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Your manuscript can be accepted based on the referee's comment.
[# PeerJ Staff Note - this decision was reviewed and approved by Monika Mortimer, a PeerJ Section Editor covering this Section #]
The quality of this paper has been substantially enhanced after thorough revision. No extra comments are given.
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Based on the referees' comments on your manuscript, major revisions are required to further improve the quality.
[# PeerJ Staff Note: Please ensure that all review and editorial comments are addressed in a response letter and any edits or clarifications mentioned in the letter are also inserted into the revised manuscript where appropriate. #]
The article is clear and unambiguous, and prior literature is appropriately referenced. The structure of the article conforms to an acceptable format.
This paper aims at proving the effectiveness of the combination of the decomposition model and the machine learning model. This kind of combination is endless. For instance, they can ensemble CNN or Transformer with other decomposition models. Why do authors not try these combinations? It can not be proved that the experimental design in the article is reasonable.
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This paper provides a nonparametric regression-based approach to explore the relationship between air pollution and PM concentration. This method can help researchers better understand the temporal and spatial variation of PM concentration, and provide scientific basis for formulating corresponding environmental protection policies. Although the research results are compelling, there are still some suggestions I would like to propose to help improve the quality.
1. Based on the 16 IMF components obtained by CEEMDAN decomposition and the 5 PF components obtained by RLMD decomposes, each component should be screened by their influencing degree on the predicted label, only components with higher degree can be selected as input indicators.
2. It is noted that correlation analysis was conducted on variables obtained by CEEMDAN decomposition and RLMD decomposes respectively, however the reason why some variables get selected as input components, and others not should be further explained.
3. The experiment needs to add more details. I suggest that you improve the description in lines 314- 344 to provide more justification for your study.
4. To test whether the model is still accurate and robust to different data, was the model re-trained when analyzing data from Beijing 1001A station？
5. In this study, to attain fluctuation characteristic, pre-decomposition process is needed for prediction validation on every run. However PM2.5 prediction is real-time prediction, how to maintain prediction accuracy when facing insufficient decompostion characteristic due to lack of contiguous data on time scale
6.It is highly recommended to provide the raw data, however, the supplemental files need more descriptive metadata identifiers to be useful to future readers.
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1) The figures representing the correlation (Figure 4, Figure 9): the numbers are not presented clearly and should not be stretched to make the numbers shrink (Figure 4a, Figure 9a).
2) The presentation of the unit in the figure (ug/m3) (Figures 5, 6, 7, 10, 11, and 12) should follow the format of PeerJ.
1. Lines 197–198: 23,956 data points belong to which particular region or monitoring station in India? It is necessary to provide a map to show the locations of all sites used in this study.
2. Also, lines 197–198 correspond to Figure 1: the data from June 4, 2019 to June 4, 2022 is 4 years, not 5 years as shown in Figure 1.
3. In addition to using PM2.5 concentration as a dependent variable in the data for PM2.5 prediction models, independent variables (often meteorological variables) are also required. What independent variables were used in this study? In this case, authors should include more information about this in the data description.
4. Is a prediction for PM2.5 concentrations for a specific future time period available? (Ex: 7-day short-term prediction).
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