PCLM-D-25-00483

Principal Component based Predictive Modelling of Particulate Matters using Air Pollutants and Meteorological Variables in Bangkok, Thailand

PLOS Climate

Dear Dr. Saeyang,

Thank you for submitting your manuscript to PLOS Climate. After careful consideration, we feel that it has merit but does not fully meet PLOS Climate’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Please submit your revised manuscript by Mar 26 2026 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at climate@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pclm/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

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We look forward to receiving your revised manuscript.

Kind regards,

Jing Chen

Academic Editor

PLOS Climate

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Additional Editor Comments (if provided):

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

-->Comments to the Author

1. Does this manuscript meet PLOS Climate’s publication criteria? Is the manuscript technically sound, and do the data support the conclusions? The manuscript must describe methodologically and ethically rigorous research with conclusions that are appropriately drawn based on the data presented.-->

Reviewer #1: No

Reviewer #2: Partly

Reviewer #3: Yes

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-->2. Has the statistical analysis been performed appropriately and rigorously?-->

Reviewer #1: No

Reviewer #2: Yes

Reviewer #3: Yes

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-->3. Have the authors made all data underlying the findings in their manuscript fully available (please refer to the Data Availability Statement at the start of the manuscript PDF file)?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception. The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.-->

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

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-->4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS Climate does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.-->

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

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-->5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)-->

Reviewer #1: The references are not well arranged and do not follow a particular referencing style (APA, Harvard, Chicago, etc.). The authors should produce a neat and well-arranged reference list with a particular/consistent style.

Find attached for major comments.

Reviewer #2: This manuscript investigates the relationships between particulate matter (PM2.5 and PM10), co-pollutants, and meteorological variables in Bangkok, Thailand, using data from four urban monitoring stations covering the period 2017–2023. The authors apply principal component analysis (PCA) to reduce dimensionality and multicollinearity and then use the resulting principal components as inputs to several machine-learning models (PCR, PC-SVR, PC-RF, and PC-XGBoost). The study finds strong positive correlations between PM2.5 and PM10 and moderate positive correlations with CO, NO2, and O3, while wind speed, wind direction, and relative humidity are generally negatively correlated with PM concentrations. Among the tested models, PC-SVR consistently outperforms the others across all stations for both PM2.5 and PM10 prediction. The topic is relevant to urban air-quality management, and the paper demonstrates a reasonable application of PCA-based machine-learning approaches. However, several methodological and presentation issues limit the scientific rigor and reproducibility of the study in its current form. I therefore recommend major revision.

Major revisions:

1. In Section 2.1 and Table 1, the dataset includes substantial proportions of missing values for several air pollutant and meteorological variables. To strengthen the robustness of the analysis, it would be helpful to include a brief assessment of the missing-data mechanism (e.g., MCAR, MAR, or MNAR) and to discuss how the extent of missingness may influence the PCA results and subsequent machine-learning models.

2. In Section 2.3 (Study Procedure), missing values are imputed using Predictive Mean Matching (PMM). To improve transparency and reproducibility, consider providing additional justification for the choice of PMM and including a sensitivity analysis, such as comparing results obtained using alternative imputation methods or reduced-variable subsets.

3. In Section 2.3, the sequence of data preprocessing, PCA, and model training would benefit from clearer description. In particular, clarifying whether PCA was fitted using only the training data or the full dataset prior to the train–test split would help readers assess the independence of model evaluation. If PCA was applied before splitting, revising the workflow accordingly would strengthen the methodological rigor.

4. In Section 3 (Results), four principal components are retained for all stations. Providing station-specific cumulative variance explained by the selected components and a quantitative rationale for using a fixed number of components across stations would enhance the statistical justification of this choice.

5. In Section 2.3, the use of a random 80:20 train–test split for daily time-series data spanning multiple years would benefit from additional discussion. Addressing temporal autocorrelation and seasonality, and clarifying the suitability of random splitting versus time-aware validation strategies, would strengthen the interpretation of predictive performance.

6. In Section 1 (Introduction), the manuscript would benefit from a more explicit statement of its primary scientific or methodological contribution relative to existing PCA–machine-learning studies of particulate matter. Clearly identifying how this work advances current understanding would help frame the significance of the study.

7. In Section 4 (Discussion), the discussion could be strengthened by linking the identified principal components and differences in model performance to known urban meteorological processes and emission characteristics in Bangkok, providing deeper physical interpretation of the results.

Minor revisions:

1. Ensure that all four air quality monitoring stations are clearly labeled and distinguishable in Figure 1, including station names or abbreviations, to improve interpretability of the study area map.

2. In Section 2.1 (Study Area and Data Descriptions), consider explicitly stating the spatial setting of each station (e.g., roadside, residential, or mixed urban environment), as this information would help contextualize differences in pollutant levels across stations.

3. In Section 2.3 (Study Procedure), include a brief clarification on the software environment (e.g., operating system, package versions beyond R version) used for data preprocessing and modeling to further support reproducibility.

4. In Section 2.2.3–2.2.5 (Machine Learning Models), specify whether default hyperparameter settings or tuning procedures were used for SVR, Random Forest, and XGBoost, and indicate the criteria used to select final model configurations.

5. In Figures 6 and 7, consider adding a short note in the figure captions specifying the evaluation dataset (training vs. testing) and the number of principal components used in each model to aid interpretation of the performance metrics.

6. In Section 3 (Results), reporting the cumulative percentage of variance explained by the selected principal components for each station would help readers better assess the effectiveness of the dimensionality reduction.

7. In Section 2.2.1 and 2.2.2, ensure consistent notation and formatting of mathematical symbols across Equations (1)–(3), and clarify the meaning of each symbol at first use to avoid ambiguity.

8. In Section 3 (Results), page 10, ensure that all correlation coefficients reported in the text use consistent decimal precision and formatting across stations and variables.

9. In the Data Availability / Availability Statement, consider providing a direct link or repository reference for the dataset, along with any relevant documentation, to fully support transparency and reproducibility in line with journal policies.

10. Throughout the manuscript, review typographic consistency in units (e.g., µg/m³, mg/m³), abbreviations, and capitalization to ensure uniform formatting across sections, tables, and figures.

Reviewer #3: PCLM-D-25-00483_comments

1. For Table 1, units not mentioned; mention the missing data as a percentage.

2. Section 2.2.1 & 2.2.2 has same titles

3. The authors mentioned in section 2.3 that data sets were imputed for each station, but it is not clear whether you actually filled in the missing data. Did you remove the outliers? Clearly mention.

4. The Pollutant concentrations will have diurnal variations. In your study, did you use the day’s mean or the peak concentration on that day?

5. It is noted that pollutant SD values are nearly 50% for almost all variables. What could be the reason for that?

6. Did you find any intra-seasonal or inter-seasonal variability?

7. What are the major seasons or months that have peak pollutant levels?

8. What are the factors contributing to increase the pollutant levels?

9. The discussion and conclusion sections were very short and inadequate. If the journal format supports, extend those sections comprehensively.

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Reviewer #1: No

Reviewer #2: No

Reviewer #3: No

**********

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Attachments

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Submitted filename: PCLM-D-25-00483_Review.docx

PCLM-D-25-00483R1

Principal Component based Predictive Modelling of Particulate Matters using Air Pollutants and Meteorological Variables in Bangkok, Thailand

PLOS Climate

Dear Dr. Saeyang,

Thank you for submitting your manuscript to PLOS Climate. After careful consideration, we feel that it has merit but does not fully meet PLOS Climate’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Please submit your revised manuscript by Jun 07 2026 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at climate@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pclm/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

• A letter that responds to each point raised by the editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
• A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
• An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

As the corresponding author, your ORCID iD is verified in the submission system and will appear in the published article. PLOS supports the use of ORCID, and we encourage all coauthors to register for an ORCID iD and use it as well. Please encourage your coauthors to verify their ORCID iD within the submission system before final acceptance, as unverified ORCID iDs will not appear in the published article. Only the individual author can complete the verification step; PLOS staff cannot verify ORCID iDs on behalf of authors.

We look forward to receiving your revised manuscript.

Kind regards,

Jing Chen

Academic Editor

PLOS Climate

Journal Requirements:

If the reviewer comments include a recommendation to cite specific previously published works, please review and evaluate these publications to determine whether they are relevant and should be cited. There is no requirement to cite these works unless the editor has indicated otherwise.

Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

Additional Editor Comments (if provided):

Reviewer #2:

The revision is a meaningful improvement. The core methodological concerns, baseline comparisons, wind direction transformation, variance explained reporting, chronological splitting, and hyperparameter disclosure, have been addressed. The remaining issues are largely matters of transparency (PCA leakage caveat, imputation sensitivity evidence, MNAR/MAR tension) and consistency (best-model claims across metrics). These can be resolved with textual clarifications and minor additions without requiring new experiments. I recommend acceptance after these minor revisions are addressed.

Minor revisions:

1. PCA applied before train-test split (data leakage concern not fully resolved). The authors acknowledge in their response that "PCA was applied to the full dataset to capture the most stable PCs" (line 229–230). While the flowchart has been updated, applying PCA to the entire dataset before splitting introduces information from the test set into the component structure, which can lead to optimistically biased performance estimates. The authors should either (a) refit PCA on the training set only and project the test set onto those components, or (b) explicitly acknowledge this as a limitation and discuss how it may affect the reported metrics. A brief justification for why the current approach was chosen (e.g., stability of components) would be acceptable if accompanied by this caveat.

2. Imputation sensitivity analysis is mentioned but not shown. The authors state they "applied alternative imputation methods, including K-Nearest Neighbors (KNN) and Support Vector Regression (SVR)" and that "MICE-PMM provides the best imputation performance." However, no results from these alternative methods appear in the manuscript, no comparison table, no diagnostic plots, and no definition of what "best imputation performance" means (which metric was used). Either include a brief supplementary table comparing imputation methods on a relevant metric or remove the claim, as it currently lacks supporting evidence.

3. The cumulative variance threshold of 60% is low and needs stronger justification. Table 3 shows the four retained PCs explain between 61.10% (NV) and 64.51% (RU) of total variance. While the Kaiser criterion (eigenvalue > 1) is satisfied, retaining only ~61–65% of variance means roughly 35–39% of the original information is discarded. The commonly cited threshold in the literature is 70–80%. The authors should briefly discuss why a ~60% threshold is acceptable for this application and whether retaining a fifth component (which would bring cumulative variance to ~70%) would change the model comparison results.

4. Figure 3 correlation matrices, significance notation needs clarification. The revised correlation plots use "*" for significant and "Na" for non-significant correlations (line 266–267). The use of "Na" may be confused with missing data ("NA"). Consider using "ns" (not significant) instead, and add the significance threshold (p < 0.05) to the figure caption rather than only in the text.

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

-->Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.-->

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

Reviewer #3: All comments have been addressed

**********

-->2. Does this manuscript meet PLOS Climate’s publication criteria? Is the manuscript technically sound, and do the data support the conclusions? The manuscript must describe methodologically and ethically rigorous research with conclusions that are appropriately drawn based on the data presented.-->

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

**********

-->3. Has the statistical analysis been performed appropriately and rigorously?-->

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

**********

-->4. Have the authors made all data underlying the findings in their manuscript fully available (please refer to the Data Availability Statement at the start of the manuscript PDF file)?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception. The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.-->

Reviewer #1: Yes

Reviewer #2: No

Reviewer #3: Yes

**********

-->5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS Climate does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.-->

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

**********

-->6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)-->

Reviewer #1: All comments have been addressed

Reviewer #2: The revision is a meaningful improvement. The core methodological concerns, baseline comparisons, wind direction transformation, variance explained reporting, chronological splitting, and hyperparameter disclosure, have been addressed. The remaining issues are largely matters of transparency (PCA leakage caveat, imputation sensitivity evidence, MNAR/MAR tension) and consistency (best-model claims across metrics). These can be resolved with textual clarifications and minor additions without requiring new experiments. I recommend acceptance after these minor revisions are addressed.

Minor revisions:

1. PCA applied before train-test split (data leakage concern not fully resolved). The authors acknowledge in their response that "PCA was applied to the full dataset to capture the most stable PCs" (line 229–230). While the flowchart has been updated, applying PCA to the entire dataset before splitting introduces information from the test set into the component structure, which can lead to optimistically biased performance estimates. The authors should either (a) refit PCA on the training set only and project the test set onto those components, or (b) explicitly acknowledge this as a limitation and discuss how it may affect the reported metrics. A brief justification for why the current approach was chosen (e.g., stability of components) would be acceptable if accompanied by this caveat.

2. Imputation sensitivity analysis is mentioned but not shown. The authors state they "applied alternative imputation methods, including K-Nearest Neighbors (KNN) and Support Vector Regression (SVR)" and that "MICE-PMM provides the best imputation performance." However, no results from these alternative methods appear in the manuscript, no comparison table, no diagnostic plots, and no definition of what "best imputation performance" means (which metric was used). Either include a brief supplementary table comparing imputation methods on a relevant metric or remove the claim, as it currently lacks supporting evidence.

3. The cumulative variance threshold of 60% is low and needs stronger justification. Table 3 shows the four retained PCs explain between 61.10% (NV) and 64.51% (RU) of total variance. While the Kaiser criterion (eigenvalue > 1) is satisfied, retaining only ~61–65% of variance means roughly 35–39% of the original information is discarded. The commonly cited threshold in the literature is 70–80%. The authors should briefly discuss why a ~60% threshold is acceptable for this application and whether retaining a fifth component (which would bring cumulative variance to ~70%) would change the model comparison results.

4. Figure 3 correlation matrices, significance notation needs clarification. The revised correlation plots use "*" for significant and "Na" for non-significant correlations (line 266–267). The use of "Na" may be confused with missing data ("NA"). Consider using "ns" (not significant) instead, and add the significance threshold (p < 0.05) to the figure caption rather than only in the text.

Reviewer #3: The manuscript improved significantly and satisfied with the revision.

**********

-->7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

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For information about this choice, including consent withdrawal, please see our Privacy Policy.-->

Reviewer #1: No

Reviewer #2: No

Reviewer #3: No

**********

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After uploading your figures to PLOS’s NAAS tool - https://ngplosjournals.pagemajik.ai/artanalysis, NAAS will process the files provided and display the results in the "Uploaded Files" section of the page as the processing is complete. If the uploaded figures meet our requirements (or NAAS is able to fix the files to meet our requirements), the figure will be marked as "fixed" above. If NAAS is unable to fix the files, a red "failed" label will appear above. When NAAS has confirmed that the figure files meet our requirements, please download the file via the download option, and include these NAAS processed figure files when submitting your revised manuscript.-->

Principal Component based Predictive Modelling of Particulate Matters using Air Pollutants and Meteorological Variables in Bangkok, Thailand

PCLM-D-25-00483R2

Dear Mr. Saeyang,

We are pleased to inform you that your manuscript 'Principal Component based Predictive Modelling of Particulate Matters using Air Pollutants and Meteorological Variables in Bangkok, Thailand' has been provisionally accepted for publication in PLOS Climate.

Before your manuscript can be formally accepted you will need to complete some formatting changes, which you will receive in a follow-up email from a member of our team.

Please note that your manuscript will not be scheduled for publication until you have made the required changes, so a swift response is appreciated.

IMPORTANT: The editorial review process is now complete. PLOS will only permit corrections to spelling, formatting or significant scientific errors from this point onwards. Requests for major changes, or any which affect the scientific understanding of your work, will cause delays to the publication date of your manuscript.

If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they'll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact climate@plos.org.

Thank you again for supporting Open Access publishing; we are looking forward to publishing your work in PLOS Climate.

Best regards,

Jing Chen

Academic Editor

PLOS Climate

***********************************************************

Additional Editor Comments (if provided):

Reviewer Comments (if any, and for reference):