Enhancing Ecological Efficiency in Biological Wastewater Treatment: A Case Study on Quality Control Information System

Round 1

Reviewer 1 Report

The manuscript entitled „Enhancing Ecological Efficiency in Biological Wastewater Treatment: A Case Study on Quality Control Information System” concerns an important aspect, a problem related to wastewater treatment plants. Treatment plants using biological purification ineffective remove bilogical and chemical pollutants from sewage, and toxins such as heavy metals, but also microorganists go to the natural environment, creating a potential threat. The work describes the Control System for monitoring the effectiveness of removal of wastewater pollution based on some polished data during purification. In my opinion, however, the work lacks several key elements that require improvement.

My comments:

Line 87: remove additional „Consequently”

Add "positive" to correlation descriptions in manuscript

2.1 is hospital sewage delivered to the treatment plant? Even though this is municipal sewage, some percentage may come from municipal hospitals, which may also have an impact on the sewage control system.

Did the authors take into account the analysis of the occurrence and quantity of antibiotics and pharmaceuticals flowing into the sewage treatment plants? These are compounds that can largely influence the composition of microorganisms in sewage during treatment processes.

There are no results regarding the identification of microorganisms, the authors claim that they monitored and examined the composition of microorganisms using a camera, but there is no information about the composition of microorganisms and photos in chapter 3. Please add results as hypotheses are made without any visible results.

Chapter 3 contains only a description of the values of selected parameters (ph, orp, conductivity, temperature) and discussions about the control system, please also expand the discussion.

2.3 - the name of the subchapter concerns microbiological testing, but no microbiological method is described in this fragment.

How was the influence of given toxic substances on changes in the composition and dynamics of microorganisms determined?

381-416 - this fragment is not a description of materials and methods, but a description of the results. Additionally, this fragment concerns results that are not included anywhere in the work.

Equation 2 is incomprehensible, while equation 3 is illegible.

Figure 14. it is worth changing the block descriptions to English, referring to the description in lines 569-573.

With such a large amount of data, it is worth considering adding a supplementary file.

Author Response

Answers to Reviewer 1

The manuscript entitled „Enhancing Ecological Efficiency in Biological Wastewater Treatment: A Case Study on Quality Control Information System” concerns an important aspect, a problem related to wastewater treatment plants. Treatment plants using biological purification ineffective remove bilogical and chemical pollutants from sewage, and toxins such as heavy metals, but also microorganists go to the natural environment, creating a potential threat. The work describes the Control System for monitoring the effectiveness of removal of wastewater pollution based on some polished data during purification. In my opinion, however, the work lacks several key elements that require improvement.

Dear Reviewer 1,

We appreciate your feedback and time dedicated to make our manuscript better.

Thank you for all the comments. As much as possible we have taken into account for the improvement of the manuscript.

Line 87: remove additional „Consequently”

My comments:

Thank you, it's been removed.

Add "positive" to correlation descriptions in manuscript

Additions have been made. All changes to the text are highlighted in green.

2.1 is hospital sewage delivered to the treatment plant? Even though this is municipal sewage, some percentage may come from municipal hospitals, which may also have an impact on the sewage control system.

Did the authors take into account the analysis of the occurrence and quantity of antibiotics and pharmaceuticals flowing into the sewage treatment plants? These are compounds that can largely influence the composition of microorganisms in sewage during treatment processes.

Sumy Municipal Hospital has local treatment facilities and only after preliminary treatment wastewater flows into the general sewerage system. It is worth noting that it was sulfur compounds that were associated with the disruption of municipal treatment facilities. Therefore, the presence of this toxicant was investigated.

There are no results regarding the identification of microorganisms, the authors claim that they monitored and examined the composition of microorganisms using a camera, but there is no information about the composition of microorganisms and photos in chapter 3. Please add results as hypotheses are made without any visible results.

Thanks, added a separate subparagraph.

Chapter 3 contains only a description of the values of selected parameters (ph, orp, conductivity, temperature) and discussions about the control system, please also expand the discussion.

An addition was made to the discussion

2.3 - the name of the subchapter concerns microbiological testing, but no microbiological method is described in this fragment.

Thanks for your comment, the methodology description has been added.

How was the influence of given toxic substances on changes in the composition and dynamics of microorganisms determined?

It was determined by direct microscopy, according to which morphometric indices of species and their composition in the activated sludge were observed.

381-416 - this fragment is not a description of materials and methods, but a description of the results. Additionally, this fragment concerns results that are not included anywhere in the work.

Agreed moved to Results.

Equation 2 is incomprehensible, while equation 3 is illegible.

thank you, the clarification has been revised.

The formulas (1), (2) and (3) presented in the article are a combination of standard formulas. More details can be found here:

Najarzadeh D. Conservative confidence intervals on multiple correlation coefficient for high-dimensional elliptical data using random projection methodology. J Appl Stat. 2020 Jul 22;49(1):64-85. doi: 10.1080/02664763.2020.1796937. PMID: 35707806; PMCID: PMC9041940; Gary Smith, Chapter 10 - Multiple Regression, Editor(s): Gary Smith, Essential Statistics, Regression, and Econometrics,Academic Press,2012, 297-331;

Robert L. Johnson, James Penny,  Split-Half Reliability, Editor(s): Kimberly Kempf-Leonard, Encyclopedia of Social Measurement, Elsevier, 2005, Pages 649-654, ISBN 9780123693983, https://doi.org/10.1016/B0-12-369398-5/00096-7

https://www.sciencedirect.com/topics/computer-science/pearson-correlation

The linear correlation coefficient is widely used to quantify the density of the relationship [15, 16, 17]. If the values of variables X and Y are given, it is calculated by the formula:

,                                                        (1)

 – average value of the product of physical quantities investigated for the presence of linear relationships, , – average values of physical quantities investigated for the presence of linear relationships and , – standard mathematical deviations of the examined for the presence of linear relationships of physical quantities.

The correlation coefficient takes on a value from -1 to +1 (σ - variance).

If |r|<0.30, then the relationship between the features is weak;

0.30≤|r|≤0.70 - moderate relationship;

|r|>0.70 - a strong or dense relationship.

The correlation coefficient can vary within the limits -1 ≤ r ≤1 and the closer it is modulo one, the tighter the linear correlation dependence - the closer the points are located to the line, the more qualitative and reliable the linear model. If either r=-1, then r=1 If either r=-1 or r=1, it is a strict linear relationship, in which all empirical points will be on the constructed line. On the contrary, the closer to zero, the more the points are scattered further, the less linear dependence is expressed.

When |r|=1, the relationship is functional.

If |r|≈0, there is no linear relationship between X and Y. However, a non-linear interaction is possible and this requires additional verification.

When assessing a linear multiple relationship, the multiple correlation coefficient is calculated. It reflects the density of the relationship between the dependent variable and the variations of all independent variables included in the analysis:

 ,                                                       (2)

where          – factor dispersion;

 – total dispersion.

The coefficient of determination is the private analog of the empirical coefficient of determination. It represents the square of the correlation coefficient: – the coefficient of determination shows the proportion of variation in the result (physical quantity) Y, which is caused by the influence of a trait-factor (physical quantity) X. The linear coefficient of determination can vary within the range of , and the closer it is to unity, the better the linear model approximates the empirical data.

When assessing the closeness of the relationship between the outcome Y and two factor attributes X1, X2, the multiple correlation coefficient can be determined by the formula:

                              (3)

where r is the pairwise correlation coefficients between the features.

The multiple correlation coefficient varies from 0 to 1 and is a positive value: 0<R<1:

R≤0.3 - there is practically no relationship (either not all important factors of the relationship are taken into account, or the wrong form of the regression equation is chosen; it is necessary to review the variables included in the model and possibly its type);

0.3<R≤0.5 - weak relationship;

0.5<R≤0.7 - moderate relationship;

R>0.7 - strong relationship.

The interpretation of Pearson's correlation coefficient values is contingent upon their absolute magnitudes [18]. Possible values of the correlation coefficient range from 0 to ±1. The greater the absolute value of r_xy, the stronger the relationship between the two variables. r_xy = 0 indicates a complete absence of a relationship. r_xy = 1 signifies the presence of an absolute (functional) relationship. To assess the strength or intensity of the correlation, commonly accepted criteria are typically employed. According to these criteria, absolute values of r_xy < 0.3 denote a weak correlation, values of r_xy from 0.3 to 0.7 indicate a moderate correlation, and values of r_xy> 0.7 suggest a strong correlation. To evaluate the strength of the correlation relationship, Cheddock's scale can be applied. According to this scale, values of the absolute correlation coefficient rxy less than 0.3 indicate a weak strength of correlation, from 0.3 to 0.5 - a moderate strength, from 0.5 to 0.7 - a significant strength, from 0.7 to 0.9 - a high strength, and more than 0.9 - a very high strength of correlation.

Figure 14. it is worth changing the block descriptions to English, referring to the description in lines 569-573.

Thanks, it was done.

With such a large amount of data, it is worth considering adding a supplementary file.

Since the study is related to the ongoing operation of the municipal wastewater treatment plant, additional materials cannot be made publicly available, but can be provided upon request.

Dear Reviewer 1,

Thank you again for your valuable comments.

Reviewer 2 Report

1.       Abstract should be corrected - please add the obtained figures from the conducted research.

2.       All abbreviations should be explained, e.g. abstract line 19 - an explanation should be added to the ORP. Please check the entire manuscript to ensure that all abbreviations used have been explained.

3.       Introduction - The utility of this study could be more clearly highlighted in the manuscript.

1.       Introduction - briefly explain the motivation for undertaking this research, its relevance and originality, where it fits into the development of the field, and why it should be of interest to Water  readers. Please answer the question what does it add to the subject area compared with other published material?

4.        Section 2.1. Wastewater from an urban wastewater treatment plant - Please add the geographical coordinates of the place where the research was conducted.

5.       Figure 9 - please enlarge the figures and check the data on the x-axis (what was written on the x-axis before 06:00 and after 18:00?)

6.       Why are they marked twice in Figure 9, which shows daily temperature values?

7.       Figure 11 - please add explanations of what is shown in the figures on the x-axis.

8.       Line 457 - The presented equation is completely illegible. Needs improvement.

9.       It is important to check that the writing text clearly expresses and explains each idea and result obtained.

10.   A better discussion would be necessary in order to emphasize the main findings.

11.   The conclusions needs improvement - highlight the most important findings and identify the added value of the main finding.

12.   In conclusion section, limitations and recommendations of this research should be highlighted.

Author Response

Reviewer 2

Dear Reviewer 2,

Thank you for all the valuable feedback, which served us to improve our manuscript.

As you will see below, we have answered each of your comments point-by-point. As well as incorporated all the changes in our manuscript, where we highlighted all the changes.

Abstract should be corrected - please add the obtained figures from the conducted research.

The abstract has been updated.

2. All abbreviations should be explained, e.g. abstract line 19 - an explanation should be added to the ORP. Please check the entire manuscript to ensure that all abbreviations used have been explained.

Explanation has been added to the ORP.

3. Introduction- The utility of this study could be more clearly highlighted in the manuscript.
4. Introduction - briefly explain the motivation for undertaking this research, its relevance and originality, where it fits into the development of the field, and why it should be of interest to Water  readers. Please answer the question what does it add to the subject area compared with other published material?

In this case, the object of the study was the treatment facilities of Sumy city (Ukraine). Sumy Municipal Hospital has local treatment facilities and only after preliminary treatment wastewater flows into the general sewerage system. It is worth noting that it was sulfur compounds that were associated with the disruption of municipal treatment facilities. Therefore, the presence of this toxicant was investigated. The relevance of this study is caused by the lack of the necessary number of measuring instruments capable of operating in aggressive environments and real-time mode. That causes the lack of necessary data arrays and, accordingly, incompleteness and blurriness of data, in particular in Ukraine. Interest is aroused by the use of mathematical and programmatic complexes and the formation of prospects for the introduction of artificial intelligence systems (neural networks).

4. Section 2.1. Wastewater from an urban wastewater treatment plant - Please add the geographical coordinates of the place where the research was conducted.

Thank you, added.

5. Figure 9 - please enlarge the figures and check the data on the x-axis (what was written on the x-axis before 06:00 and after 18:00?)

Clarification is made

6. Why are they marked twice in Figure 9, which shows daily temperature values?

The first case is temperature from a pH sensor, the second from an ORP sensor. Temperature meters are used there to correct the determination of their basic pH and ORP values.

7. Figure 11 - please add explanations of what is shown in the figures on the x-axis.

Thank you, added.

8. Line 457 - The presented equation is completely illegible. Needs improvement.

Explanation added

The formulas (1), (2) and (3) presented in the article are a combination of standard formulas. More details can be found here:

Najarzadeh D. Conservative confidence intervals on multiple correlation coefficient for high-dimensional elliptical data using random projection methodology. J Appl Stat. 2020 Jul 22;49(1):64-85. doi: 10.1080/02664763.2020.1796937. PMID: 35707806; PMCID: PMC9041940; Gary Smith, Chapter 10 - Multiple Regression, Editor(s): Gary Smith, Essential Statistics, Regression, and Econometrics,Academic Press,2012, 297-331;

Robert L. Johnson, James Penny,  Split-Half Reliability, Editor(s): Kimberly Kempf-Leonard, Encyclopedia of Social Measurement, Elsevier, 2005, Pages 649-654, ISBN 9780123693983, https://doi.org/10.1016/B0-12-369398-5/00096-7

https://www.sciencedirect.com/topics/computer-science/pearson-correlation

The linear correlation coefficient is widely used to quantify the density of the relationship [15, 16, 17]. If the values of variables X and Y are given, it is calculated by the formula:

,                                                        (1)

 – average value of the product of physical quantities investigated for the presence of linear relationships, , – average values of physical quantities investigated for the presence of linear relationships and , – standard mathematical deviations of the examined for the presence of linear relationships of physical quantities.

The correlation coefficient takes on a value from -1 to +1 (σ - variance).

If |r|<0.30, then the relationship between the features is weak;

0.30≤|r|≤0.70 - moderate relationship;

|r|>0.70 - a strong or dense relationship.

The correlation coefficient can vary within the limits -1 ≤ r ≤1 and the closer it is modulo one, the tighter the linear correlation dependence - the closer the points are located to the line, the more qualitative and reliable the linear model. If either r=-1, then r=1 If either r=-1 or r=1, it is a strict linear relationship, in which all empirical points will be on the constructed line. On the contrary, the closer to zero, the more the points are scattered further, the less linear dependence is expressed.

When |r|=1, the relationship is functional.

If |r|≈0, there is no linear relationship between X and Y. However, a non-linear interaction is possible and this requires additional verification.

When assessing a linear multiple relationship, the multiple correlation coefficient is calculated. It reflects the density of the relationship between the dependent variable and the variations of all independent variables included in the analysis:

 ,                                                       (2)

where          – factor dispersion;

 – total dispersion.

The coefficient of determination is the private analog of the empirical coefficient of determination. It represents the square of the correlation coefficient: – the coefficient of determination shows the proportion of variation in the result (physical quantity) Y, which is caused by the influence of a trait-factor (physical quantity) X. The linear coefficient of determination can vary within the range of , and the closer it is to unity, the better the linear model approximates the empirical data.

When assessing the closeness of the relationship between the outcome Y and two factor attributes X1, X2, the multiple correlation coefficient can be determined by the formula:

                              (3)

where r is the pairwise correlation coefficients between the features.

The multiple correlation coefficient varies from 0 to 1 and is a positive value: 0<R<1:

R≤0.3 - there is practically no relationship (either not all important factors of the relationship are taken into account, or the wrong form of the regression equation is chosen; it is necessary to review the variables included in the model and possibly its type);

0.3<R≤0.5 - weak relationship;

0.5<R≤0.7 - moderate relationship;

R>0.7 - strong relationship.

The interpretation of Pearson's correlation coefficient values is contingent upon their absolute magnitudes [18]. Possible values of the correlation coefficient range from 0 to ±1. The greater the absolute value of r_xy, the stronger the relationship between the two variables. r_xy = 0 indicates a complete absence of a relationship. r_xy = 1 signifies the presence of an absolute (functional) relationship. To assess the strength or intensity of the correlation, commonly accepted criteria are typically employed. According to these criteria, absolute values of r_xy < 0.3 denote a weak correlation, values of r_xy from 0.3 to 0.7 indicate a moderate correlation, and values of r_xy> 0.7 suggest a strong correlation. To evaluate the strength of the correlation relationship, Cheddock's scale can be applied. According to this scale, values of the absolute correlation coefficient rxy less than 0.3 indicate a weak strength of correlation, from 0.3 to 0.5 - a moderate strength, from 0.5 to 0.7 - a significant strength, from 0.7 to 0.9 - a high strength, and more than 0.9 - a very high strength of correlation.

9. It is important to check that the writing text clearly expresses and explains each idea and result obtained.

Thank you, improvements have been made

10. A better discussion would be necessary in order to emphasize the main findings.

Thank you, improvements have been made

11. The conclusions needs improvement - highlight the most important findings and identify the added value of the main finding.

Thank you, improvements have been made

12. In conclusion section, limitations and recommendations of this research should be highlighted.

Recommendations made

Dear Reviewer 2,

Thank you again for all your valuable feedback.

Round 2

Reviewer 1 Report

The suggested changes were implemented with satisfactory results. The manuscript after the corrections is suitable for publication in the MDPI Water journal.

Reviewer 2 Report

All the queries have been adressed by the authors and all the comments have been satisfactorily adressed  with supporting literature.

The comments are addressed properly and necessary corrections have been done. The manuscript can be accepted.