- Email: [email protected]
Application of response surface methodology to oil spill remediation
Fuel 103 (2013) 876–883
Contents lists available at SciVerse ScienceDirect
Fuel journal homepage: www.elsevier.com/locate/fuel
Application of response surface methodology to oil spill remediation Claudio Bravo-Linares a,⇑, Luis Ovando-Fuentealba a, Stephen M. Mudge b, Rodrigo Loyola-Sepulveda c a
Instituto de Ciencias Químicas, Facultad de Ciencias, Universidad Austral de Chile, Valdivia, Chile Exponent UK, Exponent, The Lenz, Hornbeam Business Park Harrogate, Harrogate HG2 8RE, UK c Laboratorio de Oceanografía Química, Departamento de Oceanografía, Facultad de Ciencias Naturales y Oceanográficas, Universidad de Concepción, Concepción, Chile b
h i g h l i g h t s " Response surface methodology significantly reduced experimental effort. " In oil spill remediation, different factors were important at different locations. " Applying biosolvent before the oil reached shore improved cleaning. " Inorganic nutrients alone were more effective than organic or mixtures. " Overall, the amount of biosolvent was the least important of the three factors.
a r t i c l e
i n f o
Article history: Received 27 April 2012 Received in revised form 24 August 2012 Accepted 15 September 2012 Available online 5 October 2012 Keywords: Oil spills Biosolvent FAMEs Hydrocarbons Response surface methodology
a b s t r a c t Spilled oil in the coastal zone may be remediated through biodegradation by naturally occurring bacteria. It is possible to enhance the removal rates through addition of nutrients and biosolvents. These rates may differ within the intertidal area due to many environmental factors including surf washing. Laboratory experimentation is complex when there are so many factors involved. In a simple three factor remediation experiment, the effect of the timing of addition of a biosolvent, the type of nutrients added and the quantity of biosolvent relative to the amount of oil spilled were examined at three levels. A response surface methodology (RSM) was used to identify the key experiments to conduct and 17 separate trials were carried out with high, mid and low tide microcosms. The petroleum hydrocarbons were quantified by GC–MS methods and the data were examined with Design of Experiments (MODDE) and Partial Least Squares (PLS) Statistical (SIMCA-P) software. Different factors were important at the different intertidal locations: at low tide, the timing of application was most important while in the mid tide location, the proportion of biosolvent was most important. Of the nutrient additions, inorganic nutrients alone were more effective than organic forms of nitrogen or mixtures of urea with inorganic nutrients. Overall, the amount of biosolvent was the least important of the three factors examined. The use of RSM significantly reduced the experimental effort needed to investigate the factors and their interactions. Ó 2012 Elsevier Ltd. All rights reserved.
1. Introduction In the past 40 years, there have been a number of large spills of crude oil from shipping incidents which have contributed to the total load of oil derived hydrocarbons in the marine environment. The International Tanker Owners Pollution Federation (ITOPF) [1] keep records of the major oil spills around the world. The amount of oil spilled since 1970 has significantly decreased despite of an overall increase in oil trading. However, several small scale oil spills still occur and may affect the coastal environment. The majority of oil spills occur in the offshore or near shore environment rather than directly on beaches. However, due to ⇑ Corresponding author. Tel.: +56 63 221529. E-mail addresses: [email protected] (C. Bravo-Linares), [email protected] (S.M. Mudge), [email protected] (R. Loyola-Sepulveda). 0016-2361/$ - see front matter Ó 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.fuel.2012.09.034
the movement of water and prevailing winds, the oil may be transported to the intertidal zones. These zones tend to be sensitive when it comes to exposure to oil [2]. The action of waves, light and other physical processes lead to the weathering of oil that manifests itself through the loss of the short chain alkanes and other light components. As these processes progress, the residual oil becomes more weathered and it is usually thick, sticky, highly absorbed on rocks and organisms, often emulsified and frequently difficult to remove by physical methods [3]. The prime amelioration mechanism of oil on a beach is the complete removal of oil mixed with sand for disposal elsewhere. This process is harder if the oil impacts coarse grain sediments and rocky or other hard surfaces. One method to clean contaminated sediments is the use of chemical cleaners; this may include organic solvents with or without surfactants, as these agents emulsify the oil dispersing it into
C. Bravo-Linares et al. / Fuel 103 (2013) 876–883
seawater [4]. These cleaners may lead to dispersion of the oil to previously unaffected areas where the oil did not initially reach, thereby increasing the total area affected and potentially allowing the oil to be transported deeper into the sediments. It is also known that these agents can be toxic and in past cases make the impact of an oil spill even worse [5,6]. Other treatments that may be used include pressurised hot water or bioremediation by local microbial activity enhanced by nutrient addition [7]. The oil distribution and the effect of removal will depend on the place within the intertidal. The cleaning of sediments in the low and mid intertidal zones may be enhanced by surf washing. However, areas in the high intertidal are only reached by waves under specific conditions such as storms or high spring tides, so physical removal by waves is not the predominant way of removing residual oil. Biosolvents based on Fatty Acid Methyl Esters (FAMEs) have been shown to dissolve residual crude oil after spills and its components form a stable mixture that is non-volatile and less dense than water; the capacity to dissolve the residual oil appears to be dependent on the type of biosolvent used, the ratio of biosolvent to oil employed and the type of substrate cleaned [3,8,9]. Laboratory experiments and field cleaning have given important information about the potential use of FAMEs to clean contaminated areas after an oil spill [3,10]. These results showed that the biosolvent effectiveness could be improved in the natural environment particularly on exposed beaches with strong wave action. Several application methods, amounts, different sources of biosolvent in micro and mesocosms have been tested [3]. However, there is little information about the interaction when several parameters are studied simultaneously. Results have demonstrated the effectiveness and potential use of this approach in the removal and the enhancement on the in situ biodegradation of crude oils. It has been demonstrated that FAMEs have a synergistic effect on the degradation of hydrocarbons by bacteria, demonstrating that FAMEs enhance the biodegradability of both diesel fuel and gasoline by means of co-metabolism [11,12]. Compared with conventional methods, design of experiments (DoE) or experimental design can offer essential information about the optimisation process indicating the individual, synergistic or antagonist effects of each factor [13]. There are some reviews that explain in detail the use of experimental design application [14,15] In this study, a factorial design with three independent variables each with three levels using a model central composite facecentered (CCF) was employed to evaluate the influence of the addition of nutrients (organic and/or inorganic), proportion of crude oil to biosolvent and the time of application of the biosolvent (a day before, the same day and a day after the oil spill) on crude oil degradation on sandy beach microcosms. It is hypothesised that, by applying biosolvents a day before the oil spill reaches the sandy beach, the amount of oil degraded can be increased, especially with regard to the heavier fractions [16] as the bacteria are ‘‘activated’’ with a benign but similar compound to the oil. It is also hypothesised that the degrading bacteria will require the addition of nutrients to maintain or enhance the degradation of the oil. However, since the bacteria will be degrading a non-polar organic phase (oil), the addition of an organic solvent soluble form of nitrogen might be a more effective way of adding this nutrient.
2. Materials and methods 2.1. Reagents and materials The crude oil employed was provided by the Chilean national petroleum company, ENAP. The type of crude oil used was a light sweet variety, Caño Limon, from Columbia. To simulate
877
environmental weathering, the crude oil was exposed for 10 days to the atmosphere and then sonicated at 30 °C for 6 h before use. After this process, the oil lost about a 2.8% of its original weight. A gas chromatogram of the crude oil can be seen in Muñoz et al. [10]. The biosolvent applied was synthesized in the laboratory through the esterification of the fatty acids from commercially bought sunflower oil with methanol in presence of sodium hydroxide at 50 °C [17]. The sand employed for the experiments was collected from a remote local beach (Curiñanco Beach, S 39° 400 48.0700 ; W 73° 210 56.9000 ). This beach is less contaminated with petroleum hydrocarbons than beaches closer to industrial or urban areas. The pore water in the collected sand had a pH in the range of 7–8. The grain size distribution was not determined, however, the samples of sand were visually categorised as fine sands (<250 lm). The sand was collected in aluminium buckets from three different locations on the intertidal zone (low, mid and high). The sand was taken with a mild steel shovel from the first 5 cm depth during low tide conditions (0.79 m). Before using the sand, it was cleaned by hand to remove major particles and macro fauna. The water content for each sample was determined by drying it at 40 °C to a constant weight; the water percentage was 0.2%, 2.2% and 15.1% for high, mid and low intertidal areas respectively. 2.2. Experiments For each intertidal area, a matrix of 17 experiments was performed (14 different experiments and the central value in triplicate for statistical validation purposes). Sand from each intertidal area (200 g) was placed in 250 mL Erlenmeyer flasks. Two mL of the partially weathered crude oil was added to each flask. For high tide samples, water was not added (to mimic the lack of tide effect) and was kept dry throughout the experiment. In samples from the mid and low tide, 100 mL of water were added every 2 days and left for 2 and 4 h respectively before decanting. The water was disposed and no analysis was performed on it. The samples were stirred every day with a glass rod to homogenise the systems. For the first 5 days, a solution made from sea salt (10% w/v) was added and then only distilled water to avoid salt saturation in the systems. The experiments were performed over 10 days. No screening was performed, as previous results had shown that during this period, the hydrocarbons degradation was around 78% but then slowed significantly up to 90 days of experimentation [16]. The same protocol was performed for control experiments with no added biosolvent. 2.3. Controls Control experiments were divided into three categories: (1). General weathering: with sand and crude oil to test the biological, physical and chemical weathering in the absence of biosolvent. (2). Physical and chemical weathering: using sterilised sand to exclude biological degradation. (3). Chemical weathering: using sterile sand and the system was sealed to minimise the evaporation. Sterile sand was obtained by autoclaving the sands for 15 min at 120 °C under atmospheric conditions. 2.4. Experimental design The experiments were repeated for each intertidal area. The experimental design was performed using the Software Modde 9.0 (Umetrics). CCF designs use points on each factor axis (star points) in addition to points at the corners of the design space (cube points) and one or more centre points, as shown in Fig. 1. CCF designs can be used for RSM experiments in which the model type is quadratic. Standard CCF designs use the Fractional Factorial or Full Factorial design for a subset of factors in the experiment. For
878
C. Bravo-Linares et al. / Fuel 103 (2013) 876–883
remaining factors outside of the subset, CCF designs use additional points that estimate quadratic effects. These designs allow highquality prediction over the entire factor space [18]. The experimental matrix design is shown in Table 1. The matrix was designed to include the following factors and levels: 1. The time of application of biosolvent (one day after, the same day and a day before contamination with oil). 2. The nutrient application were organic in the form of urea and/or inorganic containing N(15%), P(16%), K(6%), Ca(18.2%), S(7.8%) and minor constituents such as Mg, Zn, Mn, Fe, Cu and B; both nutrient sources were added at the beginning of the experiment as volume (1 mL g1) according to the amount of sand employed. The application of nutrients in the experiments varied from 100% organic (urea alone), 100% inorganic (multiple constituents) and an equal mixture of both organic and inorganic. 3. The proportion of crude oil to biosolvent (2:1, 1:2 and 1:2). 2.5. Sample extraction and preparation For oil analysis, the sand was homogenised with a nickel spatula and then 1 g of sample was taken at 0, 1, 2, 4, 6, 8 and 10 days. The samples were placed in glass flasks and then 2 g of anhydrous sodium sulphate were added to absorb the residual water. A mixture (20 mL) of 1:1 of hexane: dichloromethane were added to extract the FAMEs and petroleum hydrocarbons assisted by an ultrasonic bath for 30 min at room temperature. For hydrocarbons analysis, 450 lL of the extracted liquor was diluted with 1.25 mL of hexane: dichloromethane (1:1) and 100 lL of the internal standard (1-chlorooctadecane, 10 lg mL1 in CH2Cl2, SUPELCO). Crude oil contains a suite of compounds that have been used to establish the source of oils in many situations. For a review of these compounds, please see Wang and Stout [19]. In this case, the sesquiterpane, sterane and terpane biomarkers were analysed from a portion of the extract. This was filtered and dried with sodium sulphate anhydrous and concentrated on a rotoevaporator and dried under a stream of extra pure nitrogen. The extract was immediately resuspended with n-pentane up to 1 mL to precipitate the asphaltenes than can interfere with the chromatographic analysis. 2.6. Sample analysis The analysis of the samples for total petroleum hydrocarbons (TPHs) and FAMEs was performed simultaneously using a gas
Table 1 Experimental design matrix used to optimise crude oil degradation. Experiment number
Time of application (X1)a
Type of nutrient (X2)b
Proportion of crude oil to biosolvent (X3)c
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0
1 1 1 1 1 1 1 1 0 0 1 1 0 0 0 0 0
1 1 1 1 1 1 1 1 0 0 0 0 1 1 0 0 0
a (1) 1 day after the contamination with lightly weathered crude oil. (0) The same day of the contamination. (1) 1 day before the contamination. b (1) 100% Organic nutrient (urea). (0) 50% Organic nutrient: 50% Inorganic nutrient. (1) 100% Inorganic nutrient. c (1) 1:2 Crude oil: Biosolvent (0) 1:1 Crude oil: Biosolvent (-1) 2:1 Crude oil: Biosolvent.
chromatograph (FOCUS GC, Thermo Scientific) coupled to a mass detector (DSQ II, Thermo Scientific). One lL of sample was injected with the following conditions: The temperature program used was 70 °C held for 4 min, then a ramp up to 300 °C at a rate of 6 °C min1, maintained for 18 min at 300 °C. The transfer line was kept up to 250 °C, the carrier gas flow was 1.5 mL min1 and the injector temperature was 250 °C. The injection was done with a split ratio of 10. The ionisation energy was 70 eV. The column used was a RtxÒ-5MS (RESTEK, USA) 30 m long, 0.25 mm ID, 0.25 lm of film thickness. The samples were run in a scan mode from 40 to 450 m/z for TPHs. Biomarker analyses were performed under the following conditions: injector temperature 250 °C, transfer line at 250 °C. The temperature program was 70 °C held for 4 min, then heated up to 300 °C with a ramp of 6 °C min1 and kept at this temperature for 3 min. The analysis was performed with single ion monitoring mode (SIM) with the fragments 191, 217, 218 m/z. The identification and quantification of hydrocarbons was performed using a petroleum hydrocarbon standard from n-C10 to n-C40, even and odd chain lengths, plus pristane and phytane (S-4110-100-CY) and the internal standard 1-chlorooctadecane. The identification of the biomarkers was performed using previous chromatograms obtained in the literature [19]. No quantification was performed, and the further results were used as normalised areas to provide a chemical signature of the oil. 2.7. Statistical analyses
Fig. 1. Diagram showing the levels studied according to surface model used where the numbers represents the experiments performed according to Table 1. X1: time of application, X2: nutrient type and X3: proportion of oil to biosolvent.
The biomarker and alkane data were converted to their proportions to uniform the data. This was performed by summing up the total TPH concentrations for each sample and then each compound was divided by this amount. This methodology is used to remove any concentration related effects, as some samples can be more concentrated than other, leading to misunderstandings when the model is applied [20]. Values were subjected to partial least squares (PLS) analysis [21] with the Simca-P software from Umetrics. This approach allows a chemical signature to be defined (here the Caño Limon oil was either defined through its sesquiterpane, sterane and terpane biomarkers or through it n-alkanes profile) and the amount of variability that can be explained by that
879
C. Bravo-Linares et al. / Fuel 103 (2013) 876–883
Degradation of TPHs (%)
100
A
B
C
80
60
40
20
0 0
2
4
6
8
10
0
2
Days
4
6
8
10
0
2
4
6
8
10
Days
Days
Fig. 2. Degradation of TPHs (%) for the control experiments. A: high tide, B: mid tide and C: low tide. s: sterile sand; .: sterile and sealed system; d: untreated sand. Error bar are two standard deviations.
Table 3 Statistical validation parameters of the models applied.
Table 2 Degradation of TPHs (%) for all intertidal zones. Experiment
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
% Degradation of THPs after 10 days High tide
Mid tide
Low tide
19.1 20.6 24.4 54.5 26.6 49.3 41.6 9.9 7.8 45.3 2.1 1.2 1.1 3.3 4.0 5.8 4.0
77.9 92.9 100.0 95.3 99.2 100.0 97.8 97.9 100.0 100.0 95.1 74.1 86.7 93.8 70.4 95.8 75.4
96.2 100.0 100.0 100.0 100.0 100.0 50.0 91.9 100.0 100.0 100.0 94.1 100.0 100.0 100.0 99.6 100.0
signature in the experimental data used to indicate the degree of change. Seventeen samples of Caño Limon oil were used to generate the signature (X-block) for each analysis and this signature was applied to the 17 experimental runs (Y-block) after 30 days for high intertidal area and 10 days for mid and low intertidal area (as the amount of oil and the biomarkers signatures were low). 3. Results and discussion 3.1. Controls Fig. 2 shows the degradation of TPHs (%) for the control experiments with no added biosolvent. The control for high tide area (Fig. 2A) shows that the loss of TPHs after 10 days with sterile sand and in a sealed system was 28% (this loss is principally due to evaporation of light components), which was less than the control with untreated and sterile sand (45% and 33% respectively). This demonstrates that processes of biodegradation (14%) and evaporation (31%) are the main ways of removal of TPHs in this area of the intertidal zone where tidal effect is none or minimal. Fig. 2B and C show that the position within the intertidal area plays an important role in the removal rates of crude oil in contaminated sands by surf washing. The contact of the sediments with water can induce the removal of recently spilled crude oil up to values near 100% in low intertidal area. However, this only happens if the area is in near permanent contact with water. The
R2 Predictability (Q2) Model validity Reproducibility
High tide
Mid tide
Low tide
0.89 0.58 0.61 0.95
0.64 0.45 0.98 0.84
0.86 0.26 0.67 0.98
R2 shows the model fit; 0.5 is a model with rather low significance. Q2 shows an estimate of the future prediction precision. Q2 should be greater than 0.1 for a significant model and greater than 0.5 for a good model. Model validity is a test of diverse model problems. A value less than 0.25 indicates statistically significant model problems, such as the presence of outliers, an incorrect model, or a transformation problem. Reproducibility is the variation of the replicates compared to overall variability. A value greater than 0.5 is warranted.
removal rates for the mid tide areas showed that the loss of TPHs from sands with only the tidal washing effect was around 36% and the biodegrading process was estimated to be a 10% greater than the high intertidal area. However, these experiments do not consider the oil penetration and the fact that the surf washing effect can bring back with the next tide the crude oil washed off if it is not removed or collected by physical methods offshore. The differences within the three areas of the intertidal zone in terms of oil degradation with no added biosolvent for the controls showed that, in the mid and low tide areas, the surf washing effect was an important mechanism of crude oil removal when the oil spill is recent. However, in the high tide area where the waves do not reach very frequently, especially when the wave energy has dropped and this area is isolated from the contact of water, the principal removal mechanism is evaporation and to a lesser extent, biodegradation.
3.2. Treatments The degradation percentages of TPHs after 10 days are shown in Table 2. These experiments also showed that physical removal by surf washing is an important way of removing spilled oil from surface. The results were processed using the software MODDE 9.0 after a logarithmic transformation to normalise the data and obtain a normal distribution (the models applied are subjected to normally distributed data and they apply to a linear behaviour). After 10 days, the TPH removal for low tide area was almost 100% in most of the experiments and, in order to see differences within the studied areas, it was decided to use the results up to 6 days in order to create the response surfaces for each area.
880
C. Bravo-Linares et al. / Fuel 103 (2013) 876–883
Fig. 3. Contour plot for degradation percentage (%) of TPHs for high, mid and low intertidal sediments after 6 days.
The statistical validation parameters of the response surface models applied to each studied area are shown in Table 3. In general, the parameters for the fitness of the model were all within the
category of ‘‘good’’. The validation was calculated by the software using the central value performed in triplicate. Correct model tuning like removing non-significant model parameters or selecting
881
C. Bravo-Linares et al. / Fuel 103 (2013) 876–883
the appropriate transformation results in higher summary statistics. The best and most sensitive indicator is Q2. In order to test the degree of homogeneity within a single sample, three sub-samples from the same experiment were taken. The results were 15.9, 2.1 and 0.6%RSD for high, mid and low tide respectively. The highest value was for high tide, due to the difficulty in obtaining homogeneity of the system without water. Nevertheless, this value can still be considered acceptable, as an in real case the sample homogeneity can be very variable within few metres [10]. 3.3. Response surface methodology and model equations Fig. 3 shows the contour plots of the percentage removal of TPHs for the three intertidal areas. From the figure is possible to see that for the high tide area, the application time of biosolvent after the contamination event led to better results than the application on the same day or a day before. With posterior application, a lesser proportion of biosolvent to oil (1:2) and the use of organic nutrients led to better results in the degradation of TPHs. Also, for the application times before and the same day, the results were practically the same under the different conditions. On the other hand, results from mid tide area for all three application times, were similar but distinctly different from the high tide results. The degree of degradation of TPHs is reduced when the ratio of biosolvent to crude oil is low, inorganic nutrients are applied and the time of biosolvent application is coincident with the oil. The best results were obtained with application of the biosolvent prior to contamination by oil, potentially supporting the idea that the bacteria living in the natural system were better adapted due to the input of this supplemental carbon and nutrient source at the beginning of the experiment and were ‘‘ready’’ to degrade the crude oil. For low tide, the most important factor was the application time of biosolvent, followed by the nutrient application. The other factors, apart from the synergistic effect of application time with nutrient application, had an antagonistic effect. Overall, the synergistic effect of application time of biosolvent with the proportion of biosolvent to crude oil prove that biosolvents are one of the most important factors in the degradation of crude oil, especially for the oil that has not been removed from contaminated sands through the surf washing effect. On the other hand, nutrients tended to have an antagonistic effect with all the factors studied. Suggesting that nutrients supply may be overtaken and not needed when biosolvents are used due to the physicochemical properties of biosolvents can convert them in good solvents of crude oil and also assist the bacteria cometabolism to biodegrade the remaining oil [9,16]. The results obtained showed that the cleaning process cannot be considered the same for all the intertidal areas, as some might not need an intensive cleaning process as low tide areas. However, the cleaning process can be relevant when the surf washing effect is not the main mechanism of removal of the spilled oil. Most of the researches done in this matter point to one type of solution for the whole area, as they do not differentiate the intertidal areas. Additionally, the obtained results show that each area should be treated independently, saving time and chemicals to be used to perform the cleaning process such as biosolvents based on FAMEs. Table 4 shows the factors and their respective coefficients for each intertidal area obtained by RSM modelling. It is possible to see that main factors for all the conditions studied are the application time on its own (X1) and its quadratic effect (X1 X1). The other factors (single and as interactions), except for some in low tide, were not statistically significant due to the p-values were higher than 0.05. For instance, nutrients inputs (organic or inorganic) did not improve the degradation of TPHs implying that they might not
Table 4 Coefficients values for the model employed in the different intertidal areas. Factorsa
Coefficients
Standard error
p valueb
Low tide Constant X1 X2 X3 X1 X1 X1 X3 X2 X3
99.28 7.16 3.32 4.72 7.25 2.11 5.83
2.29 1.51 1.51 1.51 1.86 1.34 1.34
1.0E12 8.0E04 5.3E02 1.1E02 3.0E03 1.5E01 1.4E03
5.10 3.37 3.37 3.37 4.15 2.98 2.98
Mid tide Constant X1 X2 X3 X1 X1 X2 X2 X1 X2 X2 X3
70.84 2.80 0.86 5.47 9.53 2.60 3.00 3.18
4.44 2.69 2.69 2.69 3.68 3.68 2.38 2.38
2.4E07 3.3E01 7.6E01 7.7E02 2.9E02 4.9E01 2.4E01 2.2E01
10.23 6.21 6.21 6.21 8.32 8.32 5.49 5.49
High tide Constant X1 X2 X3 X1 X1 X1 X2 X1 X3 X2 X3
0.04 10.16 1.12 0.13 11.19 1.01 2.25 4.88
2.19 1.45 1.45 1.45 1.78 1.28 1.28 1.28
9.9E01 6.3E05 4.6E01 9.3E01 1.4E03 4.5E01 1.1E01 4.2E03
4.96 3.28 3.28 3.28 4.04 2.90 2.90 2.90
Confidence interval (±)
X1 = Time of application, X2 = Type of nutrient, X3 = Proportion of biosolvent. a Some factors were excluded from the models due to they were not significant and including them the values of Q2 were lower than the values accepted for a good model. b Values in italics are significant according to the p-values < 0.05.
be needed over this short experimentation time, and simple addition of biosolvents can help to improve the oil removal. The lack of importance of the nutrients might also indicate to a chemical/physical removal mechanism rather than a biologically mediated one. For mid tide area the coefficient values showed that the nutrient application and the proportion of biosolvent to oil were the most important parameters and the application time did not show an important effect in the removal of crude oil. The only synergistic effect that was important was the interaction between the application time and the proportion of biosolvent to crude oil. It may be that biological processes are more important at this location. 3.4. Statistical results Analysis of the sterane and terpane biomarkers indicated the mean change in signature compared to the initial oil was 6.2 ± 4.3% in the high tide samples; the greatest difference was 20.4% for experiment number 2 after 30 days. This is not a great change over the 30 days of the experiments and justifies the use of these compounds in signature analysis when determining the source of an oil spill. However, this is a real change and over longer periods of time or in more aggressive biodegradation environments, the rate of change will lead to a chemical signature that is sufficiently different from the original to lead to doubt in source apportionment. Care must be used when considering these signatures and the stability of the signature may influence the outcome of an interpretation. Analysis of the biomarkers in the low and mid tide areas did not show more than 3% change from the signature in the PLS analysis. A further approach to determine the change rates in relation to the treatments was undertaken using the PLS method. The alkane data for the Caño Limon crude oil as applied to the sands was used to develop a signature. The extent to which this signature can explain the variance in all the subsequent experimental samples
882
C. Bravo-Linares et al. / Fuel 103 (2013) 876–883 Table 5 The amount of variance (R2) in each sample explained by the alkane signature of the Caño Limon oil applied. This was achieved through use of a PLS model. A value of 1.0 means the signature and experimental sample are identical while a value of 0.0 means they are completely dissimilar (or absent). Intermediate values provide an indication of the amount of alkane attenuation that has occurred. The data are sorted by the signal in the mid tide after 6 days as these exhibited a wide range of possible fits.
was used as a measure of the loss rate of the oil. The results of this analysis can be seen in Table 5. The data are ordered according to the loss after 6 days in the mid tide samples as this column has a wide range values providing a good scale for display. The table shows that the greatest ‘‘loss’’ of alkanes relative to the initial signature can be seen in the low tide samples followed by the mid and finally the high tide samples. The data are colour-coded from green (values close to 1.0) through yellow to red (values close to 0.0). The high tide samples, in general, did not lose more than 50% of their alkane signature over the 10 days of the experiment. This is most likely due to the lack of water washing at these higher intertidal elevations. By comparison, the mid and low tide levels showed that most samples lost more than 50% of their signature. The role of the water in washing both the oil and the crude oil dissolved in the biosolvent out of the sands was readily apparent. The most effective treatments to remove the crude oil will be those towards the bottom of the table. In this case, the 1 treatments tend towards the bottom with the 1 values in the middle and the 0 treatments near the top where the loss rates were slowest or least effective. These data suggest that the best approach would be to apply the biosolvent BEFORE the crude oil (the 1 application) as these tend to the bottom of the table. Similarly, the more effective nutrient treatment is also 1 (inorganic only) although the 1 (organic only, in this case, urea) was more effective than the 0 treatment which was a mixture of the organic and inorganic forms. With regard to the biosolvent ratio, the data were much less clustered and no obvious trend is apparent in the results. This is interesting in itself as it implies the other factors have a greater effect on the loss rates than the ratio of the biosolvent to crude oil added. These data confirm the results shown from the Response Surface in Fig. 3. The greatest removal of the TPH occurred (in this figure) in the mid tide location with application of the biosolvent 1 day before the addition of the crude oil.
4. Conclusions When faced with multiple variables which may all interact and affect the degradation rate of spilled oil, separating out the
processes that have the greatest impact on the oil can be difficult to see without a large number of experiments. However, the Design of Experiments approach with a response surface can predict the most critical experiments to conduct to reduce the overall experimental load. Here, in the treatment of spilled lightly weathered crude oil, three factors were examined. Instead of conducting 27 experiments (3 3 3), the DoE identified just 17 to conduct and the data from these experiments clearly showed that the time of treatment relative to the spill and the form of the nutrients were more important than the amount of biosolvent added. The results show the loss rates are different within the three zones of the intertidal area (high, mid or low). Result for the low intertidal area showed that spilled crude oil can be easily removed by tidal action and may not need any additional cleaning processes. In the mid intertidal area, enhanced cleaning could be obtained by addition of the biosolvent one day prior to the arrival of the weathered crude oil. It is hypothesised that the biosolvent decreases the lag phases for the bacteria naturally present in the sand such that they are more readily able to degrade the oil through co-metabolism. Response surface methodology was useful to determine of different a treatment applied to the different intertidal areas can affect the removal of crude oil spills. Acknowledgments The authors would like to acknowledge the financial support of the FONDECYT project 11090052 and ENAP for the crude oil sample. References [1] ITOPF, International Tanker Owners Pollution Federation.. [2] Lecklin T, Ryoma R, Kuikka S. A Bayesian network for analyzing biological acute and long-term impacts of an oil spill in the Gulf of Finland. Mar Pollut Bull 2011;62:2822–35. [3] Pereira MG, Mudge SM. Cleaning oiled shores: laboratory experiments testing the potential use of vegetable oil biodiesels. Chemosphere 2004;54:297–304. [4] Fuller C, Bonner J, Dellamea S, Ussery P, Tissot P, Louchouarn P. Ecological evaluation of shoreline cleaners used on mesocosms beaches. In: Proceedings
C. Bravo-Linares et al. / Fuel 103 (2013) 876–883
[5]
[6]
[7]
[8]
[9]
[10] [11] [12]
of the twenty-third arctic and marine oil spill programme. Technical Seminar, Environment Canada, Vancouver; 2000. p. 795–803. Wu RSS. Differences in the toxicities of an oil dispersant and a surface-active agent to some marine animals, and their implication in the choice of species in toxicity testing. Mar Environ Res 1981;5:157–63. Greenwood PJ. The influence of an oil dispersant chemserve ose-dh on the viability of sea-urchin gametes – combined effects on temperature, concentration and exposure time on fertilization. Aquat Toxicol 1983;4:15–29. Gallego JR, Fernandez JR, Diez-Sanz F, Ordonez S, Sastre H, Gonzalez-Rojas E, et al. Bioremediation for shoreline cleanup: in situ vs. on-site treatments. Environ Eng Sci 2007;24:493–504. Fernandez-Alvarez P, Vila J, Garrido JM, Grifoll M, Feijoo G, Lema JM. Evaluation of biodiesel as bioremediation agent for the treatment of the shore affected by the heavy oil spill of the Prestige. J Hazard Mater 2007;147:914–22. Miller NJ, Mudge SM. The effect of biodiesel on the rate of removal and weathering characteristics of crude oil within artificial sand columns. Spill Sci Technol Bull 1997;4:17–33. Munoz J, Mudge SM, Loyola-Sepulveda R, Munoz G, Bravo-Linares C. Source apportionment in oil spill remediation. J Environ Monit 2012. Pasqualino JC, Montane D, Salvado J. Synergic effects of biodiesel in the biodegradability of fossil-derived fuels. Biomass Bioenergy 2006;30:874–9. Mudge SM, Pereira G. Stimulating the biodegradation of crude oil with biodiesel preliminary results. Spill Sci Technol Bull 1999;5:353–5.
883
[13] Mansilla HD, Bravo C, Ferreyra R, Litter MI, Jardim WF, Lizama C, et al. Photocatalytic EDTA degradation on suspended and immobilized TiO2. J Photochem Photobiol A-Chem 2006;181:188–94. [14] Leardi R. Experimental design in chemistry: a tutorial. Anal Chim Acta 2009;652:161–72. [15] Dejaegher B, Vander Heyden Y. Experimental designs and their recent advances in set-up, data interpretation, and analytical applications. J Pharm Biomed Anal 2011;56:141–58. [16] Bravo-Linares CM, Ovando-Fuentealba L, Loyola-Sepulveda RH, Mudge SM. Progress of total petroleum hydrocarbons (TPHs) treated with biosolvent in a simulated oil spill on sandy beach microcosms. J Chil Chem Soc 2011;56:941–4. [17] Zhang Y, Dube MA, McLean DD, Kates M. Biodiesel production from waste cooking oil: 1. Process design and technological assessment. Bioresour Technol 2003;89:1–16. [18] Neto B, Scarminio I, Bruns R. Statistical desing-chemometics. Netherlands: Elsevier; 2006. [19] Wang Z, Stout S. Oil spill environmental forensics fingerprinting and source identification. Academic Press; 2007. [20] Mudge SM. Multivariate statistical methods in environmental forensics. Environ Forensics 2007;8:155–63. [21] Mudge SM. Reassessment of the hydrocarbons in Prince William Sound and the Gulf of Alaska: identifying the source using partial least squares. Environ Sci Technol 2002;36:2354–60.
Contents lists available at SciVerse ScienceDirect
Fuel journal homepage: www.elsevier.com/locate/fuel
Application of response surface methodology to oil spill remediation Claudio Bravo-Linares a,⇑, Luis Ovando-Fuentealba a, Stephen M. Mudge b, Rodrigo Loyola-Sepulveda c a
Instituto de Ciencias Químicas, Facultad de Ciencias, Universidad Austral de Chile, Valdivia, Chile Exponent UK, Exponent, The Lenz, Hornbeam Business Park Harrogate, Harrogate HG2 8RE, UK c Laboratorio de Oceanografía Química, Departamento de Oceanografía, Facultad de Ciencias Naturales y Oceanográficas, Universidad de Concepción, Concepción, Chile b
h i g h l i g h t s " Response surface methodology significantly reduced experimental effort. " In oil spill remediation, different factors were important at different locations. " Applying biosolvent before the oil reached shore improved cleaning. " Inorganic nutrients alone were more effective than organic or mixtures. " Overall, the amount of biosolvent was the least important of the three factors.
a r t i c l e
i n f o
Article history: Received 27 April 2012 Received in revised form 24 August 2012 Accepted 15 September 2012 Available online 5 October 2012 Keywords: Oil spills Biosolvent FAMEs Hydrocarbons Response surface methodology
a b s t r a c t Spilled oil in the coastal zone may be remediated through biodegradation by naturally occurring bacteria. It is possible to enhance the removal rates through addition of nutrients and biosolvents. These rates may differ within the intertidal area due to many environmental factors including surf washing. Laboratory experimentation is complex when there are so many factors involved. In a simple three factor remediation experiment, the effect of the timing of addition of a biosolvent, the type of nutrients added and the quantity of biosolvent relative to the amount of oil spilled were examined at three levels. A response surface methodology (RSM) was used to identify the key experiments to conduct and 17 separate trials were carried out with high, mid and low tide microcosms. The petroleum hydrocarbons were quantified by GC–MS methods and the data were examined with Design of Experiments (MODDE) and Partial Least Squares (PLS) Statistical (SIMCA-P) software. Different factors were important at the different intertidal locations: at low tide, the timing of application was most important while in the mid tide location, the proportion of biosolvent was most important. Of the nutrient additions, inorganic nutrients alone were more effective than organic forms of nitrogen or mixtures of urea with inorganic nutrients. Overall, the amount of biosolvent was the least important of the three factors examined. The use of RSM significantly reduced the experimental effort needed to investigate the factors and their interactions. Ó 2012 Elsevier Ltd. All rights reserved.
1. Introduction In the past 40 years, there have been a number of large spills of crude oil from shipping incidents which have contributed to the total load of oil derived hydrocarbons in the marine environment. The International Tanker Owners Pollution Federation (ITOPF) [1] keep records of the major oil spills around the world. The amount of oil spilled since 1970 has significantly decreased despite of an overall increase in oil trading. However, several small scale oil spills still occur and may affect the coastal environment. The majority of oil spills occur in the offshore or near shore environment rather than directly on beaches. However, due to ⇑ Corresponding author. Tel.: +56 63 221529. E-mail addresses: [email protected] (C. Bravo-Linares), [email protected] (S.M. Mudge), [email protected] (R. Loyola-Sepulveda). 0016-2361/$ - see front matter Ó 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.fuel.2012.09.034
the movement of water and prevailing winds, the oil may be transported to the intertidal zones. These zones tend to be sensitive when it comes to exposure to oil [2]. The action of waves, light and other physical processes lead to the weathering of oil that manifests itself through the loss of the short chain alkanes and other light components. As these processes progress, the residual oil becomes more weathered and it is usually thick, sticky, highly absorbed on rocks and organisms, often emulsified and frequently difficult to remove by physical methods [3]. The prime amelioration mechanism of oil on a beach is the complete removal of oil mixed with sand for disposal elsewhere. This process is harder if the oil impacts coarse grain sediments and rocky or other hard surfaces. One method to clean contaminated sediments is the use of chemical cleaners; this may include organic solvents with or without surfactants, as these agents emulsify the oil dispersing it into
C. Bravo-Linares et al. / Fuel 103 (2013) 876–883
seawater [4]. These cleaners may lead to dispersion of the oil to previously unaffected areas where the oil did not initially reach, thereby increasing the total area affected and potentially allowing the oil to be transported deeper into the sediments. It is also known that these agents can be toxic and in past cases make the impact of an oil spill even worse [5,6]. Other treatments that may be used include pressurised hot water or bioremediation by local microbial activity enhanced by nutrient addition [7]. The oil distribution and the effect of removal will depend on the place within the intertidal. The cleaning of sediments in the low and mid intertidal zones may be enhanced by surf washing. However, areas in the high intertidal are only reached by waves under specific conditions such as storms or high spring tides, so physical removal by waves is not the predominant way of removing residual oil. Biosolvents based on Fatty Acid Methyl Esters (FAMEs) have been shown to dissolve residual crude oil after spills and its components form a stable mixture that is non-volatile and less dense than water; the capacity to dissolve the residual oil appears to be dependent on the type of biosolvent used, the ratio of biosolvent to oil employed and the type of substrate cleaned [3,8,9]. Laboratory experiments and field cleaning have given important information about the potential use of FAMEs to clean contaminated areas after an oil spill [3,10]. These results showed that the biosolvent effectiveness could be improved in the natural environment particularly on exposed beaches with strong wave action. Several application methods, amounts, different sources of biosolvent in micro and mesocosms have been tested [3]. However, there is little information about the interaction when several parameters are studied simultaneously. Results have demonstrated the effectiveness and potential use of this approach in the removal and the enhancement on the in situ biodegradation of crude oils. It has been demonstrated that FAMEs have a synergistic effect on the degradation of hydrocarbons by bacteria, demonstrating that FAMEs enhance the biodegradability of both diesel fuel and gasoline by means of co-metabolism [11,12]. Compared with conventional methods, design of experiments (DoE) or experimental design can offer essential information about the optimisation process indicating the individual, synergistic or antagonist effects of each factor [13]. There are some reviews that explain in detail the use of experimental design application [14,15] In this study, a factorial design with three independent variables each with three levels using a model central composite facecentered (CCF) was employed to evaluate the influence of the addition of nutrients (organic and/or inorganic), proportion of crude oil to biosolvent and the time of application of the biosolvent (a day before, the same day and a day after the oil spill) on crude oil degradation on sandy beach microcosms. It is hypothesised that, by applying biosolvents a day before the oil spill reaches the sandy beach, the amount of oil degraded can be increased, especially with regard to the heavier fractions [16] as the bacteria are ‘‘activated’’ with a benign but similar compound to the oil. It is also hypothesised that the degrading bacteria will require the addition of nutrients to maintain or enhance the degradation of the oil. However, since the bacteria will be degrading a non-polar organic phase (oil), the addition of an organic solvent soluble form of nitrogen might be a more effective way of adding this nutrient.
2. Materials and methods 2.1. Reagents and materials The crude oil employed was provided by the Chilean national petroleum company, ENAP. The type of crude oil used was a light sweet variety, Caño Limon, from Columbia. To simulate
877
environmental weathering, the crude oil was exposed for 10 days to the atmosphere and then sonicated at 30 °C for 6 h before use. After this process, the oil lost about a 2.8% of its original weight. A gas chromatogram of the crude oil can be seen in Muñoz et al. [10]. The biosolvent applied was synthesized in the laboratory through the esterification of the fatty acids from commercially bought sunflower oil with methanol in presence of sodium hydroxide at 50 °C [17]. The sand employed for the experiments was collected from a remote local beach (Curiñanco Beach, S 39° 400 48.0700 ; W 73° 210 56.9000 ). This beach is less contaminated with petroleum hydrocarbons than beaches closer to industrial or urban areas. The pore water in the collected sand had a pH in the range of 7–8. The grain size distribution was not determined, however, the samples of sand were visually categorised as fine sands (<250 lm). The sand was collected in aluminium buckets from three different locations on the intertidal zone (low, mid and high). The sand was taken with a mild steel shovel from the first 5 cm depth during low tide conditions (0.79 m). Before using the sand, it was cleaned by hand to remove major particles and macro fauna. The water content for each sample was determined by drying it at 40 °C to a constant weight; the water percentage was 0.2%, 2.2% and 15.1% for high, mid and low intertidal areas respectively. 2.2. Experiments For each intertidal area, a matrix of 17 experiments was performed (14 different experiments and the central value in triplicate for statistical validation purposes). Sand from each intertidal area (200 g) was placed in 250 mL Erlenmeyer flasks. Two mL of the partially weathered crude oil was added to each flask. For high tide samples, water was not added (to mimic the lack of tide effect) and was kept dry throughout the experiment. In samples from the mid and low tide, 100 mL of water were added every 2 days and left for 2 and 4 h respectively before decanting. The water was disposed and no analysis was performed on it. The samples were stirred every day with a glass rod to homogenise the systems. For the first 5 days, a solution made from sea salt (10% w/v) was added and then only distilled water to avoid salt saturation in the systems. The experiments were performed over 10 days. No screening was performed, as previous results had shown that during this period, the hydrocarbons degradation was around 78% but then slowed significantly up to 90 days of experimentation [16]. The same protocol was performed for control experiments with no added biosolvent. 2.3. Controls Control experiments were divided into three categories: (1). General weathering: with sand and crude oil to test the biological, physical and chemical weathering in the absence of biosolvent. (2). Physical and chemical weathering: using sterilised sand to exclude biological degradation. (3). Chemical weathering: using sterile sand and the system was sealed to minimise the evaporation. Sterile sand was obtained by autoclaving the sands for 15 min at 120 °C under atmospheric conditions. 2.4. Experimental design The experiments were repeated for each intertidal area. The experimental design was performed using the Software Modde 9.0 (Umetrics). CCF designs use points on each factor axis (star points) in addition to points at the corners of the design space (cube points) and one or more centre points, as shown in Fig. 1. CCF designs can be used for RSM experiments in which the model type is quadratic. Standard CCF designs use the Fractional Factorial or Full Factorial design for a subset of factors in the experiment. For
878
C. Bravo-Linares et al. / Fuel 103 (2013) 876–883
remaining factors outside of the subset, CCF designs use additional points that estimate quadratic effects. These designs allow highquality prediction over the entire factor space [18]. The experimental matrix design is shown in Table 1. The matrix was designed to include the following factors and levels: 1. The time of application of biosolvent (one day after, the same day and a day before contamination with oil). 2. The nutrient application were organic in the form of urea and/or inorganic containing N(15%), P(16%), K(6%), Ca(18.2%), S(7.8%) and minor constituents such as Mg, Zn, Mn, Fe, Cu and B; both nutrient sources were added at the beginning of the experiment as volume (1 mL g1) according to the amount of sand employed. The application of nutrients in the experiments varied from 100% organic (urea alone), 100% inorganic (multiple constituents) and an equal mixture of both organic and inorganic. 3. The proportion of crude oil to biosolvent (2:1, 1:2 and 1:2). 2.5. Sample extraction and preparation For oil analysis, the sand was homogenised with a nickel spatula and then 1 g of sample was taken at 0, 1, 2, 4, 6, 8 and 10 days. The samples were placed in glass flasks and then 2 g of anhydrous sodium sulphate were added to absorb the residual water. A mixture (20 mL) of 1:1 of hexane: dichloromethane were added to extract the FAMEs and petroleum hydrocarbons assisted by an ultrasonic bath for 30 min at room temperature. For hydrocarbons analysis, 450 lL of the extracted liquor was diluted with 1.25 mL of hexane: dichloromethane (1:1) and 100 lL of the internal standard (1-chlorooctadecane, 10 lg mL1 in CH2Cl2, SUPELCO). Crude oil contains a suite of compounds that have been used to establish the source of oils in many situations. For a review of these compounds, please see Wang and Stout [19]. In this case, the sesquiterpane, sterane and terpane biomarkers were analysed from a portion of the extract. This was filtered and dried with sodium sulphate anhydrous and concentrated on a rotoevaporator and dried under a stream of extra pure nitrogen. The extract was immediately resuspended with n-pentane up to 1 mL to precipitate the asphaltenes than can interfere with the chromatographic analysis. 2.6. Sample analysis The analysis of the samples for total petroleum hydrocarbons (TPHs) and FAMEs was performed simultaneously using a gas
Table 1 Experimental design matrix used to optimise crude oil degradation. Experiment number
Time of application (X1)a
Type of nutrient (X2)b
Proportion of crude oil to biosolvent (X3)c
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0
1 1 1 1 1 1 1 1 0 0 1 1 0 0 0 0 0
1 1 1 1 1 1 1 1 0 0 0 0 1 1 0 0 0
a (1) 1 day after the contamination with lightly weathered crude oil. (0) The same day of the contamination. (1) 1 day before the contamination. b (1) 100% Organic nutrient (urea). (0) 50% Organic nutrient: 50% Inorganic nutrient. (1) 100% Inorganic nutrient. c (1) 1:2 Crude oil: Biosolvent (0) 1:1 Crude oil: Biosolvent (-1) 2:1 Crude oil: Biosolvent.
chromatograph (FOCUS GC, Thermo Scientific) coupled to a mass detector (DSQ II, Thermo Scientific). One lL of sample was injected with the following conditions: The temperature program used was 70 °C held for 4 min, then a ramp up to 300 °C at a rate of 6 °C min1, maintained for 18 min at 300 °C. The transfer line was kept up to 250 °C, the carrier gas flow was 1.5 mL min1 and the injector temperature was 250 °C. The injection was done with a split ratio of 10. The ionisation energy was 70 eV. The column used was a RtxÒ-5MS (RESTEK, USA) 30 m long, 0.25 mm ID, 0.25 lm of film thickness. The samples were run in a scan mode from 40 to 450 m/z for TPHs. Biomarker analyses were performed under the following conditions: injector temperature 250 °C, transfer line at 250 °C. The temperature program was 70 °C held for 4 min, then heated up to 300 °C with a ramp of 6 °C min1 and kept at this temperature for 3 min. The analysis was performed with single ion monitoring mode (SIM) with the fragments 191, 217, 218 m/z. The identification and quantification of hydrocarbons was performed using a petroleum hydrocarbon standard from n-C10 to n-C40, even and odd chain lengths, plus pristane and phytane (S-4110-100-CY) and the internal standard 1-chlorooctadecane. The identification of the biomarkers was performed using previous chromatograms obtained in the literature [19]. No quantification was performed, and the further results were used as normalised areas to provide a chemical signature of the oil. 2.7. Statistical analyses
Fig. 1. Diagram showing the levels studied according to surface model used where the numbers represents the experiments performed according to Table 1. X1: time of application, X2: nutrient type and X3: proportion of oil to biosolvent.
The biomarker and alkane data were converted to their proportions to uniform the data. This was performed by summing up the total TPH concentrations for each sample and then each compound was divided by this amount. This methodology is used to remove any concentration related effects, as some samples can be more concentrated than other, leading to misunderstandings when the model is applied [20]. Values were subjected to partial least squares (PLS) analysis [21] with the Simca-P software from Umetrics. This approach allows a chemical signature to be defined (here the Caño Limon oil was either defined through its sesquiterpane, sterane and terpane biomarkers or through it n-alkanes profile) and the amount of variability that can be explained by that
879
C. Bravo-Linares et al. / Fuel 103 (2013) 876–883
Degradation of TPHs (%)
100
A
B
C
80
60
40
20
0 0
2
4
6
8
10
0
2
Days
4
6
8
10
0
2
4
6
8
10
Days
Days
Fig. 2. Degradation of TPHs (%) for the control experiments. A: high tide, B: mid tide and C: low tide. s: sterile sand; .: sterile and sealed system; d: untreated sand. Error bar are two standard deviations.
Table 3 Statistical validation parameters of the models applied.
Table 2 Degradation of TPHs (%) for all intertidal zones. Experiment
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
% Degradation of THPs after 10 days High tide
Mid tide
Low tide
19.1 20.6 24.4 54.5 26.6 49.3 41.6 9.9 7.8 45.3 2.1 1.2 1.1 3.3 4.0 5.8 4.0
77.9 92.9 100.0 95.3 99.2 100.0 97.8 97.9 100.0 100.0 95.1 74.1 86.7 93.8 70.4 95.8 75.4
96.2 100.0 100.0 100.0 100.0 100.0 50.0 91.9 100.0 100.0 100.0 94.1 100.0 100.0 100.0 99.6 100.0
signature in the experimental data used to indicate the degree of change. Seventeen samples of Caño Limon oil were used to generate the signature (X-block) for each analysis and this signature was applied to the 17 experimental runs (Y-block) after 30 days for high intertidal area and 10 days for mid and low intertidal area (as the amount of oil and the biomarkers signatures were low). 3. Results and discussion 3.1. Controls Fig. 2 shows the degradation of TPHs (%) for the control experiments with no added biosolvent. The control for high tide area (Fig. 2A) shows that the loss of TPHs after 10 days with sterile sand and in a sealed system was 28% (this loss is principally due to evaporation of light components), which was less than the control with untreated and sterile sand (45% and 33% respectively). This demonstrates that processes of biodegradation (14%) and evaporation (31%) are the main ways of removal of TPHs in this area of the intertidal zone where tidal effect is none or minimal. Fig. 2B and C show that the position within the intertidal area plays an important role in the removal rates of crude oil in contaminated sands by surf washing. The contact of the sediments with water can induce the removal of recently spilled crude oil up to values near 100% in low intertidal area. However, this only happens if the area is in near permanent contact with water. The
R2 Predictability (Q2) Model validity Reproducibility
High tide
Mid tide
Low tide
0.89 0.58 0.61 0.95
0.64 0.45 0.98 0.84
0.86 0.26 0.67 0.98
R2 shows the model fit; 0.5 is a model with rather low significance. Q2 shows an estimate of the future prediction precision. Q2 should be greater than 0.1 for a significant model and greater than 0.5 for a good model. Model validity is a test of diverse model problems. A value less than 0.25 indicates statistically significant model problems, such as the presence of outliers, an incorrect model, or a transformation problem. Reproducibility is the variation of the replicates compared to overall variability. A value greater than 0.5 is warranted.
removal rates for the mid tide areas showed that the loss of TPHs from sands with only the tidal washing effect was around 36% and the biodegrading process was estimated to be a 10% greater than the high intertidal area. However, these experiments do not consider the oil penetration and the fact that the surf washing effect can bring back with the next tide the crude oil washed off if it is not removed or collected by physical methods offshore. The differences within the three areas of the intertidal zone in terms of oil degradation with no added biosolvent for the controls showed that, in the mid and low tide areas, the surf washing effect was an important mechanism of crude oil removal when the oil spill is recent. However, in the high tide area where the waves do not reach very frequently, especially when the wave energy has dropped and this area is isolated from the contact of water, the principal removal mechanism is evaporation and to a lesser extent, biodegradation.
3.2. Treatments The degradation percentages of TPHs after 10 days are shown in Table 2. These experiments also showed that physical removal by surf washing is an important way of removing spilled oil from surface. The results were processed using the software MODDE 9.0 after a logarithmic transformation to normalise the data and obtain a normal distribution (the models applied are subjected to normally distributed data and they apply to a linear behaviour). After 10 days, the TPH removal for low tide area was almost 100% in most of the experiments and, in order to see differences within the studied areas, it was decided to use the results up to 6 days in order to create the response surfaces for each area.
880
C. Bravo-Linares et al. / Fuel 103 (2013) 876–883
Fig. 3. Contour plot for degradation percentage (%) of TPHs for high, mid and low intertidal sediments after 6 days.
The statistical validation parameters of the response surface models applied to each studied area are shown in Table 3. In general, the parameters for the fitness of the model were all within the
category of ‘‘good’’. The validation was calculated by the software using the central value performed in triplicate. Correct model tuning like removing non-significant model parameters or selecting
881
C. Bravo-Linares et al. / Fuel 103 (2013) 876–883
the appropriate transformation results in higher summary statistics. The best and most sensitive indicator is Q2. In order to test the degree of homogeneity within a single sample, three sub-samples from the same experiment were taken. The results were 15.9, 2.1 and 0.6%RSD for high, mid and low tide respectively. The highest value was for high tide, due to the difficulty in obtaining homogeneity of the system without water. Nevertheless, this value can still be considered acceptable, as an in real case the sample homogeneity can be very variable within few metres [10]. 3.3. Response surface methodology and model equations Fig. 3 shows the contour plots of the percentage removal of TPHs for the three intertidal areas. From the figure is possible to see that for the high tide area, the application time of biosolvent after the contamination event led to better results than the application on the same day or a day before. With posterior application, a lesser proportion of biosolvent to oil (1:2) and the use of organic nutrients led to better results in the degradation of TPHs. Also, for the application times before and the same day, the results were practically the same under the different conditions. On the other hand, results from mid tide area for all three application times, were similar but distinctly different from the high tide results. The degree of degradation of TPHs is reduced when the ratio of biosolvent to crude oil is low, inorganic nutrients are applied and the time of biosolvent application is coincident with the oil. The best results were obtained with application of the biosolvent prior to contamination by oil, potentially supporting the idea that the bacteria living in the natural system were better adapted due to the input of this supplemental carbon and nutrient source at the beginning of the experiment and were ‘‘ready’’ to degrade the crude oil. For low tide, the most important factor was the application time of biosolvent, followed by the nutrient application. The other factors, apart from the synergistic effect of application time with nutrient application, had an antagonistic effect. Overall, the synergistic effect of application time of biosolvent with the proportion of biosolvent to crude oil prove that biosolvents are one of the most important factors in the degradation of crude oil, especially for the oil that has not been removed from contaminated sands through the surf washing effect. On the other hand, nutrients tended to have an antagonistic effect with all the factors studied. Suggesting that nutrients supply may be overtaken and not needed when biosolvents are used due to the physicochemical properties of biosolvents can convert them in good solvents of crude oil and also assist the bacteria cometabolism to biodegrade the remaining oil [9,16]. The results obtained showed that the cleaning process cannot be considered the same for all the intertidal areas, as some might not need an intensive cleaning process as low tide areas. However, the cleaning process can be relevant when the surf washing effect is not the main mechanism of removal of the spilled oil. Most of the researches done in this matter point to one type of solution for the whole area, as they do not differentiate the intertidal areas. Additionally, the obtained results show that each area should be treated independently, saving time and chemicals to be used to perform the cleaning process such as biosolvents based on FAMEs. Table 4 shows the factors and their respective coefficients for each intertidal area obtained by RSM modelling. It is possible to see that main factors for all the conditions studied are the application time on its own (X1) and its quadratic effect (X1 X1). The other factors (single and as interactions), except for some in low tide, were not statistically significant due to the p-values were higher than 0.05. For instance, nutrients inputs (organic or inorganic) did not improve the degradation of TPHs implying that they might not
Table 4 Coefficients values for the model employed in the different intertidal areas. Factorsa
Coefficients
Standard error
p valueb
Low tide Constant X1 X2 X3 X1 X1 X1 X3 X2 X3
99.28 7.16 3.32 4.72 7.25 2.11 5.83
2.29 1.51 1.51 1.51 1.86 1.34 1.34
1.0E12 8.0E04 5.3E02 1.1E02 3.0E03 1.5E01 1.4E03
5.10 3.37 3.37 3.37 4.15 2.98 2.98
Mid tide Constant X1 X2 X3 X1 X1 X2 X2 X1 X2 X2 X3
70.84 2.80 0.86 5.47 9.53 2.60 3.00 3.18
4.44 2.69 2.69 2.69 3.68 3.68 2.38 2.38
2.4E07 3.3E01 7.6E01 7.7E02 2.9E02 4.9E01 2.4E01 2.2E01
10.23 6.21 6.21 6.21 8.32 8.32 5.49 5.49
High tide Constant X1 X2 X3 X1 X1 X1 X2 X1 X3 X2 X3
0.04 10.16 1.12 0.13 11.19 1.01 2.25 4.88
2.19 1.45 1.45 1.45 1.78 1.28 1.28 1.28
9.9E01 6.3E05 4.6E01 9.3E01 1.4E03 4.5E01 1.1E01 4.2E03
4.96 3.28 3.28 3.28 4.04 2.90 2.90 2.90
Confidence interval (±)
X1 = Time of application, X2 = Type of nutrient, X3 = Proportion of biosolvent. a Some factors were excluded from the models due to they were not significant and including them the values of Q2 were lower than the values accepted for a good model. b Values in italics are significant according to the p-values < 0.05.
be needed over this short experimentation time, and simple addition of biosolvents can help to improve the oil removal. The lack of importance of the nutrients might also indicate to a chemical/physical removal mechanism rather than a biologically mediated one. For mid tide area the coefficient values showed that the nutrient application and the proportion of biosolvent to oil were the most important parameters and the application time did not show an important effect in the removal of crude oil. The only synergistic effect that was important was the interaction between the application time and the proportion of biosolvent to crude oil. It may be that biological processes are more important at this location. 3.4. Statistical results Analysis of the sterane and terpane biomarkers indicated the mean change in signature compared to the initial oil was 6.2 ± 4.3% in the high tide samples; the greatest difference was 20.4% for experiment number 2 after 30 days. This is not a great change over the 30 days of the experiments and justifies the use of these compounds in signature analysis when determining the source of an oil spill. However, this is a real change and over longer periods of time or in more aggressive biodegradation environments, the rate of change will lead to a chemical signature that is sufficiently different from the original to lead to doubt in source apportionment. Care must be used when considering these signatures and the stability of the signature may influence the outcome of an interpretation. Analysis of the biomarkers in the low and mid tide areas did not show more than 3% change from the signature in the PLS analysis. A further approach to determine the change rates in relation to the treatments was undertaken using the PLS method. The alkane data for the Caño Limon crude oil as applied to the sands was used to develop a signature. The extent to which this signature can explain the variance in all the subsequent experimental samples
882
C. Bravo-Linares et al. / Fuel 103 (2013) 876–883 Table 5 The amount of variance (R2) in each sample explained by the alkane signature of the Caño Limon oil applied. This was achieved through use of a PLS model. A value of 1.0 means the signature and experimental sample are identical while a value of 0.0 means they are completely dissimilar (or absent). Intermediate values provide an indication of the amount of alkane attenuation that has occurred. The data are sorted by the signal in the mid tide after 6 days as these exhibited a wide range of possible fits.
was used as a measure of the loss rate of the oil. The results of this analysis can be seen in Table 5. The data are ordered according to the loss after 6 days in the mid tide samples as this column has a wide range values providing a good scale for display. The table shows that the greatest ‘‘loss’’ of alkanes relative to the initial signature can be seen in the low tide samples followed by the mid and finally the high tide samples. The data are colour-coded from green (values close to 1.0) through yellow to red (values close to 0.0). The high tide samples, in general, did not lose more than 50% of their alkane signature over the 10 days of the experiment. This is most likely due to the lack of water washing at these higher intertidal elevations. By comparison, the mid and low tide levels showed that most samples lost more than 50% of their signature. The role of the water in washing both the oil and the crude oil dissolved in the biosolvent out of the sands was readily apparent. The most effective treatments to remove the crude oil will be those towards the bottom of the table. In this case, the 1 treatments tend towards the bottom with the 1 values in the middle and the 0 treatments near the top where the loss rates were slowest or least effective. These data suggest that the best approach would be to apply the biosolvent BEFORE the crude oil (the 1 application) as these tend to the bottom of the table. Similarly, the more effective nutrient treatment is also 1 (inorganic only) although the 1 (organic only, in this case, urea) was more effective than the 0 treatment which was a mixture of the organic and inorganic forms. With regard to the biosolvent ratio, the data were much less clustered and no obvious trend is apparent in the results. This is interesting in itself as it implies the other factors have a greater effect on the loss rates than the ratio of the biosolvent to crude oil added. These data confirm the results shown from the Response Surface in Fig. 3. The greatest removal of the TPH occurred (in this figure) in the mid tide location with application of the biosolvent 1 day before the addition of the crude oil.
4. Conclusions When faced with multiple variables which may all interact and affect the degradation rate of spilled oil, separating out the
processes that have the greatest impact on the oil can be difficult to see without a large number of experiments. However, the Design of Experiments approach with a response surface can predict the most critical experiments to conduct to reduce the overall experimental load. Here, in the treatment of spilled lightly weathered crude oil, three factors were examined. Instead of conducting 27 experiments (3 3 3), the DoE identified just 17 to conduct and the data from these experiments clearly showed that the time of treatment relative to the spill and the form of the nutrients were more important than the amount of biosolvent added. The results show the loss rates are different within the three zones of the intertidal area (high, mid or low). Result for the low intertidal area showed that spilled crude oil can be easily removed by tidal action and may not need any additional cleaning processes. In the mid intertidal area, enhanced cleaning could be obtained by addition of the biosolvent one day prior to the arrival of the weathered crude oil. It is hypothesised that the biosolvent decreases the lag phases for the bacteria naturally present in the sand such that they are more readily able to degrade the oil through co-metabolism. Response surface methodology was useful to determine of different a treatment applied to the different intertidal areas can affect the removal of crude oil spills. Acknowledgments The authors would like to acknowledge the financial support of the FONDECYT project 11090052 and ENAP for the crude oil sample. References [1] ITOPF, International Tanker Owners Pollution Federation.
C. Bravo-Linares et al. / Fuel 103 (2013) 876–883
[5]
[6]
[7]
[8]
[9]
[10] [11] [12]
of the twenty-third arctic and marine oil spill programme. Technical Seminar, Environment Canada, Vancouver; 2000. p. 795–803. Wu RSS. Differences in the toxicities of an oil dispersant and a surface-active agent to some marine animals, and their implication in the choice of species in toxicity testing. Mar Environ Res 1981;5:157–63. Greenwood PJ. The influence of an oil dispersant chemserve ose-dh on the viability of sea-urchin gametes – combined effects on temperature, concentration and exposure time on fertilization. Aquat Toxicol 1983;4:15–29. Gallego JR, Fernandez JR, Diez-Sanz F, Ordonez S, Sastre H, Gonzalez-Rojas E, et al. Bioremediation for shoreline cleanup: in situ vs. on-site treatments. Environ Eng Sci 2007;24:493–504. Fernandez-Alvarez P, Vila J, Garrido JM, Grifoll M, Feijoo G, Lema JM. Evaluation of biodiesel as bioremediation agent for the treatment of the shore affected by the heavy oil spill of the Prestige. J Hazard Mater 2007;147:914–22. Miller NJ, Mudge SM. The effect of biodiesel on the rate of removal and weathering characteristics of crude oil within artificial sand columns. Spill Sci Technol Bull 1997;4:17–33. Munoz J, Mudge SM, Loyola-Sepulveda R, Munoz G, Bravo-Linares C. Source apportionment in oil spill remediation. J Environ Monit 2012. Pasqualino JC, Montane D, Salvado J. Synergic effects of biodiesel in the biodegradability of fossil-derived fuels. Biomass Bioenergy 2006;30:874–9. Mudge SM, Pereira G. Stimulating the biodegradation of crude oil with biodiesel preliminary results. Spill Sci Technol Bull 1999;5:353–5.
883
[13] Mansilla HD, Bravo C, Ferreyra R, Litter MI, Jardim WF, Lizama C, et al. Photocatalytic EDTA degradation on suspended and immobilized TiO2. J Photochem Photobiol A-Chem 2006;181:188–94. [14] Leardi R. Experimental design in chemistry: a tutorial. Anal Chim Acta 2009;652:161–72. [15] Dejaegher B, Vander Heyden Y. Experimental designs and their recent advances in set-up, data interpretation, and analytical applications. J Pharm Biomed Anal 2011;56:141–58. [16] Bravo-Linares CM, Ovando-Fuentealba L, Loyola-Sepulveda RH, Mudge SM. Progress of total petroleum hydrocarbons (TPHs) treated with biosolvent in a simulated oil spill on sandy beach microcosms. J Chil Chem Soc 2011;56:941–4. [17] Zhang Y, Dube MA, McLean DD, Kates M. Biodiesel production from waste cooking oil: 1. Process design and technological assessment. Bioresour Technol 2003;89:1–16. [18] Neto B, Scarminio I, Bruns R. Statistical desing-chemometics. Netherlands: Elsevier; 2006. [19] Wang Z, Stout S. Oil spill environmental forensics fingerprinting and source identification. Academic Press; 2007. [20] Mudge SM. Multivariate statistical methods in environmental forensics. Environ Forensics 2007;8:155–63. [21] Mudge SM. Reassessment of the hydrocarbons in Prince William Sound and the Gulf of Alaska: identifying the source using partial least squares. Environ Sci Technol 2002;36:2354–60.