IJBT 13(3) 293-300

Indian Journal of Biotechnology Vol 13, July 2014, pp 293-300

Enhancement of waste engine oil biodegradation by optimization of media using factorial design study

Munna Bhattacharya and Dipa Biswas* Department of Chemical Technology, University of Calcutta, Kolkata 700 009, India

Biodegradation of spilled or dumped waste engine oil is a challenging task due to their toxicity and persistency in the environment. Waste engine oil biodegradation can be improved by optimizing media constituents that may stimulate the microbial degradation phenomenon and contribute to the progress of oil spill bioremediation process. In the present study, the effect of different nutrients, such as, carbon, nitrogen and inorganic ion source, added externally to Bushnell-Haas (BH) medium were investigated for waste engine oil biodegradation using a novel hydrocarbon degrading bacterium, Ochrobactrum pseudintermedium C1. The bacterial strain was isolated in our laboratory from oil contaminated soil and identified by 16S rRNA gene sequencing. The results showed that the most significant variables influencing waste engine oil biodegradation were glucose, yeast extract and FeCl3. A full factorial central composite design was applied for experimental work and analysis of the data. Moreover, these three factors interacted with each other and combined to produce positive effects on waste engine oil degradation. The experimental data also allowed the development of an empirical model (P < 0.00672) describing the inter-relationship between independent and dependent variables. By solving the regression equation, the optimal values of the variables were determined as (g/L): glucose 22.028, yeast extract 1.949 and FeCl3 0.225. The BH medium with the above constituents resulted in significant enhancement of waste engine oil percent degradation from 51.24 to 71.52% within 7 d.

Keywords: Biodegradation, central composite design, Ochrobactrum pseudintermedium, optimization, waste engine oil

Introduction Waste engine oil (WEO) which is also known as

used motor oil is produced when fresh engine oil (or motor oil) is subjected to high temperature and high mechanical strain during running of the vehicle for a stipulated time. It is a brown-to-black liquid mixture consisting of low to high mol wt (C16 to C36) aliphatic and aromatic hydrocarbons, polychlorinated biphenyls, chlorodibenzofurans, lubricative additives, and decomposition products1,2 along with heavy metal contaminants, such as, zinc, lead and chromium, coming from engine parts. Thousand million gallons of WEO are generated annually from mechanical workshops, which is not recycled but spilled and dumped by automobile and generator mechanics into runoff, gutters, water drains and open vacant plots and farmlands3. Out of this, 1 L is enough to contaminate 1 million gallons of fresh water4. The illegal dumping of used engine oil is dangerous to the environment and constitutes a potential threat to human, animals and vegetations5-7. Most mechanical methods like

incineration and/or burial in secure landfills to reduce hydrocarbon pollution are expensive and time consuming. These are effective treatments but, after burning, the soil gets depleted of nutritional value and structure. These methods do not remove the contamination but only relocate the problem8. Therefore, it is needed to find out efficient, affordable and more environment-friendly methods of WEO treatment, especially in the developing countries.

Biodegradation of oil contaminated soils, which exploits the ability of microorganisms to degrade and/or detoxify organic contamination, has been established as one of the efficient, economic, versatile and environmentally sound treatment. These methods can be used to treat soils contaminated with WEO or degrade these waste oils in places where waste oil recycling is not a feasible option, and to supplement existing waste recycling treatment technologies9. Biodegradation of petroleum hydrocarbon pollutants and petrochemicals by bacteria have been extensively investigated10-12. Investigations have been carried out on the bioremediation of engine oil-contaminated soils using selected bacterial and fungal agents, mixed bacterial consortium and organic wastes with some success13-15. Since oil degradation is a natural process

*Author for correspondence: Tel: +91-33-2350-8386; Fax: +91 33-2351-9755 E-mail: [email protected]

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limited by different environmental and nutritional factors16, optimization of the culture media conditions is often needed for better degrading efficiency of the microorganisms.

Response surface methodology (RSM) is an efficient statistical technique for the optimization of multiple variables to predict the best performance conditions with minimum number of experiments17 and has already been successfully applied in many fields, such as, optimization of media18, fermentation conditions19 and enzyme-catalyzed conditions in lab experiments20. In the present investigation, Ochrobactrum pseudintermedium C1 was selected for the adaptation, which degraded WEO with higher tolerance limit. Therefore, it was worthwhile to study the effect of different media constituents that affect the biodegradation rate of WEO and to find out the optimum levels of the important factors considered for WEO degradation by the strain C1 in order to maximize degradation rate of WEO and to determine the mutual interactions between these factors simultaneously using RSM.

Materials and Methods Chemicals

WEO were collected from local automobile workshops nearby Kolkata, India. Bushnell-Haas (BH) medium and Nutrient agar medium of Hi-Media Laboratories Pvt. Ltd were used for isolation, cultivation and maintenance of culture. Other chemicals and solvents were of LR grade and purchased from local suppliers.

Organism and Cultivation Conditions The bacterium used in this present study,

O. pseudintermedium C1 was isolated in our laboratory from oil contaminated soil of ore handling plant of IISCO Steel Plant, Burnpur, India and identified by 16S rRNA gene sequencing method from Bhat Biotech India Pvt. Ltd., Bangalore, India. For isolation of the microorganism, BH medium was used as enrichment medium with the composition (g/L): K2HPO4 (1.0), KH2PO4 (1.0), NH4NO3 (1.0), MgSO4.7H2O (0.2), FeCl3.6H2O (0.05), CaCl2.2H2O (0.02) and 2% (v/v) WEO as sole carbon source. The pH of the medium was adjusted to 7.00.2. The bacterium was cultivated for 7 d in 250 mL flasks containing 50 mL BH medium at 37C incubation temperature and agitation speed of 80 rpm in a rotary shaker incubator (ORBITEK-LJE, Scigenics Biotech Pvt. Ltd., Chennai, India). For further maintenance of the culture, nutrient agar

medium was used. The composition of the nutrient agar was as follows (g/L): beef extract (5.0), peptone (10.0), NaCl (5.0) and agar (15.0). The cultures grown in nutrient agar medium for 16-18 h at 37C were used as inoculum at 2% (v/v) level throughout the study.

Media Optimization Studies The medium optimization was conducted with an

optimum WEO concentration of 4% (v/v) (34.4 g/L) in a series of experiments changing one variable at a time and keeping the other factors constant. Three factors, viz., carbon source (C), nitrogen source (N) and various inorganic salts, were chosen to obtain higher percent biodegradation of waste engine oil. For evaluation of carbon source, glucose was added as a co-substrate for degradation of WEO in varying concentrations, in the range of 5 to 20g/L. For evaluation of nitrogen sources, NaNO3, urea and yeast extract were employed at a concentration of 1 g/L with the optimum carbon source. Yeast extract was found as the most suitable nitrogen source and was further employed at varying concentrations from 0.5 to 2 g/L for determining its optimal level. To evaluate the effect of various inorganic salts, BH medium was formulated with different concentrations of the salts, such as, MgSO4, CaCl2, FeCl3, NH4NO3, MnSO4 and NaCl. The most effective inorganic salt FeCl3 was further employed at varying concentration range (between 0.1 to 0.3 g/L) for determining its optimal level. Carbon and nitrogen sources were added separately at their optimal levels. All the experiments were performed in triplicate and a control devoid of the bacterial isolates were prepared for each set of experiments.

Experimental Design and Statistical Analysis A full factorial central composite design (CCD)

was applied with the three experimental factors chosen from previous optimization studies, namely, glucose, yeast extract, and FeCl3. A total of 16 runs, performed in duplicate, were required for this procedure. Table 1 lists the coded (Xi) and actual (xi) levels of each variable. For statistical calculation, the

Table 1Coded and uncoded values of experimental variables

Variable levels Variables Coded variables

-1 0 +1 Step change

value xi Glucose conc. (g/L)

X1 10 20 30 10

Yeast extract conc. (g/L)

X2 1 2 3 1

FeCl3 conc. (g/L)

X3 0.1 0.2 0.3 0.1

BHATTACHARYA & BISWAS: ENHANCEMENT OF WASTE ENGINE OIL BIOGEGRADATION

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test factors were coded according to the following equation:

Xi = (xi -x0)/xi i = 1, 2 k

Where, Xi is the dimensionless value of an independent variable; xi is the real value of an independent variable; x0 is the real value of the independent variable at the centre point; xi is the step change value. Response surface methodology (RSM) allows modeling of the results using a quadratic equation. The experimental data obtained was fitted to the following quadratic polynomial equation:

Y=A0+A1X1+A2X2+A3X3+A4X12+A5X22+A6X32+ A7X1 X2+A8X2X3+A9X3X1

Where, Y is the predicted response value; A0 is the intercept term; A1, A2, A3 are the linear coefficients; A4, A5, A6 are the quadratic coefficients; A7, A8, A9 are the interactive coefficients.

Statistica software (version 10.0) was used for regression and graphical analysis of the experimental data obtained. The optimum levels of the selected variables were obtained by solving the regression equation and by analyzing the response surface plots.

WEO Degradation Analysis The WEO degradation was determined by

gravimetric analysis as well as gas chromatography analysis.

Gravimetric Analysis At the end of each experiment, the supernatant

phase was separated in a separating funnel after centrifuging the culture broth at 5,000 rpm (REMI R24) for 20 min. The residual waste oil was extracted by adding 20 mL of hexane and shaking thoroughly as described by Mandri and Lin21. The extracted phase was then collected in a pre-weighed beaker and the final weight was noted after evaporating the solvent. The amount of residual oil was found out from the weight difference. Then the percentage of oil degraded was calculated as follows:

% of oil degraded = [(wt of test oil sample)(wt of residual oil sample)/(wt of test oil sample)] 100

Gas Chromatography Analysis The residual waste oil sample after each

biodegradation experiment was analyzed by gas

chromatography according to the condition described by Ghazali et al22. Hexane extracts of residual oil sample (1 L) were injected for analysis by using a Polaris Q Mass Spectrometer coupled with Thermo Scientific Trace 1300 series gas chromatograph and TR-5 column (30103 cm length; 0.032 cm id; and 110-3 cm film thickness). Nitrogen was used as carrier gas. The injector and detector temperatures were maintained at 300oC and 280oC, respectively. The column was programmed at an initial temperature of 40C; this was held for 2 min, then ramped at 15C/min to 300C and held for 10 min. The relative percent degradation of WEO was calculated by the differences in summation of peak area of total petroleum hydrocarbons (TPH) present in the waste oil samples. Chromatographs were analyzed by Chromeleon 7.0 program and a library (NIST 2007) search was performed for identification of chromatogram peaks.

Results and Discussion

Experimental Optimization Using One Factor at a Time O. pseudintermedium C1 was inoculated into BH

medium with addition of different nutrient supplements, such as, glucose (10 g/L), NaNO3 (1 g/L), Urea (1 g/L), yeast extract (1 g/L), NH4NO3 (1 g/L), MgSO4 (1 g/L), CaCl2 (0.1 g/L), FeCl3 (0.2 g/L), NaCl (1 g/L) and MnSO4 (0.1 g/L). The most favourable carbon source, nitrogen source and inorganic salt were found to be glucose, yeast extract and FeCl3 according to the effect on enhancement of percent degradation of WEO as shown in Fig 1a. Supplementation of glucose as an added carbon source showed the maximum positive effect probably due to promoting growth of the bacterial species, thus enhancing ability to uptake hydrocarbons more easily24,25. The nature and concentrations of nitrogen sources are the factors stimulating the biodegradation of hydrocarbon compounds31. Of the various nitrogen sources tested (Fig. 1c), organic nitrogen source (yeast extract) showed positive effect compared to the inorganic nitrogen sources (NaNO3, NH4NO3 & Urea). Addition of FeCl3 in the growth medium showed inducing effect on biodegradation of WEO compared to other inorganic ion sources. Fe as an ion source might induce the oxidative enzymes of the bacterial species for metabolizing the hydrocarbons29,30. The optimum concentrations of the chosen variables, glucose, yeast extract and FeCl3, were determined from Figs 1(b-d), corresponding to maximum percentage of WEO degradation.


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Optimization Using Central Composite Design (CCD) Regression Modeling

In order to approach the optimum response region of maximal degradation rate of WEO, a total of 16 experiments were conducted following the CCD method using three factors with three levels of glucose (10, 20, 30 g/L), yeast extract (1, 2, 3 g/L) and FeCI3 (0.1, 0.2, 0.3 g/L). A regression model containing three linear (X1, X2, X3), three quadratic (X12,X22,X32) and three interaction (XIX2, X2X3, X3X1) terms plus one block term was employed by using STATISTICA version (10.0). The experimental results were analyzed through RSM to obtain an empirical model for the best response. The results of experimentally obtained and theoretically predicted response are shown in Table 2. The results show that the predicted data of the response from the empirical model is in good agreement with the experimentally obtained data. The quadratic model was used to explain the mathematical relationship between the independent variable and dependent responses. The mathematical expression of relationship to the WEO degradation with variables like glucose, yeast extract and FeCl3 are shown below as in terms of coded factors. All terms regardless of their significance are included in the following equation:

Y=70.52085+3.95581X19.66808X20.82952X3 7.62765X12+2.78294X225.35804X32 0.25925X1X2

0.18425X2X3+0.41975X3X1

Where, Y (%) is the percentage of WEO degradation, X1, X2 and X3 are the coded values of glucose, yeast

extract and FeCl3, respectively.

Analysis of Variance The results of ANOVA are summarized in Table 3.

The value of the determination coefficient (R2), being a measure of the goodness of fit of the polynomial model, was 0.987, which indicates that only 0.02% of the variability in the responses cannot be explained by

Fig. 1(ad)Effect of medium constituents on per cent degradation of WEO by O. pseudintermedium C1: (a) Effect of different carbon, nitrogen and inorganic ion source; (b) Effect of varying glucose concentrations; (c) Effect of varying yeast extract concentration; & (d) Effect of varying FeCl3 concentrations. [Error bars, meanSD of 3 replicates]

Table 2Full factorial central composite design matrix and their observed responses for WEO percent degradation

Coded variables % degradation No. of runs Glucose

(X1) Yeast

Extract (X2)

FeCl3 (X3)

Observed Predicted Residuals

1 10 1 0.1 55.896 56.227 -0.331 2 10 1 0.3 58.846 58.775 0.071 3 10 3 0.1 54.727 55.237 -0.510 4 10 3 0.3 58.705 58.624 0.080 5 30 1 0.1 59.725 60.626 -0.901 6 30 1 0.3 62.495 62.805 -0.310 7 30 3 0.1 58.226 59.118 -0.892 8 30 3 0.3 61.647 62.136 -0.489 9 2.362 2 0.2 70.505 70.520 -0.015

10 37.638 2 0.2 51.981 51.992 -0.011 11 20 2 0.2 60.038 58.970 1.067 12 20 0.236 0.2 59.819 59.387 0.431 13 20 3.764 0.2 58.548 57.924 0.623 14 20 2 0.024 60.823 59.731 1.091 15 20 2 0.376 64.605 64.640 -0.035 16 20 2 0.2 70.654 70.520 0.133


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the model. However, the lack of fit was observed to be insignificant (plack of fit=0.07), implying that the obtained model is adequate to represent the experimental data. A higher R2 value of 0.987 also shows that the equation is highly reliable and depicts the model to be adequate for prediction within the range of variable chosen. The significance of each coefficient of the model was determined by F-test and P-value. The smaller p-value and higher F value represent the corresponding coefficient to be more significant23. A p value less than 0.05 indicates that the model is statistically significant. In the present case, it was found that the three linear coefficients and all quadratic coefficients were highly significant.

Response Surface Analysis Effect of glucose

Fig. 2a represents maximum percentage of WEO degradation against glucose and yeast extract. The maximum percentage of WEO degradation was 70% at a particular range of glucose (20-25 g/L) and yeast extract (1.5-2.0 g/L), which is also clearly illustrated in Fig. 1b. The optimum level of WEO degradation of 70.98% occurred at glucose concentration of 22.028 g/L and yeast extract concentration of 1.949 g/L, calculated by derivatization of the regression equation and by solving the inverse matrix. The effect of glucose as shown in Fig. 1(b) indicates that increase in the concentration of glucose above (20 g/L) showed

Table 3ANOVA for regression model

Factors Co-efficient value

Sum of squares

Degrees of freedom

Mean squares

F value P value

Mean/Interaction 70.52085 - - - - 0.000672* (1) Glucose conc. (L) 3.95581 55.6389 1 55.6389 5012.29 0.008992* Glucose conc. (Q) -9.66808 243.9181 1 243.9181 21973.62 0.004295* (2) Yeast extract conc.(L) -0.82952 2.4466 1 2.4466 220.41 0.042817* Yeast extract conc. (Q) -7.62765 151.8257 1 151.8257 13677.38 0.005443* (3) FeCl3 conc. (L) 2.78294 27.5370 1 27.5370 2480.70 0.012780* FeCl3 conc. (Q) -5.35804 74.9163 1 74.9163 6748.91 0.007749* 1L by 2L -0.25925 0.1344 1 0.1344 12.11 0.178144 1L by 3L -0.18425 0.0679 1 0.0679 6.12 0.244617 2L by 3L 0.41975 0.3524 1 0.3524 31.74 0.111827 Lack of Fit 5.2424 5 1.0485 94.45 0.077953 Pure Error 0.0111 1 0.0111 Total SS 368.7333 15

R2=0.98575; Adj. R2=0.96438 *Significant for p value


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repressive effect, whereas (20 g/L) glucose resulted in only 68.14% degradation. Glucose, as a simple alternative carbon source, can enhance biodegradation of recalcitrant compounds due to its tendency to improve cell growth as reported by Wang et al24 and Saimmai et al30. Our results also show that an appropriate concentration of glucose could enhance the degradation rate of waste engine oil. The synthesis of surface active compounds, exopolysachharides etc. to enhance the hydrocarbon utilization by the cells could be taking place in presence of glucose as reported earlier by Leonie et al25.

Effect of Yeast Extract The maximum percentage of WEO degradation

was found to occur with yeast extract (1.5-2.0 g/L) and FeCl3 (0.20.3 g/L) (Fig. 2b). Optimum level of yeast extract (1.949 g/L) and FeCl3 (0.225 g/L) showed the maximum percentage of WEO degradation (70.98%). The concentration of yeast extract in BH medium (Fig. 1c) varied from 0.5 to 3.0 g/L and there was no considerable increase in the degradation beyond 2.0 g/L. However, increase in the concentration of yeast extract from 0.5 to 2.0 g/L increased the degradation from 53.47 to 62.97%. Presence of yeast extract in BH medium not only increased the bacterial yield, but also reduced the time required for completion of the degradation. A number of reports are available regarding the enhancement of growth of microorganisms on hydrocarbons using organic nitrogen source like yeast extract11,26.

Effect of Ferric Chloride Fig. 2c depicts the maximum percentage of WEO

degradation with FeCl3 (0.150.25 g/L) and glucose (20-25 g/L). Optimum level of degradation (70.9%) was observed at 0.225 g/L FeCl3 and 22.038 g/L glucose. It was observed that the growth of the bacterial strain increased with increasing concentrations of FeCl3. And that was probably due to its isolation from oil contaminated soil of iron ore handling plant. Earlier reports also described the heavy metal tolerance of the Ochrobactrum sp.27. It was also observed that they could grow in a medium with higher Fe concentration, varying from less than 0.05 to about 0.3 g/L, but their degradation activities were impaired with increasing FeCl3 concentration as observed from Fig. 1d.

Optimal Media Composite and Validation of Experiments To determine the optimum media composite, the

optimal values of the variables X1, X2 and X3 were

found out by solving the regression equation. This was achieved by putting the second order regression equation into matrix form as described by Myers and Montgomery28. The optimum values of the test variables were found to be as follows: glucose 22.028 g/L, yeast extract 1.949 g/L and FeCl3 0.225 g/L, with the predicted biodegradation of WEO at 70.98%. In order to verify the results, waste engine oil degradation was carried out using both the optimal medium and the original BH medium (not supplemented with yeast extract, glucose and FeCl3). Fig. 3 shows that 71.5% of WEO was degraded using the optimal medium within 7 d in comparison to 51.24% using the original BH medium. Saimmai et al30 also observed that the addition of nutrients stimulated the used lubricating oil degradation capabilities of indigenous microorganisms. These results show that the predicted data of the response from the empirical model is in good agreement with the experimentally obtained data and suggested that the model is satisfactory and practicable.

Biodegradation Analysis of WEO WEO composition is highly variable due to variation

in combustion conditions, so biodegradation analysis was performed based on TPH concentration measurement using gas chromatography32. The GC-MS spectra of TPH occurring in both the treated (at optimized condition) and untreated waste oil samples were expressed in Figs 4a and b. Benzene and naphthalene derivatives were the predominant hydrocarbon structures in the composition of the WEO sample. The WEO sample after biodegradation revealed significant reduction of major hydrocarbon peaks (from 6-40 min) compared to those in WEO from control samples. The most abundant peaks were located between 11-35 min of retention time and were identified as derivatives of benzene, naphthalene,

Fig. 3Degradation rate of WEO by O. pseudintermedium C1 before and after media optimization over 7 d incubation period.


299

azulene, indole, benzopyrene, dibenzophenazine etc. Naphthalene, acenaphthylenes diphenylanthracenes, benzopyrene and benzanthracene were also detected in used motor oil samples by several other researchers1,2,32. Benzoic acid and its derivatives were detected in the WEO sample recovered after biodegradation, which were presumably formed by the biodegradation of waste oil. This data suggest that the potential toxic hydrocarbon pollutants were metabolized by the Ochrobactrum sp. as alcohols, aldehydes and organic acids and these are common products of the so-called beta-oxidation of long-chain aliphatic compounds16,22.

Conclusion The CCD selected as a RSM proved to be suitable

for performing degradation studies. The true functional relationship between the dependent variable (carbon, nitrogen & inorganic metal sources) and maximum percentage of WEO biodegradation have been studied. The optimum conditions for growth and degradation by O. pseudintermedium C1 were also found out from the empirical model, which provided good quality of predictions for the above variables in terms of effective WEO degradation and good correlation coefficient (R2 0.987) was obtained. The treatment of WEO in industrial and domestic effluents is very important due to its persistent and toxic effect. The optimum culture medium obtained in these experiments gives a basis for further study with batch or fed-batch cultivation in a bioreactor for degradation of WEO in industrial effluents.

Acknowledgement The authors thankfully acknowledge the financial

grant provided by Technical Education Quality Improvement Programme (TEQIP Phase-II) and the technical support in GC-MS analysis by Mr Smritiranjan Maji, Central Instrument Facility Laboratory, Bose Institute, Kolkata (West Bengal), India.

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IJBT 13(3) 293-300