Exploring the Potential of Autonomous Underwater Vehicles for Microplastic Detection in Marine Environments: A Systematic Review

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

This paper is well organized overall in relation to the topic presented, and it seems to be well supported by various references. However, it seems that the accuracy of the flow of sentences and the referenced literature needs to be checked once more.

1. It seems that the survey was conducted on a general AUV, but it is not easy to detect microplastics in the vast ocean, and observations should be conducted from a long-term perspective. Therefore, an explanation is needed as to why the survey was conducted on a general AUV, not on a platform that can be operated for a long time, such as an underwater glider or LDUUV.

2. The equipment to be installed on the AUV must satisfy the following requirements: small size, stability, and low power. This is not limited to equipment for detecting microplastics. Therefore, an accurate explanation of the detection technology and sensors presented in the paper seems necessary. 

3. Microplastics are an important issue that needs to be resolved in relation to environmental pollution. However, I would like to request additional explanation on what aspects of the potential of AUV are discussed in this paper, other than the fact that AUVs can be utilized. The reason is that efforts to utilize AUV as a single platform for various applications have been ongoing from the past to the present, and please explain what role it can play in the field of microplastics.

Author Response

Comment 1: It seems that the survey was conducted on a general AUV, but it is not easy to detect microplastics in the vast ocean, and observations should be conducted from a long-term perspective. Therefore, an explanation is needed as to why the survey was conducted on a general AUV, not on a platform that can be operated for a long time, such as an underwater glider or LDUUV.

Response 1: Thank you for pointing this out. Regarding why we chose AUVs for microplastic detection, we agree with the Reviewer's comment and added a new paragraph and table (see the paragraph below, which was added on page 5, paragraph 2.2, lines 158-173). It states the features of platforms which include AUVs, gliders, Remotely Operated Vehicles (ROVs), and Large Displacement Unmanned Underwater Vehicles (LDUUVs), which explains why the survey was carried out on a general AUV rather than a glider, ROV or LDUUV.

“Identifying MPs in marine environments is challenging due to their small size, motility in the water column, and their wide distribution. Monitoring MP presence is important due to the threats against marine animals and other life. When considering the detection of underwater MPs, numerous platforms could be operated, including AUVs, gliders, Remotely Operated Vehicles (ROVs), and Large Displacement Unmanned Underwater Vehicles (LDUUVs). Table 1 shows the key features of the platforms, which can carry MP sensors to detect targets in-situ. A platform offering high operational flexibility, substantial payload capacity, long endurance, and low cost is preferred. Compared to other platforms, AUVs demonstrate the highest alignment with these requirements. Gliders can serve for very long-duration missions, but the drawbacks are limited maneuverability and low payload capacity[24,25]. ROVs operate in real-time but are tethered to a surface vessel and have range limitations. LDUUVs conduct longer missions and have larger payload capacity, but the cost is unbearable. AUVs, such as the International Submarine Engineering’s Explorer, provide a pragmatic compromise for autonomy, duration of operation and cost compared to gliders, ROVs and LDUUVs, and therefore are suitable for in-situ monitoring and mapping of MPs located in a deep and complex marine environment.”

Comment 2: The equipment to be installed on the AUV must satisfy the following requirements: small size, stability, and low power. This is not limited to equipment for detecting microplastics. Therefore, an accurate explanation of the detection technology and sensors presented in the paper seems necessary.

 

Response 2: Thank you for the insightful comment. In response, we have enhanced Section 5 by explaining the detection technologies and their main requirements for integration with AUVs. The following section has been added on pages 22-24, paragraph 5, lines 599-654.

 

"HSI can detect semi-transparent MP in water environments and may achieve rapid, non-destructive chemical identification through spectral signatures, but it is expensive, requires substantial computational and power resources, and few researchers have tried integrating it into AUVs. Plasmonic sensors offer high sensitivity with proper configuration, label‑free detection, strong multiplexing, and miniaturization potential. They can detect MPs in seawater without sample pretreatment. However, complicated instrumentation needs and limited validation in real marine environments limit their application to field MP detection. Fluorescent biosensors monitor MPs by measuring fluorescence intensity changes or emission wavelengths, which gives a high sensitivity. They are an emerging approach and are changing environmental monitoring. However, such sensors are still in the laboratory stages and require complex instrumentation and are subject to the possibility of fluorescent agent leakage due to the dynamic ocean environment. 

 

Electrochemical sensors are portable, low-cost platforms that can be used for in-situ analysis quickly and straightforwardly, with multiplexing capabilities for measuring different MPs. The potential for electrochemical sensors to be integrated into AUVs relies on their miniaturization possibility and low power requirements, which makes them well-suited for integration with microfluidic systems to achieve real-time monitoring. However, data processing is elaborate, and sample pH, ionic strength, matrix effects and surface/electrode fouling can interfere with detection accuracy. Portable optical sensors, handheld devices with a CCD camera for MP detection, combine the simultaneous measurement of specular laser light reflection and transmitted interference patterns from MPs. This method uses a photodiode to record reflection signals and a CCD camera to capture interference patterns, and allows screening of MP (including PET and LDPE) type and size. Currently, the method detects transparent and translucent MPs. 

 

The Machine Learning-Based Intelligent Detection with a Polarization Camera method works by simultaneously capturing holographic interference patterns and polarization states of light at four angles (0°, 45°, 90° and 135°) to characterize the polarization state of scattered light from MP samples. The system offers multiple significant features, including no sample preparation, real-time monitoring, accurate detection and automatic counting of MPs in aqueous environments. However, the detection speed is relatively slow, approximately 8 ml/min, and cannot currently be matched at AUV cruising speeds of 1.5 m/s. Polarized light scattering enables contactless MPs detection in water with long analysis times. Terahertz-based microfluidics metamaterial analysis offers rapid analysis but has constrained sample volumes due to equipment limitations. Surface nanodroplet microfluidics combined with Raman spectroscopy can have particle detection sensitivity down to the single particle level. However, they require sophisticated microfluidic devices. Triboelectric sensors have the potential to be low-cost, versatile, and rapid approaches for detecting MPs with a wide size range in real time, but the flow rate must be stable. Underwater microscopy provides a method for direct morphological observations but lacks the capability for component analysis.

 

Integrating these technologies into AUV platforms to conduct the MP detection task presents several engineering and operational challenges. High-resolution techniques like HSI require significant power, space, and computational capacity, usually exceeding general AUVs' payload and energy budgets. Microfluidic and triboelectric sensors require a stable flow rate, which may be disrupted by vehicle movement. Furthermore, most MP detection devices in deep-sea environments typically require pressure-resistant housings and robust calibration procedures to maintain performance in high-pressure, low-temperature environments. Achieving real-time onboard analysis for imaging-based or spectroscopy-based detection is constrained by the limited computational resources of AUVs, which highlights the need for lightweight data-processing algorithms. Mounting heavy devices on an AUV can increase drag and alter buoyancy, reducing its endurance and maneuverability, and lighter devices are preferable. Addressing these constraints will be critical for transitioning laboratory-based MP detection methods into practical, field-ready methods for AUV usage."

Comment 3: Microplastics are an important issue that needs to be resolved in relation to environmental pollution. However, I would like to request additional explanation on what aspects of the potential of AUV are discussed in this paper, other than the fact that AUVs can be utilized. The reason is that efforts to utilize AUV as a single platform for various applications have been ongoing from the past to the present, and please explain what role it can play in the field of microplastics.

 

Response 3: We agree with the comments of the reviewer and have added the paragraph below, which was added on pages 5-6, paragraph 2.2, lines 174-184. We discussed the potential of AUVs and their role in microplastic research.

 

"AUVs hold substantial potential for MP research because they can autonomously map MP distributions across horizontal or three‑dimensional scales in the ocean. They can perform adaptive, targeted sampling in dynamic or hard‑to‑reach environments, such as deep‑sea habitats, polar regions or areas with strong currents, where traditional platforms (including traditional vessels) are limited. Additionally, AUVs are versatile platforms for integrating advanced sensors (including optical, electrochemical, and microfluidic devices), allowing real‑time detection of MPs' physical or chemical characteristics and other key environmental parameters (including temperature, salinity, and pressure). AUVs are one of the promising solutions for MP sampling, characterization, mapping, and monitoring, as well as providing insights for uncovering the sources, sinks, and ecological effects of MPs in marine ecosystems, which helps address marine pollution."

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

This study presents a comprehensive review of current and emerging MP detection methods suitable for integration with AUVs for in-situ monitoring. Given the growing use of bibliometric approaches in mapping research landscapes, this work holds some value. However, several issues should be addressed during revision to strengthen its rigor and impact.

1. This paper adopts the research methodology outlined in Figure 4. So, is the statistical scope and quantity of literature data in this paper 74? There are two questions here. Why is the number of your literature the same as the cited literature from 2021? Additionally, for bibliometric analysis, 74 articles seem insufficient.
2. The keyword cluster analysis should be expanded to include keyword co-occurrence, clustering patterns, and burst for greater depth and insight.
3. The outlook on future MP detection methods is overly general and lacks original, critical reflections from the authors.
4. Figure 2 suffers from low resolution, impairing its clarity and interpretability.
5. Subsection 4.3 should incorporate quantitative evidence to support bibliometric findings, such as the impact of threshold settings in keyword clustering on result stability. Additionally, several key studies are missing, including: 10.1016/j.dt.2024.09.006, 10.23919/JSEE.2022.000042, and 10.3390/acoustics6040055, which should be integrated into the analysis.
6. A brief introduction to VOSviewer (e.g., its purpose, functionality, and official website) should be added, with 1–2 explanatory sentences.

Author Response

Comment 1: This paper adopts the research methodology outlined in Figure 4. So, is the statistical scope and quantity of literature data in this paper 74? There are two questions here. Why is the number of your literature the same as the cited literature from 2021? Additionally, for bibliometric analysis, 74 articles seem insufficient.

Response 1: We appreciate your detailed comment and in-depth reading. Regarding the reviewed paper number 74, first, I assume "the cited literature from 2021" refers to the article "State‑of‑the‑Art Review on Application of Unmanned Aerial Vehicles (UAVs) in the Inspection of Power Lines: Present Innovations, Trends, Future Directions," which provided a review of 75 articles. We reviewed 74 articles purely by coincidence. According to the paper searching code included in the Data Availability Statement on page 25, the primary paper search used the Semantic Scholar API with some custom code to get strongly related articles. In the searching code, keywords must appear in the title or the abstract so that the search paper's relevance to our research is highly related. After removing duplicates, we only selected high-calibre publication venues, removed poorly cited articles, and disregarded irrelevant articles. Manually, we added other related articles to the final database. Thus, this resulted in 74 articles to review.

Our literature review primarily resulted in only a few highly relevant AUV and microplastic detection publications. When we initially queried ["AUV", "Microplastic"], we only returned six papers based on our search rules (keywords must appear in the title or the abstract). In our following query of ["AUV", "Plastic"], we returned a total of 20 papers. Our eventual query: ["Microplastic", "Detection", "Method"] returned 542 papers. After filtering and identifying papers, we only kept six papers related to AUV detection of microplastics and plastics. The other 68 papers are related to the MP detection method (many papers identified had the same repeated techniques). Adding more paper in the MP detection method offers a limited additional benefit. 

Comment 2: The keyword cluster analysis should be expanded to include keyword co-occurrence, clustering patterns, and burst for greater depth and insight.

Response 2: Thank you for the comment. In response, we added a new paragraph on page 20, paragraph 4.3, lines 546-554, to provide a more detailed interpretation of the keyword co-occurrence, clustering patterns, co-occurrence relationships, and emerging research trends. 

"Co-occurrence analysis demonstrates that the nodes 'microplastic' and 'microplastics' are the most common and are associated with detection-related technologies (including Raman, microscopy and sensors) and context areas of research (including wastewater, debris, and pollution). It also shows the association between analytical approaches and environmental monitoring topics. The extensiveness of this co-occurrence analysis also demonstrates that the centre nodes connect across various research fields and help researchers identify gaps for future research. The analysis found various keywords, such as holographic, YOLOv5, and sensor, which reflect the existence of MP detection methods in the fields of imaging technology, neural network models, and advanced sensor technology."

Comment 3: The outlook on future MP detection methods is overly general and lacks original, critical reflections from the authors.

Response 3: We appreciated your comment, and we added content below on pages 22-24, paragraph 5, lines 599-654, to provide more details and insights into future MP detection methods.

"Hyperspectral imaging (HSI) can detect semi-transparent MP in water environments and may achieve rapid, non-destructive chemical identification through spectral signatures, but it is expensive, requires substantial computational and power resources, and few researchers have tried integrating it into AUVs. Plasmonic sensors offer high sensitivity with proper configuration, label‑free detection, strong multiplexing, and miniaturization potential. They can detect MPs in seawater without sample pretreatment. However, complicated instrumentation needs and limited validation in real marine environments limit their application to field MP detection. Fluorescent biosensors monitor MPs by measuring fluorescence intensity changes or emission wavelengths, which gives a high sensitivity. They are an emerging approach and are changing environmental monitoring. However, such sensors are still in the laboratory stage and require complex instrumentation and are subject to the possibility of fluorescent agent leakage due to the dynamic ocean environment. 

Electrochemical sensors are portable, low-cost platforms that can be used for in-situ analysis quickly and straightforwardly, with multiplexing capabilities for measuring different MPs. The potential for electrochemical sensors to be integrated into AUVs relies on their miniaturization possibility and low power requirements, which makes them well-suited for integration with microfluidic systems to achieve real-time monitoring. However, data processing is elaborate, and sample pH, ionic strength, matrix effects and surface/electrode fouling can interfere with detection accuracy. Portable optical sensors, handheld devices with a CCD camera for MP detection, combine the simultaneous measurement of specular laser light reflection and transmitted interference patterns from MPs. This method uses a photodiode to record reflection signals and a CCD camera to capture interference patterns, and allows screening of MP (including PET and LDPE) type and size. Currently, the method detects transparent and translucent MPs. 

The Machine Learning-Based Intelligent Detection with a Polarization Camera method works by simultaneously capturing holographic interference patterns and polarization states of light at four angles (0°, 45°, 90° and 135°) to characterize the polarization state of scattered light from MP samples. The system offers multiple significant features, including no sample preparation, real-time monitoring, accurate detection and automatic counting of MPs in aqueous environments. However, the detection speed is relatively slow, approximately 8 ml/min, and cannot currently be matched at AUV cruising speeds of 1.5 m/s. Polarized light scattering enables contactless MPs detection in water with long analysis times. Terahertz-based microfluidics metamaterial analysis offers rapid analysis but has constrained sample volumes due to equipment limitations. Surface nanodroplet microfluidics combined with Raman spectroscopy can have particle detection sensitivity down to the single particle level. However, they require sophisticated microfluidic devices. Triboelectric sensors have the potential to be low-cost, versatile, and rapid approaches for detecting MPs with a wide size range in real time, but the flow rate must be stable. Underwater microscopy provides a method for direct morphological observations but lacks the capability for component analysis.

Integrating these technologies into AUV platforms to conduct the MP detection task presents several engineering and operational challenges. High-resolution techniques like HSI require significant power, space, and computational capacity, usually exceeding general AUVs' payload and energy budgets. Microfluidic and triboelectric sensors require a stable flow rate, which may be disrupted by vehicle movement. Furthermore, most MP detection devices in deep-sea environments typically require pressure-resistant housings and robust calibration procedures to maintain performance in high-pressure, low-temperature environments. Achieving real-time onboard analysis for imaging-based or spectroscopy-based detection is constrained by the limited computational resources of AUVs, which highlights the need for lightweight data-processing algorithms. Mounting heavy devices on an AUV can increase drag and alter buoyancy, reducing its endurance and maneuverability, and lighter devices are preferable. Addressing these constraints will be critical for transitioning laboratory-based MP detection methods into a practical, field-ready method for AUV usage."

Comment 4: Figure 2 suffers from low resolution, impairing its clarity and interpretability.

Response 4: We agree and have replaced Figure 2 with a higher-resolution image of the Explorer AUV to improve clarity and interpretability, and added on page 6, paragraph 2.2, lines 192-194, using the image below.

Comment 5: Subsection 4.3 should incorporate quantitative evidence to support bibliometric findings, such as the impact of threshold settings in keyword clustering on result stability. Additionally, several key studies are missing, including: 10.1016/j.dt.2024.09.006, 10.23919/JSEE.2022.000042, and 10.3390/acoustics6040055, which should be integrated into the analysis.

Response 5: Thank you for pointing this out. We added the content below on page 18, paragraph 4.3, lines 520-528, to describe how threshold settings impact stability in clustering results. Moreover, we also added the three missing studies on page 19, paragraph 4.3, line 532.

"In the VOSviewer software, adjusting the threshold (the minimum number of occurrences of a keyword) from 2 to 4 modified the cluster group number. The total keywords are 44, when the threshold is 2, and the number of clusters is 9. When the threshold becomes 3, the total number of extracted keywords drops from 44 to 18, and the number of clusters drops to 4 with lower link density. A threshold of 4 allows for 13 keywords, and the number of clusters is 4. A lower threshold can capture broad themes and more keywords, while a higher threshold contains more stable core clusters showing more frequent, connected, and dominant themes. "

Comment 6: A brief introduction to VOSviewer (e.g., its purpose, functionality, and official website) should be added, with 1–2 explanatory sentences.

Response 6: We have included a brief introduction for VOSviewer on page 18, paragraph 4.3, lines 516-520.

"VOSviewer (v1.6.20) can be found at https://www.vosviewer.com, and was developed at Leiden University to construct and visualize bibliometric networks based on co-citation, bibliographic coupling, and co‑authorship relations. VOSviewer provides mapping functions (including Network Map, Overlay Map, and Density Map) to visualize the structure of main topics or authors and trends or emerging interdisciplinary areas."

Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

This work presents a comprehensive review of AUV-based microplastic detection. Overall, this survey is well-structured and presented. This paper successfully synthesizes a large body of literature, clearly identifies the current state-of-the-art, and outlines promising future research directions. Notably, the methodology of this survey is clearly presented and schematic, which is valuable for both the marine technology and environmental science communities. I only have some minor suggestions.

 

1. The analysis in Section 4.1.1 and Figure 5 suggests that high-quality research activity in this field "has passed its peak and is currently declining". This is a strong assertion based on data that is incomplete for 2024 and speculative for 2025. Publication counts for the most recent years are naturally lower in any mid-year analysis. Moreover, it is also possible for a fluctuation to occur within a few years. Thus, this statement should be rephrased with more caution to avoid over-interpretation of the data. For instance, the authors could state that, while there was a peak around 2020-2023, the trend in subsequent years remains inconclusive.

 

2. Section 5 discusses the potential technologies for AUV integration. To further strengthen this section, beyond the current general statements, the authors could connect the discussion more closely to the unique engineering or operational challenges associated with MP detection tasks.

 

3. The paper is generally well-written. However, some phrasing could be made more formal for an academic audience (e.g., "MPs in marine settings are vexing to detect").

 

4. The authors should ensure consistent formatting and terminology across them. For example, Table 8 lists "μ-Raman" while the text sometimes refers to "Micro-Raman Spectroscopy".

 

5. Given the keyword analysis also highlights "holographic" as a recent research area, it might be beneficial to slightly expand on how this technology works and its specific potential and challenges for in-situ MP detection beyond the single mention.

 

6. Please double-check the list of abbreviations to ensure all abbreviations used in the text are defined in this list and are used consistently after their first definition. For example, HSI should be short for Hyperspectral Imaging rather than the current Syperspectral Imaging.

Author Response

Comment 1: The analysis in Section 4.1.1 and Figure 5 suggests that high-quality research activity in this field "has passed its peak and is currently declining". This is a strong assertion based on data that is incomplete for 2024 and speculative for 2025. Publication counts for the most recent years are naturally lower in any mid-year analysis. Moreover, it is also possible for a fluctuation to occur within a few years. Thus, this statement should be rephrased with more caution to avoid over-interpretation of the data. For instance, the authors could state that, while there was a peak around 2020-2023, the trend in subsequent years remains inconclusive.

Response 1: Thank you for pointing this out. We revised this statement on page 10, paragraph 4.1.1, lines 268-271 to avoid overinterpretation.

"A peak in publications occurred between 2020 and 2023. Although there was a decline in publications in 2024 and 2025, this could reflect natural fluctuations, and since 2025 is still ongoing, the long-term trend is still uncertain."

Comment 2: Section 5 discusses the potential technologies for AUV integration. To further strengthen this section, beyond the current general statements, the authors could connect the discussion more closely to the unique engineering or operational challenges associated with MP detection tasks.

Response 2: Thank you for pointing this out. Agree. We added the following paragraph on page 24, paragraph 5, lines 642-654, to clarify these engineering constraints.

"Integrating these technologies into AUV platforms to conduct the MP detection task presents several engineering and operational challenges. High-resolution techniques like HSI require significant power, space, and computational capacity, usually exceeding general AUVs' payload and energy budgets. Microfluidic and triboelectric sensors require a stable flow rate, which may be disrupted by vehicle movement. Furthermore, most MP detection devices in deep-sea environments typically require pressure-resistant housings and robust calibration procedures to maintain performance in high-pressure, low-temperature environments. Achieving real-time onboard analysis for imaging-based or spectroscopy-based detection is constrained by the limited computational resources of AUVs, which highlights the need for lightweight data-processing algorithms. Mounting heavy devices on an AUV can increase drag and alter buoyancy, reducing its endurance and maneuverability, and lighter devices are preferable. Addressing these constraints will be critical for transitioning laboratory-based MP detection methods into a practical, field-ready method for AUV usage."

Comment 3: The paper is generally well-written. However, some phrasing could be made more formal for an academic audience (e.g., "MPs in marine settings are vexing to detect").

Response 3: Thanks for your time. Rephrased informal phrasing (e.g., “MPs in marine settings are vexing to detect”) to an academic tone throughout on page 5, paragraph 2.2, lines 158-159. 

"Identifying MPs in marine environments is challenging due to their small size, motility in the water column, and their wide distribution."

Comment 4: The authors should ensure consistent formatting and terminology across them. For example, Table 8 lists "μ-Raman" while the text sometimes refers to "Micro-Raman Spectroscopy".

Response 4: Appreciated. We kept the terminology consistent by replacing 'Micro-Raman Spectroscopy' with 'μ‑Raman' on page 14, paragraph 4.2.2, line 376 and replacing 'Micro‑Fourier Transform Infrared Spectroscopy' with 'μ‑FTIR' on page 14, paragraph 4.2.2, lines 370.

Comment 5: Given the keyword analysis also highlights "holographic" as a recent research area, it might be beneficial to slightly expand on how this technology works and its specific potential and challenges for in-situ MP detection beyond the single mention.

Response 5: Thank you for pointing this out. Added on page 12, paragraph 4.2.1, lines 306-315, a brief description of a holographic camera.

“Thevar et al. (2023)[53] developed a lightweight system called weeHoloCam, which is compatible with AUV platforms and employs a holographic imaging technique to detect fine particles in marine environments. A holographic camera reconstructs 3-dimensional images of particles via interference patterns of light waves to acquire images from a distance using weeHoloCam. It is a good option for in-situ analysis to obtain particle images in a marine environment. However, there are still challenges to overcome. The amount of intense computing required to reconstruct holographic images is very high. AUVs have limited power supplies and computing capabilities to support the analyses. If these limitations can be overcome, holographic camera systems have great potential as autonomous monitoring systems for MPs in marine environments.”

Comment 6: Please double-check the list of abbreviations to ensure all abbreviations used in the text are defined in this list and are used consistently after their first definition. For example, HSI should be short for Hyperspectral Imaging rather than the current Syperspectral Imaging.

Response 6: Thanks for your time. We double-checked the list of abbreviations to ensure all abbreviations used in the text are defined in this list, and added contents below on pages 25-26, section Abbreviations, lines 701-706 

“

MPs & Microplastics

DVL & Doppler Velocity Log

LDPE & Low-density Polyethylene

ML & Machine learning

AI & Artificial Intelligence

PET & Polyethylene Terephthalate

AUVs & Autonomous Underwater Vehicles

NSERC & Natural Sciences and Engineering Research Council

LDUUVs &  Large Displacement Unmanned Underwater Vehicles

SAN & Styrene-Acrylonitrile Copolymer

WHOI & Woods Hole Oceanographic Institution 

SPURV & Special Purpose Underwater Research Vehicle

”

A confirmed and updated list of abbreviations (adjusted HSI's abbreviations to Hyperspectral Imaging) and checked consistently for usage after their first use, corrected microplastic and microplastics. 

Author Response File: Author Response.pdf

Round 2

Reviewer 2 Report

Comments and Suggestions for Authors

A good revision, and it could now be accepted for publication.

Reviewer 3 Report

Comments and Suggestions for Authors

The authors have perfectly solved my concerns, and now I recommend acceptance.