alexa Enhanced Bioremediation of Soil Artificially Contaminated with Kerosene: Optimization of Biostimulation Agents through Statistical Experimental Design | Open Access Journals
ISSN:2157-7463
Journal of Petroleum & Environmental Biotechnology
Like us on:
Make the best use of Scientific Research and information from our 700+ peer reviewed, Open Access Journals that operates with the help of 50,000+ Editorial Board Members and esteemed reviewers and 1000+ Scientific associations in Medical, Clinical, Pharmaceutical, Engineering, Technology and Management Fields.
Meet Inspiring Speakers and Experts at our 3000+ Global Conferenceseries Events with over 600+ Conferences, 1200+ Symposiums and 1200+ Workshops on
Medical, Pharma, Engineering, Science, Technology and Business

Enhanced Bioremediation of Soil Artificially Contaminated with Kerosene: Optimization of Biostimulation Agents through Statistical Experimental Design

Agarry SE1*, Owabor CN2 and Yusuf RO3
1Biochemical Engineering and Biotechnology Laboratory, Department of Chemical Engineering, Ladoke Akintola University of Technology, Ogbomoso, Nigeria
2Department of Chemical Engineering, University of Benin, Benin-City, Nigeria
3Environmental Engineering Laboratory, Department of Chemical Engineering, University of Technology, Johor Bahru, Malaysia
Corresponding Author : Dr. Agarry SE
Biochemical Engineering and Biotechnology Laboratory
Department of Chemical Engineering
Ladoke Akintola University of Technology, Ogbomoso, Nigeria
E-mail: [email protected]
Received February 25, 2012; Accepted April 21, 2012; Published April 23, 2012
Citation: Agarry SE, Owabor CN, Yusuf RO (2012) Enhanced Bioremediation of Soil Artificially Contaminated with Kerosene: Optimization of Biostimulation Agents through Statistical Experimental Design. J Pet Environ Biotechnol 3:120. doi:10.4172/2157-7463.1000120
Copyright: © 2012 Agarry SE, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Visit for more related articles at Journal of Petroleum & Environmental Biotechnology

Abstract

The main goals of this work were to study the enhanced bioremediation of soil artificially contaminated with kerosene via biostimulation strategy, evaluate the influence of biostimulating agents on the rate of degradation and to optimize the biostimulating agents for maximum kerosene removal. The study was carried out by artificially contaminating an un-impacted tropical soil with 10% (w/w) kerosene oil in earthen pots and various concentrations of NPK fertilizer, Tween 80 and hydrogen peroxide were added and then incubated for six weeks remediation period. To optimize the range of experimentation, Response Surface Methodology (RSM) with Box Behnken Design (BBD) was used with three factors and three levels of NPK fertilizer, Tween 80 and hydrogen peroxide as independent variables and kerosene oil (total petroleum hydrocarbon) removal as dependent variable (response). The results showed that there were significant variations in the kerosene oil biodegradation pattern with respect to NPK fertilizer, Tween 80 and hydrogen peroxide. A statistically significant (P < 0.0001) second-order quadratic regression model for kerosene oil removal (using Design-Expert Statistical program (v. 6.0.8) with a coefficient of determination, R (= 0.9992) was obtained. Numerical optimization technique based on desirability function was carried out to optimize the bioremediation process. The optimum values for biostimulating agents to achieve a predicted maximum kerosene removal of 75.06% were found to be: NPK fertilizer, 4.30 g (equivalent to 0.0215 μg/kg); Tween 80, 10.03 mg/l and hydrogen peroxide, 1.13 g/l. At this optimum point, the observed kerosene oil removal was found to be 73.95%. Thus, biostimulation of indigenous microbial density and activity can reduce the period for remediation of contaminated environment and subsequently the cost of remediation. Response Surface Methodology (RSM) is a reliable and powerful tool for modeling and optimizing of kerosene oil bioremediation processes.

Keywords
Bioremediation; Biostimulation; Kerosene; NPK fertilizer; Tween 80; Hydrogen peroxide; Response surface methodology
Introduction
Petroleum products obtained from the refining of crude oil are some of the most widely used chemicals in society today. Kerosene is a colourless flammable hydrocarbon liquid obtained from the fractional distillation of petroleum at 150°C and 275°C [1,2]. Kerosene contains paraffin’s (alkanes), cycloparaffins (cycloalkanes), aromatics and olefins from approximately C9 to C20 [3]. It is used to run many types of engines, lamps, heaters and stoves. The invention of the internal combustion engine and its fast adoption in all transport forms enlarged the employment of this natural resource, thus increasing its demand production, transport, stockpiling, and distribution, as well as the raw oil and its by-products. All these activities involve pollution risks that can be minimized, but not totally eliminated, causing several problems for the environment [4].
Kerosene has many toxic effects on plants, animals and humans [1] and possesses moderate to high acute toxicity to biota with productspecific toxicity related to the type and concentration of aromatic compounds [5]. Generally, petroleum contamination results from leakages of underground and above ground storage tanks, spillage during transport of petroleum products, tanker accidents, unplanned releases and current industrial processes [6]. The application of biotechnological processes involving microorganisms, with the objective of solving environmental pollution problems, is rapidly growing, in recent decades, where petroleum and its by-products are concerned. Bioremediation processes, which take advantage of microbial degradation of organic and inorganic substances, can be defined as the use of biological systems to catalyze the destruction or transformation of various chemicals to less harmful forms [7].
It is known that biodegradation efficiencies of petroleum hydrocarbons in soil can be limited by physicochemical as well as biological factors, such as nutrients, pH, temperature, oxygen, number and type or species of microorganisms [8-10]. Also, oil spills result in an imbalance in the carbon–nitrogen ratio at the spill site, because crude-oil is essentially a mixture of carbon and hydrogen. This causes a nitrogen deficiency in an oil-soaked soil, which retards the growth of bacteria and the utilization of carbon source(s). In addition to a nitrogen deficiency in oil-soaked soil, certain nutrients like phosphorus may be growth-rate limiting [11]. Furthermore, large concentrations of biodegradable organics in the top layer of agricultural soils deplete oxygen reserves in the soil and slow down the rates of oxygen diffusion to deeper layers [12].
Petroleum hydrocarbon pollution tends to persist in soils until remediation measures, involving the application of nutrients, are resorted to, because oxygen and nitrogen are limiting factors in all types of petroleum degradation. Oxygen levels must be high enough for the breakdown of hydrocarbons. Thus, pollutant degradation rates can be enhanced by the addition of nutrients, oxygen, and primary substrates into the contaminated systems. This could increase the populations of indigenous microorganisms and thus improve the efficiency of pollutant biodegradation. In biostimulation technology, nutrient supplementation for petroleum hydrocarbon degradation has traditionally focused on addition of nitrogen (N) and phosphorus (P), either organically or inorganically. The effects of nutrients (i.e. NPK), aeration and biostimulation of indigenous soil microorganisms and inoculation of extraneous microbial consortia on the bioremediation of oil contaminated soil have been investigated [13-15]. Interest has recently been shown in the use of oxygen-release compounds (ORCs) to promote the direct oxidation of pollutants and, at the same time, to increase aerobic microbial degradation [16,17]. As most of the bioremediation methods are aerobic processes, due to its greater efficiency, both the oxygen generated and delivery of oxygen to soil (in situ bioremediation technologies) is crucial to success.
The introduction of pure oxygen rather than air can significantly increase the dissolved oxygen (DO) concentrations [18]. Injection of hydrogen peroxide (H2O2) is one of the methods to increase the DO concentrations. A significant portion of contaminants in soils has been found to be oxidized by H2O2 without any addition of soluble iron [18-20] and the mineral catalyzed Fenton-like reaction was proposed to describe the oxidation occurring in the natural soils. One of the advantages of applying Fenton-like oxidation for contaminant oxidation is that the produced oxygen during the decomposition of H2O2 would increase the DO concentration. This would enhance the aerobic biodegradation rate of contaminant [21]. Application of surfactants to soil contaminated with petroleum hydrocarbons can potentially reduce the interfacial tension, increase its solubility and bioavailability, and thus facilitate their biodegradation [22,23]. Recently, statistical design of experiment technique has been successfully applied in many fields [4,24-26] to provide information about direct effects, mutual interaction effects and curvilinear variable effects.
Biostimulation can be considered as an appropriate remediation technique for kerosene removal in soil and requires the evaluation of both the intrinsic degradation capacities of the autochthonous microflora and the environmental parameters involved in the kinetics of the in situ process. One of those parameters is aeration, which can be improved in bioremediation systems by the use of Oxygen Release Compounds (ORC). Biostimulation in systems controlling different physical and chemical factors has been well documented [27-29]. The biological removal of petroleum commercial products, such as kerosene has been reported [2,30-32] however, there is a lack of information on the use of ORC for stimulation of autochthonous microflora of kerosene contaminated soils. More also, available information regarding the effects of biodegradable surfactants’ addition on enhanced biodegradation or removal of kerosene oil as well as information on the optimization of these biostimulating factors required for the enhanced biodegradation is very limited.
Therefore, the objective of this work was to study the bioremediation of soil artificially contaminated with kerosene via biostimulation strategy as a function of nutrients (NPK fertilizer), oxygen release compound (hydrogen peroxide) and surfactant (Tween 80) through Response Surface Methodology (RSM) using full-factorial Box Behnken design in order to evaluate the influence of these biostimulating factors on the rate of degradation and to optimize the factors for maximum kerosene removal.
Materials and Methods
Soil sample and characterization
An un-impacted soil samples from Ladoke Akintola University of Technology Agricultural Farms, Ogbomoso, were collected from the surface layer of the vadose zone 15-30 cm below land surface. The soil samples were air dried, homogenized, passed through a 2 - mm (pore size) sieve and stored in a polythene bag at room temperature. Soil samples were characterized for their physicochemical and microbial parameters according to standard methods. Total organic carbon and Total Nitrogen of soil were determined using Walkley-Black and Macro-Kjeldahl methods respectively [33,34]. Soil pH was determined using pH meter fitted with a combined glass pH and reference electrode. Soil moisture content was determined by evaporation on Whatman filter paper NO 1 (BDH Chemicals England) at 103° to 105°C in an electrical oven. Available phosphorus was determined using Bray NO 1 Method [33,34]. The Total Hydrocarbon Degrading Bacteria (THDB) populations were determined by the vapour phase transfer method [35].
Chemicals
The kerosene oil was purchased from a local petroleum station in Ogbomoso, Nigeria. Hydrogen peroxide (H2O2) 50% (w/v), (a product of Merck, USA) and hexane solvent (BDH Chemicals, England) used for extraction of oil from soil were bought from a chemical store in Lagos, Nigeria and used as received. This study employed a biodegradable non ionic surfactant Tween 80 manufactured by Sigma-Aldrich, USA, which has an average molecular weight of 1310 and a Critical Micelle Concentration (CMC) value of 15 mg/l. NPK fertilizer (20:10:10) was purchased from an agro-chemical store in Ogbomoso, Nigeria.
Microcosms preparation and bioremediation experimentation
To optimize the range of experimentation for 23 full-factorial Box- Behnken design, the following experiments were performed in earthen pots maintained at room temperature. Soil samples (200 g) were placed in earthen pots (microcosm) and were artificially contaminated with kerosene fuel (obtained from a local commercial source) to a level of 10 % w/w. The kerosene-contaminated soil in each earthen pot was amended with different amounts of NPK fertilizer (2 - 10 g, equivalent to 0.01 – 0.05 μg/kg), Tween 80 (5 - 25 mg/l) and hydrogen peroxide (0.5 - 2.5 g/l), respectively. Soil used as control was not amended with any biostimulating agents. In total, 16 microcosms were settled and incubated for 42 days. All reactors were mixed manually once per week to enhance oxygenation, and kept moist during the 42 day experimental period. Samples were withdrawn at intervals of one week for residual kerosene or total petroleum hydrocarbon analysis.
Experimental design and data analysis
The Box-Behnken factorial experimental design employed had three independent variables viz, NPK (20:10:10) fertilizer, Tween 80 (surfactant) and hydrogen peroxide (ORC). Each of the independent variables was studied at three levels (1, 0, +1), with 17 experimental runs and one control. The levels were selected based on preliminary study results and literature. The variables optimized were NPK fertilizer (from 2 - 6 g or 0.01 – 0.03 μg/kg), Tween 80 (5 – 15 mg/l) and hydrogen peroxide (0.5 – 1.5 g/l), respectively Table 1. Change in kerosene oil removal was considered as experimental response. Efficiency of kerosene removal was assessed after 42 days. Table 3 shows the coded and actual values of factors and levels used in the experimental design. Kerosene contaminated soil without biostimulation was also assayed as a control. The statistical software Design Expert 6.0.8, (Stat-Ease Inc., Minneapolis, USA) was used to evaluate the analysis of variance (P < 0.05) to determine the significance of each term in the fitted equations and to estimate the goodness of fit in each case.
Estimation of total petroleum hydrocarbon (residual kerosene oil)
Total petroleum hydrocarbon (TPH) was extracted from 10 g of soil with 50 ml of hexane [36]. The extract was dried at room temperature by evaporation of the hexane solvent under a gentle nitrogen stream in a fume hood. After evaporation of the solvent, the amount of residual TPH was determined gravimetrically [36] by reading absorbance at 400 nm using visible range spectrophotometer (Model 6100 PYE UNICAM instruments England) and estimating the concentration from the standards curve, obtained from hexane extracts of fresh kerosene oil at different concentrations.
Results and Discussion
Soil parameters
The determined soil parameters values are as follows: moisture content - 5.95 ± 0.05 (%), total nitrogen - 0.25 ± 0.04 (%), available phosphorus - 0.12 ± 0.02 (%), potassium - 0.31 ± 0.05 (%), total organic carbon - 1.21 ± 0.03 (%), pH 5.9 ± 0.2, and Total hydrocarbon-degrading bacteria (THDB) - 3.7 × 105cfu-g-1. The soil characterization showed that the soil did not fulfil the nutrient (NPK) requirements for an efficient biodegradation process. Therefore, these elements were added in the form of NPK inorganic fertilizer (20:10:10) to provide the proper nutrients required for the bioremediation process.
Natural bioattenuation and enhanced bioremediation
 
After performing 17 runs of the Box-Behnken Design (BBD) and one control, the results of the statistical experiment were analyzed with regard to the coded design matrix Table 2. The regression equation shows that the kerosene oil degradation rate was an experimental function of test variables in coded units. The comparison of kerosene oil enhanced bioremediation and natural bioattenuation for each run is shown in Table 3 as well as in Figure 1. Table 3 and Figure 1 show that on the 42 day, kerosene oil content had decreased in all earthen pot reactors. In control, natural biodegradation (natural bioattenuation) removed 38.5 per cent of petroleum hydrocarbons. The reduction in hydrocarbon content of earthen pot reactors containing biostimulants was much higher (Table 3 and Figure 1) in the same period. These results indicate that the addition of biostimulants increased the rate of biodegradation. A considerable decrease in oil reduction was observed in run 12, with the highest amount of surfactant (Tween 80) and oxygen releasing compound (hydrogen peroxide), the residual oil reached 22.11 per cent of the initial kerosene oil concentration. Yuting et al. [37] showed that natural attenuation removed 13 per cent of crude oil after 33 day incubation. When the soil was supplemented with nutrients (nitrogen and phosphorus) and biomass, 26.3 per cent of the crude oil was removed.
One of the major factors limiting degradation of hydrocarbons is their low availability to the microbial cells [38,39]. In addition, hydrocarbon-oxidizing potential has also been shown to increase with hydrocarbon exposure [40]. Thus, as seen in Figure 1 (at 10 g per kg soil kerosene oil concentrations), run numbers 1 and 3 (at lower concentration of NPK and H2O2), and run numbers 11 and 12 (at higher concentration of NPK and H2O2) had same remediation conditions but with different surfactant (Tween 80) concentration, results shows that addition of surfactant can enhance kerosene degradation. Similar observations have been reported for the use of non-ionic surfactant for the remediation of environment contaminated with petroleum hydrocarbons [18,41,42].
Effect of different concentrations of hydrogen peroxide supplementation were investigated at the same condition of NPK and Tween 80 (run numbers 5 and 7, and run numbers 10 and 12) and the findings demonstrated that addition of hydrogen peroxide (ORC) can enhance the bioremediation process of soil contaminated with kerosene. This is in agreement with earlier workers observations [18,43,44]. Furthermore, at the Traverse city, Michigan, bioremediation of a contaminated site was achieved by an increase in the oxygen concentration in water through addition of hydrogen peroxide and was observed to positively affect the rate of biodegradation [45]. On the other hand; run numbers 1 and 2 and run numbers 5 and 6 through similar condition but with different amount of NPK were tested and the results shows that extra amount of NPK (from 2 to 4 g/l. equivalent to 0.01 – 0.02 μg/kg) can improve kerosene removal from contaminated soil. The results suggest that high dose nutrient amendment can accelerate the initial oil degradation rate and may shorten the period to clean up contaminated environments.
The accelerating effect of amendment is stronger when nutrient availability is a limiting factor in the biodegradation of oil. Similar observations have been reported for the use of nitrogen and phosphorus in the bioremediation of environment contaminated with petroleum hydrocarbons [2,38,46,48]. Bioremediation efficiency is a function of the microbial viability in the natural environment [49]. Microorganisms need nutrient to grow. Hence, biodegradation of hydrocarbons in the natural environment is limited by poor growth rate of microorganisms caused by nutrient deficiencies, especially in nitrogen and phosphorus [40,50]. Therefore, when bioremediation is conducted suitable nitrogen and phosphorus are usually applied to the contaminated environment to stimulate biodegradation [51].
Second order polynomial regression model and statistical analysis
The experimental data were fitted to a second order polynomial regression model (Equation 1) containing 3 linear, 3 quadratic and 3 interaction terms [52] using the same experimental design software to derive the equation (Equation (1)) for kerosene oil removal from contaminated soil.
Y= β0 + β1A + β2 B + β3 C + β11 A2 + β22 B2 + β33 C2 + β12 AB + β13 AC + β23 BC                                                                                                            (1)
where β0 is the value of the fixed response at the centre point of the design; β1, β3, β3 are linear coefficients; β12,β13, β23 are quadratic coefficients; are the interaction effect coefficients regression terms, respectively; A, B and C are the levels of independent variables The significance of each coefficient in the equation was determined by F-test and P-values. F-test indicated that all the factors and interactions considered in the experimental design are statistically significant (P < 0.05) at the 95 per cent confidence level. The regression equation obtained after analysis of variance gives the level of kerosene removal as a function of the different biostimulation variables: NPK, Tween 80, and Hydrogen peroxide. All terms regardless of their significance are included in the following Equation (2):
Y = 74.83 +1.45A + 0.38B + 0.55C – 4.78A2 – 3.53B2 – 1.03C2 + 0.27AB + 1.02AC + 0.37BC                                                                                (2) 
where A is NPK concentration, B is Tween concentration; C is Hydrogen peroxide concentrations. To test the fit of the model, the regression equation and determination coefficient (R2) were evaluated (Table 4). The model F-value of 515.92 implies the model is significant. There is only a 0.01 per cent chance that a model F-value, this large could occur due to noise alone. The low probability value (<0.0001) indicates that the model is significant. The value of the determination coefficient (R2 = 0.9985) being a measure of goodness of fit to the model indicates a high degree of correlation between the observed value and predicted values. The determination coefficient (R= 0.9992), suggests that more than 99.92% of the variance is attributable to the variables and indicated a high significance of the model. Thus, 0.08% of the total variance cannot be explained by the model. The fitted model is considered adequate if the F-test is significant (P < 0.05). The Analysis Of Variances (ANOVA) quadratic regression model demonstrated that the model was highly significant, as was evident from the very low probability (P < 0.0001) of the F - test and insignificant result from the Lack of Fit model (P = 0.1186). The Lack of Fit test is performed by comparing the variability of the current model residuals to the variability between observations at replicate settings of the factors. The Lack of Fit F -value of 3.72 implies the Lack of Fit is not significant relative to the pure error. There is an 11.86 per cent chance that a Lack of Fit F -value this large could occur due to noise.
The Lack of Fit is designed to determine whether the selected model is adequate to describe the observed data, or whether a more complicated model should be used. The Predicted R-Squared value of 0.9817 is in reasonable agreement with the Adjusted R-Squared value of 0.9966. Adequate Precision measures the signal to noise ratio. A ratio > 4 is desirable. The ratio of 63.404 obtained in this research indicates an adequate signal. This model can be used to navigate the design space. The coefficient of variation (CV) as the ratio of the standard error of estimate to the mean value of the observed response is a measure of reproducibility of the model, generally a model can be considered reasonably reproducible if its CV is not greater than 10 per cent. Hence, the low variation Coefficient value (CV = 0.29 per cent) obtained indicates a high precision and reliability of the experiments. The coefficient of the model (parameter estimation) and the corresponding P-values are presented in Table 5. The significance of regression coefficients was considered, ignoring those with an insignificant effect on the response at a significance level of 95%. The P-values of the regression coefficients suggest that among the test variables, linear, quadratic and interaction effects of NPK fertilizer, Tween 80 and hydrogen peroxide are highly significant. The insignificant effects (factors and interactions) with P-values higher than 0.05, were ignored. In this study, A, B, C, A2, B2, C2, AB, AC and BC are significant model terms.
Thus, statistical analysis of all the experimental data showed that NPK fertilizer, Tween 80 and hydrogen peroxide concentration had a significant effect on oil removal during the study. Moreover, it is observed that NPK fertilizer (nutrients) exerted more pronounced linear effect (higher coefficient values) on kerosene removal. That is, kerosene removal was mostly and positively influenced by NPK fertilizer (nutrients) followed by hydrogen peroxide (ORC) and Tween 80 (surfactant). The strong influence of nutrients on petroleum hydrocarbons removal has been clearly shown before in the previous works of Mohajeri et al. [38]. The quadratic effect of the independent biostimulating factors on the rate of kerosene removal was significant but negative.
Figure 2a shows the predicted versus actual plot of kerosene oil biodegradation. Actual values were determined for a particular run, and the predicted values were calculated from the approximating function used for the model. Figure 2b shows the studentized residuals and normal per cent probability plot. Residual shows the difference between the observed value of a response measurement and the value that is fitted under the theorized model. Small residual values indicate that model prediction is accurate. The Cooks distance and studentized residuals illustrate the normal distribution and constant variance of the residuals, the goodness of fit, linearity of the fitted model, and the independence. Figure 2c shows Cook’s distance plot; according to this plot there were no points that were potentially powerful due to their location in the factor.
Interaction among factors that influence kerosene oil removal
Table 5 showed that kerosene oil removal was influenced positively by the interaction of NPK fertilizer (A) and Tween 80 (B); NPK fertilizer (A) and hydrogen peroxide (C) and interaction of Tween 80 (B) and hydrogen peroxide (C). The graphical representation of the response shown in Figure 3a– c helped to visualize the effect of NPK fertilizer (A), Tween 80 (B) and hydrogen peroxide (C) on removal of kerosene. The effect of interaction of NPK fertilizer and Tween 80 on kerosene bioremediation is illustrated in Figure 3a. It is seen that higher rate of kerosene removal was attained with higher surfactant (Tween 80) concentration and relatively high amount of NPK fertilizer. The maximum degradation yield of kerosene (74.95 %) was obtained with 10 mg/l of Tween 80 surfactant and 4 g/l (or 0.02 μg/kg) of NPK fertilizer at a fixed hydrogen peroxide concentration of 1.0 g/l. This may be due to better bioavailability of substrate for the intrinsic microorganisms. However, availability of hydrocarbon-utilizing microorganism is a key issue in crude oil bioremediation [53]. Further increase in the NPK fertilizer concentration (> 4.0 g/l) resulted in a significant decrease in the percent kerosene oil removal.
Figure 3c shows the response surface 3D plot of the effect of interaction between Tween 80 and hydrogen peroxide concentrations. Higher rate of kerosene oil removal was observed with increase in hydrogen peroxide and Tween 80 concentration due to positive interaction effect. It is evident that due to dominating interaction effects of hydrogen peroxide, higher levels of this variable give higher yields of kerosene oil removal. Maximum kerosene oil removal (74.92%) was obtained with about 1.0 g/l of hydrogen peroxide and 10 mg/l of Tween 80. Further increase in Tween 80 concentration (> 10.0 mg/l) tends to decrease the percent kerosene oil removal.
Factor plot
The factor effect function plot (Figure 4) was used to assess the effect of each factor graphically. From the trace plot as shown in Figure 4, it can be seen that each of the three variables used in the present study has its individual effect on kerosene removal by the intrinsic microbial populations in the soil. Gradual increase in NPK fertilizer, Tween 80 and hydrogen peroxide concentrations from low level (coded value –1) to a higher level (coded value +1) resulted in both increase and decrease of kerosene oil degradation. Moreover, it is also to be noted from Figure 4 that over the range of -1 (2 g) to +1 (6 g) of NPK fertilizer, the kerosene degradation change in a wide range, which was also the case for Tween 80. However, for hydrogen peroxide the kerosene oil removal did not change over a wide range. This clearly indicates that keeping hydrogen peroxide at the optimum level, a change in NPK fertilizer and Tween 80 concentrations will respectively affect the process more severely than done otherwise.
Optimization and validation
Numerical optimization technique based on desirability function was carried out to determine the workable optimum conditions for the kerosene oil bioremediation process. In order to provide an ideal case for biodegradation, the goal for NPK fertilizer, Tween 80 and hydrogen peroxide was set in range based upon the requirements of the oil bioremediation and kerosene oil removal was set on maximize. The predicted optimum (uncoded) values of NPK fertilizer, Tween 80 and hydrogen peroxide were found to be 4.30 g (equivalent to 0.0215 μg/ kg), 10.03 mg/l and 1.13 g/l, respectively, to achieve 75.06% maximum kerosene oil removal; while desirability for the predicted optimum values was 1.000 (Figure 5). Nevertheless, validation experiment was conducted to determine the optimum kerosene oil removal when the biostimulation factors were set at the favourable optimum levels established above, through BBD and RSM. Standard deviation and percent error were investigated for validation of experiments. Errors between predicted and actual values were calculated according to Equation (3):
                                         (3)
At the optimized condition for initial kerosene oil of 10 % w/w, 73.95% kerosene removal was obtained. The percentage error between the predicted and actual values was found to be -1.50. The results clearly indicated that no significant difference was observed. Marquez-Rocha et al. [54] and Nievas et al. [55] have correspondingly reported up to 70 and 68 percent petroleum hydrocarbon removal from contaminated environment. A full factorial experimental design performed by Pala et al. [4] and Mohajeri et al. [38] to respectively assess the effects of three and four variables on the bioremediation of petroleum-contaminated soil showed that the hydrocarbon removal rate was around 80%.
Conclusions
Significant variations in the kerosene oil biodegradation pattern were observed with respect to nutrient (NPK fertilizer), surfactant (Tween 80) and oxygen contents (Hydrogen peroxide). The results of the present study indicate that biostimulation of kerosene oil contaminated soil resulted in better petroleum hydrocarbon degradation. Response Surface Methodology (RSM) is a reliable and powerful tool for modeling and optimizing of kerosene oil bioremediation processes, in the optimum conditions petroleum hydrocarbons were degraded up to 73.95% in soil.
References

Tables and Figures at a glance

Table icon Table icon Table icon Table icon Table icon
Table 1 Table 2 Table 3 Table 4 Table 5

 

Figures at a glance

Figure Figure Figure Figure Figure
Figure 1 Figure 2 Figure 3 Figure 4 Figure 5
Select your language of interest to view the total content in your interested language
Post your comment

Share This Article

Relevant Topics

Recommended Conferences

Article Usage

  • Total views: 11573
  • [From(publication date):
    May-2012 - Oct 19, 2017]
  • Breakdown by view type
  • HTML page views : 7831
  • PDF downloads :3742
 

Post your comment

captcha   Reload  Can't read the image? click here to refresh

Peer Reviewed Journals
 
Make the best use of Scientific Research and information from our 700 + peer reviewed, Open Access Journals
International Conferences 2017-18
 
Meet Inspiring Speakers and Experts at our 3000+ Global Annual Meetings

Contact Us

Agri, Food, Aqua and Veterinary Science Journals

Dr. Krish

[email protected]

1-702-714-7001 Extn: 9040

Clinical and Biochemistry Journals

Datta A

[email protected]

1-702-714-7001Extn: 9037

Business & Management Journals

Ronald

[email protected]

1-702-714-7001Extn: 9042

Chemical Engineering and Chemistry Journals

Gabriel Shaw

[email protected]

1-702-714-7001 Extn: 9040

Earth & Environmental Sciences

Katie Wilson

[email protected]

1-702-714-7001Extn: 9042

Engineering Journals

James Franklin

[email protected]

1-702-714-7001Extn: 9042

General Science and Health care Journals

Andrea Jason

[email protected]

1-702-714-7001Extn: 9043

Genetics and Molecular Biology Journals

Anna Melissa

[email protected]

1-702-714-7001 Extn: 9006

Immunology & Microbiology Journals

David Gorantl

[email protected]

1-702-714-7001Extn: 9014

Informatics Journals

Stephanie Skinner

[email protected]

1-702-714-7001Extn: 9039

Material Sciences Journals

Rachle Green

[email protected]

1-702-714-7001Extn: 9039

Mathematics and Physics Journals

Jim Willison

[email protected]

1-702-714-7001 Extn: 9042

Medical Journals

Nimmi Anna

[email protected]

1-702-714-7001 Extn: 9038

Neuroscience & Psychology Journals

Nathan T

[email protected]

1-702-714-7001Extn: 9041

Pharmaceutical Sciences Journals

John Behannon

[email protected]

1-702-714-7001Extn: 9007

Social & Political Science Journals

Steve Harry

[email protected]

1-702-714-7001 Extn: 9042

 
© 2008-2017 OMICS International - Open Access Publisher. Best viewed in Mozilla Firefox | Google Chrome | Above IE 7.0 version
adwords