Review Reports - Air Pollution Monitoring and Modeling: A Comparative Study of PM, NO<sub>2</sub>, and SO<sub>2</sub> with Meteorological Correlations

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

The article "Air Pollution Monitoring and Modeling: A Comparative Study of PM, NO2, and SO2 with Consideration of Meteorological Correlations" is a comprehensive study analyzing the spatiotemporal characteristics and atmospheric concentrations of SO2, NO2, PM2.5, and PM10 in Poland over a long period of time (2000 to 2025). The study utilizes integrated methods and data sources, including Earth remote sensing. The relevance of the study necessitated an analysis of atmospheric composition variations in the context of global increasing anthropogenic load and climate change. Despite this, there are several serious caveats that can be published in the journal after the manuscript is reviewed.
- Lines 132-134. Do you have any references to support this?
- Lines 139-146. I fully agree with the above. However, the authors failed to expand the literature on the sources of PM entering the environment. This is also true for the materials presented in Table 1. As will be shown below, the intra-annual variability of SO2, NO2, PM2.5, PM10 are characterized by U-shaped distributions. This is peculiar both for the countries of Central and Eastern Europe and for many Asian countries. This is due to the change in the conditions of self-purification of the atmosphere, as well as the change in the load on thermal power plants, which is opposite to air temperature (as shown, for example, in doi.org/10.3390/rs17030393, doi.org/10.3390/su17083585, doi.org/10.3390/app14188327, doi.org/10.3390/app14188327, doi.org/10.1007/s11869-025-01790-9). I believe this pattern of intra-annual distribution is also characteristic of Poland.
- Table 1. This comment is advisory in nature. The pie charts in the right column are difficult to read. I ask the authors to enlarge or improve the quality (resolution) of the data in the charts.
- Section 2.3. The conditions under which the station is located are not entirely clear. Perhaps some statistics on the urban/rural/background methods should be provided.
- Figures 1 and 2. Unfortunately, I didn't fully understand what R2 was calculated between? Between the UK's opinion and the year? The figure legend also requires minor adjustments: the indicator indicating the size fraction of PM2.5 (specifically, "2.5") should be indicated as a subscript. The same comment applies to the other pollutants.
- These comments are advisory in nature. I ask the authors to consider restructuring the manuscript. Specifically, organize the sections in Chapter 2 in the following order: mobile inventory, ground-based observations, and satellite observations. This will appear more logical, given Section 3, whose chapter is arranged in this order.

- While reading the manuscripts, repetitions were identified. For example: "The reasons are the introduction of flue gas desulfurization, the decommissioning of obsolete power plants, and the gradual, albeit uneven, transition from coal." This sentence appears in the manuscripts, with slight variations, on lines 132–133, 170–172, and 205–207. I ask the authors to review the manuscript several times, avoiding repetitions of meaning.

- Line 218. The sentence is not very well formulated. It is not clear what was meant by Q2? The median distribution (where Q is used as an abbreviation for the quartile)? Or the season of the year? Care should be taken to distinguish between individuals to avoid confusion within a single manuscript. "Figure 3 shows the temporal evolution of NO2 and SO2 concentrations in the atmosphere based on satellite data in Poland between the second quarter of 2019 and the second quarter of 2025."
- This begs the question: why is Section 3 devoted to the analysis of NO2 and SO2? Or should we conclude that atmospheric PM data based on the satellite Diptych have not been obtained? However, the authors do not further discuss this point in either Chapter 3.3 or Chapter 2.1.
- Figures 4 and 6. It is necessary to indicate the number of pairs of measurements used for the correlation analysis. This also raises the question of including a correct analysis of the satellite Triptych data, taking into account the different time scales of application of the datasets.
- Graph 2 shows a rather interesting point: ground-level PM2.5 concentrations, which reached results exceeding 45 µg/m3 between 2002 and 2003. What's even more unexpected and suspicious is that the PM2.5 levels are higher than the PM10 levels. This shouldn't have happened. This is incorrect in terms of what constitutes PM2.5 and what constitutes PM10. The authors don't discuss this.

Good luck!

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

The manuscript, titled "Air Pollution Monitoring and Modeling: A Comparative Study of PM, NO₂, and SO₂ with Meteorological Correlations", presents a solid foundation with a well-structured introduction and methodology. The focus on the results and statistical analysis reveals key areas for improvement, as outlined below. These revisions will enhance the study's rigor and depth, leveraging the rich dataset spanning over 20 years.

Results and Statistical Analysis: The current analysis, relying solely on Pearson correlation coefficients, is insufficient to capture the complexity of air quality modeling, where relationships between pollutants (e.g., PM, NO₂, SO₂) and meteorological variables can be nonlinear or influenced by multiple factors. Pearson may indicate trends, but fails to account for temporal dynamics or confounding effects. I suggest the authors redo this section using a more comprehensive tool like the OpenAir package in R (or similar) to explore trends and patterns, including daily, seasonal, and long-term variations. The 20-year dataset is particularly rich for identifying short-term (e.g., diurnal cycles) and long-term (e.g., climate-driven shifts) trends. For instance, investigate temperature and precipitation impacts, which could illuminate gaseous pollutant dynamics—Pearson already hints at clear trends for NO₂ and SO₂. Still, deeper exploration (e.g., via polar plots or time-series decomposition) is needed to explain these, such as how warmer temperatures might enhance NO₂ oxidation or precipitation, reducing SO₂ levels.

Discussion: The discussion should be expanded once the results and statistical analysis are revised. Incorporate the new insights from OpenAir or similar tools to compare findings with existing literature on meteorological influences in air pollution modeling. This will provide a more robust interpretation of the correlations and their implications for urban air quality management.

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

Thank you for your time.

I believe that the manuscript can be published in the journal

Author Response

Thank you very much!

Reviewer 2 Report

Comments and Suggestions for Authors

The manuscript, titled "Air Pollution Monitoring and Modeling: A Comparative Study of PM, NO₂, and SO₂ with Meteorological Correlations", was previously reviewed with a recommendation for major revisions due to its solid introduction and methodology, though the results and statistical analysis required enhancement. The authors have responded by expanding their statistical approaches, which is a step forward, but the revised submission still falls short of adequately addressing the core concerns. The current version requires further significant improvements. Below are detailed comments by section.

Methodology: Table 1 requires improvement, as the histogram text is illegible, which hinders data interpretation. The authors should enhance resolution and font size or provide a supplementary figure if necessary to ensure clarity for readers.

Results: The request for temporal pattern analysis was addressed, but the implementation remains rudimentary. Figure 5 is only briefly described, leaving the potential relationships between variables (e.g., PM and temperature trends) unclear. A detailed explanation of the observed patterns (e.g., seasonal peaks) is essential. Figure 7, while informative, is excessively large and contributes little to understanding; consider replacing it with a more concise visualization or removing it if it is redundant. Line 398 references Figure 8?, which requires enhancement in quality and labeling to support the analysis effectively. Additionally, Line 405 claims a temperature increase of 0.63°C per year, suggesting a decade-long rise of 6.3°C. This rate is implausible, as few cities globally exhibit such extreme warming; this anomaly must be verified (e.g., through data rechecking or station calibration) and discussed in detail to assess its validity.

Discussion: The discussion fails to satisfactorily incorporate the revised statistical findings or address the temperature anomaly. The addition of only 10 references, some dating back to 2012 and 2013, indicates an inadequate literature search. The authors should expand the review to include at least 10–15 recent, high-impact sources to contextualize the results within current meteorological and air quality research. The analysis suggests a need for a co-author with expertise in air quality and meteorology to strengthen the interpretation of results and the depth of discussion.

Author Response

Authors: We have improved the quality of chart.

Authors: We have improved the chart for better presenting seasonal changes (four seasons were distinguished). More detailed description of the results was added with explanation of the causes of such patterns of seasonal changes of pollutions.

Figure 7, while informative, is excessively large and contributes little to understanding; consider replacing it with a more concise visualization or removing it if it is redundant.

Authors: We have changed the figure. Number of charts in the figure was reduced and only charts which present relationship between variables which characterize weather conditions with variables which characterize pollution level are presented. Description of the figure was adjusted.

Line 398 references Figure 8?, which requires enhancement in quality and labeling to support the analysis effectively. Additionally, Line 405 claims a temperature increase of 0.63°C per year, suggesting a decade-long rise of 6.3°C. This rate is implausible, as few cities globally exhibit such extreme warming; this anomaly must be verified (e.g., through data rechecking or station calibration) and discussed in detail to assess its validity.

Authors: Number of the figure in the text was corrected (8 instead of 9). The source data were checked and they are correct. We have changed the method for preparation of trend line from Theil-Sen to linear regression and the results were changed, e.g. for temperature mean annual increase during the studied period it is 0.14 °C per year. The Fig. 8 and its description were changed.

Authors: Discussion was extended and more comparison to other similar studies were presented and more deeply analysed.