The present study was aimed towards the effective bio-treatment of actual industrial effluent containing as high as 42,000 mg/L COD (chemical oxygen demand), >28,000 ADMI (American Dye Manufacturers Institute) color value and four heavy metals using indigenous developed bacterial consortium TSR. Mineral salt medium supplemented with as low as 0.02% (w/v) yeast extract and glucose was found to remove 70% ADMI, 69% COD and >99% sorption of heavy metals in 24 h from the effluent by consortium TSR. The biodegradation of effluent was monitored by UV–vis light, HPLC (high performance liquid chromatography), HPTLC (high performance thin layer chromotography) and FTIR (Fourier transform infrared spectroscopy) and showed significant differences in spectra of untreated and treated effluent, confirming degradation of the effluent. Induction of intracellular azoreductase (107%) and NADH–DCIP reductase (128%) in addition to extracellular laccase (489%) indicates the vital role of the consortium TSR in the degradation process. Toxicity study of the effluent using Allium cepa by single cell gel electrophoresis showed detoxification of the effluent. Ninety per cent germination of plant seeds, Triticum aestivum and Phaseolus mungo, was achieved after treatment by consortium TSR in contrast to only 20% and 30% germination of the respective plants in case of untreated effluent.

INTRODUCTION

It is estimated that 280,000 tons of textile dyes are discharged in textile industrial effluent every year worldwide (Willetts & Ashbolt 2000; Shah et al. 2012). Government legislation is becoming more stringent in most developed countries regarding the removal of dyes from industrial effluents, which is in turn becoming an increasing problem for the dye industries (Banat et al. 1996). The effluents from textile and dyeing industries are relatively heavily colored, contain high concentrations of total dissolved solids (TDS), pH and heavy metals and also exhibit high biological oxygen demand (BOD)/chemical oxygen demand (COD) values (Sheth & Dave 2010). Available reports have pointed out the direct and indirect toxic effects of the dyes and metals that can lead to the formation of tumors, cancers and allergies besides growth inhibition of bacteria, protozoa, algae, plants and different animals including human beings (Jadhav et al. 2010). Many physical and chemical methods including adsorption, coagulation, precipitation, filtration and oxidation have been used for the treatment of dye-containing effluent (Dave & Dave 2009). However, the lack of their implementation is largely due to the cost, low efficiency, labor-intensive operation, need of selectivity of the process and the residual sludge generation (Banat et al. 1996; Tarley & Arruda 2004). Adsorption techniques have gained favor due to their efficiency in the removal of pollutants too stable for conventional methods and also produce a high-quality product. But, the process cannot degrade the dye (Robinson et al. 2001). Also, adsorption of dye industrial effluent is effective when the effluent volume is small. This limits the use of adsorption process for removal of dyes from effluent in large-scale industry. As an alternative, biological treatments are relatively inexpensive and eco-friendly to remove dyes and metals from wastewater (Robinson et al. 2001). Many members of bacteria, fungi, yeasts, actinomycetes and algae are capable of degrading dyes; however, they are inhibited due to the toxicity of the dye waste. Thus, it is necessary to acclimatize microbes to the toxic wastes and induce the development of resistant strains naturally, which then degrade toxic dye into less harmful forms. Among all these microbes, bacteria have proved to be a promising tool for the removal of various dyes from effluents (Saratale et al. 2011).

As can be evident from the literature, the earlier studies were conducted using pure dye solutions and individual bacterial culture; for instance, Pseudomonas luteola, Aeromonas hydrophila, Bacillus thuringiensis and many more bacterial species are reported for degradation of a single dye present in a solution (Hu 1998; Chen et al. 2009; Dave & Dave 2009). On the other hand, the use of an individual pure strain is not a practical approach at commercial/industrial scale for treatment of actual textile wastewater due to the presence of autochthonous microorganisms (Spagni et al. 2010). Therefore, nowadays utilization of microbial consortia has received more attention, having considerable advantages: for example, different strains may attack dye molecules at different positions or may use decomposed products produced by another strain for further decomposition (Jadhav et al. 2010). Saratale et al. (2010), Joshi et al. (2010) and Moosvi et al. (2007) have developed bacterial consortium-GR, natural bacterial consortium and mixed bacterial consortium JW-2, respectively, for bioremediation of mixtures of dyes. Loureno et al. (2000), Jadhav et al. (2010) and Joshi et al. (2010) have studied the bioremediation of actual textile effluent. Yusuff & Sonibare (2004) have also reported the presence of heavy metals like chromium, zinc, copper and aluminum in dye effluent due to the metal-based complex dyes. But, these reports have not taken account of the complex nature of the dye effluent, containing varying concentrations of different dyes, salts and presence of heavy metals. Much less attention has been paid to such data, which are in fact essential for the field application. Few reports are available on comprehensive study on simultaneous bioremoval of dyes and heavy metals from the effluent.

In this context, a study was undertaken to develop the native consortium for effective degradation and detoxification of the effluent along with determination of activity of various oxido-reductive enzymes of the consortium in the presence of the effluent. To the best of the authors' knowledge, this is the first study of its kind, dealing with remediation of dye manufacturing industrial effluent containing high American Dye Manufacturers Institute (ADMI) value, COD and elevated concentrations of heavy metals by the indigenous consortium.

MATERIAL AND METHODS

Source of the dye effluents and physico-chemical characterization of SSDM effluent

The industrial dye effluents were collected from the Shiv Shakti Dye Manufacturing (SSDM) Industry Pvt. Ltd, common effluent treatment plant (CETP) and Gayatri Pvt. Ltd located at Vatva GIDC, Ahmedabad, India, for the development of dye-degrading indigenous consortia. Physico-chemical analysis of SSDM effluent was carried out as it was used for all the studies. Conductivity and pH were measured using a pH meter (Systronics, India, model 362). COD was determined by the standard potassium dichromate method (APHA 1995). The heavy metals were quantified using atomic absorption spectrophotometer (AAS) (Elico, India, model SL-243). Total hardness, total solids (TS), TDS and total suspended solids (TSS) were measured by Standard Methods (APHA 1995). Percent decolorization was quantified using ADMI 3WL Tristimulus method (APHA 1995).

Enrichment and development of the bacterial consortia

Enrichment of dye-decolorizing consortia was started within 24 h of sampling. Samples of industrial dye effluents collected from different industries mentioned above were mixed thoroughly and were diluted in sterile distilled water to achieve 20% v/v concentration. When settled, 10% v/v supernatants were used as an inoculum in Erlenmeyer flasks of 250 mL containing 90 mL of sterile nutrient broth (g/L; NaCl, 5; peptone, 5; beef extract, 1.50; and yeast extract, 1.50). All the flasks were incubated under static conditions at 30 °C for 48 h. After incubation, the enriched culture was reinoculated (10% v/v) into fresh medium with addition of SSDM effluent for further enrichment purposes, and similarly seven successive transfers were given to get fully enriched consortia. For inoculum development, enriched bacterial consortium was activated in 250 mL Erlenmeyer flask, containing 100 mL nutrient broth at 30 ± 2 °C for 24 h.

Simultaneous removal of color, COD and metals

Unless otherwise mentioned, all decolorization studies were carried out in triplicate in 250 mL Erlenmeyer flasks with 70 mL of mineral salt medium supplemented with 0.2 g/L yeast extract and glucose (MSMYG) and 20% (v/v) SSDM effluent. All the test and control flasks were inoculated with 10% (v/v) activated inoculums (mixed liquor suspended solids, 5,200 mg/L) and incubated at 35 ± 2 °C in static conditions. At regular time intervals, aliquots were analyzed for ADMI, COD and heavy metals (quantitatively by AAS) from the supernatant. In the case of heavy metals, accumulation of them in bacterial cell biomass was qualitatively analyzed by X-ray diffraction (SEIFERT-FPM, model XRD7) method.

Degradation study

The metabolites produced in the broth during decolorization of the SSDM effluent were extracted with an equal volume of ethyl acetate. The extracts were dried by evaporation and the obtained residues were dissolved in small volumes of high performance liquid chromatography (HPLC) grade methanol. These samples were further analyzed using Fourier transform infrared spectroscopy (FTIR), HPLC and high performance thin layer chromatography (HPTLC). For Comet assay and phytotoxicity study, the residues were dissolved in distilled water instead of HPLC grade methanol. HPLC analysis was carried out with C18 column (250 mm × 4.6 mm, 5 μm) equipped with dual wavelength detector by isocratic method (Shimadzu SPD-20A, Japan). The mobile phase used was methanol: water (40:60) with a flow rate of 1 mL/min and 10 min run time. Ten microliters of the sample was manually injected into the injector port.

HPTLC analysis was carried out using the HPTLC system (CAMAG, Switzerland) equipped with sample loading instrument (CAMAG LINOMAT 5). In this analysis, 10 µL of the samples (SSDM effluent and its metabolites) were spotted on the pre-coated thin layer chromatography (TLC) silica gel plates (60F 254, Merck, Germany). The solvent system used was methanol:ethyl acetate:water:acetic acid:n-propanol (2:3:1:0.2:3). The chromatogram was scanned at 254 nm. FTIR was used for investigating the changes in surface functional groups of the samples. FTIR analysis (Perkin Elmer, Spectrum GX, USA) was done in the mid-IR region of 400–4,000 cm−1 with 16 scan speed. The pellets prepared using spectroscopic pure KBr (1:99) were fixed in sample holder to carry out analysis.

Enzyme activity

The bacterial cells were harvested from the broth without SSDM effluent (control) and broth containing SSDM effluent after decolorization. For cell harvesting centrifugation was done at 5,000 g for 10 min. These cells were suspended in potassium phosphate buffer (50 mM, pH 7.4) and sonicated at 4 °C by ultrasonicator (Labsonic, Sartorius, Germany) with the sonifier output set at 70 amps and giving seven strokes, each of 30 s, with 1 min time interval. These homogenates were centrifuged at 8,000 g for 10 min at 4 °C and the supernatant was used as the source of crude enzyme.

Activities of laccase, lignin peroxidase, azoreductase and 2, 6-dichlorophenol indophenol (DCIP) reductase enzymes were analyzed spectrophotometrically as reported by Telke et al. (2010). Tyrosinase activity was determined in a reaction mixture (3.0 mL) containing 2.5 mL of sodium acetate buffer (20 mM, pH 4.0) and 100 mM of L-tyrosine. The reaction was started by adding 0.2 mL of enzyme solution and increase in absorbance was measured at 280 nm (Kadam et al. 2013). All enzyme activities were assayed in both cell-free extract and the culture medium supernatant at room temperature. The reduction of DCIP was calculated using an extinction coefficient of 19 mM cm−1. Extinction coefficient of oxidized ABTS (2,2-azinobis (3-ethyl benzothiazoline-6-sulfonic acid)) was 3.6 × 104 M−1 cm−1 at 420 nm and of methyl red 23,360 M−1 cm−1 at 430 nm.

Toxicity study

The phytotoxicity test was performed to assess the toxicity of the untreated and treated SSDM effluent with respect to two kinds of seeds commonly used in Indian agriculture: Triticum aestivum and Phaseolus mungo. Ten seeds of each plant were watered every day (5 mL) with untreated and treated SSDM effluent along with tap water (control). The experiment was carried out at room temperature. Germination (%), length of shoot and root were recorded after 7 days.

Single cell gel electrophoresis (Comet assay) was performed as described by Achary et al. (2008) to assess the genotoxic effect of SSDM effluent. Small and uniform-sized Allium cepa bulbs were exposed to water for root development. Developed roots were then exposed to untreated and treated SSDM effluent and water (control). Stained slides were examined using the inverted microscope fitted with a camera. A computerized image analysis system (Comet version 1.5) was employed to measure % DNA damage (% T) and tail length (TL).

Analysis

Data were statistically analyzed by one-way analysis of variance with Tukey–Kramer multiple comparison test.

RESULTS AND DISCUSSION

Physico-chemical characterization of the SSDM effluent

Obtained physico-chemical characteristics of the SSDM effluent are reported in Table 1. Observed high TS, TDS and TSS were due to the presence of heavy metals, residual dyes and other soluble and insoluble contaminants. The SSDM effluent showed mean values of COD 42,000 mg/L, which was 42 times higher than reported value by Telke et al. (2010) for dye-containing effluent decolorization study by Pseudomonas sp. SU-EBT. ADMI color value of original SSDM effluent was 28,571, responsible for the dark color of the effluent. Apart from this, the effluent showed the presence of heavy metals Cu, Pb, Fe and Cr in the range of 3.4–20.37 mg/L in effluent. Jadhav et al. (2010) have reported the presence of heavy metals in textile effluent, but quantification of color and heavy metals in original textile effluent was not reported. Hence, observed characteristics of the SSDM effluent placed it into the hazardous category of waste and having beyond the standard permissible limits of National Environmental Quality Standards for the discharge into sewage treatment facilities (www.cpcb.nic.in).

Table 1

Physico-chemical characteristics of the SSDM effluent

Characteristic NEQS values Original SSDM effluent values 
pH 6.00–9.00 8.44 
EC – 6.42 dS/m 
λmax – 496 nm 
Color – 28,571 ADMI 
Total dissolved solids (TDS) (mg/L as CaCO33,500 45,300 
Total suspended solids (TSS) (mg/L as CaCO3400 9,780 
Total solids (mg/L as CaCO3– 55,080 
Total salts (mg/L as CaCO3– 35,300 
COD (mg/L as CaCO3400 42,000 
Metals (mg/L) 
 Cu 20.37 
 Fe 10.24 
 Zn – 
 Ni – 
 Cr 3.48 
 Pb 0.5 16.74 
Characteristic NEQS values Original SSDM effluent values 
pH 6.00–9.00 8.44 
EC – 6.42 dS/m 
λmax – 496 nm 
Color – 28,571 ADMI 
Total dissolved solids (TDS) (mg/L as CaCO33,500 45,300 
Total suspended solids (TSS) (mg/L as CaCO3400 9,780 
Total solids (mg/L as CaCO3– 55,080 
Total salts (mg/L as CaCO3– 35,300 
COD (mg/L as CaCO3400 42,000 
Metals (mg/L) 
 Cu 20.37 
 Fe 10.24 
 Zn – 
 Ni – 
 Cr 3.48 
 Pb 0.5 16.74 

NEQS – National Environmental Quality Standards (India) for effluent discharge in sewage treatment.

Screening of an efficient consortium for color removal

Three consortia, namely, consortium 1 (Con-1), consortium 2 (Con-2) and consortium TSR (Con-TSR, named after two authors, T-Tallika and SR-Shailesh R), were enriched and developed from the effluent of CETP, Gayatri industry and SSDM, respectively, for the treatment of SSDM effluent. In this study, all the three consortia were from liquid waste sources, which is in contrast to literature data on soil as source (Joshi et al. 2010; Telke et al. 2010). Consortium TSR was found to remove 70% ADMI, in contrast to 65% and 63% ADMI removal by Con-1 and Con-2 consortia, respectively, from effluent. As Consortium TSR was developed from SSDM effluent itself, it favors the significance of acclimatization of microorganisms in natural extreme environment.

Color, COD and heavy metals removal

As consortium TSR was indigenously developed from the SSDM effluent and showed higher ADMI removal efficiency, it was used further in the remaining degradation study of SSDM effluent. As shown in Table 2, ADMI and COD removal efficiency of consortium TSR was found to be 70% and 69% within 24 h and 83% and 80% after 48 h under optimized conditions, indicating the degradation of SSDM effluent by the bacterial consortium TSR.

Table 2

COD, color and metals removal by the consortium TSR

    24 h
 
48 h
 
Parameters Initial value 0 h Residual value Reduction (%) Residual value Reduction (%) 
COD (mg/L) 8,500 2,573 69.72 1,700 80.00 
Color (ADMI) 5,585 1,630 70.81 917 83.57 
Metals (mg/L) 
 Cu 4.32 BDL 100 BDL 100 
 Fe 2.78 BDL 100 BDL 100 
 Cr –     
 Pb 3.60 BDL 100 BDL 100 
 pH 7.8 8.1  8.0  
    24 h
 
48 h
 
Parameters Initial value 0 h Residual value Reduction (%) Residual value Reduction (%) 
COD (mg/L) 8,500 2,573 69.72 1,700 80.00 
Color (ADMI) 5,585 1,630 70.81 917 83.57 
Metals (mg/L) 
 Cu 4.32 BDL 100 BDL 100 
 Fe 2.78 BDL 100 BDL 100 
 Cr –     
 Pb 3.60 BDL 100 BDL 100 
 pH 7.8 8.1  8.0  

BDL – below detectable limit.

Apart from color and COD removal from the SSDM effluent, the bacteria present in consortium TSR were found to be efficient accumulators of the heavy metals. Quantitative analysis of the metals from the samples treated with the consortium TSR indicated >99.9% sorption of metals from untreated samples containing 4.32, 2.78 and 3.60 mg/L Cu, Fe and Pb, respectively. Accumulation of abovementioned heavy metals in bacterial cells of TSR was analyzed using X-ray diffraction method. The X-ray spectra obtained with the cells exposed to SSDM effluent had several distinct peaks, indicating deposition or sorption of heavy metals (data not shown). Jadhav et al. (2010) has reported only qualitative data of metal removal from textile effluent.

Biodegradation of the SSDM effluent

UV–visible light results of untreated and treated SSDM effluent were compared, which showed emergence of new peaks in the UV region and gradual decrease in the original peak height in the visible region, indicating the biodegradation of the SSDM effluent (data not shown). Mineralization of the SSDM effluent was further confirmed using HPLC, HPTLC and FTIR analysis.

HPLC analysis (Figure 1(a)) of untreated SSDM effluent showed major peaks at retention time 3.785, 5.011 and 5.267 min along with few minor peaks. As the decolorization progressed the emergence of additional peaks was observed (Figure 1(b)). Degradation of the SSDM effluent resulted in two major peaks at retention time 4.934 min and 6.446 min and two minor peaks at retention time 3.896 and 5.314 min.

Figure 1

HPLC chromatogram of (a) untreated SSDM effluent and (b) treated SSDM effluent.

Figure 1

HPLC chromatogram of (a) untreated SSDM effluent and (b) treated SSDM effluent.

In HPTLC, untreated SSDM effluent showed spots at Rf of 0.03, 0.30, 0.53 and 0.67, while after treatment Rf values for the metabolites were 0.02, 0.27 and 0.86. The HPTLC of the SSDM effluent and its metabolites showed completely different Rf values (figure not shown).

The FTIR spectra (Figure 2(a)) of untreated SSDM effluent showed specific peaks at 808, 778, 639 and 618 cm−1 representing C–Cl stretching vibrations, 528 cm−1 for C–Br stretching vibration, 1,315 cm−1 for S = O stretching for sulfonic acid, 1,000 cm−1 for P = O stretching, 1,134 and 1,412 cm−1 for C–H bending vibrations for benzene ring and 1,384 cm−1 for C–N stretching vibrations.

Figure 2

FTIR spectrum of (a) untreated and (b) treated SSDM effluent.

Figure 2

FTIR spectrum of (a) untreated and (b) treated SSDM effluent.

The group frequency region shows specific peaks for functional groups, the peaks at 1,475 cm−1 for O–H bending vibrations, 1,570 cm−1 for N = N stretching vibrations, 2,856 and 2,926 cm−1 for C–H stretching vibrations in –CH3 and –CH2 groups, 3,440 cm−1 for N–H stretching vibrations.

The FTIR spectrum of the extracted metabolite shows extensive change in peaks as compared with untreated SSDM effluent (Figure 2(b)). There are peaks at 1,075 cm−1 for C–OH stretching vibrations, 1,338 cm−1 for S = O stretching vibrations for sulfur compounds, peaks at 1,454 cm−1 for stretching vibrations in –CH2 group, 2,929 and 2,963 cm−1 for C–H stretching vibrations in –CH3 group. The change in differential spectrum obtained suggests degradation of SSDM effluent. The SSDM effluent contains azo bond N = N peak at 1,570 cm−1, which was removed due to degradation by the consortium TSR.

Enzyme activity

Induction in the activity of reductive enzymes azoreductase (107%) and DCIP reductase (128%) suggested their role in degradation of the SSDM effluent. Extracellular laccase was also found to be induced up to 489%. The activity of lignin peroxidase, intracellular laccase and tyrosinase was found to be affected adversely, which could be due to the toxicity of the effluent. Jadhav et al. (2010) also reported 211% induction of laccase and 125% induction of azoreductase during textile effluent degradation by bacterial consortium DAS. Results showed (Table 3) presence of all enzymes, leading to the efficient decolorization and degradation of SSDM effluent by consortium TSR.

Table 3

Enzyme activities in control and after decolorization state

  Enzyme activity
 
  
Enzyme assay Enzyme location Control Induced Performance (%) 
Lignin peroxidasea Intracellular 1.136 ± 0.003 0.28 ± 0.001 – 
Extracellular 3.71 ± 0.0009 2.59 ± 0.001 – 
Laccaseb Intracellular 1.51 ± 0.004 0.9 ± 0.0002 – 
Extracellular 0.92 ± 0.0006 4.5 ± 0.005** 489 
Tyrosinasea Intracellular 0.6 ± 0.003 0.45 ± 0.003 – 
Extracellular 0.11 ± 0.0009 0.07 ± 0.001 – 
Azoreductasec Intracellular 3.29 ± 0.003 3.55 ± 0.005* 107 
NADH-DCIP reductased Intracellular 11.89 ± 0.02 15.28 ± 0.01** 128 
  Enzyme activity
 
  
Enzyme assay Enzyme location Control Induced Performance (%) 
Lignin peroxidasea Intracellular 1.136 ± 0.003 0.28 ± 0.001 – 
Extracellular 3.71 ± 0.0009 2.59 ± 0.001 – 
Laccaseb Intracellular 1.51 ± 0.004 0.9 ± 0.0002 – 
Extracellular 0.92 ± 0.0006 4.5 ± 0.005** 489 
Tyrosinasea Intracellular 0.6 ± 0.003 0.45 ± 0.003 – 
Extracellular 0.11 ± 0.0009 0.07 ± 0.001 – 
Azoreductasec Intracellular 3.29 ± 0.003 3.55 ± 0.005* 107 
NADH-DCIP reductased Intracellular 11.89 ± 0.02 15.28 ± 0.01** 128 

aActivity in U/mL/min.

bμM of ABTS oxidized/mL/min.

cμM of Methyl red reduced/mL/min.

dμM of DCIP reduced/mL/min.

*,**Values are mean of three experiments (±) SEM. *, significant at 0.05% level; **, significant at 0.01% level.

Toxicity study

As shown in Table 4, the germination of both plant seeds T. aestivum (20%) and P. mungo (30%) was inhibited with untreated effluent. In contrast, 90% germination of both plant seeds resulted with treated effluent. In addition, plumule length and radicle length of both plants were significantly higher for treated effluent as compared with the untreated effluent, indicated the decrease in toxicity after the treatment.

Table 4

Phytotoxicity of the SSDM effluent after degradation

  Effluent
 
Parameters Control Untreated Treated 
Triticum aestivum 
 Germination (%) 100 20 90 
 Plumule (cm) 9.56 ± 0.52 2.52 ± 0.28* 8.35 ± 0.49 
 Radicle (cm) 6.23 ± 0.47 4.52 ± 0.32* 5.98 ± 0.37 
Phaseolus mungo 
 Germination (%) 100 30 90 
 Plumule (cm) 11.67 ± 0.65 3.23 ± 0.20* 10.77 ± 0.35 
 Radicle (cm) 5.97 ± 0.58 2.83 ± 0.12* 4.5 ± 0.05 
  Effluent
 
Parameters Control Untreated Treated 
Triticum aestivum 
 Germination (%) 100 20 90 
 Plumule (cm) 9.56 ± 0.52 2.52 ± 0.28* 8.35 ± 0.49 
 Radicle (cm) 6.23 ± 0.47 4.52 ± 0.32* 5.98 ± 0.37 
Phaseolus mungo 
 Germination (%) 100 30 90 
 Plumule (cm) 11.67 ± 0.65 3.23 ± 0.20* 10.77 ± 0.35 
 Radicle (cm) 5.97 ± 0.58 2.83 ± 0.12* 4.5 ± 0.05 

*Values are the mean of three experiments SD (±), significantly different from the seeds germinated in control at *p < 0.001, by one-way analysis of variance (ANOVA) with Tukey–Kramer multiple comparison test.

The percentage of tail DNA (% of DNA in comet tail) and TL (µm) for untreated effluent were 42.44 ± 2.34 and 20 ± 2.61, respectively, and in the case of treated effluent 22.03 ± 1.82 and 11 ± 2.36, respectively. According to results shown in Table 5, DNA damage by treated effluent was less than by untreated SSDM effluent. The removal of color, heavy metals and COD might be reducing the toxicity of the SSDM effluent.

Table 5

Comet assay for the untreated and treated SSDM effluent

Analysis Control Untreated effluent Treated effluent 
Mean TL (µm) ± SD 7 ± 1.06 20 ± 2.61* 11 ± 2.36 
Mean %T ± SD 20.02 ± 2.56 42.44 ± 2.34* 22.03 ± 1.82 
Analysis Control Untreated effluent Treated effluent 
Mean TL (µm) ± SD 7 ± 1.06 20 ± 2.61* 11 ± 2.36 
Mean %T ± SD 20.02 ± 2.56 42.44 ± 2.34* 22.03 ± 1.82 

*p < 0.001, by one-way analysis of variance (ANOVA) with Tukey–Kramer comparison test.

CONCLUSIONS

Developed indigenous bacterial consortium TSR was found to be more efficient as compared with the other two consortia used in the study, as they were developed from other dye effluents. The developed consortium TSR was found to be capable of degradation of dyes and other organic contaminants from the effluent along with removal of the metals, with low supplementation of nutrients. Moreover, the consortium has also reduced toxicity of the SSDM effluent. Thus, the consortium and the process could be used for SSDM and other dye containing effluent treatment at pilot scale treatment.

ACKNOWLEDGEMENT

We are thankful to Department of Science and Technology, New Delhi for providing the DST-INSPIRE fellowship (IF110419) to one of the authors.

REFERENCES

REFERENCES
Achary
V. M.
Jena
S.
Panda
K. K.
Panda
B. B.
2008
Aluminium induced oxidative stress and DNA damage in root cells of Allium cepa L
.
Ecotoxicol. Environ. Safety
70
,
300
310
.
APHA
1995
Standard Methods for the Examination of Water and Wastewater
,
19th edn
.
American Public Health Association
,
Greenberg, Washington, DC
.
Banat
I. M.
Nigam
P.
Singh
D.
Marchant
R.
1996
Microbial decolorization of textile-dye containing effluents: a review
.
Bioresour. Technol.
58
,
217
227
.
Joshi
S. M.
Inamdar
S. A.
Telke
A. A.
Tamboli
D. P.
Govindwar
S. P.
2010
Exploring the potential of natural bacterial consortium to degrade mixture of dyes and textile effluent
.
Int. Biodeterior. Biodegrad.
64
,
622
628
.
Loureno
N. D.
Novais
J. M.
Pinheiro
H. M.
2000
Reactive textile dye colour removal in a sequencing batch reactor
.
Water Sci. Technol.
42
,
321
328
.
Saratale
R. G.
Saratale
G. D.
Chang
J. S.
Govindwar
S. P.
2010
Decolorization and biodegradation of reactive dyes and dye wastewater by a developed bacterial consortium
.
Biodegradation
21
,
999
1015
.
Saratale
R. G.
Saratale
G. D.
Chang
J. S.
Govindwar
S. P.
2011
Bacterial decolorization and degradation of azo dyes: a review
.
J. Taiwan Inst. Chem. Eng.
42
,
138
157
.
Willetts
J. R.
Ashbolt
N. J.
2000
Understanding anaerobic decolourisation of textile dye wastewater: mechanism and kinetics
.
Water Sci. Technol.
42
,
409
415
.
Yusuff
R. O.
Sonibare
J. A.
2004
Characterization of textile industries effluents in Kaduna, Nigeria and pollution implications
.
Global Nest: Int. J.
6
,
212
221
.