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I confirm that the authors have addressed all the reviewers' comments and the manuscript is ready for publication.
[# PeerJ Staff Note - this decision was reviewed and approved by Jörg Oehlmann, a PeerJ Section Editor covering this Section #]
The paper systematically investigates the spatiotemporal evolution of
PM2.5, PM10, and O3, alongside their driving mechanisms.
Experimental results revealed the distinct seasonal geospatial patterns of these atmospheric pollutants.
It is interesting and useful to the field of fine spatiotemporal distribution and driving factors of atmospheric pollutants with mulit-source remote sensing data.
The author has addressed the issues and suggestions related to my last review. I have no other questions.
The article looks fine in its current revised version.
The article looks fine in its current revised version.
The article looks fine in its current revised version.
Based on the comments from three reviewers, your manuscript requires major revisions before it can be accepted. Particularly, there are too many figures (up to 20) showing only general data. They are required to be combined, improved, and presented in a good format. In addition, there is lack of deep analysis on the driving factors and the underlying mechanisms, which needs further elaboration. Please try to submit the revised manuscript within three weeks.
**PeerJ Staff Note:** Please ensure that all review, editorial, and staff comments are addressed in a response letter and that any edits or clarifications mentioned in the letter are also inserted into the revised manuscript where appropriate.
Language Note: The review process has identified that the English language must be improved. PeerJ can provide language editing services - please contact us at [email protected] for pricing (be sure to provide your manuscript number and title). Alternatively, you should make your own arrangements to improve the language quality and provide details in your response letter. – PeerJ Staff
The article is generally acceptable in terms of scientific structure, English language, and adherence to writing standards, but in some sections (especially the introduction and discussion) there are long and complex sentences that make it difficult for the reader to understand. It is recommended to do specialized linguistic editing.
The research background is appropriate and sufficient sources (2019-2024) are provided, and if possible, 2025 sources should also be used. However, in the introduction section, it is better to establish a clearer connection between the knowledge gap and the research objectives and for the respected author to provide more explanation in this regard.
It is also recommended that the explanation of the figures be written more completely and independently of the text so that the reader can understand the concept without referring to the text.
Raw data is provided from reliable sources (CHAP, NOAA, Landsat) and has sufficient transparency. Data links are included correctly in the text.
The explanation of HYSPLIT model input parameters and uncertainties is incomplete and needs further and more complete explanation.
The selection of government monitoring stations needs further explanation and justification (e.g. distance or geographical location criterion).
The Materials & Methods section is long, but it would be better to add subheadings and a flowchart of methods to increase readability.
The data are complete and the analyses are statistically valid. The correlations and GRA results are reasonable and consistent with field evidence and similar studies.
Limitations are not explicitly stated; the authors should point out the limitations of the satellite data (e.g., errors in cloudiness or snow) and the limitations of the HYSPLIT model.
The conclusions are data-dependent and not overly general. However, the argument could be strengthened by further comparisons with other arid metropolises.
It is suggested that the Discussion section: further address air pollution control policies and the socio-economic impacts of the findings.
Compare the results with similar studies in other arid regions of Asia.
In the Conclusion section, it is best to write recommendations in bullet points and operational terms.
Visually, maps would be better with a clearer scale, northing, and title.
Overall, the paper has high scientific value and its results will be useful for policymakers and researchers.
The paper systematically investigates the spatiotemporal evolution of
PM2.5, PM10, and O3, alongside their driving mechanisms.
Experimental results revealed the distinct seasonal geospatial patterns of these atmospheric pollutants.
It is interesting and useful to the field of fine spatiotemporal distribution and driving factors of atmospheric pollutants with mulit-source remote sensing data.
However, there are some issues or questions should be addressed:
(1) The authors validate the CHAP dataset against 12 state-controlled stations for only three summer months. It should be also validated for winter (when PM peaks), or provide seasonal validation statistics including O3 validation. The potential CHAP underestimation during heavy dust storms under high-AOD dust conditions should be discussed.
(2) Wind speed is derived from NOAA-GSOD stations via IDW interpolation at prefecture level, then averaged per month. The IDW ignores topography and the Tianshan foothills have strong katabatic flows that are smoothed out. It should compare with ERA5-Land 1-hourly 0.1° wind fields or use WRF-downscaled winds.
(3) It should perform MK on the 12 station time-series as a sensitivity test and report whether city-level trends differ from the gridded product.
(4) It should perform a robustness test for GRA results comparing with Spearman correlations using ρ = 0.3 and 0.7 and show that the ranking of meteorological drivers remains stable.
(5) It should include annual coal-use or GDP data at prefecture level as covariates in a Generalised Additive Model (GAM) alongside meteorological variables to separate policy-driven from meteorologically-driven trends
(6) some minor points should be addressed, such as typos of "R² quantifies the proportion of variance..." in Line 208, lack of captions of figures 15-17.
(7) Some related studies, such as carbon emission estimation model and analysis of influencing factors, should be included and cited in this study.
The English language requires thorough polishing; there are numerous grammatical issues and unclear wordings that make sections difficult to understand (e.g., Abstract lines 23–44). Professional editing is suggested.
Acronyms such as CHAP, PET, HYSPLIT, VOCs, and POI are not consistently defined at their first use.
The introduction provides background information on air pollution in China, but does not clearly indicate what specific knowledge gap this study will address beyond paraphrasing the existing literature.
There are some outdated or less relevant references; additionally, more recent publications (last 2–3 years) on urban air pollution in arid regions need to be incorporated.
The literature review is more descriptive than critical. Please indicate how this work goes beyond previous studies.
Figures (e.g., Figs. 3–14) are not necessarily self-explanatory. Legends need to include sufficient information to be able to interpret them without having to refer back to the text.
Some figures are not of publishable quality. Improve readability, especially maps and spatial distribution plots.
The reasoning for selecting the CHAP dataset needs to be strengthened. Why is it superior to other ground-based or satellite datasets?
The validation procedure of CHAP data (lines 317–325) is too brief. Sample size, time matching, and statistical significance should be elaborated on.
The HYSPLIT setup is not described in enough detail (e.g., emission assumptions, release heights, back trajectory duration). Readers cannot replicate the analysis from the current information.
Results combine raw descriptions and interpretations. For clarity, please separate descriptive findings (what is found in the data) from causal explanations (why it happens).
Seasonal/annual variation plots (Figs. 5–11) are presented, but quantitative plots are rarely described in the text; present key numerical findings instead of mere color maps.
Inconsistent statements are presented: e.g., Fig. 13 PM10 anomaly from terrain but also from industrial activity. Please reconcile these explanations.
Trend analyses (MK/TS results) are presented, but confidence levels and effect sizes are not provided. Without significance values, the conclusions are weak.
The discussion summarizes descriptive findings more than it synthesizes them into broader conclusions. More comparative and integrative thinking is necessary to dialogue with previous work.
Policy implications (coal-to-gas, regional coordination) are mentioned
but are general. The discussion must connect findings more directly to specific policy recommendations.
The conclusions section is too lengthy; it must be shorter and more clearly state what was new, what was confirmed, and what remains unclear.
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