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Enzymatic Approach for Bioremediation of Effluent from Pulp and Paper Industry by Thermo Alkali Stable Laccase from Bacillus Tequilensis SN4.


Pulp and paper mills generate large volumes of effluent containing various toxic compounds, BOD and COD of the effluent is very high and it is intensely colored because of the presence of lignin (1). Paper mill effluents alter the color and pH of the water bodies and soil where they are discharged.

Several physicochemical remediation treatments in the pulp-paper industry have been suggested but often are not implemented because of the high cost involved. Extensive research has been going on to study the microbial communities present in the industrial effluents (2) and to use biological remediation steps to replace or augment current treatment strategies. Most of the suggested biological treatment methods involve the use of organisms such as fungi, bacteria, algae as a single step treatment or in combination with other physical and chemical methods. Methods using single or consortium of microbial cultures have been reported to reduce COD, BOD, TS, TDS, TSS of the paper mill effluent (3-5). Although, microbial treatment of pulp and paper mills effluent showed significant results in laboratory but the treatment is not feasible at industrial level because often organisms are unable to proliferate under extreme conditions (high pH, temperature, and oxygen limitation) that are present in effluent of paper mill/s. Moreover increase in the microbial load of the treated effluent with microorganisms used for bioremediation can also pose problems.

Enzymes are extensively used in various industrial processes (6,7). One of the alternative approaches for treatment of paper mills effluent is to use enzymes. There are number of reports about the bioremediation using enzymes, such as treatment of dying industry effluent (8). Although the use of enzymes in the pulp and paper industry at various steps is well known (9-14), but the use of enzymes for bioremediation of effluent has not been explored much. One of the reason is the unavailability of lignolytic enzymes such as laccase, lignin peroxidase, manganese peroxidase etc. which can be active/stable under industrial conditions such as high temperature, pH, organic solvents etc. As most of lignolytic enzymes are of fungal origin which are not stable under extreme conditions and these enzymes in bacteria are mainly intracellular.

Earlier our laboratory has reported first bacterial true extracellular highly thermo-alkali stable laccase from Bacillus tequilensis SN4. In the present study the use of SN4 laccase for bioremediation of paper mill effluent has been explored and conditions have been optimized to achieve the optimum bioremediation of the effluent.


Microorganism and Growth Conditions

Bacillus tequilensis SN4, MTCC No. 11828, producing extracellular thermo-alkali-stable laccase was previously isolated in our laboratory from effluent of pulp and paper mill (15)

Laccase Production

Thermo alkali stable extracellular laccase from Bacillus tequilensis SN4 was produced according to the method standardized earlier in our laboratory (16).

Analysis of Effluent from Paper Industry

Effluent samples were procured from the collection tank of Kuantum Papers Ltd. Dist. Hoshiarpur Punjab, India.

The untreated and effluent treated with SN4 laccase was analyzed for various parameters. Color content in the effluent was analyzed by the standard method of Canadian Pulp and Paper Association (17). COD and BOD of the effluent were measured by the standard closed reflux method of American Public Health Association (18). Phenol and lignin content was determined by folin-ciocalteau method (19) and Pearl and Benson respectively (20).

Optimization of Parameters for the Treatment of Industrial Effluent

To standardize the conditions for optimal treatment of effluent with SN4 laccase, various parameters including reaction time, enzyme dose, temperature and aeration rate were standardized by varying one parameter at a time. In each experiment, reduction in color and lignin content of the effluent measured by slandered methods (17, 20) were taken as response. 50 ml of effluent incubated under same conditions, without enzyme, was taken as control. Parameter optimized in the preceding experiment was kept constant in the succeeding experiment.

Effect of reaction time

30 IU of SN4 laccase was added to 50 ml of effluent and incubated at 60[degrees]C, 150 rpm in a water bath shaker for 24 h. After different time intervals, percentage discoloration and lignin content of the effluent were measured.

Effect of enzyme dose

Enzyme doses in the range of 0-100 IU of SN4 laccase was added to 50 ml of effluent flasks and incubated at 60[degrees]C, 150 rpm in a water bath shaker for 5h. Percentage discoloration and lignin content of the effluent were measured.

Effect of temperature

70 IU of SN4 laccase was added to 50 ml of effluent flasks and were incubated in the temperature range of 60-800C at 150 rpm in a water bath shaker for 5h. Percentage discoloration and lignin content of the effluent were measured.

Effect of aeration rate

The effect of aeration on treatment of effluent was determined by agitating the flasks, containing 50ml of effluent and 70 IU of SN4 laccase at 650C for 5h, at different rpm in the range of 0-250 rpm. Percentage discoloration and lignin content of the effluent were measured.

Statistical Optimization of Effluent Treatment

The optimum conditions for bioremediation of effluent were further standardized statistically using response surface methodology (RSM). Four factors viz. enzyme dose, time, temperature and agitation rate were optimized through central composite design (CCD) at a value of [+ or -]2 using Design expert software (version Percentage reduction in color was taken as response in each experiment. Four parameters were studied at five different levels, high (+1, +2), medium (0) and low (-2, -1), (Supplementary Table 1). The experimental plan consisted of 30 trials (Table 1).

The performance of the model was explained by quadratic equation:

y = [[beta].sub.0] + [sigma][[beta].sub.i][X.sub.i] + [sigma][[beta].sub.ii][X.sub.i2] + [sigma][[beta].sub.ij][[beta][X.sub.i][X.sub.j] (1)

Y is the predicted response [percentage discoloration], [beta]0 is the constant term, [beta]i the linear coefficients, [beta]ii the squared coefficients, [beta]ij the interaction coefficients. Coefficient of determination [R.sup.2] expressed the quality of fitting by the polynomial model equation.

3D plots were constructed using Equation 1. By solving the obtained polynomial equation. the optimal values of the three parameters were calculated. The significance of each coefficient was determined by p-values. The statistical software was used for multiple regression analysis and to construct the plots of the obtained data.

Prediction of Optimum Values

After getting the model equation that explains the process, it was used for optimization of discoloration using numerical optimization option of the software. Criteria were set for each independent parameter and the response. The independent parameter was taken in range used by the experimental set up and the response was set to maximum. A solution was generated with predicted levels of the independent variables and a predicted maximum response.

Validation Experiments

Validation was done under optimal conditions predicted by the model and response (percentage discoloration) was calculated and compared to the predicted values. Each experiment was done in triplicates and data presented as mean [+ or -] SD.


Application of extracellular laccase from Bacillus tequilensis SN4 was explored for bioremediation of paper mill effluent

Bioremediation of paper industry effluent by batch treatment with laccase from Bacillus tequilensis SN4 has been explored.

Analysis of Untreated Effluent

The effluent sample obtained from the paper mill was analyzed for various parameters like color, COD, BOD, phenolic compounds and lignin content. As shown in Table 3 (Column 4) all the parameter found to be very high.

Optimization of Bioremediation by One Variable at Time (OVAT) Method

Bioremediation of paper industry effluent with SN4 laccase was optimized with respect to reaction time, enzyme dose, temperature and agitation rate.

Reaction time

The effect of treatment of effluent on reduction in color and lignin has been represented in Fig. 1A. Lignin content as well as color showed marked decline during first 4h of the treatment. Reduction of both the parameters was observed up to 5h and then it became constant. Therefore, 5h was selected as the optimum time for effluent treatment

Enzyme dose

Reaction was carried out in a total volume of 50 ml with different units of enzyme, reduction in color of effluent and lignin content has been represented in Fig. 1B. Reduction in the lignin content increased almost linearly up to 40 IU of enzyme per 50 ml of effluent and there was decrease in the color up to 70 IU of enzyme per 50 ml of effluent then it became constant. Therefore to achieve optimal treatment, dose of 70 IU of SN4 laccase per 50ml of effluent was selected for further optimization.


The effect of temperature was assessed in the range of 60-80[degrees]C results have been represented in Fig. 1C. Reduction in the color and lignin content increase up to optimum temperature of the enzyme i.e. 65[degrees]C and then decreased sharply.


Effluent-enzyme mixture was agitated at different rpm. The effect of aeration on reduction in color of effluent and reduction in lignin content has been represented in Fig. 1D. Maximum discoloration and reduction in lignin was achieved at 200 rpm.

Optimization of Enzymatic Treatment of Paper Industry Effluent by Statistical Methods

To study the interactive effect on bioremediation of effluent central composite design was employed taking enzyme dose, reaction time, temperature and agitation rate as variables. The experimental designs with actual and predicted responses are presented in Table 1. In 30 experiments, discoloration ranging from 5.45% to 81.67% was achieved.

Model equation

To describe the correlation between discoloration and various parameters a predictive quadratic polynomial equation was derived using multiple regression analysis:

% discoloration = +52.82 + 12.31A + 16.43B - 0.51C +3.19D + 4.55AB + 0.47AC + 1.28AD -2.15BC - 1.50BD + 0.65CD - 5.34[A.sup.2] -2.36[B.sup.2] - 2.80[C.sup.2] + 1.35[D.sup.2]

Where, R is the response (percentage discoloration), enzyme dose, reaction time, temperature and agitation rate are coded as A, B, C and D.

Analysis of variance

Analysis of variance (ANOVA) of model was calculated (Supplementary Table 2). The p-values of the model was <0.0001, which indicated that the linear, interactive and square values, all had substantial effect on discoloration of effluent. The p-value for lack of fit was 0.2310 which signified that quadratic model effectively fit into the data.

The value of determination coefficient [R.sup.2] was 0.9976 which showed that experimental and predicted values were in perfect coherence with each other. The value of adjusted [R.sup.2] was 0.9953 which suggested that the variation of 99.53% in the response can be attributed to the independent variables and only 0.047% of the total variation could not be explained.

Interaction between variables

Interaction between two variables when the other kept at its optimum value is presented in Fig. 2. Fig. 2A showed that increasing time and enzyme dose resulted in increase in discoloration of effluent in combination. While individually increase in temperature had negative effect on discoloration (Fig. 2B). A linear increase in discoloration was observed with a combination of rpm and enzyme dose (Fig. 2C). Increasing time while keeping temperature in middle range increases discoloration of effluent (Fig. 2D). Temperature and RPM in combination have significant effect on discoloration, however, increase in temperature individually had negative effect (Fig. 2E) Similarly, increase in time in combination with rpm increased discoloration (Fig. 2F).

Validation of the Mathematic Model

The numerical optimization option of the software was used to predict levels of the four parameters with the target of "maximizing" reduction in color. Consistency between experimental and predicted values proved the accuracy of the model (Table 2).

Treatment of effluent under standardized conditions

Effluent treated under optimized conditions (Table 2) and was analyzed for parameters other than color also. It was observed that under optimum conditions, color, BOD and COD of the treated effluent were reduced by 83%, 82% and 77% respectively. 74% reduction in lignin content and 62% reduction in phenolic content were also observed (Table 3).


Enzymes have been used for the bioremediation of industrial effluents (21,22). Although use of enzymes for treatment of effluent from pulp and paper industry have been suggested (23-25) still there is no report where use of enzymes for the effective bioremediation paper industry effluent has been explored in detail. Some preliminary results of the use of lignin degrading enzymes from basidiomycetous fungus NIOCC #2a for the discoloration of paper mill effluent has been reported (8). Major reasons for the less exploration of enzymes for bioremediations of paper mill effluent can be that most of the lignolytic enzymes are of fungal origin (26), which are not very stable under extreme industrial conditions, these enzymes from bacteria are stable but they are intracellular (27-29). In near future some thermoalkali stable enzymes from bacteria have been reported (15). Extracellular laccase from Bacillus tequilensis SN4 laccase is highly thermo-alkali-stable and degrades lignin without mediator (15) therefore, the use of SN4 laccase for bioremediation of paper mill effluent was explored and conditions for the optimal treatment were standardized.

As dark color of the paper mill effluent is mostly due to the presence of lignin (30) therefore both reduction in color as well as lignin content almost followed the same trend; 24 h reaction time and 1.4 IU[ml.sup.-1] of effluent were found to be optimum for the effective treatment. The optimum temperature for treatment was found to be 65[degrees]C which coincides with the optimal temperature of the enzyme (15). At higher temperature; color reduction was less effective which can be due to the inactivation of the enzyme. Discoloration of effluent was improved by agitation of the enzyme-effluent mixture which can be because of the requirement of moleular oxygen by laccase to oxidize the lignin (31).

As most of the BOD and COD in the paper mill effluent is because of the presence of lignolytic and phenolic compounds (32) after statistical optimization apart from substantial reduction in color and lignin contant significant reduction in BOD and COD was also achieved. The bioremediation achieved under standardized conditions by the use of SN4 laccase is better than the reports where a microorganism (30,33) or a consortium of microorganisms (34,35) have been used. The major improvement was in the terms of time, only 4 h were required for the effective treatment of the effluent by the SN4 laccase whereas four to five days of treatment has been reported (30,33) for the bioremediation using microorganisms. Moreover while using the microorganisms for the treatment process most of the times sequential treatment is required (34). Efficiency of the enzymatic treatment can be further improved by techniques such as immobilization of the enzyme etc (36).


Under standardized conditions significant reduction in all the polluting parameters of paper mill effluent was achieved in a very short treatment time with laccase from Bacillus tequilensis SN4. Efforts are being made to immobilize the enzyme and develop a continuous process for the efficient treatment of effluent from paper mill with SN4 laccase.


This work was supported by University Grant Commission, Government of India, New Delhi, India.


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Sonica Sondhi

Department of Microbiology BMS Block 1 South Campus, Sec 25 Panjab University, Chandigarh India Pin 160014

Deepak Kumar

Department of Microbiology BMS Block 1 South Campus, Sec 25 Panjab University, Chandigarh India Pin 160014

Steffy Angural

Department of Microbiology BMS Block 1 South Campus, Sec 25 Panjab University, Chandigarh India Pin 60014

Prince Sharma

Department of Microbiology BMS Block 1 South Campus, Sec 25 Panjab University, Chandigarh India Pin 160014

Naveen Gupta

Department of Microbiology BMS Block 1 South Campus, Sec 25 Panjab University, Chandigarh India Pin 160014

doi: 10.5912/jcb799

Correspondence: Naveen Gupta, Panjab University Chandigarh, India. Email:

Caption: Figure 1: Effect of enzymatic treatment on bioremediation (discoloration and reduction in lignin content) of pulp and paper industry effluent with respect to: Effect of time; Fig A, Effect of enzyme dose; Fig B, Effect of temperature; Fig c, effect of aeration; Fig D Values represent mean [+ or -] SD, n = 3; The asterisks indicated significant differences: ***p <0.001 analyzed by unpaired student's t-test

Caption: Figure 2: Contour plot of combined effects of (A) enzyme dose and time (B) enzyme dose and temperature (C) rpm and enzyme dose (D) temperature and incubation time (E) rpm and time (F) rpm and temperature on percentage reduction in color of the effluent when other factors were kept constant

Caption: Figure 3: Discoloration of paper industry effluent (A) untreated effluent (B) after SN4 laccase treatment
Table 1: Actual and predictive values of reduction in color of
paper industry effluent Central composite design matrix

Run       (IU/50ml     Reaction   Temperature    Agitation
Order   of effluent)   time (h)   ([degrees]c)     rate

1            90           17          65           175
2            30           10          60           150
3            30           10          70           150
4            70           10          60           200
5            50           17          65           175
6            70           24          70           150
7            50           17          55           175
8            70           24          60           150
9            50           17          65           175
10           30           24          70           200
11           50           17          65           175
12           30           10          60           200
13           50           31          65           175
14           50           17          65           175
15           30           24          70           150
16           50           3           65           175
17           50           17          65           125
18           50           17          65           225
19           50           17          75           175
20           70           24          70           200
21           70           10          70           150
22           10           17          65           175
23           30           24          60           150
24           50           17          65           175
25           30           10          70           200
26           30           24          60           200
27           50           17          65           175
28           70           24          60           200
29           70           10          70           200
30           70           10          60           150

Run        Actual value        Predicted value    Residual (%
Order    (% discoloration)    (% discoloration)   discoloration)

1       57.26 [+ or -] 0.14        56.11              1.15
2       16.45 [+ or -] 0.32        15.56              0.89
3       16.56 [+ or -] 0.24        16.59             -0.028
4       37.15 [+ or -] 0.05        38.22             -1.07
5       52.17 [+ or -] 0.12        52.82             -0.65
6       72.25 [+ or -] 1.25        71.15              1.10
7       43.42 [+ or -] 0.56        42.63              0.79
8       75.15 [+ or -] 0.25        76.82             -1.67
9       53.67 [+ or -] 0.28        52.82              0.85
10      40.78 [+ or -] 1.18        41.17             -0.39
11      51.45 [+ or -] 0.39        52.82             -1.37
12      20.15 [+ or -] 0.28        21.06             -0.91
13      76.27 [+ or -] 0.39        76.23              0.038
14      52.67 [+ or -] 0.25        52.82             -0.32
15      40.18 [+ or -] 1.04        39.05              1.13
16      10.24 [+ or -] 0.48        10.53             -0.29
17      50.28 [+ or -] 1.26        51.83             -1.55
18      65.89 [+ or -] 0.49        64.59              1.30
19      39.56 [+ or -] 0.24        40.60             -1.04
20      77.57 [+ or -] 2.49        78.40             -0.83
21      30.89 [+ or -] 0.25        30.50              0.39
22       5.45 [+ or -] 0.59         6.85             -1.40
23      47.57 [+ or -] 1.28        46.61              0.96
24      54.46 [+ or -] 0.48        52.82              1.64
25      26.45 [+ or -] 0.29        24.71              1.74
26      45.78 [+ or -] 0.56        46.11             -0.33
27      52.50 [+ or -] 0.26        52.82             -0.32
28      81.67 [+ or -] 0.14        81.46              0.21
29      42.98 [+ or -] 0.26        43.75             -0.77
30      28.15 [+ or -] 0.44        27.57              0.58

Values represent mean [+ or -] SD, n = 3

Table 2: Validation of predicted model

                          Variables                      Response
                                                         (% reduction
                                                         in color)
Std run   (IU/50 ml)   RPM   Temperature    Time         Actual
                             ([degrees]C)   (h)

1            62        198       63.52      4.5    82.34 [+ or -] 0.46

Std run   Predicted   Residual   Error (%)

1           82.96       0.62       0.75

values represent mean [+ or -] SD (n=3)

Table 3: Analysis of paper industry effluent for various pollution
parameters before and after treatment with SN4 laccase

S. No.   Parameters   Units           Values before treatment

1        pH           --                     7.58
2        cod          mg [l.sup.-1]     1925 [+ or -] 80.24
3        bod          mg [l.sup.-1]      525 [+ or -] 15.241
4        Lignin       Ppm               6797 [+ or -] 5.27
5        Phenol       mg [l.sup.-1]   218.75 [+ or -] 8.94
6        TDS          mg [l.sup.-1]     1341 [+ or -] 9.32
7        TSS          mg [l.sup.-1]      158 [+ or -] 12.54
8        color        --              2136.4 [+ or -] 30.46

S. No.   Values after treatment   % reduction

1                8.5
2         447.95 [+ or -] 54.14      76.73
3          95.25 [+ or -] 6.48       81.85
4        1797.57 [+ or -] 70.25      73.56
5          27.34 [+ or -] 2.34       61.54
6            967 [+ or -] 4.89       27.88
7             26 [+ or -] 6.79       33.54
8          358.8 [+ or -] 23         83.20
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Author:Sondhi, Sonica; Kumar, Deepak; Angural, Steffy; Sharma, Prince; Gupta, Naveen
Publication:Journal of Commercial Biotechnology
Article Type:Report
Geographic Code:9INDI
Date:Oct 1, 2017
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