Response Surface Methodology Optimization and Kinetics of Diesel Degradation by a Cold-Adapted Antarctic Bacterium, Arthrobacter sp. Strain AQ5-05
, Nur Nadhirah Zakaria 1, Ahmad Fareez Ahmad Roslee 1, Khalilah Abdul Khalil 4, Suhaimi Napis 5, Peter Convey 6, Claudio Gomez-Fuentes 7 and Siti Aqlima Ahmad 1,*Round 1
Reviewer 1 Report
Dear Authors,
In my opinion the theme of the article is innovate and very interesting for the readers of the journal.
The authors showed that the cold-tolerant diesel-degrading Arthrobacter strain AQ5-05 from Antarctica is capable of degrading of up to 3% (v/v) diesel at 10°C within 7 days.
Optimising the environmental variables influencing diesel degrading activity via RSM greatly enhanced degradation.
This study showed that the application of mathematical growth models revealed important growth constants, which are useful in estimating diesel utilisation and the diesel concentration that may be significant during up-scaling of any diesel bioremediation process. Studies are ongoing to identify the pathways used for diesel degradation using a whole genome sequencing approach, which will support in the further improvement and application of this bacterium in area of bioremediation techniques.
The manuscript under revision is well structured; the language is well correct and clear. The title and abstract clearly describe the content of the manuscript.
Best regards
Author Response
Thank you very much.
Reviewer 2 Report
The manuscript reports on response surface methodology optimisation and kinetics of diesel degradation by a cold-adapted Antarctic bacterium, Arthrobacter sp. strain AQ5-05. It is an interesting subject in the area of bioremediation. It is written well with fluent English and clear procedures and presentation of the results. There are some comments as below that need to be addressed:
1)L36: Please include in parenthesis that 34.5% is at what condition?
2)L40: Why percentage units for Ks and Ki are in (v/v)? Why not (w/v)?
3)L45: Should be ’15-min autoclaving’ instead of ‘autoclaving 15 min’.
4)L117: investigates should be replaced by experiments.
5)L140: What column was used for GC analysis?
6)How the microorganism used was named in this study?
7)Why 2 experimental designs were used in this study with 2 tables presented?
8)Table 1: Why only 2 levels were considered for each variable in this experimental design? This would reduce the accuracy of the model.
9)Eq. 2: Square of the factors should be indicated by power 2.
10)Title 2.4: title is not clear. Do authors mean ‘Determination of growth kinetic parameters in diesel’?
11)Why squares of the parameters are not included in Eq.3 and Table 3?
12)There is no reason in the text why Monod model did not work? Normally it works good in describing growth kinetics.
13)Y-axis is not clear.
14)Was there any aeration into the flasks? It is not indicated what material used to plug the flasks?
Author Response
Comment 1: L36: Please include in parenthesis that 34.5% is at what condition?
Answer: The 34.5% degradation reported here was attained at the initial pre-screening when the growth parameters has not been optimised (temperature 10°C, broth media pH 7 .0, 1% of salinity). upon optimisation via RSM the degradation was enhanced. Page 1, line 36-37.
Comment 2: L40: Why percentage units for Ks and Ki are in (v/v)? Why not (w/v)?
Answer: Considering the nature of the substrate (diesel), the diesel concentration used was measure in volume per volume and not in weight per volume.
Comment 3: L45: Should be ’15-min autoclaving’ instead of ‘autoclaving 15 min’
Answer: Corrected, Page 3, Line 115
Comment 4: L117: investigates should be replaced by experiments
Answer: Corrected, Page 3, Line 117
Comment 5: L140: What column was used for GC analysis?
Answer: (30 m × 0.25 mm × 0.25 µm), Page 3, Line 140.
Comment 6: How the microorganism used was named in this study?
Answer: Arthrobacter sp. strain AQ5-05. This strain was identified based on 16S rRNA and was tentative named Arthrobacter sp. strain AQ5-05.
Comment 7: Why 2 experimental designs were used in this study with 2 tables presented?
Answer: 2 experimental design (1st order design and 2nd order model) was used in this study to collectively optimize all the affecting parameters to eliminate the limitations of a single-factor optimization process. 4 tables were presented tables as indicated n the manuscript 1, 3, 4, and 5.
Comment 8: Table 1: Why only 2 levels were considered for each variable in this experimental design? This would reduce the accuracy of the model.
Answer: In the Plackett-Burman design each variable was examined at two levels: –1 for the low level and +1 for the high level. This is to efficiently identify the important factors significantly influencing diesel degradation. In the CCD five level was used to further optimised and study the interaction which will further enhance the accuracy of the model. The coefficient of determination (R2) 0.953 for diesel degradation, indicate good agreement between the experimental and the predicted values, thus the model was very accurate.
Comment 9: Eq. 2: Square of the factors should be indicated by power 2.
Answer: Noted. However, the model used the equations are written as presented.
Comment 10: Title 2.4: title is not clear. Do authors mean ‘Determination of growth kinetic parameters in diesel’?
Answer: Noted and corrected. Page 5, Line 183.
Comment 11: Why squares of the parameters are not included in Eq.3 and Table 3?
Answer: Noted and corrected. Page 7.
Comment 12: There is no reason in the text why Monod model did not work? Normally it works good in describing growth kinetics.
Answer: Table 6. based on statistical analysis, the model with the lowest values for RMSE and AICc, highest adjusted R2 values, F-test and with Bias Factor and Accuracy Factor nearest to unity (1.0) are considered the good model. However, Monod values is the highest indicating that it is not a good model in this study. Page 11.
Comment 13: Y-axis is not clear.
Answer: Corrected. Page 10
Comment 14: Was there any aeration into the flasks? It is not indicated what material used to plug the flasks?
Answer: Yes. The aeration was achieved via shaken incubator at 150 rpm.
Reviewer 3 Report
This paper, entitled Response surface methodology optimisation and kinetics of diesel degradation by a cold-adapted Antarctic bacterium, Arthrobacter sp. strain AQ5-05, is a scholarly work and can increase knowledge on this domain. The authors provide an interesting study and the content is relevant to Sustainability. The manuscript is quite well written and well related to existing literature. The abstract and keywords are meaningful.
I have some specific and general comments:
- Please give more details about the choice of this strain. Why this strain? Is this choice made after a benchmark and previous screening or not? It's quite confusing because the authors mentioned that they carried out screening for diesel degrading competence. How was made the choice of working with this strain?
- If I understand right, all the experiments were carried out in synthetic media, why not wortking on raw effluents with real or artiticial diesel contamination? The use of microorganims for degradation is needed in real conditions and real site, so it's very important to test the capability of this strain in real conditions (growth, activity, ...).
- Why the choice of Plackett-Burman Design (PBD) tool? Please discuss about this in the manuscript? Why not working with another experimental design tool such Box-Benkhen, Doelhert, … ? What are the advantages to work with this one? and limits?
- In Table 4, the authors give date predictive and experimental data. It's not really clear why and how they are obtained and compared. The experimental design tool is usually used for the determination of an optimum not for simulation or modeling. Please discuss this point.
- Please modify the Figure 5 in terms of quality and presentation.
- What are the next steps? Is there any experiments scheduled in real conditions or on real effluents contaminated with diesel?
As it, the manuscript is not fully acceptable for publication and requires some amendments.
Author Response
Comment 1: Please give more details about the choice of this strain. Why this strain? Is this choice made after a benchmark and previous screening or not? It's quite confusing because the authors mentioned that they carried out screening for diesel degrading competence. How was made the choice of working with this strain?
Answer: This strain was previously isolated from Antarctica and was used for phenol degradation study. Upon found competent of phenol degradation it was screening for diesel degradation. Similarly, whole genome study shows that this strain harbor completely enzyme alkane and aromatic degradation.
Comment 2: If I understand right, all the experiments were carried out in synthetic media, why not wortking on raw effluents with real or artiticial diesel contamination? The use of microorganims for degradation is needed in real conditions and real site, so it's very important to test the capability of this strain in real conditions (growth, activity, ...).
Answer: This study is a lab scale study to first test it competency to degrade diesel. Study is ongoing using this particular strain for field study in the Antarctica by our team.
Comment 3: Why the choice of Plackett-Burman Design (PBD) tool? Please discuss about this in the manuscript? Why not working with another experimental design tool such Box-Benkhen, Doelhert, … ? What are the advantages to work with this one? and limits?
Answer: PBD is useful for fitting first order models (which identify linear effects) and can give indication of the existence of second-order curvature effects when the design includes centre points. Comparing PBD and full factorial design, in the latter there would be five parameters with two levels for each parameter. In this case, a total of 32 different experiments would have to be conducted to characterize the response. In PBD a total of 12 different experiments were required with the five parameters, saving time, resources and energy, and the response is larger when compared with full factorial design. More so, PBD is use to screen the significant parameters that affect diesel degradation by Arthrobacter sp. AQ5-05. In addition, PBD predict the interactions/relationship between actual and predicted values of diesel oil degradation for Arthrobacter sp. AQ5-05.
Central Composite designs can fit a full quadratic model. They are often used when the design plan calls for sequential experimentation because these designs can include information from a correctly planned factorial experiment while Box-Behnken designs usually have fewer design points than central composite designs, thus, they are less expensive to run with the same number of factors. They can efficiently estimate the first- and second-order coefficients; however, they can't include runs from a factorial experiment. Box-Behnken designs always have 3 levels per factor, unlike central composite designs which can have up to 5. Also unlike central composite designs, Box-Behnken designs never include runs where all factors are at their extreme setting, such as all of the low settings.
Comment 4: In Table 4, the authors give date predictive and experimental data. It's not really clear why and how they are obtained and compared. The experimental design tool is usually used for the determination of an optimum not for simulation or modeling. Please discuss this point.
Answer: Here the interaction between the significant parameter is study by further optimizing the parameters. This experimental runs are chosen for the purpose of getting more information that can lead to the determination of optimum operating conditions on the control variables using the second-degree model. The design matrix of tested variables and the experimental results are represented in Table 4. Multiple regression analysis was used to analyze the data and thus a second-order polynomial equation was derived. The experimental data obtained from the CCD model experiments is represented in equation 4, where Y denote the predicted response. The adequacy of the model was checked using ANOVA, as shown in Table 5. The “F-value” of the model was 22.46, and the value of “Prob > F” < 0.0001, suggesting that the model was highly significant. Linear terms of X1, X2, X3, X4, and quadratic terms of X12, X22, X32 and X42 were significant for diesel degradation. Interactive terms of X1 X4, X2 X4, and X3 X4 were also significant for diesel degradation. The coefficient of determination (R2) was calculated as0.953 for diesel degradation, indicating good agreement between the experimental and the predicted values. The pred-R2 of 0.712 was in reasonable agreement with adj-R of 0.910. The adequate precision, the signal to noise ratio of 13.049, suggested an adequate signal. The coefficient of variation (CV) indicates the degree of precision with which the treatments are compared. The lower value of CV (21.97%) demonstrated that the performed experiment was highly reliable. The “Lack of Fit F-value” of 0.118 implied that the Lack of Fit was not significant relative to the pure error. There was a 12.79% chance that a large “Lack of Fit F-value” could occur due to noise. The model was found to be adequate for prediction within the range of variables employed.
Comment 5: Please modify the Figure 5 in terms of quality and presentation.
Answer: Noted and corrected. Page 11. Figure 5.
Comment 6: What are the next steps? Is there any experiments scheduled in real conditions or on real effluents contaminated with diesel?
Answer: Yes. Research is ongoing with this strain for field trial in Antarctica. We have collaboration with Dr. Claudio Gomez-Fuentes from CIMAA/ Universidad de Magallanes, Región de Magallanes y Antártica Chilena, Chile for 5 years 2018-2022. Due to the covid19 pandemic we cannot finish the research during Antarctic expedition 2020.
Round 2
Reviewer 3 Report
The authors provide a revised version of their manuscript taking into account all the comments and requests of amendments. I agree with all the anwsers and comments. The manuscript is now fully acceptable for publication and I recommend the following decision: ACCEPT IN PRESENT FORM.
