Deciphering Air Pollution Dynamics and Drivers in Riverine Megacities Using Remote Sensing Coupled with Geospatial Analytics for Sustainable Development

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

Review of “Deciphering Air Pollution Dynamics and Drivers in Riverine Megacities Using Remote Sensing Coupled with Geospatial Analytics for Sustainable Development” by Ayek et al.

 

This manuscript analyzes 2012–2023 air quality in Baghdad using products accessed through NASA’s Giovanni platform. Seven pollutants (CO, CO₂, SO₂, SO₄, O₃, CH₄, and AOD) are examined for long-term trends, seasonal cycles, spatial patterns, and inter-pollutant correlations. The authors report notable changes such as a sharp post-2021 decline in CO, steady increases in CO₂ and CH₄, and stable but seasonally variable SO₂ and AOD. Spatial mapping highlights urban–rural gradients, industrial and transport hotspots, and dust-related AOD peaks. The authors further claim the study advances Sustainable Development Goals (SDGs) and offers a transferable monitoring framework for other megacities.

However, several critical issues limit the manuscript’s contribution in its current form. First, although the work is framed as a “satellite remote sensing” study, most of the datasets are MERRA-2 reanalysis products, which are model–data assimilation outputs rather than direct satellite observations. These have relatively coarse spatial resolution and may not capture fine-scale urban variability, especially in regions without dense observational constraints. Second, the atmospheric chemistry discussions are often oversimplified or inaccurate. For example, the explanations of the SO₄–O₃ and SO₂–AOD correlations do not reflect the dominant chemical pathways under Baghdad’s climatic conditions, and key processes such as OH-driven oxidation are overlooked. Third, the section on SDG contributions is highly generalized and largely qualitative. It lacks quantitative linkage between the presented results and specific SDG indicators (e.g., changes in mortality risk for SDG 3.9 or PM guideline exceedances for SDG 11), making the policy relevance appear overstated.

In its current form, the manuscript reads more as a descriptive summary of reanalysis trends than as a robust, observation-based or process-resolving study. To strengthen it, the authors should clarify the nature and limitations of the data, improve the accuracy and depth of the atmospheric chemistry discussion, and support the SDG claims with quantitative, indicator-based analysis. Without these revisions, the scientific rigor and policy relevance remain insufficient for publication.

Author Response

Response to Reviewer (1)

Comments and Suggestions for Authors:

Review of “Deciphering Air Pollution Dynamics and Drivers in Riverine Megacities Using Remote Sensing Coupled with Geospatial Analytics for Sustainable Development” by Ayek et al.

This manuscript analyzes 2012–2023 air quality in Baghdad using products accessed through NASA’s Giovanni platform. Seven pollutants (CO, CO₂, SO₂, SO₄, O₃, CH₄, and AOD) are examined for long-term trends, seasonal cycles, spatial patterns, and inter-pollutant correlations. The authors report notable changes such as a sharp post-2021 decline in CO, steady increases in CO₂ and CH₄, and stable but seasonally variable SO₂ and AOD. Spatial mapping highlights urban–rural gradients, industrial and transport hotspots, and dust-related AOD peaks. The authors further claim the study advances Sustainable Development Goals (SDGs) and offers a transferable monitoring framework for other megacities.

However, several critical issues limit the manuscript’s contribution in its current form.

Comment 1: First, although the work is framed as a “satellite remote sensing” study, most of the datasets are MERRA-2 reanalysis products, which are model–data assimilation outputs rather than direct satellite observations. These have relatively coarse spatial resolution and may not capture fine-scale urban variability, especially in regions without dense observational constraints.

Response: Thank you for this insightful and constructive comment. We sincerely appreciate your careful attention to this important methodological distinction, and we fully acknowledge the validity of your concern regarding the nature of MERRA-2 reanalysis products versus direct satellite observations. You are absolutely correct in noting that most of our datasets are derived from MERRA-2 reanalysis products, which represent model-data assimilation outputs rather than pure satellite observations. In response to your valuable feedback, we have made several significant improvements to enhance methodological transparency and address the limitations you have identified.

• We have explicitly clarified in the revised manuscript (lines 266-273 and 311-316) that MERRA-2 integrates multiple satellite retrievals with atmospheric model assimilation techniques. While this approach ensures temporal consistency and gap-free coverage, we now clearly acknowledge that it introduces certain limitations compared to direct observational datasets, as you have correctly pointed out. This clarification ensures that readers understand the nature of our data sources and their associated uncertainties.
• To address your concern regarding the relatively coarse native resolution of MERRA-2 (0.5° × 0.625°), we applied cubic convolution resampling following the Keys (1981) interpolation method to achieve enhanced spatial detail of approximately 0.01° (lines 361-375). This spatial enhancement technique, widely validated in geospatial research, helps capture finer-scale urban variability in Baghdad where in-situ monitoring infrastructure is sparse or compromised. We emphasize that while this interpolation improves visualization and spatial comparability, it does not create new information beyond the original data resolution.
• Following your suggestion for platform justification, we have expanded our discussion (lines 300-307 and 143-147) explaining why NASA Giovanni was selected over alternative platforms such as Sentinel-5P TROPOMI. Although Sentinel-5P provides superior spatial resolution (~1 km for recent observations), its temporal coverage begins only in late 2017, which would not support our comprehensive decadal analysis (2012-2023). Giovanni's integrated framework provides consistent, long-term datasets essential for trend analysis and temporal pattern identification across our study period.
• We acknowledge in the study area description (lines 244-247) that ground-based monitoring infrastructure in Baghdad has been compromised, as mentioned where the text states "as the latter often have sparse coverage and cannot capture the full extent of long-range transport phenomena." Additionally, we discuss in the introduction (lines 132-139) that "Regional instability, population displacements, and substantial gaps in ground-based sensor networks have left major cities without continuous, reliable air quality data." However, we emphasize that satellite-based approaches remain among the most feasible methods for long-term, spatially consistent air quality assessment in data-scarce environments, particularly where traditional monitoring infrastructure has been compromised. The study framework maintains scientific rigor while acknowledging these inherent limitations, providing valuable baseline data for air quality assessment in regions where conventional monitoring is unfeasible.

Comment 2: Second, the atmospheric chemistry discussions are often oversimplified or inaccurate. For example, the explanations of the SO₄–O₃ and SO₂–AOD correlations do not reflect the dominant chemical pathways under Baghdad’s climatic conditions, and key processes such as OH-driven oxidation are overlooked.

Response: Thank you for this valuable and important observation. We sincerely appreciate your expertise in atmospheric chemistry and acknowledge that the initial discussion required significant improvement to accurately reflect the complex chemical processes occurring under Baghdad's specific climatic conditions. You are absolutely correct in pointing out that the explanations of SO₄–O₃ and SO₂–AOD correlations needed to be more scientifically rigorous and contextually appropriate. In response to your feedback, the atmospheric chemistry discussion has been substantially enhanced throughout the revised manuscript to ensure accuracy and relevance to Baghdad's semi-arid environment.

1. The discussion of sulfate formation has been revised to explicitly account for OH-initiated gas-phase oxidation of SO₂, which is indeed the dominant pathway under hot, sunny, semi-arid conditions such as those prevailing in Baghdad. These reactions are now included in the Results section (lines 471-477), with full chemical equations provided (Equations 8-10). This OH-driven pathway is critical under Baghdad's intense solar radiation conditions and high temperatures, which promote rapid photochemical activity.
2. Following your suggestion, comprehensive explanations of aqueous-phase oxidation by H₂O₂ and O₃ as well as heterogeneous metal-catalyzed reactions on particle surfaces have been added, which may also accelerate SO₄²⁻ production during stagnant air episodes. These processes are now discussed in detail in lines 474-478, with specific chemical equations (11-12) showing the aqueous-phase pathways. Additionally, NO₂/HONO multiphase pathways and heterogeneous metal-catalyzed reactions that become important during stagnant atmospheric conditions are discussed.
3. The interpretation of the SO₄–O₃ relationship has been corrected to clarify that this reflects their shared oxidation chemistry rather than direct causality, as you correctly noted (lines 645-648). The negative correlation (r = -0.51, p < 0.01) occurs because ozone acts as an oxidant in SO₂-to-sulfate conversion, consuming ozone during high photochemical activity periods. Similarly, the explanation of the SO₂–AOD correlation has been revised to emphasize that AOD represents an integrated measure of multiple aerosol types including dust, black carbon, and sulfates (lines 496-502), and thus is not linearly controlled by SO₂ emissions alone.

The revised text now places all atmospheric chemistry discussions within the proper context of Baghdad's semi-arid climate, characterized by intense solar radiation, frequent dust events, limited humidity, and extreme temperatures (lines 103-105 and 471-477). These environmental conditions strongly influence the dominant oxidation pathways and secondary aerosol formation processes, which are now addressed comprehensively throughout the manuscript.

Comment 3: Third, the section on SDG contributions is highly generalized and largely qualitative. It lacks quantitative linkage between the presented results and specific SDG indicators (e.g., changes in mortality risk for SDG 3.9 or PM guideline exceedances for SDG 11), making the policy relevance appear overstated.

Response: Thank you for this valuable and constructive observation. Your feedback regarding the need for more quantitative linkages between our study results and specific SDG indicators is absolutely correct, and this concern has been addressed comprehensively in the revised manuscript.

You are right in pointing out that the initial SDG discussion was too generalized and lacked the specific quantitative connections necessary to demonstrate genuine policy relevance. In response to your feedback, the SDG contributions section has been substantially revised to provide concrete, measurable linkages between the atmospheric pollutant findings and relevant SDG indicators.

For SDG 3.9 (mortality risk from air pollution), quantitative health impact assessments have been incorporated using WHO guideline thresholds. The study findings show that annual PM₂.₅ concentrations in Baghdad exceed 70 µg/m³, which is more than 14 times the WHO guideline limit of 5 µg/m³ (lines 76-77). This exceedance level directly supports indicator 3.9.1 (Mortality rate attributed to household and ambient air pollution) by providing measurable baseline data for health risk assessment. Additionally, the dramatic CO reduction from 0.35-0.40 ppm during 2012-2020 to 0.10-0.15 ppm during 2021-2023 represents a quantifiable improvement that correlates with reduced cardiovascular and respiratory health risks (lines 695-699).

Regarding SDG 11.6 (air quality in cities), specific quantitative metrics have been established for indicator 11.6.2 (Annual mean levels of fine particulate matter in cities). The study documents pronounced urban-rural concentration gradients, with central Baghdad CO levels exceeding 0.40 ppm compared to peripheral regions below 0.20 ppm (lines 727-730). Additionally, winter SO₂ concentrations consistently reach 1.8-2.0 × 10⁻⁵ kg/m², representing measurable exceedances of international air quality standards that can be directly monitored for SDG progress assessment.

The comprehensive quantitative framework is systematically presented in Table 2 (lines 829-831), which demonstrates how this research addresses 12 different SDG targets across four key dimensions. The table provides specific contribution percentages: Environmental Health and Public Well-being (30%), Sustainable Cities and Climate Action (25%), Economic Development and Resource Management (25%), and Institutional Development and Partnership (20%). Each dimension includes direct and indirect contributions with measurable indicators. For instance, the Environmental Health dimension includes quantifiable metrics such as CO reduction from 0.35-0.40 ppm to 0.10-0.15 ppm supporting SDG 3.9.1, winter SO₂ loading of 1.95 × 10⁻⁵ kg/m² affecting water quality for SDG 6.3.2, and AOD spring maxima of 0.52-0.65 impacting agricultural sustainability for SDG 2.4.1.

The Climate Action dimension demonstrates quantifiable contributions through the documented 17 ppm CO₂ increase representing approximately 0.31 W/m² additional radiative forcing for climate planning (lines 742-744), while the Economic Development dimension provides measurable evidence such as the 60-70% CO reduction during COVID-19 lockdowns and documentation that transportation accounts for 75% of urban CO burden. The Institutional Development dimension showcases the integration of NASA, ESA, and Google platforms as a concrete example of international technology cooperation supporting SDG 17.6.1.

These comprehensive revisions ensure that the SDG discussion is anchored in measurable, evidence-based indicators rather than qualitative statements, directly addressing your concern about policy relevance and providing concrete metrics for monitoring sustainable development progress across multiple interconnected goals.

Other comments:

In its current form, the manuscript reads more as a descriptive summary of reanalysis trends than as a robust, observation-based or process-resolving study. To strengthen it, the authors should clarify the nature and limitations of the data, improve the accuracy and depth of the atmospheric chemistry discussion, and support the SDG claims with quantitative, indicator-based analysis. Without these revisions, the scientific rigor and policy relevance remain insufficient for publication.

Response: Thank you for this comprehensive summary and constructive feedback. Your assessment has been invaluable in guiding the substantial improvements made to enhance the scientific rigor and policy relevance of this manuscript. You are absolutely correct that the initial version required significant strengthening to move beyond descriptive analysis toward a more robust, scientifically rigorous study. In response to your feedback, comprehensive revisions have been implemented across all the areas you identified as needing improvement.

Regarding data nature and limitations, the manuscript now explicitly clarifies that most datasets are MERRA-2 reanalysis products rather than direct satellite observations (lines 266-273 and 311-316). The revised text thoroughly explains the assimilation-based nature of these products and outlines their spatial resolution constraints. The cubic convolution resampling methodology used to achieve enhanced spatial representation (~0.01°) is now clearly described (lines 361-375), while acknowledging that this technique improves visualization without creating new information beyond the original data resolution.

The atmospheric chemistry discussion has been substantially enhanced with detailed explanations of OH-driven oxidation as the dominant sulfate formation pathway under Baghdad's hot, semi-arid conditions (lines 471-477, Equations 8-10). Comprehensive coverage of aqueous-phase and heterogeneous reactions has been added (lines 474-478, Equations 11-12), and the interpretation of pollutant correlations has been corrected to reflect established atmospheric chemistry principles (lines 645-648 and 496-502).

The SDG contributions have been transformed from qualitative statements to quantitative, indicator-based assessments as demonstrated in Table 2 (lines 829-831). Specific measurable metrics now support each SDG claim, including WHO threshold exceedances for health impacts and quantifiable air quality improvements.

Beyond these targeted improvements, the study provides significant analytical depth through advanced statistical modeling including SARIMA analysis and comprehensive correlation assessments (lines 407-410, Figure 11). The research identifies three distinct pollutant trajectory categories reflecting complex emission-atmosphere interactions (lines 431-522), demonstrating process-level understanding rather than mere trend description. The spatial analysis reveals pronounced urban-rural gradients and source attribution patterns (lines 534-607), providing mechanistic insights into emission sources and transport processes.

The study addresses critical knowledge gaps in conflict-affected regions where traditional monitoring is compromised (lines 132-139), providing essential baseline data for evidence-based environmental management. The methodological framework demonstrates reproducibility and transferability to similar urban environments globally (lines 892-908), establishing a robust analytical approach for data-scarce regions. The integration of multiple satellite platforms and statistical techniques creates a comprehensive monitoring system that advances both scientific understanding and practical policy applications for sustainable urban development.

Quality of English Language: The English is fine and does not require any improvement.

Response: Thank you for confirming that the English language quality in the manuscript meets the publication standards.

We are confident that the manuscript now adheres to the publication standards. Your constructive feedback during the major revision process was invaluable, and we thank you again for your time and dedication in helping us improve our work for publication in the Atmosphere.

Reviewer 2 Report

Comments and Suggestions for Authors

1.1. Recommendation
Minor Revision
Title: Deciphering Air Pollution Dynamics and Drivers in Riverine Megacities Using Remote Sensing Coupled with Geospatial Analytics for Sustainable Development

Overview and General Recommendations:
This study analyzed air pollutants (CO, CO₂, SO₂, SO₄, O₃, CH₄, and AOD) in Baghdad, Iraq, over an 11-year period. Using statistical techniques, it identified temporal, spatial, and seasonal variations. This study will contribute significantly to understanding air pollution sources in riverine megacities in the Middle East. The various statistical techniques used in this paper, along with the use of NASA Giovanni and Google Colab analysis tools, will be helpful to researchers.

This paper, consisting of an introduction, materials and methods, results and discussion, and conclusion, is eligible for publication in Atmosphere after a minor revision.

The main comments of the paper are as follows:

1. Introduction
The introduction provides a detailed and well-written introduction to the major air quality issues in urban areas in the Middle East, including greenhouse gases, health impacts, and data utilization methods. However, references need to be identified.

2. Materials and Methods
2.1 Study Area
The study area is clearly identified and explained. However, Figure 1 (a) and (b) are missing.
2.2 Methodological Framework
Data from NASA's Giovanni platform, along with data preparation and calculation equations, are clearly presented.
References need to be identified.

3. Results and Discussion
3.1 Temporal Trends and Long-Term Changes in Air Pollutants
The results are clearly presented and are free of problems.
Figure 3 requires identification of (a)-(g).
3.2 Spatial Distribution Patterns and Source Attribution
The spatial distribution of each pollutant source is clearly identified and is well represented in Figures 4, 5, 6, 7, 8, 9, and 10. 3.3 Analysis of Interrelationships and Correlations between Pollutants
Figure 11 illustrates the correlations between pollutants well, and the explanations are also sound.
3.4. Contribution to Sustainable Development Goals <- Separate Discussion
While well-written, it would be easier for other researchers to understand the evidence using the results data by writing a separate Discussion section.

4. Conclusion
The overall results above are summarized well.

5. References

The MDPI format for the references needs to be confirmed.

Conclusively, this paper can be published after minor revisions.

Author Response

Response to Reviewer (2)

1.1.Recommendation: Minor Revision

Title: Deciphering Air Pollution Dynamics and Drivers in Riverine Megacities Using Remote Sensing Coupled with Geospatial Analytics for Sustainable Development

Overview and General Recommendations:

This study analyzed air pollutants (CO, CO₂, SO₂, SO₄, O₃, CH₄, and AOD) in Baghdad, Iraq, over an 11-year period. Using statistical techniques, it identified temporal, spatial, and seasonal variations. This study will contribute significantly to understanding air pollution sources in riverine megacities in the Middle East. The various statistical techniques used in this paper, along with the use of NASA Giovanni and Google Colab analysis tools, will be helpful to researchers.

This paper, consisting of an introduction, materials and methods, results and discussion, and conclusion, is eligible for publication in Atmosphere after a minor revision. The main comments of the paper are as follows:

Comment 1: Introduction

The introduction provides a detailed and well-written introduction to the major air quality issues in urban areas in the Middle East, including greenhouse gases, health impacts, and data utilization methods. However, references need to be identified.

Response: You are absolutely correct in noting that the introduction required strengthened referencing to properly support the discussion of major air quality challenges. In response to your constructive feedback, extensive efforts have been made to enhance the scholarly foundation of the introduction through comprehensive citation improvements.

The discussion of greenhouse gases and regional air quality challenges now includes appropriate references to support claims about regional GHG emissions and their impacts on air quality (lines 62-68). The text addressing carbon dioxide as the most significant anthropogenic greenhouse gas and its role in climate change is now properly supported with citations (lines 93-97), while methane's contribution to global warming and its various sources are backed by appropriate literature references (lines 109-113).

The health impacts section has been substantially strengthened with citations from WHO guidelines and peer-reviewed epidemiological studies that establish the linkages between pollutant exposure and respiratory and cardiovascular outcomes (lines 68-76). The discussion of carbon monoxide's health risks, including its binding affinity to hemoglobin and potential for fatal poisoning, is now supported by appropriate medical literature (lines 88-92). Similarly, the health impacts of sulfur compounds and aerosols are now properly referenced (lines 98-105 and 114-118).

The data utilization methods discussion has been enhanced with citations supporting the use of satellite remote sensing and reanalysis datasets for air quality monitoring in data-scarce regions (lines 80-83). The methodological approaches section now includes proper references for statistical techniques and remote sensing applications in atmospheric monitoring.

Economic impact assessments, including the World Bank estimates that air pollution-related health damages cost approximately 2-4% of GDP annually in the Middle East and North Africa region, are now properly cited (lines 122-124). These additions ensure that all major claims in the Introduction are substantiated with credible scholarly sources, thereby strengthening the academic rigor and foundation of the study.

Comment 2: 2. Materials and Methods

2.1 Study Area

The study area is clearly identified and explained. However, Figure 1 (a) and (b) are  missing.

Response: Thank you for this constructive feedback and for acknowledging that the study area is clearly identified and explained. Your observation regarding Figure 1 (a) and (b) has been carefully addressed in the revised manuscript.

Figure 1 has been revised to include the appropriate labels (a) and (b) as referenced in the text. Part (a) now clearly shows the location of Iraq within the Middle East region, providing the broader geographical context for the study area. Part (b) displays the administrative boundaries of Baghdad Governorate, illustrating its strategic position along the Tigris River in central Iraq.

The figure caption has been updated accordingly to read: "Figure 1. Geographic location of Baghdad Governorate within Iraq: (a) Location of Iraq in the Middle East region, (b) Administrative boundaries of Baghdad Governorate showing its position along the Tigris River in central Iraq" (lines 252-254).

Comment 3: 2.2. Methodological Framework

Data from NASA's Giovanni platform, along with data preparation and calculation equations, are clearly presented.

References need to be identified.

Response: Thank you for your positive assessment of the methodological framework section and for acknowledging that the data from NASA's Giovanni platform and calculation equations are clearly presented. Your feedback regarding the need for improved referencing in this section has been thoroughly addressed in the revised manuscript.

The use of NASA's Giovanni platform is now properly supported with appropriate documentation and methodological references, including Gelaro et al. (2017) for MERRA-2 reanalysis validation and Acker & Leptoukh (2007) for Giovanni platform capabilities (lines 280-286). These citations establish the platform's credibility and provide readers with access to detailed technical specifications and validation studies.

The data preparation steps and statistical techniques are now backed by appropriate methodological literature. The cubic convolution resampling technique is supported by the original Keys (1981) reference and additional literature by Lehmann et al. (1999) demonstrating its application in geospatial research (lines 364-365). The calculation equations, particularly Equations 1 and 2 for monthly averaging and spatial aggregation, are now supported by relevant statistical references including Radmanesh et al. (2023) for climatological data analysis (lines 352-353 and 378-389).

The computational framework utilizing Google Colab is now properly cited with Bisong (2019) reference (lines 377-379), and the specialized libraries used for geospatial analysis and atmospheric data processing are referenced appropriately, including Harris et al. (2020) for NumPy and Hunter (2007) for Matplotlib (lines 380-384). The time series analysis methods, including ordinary least squares regression and trend assessment techniques, are supported by robust statistical literature such as Paul et al. (2017) for trend analysis (lines 407-410).

These comprehensive referencing improvements ensure that all methodological approaches, from platform selection through analytical techniques, are properly grounded in established literature, thereby enhancing the reproducibility and scientific credibility of the study framework.

Comment 4: 3. Results and Discussion

3.1 Temporal Trends and Long-Term Changes in Air Pollutants

The results are clearly presented and are free of problems.

Figure 3 requires identification of (a)-(g).

Response: Thank you for your positive assessment of the Results and Discussion section and for acknowledging that the temporal trends and long-term changes in air pollutants are clearly presented without problems. You are absolutely correct regarding Figure 3 labeling. This formatting issue has been corrected in the revised manuscript. Figure 3 now includes proper identification labels (a) through (g) for all seven atmospheric pollutants: (a) CO, (b) CO₂, (c) SO₂, (d) SO₄, (e) O₃, (f) CH₄, and (g) AOD concentrations, as referenced in the figure caption (lines 531-532).

Comment 5: 3.2. Spatial Distribution Patterns and Source Attribution

The spatial distribution of each pollutant source is clearly identified and is well represented in Figures 4, 5, 6, 7, 8, 9, and 10.

Response: Thank you for your positive feedback on the spatial distribution analysis. We appreciate your acknowledgment that the spatial distribution patterns of each pollutant source are clearly identified and well represented in Figures 4-10.

Comment 6: 3.3. Analysis of Interrelationships and Correlations between Pollutants
Figure 11 illustrates the correlations between pollutants well, and the explanations are also sound.

Response: Thank you for your positive feedback on both the spatial distribution analysis and the interrelationships and correlations analysis. We appreciate your acknowledgment that the spatial distribution patterns of each pollutant source are clearly identified and well represented in Figures 4-10, and that Figure 11 effectively illustrates the correlations between pollutants with sound explanations.

We sincerely appreciate your positive assessment, which validates both the comprehensive spatial mapping approach using cubic convolution resampling methodology and the correlation matrix analysis based on established atmospheric chemistry principles. In the revised manuscript, additional clarifications and references have been incorporated in these sections in response to comments from another reviewer, while ensuring clear explanation of pollutant interrelationships, robust scientific interpretations, and precision in statistical correlation analysis.

Comment 7: 3.4. Contribution to Sustainable Development Goals <- Separate Discussion
While well-written, it would be easier for other researchers to understand the evidence using the results data by writing a separate Discussion section.

Response: Thank you for your constructive feedback and positive assessment of the SDG contribution section. Your suggestion regarding creating a separate Discussion section is valuable and has been carefully considered. You are correct that separating the Discussion could potentially enhance accessibility for researchers seeking to understand the evidence and its implications. However, after thorough consideration of the manuscript structure and reader experience, the decision has been made to maintain the combined Results and Discussion format for several methodological reasons.

Given the comprehensive nature of this study with seven atmospheric pollutants analyzed across multiple temporal and spatial dimensions, the manuscript contains extensive visual evidence including Figures 3-11, with many containing multiple sub-panels (particularly the spatial distribution maps in Figures 4-10, each containing 12 monthly panels). Separating the Discussion would require readers to continuously navigate between sections and figures, potentially disrupting the analytical flow and creating cognitive burden rather than enhancing clarity.

The integrated Results and Discussion approach allows for immediate contextualization of findings within their scientific and policy frameworks, particularly important for the complex atmospheric chemistry relationships and SDG linkages presented. This format enables direct connection between quantitative results and their broader implications without requiring readers to repeatedly cross-reference between distant sections of the manuscript.

The SDG section specifically benefits from this integrated approach as it draws upon multiple result components (temporal trends, spatial patterns, and correlation analyses) to demonstrate quantitative contributions across 12 SDG targets. Table 2 synthesizes these multi-dimensional findings into concrete policy-relevant metrics that would be difficult to present coherently in a separated format.

While fully respecting your valuable suggestion, this integrated structure maintains analytical coherence and reader accessibility for this complex, multi-pollutant urban air quality assessment.

Comment 8: 4. Conclusion

The overall results above are summarized well..

Response: Thank you for your positive assessment of the Conclusion section. We appreciate your acknowledgment that the overall results are well summarized, effectively capturing the key findings and implications of this comprehensive air quality study.

Comment 9: 4 5. References

The MDPI format for the references needs to be confirmed.

Response: Thank you for pointing out this formatting concern. The references have been carefully reviewed and updated to ensure full compliance with MDPI formatting guidelines and journal standards in the revised manuscript.

 

Other comments: Conclusively, this paper can be published after minor revisions.

Response: We are confident that the manuscript now adheres to the publication standards. Your constructive feedback during the major revision process was invaluable, and we thank you again for your time and dedication in helping us improve our work for publication in the Atmosphere.

 

Quality of English Language: The English is fine and does not require any improvement.

Response: Thank you for confirming that the English language quality in the manuscript meets the publication standards.

Reviewer 3 Report

Comments and Suggestions for Authors

Spatial-temporal dynamics of atmospheric pollution in Baghdad, Iraq, during 2012-2023 are analyzed with NASA's Giovanni platform coupled with Google Colab analytics. Three distinct pollutant trajectory categories are identified, reflecting complex emission-atmosphere interactions. Strong relationships between meteorological factors and pollutant concentrations are revealed. Here are my comments. 

1. In the introduction, please provide more quantitative information on Baghdad’s air quality crisis, including some representative metrics.
2. Please indicate the similarity and difference of air quality behaviors between Baghdad and general Middle East regions.
3. “Estimates suggest that air quality-related economic losses in regions like Iraq may reach several percent of annual GDP”, please be more accurate and give exact reference source.
4. In addition to the NASA's Giovanni platform, what are the other tools available for analyses, and why they are not used in the present work.
5. If the map in fig.1 is reproduced from other sources, the reference should be provided.
6. Line 264, for the chosen seven key atmospheric indicators, reasoning for selecting particularly these indicators but not the others should be given to justify that these indicators are sufficiently representative to provide a comprehensive air quality analysis.
7. Line 330, do authors derive the cubic Keys function, if not, the derivation should be cited.
8. Trivial equations such as average or linear regression can be neglected.
9. Discuss possible applications of the proposed model for air quality analyses in other regions, what are the key technical aspects when generalize the analysis tools.

Author Response

Response to Reviewer (3)

Spatial-temporal dynamics of atmospheric pollution in Baghdad, Iraq, during 2012-2023 are analyzed with NASA's Giovanni platform coupled with Google Colab analytics. Three distinct pollutant trajectory categories are identified, reflecting complex emission-atmosphere interactions. Strong relationships between meteorological factors and pollutant concentrations are revealed. Here are my comments:

Comment 1: In the introduction, please provide more quantitative information on Baghdad’s air quality crisis, including some representative metrics.

Response: Thank you for this valuable feedback regarding the need for more quantitative information about Baghdad's air quality crisis in the introduction. Your suggestion has been addressed by incorporating specific representative metrics that establish the severity of the air quality challenges.

In response to your recommendation, quantitative data has been added to the introduction to provide concrete evidence of Baghdad's air quality crisis. The revised text now includes specific measurements showing that annual mean PM₂.₅ concentrations in Baghdad exceed 70 µg/m³, which represents more than 14 times the WHO guideline limit of 5 µg/m³ (lines 76-77). Additionally, the introduction now specifies that NO₂ and SO₂ levels frequently surpass international standards during both summer and winter seasons (lines 77-78).

The quantitative context has been further strengthened by including regional comparative information, noting that Baghdad shares characteristics with other Middle Eastern cities such as frequent dust intrusions and seasonal photochemical smog events, but is distinguished by higher contributions from local anthropogenic sources and limited monitoring infrastructure (lines 78-83).

Furthermore, economic impact metrics have been incorporated, referencing World Bank estimates that air pollution-related health damages in the Middle East and North Africa cost approximately 2-4% of GDP annually, with Iraq among the most affected countries (lines 122-124). These specific quantitative indicators provide readers with concrete evidence of the magnitude of Baghdad's air quality challenges and establish the critical need for comprehensive monitoring and analysis as undertaken in this study.

These additions transform the introduction from general descriptions to evidence-based quantitative assessment, directly addressing your concern about the need for representative metrics to demonstrate the severity of Baghdad's air quality crisis.

Comment 2: Please indicate the similarity and difference of air quality behaviors between Baghdad and general Middle East regions.

Response: Thank you for this valuable feedback requesting clarification on the similarities and differences between Baghdad's air quality behaviors and those of the broader Middle East region. This important comparative context has been addressed in the revised manuscript.

The similarities between Baghdad and other Middle Eastern cities have been clearly established in the introduction (lines 78-83). Baghdad shares several regional characteristics common throughout the Middle East, including frequent dust intrusions from desert regions, arid climatic conditions that promote photochemical reactions, and seasonal dust storm events that significantly impact air quality. The city also experiences similar meteorological phenomena such as intense solar radiation, high temperatures exceeding 45°C during summer months, and low annual precipitation patterns (<150 mm) that characterize the broader regional climate (lines 216-220).

However, Baghdad exhibits several distinctive features that differentiate it from other Middle Eastern urban areas. The revised text now emphasizes that Baghdad is distinguished by a higher contribution from local anthropogenic sources, with over 90% of hazardous fine particulate matter arising from diesel generators, chronic traffic congestion, and unregulated industrial emissions (lines 67-71). This contrasts with many regional cities where transboundary pollution or natural phenomena dominate air quality patterns.

Additionally, Baghdad's unique challenges include limited air quality monitoring infrastructure and socio-economic constraints that hinder effective mitigation efforts, conditions exacerbated by decades of conflict and sanctions (lines 70-72). The city's position along the Tigris River creates specific microenvironmental conditions that influence pollutant dispersion patterns differently from other regional urban centers.

The economic burden is also specifically quantified, with the study noting that air pollution-related damages in the Middle East and North Africa region cost approximately 2-4% of GDP annually, with Iraq among the most severely affected countries (lines 122-124), highlighting Baghdad's particular vulnerability within the regional context..

 

Comment 3: “Estimates suggest that air quality-related economic losses in regions like Iraq may reach several percent of annual GDP”, please be more accurate and give exact reference source.

Response: Thank you for requesting more precision on this important economic data. You are absolutely correct that specific and accurate referencing is essential for such claims, and this has been addressed in the revised manuscript.

The revised text now provides exact figures with proper source attribution. Based on World Bank data, the economic losses from air pollution in the Middle East and North Africa region have been precisely quantified. According to the World Bank's 2022 report by Heger et al. (2022), the economic costs of air pollution are immense – around $141 billion per year, or 2% of regional GDP.

More specifically, for individual countries in the region, the World Bank's 2013 data shows that estimated welfare losses ranged from around 0.4 percent in Qatar to more than 3 percent in Egypt, Lebanon, and the Republic of Yemen. For Iraq specifically, the total welfare loss from air pollution was estimated at 2.67% of GDP, confirming that the statement about "several percent of annual GDP" is accurate but was indeed too vague in the original text.

The revised manuscript now includes the precise reference: Heger, M., Vashold, L., Palacios, A., Alahmadi, M., Bromhead, M. A., & Acerbi, M. (2022). Blue skies, blue seas: air pollution, marine plastics, and coastal erosion in the Middle East and North Africa. World Bank Publications. This source provides comprehensive economic impact assessments for the region, including Iraq-specific data that supports the quantitative claims about GDP losses from air pollution.

This correction ensures that all economic impact statements are backed by authoritative, peer-reviewed World Bank analyses rather than general estimates, thereby enhancing the credibility and precision of the economic arguments presented.

Comment 4: In addition to the NASA's Giovanni platform, what are the other tools available for analyses, and why they are not used in the present work.

Response: Thank you for this valuable question regarding alternative analytical platforms and tools. Your inquiry about platform selection rationale is important for methodological transparency and has been addressed comprehensively in the revised manuscript. Several alternative platforms and tools are available for atmospheric pollution analysis, each with specific advantages and limitations that influenced our platform selection decision. The revised manuscript now discusses these alternatives more thoroughly to justify our methodological choices.

Google Earth Engine (GEE) represents one major alternative platform that provides access to extensive satellite data archives with powerful cloud computing capabilities. However, while GEE offers superior computational resources and programming flexibility, it requires substantial technical expertise in JavaScript or Python programming and lacks the user-friendly interface that Giovanni provides for atmospheric data extraction and preliminary analysis (lines 143-147).

The Copernicus Atmosphere Monitoring Service (CAMS) offers another comprehensive alternative, providing near-real-time atmospheric composition data and forecasts. However, CAMS datasets have shorter temporal coverage compared to the decadal scope required for this study (2012-2023), and the platform's focus on European regions limits its optimization for Middle Eastern atmospheric conditions.

Sentinel-5P TROPOMI data, accessible through various platforms including GEE, provides superior spatial resolution (~1 km) compared to MERRA-2 products. However, as discussed in the methodology section (lines 300-307), TROPOMI's operational period beginning in late 2017 would not support our comprehensive decadal trend analysis. Additionally, TROPOMI coverage is limited to certain atmospheric constituents (NO₂, SO₂, CO, CH₄, O₃) and does not include sulfates (SO₄) and aerosol optical depth (AOD) measurements that are crucial for this comprehensive study.

NASA Giovanni was selected due to its optimal balance of accessibility, dataset integration, temporal coverage, and compatibility with our study's specific requirements. The platform's integrated framework provides consistent, long-term datasets from multiple satellite missions with standardized processing protocols, enabling robust trend analysis across our complete study period while maintaining methodological consistency across all seven atmospheric pollutants examined.

Comment 5: If the map in fig.1 is reproduced from other sources, the reference should be provided..

Response: Thank you for this important clarification regarding Figure 1. We appreciate your attention to proper attribution and referencing standards. Figure 1 was created entirely by the authors using original cartographic work. The map was designed and produced specifically for this study using ArcGIS 10.8 software, with all geographical boundaries, administrative divisions, and visual elements being drawn and compiled by the research team members.

The base geographical data for Iraq's administrative boundaries and Baghdad Governorate's boundaries were derived from publicly available administrative datasets, but the final map composition, design, symbology, and cartographic presentation are entirely original work created specifically for this manuscript. No existing maps or copyrighted materials were reproduced or modified from external sources.

Comment 6: Line 264, for the chosen seven key atmospheric indicators, reasoning for selecting particularly these indicators but not the others should be given to justify that these indicators are sufficiently representative to provide a comprehensive air quality analysis..

Response: We thank the reviewer for raising this important point. In the revised manuscript, we have clarified the rationale for selecting the seven atmospheric indicators (CO, CO₂, SO₂, SO₄, O₃, CH₄, and AOD). As now explained in the Methods section (lines 330–336), these indicators were chosen because they collectively represent the major categories of urban air quality drivers: primary pollutants from combustion (CO, SO₂), greenhouse gases with strong climate forcing (CO₂, CH₄), secondary aerosol formation (SO₄²⁻), atmospheric oxidants (O₃), and the integrated aerosol burden (AOD). Together, they provide a sufficiently representative set to capture both health-related and climate-relevant aspects of air quality. This selection is supported by established literature on air pollution and climate–health linkages.

Comment 7: Line 330, do authors derive the cubic Keys function, if not, the derivation should be cited..

Response: Thank you for this important methodological clarification regarding the cubic Keys function derivation. You are absolutely correct that proper citation is essential for mathematical formulations used in the study.

The cubic Keys function was not derived by the authors but represents an established interpolation method in the geospatial literature. In the revised manuscript, appropriate citations have been added to acknowledge the original derivation and subsequent applications of this interpolation technique.

The cubic convolution interpolation method and its mathematical formulation are now properly referenced to Keys (1981), who originally developed this technique for digital image processing (line 362). Additionally, the specific mathematical equations (Equations 2 and 3) presented in lines 370-374 are supported by Lehmann et al. (1999), who provided detailed mathematical descriptions and validation of cubic interpolation methods in spatial data processing applications.

The Keys interpolation method was selected over bilinear or nearest-neighbor approaches because it preserves gradient continuity while avoiding excessive smoothing, which is essential for detecting subtle pollutant concentration differences in urban environments (lines 363-365). This established technique has been widely validated in geospatial research for enhancing spatial resolution of atmospheric datasets while maintaining data integrity.

Comment 8: Trivial equations such as average or linear regression can be neglected.

Response: Thank you for this thoughtful suggestion regarding the inclusion of fundamental equations in the manuscript. We sincerely appreciate your perspective on mathematical presentation and have carefully considered your recommendation.

While we fully acknowledge that equations such as averaging and linear regression represent well-established fundamental concepts for atmospheric sciences audiences, the decision has been made to retain these equations for important methodological reasons. Given the interdisciplinary nature of environmental health research, this study may be accessed by researchers from related disciplines including geography, public health, urban planning, and policy studies, who benefit from explicit mathematical formulations. The equations provide methodological transparency that enables researchers from diverse backgrounds to understand and replicate the analytical approaches employed.

Additionally, the explicit presentation of fundamental equations enhances reproducibility, a critical aspect of scientific research. By providing complete mathematical specifications, even for standard procedures, the methodology becomes fully transparent and enables exact replication by other research groups working in data-scarce environments.

Furthermore, given the manuscript's contribution to Sustainable Development Goals and potential use in policy contexts, non-specialist readers including policymakers and environmental managers may engage with this literature. The explicit mathematical presentation ensures accessibility to this broader audience while maintaining academic rigor and supporting the interdisciplinary objectives of environmental science research.

Comment 9: Discuss possible applications of the proposed model for air quality analyses in other regions, what are the key technical aspects when generalize the analysis tools..

Response: Thank you for this valuable suggestion regarding the broader applicability of our analytical framework. This recommendation has been addressed through the addition of a comprehensive Recommendations section in the revised manuscript.

The proposed methodological framework demonstrates strong transferability to other urban environments sharing similar environmental and socio-economic conditions. The Recommendations section (lines 833-849) now outlines key technical aspects for generalization, including: selection of consistent long-term atmospheric datasets from reanalysis and satellite sources, harmonization of spatial and temporal resolutions across pollutants, application of time-series decomposition and correlation analyses to separate seasonal, meteorological, and anthropogenic influences, and integration of results into sustainable development planning frameworks.

The framework's scalability is enhanced through its cloud-based computational approach using Google Colab and standardized satellite data platforms, making it accessible to research groups with limited computational resources. Key technical considerations for generalization include adapting the cubic resampling methodology to local spatial scales, calibrating statistical models to region-specific meteorological patterns, and incorporating ground-based validation networks where available.

Future extensions should integrate Sentinel-5P data for higher spatial resolution once longer temporal archives become available, combine multiple satellite platforms for comprehensive pollutant coverage, and incorporate local emission inventories to enhance source attribution capabilities. The framework is particularly suitable for conflict-affected regions, rapidly urbanizing cities, and areas with limited monitoring infrastructure, providing essential baseline data for evidence-based environmental management and supporting comparative air quality assessments across multiple urban centers globally.

Quality of English Language: The English is fine and does not require any improvement.

Response: Thank you for confirming that the English language quality in the manuscript meets the publication standards.

Round 2

Reviewer 2 Report

Comments and Suggestions for Authors

The current paper has been revised to address all of the issues previously mentioned, so there are no major issues with its publication in atmosphere. However, the MDPI format for the references needs to be reconfirmed.

Reviewer 3 Report

Comments and Suggestions for Authors

The manuscript can be accepted for publication in its present form.