Assessing Bacillus subtilis biosurfactant effects on the

Environ Monit Assess (2015) 187:4116 DOI 10.1007/s10661-014-4116-8

Assessing Bacillus subtilis biosurfactant effects on the biodegradation of petroleum products Renato Nallin Montagnolli & Paulo Renato Matos Lopes & Ederio Dino Bidoia

Received: 1 April 2014 / Accepted: 28 October 2014 # Springer International Publishing Switzerland 2014

Abstract Microbial pollutant removal capabilities can be determined and exploited to accomplish bioremediation of hydrocarbon-polluted environments. Thus, increasing knowledge on environmental behavior of different petroleum products can lead to better bioremediation strategies. Biodegradation can be enhanced by adding biosurfactants to hydrocarbon-degrading microorganism consortia. This work aimed to improve petroleum products biodegradation by using a biosurfactant produced by Bacillus subtilis. The produced biosurfactant was added to biodegradation assays containing crude oil, diesel, and kerosene. Biodegradation was monitored by a respirometric technique capable of evaluating CO2 production in an aerobic simulated wastewater environment. The biosurfactant yielded optimal surface tension reduction (30.9 mN m−1) and emulsification results (46.90 % with kerosene). Biodegradation successfully occurred and different profiles were observed for each substance. Precise mathematical modeling of biosurfactant effects on petroleum degradation profile was designed, hence allowing long-term kinetics prediction. Assays containing biosurfactant yielded a higher overall CO2 output. Higher emulsification and an enhanced CO2 production dataset on assays containing biosurfactants was observed, especially in crude oil and kerosene. R. N. Montagnolli : P. R. M. Lopes : E. D. Bidoia (*) Departamento de Bioquímica e Microbiologia, Instituto de Biociências, UNESP—Univ Estadual Paulista, Avenida 24 A, 1515—Bela Vista, 13506-900 Rio Claro, SP, Brazil e-mail: [email protected]

Keywords Bioremediation . Respirometry . Kinetics . Hydrocarbon

Introduction Large accidents involving oil tankers, rigs and pipelines have attracted public attention to the fate of petroleum hydrocarbons in various environments. For example, the two worst spills in US history: Exxon Valdez spill in 1989 and British Petroleum Deepwater Horizon spill in 2010 (Atlas and Hazen 2011), and the accident in Dalian, China in July 2010 (Xu et al. 2012). However, accidents are not the sole cause of petroleum contamination scenarios. Exploration, transportation, and consumption of oil products causes a continuous release of hydrocarbons into previously pristine areas through improper disposal or leakage in storage systems. Oil production reaches 3 billion tons a year worldwide. Half of the produced amount is transported by sea. It is estimated that over 2 million tons are lost each year by inadequate oil handling (Readman et al. 1992). The increasing hydrocarbons insertion in the environment can lead to serious environmental problems (Oluwole et al. 2005; Okoh and Trejo-Hernandez 2006). Once crude oil is spilled at the sea, it spreads rapidly to form oil slicks on the sea surface (Garrett et al. 2003). The oil is dispersed through the surface and beneath it the volatile compounds are released into the atmosphere. Various weathering processes occur as well, including dissolution, photooxidation, and microbial degradation (Wolfe et al. 1994; Garrett et al. 2003; Harayama et al. 1999; Dutta and

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Harayama 2000; Medina-Bellver et al. 2005). Although a substantial fraction of petroleum is removed by evaporation, a portion of petroleum inflow to marine environments is dispersed into the water column, consequently affecting the local marine biota (Chouksey et al. 2004). Petroleum biodegradation and bioremediation Petroleum is a complex mixture of hydrocarbons and other organic compounds, including some organometallic constituents, particularly involving vanadium and zinc. Crude oil is an extremely complex assemblage of chemicals mainly consisting of aliphatics, aromatics, and polar compounds. Furthermore, oil from different areas around the globe varies considerably in their composition and physical-chemical properties (Prince and Walters 2007). Mechanical removal of hydrocarbons from the environment relies on expensive, slow, and inefficient methodologies (Mandri and Lin 2007). Biological treatment, or bioremediation, is a desirable alternative due to its cost-effectiveness (Juhasz and Naidu 2000; Stallwood et al. 2005; Karhu et al. 2009). The first bioremediation technologies were introduced in the early 1980s as an alternative to conventional methods involving excavation, landfill, pumping, treatment, and addition of absorbent material. The goal of bioremediation is to breakdown contaminants with microorganisms, ultimately reaching full mineralization. Bioremediation processes aim to decrease contaminant concentrations, toxicity, and risk to potential receptors (Sanscartier et al. 2009). Thus, research on petroleum hydrocarbons biodegradation is imperative in terms of further understanding its properties and how to best handle it. Designing bioremediation strategies for various substances is a major challenge, as there is no single set of contaminant and environmental characteristics able to effectively predict bioremediation potential. There is an increasing practical interest in developing short-term assays and models that can effectively predict availability and biodegradation profiles of contaminants. Hence, ruling out appropriate technologies for environmental cleanup (Semple et al. 2007). Biodegradation processes reduce petroleum hydrocarbons damage in contaminated environments. However, these recalcitrant pollutants are broken down very slowly under normal conditions because of specific characteristics (Yanto and Tachibanac 2013). The effect of temperature, salinity, and nutrients in oil biodegradation kinetics has already been extensively researched (Atlas 1981). There are also reports on biodegradation of petroleum

Environ Monit Assess (2015) 187:4116

constituents such as polycyclic aromatic hydrocarbons (PAHs) (Juhasz and Naidu 2000; Li et al. 2008), sulfur and nitrogen compounds (Benedik et al. 1998), and metabolism anaerobic hydrocarbons (Frazer et al. 1995). Total petroleum degradation is a result of a microbial consortium action, which is composed of different species with specific biochemical roles. Each one presents a required enzymatic mechanism capable of degrading a wide array of oil compounds (Solano-Serena et al. 1999; Yemashova et al. 2007; Obayori et al. 2009). Isolated organisms do not usually have all the necessary enzymes to metabolize complex substrates. However, some genera found in contaminated environments have a reasonable performance during hydrocarbons biodegradation, such as Pseudomonas, Sphingomonas, Acinetobacter, Alcaligenes, Micrococcus, Bacillus, Flavobacterium, Arthrobacter, Alcanivorax, Mycobacterium, Rhodococcus, and Actinobacter (Nadarajah et al. 2002; Okoh and Trejo-Hernandez 2006; Jacques et al. 2008). Moreover, studies by Atlas and Bartha (1992) and Bragg et al. (1994) determined that the concentrations of available nitrogen and phosphorus in sea water influence hydrocarbon-degrading microorganisms growth. By adjusting biodegradation conditions, it is possible to stimulate oil biodegradation and turn it into a more efficient process (Chaîneau et al. 2005). Biosurfactants as an enhancement of biodegradation processes Oil droplets dispersion in water column is naturally induced by wave action, while emulsification of diverse oil components occurs by emulsification agents. Still, most petroleum hydrocarbons are highly insoluble in water. Hydrocarbon biodegradation takes place at the hydrocarbon-water interface. Thus, the surface area to volume ratio of the oil can significantly impact biodegradation rates. Surface-active substances are capable of reducing interfacial tension between oil and water. As a result, droplets are dispersed much more efficiently in water column (Tjessen et al. 1984). Chemical dispersants, such as Corexit 9500 (used during the BP Deepwater Horizon spill), increased available surface and thus the overall biodegradation rates and efficiency (Atlas and Hazen 2011). Bioremediation of areas contaminated with hydrocarbons can be achieved by bioaugmentation with biosurfactants. Biosurfactants are structurally diverse


amphipathic surface-active compounds produced by a wide array of microorganism genera. As compared to their synthetic counterparts, biosurfactants are biodegradable, environmentally safe, stable under extreme conditions, and they can be produced in situ from inexpensive renewable substrates (Desai and Banat 1997; Benincasa et al. 2010). Low toxicity and the high biodegradability make biosurfactants advantageous when compared to synthetic compounds. These are desirable properties to bioremediation strategies and processes. The properties of various microbial produced biosurfactants had been studied (Haferburg et al. 1986; Cooper and Goldenberg 1987; Georgiou et al. 1992; Rosenberg and Ron 1997). Biosurfactant properties make them suitable for a wide range of industrial applications involving: detergency, emulsification, lubrication, foaming ability, wetting ability, solubilization, and phases dispersion (Ashis and Das 2010). World production of surfactants exceeds 3 million tons/year, whose use is concentrated in oil industry, cosmetics, toiletries, and cleaning (Banat et al. 2000). In the petroleum industry, they can be applied in enhanced oil recovery, cleaning oil spills, oil-contaminated tanker cleanup, viscosity control, oil emulsification, and removal of crude oil from sludge (Urum and Pekdemir 2004; Mulligan 2005; Perfumo et al. 2010). Arima et al. (1968) discovered the existence of a novel compound produced by Bacillus subtilis, which was named surfactin due to its high surface activity. Surfactants produced by different strains of B. subtilis are obtained by less aggressive processes from environmental point of view considered and can be presented as an attractive alternative to replace less environmentfriendly synthetic surfactants (Maier 2003). In situ bioremediation experiments with biosurfactantproducing microorganisms were already successfully able to accelerate the biodegradation of oil sediments (Radwan et al. 1998). Harvey et al. (1990) assays with Pseudomonas aeruginosa Exxon Valdez accident in Alaska observed that samples containing a 1 % biosurfactant solution had an oil removal success rate up to three times greater than control assays. These results demonstrate the ability of biosurfactants on removal of environmental pollutants, acting as facilitators during substrates uptake by microorganism cells (Banat 1994). Besides, many techniques have been developed with microbial populations and the introduction of nutrients to stimulate the production of biosurfactants and use of oil as substrate (Muller-Hurtig et al. 1993).

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This study evaluated the biodegradation of crude oil, kerosene, or diesel in a simulated wastewater environment. The biodegradation process was enhanced by adding biosurfactant produced by B. subtilis. Mathematical models were used to demonstrate and predict how biosurfactant influences biodegradation kinetics of petroleum hydrocarbons. Analysis of optimal biosurfactant production by B. subtilis was also conducted.

Material and methods Biosurfactant production Biosurfactant-optimized production conditions were analyzed with its physical properties. All subsequent biodegradation steps relied on the biosurfactant produced through methods described in this section. Preparation and selection of culture media Biosurfactant was produced by B. subtilis ATCC 6633 originally stored under refrigeration (4 °C) in nutrient agar (NA) in accordance to Difco (1984). The microorganism was reactivated in nutrient broth medium (Table 1), for 24 h and then inoculated (1.0 ml) in a 250 ml Erlenmeyer flasks, each containing 100 ml media listed at Table 1. The flasks were incubated at 35 °C with stirring Solab Shaker at 180 rpm to allow B. subtilis growth. Weight measurements were performed using the Sartorious BL201 S analytical scale. The medium that produced the most biomass was selected in as a first step to optimal biosurfactant production. There is a proportional ratio between biomass growth and biosurfactant production (Vater 1986). A 1.0-ml broth aliquot was removed for plate count agar (PCA) quantification. Gram stain was performed to ensure pure B. subtilis growth occurred. Biomass measurement Biomass was determined from BHI + Mg broth. Samples were periodically removed from the fermentation broth every 4 h. The broth was centrifuged at 104×1.9g (16,000 rpm) during 30 min using a K24 MLW centrifuge. Samples had their supernatant removed. Centrifugation process was performed to extract any reminiscent intracellular surfactant. Biosurfactant collected from

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Table 1 Culture broths for B. subtilis growth and biosurfactant production Broth

Components concentration

NA (nutrient agar)

3.0 g L−1 meat extract, 5.0 g L−1 peptone, 8.0 g L−1 NaCl and 15 g L−1 agar

BH (Bushnell Haas) broth

0.2 g L−1 MgSO4, 0.02 g L−1 CaCl2, 1.0 g L−1 KH2PO4, 1.0 g L−1 K2HPO4, 1.0 g L−1 NH4NO3, and 0.05 g L−1 FeCl3

BH + oil broth

0.2 g L−1 MgSO4, 0.02 g L−1 CaCl2, 1.0 g L−1 KH2PO4, 1.0 g L−1 K2HPO4, 1.0 g L−1 NH4NO3, 0.05 g L−1 FeCl3, and 1.0 mL of oil

NB (nutrient broth)

3.0 g L−1 meat extract, 5.0 g L−1 peptone and 8.0 g L−1 NaCl

BHI (brain heart infusion) broth 14.5 g L−1 casein, 10 g L−1 brain heart infusion, 5 g L−1 peptone, 3.0 g L−1 NaCl, 2.5 g L−1 Na2HPO4, and 2.0 g L−1 glucose BHI + Mg

14.5 g L−1 casein, 10 g L−1 brain heart infusion, 5 g L−1 peptone, 3.0 g L−1 NaCl, 2.5 g L−1 Na2HPO4, and 2.0 g L−1 glucose and 0.5 g L−1 Mg

supernatant was separated for further analysis. Precipitate in centrifuge tubes was dried for 48 h and weighed. Surface tension measurement Surface tension values were obtained by using a bench tensiometer Krüss model K6. This equipment allows analog readings using the platinum-iridium ring method, by submerging a 6.0 cm ring into a 50.0-mm diameter glass vessel where the sample is placed. The result was a direct readout in millinormal per meter. The measurement was performed at 25 °C. The force required to detach the annulus fluid is indicated as surface tension. Surface tension measurements were made directly in the supernatant collected after centrifuging the culture medium containing B. subtilis. Yielded surfactant Surfactant was isolated from crude cell-free broth by acid precipitation (Queiroga et al. 2003; Nitschke et al. 2004; Dehghan-Noudeh et al. 2005). This technique consists of an acid precipitation in the surfactant containing broth pH from 2.0 to 6.0 M HCl addition at a reduced temperature (4 °C). The precipitate formed overnight was then collected, centrifuged at 2100g for 20 min, dried, and weighed. Surfactant extraction Surfactant was extracted using 10.0 mL of the solvent dichloromethane and then added to dried pellets. The mixture was stirred for 5 min in a Phoenix AP56 vortex. After pellet solubilization, the organic phase was removed though solvent evaporation. A yellowish powder

was then resolubilized in water and basified to pH 11.0 with a 6.0 M NaOH solution. Such technique ensures a 52 to 55 % purity according to previous studies from Chen and Juang (2008) and Gong et al. (2009), who analyzed the substance by HPLC. E24 Emulsification test Emulsifying activity is an important characteristic in surfactants. An emulsion occurs when a liquid phase is dispersed as microscopic droplets in another (liquid continuous phase) even when two phases were previously immiscible (Desai and Banat 1997). We analyzed the E24 emulsification index of the biosurfactant obtained from B. subtilis. The results were compared to a synthetic detergent (Tween 80®) emulsification capability. The Tween 80® concentration we used was matched to our biosurfactant yield. The E24 was adapted from Cooper and Goldenberg’s (1987) original method, similar to the study by Ilori et al. (2005). Biosurfactant activity was evaluated by adding 2.0 ml of the fermented broth for 48 h or 2.0 mL of cellfree (centrifuged) surfactant and 2.0 mL of the substance under examination in test tubes. The resulting biosurfactant concentration in each assay was 2.165 g L−1. E24 was calculated for the following substances: crude oil, diesel, and kerosene. The test was performed at 25 °C. The flasks were stirred in a vortexer for 1.0 min at high speed. After 24 h, the ratio between the height of the region and emulsified total height was calculated (Eq. 1). . E24 ¼ Hem Hs 100 ð1Þ where: Hem = height of region emulsified, Hs = total height of the solution.


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Respirometry This study used the respirometric methodology of Bartha and Pramer (1965) to determine CO2 concentrations from the micro-atmosphere created inside a respirometer flask. Respirometry allows for weekly monitoring of CO2 production. The CO2 produced during microbial respiration processes can be neutralized in a KOH solution located in a side arm connected to the respirometric flask. The absorbed CO2 amount was analyzed by titration. Inoculum preparation Microorganisms capable of biodegradation used in the respirometric assays, were preselected by a soil contamination simulation though a plastic bag filled with 3.0 kg of soil, 100 mL of distilled water, and 50 ml of a mixture containing equal parts of crude oil and petroleum derivatives. The container was then filled with small holes with approximately 1.0 mm diameter, spaced 1 in. apart. This interface allowed microbial exchange between outer and inner soil media. Such inoculum preparation followed a L6.350 CETESB—Companhia de Tecnologia e Saneamento Ambiental (1990) technical standard with some adaptations. After 15 days, it was considered that there was a microbial pool adapted to oil degradation. The soil was then immersed in 1500 ml of water, thus defining the “base aqueous media” to be used in all biodegradation assays. Assays preparation Biodegradation assays were carried out in Bartha and Pramer (1965) respirometric flasks (Fig. 1). The respirometer consists of a fully enclosed system, with two connected chambers: a major chamber where biodegradation takes place in whichever analyzed effluent (Fig. 1(G)); and another chamber where an alkaline solution is placed to neutralize all CO2 produced by microbial respiration (Fig. 1(D)). Respirometry applied to bioremediation studies offers several advantages for obtaining data on CO2 production that would be otherwise difficult by other means (Graves et al. 1991). This study used a methodology adapted for aqueous medium respirometry (Lopes and Bidoia 2009; Montagnolli et al. 2009) that made it possible to obtain information about a simulated wastewater environment polluted by petroleum substances. In

Fig. 1 Bartha and Pramer respirometer. (A) Cannula cap, (B) Cannula (diameter 1–2 mm). (C) Rubber cork. (D) Lateral arm (diameter 40 mm, height 100 mm). (E) KOH solution. (F) Medium with aqueous analyte. (G) Erlenmeyer flask (250 ml). (H) Valve. (I) Support (glass, wool or cotton). (J) Air filter (diameter 15 mm, height 40 mm)

our paper, we replicated urban and domestic wastewater, not industrial wastewater. The experimental design simulated riverside and/or lakeside areas vulnerable to oil pollution. For a bioremediation study, it is necessary measure biodegradation over time (Wu et al. 2004), involving different stages of adaptation, degradation, and termination a biodegrading microbiota. Such data can be immediately related to biodegradation rate and biomass changes over time (Fiúza and Vila 2004). Bartha and Pramer (1965) respirometry monitors CO2 evolution rate though a simple analytical chemistry process. Carbon dioxide evolved during metabolism is captured in a potassium hydroxide (KOH) solution located on the side arm connected to the respirometric chamber. The CO2 amount was then neutralized and analyzed by titration of residual KOH with a standard solution of hydrochloric acid (HCl). Next, barium chloride (BaCl2) was added to precipitate carbonate ions. Levels of carbon dioxide produced were calculated and plotted as a function of incubation time (Balba et al. 1998). The substances subject to analysis were crude oil from the REPLAN petroleum refinery (Paulínia-SP,

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promote a better handling of bioremediation scenarios. The proposal of different models of biodegradation for each substance biodegraded, allows for determination of how the profiles may vary for each substance. Data was analyzed by statistical tools from SigmaStat 10 SYSTAT. For the modeling of the data, equations were used in setting the platform (WOLFRAM MATHEMATICA 6 and SigmaPlot 10 from SYSTAT). A different model was used in accumulated and weekly datasets. Many models for data fitting were considered and tested. The selection factor for the best model was based on the highest R2 mean along adjusts. Most models were originally related to growth kinetics description in microbial populations. The growth models were found to be suitable for biodegradation data. The first proposed data fitting model was an adapted logistic equation by Schmidt et al. (1985). This model was initially used by the author to describe microbial growth in various culture media (Eq. 3). In this study, the model provided parameters to predict maximum production of CO2 by the microorganisms in the biodegradation process. . Bmax −Bo −rt B ¼ Bmax 1þ e ð3Þ Bo

Brazil), diesel oil (Diesel Extra Additives Petrobrás® S-50) and kerosene (Buffalo®—no preservatives). The biosurfactant obtained was added to the biodegradation assays in order to evaluate its effect in biodegradation process improvement in a 1 g L−1 concentration. The assays and controls setup followed the quantities described in Table 2. Following the parameters in Eq. 2, CO2 calculation is made in function to consumed HCl volume in KOH titration. GCO2 ¼ ðA−BÞ 50 θHCl 0:044

ð2Þ

where: CO2 =carbon dioxide generation in mg, A= HCl 0.1 N volume spent in blank titration (ml), B=HCl 0.1 N volume spent in sample titration (ml), θHCl= Specific constant as HCl 0.1 N factor. The 50 and 0.044 values stand as transformation factors to CO2 milligrams from millimoles. The dataset was presented in two ways: accumulated CO2 production and weekly CO2 production. Quantifying the CO2 accumulation along biodegradation days in different kinds of oils generates a curve. This defines the most biodegradable oil and shows which conditions were most efficient in the biodegradation process. This reveals how both inoculum and biosurfactant can accelerate degradation of the compound. Quantification of weekly CO2 provides a curve that allows evaluation of biodegradation rates in different respirometry assays and substances over time.

where: B=CO2 produced, Bmax =maximum CO2 production; Bo =initial CO2 production, r=maximum production rate specified for a particular oil, t=time. Regarding weekly respirometry curves, another model was adjusted to better describe changes in biodegradation rate over time. The model proposed by Membre et al. (1996) for growth and death of microbial communities (Eq. 4) was adapted to biodegradation parameters.

Mathematical modeling Carbon dioxide datasets from respirometry was applied to mathematical models which best describe the biodegradation process. Data fit to models allows a better understanding of biodegradation kinetics, thus allowing one to predict and optimize the process (Kernanshani et al. 2006). Mathematical and statistical tools are effective ways to describe parameters involving pollution removal during biodegradation (Pala et al. 2006) and Table 2 Respirometric assays setup

a Crude petroleum oil, diesel, or kerosene

B¼

1 K1

e−m1:t

þ

−1 1 em2:t K2

ð4Þ

where: B=CO2 produced, K1=production increase constant; K2=production decrease constant; m1=rate of

Assay ID

Analyzed substance volume

“Base aqueous media” volume

Biosurfactant volume (1.0 g L−1)

Media control

–

99.0 mL

–

Surfactant control

–

99.0 mL

1.0 mL

Oil assay

1.0 mLa

99.0 mL

–

Full assay

1.0 mLa

99.0 mL

1.0 mL


increase in CO2 production, m2=Rate of decrease in CO2 production, t=time. Both models were originally created to describe microbial growth. A correlation between the microbial population dynamics and CO2 production was successfully applied to the respirometry data set, thus allowing accurate biodegradation kinetics study.

Results and discussion Biosurfactant production Biomass growth B. subtilis growth was observed in various culture media. A higher biomass growth was observed in BHI + Mg (11.95 g L−1). NB and BHI production was 5.74 and 9.34 g L−1 respectively. B. subtilis biomass production in BH was about ten times lower when compared to BHI + Mg. Due to a higher biomass found in BHI + Mg after 48 h, it was the medium of choice for biosurfactant production. The B. subtilis growth curve in this medium was then monitored for 168 h (Fig. 2). The growth of B. subtilis in BHI + Mg reached its maximum after 100 h of fermentation. A higher biomass growth was aimed at this study. Reports indicate that higher yields of biosurfactant are related to greater biomass production (Vater 1986; Gong et al. 2009). On the contrary, a few studies state that in some situations biosurfactant production is inhibited in B. subtilis cultures (i.e., for adding hexadecane).

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Although even in these instances an increased biomass still occurred (Haferburg et al. 1986). Interestingly, oil added to BH broth as a sole carbon source resulted in the lowest B. subtilis biomass production (0.67 g L−1). This result is opposite to other biosurfactant producing genera such as Pseudomonas sp., which tend to have their surfactant production stimulated near oily residues and hydrocarbon-based substances (Uysal and Turkman 2011). Such oil inhibition can be exclusive to the Bacillus genus, as Bacillus licheniformis JF-2 was also reported to have inhibited growth in the presence of hydrocarbons (Javaheri et al. 1985). In a previous study by Cooper et al. (1981), low growth rates occurred in the presence of hydrocarbons for Bacillus sp. cultures. This could be a limiting factor to some discussions in literature that propose in situ oceanic applications of B. subtilis for treating areas contaminated with hydrocarbons (Barros et al. 2007). Yielded surfactant and surface tension Surface tension in media without biosurfactant was 56.0 mN m−1. There was a decrease in surface tension to 30.9 mN m −1 at 16 h, which is evidence of biosurfactant production (Queiroga et al. 2003). An iridiscent foam was a visual confirmation of biosurfactant presence. Yielded biosurfactant and medium surface tension is presented in Fig. 3. There was no significant difference (P=0.223, t test) between surface tension in culture media containing biomass and without biomass. The critical micelle concentration (CMC) was reached before maximum yield of biosurfactant (4.330 g L−1) within 48 h. At this point, surface tension reached a constant minimum independent of further increase in biosurfactant amount. Current literature refers to B. subtilis biosurfactant as surfactin. Such biosurfactant is considered one of the most effective surfactants to reduce surface tension between fluids (Das and Mukherjee 2007). Index emulsification E24

Fig. 2 B. subtilis biomass growth in BHI + Mg

Emulsification index (E24) results are presented in Fig. 4. All substances containing biosurfactant had greater emulsification rates when compared to control experiments without biosurfactant. Biosurfactant effect was relatively higher in crude petroleum and kerosene. The biosurfactant produced had a comparable performance to synthetic surfactant, and performed even better

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Fig. 3 B. subtilis biosurfactant production and surface tension in BHI + Mg

in kerosene containing assays. The highest emulsification was obtained with kerosene (46.90±3.12 %). Most of the surfactants are microbial specific, solubilizing, or emulsifying distinctly different hydrocarbons (Ilori et al. 2005). This explains the range of values found for each substance E24. Emulsification index varies depending on viscosity, droplet size, volumes, temperature, pH, emulsion time, and stirring in various substances (Singh et al. 2007). Differences in the emulsification of some substrates come from the inability of the surfactant produced by B. subtilis in stabilizing specific microscopic droplets in two liquid phases (Obayori et al. 2009). Still, biosurfactants have a positive effect in increasing emulsification in all tests, thus helping to increase bioavailability and promote biodegradation. The effect of surfactants on biodegradation will be further discussed in this paper.

In a recent study by Cai et al. (2014), they observed that both Bacillus and Rhodococcus strains isolated from oily contaminated coastal samples efficiently dropped surface tension, similarly to the B. subtilis in our paper. However, they noticed that an Acinetobacter isolates could significantly emulsify hydrocarbons at 62.5 % E24, whereas B. subtilis strains reached up to 40 % E24. Ibrahima et al. (2013) also found another efficient bioemulsificant from Serratia marcescens isolates, with an E24 value of 87 % with crude oil—much higher than the results found in our study with a B. subtilis strain. The biosurfactant production, however, was still higher within the Bacillus genus. A reasonable balance of lowering surface tension and increasing E24 are desirable when optimizing production and application of biosurfactants in the environment. Moreover, assessing emulsification of petroleum products is important due to daily losses at various stages of exploration, production, and recovery of oil due to this property (Manning and Thompson 1995; Martínez-Palou et al. 2011). This is important when choosing proper emulsifiers in an oil industry. Such processes are useful, for example, in pipelines during oil recovery (Lee 1999; Ashrafizadeh and Kamran 2010; Cerón-Camacho et al. 2013). Respirometry

Fig. 4 Emulsification index (E24) for tested substances

Biodegradation kinetics obtained from each respirometers containing petroleum derivative was evaluated through weekly and accumulated CO2 production models.


CO2 production rate Each substance presented a distinct biodegradation profile (Fig. 5). Each simulated wastewater assay demonstrated different biodegradation rate profiles; this

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explains petroleum products environmental behavior over time. Figure 5 shows data from weekly CO2 production adjusted to the Membre et al. (1996) model. Respirometry reached a maximum CO 2 output followed by a decline in production in every assay. It

Fig. 5 Weekly CO2 production profile according to the Membre et al. (1996) model adjusted to dataset

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was possible to predict the biodegradation kinetic of each type of oil within their respective rates of CO2 production per week. The maximum biodegradation rate (BRmax) corresponds to the curve peak in Fig. 5. The model can return the expected maximum CO2 production time expected in each substance (BRmaxT). In Fig. 5, the time axis has been extended to 400 days, which is greater than the data collection interval. Thus, we used this kinetic model to predict CO2 production values to up to 400 days of biodegradation in each set environment. As expected, control assays had very low weekly CO2 production (8.1 mg in average) compared to other assays. Control assays only had “base liquid media” as substrate, and there was no further carbon source from petroleum hydrocarbon provided in the medium. Similarly, low CO2 detection occurred at surfactant controls (8.9 mg in average). The addition of biosurfactant caused an average CO2 gain of 18.3 % compared with assays without biosurfactant. In crude oil assays, a much larger increase in average CO2 production (137.5 %) occurred. Crude oil and biosurfactant peaked their CO2 production at 22.54±1.3 mg, whereas in the absence of biosurfactant CO2 weekly production was as low as 12.99±1.9 mg at its peak. According to the model, it is possible to predict a continuous and lasting CO2 production in crude oil and surfactant assay. The biodegradation process is supposed to last up to 2.5 years according to projections established by the proposed kinetic model. Similarly, diesel oil presented a steady and lasting CO2 production profile after its peak production of 17.95±2.8 mg of CO2, with a reduced decreasing trend after 60 days. Diesel oil biodegradation was predicted to last for 527.22 days according to model parameters. Diesel control assays (without biosurfactant) presented a similar profile, but with lower CO2 values. Modeling parameters determined that kerosene is more likely to reach the highest maximum production of CO2 (27.75±2.3 mg). However, an increased biodegradation rate did not remain constant throughout the course of experiments. After 40 days, the weekly production of CO2 dropped and later ceased. This may be due to the formation of less biodegradable secondary metabolites (Zhengkai and Wrenn 2008) and even toxic compounds to the microbial community. A complete biodegradation of kerosene could have occurred in respirometers. This could explain the full depletion of carbon source in


earlier experiment days, explaining subsequent lower CO2 values thereafter. Low CO2 peak production time (BRmaxT) indicate a higher affinity and adaptation of microorganisms to certain substances; which explains a sooner peak in diesel maximum weekly CO2 production (25.56 ± 2.0 mg) compared to other assays (ranging from 35.89 to 82.41 mg). Biosurfactants increased CO2 production during the entire biodegradation process. This is related to organic matter access facilitation to microorganisms. In turn, making possible further degradation steps and molecular breaks as also reported in studies of Christofi and Ivshina (2002) and Calvo et al. (2008). A shift in CO2 peak production time (BRmaxT) was also observed. The addition of biosurfactant caused a delayed CO2 production peak in all assays. This result is contrary to the expected acceleration in the biodegradation process. However, it is possible that with increased organic matter availability, as reflected in higher maximum biodegradation rate (BRmax) values, the microbiota metabolized the substrate for a longer time, causing an overall extension in process. The CO2 production peak (BRmax) was also higher than surfactant absent assays. Even though some references state a decrease in time required for the biodegradation when surfactants are added (Kosaric 2001), biosurfactants may promote the appearance of intermediates previously unavailable by emulsifying and solubilizing oil phase. This could provide a more complete biodegradation process toward mineralization and increase the total time of biodegradation (Norman et al. 2002; Bordoloi and Konwar 2009). Accumulated CO2 production The accumulated sum of all CO2 produced in each respirometric throughout biodegradation process is presented in Fig. 6. Data was adjusted using the Schmidt et al. (1985) model. The most important parameters are the expected maximum biodegradation (Bmax) and time of total biodegradation (BmaxT). It was possible to predict the total amount of CO2 that would be produced from each substance during the entire biodegradation process. An overall slowdown in CO2 was found in all assays after 100 days of biodegradation (Fig. 6). Thus explaining the use of a non-linear logistic model instead of a linear model. A linear model would not fit accurately in most datasets.


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Fig. 6 Accumulated CO2 production profile according to Membre et al. (1996) model adjust to dataset

The maximum production of CO2 (Bmax) is the expected CO2 amount yielded from within each respirometric by the time all biodegrading microbial activity ceases. Bmax value is established when the curve is stabilized and no increase occurs in the B parameter (accumulated CO2 production) over time. Significant

increase was defined by values of B branched alkanes > aromatic low molecular weight>cycloalkanes>polyaromatic compounds>polar compounds. While relating to this scale, the results found in the present study also had biodegradation rates boosted by the biosurfactant produced by B. subtilis.

Discussions on petroleum products biodegradability Biosurfactants influence on biodegradation kinetics Biodegradation of different petroleum products are influenced by their wide range of variable components of biodegradability (Widdel and Rabus 2001). For example, it is possible to infer the biodegradability of various derivatives based on chain length n-alkanes, branched chains concentration, and/or polycyclic aromatic hydrocarbons presence (Feitkenhauer et al. 2003; Jovančićević et al. 2008). Oil fractions can be obtained by distillation and refining. In general, there is an average composition for all derivatives being formed by a variety of alkanes, unsaturated chains, aromatic rings, and polycyclic structures (Merck 1976). The different biodegradation profiles found ranged from 547.61 mg of CO2 produced in crude oil aided by biosurfactants, and a minimum of 150.56 mg of CO2 for kerosene without biosurfactant. Such variation is attributed to specific biodegradation susceptibility of many compounds. These differ in size of carbon chains, and each individual components present in substances to be biodegraded (Peters et al. 2005). Diesel oil and crude oil are denser compounds when compared to kerosene. Crude oil contains all types of hydrocarbons because it has not undergone any refining process. Diesel oil contains heavy chains ranging from C8 to C22. According to the degradation profiles seen in Figs. 5 and 6, the chain length had a great influence in long-term biodegradability of these compounds. Heavier substances ended up producing the highest accumulated CO2, whereas a quick burst of CO2 output was found in kerosene (a lighter compound). Biodegradation of kerosene aided by biosurfactant produced the highest of CO2 production rate in our respirometers. For instance, the C6 to C16 chains found in kerosene have high concentrations of alkene compared to other petroleum products. Small molecules are more readily biodegraded hydrocarbons, and aromatic compounds are degraded at a rate slower than alkanes in aqueous environments (Sasaki et al. 1998; Prince and Walters 2007). On this matter, Peters et al. (2005)

It was observed in this study that biosurfactant improved hydrocarbon biodegradation, thus enabling better performance of microbial communities in a simulated wastewater contamination. Biosurfactants caused an overall gain of 24.4 % in accumulate CO2 production. This study supports the argument that a good emulsifying biosurfactant should increase bioavailability and increase microbial metabolism, as found in other studies by Amund and Adebiyi (1991), West and Harwell (1992), Volkering et al. (1998), and Millioli and Sobral (2007). In our study, we demonstrated that B. subtilis could effectively improve biodegradation of a wide variety of petroleum hydrocarbons. Some authors report the occurrence of biosurfactants inhibitory effects in microbial activity (Das et al. 2008; Monteiro et al. 2011), which was not observed in this study. As previously discussed, hydrocarbons properties are a decisive factor in biodegradation kinetics and play an important role in bioremediation strategies. All respirometric tests showed a better yield in CO2 production after biosurfactant addition. Thus, biosurfactants, such as surfactin produced by B. subtilis, provided an environment-friendly and efficient way to facilitate the metabolic activity of microorganisms and enhance biodegradation processes.

Conclusions Improved petroleum biodegradation occurred by a conjoint action of microbial consortium and biosurfactant augmentation. It was possible to mathematically model and predict the biodegradation behavior of the studied substances. The methods discussed in this paper investigated the biodegradability of petroleum products alongside biosurfactants, thus increasing current knowledge of oil-contaminated environments, and promoting

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better handling and predictability of petroleum bioremediation scenarios. Different biodegradation profiles were related to specific characteristics of each substance, such as the size of the carbon chains or the presence of aromatic rings. The biosurfactant production by B. subtilis successfully decreased surface tension in culture media. In general, application of biosurfactant caused an increased biodegradability. Biochemistry associated with oil degrading microorganisms is not uniform to establish relationship patterns between substances and micro-organisms. A wide variety of reactions involved in oil biodegradation explain these differences, and these reactions can vary depending on environmental conditions and nutrients. Thus, the proposed knowledge and profiling of biodegradation in different substances allows an in depth comprehension on the environmental behavior of such substances. Acknowledgments We gratefully acknowledge the thoughtful review and important insights provided by Sarah Mae Wachlin and Brent Perumal, providing comments on the manuscript in its entirety and working with the authors as we made revisions to the text. We alone, of course, take responsibility for the final text and any errors that appear therein. Our research group acknowledges CAPES (Coordenação de Aperfeiçoamento de Pessoal de Nível Superior), CNPq (Conselho Nacional de Desenvolvimento Científico e Tecnológico), FUNDUNESP (Fundação para o Desenvolvimento da UNESP), PRH-ANP/MCT (Programa de Formação de Recursos Humanos em Geologia do Petróleo e Ciências Ambientais Aplicadas ao Setor de Petróleo e Gás) and UNESP (Universidade Estadual Paulista “Julio de Mesquita Filho”) for the financial support.

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