Novel Insights into the Vertical Distribution Patterns of Multiple PM_2.5 Components in a Super Mega-City: Responses to Pollution Control Strategies

Round 1

Reviewer 1 Report (Previous Reviewer 1)

Comments and Suggestions for Authors

Compared to the previous version, the author has made certain modifications to the manuscript, particularly strengthening the analysis of the correlation between the research content and remote sensing technology in the section of "Introduction". However, the following issues still persist:

1. In the paper's structure, the author did not separate the Results and Discussion sections. Therefore, please incorporate some discussion appropriately within the Results section.
2. The resolution of all figures in the paper requires improvement.

Author Response

Point-by-point responses are attached.

Author Response File: Author Response.pdf

Reviewer 2 Report (Previous Reviewer 3)

Comments and Suggestions for Authors

Comments:

1. The CNN-BiLSTM-BO model is the core methodology of the manuscript, but its details are barely presented in the text. There is a lack of background discussion on the method, as well as a detailed description of the model. Simply referring to previously published methods is not acceptable—academic articles must be self-contained, meaning that even if existing methods are used, they must still be explained.

2. While the results analysis appears comprehensive, the core deep learning model requires performance evaluation metrics to assess its accuracy.

3. The evaluation method for the retrieved data is also insufficiently described and lacks detail.

4. The data sources section (Chapter 2) is currently fragmented and does not clearly specify the temporal and spatial coverage of the data. It is recommended to use an integrated map to mark the data sources used in the study.

5. There is no explanation of how LiDAR and sun photometer data are integrated to obtain the vertical distribution of PM2.5 components. The specific data fusion method should be clarified.

6. Data presentation should include concrete examples, such as showcasing the data structure for a specific date.

At present, the clarity of the methodology in the manuscript must be improved. In its current form, I do not recommend acceptance.

Author Response

 point-by-point response as attached. 

Author Response File: Author Response.pdf

This manuscript is a resubmission of an earlier submission. The following is a list of the peer review reports and author responses from that submission.

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

I don't think the content of this article is particularly in line with the scope of remote sensing journals, as there hasn't been much use of remote sensing in terms of data and methods. It may be more suitable for environmental journals.

Reviewer 2 Report

Comments and Suggestions for Authors

Review of Manuscript-RS-3419988:

In this study, an aerosol composition and vertical distribution dataset obtained by Lidar was used to study pollution events in the Beijing region. The contributions of emission and meteorological parameters to pollution were studied by using KZ filtering method and reanalysis data. The variation characteristics of pollutants in different periods were analyzed. The study is detailed and well organized, and my comments before considering publication are as follows:

Comments:

1)      I suggest adding a sentence or two to the Abstract and Introduction sections describing the research method or technique to clarify how the “first-ever vertical-temporal continuous dataset” was obtained.

2)      Considering that the generation of a dataset is a focus of this study, so whether there are corresponding validation and the validation results are also suggested to be reflected in the Abstract.

3)      The dataset in this paper is realized based on Lidar observation, so the corresponding Lidar technology for aerosol component should also be explained and summarized in the Introduction section. Such as Veselovskii et al. 2024 AMT, 10.5194/amt-17-4137-2024

4)      Abbreviations for each aerosol component are recommended in the form of initial capital letters or ions, such as SO₄→SO₄⁺, to avoid misunderstanding.

5)      If possible, please add a main formula to illustrate the application of KZ filtering in this study. In addition, please clarify how seasonal and long-term contributions can be inferred from around 1 month of observational data.

6)      Fig.1S and Fig.2S show the comparison between observations and Lidar results. It is good! But what is the source of the observational data, such as the observation instrument, frequency and vertical resolution, which need to be clarified.

7)      Lines 168-170: What kind of pollutant does this describe?

8)      I feel that the header of Table 2 may be wrong, please check and clarify.

9)      Line 229-230: How to explain the correlation between RH and NO3 seems to show an altitude-related trend.

10)   Fig.4c lacks legend.

Reviewer 3 Report

Comments and Suggestions for Authors

The article is highly timely, with rich results and offers references for policy-making. However, there is room for improvement in highlighting its innovations, providing methodological details and optimizing figures. Below are my specific comments:

1. In the introduction, it is recommended to further clarify the differences between this study and previous research, such as the advantages of high temporal resolution data and the uniqueness of the research methodology.

2. The article needs to explicitly explain how the study contributes to more precise pollution control strategies.

3. The clarity of the methodology must be improved. The manuscript does not adequately present the structure of the deep learning model used. Simply referencing previous studies in a concise manner is insufficient. If the model used is the same as in other studies, the contribution of this paper may appear limited to using a different dataset.

4. It is suggested to reorganize the data section into a standalone chapter. The article should provide detailed descriptions of the data used, avoiding references to other studies. Additionally, including a map for the studied area is recommended to better illustrate the research context.

5. The figures currently appear to have low resolution, possibly due to compression. This should be optimized in the final version.