Open Access

Occurrence, risk assessment, and source apportionment of heavy metals in surface sediments from Khanpur Lake, Pakistan

Journal of Analytical Science and Technology20145:28

DOI: 10.1186/s40543-014-0028-z

Received: 20 December 2013

Accepted: 1 May 2014

Published: 2 May 2014

Abstract

Background

The present study was carried out to assess the seasonal variations, source apportionment, and risk assessment of heavy metals (Cd, Cr, Cu, Fe, Mn, Pb, and Zn) in the surface sediments from the Khanpur Lake, Pakistan.

Methods

Composite samples are collected and processed to measure the concentrations of heavy metals in Ca(NO3)2 extract and acid extract of the sediments using flame atomic absorption spectrophotometry.

Results

The highest concentrations in acid extracts of the sediments are found for Fe, followed by Mn, while the least concentrations are noted for Cd. Relatively higher extraction efficiencies in Ca(NO3)2 extract are observed for Pb and Cd, which also reveal extremely severe enrichment in the sediments as shown by the enrichment factor. Geoaccumulation index shows moderate and strong to extreme pollution of Pb and Cd, respectively, whereas potential ecological risk factor exhibits low to very high risk by Cd; the cumulative ecological risk index reveals low to very high risk of contamination in the sediments as a whole. Principal component analysis and cluster analysis reveal dominant anthropogenic contributions of Cd, Pb, Cr, and Zn.

Conclusion

Measured concentrations of Cd, Cr, Cu, Mn, and Pb in the sediments exceed the sediment quality guideline for the lowest effect levels (LEL), while the concentrations of Cd and Pb are also higher than the effects range low (ERL) values, manifesting occasional adverse biological effects to the surrounding flora and fauna. Moreover, the mean effects range medium (ERM) quotient reveals 21% probability of toxicity in the sediments.

Keywords

Sediment Metal Risk assessment Multivariate analysis AAS Pakistan

Background

Contamination of aquatic ecosystems with heavy metals has received much attention due to their toxicity, abundance, and persistence in the environment and subsequent accumulation in aquatic habitats (Arnason and Fletcher [2003]). Elevated levels of heavy metals in environmental compartments, such as aquatic sediments, may pose a risk to human health due to their transfer in aquatic media and uptake by living organisms, thereby entering the food chain (Sin et al. [2001]; Varol and Sen [2012]). Heavy metals may enter a freshwater reservoir from a variety of sources, either natural or anthropogenic (Adaikpoh et al. [2005]; Akoto et al. [2008]). Generally, in natural ecosystems, most of the metals are present in very low concentrations and are mostly derived from rock and soil weathering (Reza and Singh [2010]; Varol and Sen [2012]). Major anthropogenic sources of heavy metal pollution are mining and smelting activities, atmospheric deposition, disposal of untreated/partially treated urban and industrial effluents, metal chelates from different industries, and haphazard use of heavy metal-containing fertilizers and pesticides during agricultural activities (Martin [2000]; Nouri et al. [2008]; Reza and Singh [2010]).

Sediments are ecologically sensitive components of the aquatic ecosystems and are also a reservoir of the contaminants, which take part considerably in maintaining the trophic status for any water reservoir (Singh et al. [2005]). Depending upon the physicochemical conditions, sediments can act both as source and sink for nutrients and heavy metals. Hence, sediments are not only considered as carriers of contaminants but also potential secondary sources of contaminants in an aquatic ecosystem. Consequently, the analysis of sediments is a useful method to study the heavy metal pollution in any area (Gielar et al. [2012]; Varol and Sen [2012]). The toxicity and mobility of the metals in sediments vary among different chemical forms (Cuong and Obbard [2006]; Yu et al. [2010]). Therefore, the evaluation of distribution and mobility/potential bioavailability of heavy metals in surface sediments is an important step to evaluate the degree of contamination of an aquatic ecosystem (Martin et al. [2009]; Sprovieri et al. [2007]). Assessment of biologically available fractions of heavy metals helps to evaluate their potential for mobilization and availability to benthic organisms (Rodrigues et al. [2010]). Various chemical extraction methods have been suggested to determine the bioavailable fractions of the metals in sediments. Generally, weak acids/electrolytes are used to extract the bioavailable fractions of the metals in sediments (An and Kampbell [2003]).

Major objectives of the present study are (i) to measure the concentrations of heavy metals (Cd, Cr, Cu, Fe, Mn, Pb, and Zn) in sediments during summer and winter; (ii) to determine potential ecological risk using enrichment factor (EF), geoaccumulation index (Igeo), potential ecological risk factor (E i ), and potential ecological risk index (RI); (iii) to identify risks of potential toxicity by comparison with sediment quality guidelines (SQGs); (iv) to determine potential bioavailability and mobility of the metals; and (v) to define their natural/anthropogenic contributions using multivariate statistical methods. It is anticipated that the study would provide a baseline data regarding the distribution and accumulation of heavy metals in the sediments and would help reduce the contamination by identifying the major pollution sources.

Methods

Study area

Khanpur Lake (longitude 72°56′E and latitude 33°48′N) is situated on the Haro river near the town of Khanpur, about 40 km northwest of Islamabad, Pakistan (Figure 1). It supplies drinking water to the inhabitants of twin cities of Islamabad and Rawalpindi, Pakistan, and irrigation water to the agricultural areas surrounding the cities. It was built in 1983 with the storage capacity of 140 million m3 of water. It is 51 m high with an average depth of 15 m. The gross storage capacity of the reservoir is 0.132 km3 with a total catchment area of 798 km2. The surface area of the reservoir varies from maximum of 1,806 ha to minimum of 215 ha. In past, the lake was leased for commercial exploitation. The area around the lake has been planted with flowering trees and laid out with gardens, picnic spots, and secluded paths. The lake is used for picnics, fishing, boating, sailing, water skating, and diving. Untreated and/or partially treated urban and industrial effluents, road and agricultural run offs, poultry farms wastes, and contaminants released during the recreational use of motorboats are among the suspected sources of pollution in the lake.
https://static-content.springer.com/image/art%3A10.1186%2Fs40543-014-0028-z/MediaObjects/40543_2014_Article_28_Fig1_HTML.jpg
Figure 1

Location of the sampling points in the study area.

Sampling and preservation

A total of 100 composite surface sediment samples from Khanpur Lake, Pakistan, were collected in the summer and winter of 2008. Each sediment sample was a composite of three to five sub-samples from an area of 1 to 2 m2 and collected using a snapper (Ø 5 cm) in top layer (0 to 10 cm). The sediment samples were taken from the central portion of the snapper with a plastic spatula to avoid any contamination from the metallic parts of the sampler. Before transferring the samples in pre-cleaned Ziploc polythene bags (S. C. Johnson & Son, Inc., Racine, WI, USA), the above water was decanted. The samples were kept in airtight large plastic containers for transport to the laboratory. The sediment samples were then oven-dried, grounded, homogenized, and sealed in pre-cleaned polythene bags and stored in a refrigerator until further processing (Radojevic and Bashkin [1999]).

Sample processing and analysis

The samples were processed to assess the Ca(NO3)2-extractable and acid-extractable fractions of heavy metals. A single-step extraction procedure using 0.1 M Ca(NO3)2 was applied to the sediment samples at room temperature in order to evaluate the bioavailable metal fractions (An and Kampbell [2003]). An aliquot of 5 g of the sample was added to 50-mL solution of 0.1 M Ca(NO3)2, and the extraction was performed in pre-cleaned glass vessel by shaking on an auto-shaker at 240 vibrations/min for 16 h. A blank sample was also processed with the same amount of reagents without sediment sample. Three replicate extractions were performed for each sample. The final extracts were separated from the solid residues through filtration using a fine (0.45-μm pore) filter paper (An and Kampbell [2003]; Radojevic and Bashkin [1999]; Rodrigues et al. [2010]). To measure the acid-extractable fractions, 1- to 2-g dried sediment sample was digested in a microwave system using an acid mixture of 9 mL HNO3 and 3 mL HCl (USEPA [2007]). Three replicate extractions were performed for each sample. The digests were then filtered through the fine filter paper and made up to 50 mL with double distilled water and stored at 4°C. A blank sample was also processed with the same amount of chemical reagents without sediment sample. Heavy metals (Cd, Cr, Cu, Fe, Mn, Pb, and Zn) in the sediment samples were analyzed using a flame atomic absorption spectrophotometer (Shimadzu AA-670, Kyoto, Japan). The calibration line method was used for quantification of the metals, and the samples were appropriately diluted whenever required (Radojevic and Bashkin [1999]; Shah et al. [2012]). The optimum analytical conditions used for the quantification of the selected metals on the spectrophotometer are given in Table 1. During sample collection and analysis, strict QA/QC measures were taken including method blanks, analysis of standard reference material, and analysis of duplicate samples. The reagents for the blanks were prepared during each extraction, and all samples were blank-corrected. Standard reference material (NIST SRM-2709) was also used to ensure the reliability of the metal data as shown in Table 1. The measured metal levels closely matched with the certified values. Moreover, reliability of the finished data was also ensured using known spikes and by conducting interlaboratory comparison, and the results were within ±1.5%. Working standards of the metals were prepared from a stock solution of 1,000 mg/L (E-Merck, Darmstadt, Germany) by successive dilutions. The moisture content of each sediment sample was determined by drying separate 5-g sample in an oven (105°C ± 2°C) to constant weight. From this, a correction to dry mass was obtained, which was applied to all reported metal concentrations. All the measurements were made in triplicate.
Table 1

Description of optimum analytical conditions and analysis of selected metals in SRM

 

Cd

Cr

Cu

Fe

Mn

Pb

Zn

Wavelength (nm)

228.8

357.9

324.8

248.3

279.5

217.0

213.9

HC lamp current (mA)

4.0

5.0

3.0

8.0

5.0

7.0

4.0

Slit width (nm)

0.3

0.5

0.5

0.2

0.4

0.3

0.5

Fuel gas flow rate (L/min)

1.8

2.6

1.8

2

1.9

1.8

2

Detection limit (μg/L)

4.0

6.0

4.0

6.0

3.0

10.0

2.0

SRM-certified level (mg/kg)

0.38

130

34.6

35,000

538

18.9

106

SRM-measured level ± SD (mg/kg)

0.36 ± 0.03

138 ± 8

35.2 ± 1.2

34,300 ± 385

547 ± 11

19.3 ± 1.4

109 ± 3.2

The analytical conditions were maintained on AAS using air-acetylene flame, and the standard reference material is SRM-2709.

Statistical analysis

Statistical analysis can be used to evaluate the complex eco-toxicological processes by showing the relationship and interdependency among the variables and their relative weights. Basic statistical parameters, such as minimum, maximum, mean, median, standard error (SE), and skewness, were computed along with correlation study. Multivariate techniques have been used for evaluation and characterization of analytical data (Fadigas et al. [2010]). Principal component analysis (PCA) and cluster analysis (CA) are among the most popular methods. The PCA finds out the diagonalization of the covariance or correlation matrix transforming the original chemical measurements into linear combinations of these measurements, which are the principal components (PCs). It rotates the coordinate space axes so that the explained variance of each PC is maximized. This technique allows for data reduction from higher to lower dimensional spaces to simplify their representation. Nonetheless, CA demonstrates the similarities between variables by examining the interpoint distances representing all possible variables in the higher dimensional space. The PCA was performed using varimax normalized rotation on the dataset, and the CA was applied to the standardized matrix of the samples using Ward's method, and the results are reported in the form of dendrograms. PCA and CA complement each other and have been widely used in environmental studies (Gielar et al. [2012]; Iqbal and Shah [2011]; Shah et al. [2012]; Singh et al. [2005]).

Pollutant indicators and risk assessment

To gauge the degree of contamination and to distinguish natural and anthropogenic inputs, EFs, Igeo, E i , and RI are computed (Cukrov et al. [2011]; Hakanson [1980]; Muller [1969]). EFs are calculated (Cukrov et al. [2011]; Iqbal and Shah [2011]; Luoma and Rainbow [2008]; Tessier et al. [2011]) by comparing the measured metal levels to the pre-industrial levels (Lide [2005]). In order to avoid the overestimation or underestimation of the enrichment; geochemical normalization based on the concentration of a conservative element is commonly employed. The purpose of normalization is to correct changes in the nature of sediments, which may influence the contaminant distribution. Various conservative elements may be used: Al, Fe, Th, Ti, Zr, etc. (Larrose et al. [2010]; Reimann and de Caritat [2005]). Iron is chosen as the conservative element for normalization in this work. The interest of using Fe content is its relationship to the abundance of clay and other aluminum silicates in the sediments. Its contents are influenced by natural sedimentation and the effects of enhanced erosion, but not by pollution (Iqbal and Shah [2011]). The normalized EF is usually computed as double ratios of the target element and Fe as a reference element in the examined sediments and Earth's crust using the following relationship:
EF = X / Fe sample X / Fe crust ,
(1)
where [X/Fe]sample and [X/Fe]crust refer, respectively, to the ratios of mean concentrations (mg/kg, dry weight) of the target element and Fe in the sediments and continental crust (Lide [2005]).
The Igeo enables the assessment of contamination by comparing the measured and pre-industrial concentrations of the metals in the Earth's crust (Loska et al. [2004]; Muller [1969]). It is computed using the following relationship:
I geo = log 2 C n 1.5 B n ,
(2)
where C n is the measured concentration of the element in the sediment samples, and B n is the geochemical background value in the Earth's crust (Lide [2005]). Factor 1.5 is introduced to minimize the effect of possible variations in the background values which may be attributed to lithogenic variations.
RI is introduced to assess the degree of heavy metal pollution in sediments, which was originally introduced by Hakanson ([1980]), according to the toxicity of heavy metals and the response of the environment:
RI = E i
(3)
E i = T i f i
(4)
f i = C i / C b ,
(5)
where RI is computed as the sum of all risk factors in sediments, E i is the monomial potential ecological risk factor for individual factors, and T i is the metal toxic factor. Based on the standardized heavy metal toxic factor developed by Hakanson ([1980]), the order of the level of heavy metal toxicity is Cd > Pb = Cu > Cr > Zn. The toxic factors for the metals are 30, 5, 5, 2, and 1, respectively. f i is the metal pollution factor, C i is the concentration of metal in the sediments, and C b is the reference value of a given metal in the Earth's crust (Lide [2005]).
Multiple contamination which is often encountered in natural environments affected by human activities is also calculated in terms of mean-effects range medium-quotient (m-ERM-Q) by the following relationship (de Vallejuelo et al. [2010]; Long and MacDonald [1998]; Tessier et al. [2011]):
m ERM Q = i = 1 n C i / ERM i n ,
(6)
where C i is the concentration of a metal in a sediment, ERM i is the ERM value for metal i, and n is the number of metals.

Results and discussion

Distribution of heavy metals in the sediments

Concentrations of heavy metals in acid extracts of the sediments during summer and winter in terms of statistical distribution parameters are shown in Table 2. During summer, the data reveal dominant mean level of Fe (4,630 mg/kg), followed by Mn (447.5 mg/kg), while the average concentration of Cd (1.883 mg/kg) is the lowest. On the average basis, the metals follow a decreasing concentration order: Fe > Mn > Zn > Cu > Cr > Pb > Cd. Among the metals, Fe indicates almost comparable mean and median levels with lower skewness, indicating relatively symmetrical distribution in acid extract of the sediments. The counterpart statistical data during winter show the highest average levels of Fe (3,791 mg/kg), followed by Mn (321.4 mg/kg), whereas Pb (18.24 mg/kg) and Cd (2.457 mg/kg) are found at relatively lower levels. On the mean basis, the metals exhibit a decreasing concentration order: Fe > Mn > Zn > Cr > Cu > Pb > Cd. Relatively normal distribution is revealed by Cd and Pb, which are also associated with lower skewness. Maximum dispersion in terms of SE is exhibited by Fe. Overall, significantly elevated average levels of the metals (except Cd and Cr) are noticed during summer compared with winter (Table 2). It could be due to the leaching of the metals into the reservoir from the roadside and agricultural runoffs during wet summer season.
Table 2

Statistical summary of heavy metal distribution in acid extract and Ca(NO 3 ) 2 extract of the sediments

  

Summer (n = 50)

Winter (n = 50)

pvalue

  

Min

Max

Mean

SE

Skew

Min

Max

Mean

SE

Skew

Acid extract

Cd

0.196

4.500

1.883

0.234

0.584

0.149

5.183

2.457

0.235

−0.084

<0.05

Cr

11.35

63.45

34.66

2.293

−0.262

23.82

68.97

37.65

1.543

1.790

Non-significant

Cu

25.15

49.39

36.84

1.285

−0.072

18.22

51.53

28.05

1.314

1.166

<0.05

Fe

3,835

5,186

4,630

57.83

−0.350

3,523

4,182

3,791

30.95

0.426

<0.05

Mn

236.2

836.7

447.5

32.97

0.713

167.7

886.0

321.4

26.00

2.234

<0.05

Pb

9.739

78.48

33.71

3.419

0.771

0.412

39.03

18.24

1.966

−0.003

<0.01

Zn

70.71

114.4

86.09

2.032

0.650

42.24

115.2

61.90

2.459

2.190

<0.05

Ca(NO3)2 extract

Cd

0.004

0.122

0.058

0.006

−0.081

0.016

0.146

0.071

0.006

0.175

Non-significant

Cr

0.042

0.546

0.217

0.027

0.794

0.008

0.478

0.230

0.023

0.061

Non-significant

Cu

0.008

0.220

0.098

0.008

0.212

0.012

0.134

0.073

0.006

−0.201

<0.05

Fe

0.020

28.50

2.069

0.985

4.415

0.248

1.218

0.658

0.053

0.314

<0.01

Mn

0.004

0.274

0.078

0.012

1.149

0.010

0.072

0.042

0.003

0.178

<0.01

Pb

0.206

2.032

1.192

0.085

−0.343

0.140

2.082

1.205

0.087

−0.254

Non-significant

Zn

0.010

0.656

0.179

0.023

1.972

0.056

0.186

0.118

0.007

0.175

<0.05

The heavy metal distribution is expressed in milligrams per kilogram.

Correlation study

The correlation coefficient matrix of heavy metals in the acid extract of the sediments during summer and winter is given in Table 3. During summer, strong correlations of Fe with Mn and Cu, Cr with Zn, and Cu with Mn are noted. Some other significant relationships of Pb with Cd and Cr are also observed. However, Pb and Zn show negative associations with Cu, Fe, and Mn, revealing their opposing distribution in the sediments during summer. The counterpart data related to the metal levels in the sediments during winter indicate strong correlations of Zn with Cu and Mn, Cu with Mn, and Cr with Cd and Cu, thus manifesting close association of these metals which might share common sources. Some significant correlations for Pb with Cr, Cu, Mn, and Zn are also observed. Fe does not show any significant relationship with other heavy metals in the sediments during winter, suggesting its independent variations in the sediments.
Table 3

Correlation coefficients ( r )* matrix for heavy metals in acid extract of sediments during summer and winter

 

Cd

Cr

Cu

Fe

Mn

Pb

Zn

Cd

1

0.580

0.389

−0.029

0.473

0.181

0.223

Cr

0.320

1

0.657

0.087

0.319

0.412

0.345

Cu

0.171

0.153

1

0.143

0.685

0.480

0.803

Fe

0.071

−0.056

0.615

1

0.126

0.151

−0.016

Mn

0.086

−0.031

0.863

0.551

1

0.425

0.619

Pb

0.362

0.432

−0.068

−0.187

−0.087

1

0.427

Zn

0.103

0.540

−0.028

−0.208

−0.245

0.062

1

Values for summer are below the diagonal, and those for winter are above the diagonal. *r values >0.330 or <−0.330 are significant at p < 0.01.

Pollution indices

The range and mean EF values of heavy metals in acid extract of the sediments during summer and winter are shown in Figure 2a. Seven degrees of contamination are commonly defined (Birch et al. [2003]): EF < 1 indicates no enrichment, EF < 3 minor enrichment, EF = 3 to 5 moderate enrichment, EF = 5 to 10 moderately severe enrichment, EF = 10 to 25 severe enrichment, EF = 25 to 50 very severe enrichment, and EF > 50 extremely severe enrichment. During summer, on the average basis, Cr reveals moderate enrichment, Cu and Mn indicate moderately severe enrichment, Zn manifests severe enrichment, Pb shows very severe enrichment, and Cd illustrates extremely severe enrichment in the sediments. The geochemical normalization study during winter reveals that Cr, Cu, and Mn indicate moderately severe enrichment; Pb and Zn explicate severe enrichment, and Cd illuminates extremely severe enrichment. Overall, Cd emerge as the major pollutant during both seasons; Pb poses severe to extremely severe enrichment during summer and minor to very severe enrichment during winter. Zn causes severe enrichment during both seasons. Mostly, elevated degree of pollution by the metals is noted during summer than during winter.
https://static-content.springer.com/image/art%3A10.1186%2Fs40543-014-0028-z/MediaObjects/40543_2014_Article_28_Fig2_HTML.jpg
Figure 2

Description of the different parameters. Description of (a) enrichment factor (EF), (b) geoaccumulation index (Igeo) and (c) potential ecological risk factor (E i ) of heavy metals in acid extract of sediments during summer (S) and winter (W).

The lowest, mean, and highest values of Igeo in acid extract of the sediments during summer and winter are illustrated in Figure 2b. The following categorizations are given by Muller ([1969]) for geoaccumulation index: Igeo < 0 indicates unpolluted, Igeo = 0 to 1 unpolluted to moderately polluted, Igeo = 1 to 2 moderately polluted, Igeo = 2 to 3 moderately to strongly polluted, Igeo = 3 to 4 strongly polluted, Igeo = 4 to 5 strongly to extremely polluted, and Igeo > 5 demonstrates extremely polluted. The highest category reflects at least a 100-time enrichment above the background values. As shown in the figure, during summer, Cd and Pb pose strong to extreme contamination and moderate contamination, respectively. However, the remaining metals exhibit practically un-contamination in the sediments. During winter, Cd indicates strong to extreme pollution; Zn causes unpolluted to moderate pollution, whereas Pb shows least to moderate contamination.

Ecological risk assessment

The range and mean E i values of the heavy metals in acid extract of the sediments during summer and winter are shown in Figure 2c. The following categorization is given by Hakanson ([1980]) for E i : E i  < 40 demonstrates low risk, E i  = 40 to 80 moderate risk, E i  = 80 to 160 considerable risk, E i  = 160 to 320 great risk, and E i  > 320 demonstrates very great risk. The categorization related to RI is also suggested by Hakanson ([1980]): RI < 65 explicates low risk, RI = 65 to 130 moderate risk; RI = 130 to 260 considerable risk, and RI > 260 explicates very high risk. The results elucidate that Cd causes low to very high risk, while the rest of the metals explicate low risk in the sediments during both seasons. Overall, the cumulative potential risk index (RI = 45.91 to 935 during summer and RI = 31.87 to 1,058 during winter) reveals low to very high risk of the sediments during both seasons. However, relatively higher potential ecological risk is observed during winter compared to summer.

Source apportionment

One of the important aspect of the present study is the source apportionment of the metals in sediments using PCA and CA. The principal component loadings of the heavy metals in acid extract of the sediments during summer and winter are given in Table 4, whereas the corresponding CA is shown in Figure 3. During summer, two PCs are extracted with eigenvalues more than 1, explaining about 60% of the total variance. The first PC (36.14% variance) reveals elevated loadings of Fe, Mn, and Cu, supported by their mutual cluster in CA. These metals are likely to be contributed by lithogenic processes such as soil erosion and rock weathering. The second PC (23.77% variance) shows significant loadings of Pb, Cd, Cr, and Zn supported by their shared cluster and are mainly contributed by automobile emissions, agricultural runoff, and untreated urban wastes. The counterpart data during winter also yield two PCs with eigenvalues greater than 1, explaining more than 66% of the total variance. PC1 (51.09% variance) exhibits higher loadings for Zn, Cu, Cr, Mn, Pb, and Cd, which are predominantly contributed by transportation activities, untreated urban wastes, and agricultural runoff. The cluster analysis also shows a joint cluster for these metals. PC2 (15.22% variance) reveals the natural/lithogenic contribution as manifested by the elevated loadings of Fe only which shows almost independent pattern in CA.
Table 4

Principal component loadings of heavy metals in acid extract of sediments during summer and winter

 

Summer

Winter

 

PC1

PC2

PC1

PC2

Eigenvalue

2.530

1.664

3.576

1.065

Percentage of total variance

36.14

23.77

51.09

15.22

Percentage of cumulative variance

36.14

59.91

51.09

66.30

Cd

0.131

0.733

0.506

−0.139

Cr

0.284

0.666

0.838

−0.015

Cu

0.914

0.144

0.884

0.179

Fe

0.800

−0.108

−0.035

0.951

Mn

0.913

0.020

0.835

0.140

Pb

−0.179

0.714

0.557

0.388

Zn

−0.273

0.393

0.876

0.011

https://static-content.springer.com/image/art%3A10.1186%2Fs40543-014-0028-z/MediaObjects/40543_2014_Article_28_Fig3_HTML.jpg
Figure 3

Cluster analyses of heavy metals in acid extract of sediments during (a) summer and (b) winter.

Sediment quality guidelines

The assessment of acid-extractable metal levels in the sediments is the first step to gauge the pollution of the water reservoir. However, it does not provide information on the potential toxicity to the benthic flora and fauna in the reservoir. For this purpose, numerous sediment quality guidelines are used to protect aquatic biota from the harmful and toxic effects related with sediment-bound contaminants (Caeiro et al. [2005]; McCready et al. [2006]; Spencer and Macleod [2002]). These guidelines evaluate the degree to which the sediment-associated chemical status might adversely affect the aquatic organisms and therefore are designed for the interpretation of sediment quality. SQGs have been developed for both freshwater and marine ecosystems to represent threshold chemical concentrations associated with the presence or absence of biological effects on communities (Caeiro et al. [2005]; Long and MacDonald [1998]; MacDonald et al. [2000]; Thompson et al. [2005]; Wenning et al. [2005]). These guidelines have been widely used to screen sediment contamination by comparing the concentrations in sediments with the corresponding quality guidelines in aquatic ecosystems (Caeiro et al. [2005]; MacDonald et al. [2000]). It is important to determine whether the estimated concentrations of heavy metals in sediments pose a threat to aquatic life, and they are assessed by two sets of sediment quality guidelines: (i) lowest effect level (LEL) and severe effect level (SEL) and (ii) effects range low (ERL) and effects range medium (ERM) (MacDonald et al. [2000]). These two sets of numerical SQGs are directly applied to assess the possible risk associated with heavy metal contamination in the sediments. It is interpreted that LEL and ERL as the concentrations below which adverse biological effects rarely occur. Hence, these are considered to provide a high level of protection for aquatic organisms. Similarly, SEL and ERM refer to the concentrations above which adverse biological effects frequently occur. Hence, these are considered to provide a lower level of protection for aquatic organisms (Long and MacDonald [1998]; MacDonald et al. [2000]).

The description of SQGs and sediment classification along with the results related to the sediments from Khanpur Lake during summer and winter is presented in Table 5, while the percent contribution of heavy metals towards potential acute toxicity in the sediments during summer and winter is depicted in Figure 4. During summer, the measured levels of Cd, Cr, Cu, Mn, and Pb are found to be higher than the LEL values in 87%, 100%, 100%, 37%, and 37% sediment samples, respectively. It depicts that these metals could pose moderate impact on the biota (Graney and Eriksen [2004]). On the other hand, the concentrations of Fe and Zn are found to be lower than the LEL levels in 100% sediment samples, demonstrating that these metals cause little or no impact on biota in the lake. Similarly, the measured levels of Cd, Cr, Cu, and Zn are found to be lower than the ERL values in 100% sediment samples, revealing that these metals are not associated with adverse health effects to the dwelling biota (MacDonald et al. [2000]). However, Pb levels are found to be higher in 37% sediment samples, manifesting that Pb is associated with frequent adverse biological effects to the underlying organisms (MacDonald et al. [2000]). Furthermore, potential acute toxicity (∑TUs) study shows that the mean levels of toxic units (TUs) for heavy metals follow a decreasing order: Cd > Cr > Pb > Zn > Cu. It indicates relatively higher contributions of Cd, Cr, and Pb to ∑TUs (i.e., 31%, 22%, and 21%, respectively; Figure 4) (Pedersen et al. [1998]). Nevertheless, Cu (11%) is the minor contributor to ∑TUs compared with the other heavy metals. The levels of ∑TUs range from 0.64 to 3.45 with a mean value of 1.75 in the sediments. Based on the USEPA sediments classification (Giesy and Hoke [1990]), Cr, Cu, and Zn show moderate contamination, Mn and Pb exhibit heavy pollution, and Cd and Fe reveal little or no contamination in the sediments during summer. It demonstrates that Cr, Cu, Zn, Pb, and Mn are the major contributors toward the gross pollution of the water reservoir.
Table 5

Description of sediment classification and sediment quality guidelines in acid extract of sediments in two seasons

  

Cd

Cr

Cu

Fe

Mn

Pb

Zn

Sediment classification

Non-polluted

-

<25

<25

<17,000

<300

<40

<90

Moderately polluted

-

25 to 75

25 to 50

17,000 to 25,000

300 to 500

40 to 60

90 to 200

Heavily polluted

>6

>75

>50

>25,000

>500

>60

>200

Sediment quality guidelines (SQGs)

LEL

0.6

26

16

20,000

460

31

120

SEL

10

110

110

40,000

1,100

250

820

ERL

5

80

70

-

-

35

120

ERM

9

145

390

-

-

110

270

Percentage of samples (summer)

Non-polluted

100

-

-

100

23

63

67

Moderately polluted

-

100

100

-

43

23

33

Heavily polluted

-

-

-

-

34

14

-

<LEL

13

-

-

100

63

63

100

≥LEL and <SEL

87

100

100

-

37

37

-

>SEL

-

-

-

-

-

-

-

<ERL

100

100

100

-

-

63

100

≥ERL and <ERM

-

-

-

-

-

37

-

>ERM

-

-

-

-

-

-

-

Percentage of samples (winter)

Non-polluted

100

3.0

40

100

53

100

97

Moderately polluted

-

97

57

-

44

-

3.0

Heavily polluted

-

-

3.0

-

3.0

-

 

<LEL

10

3.0

-

100

90

93

100

≥LEL and <SEL

90

97

100

-

10

7.0

-

>SEL

-

-

 

-

-

-

-

<ERL

97

100

100

-

-

93

100

≥ERL and <ERM

3.0

-

-

-

-

7.0

-

>ERM

-

-

-

-

-

-

-

The units of metals are expressed in milligrams per kilogram. LEL, lowest effect level; SEL, severe effect level; ERL, effect range low; ERM, effect range median.

https://static-content.springer.com/image/art%3A10.1186%2Fs40543-014-0028-z/MediaObjects/40543_2014_Article_28_Fig4_HTML.jpg
Figure 4

Percent contribution of heavy metals to ∑TUs in acid extract of sediments during summer and winter.

During winter, the measured levels of Cd, Cr, Cu, Mn, and Pb in the sediments are found to be higher than the LEL values in 90%, 97%, 100%, 10%, and 7.0% samples, respectively. It reveals moderate impact on the biota health. The observed values of Fe and Zn are found to be lower than the LEL values in 100% sediment samples, indicating that these metals are not associated with adverse impact on the biota (Graney and Eriksen [2004]). The ERL and ERM SQGs manifest that Cd and Pb levels exceed the ERL values in 3.0% and 7.0% sediment samples, respectively, demonstrating that these metals are associated with occasional adverse health hazards to the surrounding biota (MacDonald et al. [2000]). The concentrations of Cr, Cu, and Zn are lower than the ERL values in 100% sediment samples, demonstrating little or no undesirable health hazards. The potential acute toxicity study reveals that the average levels of TUs for heavy metals follow a decreasing order: Cd > Cr > Pb > Zn > Cu. It illustrates that Cd, Cr, and Pb are the major contributors to ∑TUs (i.e., 42%, 25%, and 12%, respectively; Figure 4), while Cu (8.6%) is a minor contributor (Pedersen et al. [1998]). The values of ∑TUs range from 0.54 to 3.29 with an average value of 3.29 in the sediments. Based on the USEPA sediments classification (Giesy and Hoke [1990]), Cd, Fe, and Pb may pose little or no pollution. Cr and Zn cause moderate contamination, and Cu and Mn exhibit heavy pollution in the sediments. Consequently, Cr, Cu, Mn, and Zn emerge as the major pollutants in the water reservoir during winter. Overall, the SQG results lead to the conclusion that the metals, such as Cd, Cr, Cu, Mn, and Pb are of concern during both seasons. Potential acute toxicity results demonstrate that Cd, Cr, and Pb are the major toxicants, while Zn and Cu are the minor pollutants during both seasons. However, relatively higher potential acute toxicity is observed during summer than during winter.

From the ecotoxicological dataset obtained for the US Coasts, Long et al. ([1998]) have defined several classes of toxicity probability for benthic biota: m-ERM-Q < 0.1 has a 9% probability of being toxic (based on amphipod survival test), m-ERM-Q between 0.11 and 0.5 has 21% probability of toxicity, m-ERM-Q between 0.51 and 1.5 has a probability of 49% to be toxic, and m-ERM-Q > 1.50 has 76% probability of toxicity. In the present study, the m-ERM-Q values range from 0.159 to 0.408 and 0.126 to 0.337 with the average values of 0.247 and 0.200 during summer and winter, respectively. Consequently, the metals pose approximately 21% probability of toxicity to the benthic organisms in the lake during both seasons.

Bioavailability of heavy metals in the sediments

Potential toxicity of heavy metals in the sediments is also assessed by the measurement of mobile metal concentrations. The statistical distribution parameters related to the concentrations of heavy metals in Ca(NO3)2 extract of the sediments during summer and winter are given in Table 2, whereas their percent extraction in Ca(NO3)2 extract is shown in Figure 5. The summer results reveal Fe as having the highest contributions (2.069 mg/kg), while the measured levels of Mn and Cd are the least. Nonetheless, the winter results demonstrate an elevated concentration of Pb (1.205 mg/kg), and the mean level of Mn is the lowest. On the percent extraction basis, the metals follow identical decreasing sequence during summer and winter: Pb > Cd > Cr > Cu > Zn > Fe > Mn. Moreover, there are no direct relationships among the Ca(NO3)2-extractable and acid-extractable fractions of the metals in sediments. The Ca(NO3)2-extractable recoveries are found to be within approximately 11% during summer and 15% during winter of the acid-extractable metal concentrations (Figure 5). Since element bioavailability is related to its solubility, extractable metal concentrations may correspond to the bioavailable concentrations (An and Kampbell [2003]). The results demonstrate that Pb and Cd show the maximum extraction efficiencies, mobilities, and bioavailabilities, followed by Cr, while Fe and Mn manifest the least during both seasons. Accordingly, Pb, Cd, and Cr exhibit higher mobility and higher potential toxicity to the surrounding biota, while Fe and Mn show least mobility and bioavailability to the benthic biota in the water reservoir.
https://static-content.springer.com/image/art%3A10.1186%2Fs40543-014-0028-z/MediaObjects/40543_2014_Article_28_Fig5_HTML.jpg
Figure 5

Percent extraction of heavy metals in Ca(NO 3 ) 2 extract of sediments during summer and winter.

Conclusions

The present study is primarily related to the evaluation of the distribution, correlation, source apportionment, contamination, and risk assessment of the heavy metals in surface sediments from Khanpur Lake, Pakistan. The study shows significantly divergent metal levels for most of the cases in the sediments during summer and winter. Most of the metals exhibit random distribution and diverse correlations in the sediments. Extremely severe enrichment is noted for Cd and Pb, while Zn shows severe enrichment. Moderate pollution is associated with Pb levels; strong to extreme pollution is shown by Cd, which is also associated with very high risk. On the whole, RI shows low to very high risk of contamination in the sediments. Multivariate PCA and CA manifest dominantly anthropogenic contributions of Pb, Cd, Cr, and Zn in the sediments. Comparison of heavy metal contents in the sediments with quality guidelines indicates adverse biological effects to the surrounding flora and fauna due to elevated levels of the metals. The m-ERM-Q study reveals 21% probability of toxicity due to the metals in the sediments. The potential toxicity, mobility, and bioavailability manifest that Cd and Pb are more mobile and available to the benthic flora and fauna. The present investigation clearly indicates that the sediments from freshwater reservoir are contaminated with some toxic heavy metals. Consequently, there is a dire need to reduce/regulate the anthropogenic sources of pollution in the study area.

Declarations

Acknowledgements

The research fellowship awarded by Quaid-i-Azam University, Islamabad, to carry out this project is appreciatively accredited. We are also grateful to the administration of Khanpur Lake, Islamabad, for their assistance and help during the sampling campaign.

Authors’ Affiliations

(1)
Department of Chemistry, Quaid-i-Azam University

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Copyright

© Iqbal and Shah; licensee Springer. 2014

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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