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The reviewers have revised the paper and suggested publication as is. I concur with the reviewers on accepting the article. Congratulations.
[# PeerJ Staff Note - this decision was reviewed and approved by Monika Mortimer, a PeerJ Section Editor covering this Section #]
The authors have revised the manuscript well.
The authors have revised the manuscript well.
The authors have revised the manuscript well.
The authors have revised the manuscript well.
better after addressing my previous concerns.
can be improved.
limited to 2 provinces.
no
The authors have addressed all comments satisfactorily, and the revisions have improved the clarity and quality of the paper. I recommend acceptance in its current form.
Adequate
Adequate
The article requires significant revisions, particularly in terms of clarifying the methods used, ensuring the appropriateness of distribution comparisons, and validating the findings with appropriate goodness-of-fit tests. These revisions are necessary to ensure the scientific validity and robustness of the study. Please attend all reviewer's comment, especially from reviewer 3 related to the clarity and issues regarding the methodology used.
See the attached file.
See the attached file.
See the attached file.
See the attached file.
it is well written. English is good. logic is clear. and method and results are convincing. I only have a few concerns. please see following comments.
1. the manuscript talk about wind speed (absolute speed), as for air pollution, it is useful that author consider both wind speed and wind directions at the same time.
2. maybe a representative figure of rose map for wind (speed and direction) for the 2 provinces you apply your model.
1. it will be better the author explain why choose those 2 provinces for empirical application. 1) how is the location of site matter? 2) are only two provinces representatively enough to support the conclusion. are more sites needed? 3) please explain more how raw data from provinces are preprocessed before applying 'empirical application'.
1. Line 42, need reference or data support that Thailand face high pm2.5 in this period. and what standard used (WHO?) to be considered high?
2. line 41, PM2.5 is NOT identical to dust, please reword. same as line 53.
3. line 48, need reference that pm2.5 pose risk to vulnerable population.
4. line 483-531. please explain why those 2 provinces are chosen? is there more sites available. and how the raw wind data is pre-processed?
5. line 533-540. this paragraph talk about clean energy, which is odd when in introduction the author focus on air pollution relation to wind. please reword and keep one focus.
The paper is generally well-written and structured. The introduction and methodology provide a solid foundation for understanding the statistical methods employed. However, the motivation and novelty of the study could be more explicitly highlighted, particularly in comparison to existing literature. Additionally, the manuscript discusses the use of AIC and BIC for model selection but does not explicitly state the method used to estimate the distribution parameters. This omission is significant and needs to be addressed. A thorough proofreading to correct minor typographical errors is also necessary to enhance the paper's professional quality.
The research question is relevant and meaningful, addressing an identified knowledge gap in the modeling of wind speed data with zero values. However, the investigation could be strengthened with a more detailed rationale for the selection of parameters in the simulation study and a clearer explanation of the method used for data generation in the algorithms, particularly regarding the determination of distribution parameters. These aspects are crucial for ensuring the replicability and applicability of the study's findings and must be clarified.
While the study's conclusions are linked to the original research question, the validity of the findings is compromised by the lack of goodness-of-fit tests to confirm the adequacy of the chosen distribution. AIC and BIC are useful for model selection, but they do not directly assess the fit of the distribution. Incorporating goodness-of-fit tests, such as the Kolmogorov-Smirnov or Anderson-Darling tests, is essential to provide a robust validation of the results. Additionally, some of the distributions compared, such as the Normal and Uniform, are not appropriate given the right-skewed, unimodal nature of the wind speed data. This mismatch in comparison requires revision to ensure the relevance and accuracy of the conclusions drawn.
The manuscript includes a comparison of various distributions for fitting wind speed data, but some of these distributions, like the Normal, Exponential, and Uniform, are not well-suited to the characteristics of the data. More appropriate comparisons would involve distributions that better handle the skewness and shape of wind speed data. Additionally, if Maximum Likelihood Estimation (MLE) was used for parameter estimation, this must be explicitly stated in the methodology section. These issues are significant and need to be addressed to improve the clarity, robustness, and validity of the findings.
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