Spatial distribution, environmental assessment and ...

Chemie der Erde 76 (2016) 171–177

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Spatial distribution, environmental assessment and source identification of metals content in surface sediments of freshwater reservoir, Pakistan Javed Iqbal ∗ , Muhammad Saleem, Munir H. Shah ∗ Department of Chemistry, Quaid-i-Azam University, Islamabad 45320, Pakistan

a r t i c l e

i n f o

Article history: Received 26 June 2015 Accepted 6 February 2016 Editorial handling - H. Guo Keywords: Metal Risk assessment Contamination Pollution load index Multivariate analysis Pakistan

a b s t r a c t Surface sediments were collected from different sites of a freshwater reservoir, Pakistan, and analyzed for eight metals (Cd, Co, Cr, Cu, Fe, Mn, Pb and Zn) using flame atomic absorption spectrometry. The estimated metals levels were found higher than other reported studies. The environmental indices including geoaccumulation index, enrichment factor and contamination factor identified Cd, Co, Pb and Zn as the priority pollutants of concern. Chromium, Cu and Mn were also found to be enriched in some areas. The pollution load index (≥1) indicated progressive deterioration of the sediments quality. Principal component and cluster analyses revealed that Cd, Co, Pb and Zn were mainly originated from agricultural activities, domestic wastes, road runoffs and recreational activities. Chromium, Cu, Fe and Mn were mainly derived from natural sources though Cr, Cu and Mn were partially contributed by human inputs. Based on spatial distribution, inlet and middle sites of the reservoir were found more contaminated. This study would drive urgent attention to develop preventive actions and remediation processes for aquatic system protection and future restoration of the reservoir. © 2016 Elsevier GmbH. All rights reserved.

1. Introduction Heavy metal pollution in aquatic ecosystems has got sizeable consideration owing to their toxicity, persistence and biological buildup (Varol, 2011; Jiang et al., 2012; Li et al., 2013). Concentrations of heavy metals in sediments are affected by both geogenic and anthropogenic factors (Lalah et al., 2008). Natural factors include benthic agitation, flow changes, rocks weathering and natural erosion etc., while anthropogenic factors include sewage discharge, industrial wastewater discharge, atmospheric deposition, agricultural runoff and fertilizer leaching etc. (Romic and Romic, 2003; Tang et al., 2010; Choi et al., 2012; Srebotnjak et al., 2012; Rodriguez-Martin et al., 2013; Su et al., 2013; Islam et al., 2014). In aquatic environment, heavy metals exhibit high attraction for particular matter and will hence accumulate in sediments (Sundaray et al., 2011). Once absorbed and accumulated on sediments, though, chemical and biological processes may permit heavy metals to be desorbed from surface sediments as a result of which they are discharged into the water column (Li and Davis,

∗ Corresponding authors. Fax: +92 51 90642241. E-mail addresses: [email protected], [email protected] (J. Iqbal), munir [email protected], [email protected] (M.H. Shah). http://dx.doi.org/10.1016/j.chemer.2016.02.002 0009-2819/© 2016 Elsevier GmbH. All rights reserved.

2008; Dong et al., 2012; Cheng et al., 2013). Sediments, afterward, can act as a sink and potential secondary source of contaminants in aquatic environment (Caeiro et al., 2005; Segura et al., 2006; Yu et al., 2008; Bai et al., 2011). Therefore, the research on heavy metals in surface sediments provides significant insights into the metal pollution and associated risks in order to protect corresponding aquatic ecosystem. The objectives of this study were to (1) assess the concentration and spatial distributions of selected metals (Cd, Co, Cr, Cu, Fe, Mn, Pb and Zn) in surface sediments from Simly Lake, Pakistan, (2) determine the potential environmental risk employing enrichment factor (EF), geo-accumulation index (Igeo ), contamination factor (CF) and pollution load index (PLI), and (3) indentify the natural and/or anthropogenic sources of these metals using statistical techniques such as, principal component analysis (PCA) and cluster analysis (CA). It is further anticipated that this study would provide a geochemical data related to the spatial distribution and contamination of the metals in the freshwater reservoir which would help to provide essential information to support environmental control actions for the anthropogenic pollutants in the natural ecosystem.

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J. Iqbal et al. / Chemie der Erde 76 (2016) 171–177

Fig. 1. Location map of the study area.

2. Materials and methods

2.4. Chemical analyses and quality control

2.1. Study area

The pH was measured with a multimeter (Bench Meter, Martini Instrument Mi 180) in a 1:2 sediment:water suspension (Radojevic and Bashkin, 1999). For the measurement of pseudo-total metal levels, an aliquot of 1–2 g dried sediments was digested in a microwave oven using a freshly prepared acid mixture (9 mL HNO3 and 3 mL HCl) (USEPA, 2007). After microwave digestion, the sample solutions were filtered (0.45 ␮m, pore size) and stored at 4 ◦ C in a refrigerator until further analysis. A reagent blank was also prepared containing same amounts of the reagents without sediment sample along with each batch of samples. The samples were then analyzed for Cd, Co, Cr, Cu, Fe, Mn, Pb and Zn by flame atomic absorption spectrometry. Calibration line method was followed for quantification of the selected metals and the samples were appropriately diluted whenever required (Radojevic and Bashkin, 1999; Iqbal and Shah, 2011). All measurements were made in triplicate and the results were shown as the mean. Instrument settings were as recommended in the manufacturer’s manual, with wavelengths (nm) of 228.8 (Cd), 240.7 (Co), 357.9 (Cr), 324.8 (Cu), 248.3 (Fe), 279.5 (Mn), 217.0 (Pb) and 213.9 (Zn). For quality assurance and quality control, the precision and accuracy of the chemical methods were ensured by analyzing the standard material (SRM 2709) and reagents blanks, with each batch of samples. The percentage recoveries of the metals in the standard reference material samples ranged from 93 to 104%. Some sediment samples were also analyzed for cross comparison at an independent laboratory and a maximum of ±2.5% difference was found in the two results.

Simly Lake (longitude: 73◦ 20 E and latitude: 33◦ 43 N) is a freshwater source for the residents of Islamabad, the capital city of Pakistan (Fig. 1). It is an 80 m high, earthen embankment reservoir on the Soan river, near Bhara Kahu town, 30 km east of Islamabad. It was constructed in 1983 and is spread over an area of 28,750 acres. The water stored in this lake is fed by the melting snow and natural springs from Murree hills and other surrounding areas. It is a popular picnic spot because of its scenic location. This lake provides recreation facilities like boating, sailing, fishing and water skiing. Untreated domestic wastewater effluents, agricultural and road runoffs, and pollutants released during recreational activities are among the major metal contamination sources in this lake. 2.2. Sample collection and storage A total of 50 composite surface sediments (0–15 cm, top layer) were collected from Simly Lake, Pakistan in June 2013. Each composite sample was composed of 3–5 subsamples from an area of about 20 m2 . The sediments samples were collected in pre-cleaned Zip-locked polythene bags by using a sediments snapper (Ø5 cm). The collected samples were placed in an ice-cooler and transported to the laboratory immediately. Then the samples were dried, grounded, homogenized, sieved through a 2 mm nylon mesh after removing stones or other debris, sealed in pre-cleaned polythene bags and stored in a refrigerator until further processing (Kannan et al., 2008; Nemati et al., 2011; Saleem et al., 2013).

2.5. Environmental risk assessment 2.3. Reagents and glassware All reagents used were of analytical grade (certified purity > 99.9%) procured from E. Merck, Germany or BDH, UK. Double distilled water was used for the preparation of all solutions. Metal standard solutions used for the calibration were prepared by diluting the stock solutions (1000 mg/L) of each metal. All glassware used was cleaned by soaking in dilute acid (20% HNO3 , v/v) for at least 24 h, followed by repeated rinsing with double distilled water (Diaz-de Alba et al., 2011). Finally the glassware was dried in an electric oven maintained at 80 ◦ C for about eight hours prior to use.

Metal contamination in the sediments was assessed using geoaccumulation index (Igeo ), enrichment factor (EF), contamination factor (CF) and pollution load index (PLI). Igeo , CF and EF are the most common methods to assess the ecological risk by a single element, whereas PLI evaluates the environmental risk posed by multiple elements (Caeiro et al., 2005; Li et al., 2012; Zhao et al., 2012; Cheng et al., 2013; Hou et al., 2013; Wang et al., 2014a). 2.5.1. Geo-accumulation index (Igeo ) The Igeo enables the assessment of contamination by comparing the current and pre-industrial concentrations of the metals


(Zhao et al., 2012; Wang et al., 2014a). The geo-accumulation index (Igeo ) values were calculated for the studied metals as introduced by Muller (1969) as follows: Igeo = log2

C n 1.5Bn

(1)

where (Cn ) is the measured concentration of examined element(n) in the sediment sample and (Bn ) is the geochemical background for the element(n) in pre-civilization (pre-industrial) reference sediments (Lide, 2005). The factor 1.5 is introduced to minimize the effect of possible variations in the background values which may be attributed to geogenic variations. The index consists of seven classes as proposed by Muller (1969): Class 0 (practically unpolluted) — Igeo ≤ 0; Class 1 (unpolluted to moderately polluted) — 0 < Igeo < 1; Class 2 (moderately polluted) — 1 < Igeo < 2; Class 3 (moderately to heavily polluted) — 2 < Igeo < 3; Class 4 (heavily polluted) — 3 < Igeo < 4; Class 5 (heavily to extremely polluted) — 4 < Igeo < 5; and Class 6 (extremely polluted) — Igeo > 5. 2.5.2. Enrichment factor (EF) Enrichment factor (EF) is a useful approach to determine the magnitude of anthropogenic metals pollution in the environment using a normalization element in order to assuage the variations produced by heterogeneous sediments (Martin et al., 2012; Wang et al., 2014a). It is a ratio of the abundances of a potentially enriched element with respect to a reference element. The normalizing element is selected so as to have minimum variability of occurrence or is present in such large concentrations in the studied environment, that neither small concentration variations nor other synergistic or antagonistic effects toward the examined elements are significant (Abrahim and Parker, 2008; Iqbal and Shah, 2011). Different elements (Al, Fe, K, Li, Sc, Ga, Zr and Ti) are used as normalizing element (Daskalakis and Connor, 1995; Van der Weijden, 2002; Zhang et al., 2007; Amin et al., 2009; Hasan et al., 2013), but in this study Fe was selected as normalization element for the following reasons: (i) it is associated with fine solid surfaces; (ii) is a major sorbent phase for trace metals; (iii) its geochemistry is similar to that of many trace metals; and (iv) is a quasiconservative tracer of the natural metal-bearing phases in the sediments (Daskalakis and Connor, 1995; Hasan et al., 2013). In present study, enrichment factors (EF) were computed by using the following relationship:

X sample Fe EF = X crust Fe

(2)

where [X/Fe]sample and [X/Fe]crust refer, respectively, to the ratios of mean concentrations (mg/kg, dry weight) of the target metal and Fe in the examined sediments and continental earth crust (Lide, 2005). The EF values are interpreted as suggested by Sakan et al. (2009): EF < 1 indicates no enrichment; EF < 3, minor enrichment; EF = 3–5, moderate enrichment; EF = 5–10, moderately severe enrichment; EF = 10–25, severe enrichment; EF = 25–50, very severe enrichment; and EF > 50 shows extremely severe enrichment. 2.5.3. Contamination factor (CF) and pollution load index (PLI) Contamination factor (CF) is the ratio obtained by dividing the concentration of each metal in the studied sediment (Csample ) by the earth crust value (Ccrust ) (Hakanson, 1980): CF =

Csample Ccrust

(3)

The CF values are interpreted as suggested by Hakanson (1980): CF < 1 indicates low contamination; 1 < CF < 3, moderate contam-

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ination; 3 < CF < 6, considerable contamination; and CF > 6 shows very high contamination. To assess the level of metal pollution, Tomlinson et al. (1980) proposed a simple and comprehensive tool namely pollution load index (PLI). The PLI value higher than 1 indicates a polluted condition, while PLI lower than 1 means no significant pollution (Tomlinson et al., 1980). It is determined as the nth root of the n CF multiplied together and calculated using the following equation: 1

PLI = (CF1 × CF2 × CF3 × · · · · · · · × CFn) n

(4)

2.6. Statistical analyses Multivariate statistical analyses of the sediments data were performed using a statistical software package (STATISTICA). Principal component analysis (PCA) is a multivariate method that is predominantly used to reduce data and extract a smaller number of independent factors (principal components) to examine the relationships among the analyzed variables. Varimax rotation was further applied to reduce the number of variables that have high loadings on each component by giving to those variables maximum weight and minimum weight to the variables less correlated to the axis, thereby shortening the transformed data matrix and simplify the interpretation (Sun et al., 2013; Wang et al., 2014b). Cluster analysis (CA) organizes a set of variables into two or more mutually exclusive unknown groups based on a combination of internal variables (Lu et al., 2010). The variables with smaller distance are more similar than those with longer distances and therefore could be grouped within the same cluster (Cesari, 2007). The obtained results are displayed in a dendrogram, which shows degree of similarity among the different variables. This is a very efficient method to explore highly stable groups’ structures (Zupan et al., 2000; Li et al., 2013). PCA and CA are complementary techniques; both compress a large amount of data into more manageable groups and increase significance. The difference is that CA is considered more efficient in producing structures and groups clearly, and is relatively more stable (Guillen et al., 2012). 3. Result and discussion 3.1. Metal concentrations in the surface sediments Table 1 shows descriptive statistics for selected metals and pH in surface sediments from Simly Lake, Pakistan. In this study, the sediments displayed an alkaline pH ranging from 7.44 to 8.08 with an average value of 7.82. Overall the concentration ranges and mean values of selected metals in surface sediments were found as follows: Cd, 0.29–3.60 (1.55); Co, 37.3–65.9 (53.2); Cr, 29.0–81.3 (41.0); Cu, 16.5–43.3 (23.4); Fe, 5600–6380 (6122); Mn, 574–1026 (647); Pb, 15.2–57.0 (41.0); and Zn 90.0–173 (132) mg/kg, dry weight. On the average basis, the metal concentrations in surface sediments collected from the Simly Lake, in general, were ranked in decreasing order, as follows: Fe > Mn > Zn > Co >Cr > Pb > Cu > Cd. The measured concentrations of these metals were compared with other national and international reported levels (Table 2). Cadmium levels were found higher than those of Mangla Lake (Pakistan), Algeciras Bay (Spain), Poxim River Estuary (Brazil), Sungai Buloh (Malaysia) and Poyang Lake (China). Cobalt, Mn, Pb and Zn levels were found to be higher than most of the reported studies, whereas Cr, Cu and Fe concentrations were lower than some of the reported studies. Overall, the estimated metal levels of the present study were found relatively higher than other reported studies, indicating that the reservoir needs much attention for control and remedial measures to reduce the toxic metals pollution.

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Table 1 Descriptive statistics for selected metals (mg/kg, dry weight) and pH at various sites. Cd

Co

Cr

Cu

Fe

Mn

Pb

Zn

pH

Overall

Min Max Mean

0.29 3.60 1.55

37.3 65.9 53.2

29.0 81.3 41.0

16.5 43.3 23.4

5600 6380 6122

574 1026 647

15.2 57.0 41.0

90.0 173 132

7.44 8.08 7.82

S-1

Min Max Mean

0.29 2.70 0.99

37.3 43.3 40.6

38.5 81.3 52.4

23.2 43.3 30.1

5981 6380 6244

620 1026 726

22.6 41.5 29.3

90.0 173 110

7.44 7.81 7.65

S-2

Min Max Mean

2.08 3.59 2.71

51.7 60.4 56.1

33.1 39.5 36.6

19.0 22.0 20.4

5600 6199 5926

574 607 590

15.2 56.6 37.8

131 149 139

7.71 7.99 7.81

S-3

Min Max Mean

0.63 1.87 1.12

59.4 65.9 63.0

29.0 31.3 30.2

16.5 18.7 17.4

5993 6327 6178

578 626 597

42.5 57.0 52.1

133 155 146

7.76 8.08 7.95

Table 2 Comparison of the investigated metals levels (mg/kg, dry weight) with other international and national studies. Location

Cd

Co

Cr

Cu

Fe

Mn

Pb

Zn

Reference

Simly Lake, Pakistan Mangla Lake, Pakistan (Summer) Mangla Lake, Pakistan (Winter) Khanpur Lake, Pakistan (Summer) Khanpur Lake, Pakistan (Winter) Rawal Lake, Pakistan Mahanadi River, India Algeciras Bay, Spain Poxim River Estuary, Brazil Sungai Buloh, Malaysia Poyang Lake, China

1.55 1.33 1.52 1.883 2.457 2.13 4.2 0.3 0.23 0.316 0.56

53.2 – – – – 3.901 – 11 – 4.01 –

41.0 21.3 42 34.66 37.65 12.15 72 112 7.98 39.51 70.77

23.4 13.4 23.3 36.84 28.05 7.155 36 17 8.79 34.73 27.71

6122 3870 3800 4630 3791 14,979 60,000 28,129 – – –

647 324 612 447.5 321.4 306.4 1133 534 – – –

41.0 17.2 30.6 33.71 18.24 16.94 131 24 12.04 37.27 48.67

132 37.4 50.1 86.09 61.9 18.18 137 73 19.02 93.98 65.78

Present study Saleem et al., 2013

3.2. Spatial distribution of analyzed variables The whole study area has been divided into three sites: S1 (close to spill way area), S-2 (close to picnic points, rest house and boating activities areas) and S-3 (close to inflow area), (Table 1 & Fig. 1). Spatial distribution of selected metals results revealed that the average metal concentrations showed the order: Fe > Mn > Zn > Cr > Co > Cu > Pb > Cd at site S-1 whereas at sites S-2 & S-3 the order was: Fe > Mn > Zn > Co > Pb > Cr > Cu > Cd. The measured concentrations of Cr, Cu, Mn and Fe were higher at site S-1; Co, Pb and Zn levels were higher at site S-3; and Cd contents were higher at site S-2. Overall, Co, Pb and Zn had concentration order: S-3 > S-2 > S-1; Cr and Cu: S-1 > S-2 > S-3; Fe and Mn: S-1 > S-3 > S-2; and Cd exhibited order as S-2 > S-3 > S-1. 3.3. Ecological risk assessment 3.3.1. Index of geo-accumulation (Igeo ) The Igeo class 0 indicates the absence of contamination, while the Igeo class 6 represents the extremely contamination of sediments. In this study (Fig. 2a), the mean Igeo values for Cr, Cu, Fe and Mn were found lower than 0, indicating practically unpolluted; Co, Pb and Zn, unpolluted to moderately polluted; and Cd indicated moderately to heavily polluted sediment quality. Cobalt and Zn indicating unpolluted to moderately pollution at all sites; Pb moderately pollution at site S-3; and Cd manifested heavily pollution at site S-2. Overall the studied sediments were polluted by Cd, Co, Pb and Zn, indicating that these metals might be included by anthropogenic inputs in the lake sediments. 3.3.2. Enrichment factor (EF) Enrichment Factor (EF) values less than 1 indicate that the metal is entirely from geogenic origin, whereas EF values greater than 10 suggest that the metal is likely to be originated from anthropogenic sources. In current investigation (Fig. 2b), the average EF values of

Iqbal and Shah, 2014 Iqbal et al., 2013 Sundaray et al., 2011 Diaz-de Alba et al., 2011 Passos et al., 2010 Nemati et al., 2011 Yuan et al., 2011

Table 3 Metal contamination factors (CFs) and pollution load indices (PLIs) for selected metals at various sites.

S-1 S-2 S-3

Cd

Co

Cr

Cu

Fe

Mn

Pb

Zn

PLI

6.7 18.1 7.4

1.6 2.2 2.5

0.5 0.4 0.3

0.5 0.3 0.3

0.1 0.1 0.1

0.8 0.6 0.6

2.1 2.7 3.7

1.6 2.0 2.1

0.97 1.07 0.98

the Lake indicated that Cr and Cu were between 3 and 5, indicating a moderate enrichment; Mn was between 5 and 10, revealing moderately severe to severe enrichment; Zn and Co were between 10 and 25, showing severe enrichment; Pb indicated very severe enrichment; and Cd revealed extremely severe enrichment. The mean EF levels of Cd, Co, Pb and Zn were greater than 10, suggesting high anthropogenic impact of these metals in the lake sediments. Cadmium showed extremely severe enrichment at all sites; Pb, very severe enrichment at site S-3; and Co & Zn, manifesting severe enrichment at all sites. Chromium, Cu and Mn also indicated some enrichment, but they were considered to be contributed by mixed sources. 3.3.3. Contamination factor (CF) and pollution load index (PLI) The degree of contamination by toxic elements in the sediments is often expressed in terms of a contamination factor (Hakanson, 1980). The description of CF values in the sediments is explained in Table 3. The mean CF values for Cr, Cu, Fe and Mn were lower than 1, manifesting low contamination; Co, Pb and Zn, moderate contamination; and Cd indicated very high contamination in the sediments. Chromium, Cu, Fe and Mn indicated low contamination at all sites; Co, Pb and Zn manifested moderate contamination at site S-3; and Cd indicated very high contamination at site S-2. Pollution load index (PLI) provides some understanding to the inhabitants of the area about sediments quality of their environment. It also offers important information and guidance to the policy makers on the pollution level of the studied area (Harikumar


Geoaccumulation Index (Igeo)

4.0

(a) S-1

S-2

S-3

2.0

0.0

-2.0

-4.0 Cd

Co

Cr

Cu

Fe

Mn

Pb

Zn

1000

(b)

Enrichment Factor (EF)

S-1

S-2

S-3

100

10

1 Cd

Co

Cr

Cu

Mn

Pb

Zn

Fig. 2. Description of geoaccumulation indices (Igeo , a) and enrichment factors (EF, b) of selected metals at different sites.

175

ance) included Cd and Zn; and PC3 grouped metals such as Co and Pb explaining 14.6% of the total variance. The CA was applied to determine similarities among the metals and find out homogenous groups which were mutually correlated within the sediments data and the obtained dendrogram (Fig. 3) indicated various stable clusters/associations for the studied metals. The first association included Cd and Zn (Cluster-I); Co and Pb formed Cluster-II; and the last Cluster (III) exhibited Cu, Cr, Fe and Mn. Several studies have established that the association of elements with factors can be indicated by anthropogenic or a lithogenic origin (Romic and Romic, 2003; Davis et al., 2009; Gu et al., 2012; Rodriguez-Martin et al., 2013; Gu et al., 2014). Chromium, Cu, Fe and Mn were relatively not much higher in the studied sediments, indicating that these metals were probably controlled by natural sources like parent material and subsequent pedogenesis (Zhao et al., 2014). On contrary elements such as, Cd, Co, Pb and Zn and partially Cr, Cu and Mn were considered to be related to anthropic intrusions as the average EF values of Cr and Cu were between 3 and 5, indicating a moderate enrichment; Mn was between 5 and 10, revealing moderately severe to severe enrichment; Zn and Co were between 10 and 25, showing severe enrichment; Pb indicated very severe enrichment; and Cd revealed extremely severe enrichment. It indicated that these metals were likely to be contributed by human inputs. There are many farmlands located in the surrounding areas of the lake and local farmers generally used a lot of fertilizers, pesticides and cattle slurry which always contained elements Cd, Co, Pb and Zn in agricultural activities to increase production and improve economic profits. Accordingly, it seems sensible to conclude that Cd, Pb and Zn might be originated from agricultural activities (Gu et al., 2014; Wang et al., 2014b). Cadmium, Co, Pb, Mn, Cr, Cu and Zn could be associated with anthropogenic wastes including sewage sludge, industrial wastes and domestic solid wastes released from the nearby villages, rest house, picnic points and boating activities (Charlesworth et al., 2003; Varol, 2011; Zhao et al., 2014).

Table 4 Principal component loadings for selected metals.

Eigen value Total variance (%) Cumulative variance (%) Cd Co Cr Cu Fe Mn Pb Zn

PC1

PC2

PC3

4.77 49.6 49.6 0.13 0.22 0.99 0.97 0.94 0.97 0.25 −0.08

1.47 22.4 72.0 0.97 0.39 0.05 0.13 −0.15 −0.20 0.37 0.99

1.33 14.6 86.6 0.12 0.76 0.05 −0.08 −0.21 0.02 0.67 0.03

and Jisha, 2010; Mohiuddin et al., 2010). The results of PLI for all three sites are described in Table 3. The PLI levels ranged from 0.97 to 1.07 with an average of 0.98. The PLI value was found higher for site S-2. 3.4. Sources identification PCA was applied to find out associations and the possible contributing sources for the studied metals. The resulting principal component loadings for the selected metals are explicated in Table 4. Three principal components (PC) were obtained with eigenvalue greater than 1, explaining more than 86% of total variance. PC1 explained the 49.6% of the total variance and exhibited high loading values for Cr, Cu, Fe and Mn; PC2 (22.4% of total vari-

4. Conclusions In this study, spatial distribution of eight selected metals (Cd, Co, Cr, Cu, Fe, Mn, Pb and Zn) in surface sediments from Simly Lake, Pakistan was examined. Then potential environmental risk caused by these metals was assessed by computing geoaccumulation index, enrichment factor, contamination factor and pollution load index. Sources were identified with the aid of multivariate analyses such as, principal component analysis (PCA) and cluster analysis (CA). On average basis, the measured levels of the studied metals followed the decreasing concentration order: Fe > Mn > Zn > Co >Cr > Pb > Cu > Cd. According to the environment risk assessment results, Cd, Co, Pb and Zn were identified as the priority pollutants of concern though Cr, Cu and Mn could not be ignored as they indicated some enrichment in some areas. Multivariate analyses indicated that Cd, Co, Pb and Zn were mainly originated from agricultural activities, sewage sludge, untreated domestic solid wastes and boating activities. Cr, Mn and Cu were partially added by anthropic sources. However, Cr, Cu, Fe and Mn were mainly derived from lithogenic sources. Moreover, selected metals pollution was found relatively higher at site S-2 and S-3 which receive anthropic inputs from the nearby areas such as farmlands, villages, picnic points and other catchments. Therefore, the remedial measures, to develop strategies of contamination control and management with the inclusive consideration of the entire area are required for aquatic system/human health protection and future restoration of the lake.

176


Ward`s method (Pearson r)

Cd Zn Co Pb Cr Fe Mn Cu 0.0

0.5

1.0

1.5

2.0

2.5

Fig. 3. Cluster analysis of the analyzed metals.

Conflict of interests The authors do not have any conflict of interests.

Acknowledgements Authors are grateful to the administration of Simly Lake, Islamabad, Pakistan for their help during sampling campaign. Technical and financial help by Quaid-i-Azam University, Islamabad, Pakistan to accomplish this project is also accredited.

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