Assessment of metal species in river Ganga sediment at Varanasi, India using sequential extraction procedure and SEM–EDS

Chemosphere 134 (2015) 466–474

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Assessment of metal species in river Ganga sediment at Varanasi, India using sequential extraction procedure and SEM–EDS Mayank Pandey a, Ashutosh Kumar Pandey a, Ashutosh Mishra a, B.D. Tripathi b,⇑ a b

Institute of Environment and Sustainable Development, Banaras Hindu University, Varanasi 221005, India Department of Botany, Banaras Hindu University, Varanasi 221005, India

h i g h l i g h t s  The present study is first of its kind in the studied region.  It deals with impact assessment of urban drains on river water and sediment.  Metal speciation analysis was carried out by sequential extraction procedure.  EDX study of the sediment was conducted to assess the elemental composition.

a r t i c l e

i n f o

Article history: Received 12 July 2014 Received in revised form 12 April 2015 Accepted 19 April 2015 Available online 23 May 2015 Keywords: River Ganga Metal speciation Sequential extraction process Chemometric analysis SEM–EDS

a b s t r a c t Aim of the present study was to assess impact of urban drains over river water and sediments by physico-chemical and metal analysis. Metal speciation (Sequential Extraction Procedure) and elemental composition analysis (SEM–EDS) was used to quantify metal pollution load in river sediments. Metal speciation analysis showed dominance of available and labile fractions of all heavy metals (Cr, Ni, Cu, Zn, Cd and Pb) except Mn and Fe which were dominant in residual forms. Cluster analysis (CA), Principal Components Analysis (PCA) and Partial Least Square Regression (PLSR) were applied as source receptor modeling for pollutants. Results classified river stretch into three zones i.e. moderately, severely and extremely polluted, on the basis of pollutant concentration released from anthropogenic sources. SEM– EDS study revealed the elemental composition percentage in river sediments. Pollution Load Index (PLI) varied from 1.8 (S1)–3.9 (S15). The Geo accumulation index (GAI) was found highest for Cd (6.88–8.97) and Pb (2.41–3.24). Ó 2015 Elsevier Ltd. All rights reserved.

1. Introduction Fast industrial growth rate and rapid urbanization are prominent cause for environmental deterioration. Industrial and urban effluents contain high concentration of heavy metals (HMs) ultimately polluting water, sediment and soil. Metals discharged into the river get precipitated and accumulated onto river sediments and eventually enter the food chain. Therefore, river sediments may act both as source and sink of the metals (Varol, 2011). High concentration of HM in environment attracted the scientific community due to its non-biodegradability, long biological half life and toxicity potential (Jain, 2004; Chabukdhara and Nema, 2012; Kelepertzis et al., 2012). In an unpolluted environment, HM are ⇑ Corresponding author. E-mail addresses: [email protected] (M. Pandey), ashutosh.cest@ gmail.com (A.K. Pandey), [email protected] (A. Mishra), [email protected] (B.D. Tripathi). http://dx.doi.org/10.1016/j.chemosphere.2015.04.047 0045-6535/Ó 2015 Elsevier Ltd. All rights reserved.

attached to silicate and minerals. However, under anthropogenic induced environmental stress, metals may occur in such labile forms as oxides, hydroxides, carbonates, sulfides etc. and may join the liquid matrix (water). Hence, sediment may behave as source and sink of HM (Passos et al., 2010; Medici et al., 2011). Therefore, regular sediment quality monitoring, with special reference to metal speciation, is necessary as sediments decipher short and long term pollution load (Kwon and Lee, 2001). Metal speciation analysis using sequential extraction process (SEP) in river sediment helps to differentiate the metal into exchangeable/bound to carbonate, reducible/bound to Fe–Mn oxide, bound to organic matter and residual form (Tessier et al., 1979; Rauret et al., 2001; Sutherland, 2010). It also helps to understand the biogeochemical cycle of the ecosystem (Heltai et al., 2000). The obtained species can be grouped into functional (species which can be assimilated by plants), operational (species which can be extracted chemically) and specific characters (specific states like oxidation state etc.) (Naji et al., 2010).

M. Pandey et al. / Chemosphere 134 (2015) 466–474

River Ganga (2525 km), carrying a huge sediment load (1600  1012 g Year1), forms a vast alluvial deposited basin (1.086  106 km2) (Purushothaman and Chakrapani, 2007; Singh et al., 2007). Densely industrialized and urbanized cities discharge their effluent containing HM into the river. The present work is first of its kind in the study region where physico-chemical characterization of wastewater was done to assess its impact on river water and sediment. SEP coupled with SEM–EDS was done on river sediment to evaluate the metal pollution. 2. Material and methods 2.1. Study region Varanasi (25°160 5500 N 82°570 2300 E, 76 m amsl), situated on the bank of river Ganga, is an important industrial and residential center in northern India. Varanasi region has about 5000 registered industries which can be categorized into textile and fabric, carpet, Diesel Locomotive Works (DLW), wood, leather, metal, paper products, food processing, plastic–rubber and glass industries (DIP Varanasi) (DIP, 2014). Industrial effluents carrying HM directly or indirectly (get mixed with the city sewage system) discharged into the river. 2.2. Sampling and analysis Prewashed and acidified PTFE bottles (Tarsons, India) were used for the water sampling. Drain samples were collected from the exit point. River water was collected at drain-river confluence from fifteen sampling stations fortnightly from July 2012–June 2013 (Fig. 1, Table 1a). Sampling, physico-chemical and HM analysis of water samples was carried out by following standard protocols (APHA, 2005). Heavy metal analysis in acidified water and digested sediment samples (USEPA, 1996) was done by atomic absorption spectrophotometer (Perkin Elmer AAnalyst 800). Sediment samples collected with the help of core sampler from 15 stations and transported to lab in airtight plastic bags at 4 °C. Air dried, crushed and sieved (75 lm) samples were used for further analysis. Metal speciation analysis by SEP was conducted on sediment samples (Tessier et al., 1979; Rauret et al., 2001; Sutherland, 2010). Total acid digestion of samples was done following USEPA 3050B. The degree of metal accumulation was assessed by contamination factor (CF), enrichment factor (EF), pollution load index (PLI) and geo-accumulation index (GAI). Contamination factor gives the concentration of metal higher than the baseline concentration of same metal in an uncontaminated site. Contamination factor (CF) in the present study is calculated against background elemental composition in sediments (Fukue et al., 2006). Baseline concentration was taken from Salomons and Förstner (1984). CF ¼ Concentration of metal in sample=Baseline concentration of metal

Enrichment Factor represents the anthropogenic influx of metals in a system (Salomons and Förstner, 1984). EF ¼ ðConcentration of Metal in sediment  Baseline concentration of FeÞ=ðBaseline concentration of metal  Concentration of Fe in sedimentÞ

Pollution Load Index (PLI) gives the picture of pollution load in totality at a particular site (Angulo, 1996). PLI was calculated as

PLI ¼ ðCF1  CF2  CF3      CFnÞ1=n Geo-accumulation Index (Igeo) suggested by Muller which gives the level of metal pollution in terms of metal accumulation (Salomons and Förstner, 1984).

Igeo ¼ log 2½Cn=1:5  Bn

467

Cn = concentration of element n, Bn = geochemical background. SEM–EDS analysis (FEI Quanta 200F) was done for semi-quantitative analysis for four samples from upstream (S1), midstream (S8 and S9) and downstream (S15). S8 (mass bathing) and S9 (cremation) bear heavy anthropogenic load, therefore they were chosen as midstream samples. 3. Results and discussion 3.1. Drain samples and river water Annual mean concentration of physico-chemical parameters of drain samples and river water is given in supplementary. D7 (cremation ground), D8 (bathing ghat), D9 (cremation ground) and D15 (Drain Varuna) were found highly polluted, thus contributing maximum pollutant to the river Ganga in downstream. Drain samples were slightly less alkaline (8.06 ± 0.14) than the river water (8.36 ± 0.07). Low DO (2.8 ± 0.35 mg L1) with high BOD (91.74 ± 9.84 mg L1) and COD (131.78 ± 7.84 mg L1) indicated high organic load carried by the drains to the river (Table 1b). Phosphate (5.72 ± 0.417 mg L1) and nitrate (20 ± 3.2 mg L1) concentration were also significant in drain samples. Various industries of different scale are the principal sources of HMs in the study region. The industrial effluents get mixed with the city sewage system. Due to lack of metal removing techniques in the treatment plants, HMs in wastewater are ultimately get discharged into the river and deposited onto the suspended and bed sediments. Concentration of hazardous HMs in the drain samples were as follows: Cr (97.3 ± 10.86 lg L1), Cu (56.1 ± 12.68 lg L1), Zn (102.8 ± 15.54 lg L1), Cd (62.5 ± 14.4 lg L1) and Pb (270.68 ± 21 lg L1). Quantification of river water showed significant dilution of drain water meeting into the river. BOD and COD, organic pollution indicator in the water, were found 178.37 ± 27.3 mg L1 and 266.43 ± 37 mg L1 respectively. Cr (50.2 ± 7.14 lg L1), Cu (31.28 ± 7.06 lg L1), Cd 1 (21.63 ± 7.44 lg L ) and Pb (95.7 ± 13.5 lg L1) were found in significant concentration in the river water. 3.2. River sediment Chromium concentration in the sediment varied between 133.75 mg kg1 (S1) and 247.05 mg kg1 (S15). The labile form of Chromium (exchangeable and reducible) was significant in river downstream. However, the oxidizable and residual fraction was predominant all over the stretch (Fig. 2). The EF (8.76–14.16), CF (1.86–3.43) and GAI (0.31–1.19) depicted moderate to severely contamination of the sediment (Table 2) (Salomons and Förstner, 1984; Fukue et al., 2006; Chabukdhara and Nema, 2012). In the similar studies, Chromium concentration was found in the range of 2.22 mg kg1–19.13 mg kg1 in the river Gomti (India) sediments while in river Jhanji (India) sediment concentration was found between 28.5 mg kg1–513.8 mg kg1 (Baruah et al., 1996; Singh et al., 2005c). Reducible (41.8 ± 10.5) and residual fractions (34.5 ± 11.4) of Cr were present in highest percentage in Perl river (China) sediments with significant percentage of bound to carbonate form (10.8 ± 9.9) (Li et al., 2007). Lowest and highest concentration of Manganese was found at S11 (322.43 mg kg1) and S3 (439.75 mg kg1) respectively with little enrichment of Manganese (1.88–2.91). Residual fraction of Manganese was predominant in the sediment. The CF was almost constant all over the stretch (0.42–0.57) while negative GAI was found showing practically uncontaminated sites (Chabukdhara and Nema, 2012). Concentration of Iron was found highest and lowest at S15 (10 343 mg kg1) and S2 (7493.91 mg kg1) with narrow range of CF (0.18–0.25). The GAI of iron was found negative

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M. Pandey et al. / Chemosphere 134 (2015) 466–474

Fig. 1. Sampling stations.

along the whole stretch. Residual fraction of Fe was found as prominent species (74.31–82.58%). It suggested that the concentration of iron in the sediment may be of lithogenic origin. Nickel concentration was highest at S15 (97.1 mg kg1) followed by S14 (86.02 mg kg1) and lowest at S2 (18.38 mg kg1). Labile (fraction 1 + 2) and residual (fraction 4) forms of Nickel were predominant in the sediment (Fig. 2). Although, the GAI was negative along the entire river stretch except at S9, S14 and S15, the EF (1.93–7.4) of Nickel showed moderate to moderately severe enrichment. Ni was predominantly present in residual fraction in Perl river sediments (67.2 ± 8.4%) (Li et al., 2007).

Copper concentration varied between 15.3 mg kg1 (S1) and 70.7 mg kg1 (S15). Acid extractable, reducible and oxidizable species collectively formed bulk fraction (more than 60%) of metal in the sediment (Fig. 2). Sediment samples were found significantly enriched (2.51–9.21) having wide range of CF (0.46–2.14). However, GAI showed that Copper was moderately accumulated in sediment from middle to last stretch of the river. Copper, prominent in Fe–Mn oxide, organic matter and residual forms was reported in the river Jhanji (26.2 mg kg1–69.5 lg g1) and Hughly (India) (21.1 mg kg1–36.8 lg g1) sediment (Baruah et al., 1996; Massolo et al., 2012). Oxidizable (30.8 ± 13.8%) and

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M. Pandey et al. / Chemosphere 134 (2015) 466–474 Table 1a Details of sampling station.

a

River sample (River-Drain Confluence)

Drain sample Drain

a

Drain Flow Rate (mld)/Year 2000

Sediment sample (River Drain Confluence)

Latitude longitude

(Samne Ghat) R1

(Samne Ghat Drain) D1

–

(Samne Ghat) S1

(Ravidas Ghat) R2

(Ravidas Ghat Drain) D2

–

(Ravidas Ghat) S2

(Assi Ghat) R3

(Assi Drain) D3

44.5

(Assi Ghat) S3

(Ganga Mahal Ghat I) R4

–

(Ganga Mahal Ghat I) S4

(Chetsingh Ghat) R5

(Ganga Mahal I Ghat Drain) D4 (Chetsingh Ghat Drain) D5

–

(Chetsingh Ghat) S5

(Shivala Ghat) R6

(Shivala Ghat Drain) D6

5.5

(Shivala Ghat) S6

(Harishchandra Ghat) R7

(Drain + Cremation) D7

2.5

(Harishchandra Ghat) S7

(RP Ghat) R8

(Ghora Nala) D8

25

(RP Ghat) S8

(Manikarnika Ghat) R9

(Drain + Cremation) D9

–

(Manikarnika Ghat) S9

(Bhosle Ghat) R10

(Bhosle Ghat Drain) D10

–

(Bhosle Ghat) S10

(Panchaganga Ghat) R11

–

(Panchaganga Ghat) S11

(Teliyanala Ghat) R12

(Panchaganga Ghat Drain) D11 (Teliyanala Drain) D12

3

(Teliyanala Ghat) S12

(Rajghat I) R13

(Rajghat Drain) D13

0.03

(Rajghat I) S13

(Rajghat II) R14

(Rajghat Outfall) D14

130

(Rajghat II) S14

(Ganga-Varuna Confluence) R15

(Varuna River cum Drain) D15

–

(Ganga-Varuna Confluence) S15

N25°160 26.3400 E83°000 52.8400 N25°160 51.0900 E83°000 38.4500 N25°170 13.8000 E83°000 25.4500 N25°170 27.9900 E83°000 24.6900 N25°170 40.4000 E83°000 26.5600 N25°170 51.3900 E83°000 28.9300 N25°180 11.6400 E 83°000 27.900 N25°180 2200 E83°000 34.7800 N25°180 39.400 E83°000 5200 N25°180 51.9900 E83°10 01.6700 N25°190 4.1900 E83°010 15.600 N25°190 14.700 E83°10 36.200 N25°190 23.100 E83°10 51.700 N25°190 25.3500 E83°010 57.8700 N25°190 33.600 E83°20 15.900

Uttar Pradesh Jal Nigam.

Table 1b Physico-chemical characterization of water samples: Annual mean concentration. Parameter

Temperature (°C) pH EC (lS cm1) TSS (mg L1) TDS (mg L1) DO (mg L1) BOD (mg L1) COD (mg L1) Total hardness (mg L1) Alkalinity (mg L1) Acidity (mg L1) F (mg L1) Cl (mg L1) (SO4)2 (mg L1) (PO4)3 (mg L1) (NO3) (mg L1) Chromium (Cr) (lg L1) Manganese (Mn) (lg L1) Iron (Fe) (lg L1) Nickel (Ni) (lg L1) Copper (Cu) (lg L1) Zinc (Zn) (lg L1) Cadmium (Cd) (lg L1) Lead (Pb) (lg L1)

Method/Instrument

Eutech PCSTestr 35 Eutech PCSTestr 35 Eutech PCSTestr 35 Gravimetric method Eutech PCSTestr 35 Titration Titration Titration Titration Titration Titration Ion selective electrode Titration Spectrophotometer Spectrophotometer Ion selective electrode AAS AAS AAS AAS AAS AAS AAS AAS

River samples

Drain samples

Range

Mean

SD

Range

Mean

SD

23.22–23.3 8.2–8.45 339.1–553.1 60.44–116.61 266.72–344.18 3.73–5.82 37.4–58.66 53.5–79.55 276.85–341.77 230.17–279.0 18.11–28.55 2.16–7.81 21.42–94.7 37.85–59.24 3.27–4.33 2.78–3.57 41.8–70.16 40.62–68.83 83.17–117-7 31.28–61.11 19.42–43.72 31.73–71.37 11.41–39.24 80.55–134.8

23.26 8.36 430.35 78.37 302.53 4.85 41.76 60.28 297.72 247.53 23.28 3.76 49.58 44.63 3.7 2.43 50.2 49.78 94.2 40.52 31.28 50.41 21.63 95.7

0.03 0.07 71.7 15.0 26.5 0.71 5.18 6.82 20.3 15.8 4.15 1.43 18.5 5.1 0.3 0.52 7.14 7.73 10.4 8.31 7.06 12.5 7.44 13.5

23.33–24.18 7.68–8.23 1583–2135 167.53–253.86 737.61–2187.52 2.38–3.62 81.22–114.44 118.61–145.74 1337.3–2162.88 289.66–343.28 22.27–39.85 6.3–12.1 212–373 423–671 5.17–6.58 16.4–27.5 83.22–119.51 82.13–107.0 103.2–135.51 48.11–76.33 41.12–82.6 87.23–137.88 47.18–95.82 241.77–311.8

23.77 8.06 1818.35 190.78 1059.74 2.8 91.74 131.78 1626.34 310.1 30.81 8.5 277.6 528.1 5.72 20 97.3 94.27 113.71 57.66 56.1 102.8 62.5 270.68

0.21 0.14 144.07 23.27 345.83 0.35 9.84 7.84 240.56 16.64 7.03 1.63 43.78 87.87 0.417 3.2 10.86 7.67 9.95 8.08 12.68 15.54 14.4 20.9

EC – Electrical Conductivity, TSS – Total Suspended Solids, TDS – Total Dissolved Solids, DO – Dissolved Oxygen, BOD – Biochemical Oxygen Demand, COD – Chemical Oxygen Demand, AAS – Atomic Absorption Spectrophotometer, SD – Standard Deviation, AAS – Atomic Absorption Spectrophotometer.

residual fractions (58.8 ± 13.6%) of Cu were the major fractions in river Perl estuary sediments (Li et al., 2007). Highest and lowest concentration of Zinc was found at S15 (278.61 mg kg1) and S5 (185.15 mg kg1). Oxidizable and residual

fractions were prominent in all the samples while the labile fraction (fraction 1 + 2) was also found in the significant percentage along the middle to last stretch of the river. CF (1.95–2.93) and EF (8.62–11.96) along with GAI (0.38–0.97) indicated moderately

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M. Pandey et al. / Chemosphere 134 (2015) 466–474

Fig. 2. Heavy metal concentration and fraction in river sediment.

severe contamination of Zinc in the sediment. Exchangeable (43.1 ± 6.3%), bound to carbonate (16.8 ± 7.5%) and residual fraction (32.6 ± 6.2%) of Zn were dominant in the sediments of Perl river. Cadmium concentration in the present study broadly varied 30.01 mg kg1 (S1) to 128.13 mg kg1 (S15) predominantly in the labile and oxidizable form (74.32–75.5%). Extremely high CF (176.53–753.71), EF (954.48–2987.51) and GAI (6.88–8.97) suggested that Cadmium has reached up to hazardous concentration in the sediment. Similar studies in rivers like Yamuna, Gomti and Hughly revealed the dominance of available fraction of Cadmium (Jain, 2004; Singh et al., 2005c; Massolo et al., 2012). Significant fractions of Cd in Perl river sediments were exchangeable (38.3 ± 7.9%), bound to carbonate (18.3 ± 8.6%) and residual (35.7 ± 6.2%) (Li et al., 2007). Lead concentration in the present study varied between 151.85 mg kg1 (S2) and 269.38 mg kg1 (S15) prominent in available fractions with significant percentage of oxidizable and residual species. Lead accumulation was found to be moderate to strong in the samples having wide range of CF (8.0–14.18) and EF (43.6–56.2). Lead concentration dominant in labile fraction, in river Gomti sediments was observed comparatively less than the present study (Singh et al., 2005c; Massolo et al., 2012). Highest Pollution Load Index (PLI) was recorded at S15 (3.9) (Angulo, 1996). Pb was predominantly present in exchangeable (43.2 ± 8.9%), reducible (14.4 ± 5.9%) and residual fractions (32.8 ± 3.9%) in the sediments of river Perl estuary (Li et al., 2007). 4. Multivariate analysis Multivariate analysis was carried out with the help of 2 ways cluster analysis (CA), Principal Components Analysis (PCA) and Partial Least Square Regression (PLSR). Pretreatment of data was done by z-score transformation to counterbalance the multidimensionality and different unit effect to evade misclassification of data

without loss of information. Z-score normalizes the data with mean and variance with zero and one respectively. It helps to reduce the weight of variable having large variance and increases the weight of variables having small variance (Wunderlin et al., 2001; Singh et al., 2005a, 2005b). 4.1. Cluster analysis (CA) Two-way cluster analysis was operated separately on the matrices of z-score transformed dataset of river water, sediment and drain water. Heatmap obtained by the two-way cluster analysis classifies the cases and variables into different clusters depending upon the resemblance among them. For sediments, three clusters were obtained for sampling stations viz. C1 (S1, S2, S7, S10), C2 (S4, S8, S14, S15) and C3 (S3, S5, S6, S9, S13, S11, S12). The behavior and origin of Cr (C1) might be different from rest of the metals (C2) in the river sediments (Fig. 3A(i)). For the river water dataset, parameters were grouped into 3 clusters viz. C1 (Cd, Pb), C2 (Sulphate, Nitrate, Chloride, Phosphate, Cr, Mn, Fe, Cu, Ni, Zn) and C3 (Temp, pH, EC, TSS, TDS, DO, BOD, COD, Fluoride, Hardness, Alkalinity, Acidity) (Fig. 3A(ii)). Drain water quality parameters formed complex clusters and were broadly grouped into two classes viz. C1 (Cd, Pb) and C2 (rest parameters) (Fig. 3A(iii)). 4.2. Principal Components Analysis (PCA) One component was obtained for HMs in sediment samples (Fig. 3A). All the metals except Mn (0.468) were found to be significant having un-rotated component extraction from 0.844 (Fe) to 0.974 (Ni) suggesting distinct origin or behavior of Mn from rest of the studied metals. Two components were achieved for river water dataset (Table 3). PC1 (83.1%) contain all the parameters having significant extraction except temperature and phosphate which were significant in PC2 (7.76%) indicating distinct behavior

Table 2 Enrichment factor, contamination factor, pollution load index and geo-accumulation index in river sediment.

S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 S12 S13 S14 S15

Contamination factor

PLI

Cr

Mn

Fe

Ni

Cu

Zn

Cd

Pb

1.86 1.95 2.29 2.01 2.16 2.21 3.09 2.67 2.79 2.60 2.64 2.26 2.66 2.95 3.43

0.54 0.52 0.57 0.53 0.50 0.49 0.46 0.46 0.46 0.43 0.42 0.45 0.52 0.51 0.49

0.18 0.18 0.20 0.23 0.22 0.23 0.22 0.24 0.24 0.23 0.22 0.22 0.22 0.23 0.25

0.44 0.35 1.12 1.10 0.99 0.97 1.19 1.43 1.65 1.46 1.40 1.33 1.45 1.65 1.87

0.46 0.66 1.59 1.55 1.24 1.51 1.79 1.73 1.74 1.58 1.44 1.46 1.50 2.11 2.14

2.06 2.17 2.11 1.99 1.95 1.95 2.22 2.40 2.71 2.47 2.43 2.41 2.60 2.74 2.93

176.53 232.94 312.53 287.29 231.65 248.41 390.82 467.35 607.18 463.35 435.76 417.18 494.41 615.53 753.71

8.06 7.99 11.63 10.34 9.74 8.77 10.92 12.08 12.16 11.71 11.73 11.55 10.81 11.12 14.18

1.80 1.90 2.77 2.65 2.45 2.50 2.98 3.16 3.40 3.08 2.97 2.89 3.12 3.50 3.90

Enrichment factor

Geo-accumulation index

Cr

Mn

Ni

Cu

Zn

Cd

Pb

Cr

Mn

Fe

Ni

Cu

Zn

Cd

Pb

10.04 10.68 11.67 8.76 9.68 9.76 14.16 10.98 11.47 11.08 12.04 10.47 12.09 12.87 13.60

2.90 2.83 2.91 2.32 2.22 2.18 2.11 1.88 1.89 1.84 1.91 2.08 2.35 2.24 1.96

2.40 1.93 5.72 4.78 4.43 4.28 5.46 5.88 6.77 6.21 6.36 6.16 6.58 7.21 7.40

2.51 3.63 8.08 6.74 5.56 6.66 8.20 7.10 7.17 6.72 6.58 6.76 6.81 9.21 8.49

11.14 11.86 10.75 8.65 8.72 8.62 10.19 9.87 11.13 10.53 11.09 11.19 11.84 11.96 11.62

954.48 1274.45 1589.74 1251.13 1035.79 1098.62 1792.72 1921.45 2496.90 1974.10 1986.85 1937.29 2246.71 2682.23 2987.51

43.58 43.73 59.17 45.04 43.54 38.80 50.11 49.68 50.01 49.91 53.49 53.63 49.11 48.47 56.20

0.31 0.38 0.61 0.42 0.53 0.56 1.04 0.83 0.90 0.79 0.82 0.59 0.83 0.98 1.19

1.48 1.53 1.39 1.50 1.59 1.61 1.70 1.71 1.71 1.80 1.84 1.75 1.54 1.54 1.60

3.02 3.04 2.93 2.71 2.75 2.73 2.78 2.62 2.62 2.68 2.77 2.80 2.77 2.71 2.57

1.76 2.09 0.42 0.45 0.60 0.63 0.33 0.07 0.13 0.04 0.10 0.18 0.05 0.14 0.32

1.69 1.18 0.08 0.05 0.27 0.01 0.25 0.20 0.22 0.07 0.05 0.04 0.00 0.49 0.51

0.46 0.53 0.49 0.40 0.38 0.38 0.57 0.68 0.85 0.72 0.70 0.68 0.80 0.87 0.97

6.88 7.28 7.70 7.58 7.27 7.37 8.03 8.28 8.66 8.27 8.18 8.12 8.36 8.68 8.97

2.43 2.41 2.96 2.79 2.70 2.55 2.86 3.01 3.02 2.97 2.97 2.94 2.85 2.89 3.24

M. Pandey et al. / Chemosphere 134 (2015) 466–474

Site

PLI – Pollution Load Index.

471

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M. Pandey et al. / Chemosphere 134 (2015) 466–474

(i) River Sediment

(ii) River Water

(iii) Drain Water

(v) River Water

(iv) River Sediment

(vi) Drain Water

Fig. 3A. Cluster analyses and principal components analysis.

Table 3 Rotated components matrix. Parameters

Temp pH EC TSS TDS DO BOD COD Hardness Alkalinity Acidy Fluoride Chloride Sulphate Phosphate Nitrate Cr Mn Fe Ni Cu Zn Cd Pb

Drain water

River water

PC1 (75.12%)

PC2 (11%)

PC3 (4.53%)

PC1 (83.1%)

PC2 (7.76%)

0.093 0.847 0.674 0.748 0.77 0.199 0.628 0.875 0.512 0.408 0.105 0.849 0.692 0.294 0.768 0.742 0.65 0.361 0.602 0.834 0.598 0.945 0.674 0.614

0.21 0.062 0.59 0.607 0.526 0.131 0.695 0.424 0.835 0.885 0.942 0.41 0.53 0.891 0.503 0.634 0.712 0.808 0.676 0.511 0.764 0.215 0.691 0.739

0.875 0.322 0.316 0.019 0.192 0.76 0.294 0.082 0.104 0.176 0.175 0.015 0.291 0.228 0.275 0.061 0.097 0.206 0.146 0.008 0.198 0.076 0.107 0.227

0.003 0.764 0.936 0.83 0.897 0.792 0.936 0.921 0.845 0.856 0.873 0.73 0.777 0.586 0.221 0.821 0.626 0.715 0.705 0.776 0.891 0.901 0.822 0.733

0.926 0.125 0.289 0.523 0.331 0.551 0.329 0.331 0.509 0.49 0.258 0.591 0.55 0.696 0.919 0.535 0.705 0.614 0.631 0.602 0.388 0.36 0.5 0.64

of temperature and phosphate than other parameters. DO was negative in both the components suggesting independent DO behavior. Three components viz. PC1 (75.12%), PC2 (11%) and PC3 (4.53%) were extracted from drain dataset (Fig. 3A(vi)). Parameters like EC, TSS, TDS, COD, fluoride, chloride, phosphate,

nitrate, Ni and Zn were prominent in PC1. Hardness, alkalinity, acidity, sulfate, Cr, Mn, Fe, Cu, Cd and Pb were significantly extracted in PC2. BOD was significant in both the components i.e. PC 1 (0.628) and PC2 (0.695). Temperature (0.875) was significant in PC3 showing the independence of temperature with other parameters.

4.3. Partial Least Square Regression (PLSR) PLSR dissociated the data set, having drain (x-independent) and river water samples (y-dependent), into 13 components. The cumulative Q2, R2X and R2Y in the thirteen components varied from 0.74–0.86, 0.75–1.00 and 0.78–0.99 respectively (Fig. 3B(i)). The bi-plot obtained by PLSR shows that the sampling stations formed three groups i.e. moderately polluted (S1–S7), severely polluted (S8–S14) and extremely polluted (S15) stations (Fig. 3Bii). All the parameters of X and Y matrix were closely grouped except temperature, pH and DO of drain (X) and river (Y). This shows that the pollutant released from the drains proportionally affect the river along the whole stretch which in turn may affect the river sediment quality.

5. Energy dispersive X-ray spectroscopy (EDS) EDS study revealed that oxygen, aluminum and silica were abundant (73.25 wt%) in the S1 sediments. Boron was the chief component in the S8 (76.46 wt%) and S9 (61.36%). Highest Wt% of lead was found at S15 (3.42%) followed by S9 (1.2%) and S8 (0.84%) (Fig. 4). Significant presence of Titanium and Vanadium along with other metals at S9 indicated that metals may have arisen from anthropogenic sources.

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(i): Components of PLSR

(ii): Bi-Plot PLSR

Model quality by number of components 1

1 0.9

0.75

0.8

D DO

0.5

0.7

VG Confluence D Zn D COD D R PO42D FTDS R Temperature Samne Ghat D Ni Assi Ghat Ganga Mahal (I) RRD DSO42TSS Cr Cd D Fe Ghat Harishchandra DF-NO3DRPO42Chetsingh Ghat Ghat RD Pb Cr Shivala DRClMn RP Ghat D EC Ni RRDD Fe Pb ClManikarnika D Cu BOD TSS TH RRD Cd RajGhat Ghat II NO3RR TH Bhosle Ghat R Alkalinity R pH D Panchaganga D Mn Teliyanala Ghat Raj Ghat I RSO42Cu Ghat D RR COD BOD Zn EC RR TDS D Acidity D TemperatureR Acidity Ravidas Ghat

0.25

0.5

R DO

t2

Index

0.6

0.4

0

0.3 -0.25 0.2 0.1

-0.5

0 1

2

3

4

5

6

7

8

9

10

11

12

13

X

D pH

-0.75

Components Q² cum

R²Y cum

Y

-1

R²X cum

-1

-0.75

-0.5

-0.25

0

0.25

0.5

0.75

1

t1 Fig. 3B. Partial least square regression.

EDS- S1

EDS-S8

EDS-S9

EDS-S15

Fig. 4. SEM–EDS analyses of sediment.

Studies on river Ganga water quality assessment has been done for over a long period. However, the impact assessment of drains on river water and sediments using SEP and SEM–EDS has not been

reported elsewhere. Present work fills this gap which would be helpful for scientists and policy makers to draw the future work outline accordingly.

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6. Conclusions For the first time, impact assessment of city sewage on the river water and sediments has been done in the present study using SEP and SEM–EDS. The physico-chemical, metal and Chemometric (CA–PCA–PLSR) characterization revealed the hazardous impact of urban drains on the river water and sediment. The whole stretch of the river may be divided into moderately (S1–S7), severely (S8–S14) and extremely polluted zones (S15). The Chemometric analysis classified the lithogenic and anthropogenic origin of pollutants. The metal speciation study on the river sediment showed the dominance of labile metal fractions. The concentration of metals, especially Cd and Pb, in the river sediment showed long term deposition of labile and available fraction of the metals studied. Hence, the river sediment may behave as source of the labile metals fractions under favorable environmental conditions. PCA analysis on the river water and drain samples showed the outline behavior of pH, temperature and DO. The application of CA–PCA–PLSR acts as source receptor model for the present study. Acknowledgements Authors are thankful to University Grants Commission, India for providing financial support (UGC-REF. No. 20591/NET-DEC. 2009). Authors want to acknowledge Institute of Environment and Sustainable Development and Centre for Advanced Study in Botany, Banaras Hindu University for providing necessary infrastructure. Authors also thank Coordinator, DST-CIMS, Banaras Hindu University for using SPSS and MATLAB. SEM–EDS analysis was done from National Microscopy Centre, Department of Metallurgy, IIT-BHU. References Acid Digestion of Sediment (USEPA), Sludges and Soils, Method 3050B, 1996. US Environmental Protection Agency. Angulo, E., 1996. The Tomlinson Pollution Load Index applied to heavy metal, ‘Mussel-Watch’ data: a useful index to assess coastal pollution. Sci. Total Environ. 187, 19–56. Baruah, N.K., Kotoky, P., Bhattacharyya, K.G., Borah, G.C., 1996. Metal speciation in Jhanji River sediment. Sci. Total Environ. 193, 1–12. Chabukdhara, M., Nema, A.K., 2012. Assessment of heavy metal contamination in Hindon River sediments: a chemometric and geochemical approach. Chemosphere 87, 945–953. District Industrial Profile (DIP), 2014. Varanasi; Ministry of Micro, Small and Medium Enterprises, Govt. of India. Fukue, M., Yanai, M., Sato, Y., Fujikawa, T., Furukawa, Y., Tanic, S., 2006. Background values for evaluation of heavy metal contamination in sediment. J. Hazard. Mater. 136, 111–119.

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