Liu et al. 2003; Ruiz 2001) e.g., Pearson’s correlation. Pearson’s correlation (PC) matrix for analyzed sediment parameters was calculated to see if some of the parameters interrelated with each other and the results are presented in Table 3. During winter season, most of the elements in sediments showed significant positive correlation with each other except As with Cr that did not show any significant correlation. However, in summer season, all metals showed significant positive correlation except Cr with other metals that did not show any correlation. The significant relationship between all heavy metals suggests similar sources of input (human or natural) for these metals in the river sediment (Bastami et al. 2012). The statistical analysis of intermetallic relationship revealed that the high degree of significant correlation among the metals indicate the identical behavior of metals in the aquatic environment. High correlations between specific heavy metals in sediments may reflect similar levels of contamination and/or release from the same sources of pollution, mutual dependence and identical behavior during their transport (Jiang et al. 2014; Chen et al. 2012; Suresh et al. 2012; Li et al. 2009; Ha˚kanson and Jansson 1983). Assessment of metal pollution
15 (13–17)
10 (8.2–12)
32 (28–33)
10 (8.1–15)
16 (12–18)
19 (16–20)
49 (47–51) 41 (38–42)
19 (19–21)
24 (20–30)
52 (48–56)
15 (14–17)
24 (22–25)
30 (28–34)
52 (48–54) 65 (60–68)
Summer Winter
Cu
By comparing with toxicological reference values To predict the metal pollution in sediment of the studied river in Bangladesh, the available data for a comparative analysis with background and toxicological reference Table 3 Pearson correlation coefficient matrix for heavy metals in sediments of Paira River, Bangladesh
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59 (55–62) 41 (39–43) 63 (54–74) 54 (52–55)
27 (25–28) 26 (23–31)
42 (40–45) 45 (42–46) 93 (90–96) 84 (78–90)
37 (33–39)
18 (14–20)
14 (11–17) 34 (28–38)
15 (14–16) 35 (30–39)
53 (49–60) 63 (60–65)
25 (23–26)
28 (22–30)
37 (32–42) 46 (35–55) 57 (50–62)
18 (15–22)
13 (11–18)
35 (33–38)
17 (15–18)
32 (20–38) 27 (25–35)
30 (27–32)
46 (40–50)
58 (48–65)
Ni
Cu
As
P7 P8
P6
P5
P4
P3
P1
Cd
Pb
Winter (n = 24) Cr
1
Ni
0.842**
1
Cu
0.767*
0.819*
1
As
0.598
0.857**
0.838**
1
Cd
0.894**
0.917**
0.872**
0.738*
1
Pb
0.739*
0.900**
0.863**
0.742*
0.888**
1
Summer (n = 24) Cr
1
Ni
0.153
Cu
0.293
0.961**
1
As
0.395
0.755*
0.810*
1
Cd
0.541
0.737*
0.812*
0.785*
1
Pb
0.100
0.938**
0.926**
0.801*
0.803*
1
* Correlation is significant at 0.05 level (2-tailed) P2
Winter Winter
Cr
Summer
Ni
Summer
Cr
Sites
Table 2 Concentration of heavy metal (mg/kg dw) in sediments of Paira River at different sampling sites [mean (range)] (n = 3)
10 (8.4–11)
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Summer
1842
** Correlation is significant at 0.01 level (2-tailed)
1
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Table 4 Comparison of metal concentration (mg/kg dw) in sediment of Paira River with some reference values and other reported values of other countries Sampling area
Cr
Ni
Cu
As
Cd
Pb
References
Paira River, Bangladesh
45 (17–93)a
34 (13–63)
30 (10–65)
12 (2.6–29)
0.72 (0.15–1.6)
25 (9.1–58)
This study
Bangshi River, Bangladesh
98.10
25.67
31.01
1.93
0.61
59.99
Rahman et al. (2013)
Padma River, Bangladesh
97
28
25
–
–
17
Datta and Subramanian (1998)
Yellow River, China
41–128
NA
30–102
14–48
NA
26–78
Liu et al. (2009)
River Ganges (India)
1.8–6.4
NA
0.98–4.4
NA
0.14–1.4
4.3–8.4
Gupta et al. (2009)
Gomti River (India)
8.15
15.7
5
NA
2.42
40.3
Singh et al. (2005)
Guidelines for metals contamination in sediment ASV (average shale value)
90
68
45
13
0.3
20
Turekian and Wedepohl (1961)
CUC (continental upper crust)
92
47
28
5
0.09
17
Rudnick and Gao (2003)
TRV (toxicity reference value)
26
16
16
6
0.6
31
US EPA (1999) MacDonald et al. (2000)
LEL (lowest effect level)
26
16
16
6
0.6
31
TEL (threshold effect level)
37.3
18
35.7
5.9
0.59
35
PEL (probable effect level)
90
36
197
17
3.5
91
SEL (severe effect level)
110
75
110
33
10
250
NA not analyzed Values in parenthesis as range
Conc. of total metals (mg/kg dw)
a
500 450 400 350 300 250 200 150 100 50 0
P1
P2
P3
P4
P5
P6
P7
P8
Sampling sites
Fig. 2 Total concentrations of six metals in sediment (mg/kg dw) at the different sampling sites of Paira River, Bangladesh
values and some studied river sediment values are summarized in Table 4. It was noted that the average concentration of As, Cd and Pb in the sediment samples exceeded the geochemical background, i.e., average worldwide shale standard and continental upper crust value (Turekian and Wedepohl 1961). Similar comparison results were observed by Mohiuddin et al. (2011) for heavy metals in surface sediment from an urban river in Bangladesh. The mean concentrations of all the analyzed heavy metals were higher than those of the US Environmental Protection Agency’s (US EPA) toxicity reference values (TRV),
lowest effect levels (LEL) and probable effect levels (PEL) (Table 4). The benchmarks applied were the lowest effect level (LEL) and the severe effect level (SEL) for the protection of aquatic organisms. If both LEL and SEL criteria are exceeded, the metal may severely impact biota health. If only the LEL criterion is exceeded, the metal may have moderately impact on biota health, while concentrations above PEL are expected to be frequently associated with adverse biological effects. The incidence of toxicity was determined among samples in which none of the substances were exceeded the TELs and PELs concentrations. The mean concentrations of heavy metals in sediments of this river were higher than those of the sediment of the rivers Bangshi and Padma in Bangladesh and other study rivers from other countries (Table 4). The results indicated that the levels of heavy metals found in the studied river might create an adverse effect on the aquatic ecosystem associated with the wetland that receives wastewaters from the nearby district. Toxic unit analysis Potential acute toxicity of contaminants in sediment samples can be estimated as the sum of the toxic units, defined as the ratio of the determined concentration to probable effect levels (PELs) value (Zheng et al. 2008; Pedersen et al. 1998). Toxic unit (TU) and sum of toxic units
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Environ Earth Sci (2015) 73:1837–1848
Fig. 3 Estimated sum of the toxic units (
Table 5 Contamination factors, degree of contamination, contamination level and pollution load of heavy metals in sediments of Paira River, Bangladesh
Sites
P1
P TUs) in surface sediments of Paira River, Bangladesh
Seasons
Contamination factors Cfi
Contamination level
Cr
Ni
Cu
As
Cd
Pb
Winter
0.77
0.47
0.42
0.42
2.62
0.76
5.2
Low Low
Summer
0.45
0.19
0.34
0.20
0.86
0.48
2.4
P2
Winter
0.96
0.51
0.54
0.52
1.78
1.07
5.1
Low
P3
Summer Winter
0.50 0.95
0.26 0.68
0.23 1.15
0.37 1.30
0.96 3.81
0.82 2.18
3.0 9.7
Low Medium
Summer
0.29
0.55
0.72
0.72
2.40
1.79
6.4
Low
Winter
0.41
0.52
0.33
1.16
1.39
0.84
4.5
Low
Summer
0.47
0.23
0.22
0.62
1.63
0.59
3.6
Low
Winter
1.05
0.50
0.53
0.52
2.69
0.71
5.6
Low
Summer
0.88
0.21
0.36
0.27
1.92
0.46
3.8
Low
P4 P5 P6 P7 P8
Winter
0.62
0.39
0.67
0.52
2.02
1.20
5.2
Low
Summer
0.28
0.39
0.43
0.38
0.51
0.73
2.6
Low
Winter
1.56
0.92
1.15
1.62
5.50
2.89
13.1
Summer
0.70
0.87
1.09
1.31
2.86
1.98
8.6
Medium
Winter
1.40
0.80
1.45
2.22
4.62
1.99
12.0
Medium
Summer
0.75
0.60
0.91
2.03
2.82
1.79
8.7
Medium
P ( TUs) for heavy metals in surface sediments of Paira River were presented in Fig. 3. Heavy metal toxic units in the studied river decreased in the order of Ni [ As [ Cr [ Pb [ Cd [ Cu. The average toxic unit values of Cr, Ni, Cu, As, Cd and Pb in river sediments were 0.64, 1.13, 0.18, 0.79, 0.26 and 0.32 during winter whereas they were 0.36, 0.78, 0.12, 0.56, 0.15 and 0.24 during summer season, respectively. The sum of toxic units at sites P7 and P8 were greater than 4, indicating a moderate to serious toxicity of heavy metals to sediment-dwelling fauna in the study river (Wang et al. 2011; Xiao et al. P 2012). The sum of toxic units ( TUs) for the studied metals for sites P3, P7 and P8 were higher than the other
123
Degree of contamination Cd
Medium
sites, which were in the similar trends of metal concentrations in sediments (Figs. 2, 3). Potential ecological risk Ha˚kanson (1980) developed a methodology to assess ecological risks for aquatic pollution control. The methodology is based on the assumption that the sensitivity of the aquatic system depends on its productivity. In the present study, values of contamination factor Cfi existed in the order of Cd [ Pb [ As [ Cr [ Cu [ Ni in the sediments of Paira River (Table 5). The contamination factor and
Environ Earth Sci (2015) 73:1837–1848
1845
degree of contamination were classified into four groups by Ha˚kanson (1980). Values of the contamination factor are characterized as follows: Cfi \ 1; 1 B Cfi \ 3; 3 B Cfi \ 6; and Cfi C 6; indicate low, moderate, considerable, and very high contamination factor, respectively. The degree of contamination (Cd ) defined the quality of the environment in the following way: Cd \ 8; 8 B Cd \ 16; 16 B Cd \ 32; and Cd C 32; indicate low, moderate, considerable, and very high degree of contamination accordingly (Rahman et al. 2013). The assessment of integrated pollution degree of sediments is based on the degree of contamination (Cd ). The ranges of Cd is 2.4–13.1 with an average 6.2, indicating low contamination of the
sediment environment (Fu et al. 2009). However, the degree of contamination (Cd ) of the selected heavy metals in sediments were low to medium level (2.4–13.1) and winter season showed slight higher contamination factor (Cfi ) than summer (Table 5). Pollution load index (PLI) value equal to zero indicates perfection; value of one indicates the presence of only baseline level of pollutants and values above one indicates progressive deterioration of the site and estuarine quality (Suresh et al. 2012, 2011; Mohiuddin et al. 2011). Extend of pollution increase with the increase of numerical PLI value. As per above grade, present sediments were polluted considerably, since PLI of three sites (P3, P7 and P8) were higher than one. The PLI values for winter ranged from 0.63 to 1.75 with an average 1.03 and during summer ranged from 0.34 to 1.22 with an average of 0.66 (Fig. 4), which confirmed the progressive deterioration of sediments by selected heavy metals. The PLI can provide some understanding to the public about the quality of an aquatic environment (Suresh et al. 2012). Potential ecological risk for single regulator was classified into following five groups: Eri \ 40; 40 B Eri \ 80; 80 B Eri \ 160; 160 B Eri \ 320 and Eri C 320; indicate low, moderate, considerable, high and very high risk and potential ecological risk index (PER) for all factors were classified into following four groups: PER \95; 95 B PER \ 190; 190 B PER \ 380 and PER C380; indicate low, moderate, considerable and very high ecological risk (Suresh et al. 2012; Yi et al. 2011; Guo et al. 2010; Ha˚kanson 1980). The results of potential ecological risk factor Eri and the potential ecological risk index (PER)
Fig. 4 Pollution load index (PLI) of heavy metals in sediment at different sites of Paira River, Bangladesh Table 6 Potential ecological risk factors Eri and potential ecological risk indexes (PER) of heavy metals in sediments of Paira River, Bangladesh
Sites
P1
Ni
Cu
As 4.2
Winter
1.5
2.8
2.1
Summer
0.9
1.1
1.7
Winter
1.9
3.1
2.7
Summer
1.0
1.5
1.2
Winter
1.9
4.1
5.8
Summer
0.6
3.3
3.6
Winter
0.8
3.1
1.7
Summer
0.9
1.4
1.1
Winter
2.1
3.0
2.7
Summer
1.8
1.3
1.8
Winter
1.2
2.3
3.4
Summer
0.6
2.3
2.1
P7
Winter
3.1
5.5
5.8
P8
Summer Winter
1.4 2.8
5.2 4.8
Summer
1.5
3.6
P3 P4 P5 P6
Indicates the moderate ecological risk for Cd in sediment
Potential ecological risk factor Eri Cr
P2
a
Seasons
2.0 5.2 3.7
Cd
Pb
Risk index (PER)
Pollution degree
79
3.8
93.1
Low
26
2.4
33.8
Low
53
5.4
71.8
Low
29
4.1
40.2
Low
114a
11
150
Moderate
72
8.9
95.7
Moderate
42
4.2
63.2
Low
49
3.0
61.4
Low
81
3.5
97.1
Low
58
2.3
67.3
Low
61
6.0
78.7
Low
15
3.7
28.0
Low
16
165a
14
210
Considerable
5.4 7.2
13 22
86a 139a
9.9 10
121 186
Moderate Moderate
4.6
20
85a
9.0
124
Moderate
13 7.2 12 6.2 5.2 2.7 5.2 3.8
123
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are summarized in Table 6. The order of PER in sediments were in the following descending order of Cd [ As [ Pb [ Cu [ Ni [ Cr and the potential ecological risk for single metal was low to high risk group. When combining the potential ecological risk index of individual metal Eri (Table 6) with its grade classifications, each single metal showed low potential ecological risk. However, Cd in the present study showed higher ecological risk. Generally, applications of phosphate fertilizers to the agricultural field beside the river and waste disposal from the town are the main sources of cadmium in sediment (ATSDR 2008). The potential ecological risk index (PER) in the sampling sites were 33.8–210, indicates low to considerable risk (Table 6). Conclusion Contamination of heavy metals was investigated in the surface sediments of Paira River located at the southern part of Bangladesh. The levels of metal in sediments were compared with the results of other studies as well as toxicological reference values. Comparative study showed that metals were slightly higher than the corresponding values and might create an adverse effect on this riverine ecosystem. From the potential ecological risk index (PER) values, each single metal had low potential ecological risk with the exception of Cd. Sources of Cd in the present study were metal processing industries, agricultural lands and the activities from the residents. Higher potential ecological risk was observed at sites P7 and P8, indicated moderate to considerable risk level. This study confirmed the consequences of As, Cd and Pb levels in sediments. Finally, it is concluded that further detailed assessment of these three vital metals are highly recommended for the study area. In addition, chemical speciation and bioavailability of heavy metals is needed to accomplish a comprehensive ecological risk assessment. Acknowledgments The authors thank the authority of Patuakhali Science and Technology University (PSTU), Bangladesh and Yokohama National University, Japan for providing laboratory facilities to analyze samples. The authors also delighted to express their gratefulness and sincerest thanks to Professor Dr. Syed Shakhwat Husain (Ex Vice Chancellor, PSTU), for his valuable suggestions and cooperation to carry out this research. Furthermore, we are thankful for the kind help from the members of the Department of Soil Science Patuakhali Science and Technology University (PSTU), Bangladesh during the field sampling.
References Agency for Toxic Substances and Diseases Registry, ATSDR (2008) Division of Toxicology and Environmental Medicine. Public
123
Environ Earth Sci (2015) 73:1837–1848 Health Statement of Cadmium. US Department of Health and Human Services, Atlanda Ahmad MK, Islam S, Rahman S, Haque MR, Islam MM (2010) Heavy metals in water, sediment and some fishes of Buriganga River, Bangladesh. Int J Environ Res 4(2):321–332 Alam AMS, Islam MA, Rahman MA, Siddique MN, Matin MA (2003) Comparative study of the toxic metals and non-metal status in the major river system of Bangladesh. Dhaka Univ J Sci 51(2):201–208 Al-Khashman OA (2007) Determination of metal accumulation in deposited street dusts in Amman, Jordan. Environ Geochem Health 29:1–10 Armitage PD, Bowes MJ, Vincent HM (2007) Long-term changes in macroinvertebrate communities of a heavy metal polluted stream: The River Nent (Cumbria, UK) after 28 years. River Res Appl 23(9):997–1015 Baeyens W, De Brauwere A, Brion N, De Gieter M, Leermakers M (2007) Arsenic speciation in the River Zenne, Belgium. Sci Total Environ 384:409–419 Bai J, Cui B, Chen B, Zhang K, Deng W, Gao H, Xiao R (2011) Spatial distribution and ecological risk assessment of heavy metals in surface sediments from a typical plateau lake wetland, China. Ecol Model 222(2):301–306 Bastami KD, Hossein B, Sarah H, Farzaneh S, Ali H, Mousa DB (2012) Geochemical and geo-statistical assessment of selected heavy metals in the surface sediments of the Gorgan Bay, Iran. Mar Pollut Bull 64(12):2877–2884 BBS (2011) Bangladesh Bureau of Statistics. Statistics and Informatics division, Ministry of Planning, Population and Housing Census, National Series, Bangladesh Calmano W, Hong J, Fo¨rstner U (1993) Binding and mobilization of heavy metals in contaminated sediments affected by pH and redox potential. Water Sci Technol 28:223–235 Chen B, Liang X, Xu W, Huang X, Li X (2012) The changes in trace metal contamination over the last decade in surface sediments of the Pearl River Estuary, South China. Sci Total Environ 439:141–149 Datta DK, Subramanian V (1998) Distribution and fractionation of heavy metals in the surface sediments of the Ganges-Brahmaputra-Meghna River system in the Bengal Basin. Environ Geol 36(1–2):93–101 Dı´az-Somoano M, Kylander ME, Lo´pez-Anto´n MA, Sua´rez-Ruiz I, Martı´nez-Tarazona MR, Ferrat M, Kober B, Weiss DJ (2009) Stable lead isotope compositions in selected coals from around the world and implications for present day aerosol source tracing. Environ Sci Technol 43:1078–1785 ElNemr AH, El Sikaily A, Khaled A (2007) Total and leachable heavy metals in muddy and sandy sediments of Egyptian coast along Mediterranean Sea. J Coast Shelf Sci 129:151–168 Facetti J, Dekov V, Van Grieken R (1998) Heavy metals in sediments from the Paraguay River: a preliminary study. Sci Total Environ 209:79–86 Fu C, Guo J, Pan J, Qi J, Zhou W (2009) Potential ecological risk assessment of heavy metal pollution in sediments of the Yangtze River within the wanzhou section, China. Biol Trace Elem Res 129:270–277 Fu J, Zhao C, Luo Y, Liu C, Kyzas GZ, Luo Y, Zhao D, An S, Zhu H (2014) Heavy metals in surface sediments of the Jialu River, China: their relations to environmental factors. J Hazard Mater 270:102–109 Gao X, Chen CTA (2012) Heavy metal pollution status in surface sediments of the coastal Bohai Bay. Water Res 46:1901–1911 Guo W, Liu X, Liu Z, Li G (2010) Pollution and potential ecological risk evaluation of heavy metals in the sediments around Dongjiang Harbor, Tianjin. Proc Environ Sci 2:729–736
Environ Earth Sci (2015) 73:1837–1848 Gupta A, Rai DK, Pandey RS, Sharma B (2009) Analysis of some heavy metals in the riverine water, sediments and fish from river Ganges at Allahabad. Environ Monit Assess 157(1–4):449–458 Ha˚kanson L (1980) An ecological risk index for aquatic pollution control: a sedimentological approach. Water Res 14:975–1001 Ha˚kanson L, Jansson M (1983) Principles of lake sedimentology. Springer, Berlin Haque MR, Ahmed MK, Rahman MM, Mannaf MA (2007) Physicochemical parameters and heavy metal contamination in sediment and water of Passur River, Mongla port, Bangladesh. J NOAMI 23(2):97–108 Hilton J, Davison W, Ochsenbein U (1985) A mathematical model for analysis of sediment coke data. Chem Geol 48:281–291 Hou D, He J, Lu¨ C, Ren L, Fan Q, Wang J, Xie Z (2013) Distribution characteristics and potential ecological risk assessment of heavy metals (Cu, Pb, Zn, Cd) in water and sediments from Lake Dalinouer, China. Ecotoxicol Environ Saf 93:135–144 Huang L, Pu X, Pan JF, Wang B (2013) Heavy metal pollution status in surface sediments of Swan Lake lagoon and Rongcheng Bay in the northern Yellow Sea. Chemosphere 93(9):1957–1964 Islam MS, Han S, Masunaga S (2014) Assessment of trace metal contamination in water and sediment of some rivers in Bangladesh. J Water Environ Technol 12(2):109–121 Jiang X, Teng A, Xu W, Liu X (2014) Distribution and pollution assessment of heavy metals in surface sediments in the Yellow Sea. Mar Pollut Bull. doi:10.1016/j.marpolbul.2014.03.020 Li C, Lu FY, Zhang Y, Liu TW, Hou W (2008) Spatial distribution characteristics of heavy metals in street dust in Shenyang city. Ecol Environ 17:560–564 Li FY, Fan ZP, Xiao PF, Oh K, Ma XP, Hou W (2009) Contamination, chemical speciation and vertical distribution of heavy metals in soils of an old and large industrial zone in Northeast China. Environ Geol 54:1815–1823 Li HB, Yu S, Li GL, Liu Y, Yu GB, Deng H, Wu SC, Wong MH (2012) Urbanization increased metal levels in lake surface sediment and catchment topsoil of waterscape parks. Sci Total Environ 432:202–209 Liang Y, Wong MH (2003) Spatial and temporal organic and heavy metal pollution at Mai Po Marshes Nature Reserve, Hong Kong. Chemosphere 52:1647–1658 Liu WX, Li XD, Shen ZG, Wang DC, Wai OWH, Li YS (2003) Multivariate statistical study of heavy metal enrichment in sediments of the Pearl River estuary. Environ Pollut 121(3):377–388 Liu C, Xu J, Zhang P, Dai M (2009) Heavy metals in the surface sediments in Lanzhou Reach of Yellow River, China. Bull Environ Contam Toxicol 82(1):26–30 Lourin˜o-Cabana B, Lesven L, Charriau A, Billon G, Ouddane B, Boughriet A (2011) Potential risks of metal toxicity in contaminated sediments of Deuˆle River in Northern France. J Hazard Mater 186(2–3):2129–2137 MacDonald DD, Ingersoll CG, Berger TA (2000) Development and evaluation of consensus-based sediment quality guidelines for freshwater ecosystems. Arch Environ Contam Toxicol 39(1):20–31 Mandal BK, Suzuki KT (2002) Arsenic round the world: a review. Talanta 58:201–235 McCready S, Birch GF, Long ER (2006) Metallic and organic contaminants in sediments of Sydney Harbour, Australia and vicinity—a chemical dataset for evaluating sediment quality guidelines. Environ Int 32:455–465 Mohiuddin KM, Ogawa Y, Zakir HM, Otomo K, Shikazono N (2011) Heavy metals contamination in water and sediments of an urban river in a developing country. Int J Environ Sci Technol 8(4):723–736
1847 Mucha AP, Vasconcelos MTSD, Bordalo AA (2003) Macrobenthic community in the Douro Estuary: relations with trace metals and natural sediment characteristics. Environ Pollut 121:169–180 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 Pravin US, Trivedi P, Ravindra MM (2012) Sediment heavy metal contaminants in Vasai Creek of Mumbai: pollution impacts. Am J Chem 2:171–180 Rahman MS, Saha N, Molla AH (2013) Potential ecological risk assessment of heavy metal contamination in sediment and water body around Dhaka export processing zone. Bangladesh Environ Earth Sci 71:2293–2308 Rudnick RL, Gao S (2003) Composition of the continental crust. Treatise Geochem 3(1):1–64 Ruiz F (2001) Trace metals in estuarine sediments from the Southwestern Spanish coast. Mar Pollut Bull 42(6):481–489 Saha N, Zaman MR (2013) Evaluation of possible health risks of heavy metals by consumption of foodstuffs available in the central market of Rajshahi city, Bangladesh. Environ Monit Assess 185(5):3867–3878 Shikazono N, Tatewaki K, Mohiuddin KM, Nakano T, Zakir HM (2012) Sources, spatial variation and speciation of heavy metals in sediments of the Tamagawa River in Central Japan. Environ Geochem Health 34:13–26 Sin SN, Chua H, Lo W, Ng LM (2001) Assessment of heavy metal cations in sediments of Shing Mun River, Hong Kong. Environ Int 26(5–6):297–301 Singh KP, Mohan D, Singh VK, Malik A (2005) Studies on distribution and fractionation of heavy metals in Gomti River sediments: a tributary of the Ganges, India. J Hydrol 312(1–4):14–27 Suresh G, Ramasamy V, Meenakshisundaram V, Venkatachalapathy R, Ponnusamy V (2011) Influence of mineralogical and heavy metal composition on natural radionuclide contents in the river sediments. Appl Radiat Isot 69:1466–1474 Suresh G, Sutharsan P, Ramasamy V, Venkatachalapathy R (2012) Assessment of spatial distribution and potential ecological risk of the heavy metals in relation to granulometric contents of Veeranam lake sediments, India. Ecotoxicol Environ Saf 84:117–124 Turekian KK, Wedepohl KH (1961) Distribution of the elements in some major units of the earth’s crust. Geol Soc Am Bull 72:175–192 Uluturhan E, Kontas A, Can E (2011) Sediment concentrations of heavy metals in the Homa Lagoon (Eastern Aegean Sea): assessment of contamination and ecological risks. Mar Pollut Bull 62(9):1989–1997 US EPA (1999) Screening level ecological risks assessment protocol for hazardous waste combustion facilities vol. 3. Appendix E: toxicity reference values, EPA US EPA (2001) Methods for collection, storage and manipulation of sediments for chemical and toxicological analyses: technical manual. EPA-823-B-01-002, Office of Water, Washington, DC Varol M, Sen B (2012) Assessment of nutrient and heavy metal contamination in surface water and sediments of the Upper Tigris River, Turkey. Catena 92:1–10 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 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
123
1848 Xu ZQ, Ni SJ, Tuo XG (2008) Calculation of heavy metals toxicity coefficient in the evaluation of potential ecological risk index. Environ Sci Technol 31:112–115 Yan C, Li Q, Zhang X, Li G (2010) Mobility and ecological risk assessment of heavy metals in surface sediments of Xiamen Bay and its adjacent areas, China. Environ Earth Sci 60:1469–1479 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 Yi Y, Yang Z, Zhang S (2011) Ecological risk assessment of heavy metals in sediment and human health risk assessment of heavy metals in fishes in the middle and lower reaches of the Yangtze River basin. Environ Pollut 159:2575–2585 Yu G, Liu Y, Yu S, Wu S, Leung A, Luo X, Xu B, Li H, Wong M (2011) Inconsistency and comprehensiveness of risk assessments for heavy metals in urban surface sediments. Chemosphere 85:1080–1087
123
Environ Earth Sci (2015) 73:1837–1848 Yuan GL, Liu C, Chen L, Yang Z (2011) Inputting history of heavy metals into the inland lake recorded in sediment profiles: Poyang Lake in China. J Hazard Mater 185(1):336–345 Zhan S, Peng S, Liu C, Chang Q, Xu J (2010) Spatial and temporal variations of heavy metals in surface sediments in Bohai Bay, North China. Bull Environ Contam Toxicol 84:482–487 Zhang R, Zhou L, Zhang F, Ding Y, Gao J, Chen J, Yan H, Shao W (2013) Heavy metal pollution and assessment in the tidal flat sediments of Haizhou Bay, China. Mar Pollut Bull 74:403–412 Zheng N, Wang Q, Liang Z, Zheng D (2008) Characterization of heavy metal concentrations in the sediments of three freshwater rivers in Huludao City, Northeast China. Environ Pollut 154:135–142 Zhu YG, Ioannidis JP, Li H, Jones KC, Martin FL (2011) Understanding and harnessing the health effects of rapid urbanization in China. Environ Sci Technol 45:5099–5104
