Enrichment and geo-accumulation of heavy metals and risk assessment of sediments of the Kurang Nallah—Feeding tributary of the Rawal Lake Reservoir, Pakistan

Science of the Total Environment 470–471 (2014) 925–933

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Enrichment and geo-accumulation of heavy metals and risk assessment of sediments of the Kurang Nallah—Feeding tributary of the Rawal Lake Reservoir, Pakistan Azmat Zahra a, Muhammad Zaffar Hashmi b, Riffat Naseem Malik a,⁎, Zulkifl Ahmed c a b c

Environmental Biology and Ecotoxicology Laboratory, Department of Environmental Sciences, Faculty of Biological Sciences, Quaid-i-Azam University, Islamabad, PO 45320, Pakistan Department of Environmental Engineering, College of Environmental & Resource Sciences, Zhejiang University, Hangzhou 310058, People's Republic of China Department of Building and Architecture Engineering, Bahauddin Zakariya University, Multan, Pakistan

H I G H L I G H T S • • • •

EF, Igeo and MPI were used to determine metal enrichment in sediments of the Kurang stream. Cd, Zn, Ni and Mn enrichment was more in sediments. Ni and Zn were above ERL values; however, Ni exceeded the ERM values. Ni and Zn threats to aquatic ecosystem should not be ignored.

a r t i c l e

i n f o

Article history: Received 13 June 2013 Received in revised form 2 October 2013 Accepted 3 October 2013 Available online 14 November 2013 Editor: Gisela de Aragão Umbuzeiro Keywords: Sediments Multivariate analysis Enrichment factor Geo-accumulation index Heavy metals

a b s t r a c t Heavy metal concentrations in sediments of the Kurang stream: a principal feeding tributary of the Rawal Lake Reservoir were investigated using enrichment factor (EF), geoaccumulation index (Igeo) and metal pollution index (MPI) to determine metal accumulation, distribution and its pollution status. Sediment samples were collected from twenty one sites during two year monitoring in pre- and post-monsoon seasons (2007–2008). Heavy metal toxicity risk was assessed using Sediment Quality Guidelines (SQGs), effect range low/effect range median values (ERL/ERM), and threshold effect level/probable effect level (TEL/PEL). Greater mean concentrations of Ni, Mn and Pb were recorded in post-monsoon season whereas metal accumulation pattern in pre-monsoon season followed the order: Zn N Mn N Ni N Cr N Co N Cd N Pb N Cu N Li. Enrichment factor (EF) and geoaccumulation (Igeo) values showed that sediments were loaded with Cd, Zn, Ni and Mn. Comparison with uncontaminated background values showed higher concentrations of Cd, Zn and Ni than respective average shale values. Concentrations of Ni and Zn were above ERL values; however, Ni concentration exceeded the ERM values. Sediment contamination was attributed to anthropogenic and natural processes. The results can be used for effective management of fresh water hilly streams of Pakistan. © 2013 Elsevier B.V. All rights reserved.

1. Introduction Sediment quality is an indicator of water pollution that manifests pollutant variations. Sediment provides a site for biogeochemical cycling and the foundation of the food web (Burton et al., 2001). Sediments have been used as an important tool to assess the health status of aquatic ecosystems (Birch et al., 2001) and are an integral component for functioning of ecological integrity. Sediments act as a sink of organic as well as inorganic pollutants (heavy metals) and provide a history of anthropogenic pollutant input (Santos Bermejo et al., 2003) ⁎ Corresponding author at: Environmental Biology and Ecotoxicology Laboratory, Department of Environmental Sciences, Faculty of Biological Sciences, Quaid-i-Azam University, Islamabad, PO 45320, Pakistan. Tel.:+92 5190643017. E-mail address: [email protected] (R.N. Malik). 0048-9697/$ – see front matter © 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.scitotenv.2013.10.017

and environmental changes (Shomar et al., 2005). Heavy metals enter the aquatic ecosystems through point sources such as industrial, municipal and domestic waste water effluents as well as diffuse sources which include surface runoff, erosion, and atmospheric deposition. Sediment pollution with heavy metals is a worldwide problem (Fernandes et al., 2008; Kucuksezgin et al., 2008) and is considered to be a serious threat to the aquatic ecosystem because of their toxicity, ubiquitous and persistence nature, non-biodegradability and ability to bio-accumulate in food chain (Duman et al., 2007). Sediments serve as the largest pool of metals in aquatic environment. More than 90% of the heavy metal load in the aquatic systems has been found to be associated with suspended particulate matter and sediments (Amin et al., 2009; Zheng et al., 2008). Metals in suspended particulates settle down and pool up in sediments (Kucuksezgin et al., 2008), while the dissolved metals adsorb onto fine particles which may carry them to

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bottom sediments (K.P. Singh et al., 2005). Distribution of heavy metals is influenced by the mineralogical and chemical composition of suspended material, anthropogenic influences, deposition, sorption, enrichment in organism (Jain et al., 2007), and various physico-chemical characteristics (K.P. Singh et al., 2005). Sediment has widely been studied for anthropogenic impacts on the aquatic environment (Sayadi et al., 2010). Various studies have reported sediment quality assessments, distribution and contamination of heavy metals and quantification of pollution load in sediments of different rivers such as the Po River, Italy (Viganò et al., 2003), the River Gomti, India (V.K. Singh et al., 2005), the Songhua River, China (Lin et al., 2008), and the Shur River (Karbassi et al., 2008) and the Khoshk River (Salati and Moore, 2010) in Iran. However, river sediment contamination has sparsely been investigated in Pakistan in general and no information is available for the Kurang Nallah and its associated streams. The Kurang is a main feeding tributary of the Rawal Lake Reservoir and is the cheapest source of drinking water for the local population. The Kurang has been subjected to heavy metal pollution due to a rapid increase in population and unplanned human settlements in its catchment area, washing activities (human, animal and laundry), recreational activities, poultry waste discharge, dumping of solid waste and direct and/or indirect discharge of untreated domestic effluents. During recent years, annual fish production has been reduced from 450 to 600 tons in the Kurang and Soan Rivers. Therefore, the importance to investigate heavy metal contamination, distribution and possible sources. The present study aimed to (1) determine the accumulation, spatial and temporal distribution trends, and source identification of heavy metals in sediments of the Kurang and its tributaries, (2) quantify the extent of metal pollution using enrichment factor (EF), geoaccumulation indices (Igeo) and metal pollution index (MPI), and (3) assess ecological risk of sediments using sediment quality guidelines viz., effect range low/effect range median values (ERL/ERM), and threshold effect level/probable effect level (TEL/PEL).

during the period of two years on seasonal basis viz., pre-monsoon season (April 2007 and 2008), and post-monsoon season (October 2007 and 2008). Samples that showed no evidence of surface disturbance were retained. The top 2–3 cm was removed, transferred to pre-cleaned polythene bags and sealed. Sediment samples were kept at ~4 °C before laboratory processing. Sediment samples were air dried, crushed, sieved (b 2 mm) and stored in pre-washed glass containers at room temperature. Global Positioning System (GPS) was used to locate the sites.

2. Materials and methods

2.4. Measurement of other sediment parameters

2.1. Study area

Organic matter (OM) was determined by Tyurin's method (Nikolskii, 1964). Sediment pH, electric conductivity (EC) and total dissolve solids (TDS) were determined using a portable pH, EC and TDS millimeter (Milwaukee, model SM 802). Sediment suspension was prepared in 1:9 ratio of sediment to deionized water (10 g soil; 90 ml distilled water). Before the determination of pH, EC, and TDS, the mixture was stirred for sixty seconds at 10 minute intervals for 30 min. The proportion of sand, silt and clay (%) was calculated to determine soil textural class using the Bouycous hydrometer method.

The Kurang is an important watercourse of the Rawalpindi district and is the principal feeding tributary of the Rawal Lake Reservoir, catering for 50% water demand of the fourth largest city (Rawalpindi) of the country. The Kurang makes its origin from numerous natural springs in the Murree Hills and is fed by numerous seasonal and perennial streams. It passes through undulating terrain which is dissected by gullies and ravines, ranging from steep slopes to relatively plain areas. The Kurang is characterized by sluggish flow throughout the year, except during monsoon season when heavy rain fall causes a manifold increase in its runoff. Three important streams join the river along its course: Baroha, Malachh and Shahdara. The mean annual precipitation ranges from 1000 mm to 1500 mm. The bulk of the monsoon precipitation is received during July and August, with monthly averages of 267 mm and 309 mm, respectively. The average monthly maximum and minimum temperature ranges from 16.9 °C to 40.1 °C and from 3.1 °C to 24.7 °C, respectively. The lowest temperature was recorded in the month of January (−4 °C) and the highest was reached in the month of June (48 °C). Relative humidity ranges from 19% to 54% recorded in the month of May and August. Wide seasonal variations in temperature and precipitation characterize the climate as sub-humid. The elevation ranges from 525 to 2181 m. Rocks are composed of red and purple sand stone, limestone, shale, and siltstone. Soils are derived from wind and water laid deposits. Effects of erosion are more pronounced throughout the study area. 2.2. Sediment sampling Surface pore water sediment samples were collected from twenty one sites along the Kurang River and its feeding tributaries (Fig. 1)

2.3. Determination of metals For the measurement of total metal concentrations, acid digests of each sediment sample were prepared using USEPA method 3051. Each sediment sample measuring 0.5 g was digested in 10 ml of ultrapure HNO3 using Microwave Accelerated Reaction System (MARS, CEM®), filtered, and diluted. Total metal concentration of Cr, Mn, Co, Ni, Cu, Cd, Zn, Ca, Mg, Fe, K, Na, Pb, and Li was determined in triplicate in air/acetylene flame using Fast Sequential Atomic Absorption Spectrophotometer (Varian FSAA-240). Results of triplicate analyses revealed good reproducibility of the equipment. Analytical blanks and standard reference material were run in the same way as the samples and heavy metal concentrations were determined using standard solutions prepared in the same acid matrix. Sediment reference material CRM 320 was used (N = 3) to ensure the validation of data and the accuracy and precision of analytical method. The recoveries were 84–105% for all metals regarding their certified/noncertified values, which in general are considered satisfactory. Total heavy metal concentrations were expressed in mg/kg dry sediments. All the reagents used were of supra quality and of analytical grade. All solutions were prepared using ultra pure water. All plastic, quartz and glassware were soaked in HNO3 (10%) for at least 24 h and rinsed repeatedly with ultra pure water.

2.5. Quantification of sediment pollution 2.5.1. Enrichment factor (EF) Normalized enrichment factor is applied (Salati and Moore, 2010) to differentiate metal source originating from anthropogenic and natural means (Selvaraj et al., 2004). This involves normalization of the sediment with respect to reference elements such as Al, and Fe (Acevedo-Figueroa et al., 2006; Amin et al., 2009; Huang and Lin, 2003; Karbassi et al., 2008), Mn, Ti and Sc (Salati and Moore, 2010), and Li and Cs (Pereira et al., 2007). Geochemical normalization has also extensively been used to calculate enrichment and to reduce heavy metal variability caused by grain size and mineralogy of sediments (Zhang and Shan, 2008). Normalized EF of metals in Kurang pore water sediments of each site was calculated using Eq. (1). Manganese (Mn) was used as a reference element to calculate anthropogenic metal enrichments as described by Loska et al. (1997). World average concentration of metals reported for the shale by Turekian and Wedepohl (1961) was used as background values for heavy metals (Cr, Co, Ni, Cu, Cd, Zn, Fe, Pb, and Li). Based on EF values, all the sites were categorized into five main classes (Table 1) (Birch and Olmos, 2008).

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Fig. 1. Location of study area and sampling sites on the Kurang and associated tributaries.

EF ¼

ðCx =CMn ÞsampleÞ

.

ð1Þ

ðCx =CMn ÞshaleÞ

where, (Cx/CMn) is the ratio of concentration of the element of concern (Cx) to that of Mn (CMn) in the sediment sample (μg/g dry weight) and (Cx/CMn) is the same ratio in an unpolluted reference sample. 2.5.2. Geoaccumulation index (Igeo) Geoaccumulation index (Igeo) was developed by Müller (1979) and had widely been used in trace metal studies of sediments and soils (Amin et al., 2009; V.K. Singh et al., 2005). To quantify the degree of heavy metal pollution in Kurang pore water sediments, Igeo was calculated according to Muller and is given in Eq. (2). The results were interpreted using Igeo classes given in Table 1.

Igeo ¼ ðlog2 Cn =1:5 Bn Þ

ð2Þ

where Cn is the concentration of the examined metal in the sediment, Bn is the geochemical background value of a given metal in the shale (Turekian and Wedepohl, 1961) and the factor 1.5 is used to account the possible variations in the background values.

Table 1 Enrichment factor (EF) and Igeo classes in relation to sediment quality.

.

1

MPIn ¼ ðCf 1  Cf 2  …  Cf n Þ

n

ð3Þ

where Cfn is the concentration of the metal n in the sample. 2.6. Statistical analysis Analysis of variance (ANOVA) was carried out to assess mean significant differences of studied parameters between the two seasons. Cluster analysis was used to identify spatial variability between the sites based on physicochemical parameters. Euclidean distance was used as dissimilarity matrix, whereas Ward's method was used as a linkage method. Factor analysis based on principal component analysis (PCA/FA) was used to ascertain sources of contamination (natural and anthropogenic). Varimax rotation was applied to minimize the number of variables with high loading on each factor (Varimax factors) and facilitate interpretation of results. PCA/FA was applied on total data. Varimax rotation also maximizes sum of variance of the factor coefficients. Correlation matrix using Pearson's moment correlation coefficient was used to identify interrelationship between metals and other parameters and to support results obtained by PCA/FA. 3. Results and discussion

Igeo class Sediment quality

EF classes Sediment quality

Igeo

EF b1 EF b3

No enrichment Minor enrichment

0–0 0 0–1 1

EF 3–5 EF 5–10

Moderate enrichment Moderately severe enrichment Severe enrichment Extremely severe enrichment

1–2 2 2–3 3

EF 10–25 EF 25–50

2.5.3. Metal pollution index The overall metal load in sediments at each site was compared using metal pollution index (MPI) and was calculated after Usero et al. (1997) using Eq. (3)

3–4 4 4–5 5 5–6 N5

Unpolluted Unpolluted to moderately polluted Moderately polluted Moderately to highly polluted Highly polluted Highly to very highly polluted Very highly polluted

3.1. Metal concentrations in sediments and comparison with regional studies Concentrations of Mn, Co, Ni, Cd, Pb and Li were significantly different between seasons (p ≤ 0.05) and greater at sampling sites viz., K1, K2, K3, K8, K10, K11, K12, S1, S2, S3, S4, and M1 located in close vicinity of urban and semi-urban areas with anthropogenic activities. In pre-monsoon season heavy metal concentrations in pore water sediment samples followed the order: Zn N Mn N Ni N Cr N Co N Cd N Pb N Cu N Li. In post-monsoon season concentrations of Co, Cd, Zn

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and Fe were lower in pore water sediment samples. The results suggest that may be due to dilution during monsoon rainfall which mixes polluted and unpolluted water and decrease the heavy metal concentrations in post-monsoon season (Collvin, 1985). However, higher concentration of these metals in pre-monsoon season in pore water sediment samples could be attributed to the decrease in water level and drought condition (Gupta et al., 2009). Sometimes, the variations in metal concentrations may also be influenced by changes in lithological inputs, hydrological effects, geological features, cultural influences and type of vegetation cover (Jain et al., 2007). Nickel was only detected at five sampling sites (S1, S2, S3, B3 and M1). During post-monsoon season the concentrations of Ni were higher in all the sites which ranged from 0.50 to 70.80 μg/g. The highest concentration of Ni was recorded in site S1 (51.70 μg/g) pore water sediment samples, whereas the concentrations of Zn and Mn were relatively higher in pore water sediment samples at the sites K1 (317.65 μg/g) and M1 (122.60 μg/g) during post-monsoon season. However, higher Zn and Mn concentrations were recorded at the S4 (381.92 μg/g) and K8 (52.18 μg/g) sites. The higher concentrations of Zn, Mn and Ni at the sampling sites S4, K8, K1, S1, M1 and B3 may be attributed to several anthropogenic activities. For example the site S4 was located in the highly urbanized area of Bhara Kahu, and the stream at this site directly receives untreated domestic sewage, urban runoff, and wastes from construction of residential and commercial areas. The site K8 was near a motorway and receives metal input from automobile activities. The site K1 was adjacent to a tuberculosis sanatorium and received untreated discharge of municipal sewage, hospital and solid waste, while the sampling sites S1, M1 and B3 were located near agricultural areas. The concentration of Cr was higher in pore water sediment samples at the site K8 (3.14 μg/g) in pre-monsoon season and S2 (2.82 μg/g) in post-monsoon season. The concentrations of Co exhibited greater variations (0.001–5.47 μg/g) in pre-monsoon season, and its concentration was higher in the sediment samples collected from urban area (site K12). However, the concentrations of Co decreased during postmonsoon season (0.001–1.60 μg/g). Concentrations of Cd detected in pore water sediment samples were varied (0.18–0.57 μg/g) in premonsoon season while the Cd concentrations decreased in all the sampling sites during post-monsoon season (ranged from 0.01 to 0.17 μg/g). In pre-monsoon season the concentrations of Cd in all the pore water sediments exceeded the average shale value. The concentrations of Pb showed fluctuating results in both seasons with the range of 0.14–0.58 μg/g. Pb higher concentration was recorded at the sampling site S4. However, during post-monsoon season Pb concentration was higher in all the sites. Cu and Li concentrations were higher at the site M1 (0.43 μg/g) and K8 (0.26 μg/g) in premonsoon season while during post-monsoon season the higher contents were recorded at the sampling sites S4 (0.82 μg/g) and S3 (0.14 μg/g). Based on the spatial similarities of physiochemical parameters measured in the pore water sediment samples three main groups/ clusters were identified (Fig. 2) using HACA. Group 1 comprised six sites (B3, B1, K5, M1, K7 & K4) with lowest metal concentrations in sediment samples. Group 2 comprised eight sites (B2, S3, S2, K9, K1, M2, S1 & K1) which were surrounded by semi urban areas. Group 3 comprised seven sites (K1, K12, K11, K8, K3, B4 & K2) and most of the sites were located downstream except K2. The pore water sediment samples were highly impaired at these sites. Anthropogenic activities such as urbanization, dumping of solid waste, raw sewage, automobile washing and auto-workshops near these sites were main sources of metal pollution in pore water sediment samples. 3.2. Properties of pore water sediments Kurang pore water sediments pH was slightly acidic to alkaline in nature. TDS ranged from 6.6 to 7.8 (mg/L) and from 80 to 450 (mg/L)

Ward`s method Euclidean distances 120 100

(Dlink/Dmax)*100

928

80 60 40 20 0

K10 K8 K3 K2 B1 M1 K4 S3 K9 M2 K1 K12 K11 S4 B3 K5 K7 B2 S2 K6 S1

Group 3

Group 1

Group 2

Fig. 2. Hierarchical cluster diagram of sites obtained using Wards methods as linkage method and Euclidean distance matrix (the distances reflect the degree of association between different sites based on the dissimilarity of physiochemical properties of surface sediments).

in pre-monsoon season and from 4.0 to 9.3 (mg/L) and from 40 to 340 (mg/L) in post-monsoon season. Pore water sediment samples collected in pre-monsoon season showed relatively higher values of pH (7.71) and OM (4.28%) than the post-monsoon season. However, OM and pH were significantly different (p ≤ 0.05) between both seasons. Comparison among the sites revealed that the sites K1, K2, K5, K7, B1 and B2 showed acidic pH as compared to the other sites. Lower pH values suggested that there may be a potential risk for metal resolubilization, as it regulates concentration of dissolved metals in water and sediment (Praveena et al., 2007), while heavy metals at alkaline pH generally precipitate (Jain et al., 2007). Organic matter and pH play an important role in the heavy metal retention in sediment implying their important role on sediment quality. High pH generally decreases the solubility of heavy metals in water (Avila-Pérez et al., 1999), however, at low pH competition between metals and hydrogen ions for binding sites increases which may dissolve metal complexes releasing free metal ions into the water column. The concentrations of organic matter exhibited great variations and ranged from 0.06 to 11.29% in pre-monsoon season and from 0.27 to 36.58% in postmonsoon season. The higher concentrations of OM were at the site K1 which might be due to direct discharge of raw sewage and solid waste from its catchment area. Fine grain sediments and more organic matter facilitate the accumulation of more heavy metal contents. Organic matter may increase the Pb and Cu contents in our study. Cu forms complexes with organic matter due to its high formation of organic– copper compounds (Li et al., 2000). The sites K1, K3, K8, K11, K12, S1, S2, S3, S4, B2, B3 and M1 were characterized with relatively greater silt, clay and OM content. Previous studies demonstrated that grainsize acts as a major factor in controlling sedimentary heavy metal concentrations (Lin et al., 2008). Texture plays an important role in bioavailability and toxicity of heavy metals in sediments, as metals are not homogeneously distributed over various grain size fractions. Grain size and OM have greater surface area for metal adsorptions (Yan and Tang, 2009) and influence their distribution (Huang and Lin, 2003; Rodríguez-Barroso et al., 2010; Wakida et al., 2008). 3.3. Enrichment factor (EF) A comparison of metal concentration in sediments with background reference values is generally used to assess metal enrichment (Tuna et al., 2007). Mean EF values of studied metals with respect to the average shale (Turekian and Wedepohl, 1961) are presented in Table 3. Enrichment of metals with respect to their background standard values in our study indicated that metal accumulation was either by

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Table 2 Metal concentrations (μg/g) in sediments of the Kurang Nallah during pre- and post-monsoon seasons. Seasons

Cr

Mn

Co

Ni

Mean post-monsoon season Min post-monsoon season Max post-monsoon season Mean pre-monsoon season Min pre-monsoon season Max pre-monsoon season Mean sedimentb Background level Aver. shalea ERLc ERMc TELd PELd

1.69 0.9 2.82 1.67 0.33 3.14 72 14.0 90 81 370 52 160

38.4 9.8 122.6 26.7 9.97 52.18 n.a.

0.45 0 1.6 1.26 0 5.47 n.a

850 n.a. n.a. n.a. n.a.

19 n.a n.a n.a n.a

28.42 0.5 70.8 6.8 BDL 52 52 5.0 68 20.9 51.6 n.a. n.a.

Cu 0.14 0.02 0.82 0.17 0.05 0.43 33 45 34 270 19 108

Cd

Zn

Ca

Mg

Fe

K

Na

0.09 0.01 0.17 0.42 0.18 0.57 0.17 0.30 0.30 1.2 9.6 0.7 4.2

54.25 27.7 317.65 58.57 0 381.92 95

1410.36 230.84 2673.14 1164.35 474.41 1969.3 n.a.

88.91 48.44 210.53 105.97 40.04 178.26 n.a.

378.34 54.1 2256.14 911.17 97.56 3559.52 n.a.

227.1 23.42 1449.32 227.48 66.68 520.82 n.a.

95 150 410 124 271

22,100 n.a. n.a. n.a. n.a.

15,000 n.a. n.a. n.a. n.a.

185.51 91.92 403.56 235.09 112.59 440.49 n.a. 46.0 47200 n.a. n.a. n.a. n.a.

Pb

2.66 n.a. n.a. n.a. n.a.

96 n.a. n.a. n.a. n.a.

0.69 0.5 1.03 0.3 0.14 0.58 19 20 47 218 30 112

Li 0.07 BDL 0.14 0.09 0.01 0.26 n.a. 66 n.a. n.a. n.a. n.a.

BDL = below detection limit. a Turekian and Wedepohl (1961). b Salomons and Förstner (1984). c Long et al. (1995, 1998). d MacDonald et al. (2000).

natural or anthropogenic sources (Sayadi et al., 2010). Mean EF values of Cr, Co, Cu, Cd, Zn, Fe and Li were greater in pre-monsoon season, and followed the order: Cd N Zn N Ni N Co N Cr N Pb N Fe N Cu N Li. Lower values

of metal EF in post-monsoon season can be related to high flow rate which may cause transport of sediments. Mean concentration of Ni, at the two sites (B3 and S2), and Cd in post-monsoon season were higher

Table 3 Enrichment factor and metal pollution index values of heavy metals in the Kurang Nallah pore water sediments during pre- and post-monsoon seasons. Sites

Seasons

EF Cr

EF Co

EF Ni

EF Cu

EF Cd

EF Zn

EF Fe

EF Pb

EF Li

EF MPI

B1 B1 B2 B2 B3 B3 K1 K1 K10 K10 K11 K11 K12 K12 K2 K2 K3 K3 K4 K4 K5 K5 K6 K6 K7 K7 K8 K8 K9 K9 M1 M1 M2 M2 S1 S1 S2 S2 S3 S3 S4 S4 Mean Mean

Post-monsoon Pre-monsoon Post-monsoon Pre-monsoon Post-monsoon Pre-monsoon Post-monsoon Pre-monsoon Post-monsoon Pre-monsoon Post-monsoon Pre-monsoon Post-monsoon Pre-monsoon Post-monsoon Pre-monsoon Post-monsoon Pre-monsoon Post-monsoon Pre-monsoon Post-monsoon Pre-monsoon Post-monsoon Pre-monsoon Post-monsoon Pre-monsoon Post-monsoon Pre-monsoon Post-monsoon Pre-monsoon Post-monsoon Pre-monsoon Post-monsoon Pre-monsoon Post-monsoon Pre-monsoon Post-monsoon Pre-monsoon Post-monsoon Pre-monsoon Post-monsoon Pre-monsoon Post-monsoon Pre-monsoon

0.64 0.66 0.28 0.31 0.46 0.53 0.95 0.72 0.17 0.42 0.61 0.69 0.42 1.16 1.04 1.28 0.39 0.65 0.4 0.58 1.24 0.54 0.16 0.11 0.41 0.48 0.57 0.57 0.34 0.29 0.1 0.41 0.22 0.34 0.90 0.98 0.84 0.63 0.78 1.08 0.75 2.06 0.54 0.70

0.46 3.36 0.12 0 0.32 0 0.61 0 1.13 2.55 0.10 8.28 1.02 10.35 0.82 0 0 0.48 2.52 0.79 7.59 2.95 0.43 0 0.51 0 0 0.4 1.04 0 0.07 1.26 0.17 3.72 0.81 0 0.46 0 0.45 3.28 0 14.93 0.62 2.75

10.26 0 7.83 0 17.64 2.81 37.63 0 1.55 0 18.8 0 3.52 0 18.74 0 0 22.16 0 6.73 0 4.7 0.12 0 2.33 0 0 7.51 3.36 0 0.57 6.64 1.68 0 25.58 34.97 27.96 16.4 22.73 14.84 28.18 0 12.84 3.60

0.11 0.10 0.04 0.06 0.05 0.10 0.14 0.25 0.02 0.05 0.04 0.09 0.04 0.12 0.33 0.40 0.10 0.04 0.06 0.09 0.25 0.06 0.01 0.03 0.04 0.07 0.06 0.06 0.02 0.04 0.02 0.26 0.02 0.05 0.12 0.21 0.15 0.12 0.07 0.19 0.86 0.75 0.11 0.16

14.75 65.53 3.77 26.31 4.01 40.32 2.61 50.12 6.4 31.35 3.02 66.14 9.81 59.22 5.78 89.9 37.55 4.53 46.18 11.19 129.84 14.59 4.28 32.43 8.61 43.02 18.84 8.62 6.63 37.37 3.93 15.67 7.44 44.07 8.00 60.32 6.10 28.5 15.59 65.33 5.65 147.97 7.40 54.09

10.79 8.78 5.1 4.13 7.98 2.87 137.76 96.96 3.93 5.36 12.98 7.69 8.23 17.07 50.05 30.30 15.85 10.22 5.9 26.06 43.84 12.57 4.56 0.37 10.53 4.61 6.63 11.14 6.13 0 2.23 17.04 5.96 6.52 18.23 24.23 16.05 16.32 20.21 16.06 23.61 342.54 19.25 32.05

0.12 0.17 0.04 0.09 0.1 0.11 0.24 0.15 0.04 0.11 0.15 0.25 0.1 0.32 0.17 0.27 0.08 0.1 0.12 0.07 0.33 0.09 0.04 0.07 0.09 0.12 0.14 0.14 0.06 0.09 0.02 0.17 0.06 0.10 0.21 0.22 0.23 0.17 0.15 0.32 0.16 0.43 0.11 0.18

0.88 0.29 0.54 0.3 0.56 0.6 1.83 0.47 0.47 0.16 1.01 0.51 0.77 0.52 4.46 1.31 0.45 1.23 0.32 0.82 0.91 1.37 0.53 0.21 0.91 0.59 0.27 1.01 0.59 0.32 0.2 0.71 0.38 0.33 1.22 0.9 0.94 0.61 0.91 0.40 2.07 2.47 1.08 0.60

0.06 0.04 0.02 0.05 0.02 0.08 0.01 0.03 0.02 0.02 0.02 0.01 0.03 0.06 0.01 0.08 0.03 0.01 0.02 0.04 0.03 0.05 0.02 0.03 0.02 0.05 0.07 0.03 0.03 0.03 0.01 0.06 0.03 0.01 0.04 0.08 0.03 0.08 0.07 0.07 0.01 0.04 0.03 0.05

2.36 1.82 2.02 1.56 2.7 1.68 2.26 2.25 2.14 2.25 1.59 1.49 2.02 2.64 1.17 1.77 1.75 1.69 1.71 2.18 1.64 1.92 1.12 0.85 1.65 1.51 2.59 1.72 2.07 0.96 1.93 2.31 1.84 1.67 2.62 3.04 2.91 3.34 2.48 3.56 2.32 2.74 2.03 2.05

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with respective to average shale values (Table 2). Mean concentration of Zn at the sites K1 and S4 was above than average shale values. The results suggested that these sites were located in urban areas and received municipal effluents, solid waste dumping and surface runoff. EF values of Cu, Fe, Pb (except in K2 and S4 where EF values for Pb were 1.31 and 2.47) and Li were b1 and suggested no enrichment of these metals. Average EF values for Co and Cr were b3 and suggested a minor enrichment of these metals. The EF values of the sites S3 (3.28), B1 (3.36) and M2 (3.72) showed moderate enrichment; the sites K5 (7.59) and K11 (8.28) exhibited moderately severe enrichment and the sites K12 (10.35) and S4 (14.93) showed severe enrichment for Co. Pb showed no enrichment or minor enrichment, except the site K2, which showed moderate enrichment with EF value of 4.46. Pb EF values were greater at most of the sites during post-monsoon season. Greater EF values at the site S4 for Zn (342.54), Cd (147.97), Co (14.93), Pb (2.47) and Cr (2.06) indicated extremely severe enrichment of the site for Zn and Cd, severe enrichment for Co, and minor enrichment for Pb and Cr. During post-monsoon season Cr, Co, Cu, and Li showed no-enrichment with EF values b 1.5. EF values of Cr at the site K2 was b3, indicating minor enrichment, while all other sites had EF value b 1, indicating no enrichment. Principal sources of these metals can be associated with municipal and domestic waste, fossil fuel incineration and agriculture runoff. The world average background concentration of Fe was much higher than other metals (467,200 μg/g), indicating its geological occurrence. The EF values for Fe indicated insignificant effects of anthropogenic inputs when compared to background concentrations of Fe in uncontaminated sediments. The mean EF value for Zn in pore water sediments of Kurang reached 19.25 and showed severe enrichment at most of the sites. Zn was the most enriched element in post-monsoon season followed by Cd and Ni. The mean EF value for Zn was 32.05 in pre-monsoon season and 19.25 during post-monsoon season. The results suggested that greater EF values of Zn can be attributed to surface runoff (Boxall et al., 2000) and input of organic wastes which is associated from municipal sewage and solid waste (Alagarsamy, 1991). Metals such as Cu, Zn and Pb have a high affinity to humic substances present in organic matter. The presence and quantity of organic matter differentially influence the binding of metals within the sediments and reduce the adsorption of Cd and Co as well as increased adsorption of Zn (Tomlinson et al., 1980). EF values for Cd were highest (54.09) in pre-monsoon season. The EF values of Cd indicated severe enrichment at the sampling sites K4, K5, S3 and B1 and moderate to severe enrichment at the sites K2, K7, K8, K9, K10, K12, S1, S2, S4, M2, K3, K6, K11, B2, B3 and M1. Cd is a typical anthropogenic metal associated with by human activities (Zhang and Shan, 2008) and showed severe enrichment at all the sites with highest EF value (147.97) recorded at the site S4 located downstream. Cd has been associated to a greater extent with colloidal materials in surface runoff that can easily be transported in river flow (Wakida et al., 2008). Ni showed significant accumulation at the five sites (S1, S2, S3, M1 and B3) in pre-monsoon season, whereas EF values for Ni exhibited enrichment at all sites during post-monsoon season. Similarly, Pb showed high EF values at all sites during post-monsoon season, except at the site K2 where it showed moderate enrichment. Trends in the EF values of Ni and Pb in post-monsoon season revealed common sources of these metals related to automobile activities. Ni can be generated by the wear of bearings, bushings and other moving parts in engines, while lead is used as a filler material in tyres (Makepeace et al., 1995). Surface runoff may therefore contain higher concentrations of these metals. Ni is generally associated with the asphaltene component of petroleum (Priju and Narayana, 2006). Leakage from septic tanks and leaded gasoline can be other possible sources for Ni and Pb in Kurang pore water sediments. Pb has lower solubility in water and is an extremely stable element; however, it is toxic to humans and animals (Sayadi et al., 2010). Praveena et al. (2007) reported that Pb most probably

arises from indirect sources such as atmospheric deposition. Pb is the fifth most commonly used metal in the world and canned foods that have acidic reactions will tend to solubilize Pb from the container in which the products were stored.

3.4. Index of geoaccumulation (Igeo) Igeo values have been used to explain sediment quality (Karbassi et al., 2008); however, Igeo is not readily comparable to the other indices of metal enrichment due to the nature of the Igeo calculation, which involves a log function, and a background multiplication of 1.5 (Abrahim and Parker, 2008). The Igeo of sampling sites during the preand post-monsoon seasons are presented in Tables 4 and 5. The Igeo values of Cr, Co, Cu, Pb and Li indicated no pollution in pore water sediment samples. Among the metals Mn showed the highest accumulation in pre-monsoon season. Mn Igeo values ranged from 2.35 (site S4) to 12.31 (site K8) which corresponded to class 3 of moderately to strongly polluted sediment samples and class 6 of very strongly polluted sediment samples. Greater Igeo value of 28.94 was recorded at M1 indicating highly polluted sediment samples during post-monsoon season. Mn is the eleventh abundant element in earth crust (Duman et al., 2007) and its accumulation and enrichment are influenced by both natural processes and anthropogenic activities. Dumping of sewage sludge and domestic wastes were the major anthropogenic sources of Mn in our study area. The results indicated that 76% of all the sites showed no Ni geo-accumulation in pre-monsoon season. Ni (mean: Igeo 0.08) and Zn (mean: Igeo 0.11) showed moderate pollution during post-monsoon season at all the sites. Mn and Ni showed more accumulation in post-monsoon season as compared to Cd, Zn and Fe that had higher Igeo values in pre-monsoon season. Sediment samples collected from the sites viz., K8, K10, K11, K12, S4, M1, S2 and S3 were moderately to strongly polluted with Fe in pre-monsoon. In general, 24% of the total sediment samples were classified into moderately to strongly polluted with Fe while 76% as moderately polluted during post-monsoon season. The EF values of Cd in both seasons fall into class 1 of unpolluted to moderately polluted sediment samples.

Table 4 Geoaccumulation index values of analyzed metals in sediments of the Kurang Nallah and their classes during pre-monsoon season. Igeo values based on shale

Igeo class

Sites

Mn

Co

Ni

Cd

Zn

Fe

Mn

Co

Ni

Cd

Zn

Fe

K1 K2 K3 K4 K5 K6 K7 K8 K9 K10 K11 K12 S1 S2 S3 S4 B1 B2 B3 M1 M2 Mean

5.52 2.75 6.73 8.12 2.75 6.58 5.65 12.31 5.97 11.62 4.72 5.57 4.36 7.09 5.8 2.35 5.82 7.95 5.04 7.46 7.95 6.29

0 0 0 0.02 0.02 0 0 0 0 0.02 0.03 0.05 0 0 0.01 0.03 0.01 0 0 0 0.02 0.01

0 0 0 0 0 0 0 0 0 0 0 0 0.15 0.11 0.08 0 0 0 0.01 0.04 0 0.02

0.27 0.24 0.25 0.37 0.37 0.21 0.23 0.23 0.22 0.36 0.31 0.32 0.26 0.2 0.38 0.34 0.38 0.2 0.2 0.12 0.34 0.28

0.53 0.08 0.1 0.04 0.12 0 0.02 0.08 0 0.06 0.03 0.09 0.1 0.11 0.09 0.8 0.05 0.03 0.01 0.12 0.05 0.12

0.81 0.75 0.54 0.95 0.89 0.47 0.68 1.75 0.56 1.26 1.18 1.77 0.93 1.22 1.87 1.01 0.97 0.68 0.57 1.27 0.76 0.99

6 3 6 6 3 6 6 6 6 6 5 6 5 6 6 3 6 6 6 6 6 6

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 1 1 0 0

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

1 1 1 1 1 0 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1

1 1 1 1 1 1 1 2 1 2 2 2 1 2 2 2 1 1 1 2 1 1

A. Zahra et al. / Science of the Total Environment 470–471 (2014) 925–933 Table 5 Geoaccumulation index values of analyzed metals in sediments of the Kurang Nallah and their classes during post-monsoon season. Igeo values Sites

Mn

Co

Ni

Cd

Zn

Fe

Pb

Mn

Co

Ni

Cd

Zn

Fe

Pb

K1 K2 K3 K4 K5 K6 K7 K8 K9 K10 K11 K12 S1 S2 S3 S4 B1 B2 B3 M1 M2 Mean

1.5 2.31 6.35 6.57 5.27 12.82 7.54 6.13 10.7 15.04 6.87 8.04 6.02 7.46 6.05 4.26 6.84 13.48 11.84 28.94 13.03 8.91

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0.18 0.04 0.14 0.04 0.02 0 0.01 0.04 0.03 0.02 0.12 0.02 0.15 0.2 0.13 0.12 0.07 0.1 0.2 0.01 0.02 0.08

0.01 0.01 0.02 0.07 0.08 0.05 0.06 0.05 0.07 0.09 0.02 0.08 0.04 0.04 0.09 0.04 0.1 0.05 0.04 0.11 0.1 0.06

0.67 0.11 0.06 0.17 0.06 0.05 0.07 0.06 0.06 0.05 0.08 0.06 0.1 0.11 0.12 0.1 0.07 0.06 0.09 0.06 0.07 0.11

1.16 0.39 0.65 0.48 0.49 0.48 0.64 0.84 0.59 0.52 1.01 0.83 1.27 1.71 0.92 0.66 0.79 0.59 1.15 0.46 0.83 0.78

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

2 3 6 6 6 6 6 6 6 6 6 6 6 6 6 5 6 6 6 6 6 6

0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0

1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

2 1 1 1 1 1 1 1 1 1 2 1 2 2 1 1 1 1 2 1 1 1

0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

The values of Igeo values based on shale and Igeo class of Cr, Cu, and Li were “0” and are not indicated.

3.5. Metal pollution index (MPI) and comparison with sediment quality guidelines (SQGs) The MPI values of Cr, Mn, Co, Ni, Cu, Cd, Zn, Fe, Pb, and Li in all sediment samples are summarized in Table 3. MPI indicated metal pollution load at the each site. In pre-monsoon season, higher MPI values were measured at the sites viz., K2, K4, K5, K10, K12, S1, S2, S3, S4 and M1 whereas in post-monsoon season, the higher MPI values were recorded at the sites, K3, K6, K7, K8, K9, K11, B1, B2, B3 and M2. In pre-monsoon season the highest MPI value was measured at the site S3 (3.56) and in post-monsoon season at the site S2 (2.91). All the sites on the Shahdra stream (S) had a greater metal pollution load in pre-monsoon season indicating impact from human related activities. In contrast, the sites located on Baroha stream (B) showed higher MPI in post-monsoon season indicating contribution of surface runoff. Total heavy metal concentrations have increasingly been used in the assessment of the environmental status of aquatic environments, but more important is whether toxicants are available to living organisms and whether they are entering the food chain (Birch and Taylor, 1999). Sediment quality guidelines (SQGs) were used to assess the quality of sediments and provide tolerable concentrations of sedimentbound contaminants in order to protect the living organisms living in or near sediments and the comparison of measured concentrations of various contaminants (Violintzis et al., 2009). To evaluate possible environmental consequences of studied metals, comparison was made with concentrations of Cr, Cu, Ni, Cd, Zn and Pb measured in sediments to the numerical sediment quality guidelines of effect range low (ERL) and effect range median (ERM) threshold effect level (TEL) and probable effect level (PEL). ERL and TEL values represent chemical concentrations below which adverse biological effects were rarely observed (Long et al., 1998; MacDonald et al., 2000). In contrast, ERM and PEL values represent chemical concentrations above which effects are more frequently expected. The results showed that Cr, Cu and Pb concentrations during pre-monsoon season and post-monsoon season were below the ERL values (81 μg/g, 34 μg/g and 46.7 μg/g, respectively) and ERM values (370 μg/g, 270 μg/g and 218 μg/g, respectively) and were below the background concentration of uncontaminated sediment samples (72 μg/g, 33 μg/g and 19 μg/g, respectively) as suggested by Salomons and Förstner (1984).

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Cd concentrations in the seven sites (K4, K5, K10, S3, S4, B1 and M2) during pre-monsoon season were close to the TEL value (0.68 μg/g) but none exceeded the ERL (1.2 μg/g) and ERM (9.6 μg/g) values. In postmonsoon season, Cd concentrations at all the sites were below the ERL and ERM values. However, the concentrations of Cd at the all sites in pre-monsoon season were above the background concentration of uncontaminated sediments (0.17 μg/g). In pre-monsoon season, Ni concentrations in sediment samples from the sites S1 (51.70 μg/g), S2 (39.40 μg/g) and S3 (29.20 μg/g) exceeded the ERL value of 20.9 μg/g. The Ni concentration at the site S1 exceeded the ERM value of 51.6 μg/g. The Ni at the sampling sites, K1 (62.10 μg/g), K3 (47.70 μg/g), K11 (43.80 μg/g), S1 (52.20 μg/g), S2 (70.70 μg/g), S3 (46.60 μg/g), S4 (40.70 μg/g), B1 (23.80 μg/g), B3 (70.80 μg/g) and B2 (35.80 μg/g) exceeded the ERL value of 20.9 μg/g. The Ni concentration at K1, S1, S2 and B3 was also higher than the ERM values of 51.60 which signify that any living organism living within or close to these sites could potentially experience adverse effects. However, the two sites (B3 and S2) had higher Ni concentrations than the world average shale values (68 μg/g) and background concentration of non-contaminated sediment (52 μg/g). Ni is highly mobile in the soil and sediment. It is the only metal which can potentially be harmful to aquatic organisms in the Kurang as its concentration at four sites was above the ERM values indicating its potential toxicity for aquatic organisms and can potentially be harmful to aquatic biota. The Zn concentration at K1 during both seasons (pre—253.63, post— 317.65μg/g) and S4 (381.92μg/g) during pre-monsoon season exceeded the ERL value of 150 μg/g. However, none of the sites exceeded the ERM value. The Zn concentration at S4 was close to the ERM value and could possibly pose a potential risk to the aquatic environment. It is therefore suggested that follow-up monitoring should be continued at these sites. 3.6. Pollution source identification based on PCA/FA and correlation matrix (CM) The results of the PCA/FA of physicochemical parameters are shown in Table 6. PCA/FA leads to a reduction of the initial dimension of the data set to nine Varimax factors (VFs) which explain 85.22% of the data variation. A total of nine significant VFs were extracted with an eigenvalues N 1. VF1 explained 16.66% of the total variance and an eigenvalue of 4.17 with strong negative correlation with OM and Zn, positive loadings on sand (%). VF1 is the most important component and is explained as an anthropogenic source of OM and Zn that is mainly related to direct input from municipal effluents, and to geochemical weathering of parent rock material characterized by sand and limestone. The distribution and transport of Zn in sediment is dependent on the species of Zn present, and the characteristics of the environment. Zn is unlikely to be leached from the soil owing to its adsorption on clay and organic matter. Acidic soils and sandy soils with a low organic content reduce Zn absorption. VF2 explained 15.77% of the total variance and an eigenvalue of 3.94 with Cr, Mg, and Fe. VF3 explained 11.87% of the total variations and an eigenvalue of 2.97. This VF had strong negative loadings on Pb, Ni, and Cd. High concentrations of these metals are possibly caused by anthropogenic inputs from surface runoff and atmospheric deposition. Heavy metals in the atmosphere might be originated from fossil fuel burning, traffic, and waste burning, which were widespread throughout the study area. VF4 explained 10.19% of the total variation with an eigenvalue of 2.55. This VF showed negative loading on TDS indicating input from domestic sources, animal and human washing as well as agricultural activities. VF5 explained 6.83% of the total variance and eigenvalue of 1.71. There was a positive loading on silt (%) and Cu indicating its input from surface runoff and municipal effluents. Cu containing agrochemicals are commonly used for disease control in livestock and poultry. VF6 and VF7 explained 7.42% and 5.65% of the total variance with eigenvalues of 1.85 and 1.41, respectively. Having strong positive loadings on Mn, K, Na and clay particles. The results suggested that

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Table 6 Loadings of environmental variables on significant Varimax rotated principal components and marked loadings are N0.70. Parameters

PCA 1

PCA 2

PCA 3

PCA 4

PCA 5

PCA 6

PCA 7

PCA 8

PCA 9

pH EC TDS OM Clay Sand Silt Cr Mn Co Ni Cu Cd Zn Ca Mg Fe K Na Pb Li Eigenvalues Explained variance (%)

−0.04 0.02 0.02 0.78 −0.17 −0.92 −0.01 0.16 −0.01 −0.03 0.06 0.20 0.04 0.82 −0.18 −0.17 0.09 0.05 0.05 0.06 −0.20 4.17 16.66

−0.01 −0.06 −0.06 0.05 0.18 −0.02 −0.05 0.90 −0.10 0.22 0.47 0.19 0.20 0.11 0.07 0.86 0.89 0.46 0.07 −0.10 0.53 3.94 15.77

−0.33 −0.07 −0.07 −0.40 0.03 0.08 0.05 −0.09 −0.11 0.36 −0.73 0.00 0.87 −0.06 −0.12 0.05 0.10 0.22 −0.08 −0.90 0.20 2.97 11.87

0.60 −0.96 −0.96 0.02 0.12 0.02 0.14 0.04 −0.16 −0.10 0.03 −0.01 −0.01 −0.04 −0.19 0.06 0.05 0.01 0.00 −0.04 −0.06 2.55 10.19

0.14 −0.02 −0.02 −0.21 0.09 −0.10 0.70 0.09 −0.22 0.11 0.05 0.85 −0.04 0.19 −0.17 −0.09 0.12 0.11 0.00 0.20 −0.12 2.07 8.26

0.19 0.06 0.06 −0.09 0.78 −0.14 0.46 0.04 0.79 0.02 −0.14 −0.14 −0.10 −0.21 0.06 −0.08 0.11 0.12 −0.01 −0.03 0.45 1.85 7.42

0.15 0.04 0.04 −0.13 0.12 −0.05 −0.03 0.18 −0.07 0.06 0.02 −0.14 −0.02 −0.05 0.06 0.02 0.09 0.70 0.94 0.03 −0.07 1.41 5.65

0.06 0.05 0.05 0.09 0.06 0.00 0.36 0.06 −0.06 0.83 −0.21 −0.10 0.32 0.17 0.25 0.01 0.16 0.18 −0.01 0.00 −0.47 1.32 5.28

0.12 0.13 0.13 −0.22 −0.17 0.20 −0.03 0.01 0.23 0.02 0.01 0.07 −0.06 −0.04 0.84 0.24 −0.19 −0.23 0.08 0.16 −0.11 1.03 4.12

Bold or marked values are significant at b0.70.

their origin can be explained by the significant role of clay that is bound closely with metals. These two factors indicated variability of the metals seemingly to be controlled by parent rocks. VF8 and VF9 explained 5.65% and 4.12% of the total variance and had strong positive loadings on Co and Ca, indicating their origin from parent rock material. Interrelation between metals can provide information on sources of contamination and heavy metal pathways. Correlation matrices (CM) confirmed the results obtained from PCA/FA, metal pollution index and enrichment factor. CM provided very similar correlations as obtained from PCA/FA. However, new associations between metals that were not clearly stated in previous analysis were also identified; e.g., Cr was positively correlated with Ni, Mg, Fe, and Li indicating their common origin mainly from surface runoff and washing of vehicles which is common along the stream banks in the study area. The Mn association with Li indicated a common origin from natural sources. Co and Cu were associated with Cd, highlighting input from surface runoff. Cd content in sediments can also be due to the use of phosphate fertilizers. The negative correlation of Cd and Pb indicated

their origin from automobile activities, whereas, Pb showed positive association with Cu and Zn indicating an additional source from urban surface runoff. Mg, Li, K and Fe were significantly correlated which highlighted their input from lithogenic origin since these metals are generally present in the parent rock material. The present study identified similar sources of metal contamination as by Adomako et al. (2008). Their results indicated direct atmospheric deposition, geological weathering or through the discharge of agriculture, municipal, residential or industrial waste products. Sediment pH and TDS showed a insignificant correlation between heavy metal content, corresponding with the findings of Bhuiyan et al. (2011) previously (Table 7). 4. Conclusion Results showed that Cd concentrations in pre-monsoon season were higher than the average shale values. Zn and Ni concentrations were also higher in both seasons. Similarly, these were the most enriched in the Kurang pore water sediments. Igeo indicated that sediments are

Table 7 Correlation of metals with soil properties.

pH EC TDS OM Clay Sand Silt Cr Mn Co Ni Cu Cd Zn Ca Mg Fe K Na Pb Li

pH

EC

TDS

OM

Clay

Sand

Silt

1 −0.33 −0.33 −0.07 0.25 −0.03 0.28 0.11 −0.21 0.05 0.13 0.17 −0.11 −0.01 −0.21 −0.16 0.21 0.13 −0.03 0.21 0.1

1 1 0.06 −0.14 −0.05 −0.17 0.08 0.33 0.32 −0.07 −0.06 0.26 0.05 0.40 0.01 0.07 0.23 0.23 −0.04 −0.01

1 0.06 −0.14 −0.06 −0.17 0.07 0.33 0.32 −0.07 −0.07 0.26 0.05 0.40 0.01 0.06 0.23 0.24 −0.04 −0.01

1 −0.25 −0.91 −0.22 0.14 −0.11 −0.3 0.23 0.16 −0.22 0.77 −0.37 −0.19 0.12 −0.04 0.0 0.21 −0.19

1 −0.05 0.42 0.20 0.52 0.18 0.08 −0.10 −0.04 −0.35 −0.05 0.16 0.40 0.28 0.22 −0.24 0.45

1 −0.09 −0.2 −0.01 0.04 −0.17 −0.25 0.10 −0.77 0.37 0.23 −0.24 −0.15 −0.10 −0.18 0.19

1 0.05 1 0.13 −0.25 1 0.49 0.13 −0.03 1 −0.21 0.63 −0.24 −0.34 1 0.55 0.28 −0.21 0.04 0.28 1 0.22 0.18 −0.16 0.72 −0.26 −0.05 1 0.17 0.24 −0.34 −0.03 0.26 0.68 −0.04 1 −0.11 −0.01 0.15 0.27 −0.14 −0.01 0.29 −0.24 1 −0.20 0.67 −0.10 −0.08 0.64 0.03 0.05 −0.17 0.41 1 0.20 0.91 −0.05 0.23 0.56 0.16 0.15 0.13 −0.17 0.55 1 0.31 0.47 −0.13 0.43 −0.09 0.07 0.07 0.08 −0.05 0.01 0.47 1 0.02 0.04 −0.11 0.16 0.1 −0.08 −0.09 0.02 0.14 −0.06 0.05 0.52 1 0.28 0.75 −0.47 0.55 −0.07 −0.06 −0.03 0.19 0.17 1 0.17 0.10 −0.34 −0.24 −0.07 0.5 0.45 −0.19 0.24 −0.18 −0.07 −0.41 0.03 0.53 0.57 0.04 −0.41 −0.34 1

Bold or marked values are significant at b0.70.

Cr

Mn

Co

Ni

Cu

Cd

Zn

Ca

Mg

Fe

K

Na

Pb

Li

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