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Urban Air Pollution Exposure Impact on COVID-19 Transmission in a Few Metropolitan Regions

Sustainability 2024, 16(14), 6119; https://doi.org/10.3390/su16146119

by Maria Zoran^*

, Roxana Radvan, Dan Savastru and Marina Tautan

Reviewer 1: Anonymous

Reviewer 2: Anonymous

Reviewer 3: Anonymous

Sustainability 2024, 16(14), 6119; https://doi.org/10.3390/su16146119

Submission received: 26 April 2024 / Revised: 25 June 2024 / Accepted: 10 July 2024 / Published: 17 July 2024

(This article belongs to the Section Pollution Prevention, Mitigation and Sustainability)

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

see attachment

Comments for author File: Comments.pdf

Comments on the Quality of English Language

see attachment

Author Response

Dear Colleague Reviewer,

I am very thankful for all your comments. I have addressed all your points of view and I believe that based on your comments, this paper was significantly improved.

Please, see my point-by-point answers below.

Overall Comment: The author studied the Urban Air Pollution Exposure Impact on COVID-19 2 Transmission in Few Metropolitan Regions. The results underscore the significant impact of environmental factors, particularly outdoor air pollution and meteorological conditions, on the transmission dynamics of COVID-19 in major European metropolises. By analyzing comprehensive datasets spanning from March 2020 to March 2022, including air pollutants such as PM2.5, PM10, NO2, and O3, along with key climate parameters, the study revealed a direct positive association between aerosol loading and the spread and severity of COVID-19. Despite urban variations, negative correlations were observed between COVID-19 cases and deaths and ground-level ozone concentrations, air temperature, Planetary Boundary Layer height, and surface solar irradiance during spring-summer periods. These findings highlight the intricate interplay between particulate matter and viral pathogen dispersion, influenced by climate variability, in shaping COVID-19 transmission dynamics in densely populated urban areas. Such insights can inform policymakers and stakeholders in developing targeted interventions to mitigate the impact of epidemics in urban settings. However, I would like to give some comments in order to make the study more informative.

Specific Comments

# Comment 1: 1. Clarify the specific statistical methods employed to analyze the multiple datasets of air pollutants and climate parameters. Providing more detail on the statistical techniques used would enhance the reproducibility and rigor of the study.

Response:

At Section 2.2 Methods I added 2 phrases:

In order to investigate the mutual influence between the number of COVID-19 daily new cases (DNC) and COVID-19 daily cases new deaths (DND) and the climate variables, we must consider two elements: first, the phenomena involved are strongly nonlinear and secondly the data are quite sparse.

The Spearman’s rank correlation coefficient was considered as a nonparametric statistical indicator, and a measure of the dependence between the rankings of two variables. Spearman’s r quantifies how well the relationship between two variables can be represented by a monotonic function, without any linearity assumption. In other words, Spearman’s correlation quantifies the monotonic relationships whether they are linear or not.

However, at your suggestion I improved my article.

Thanks a lot.

# Comment 2: Clarify the rationale behind the selection of the specific metropolitan areas. Provide more detailed explanations regarding why Berlin, London, Madrid, and Paris were chosen as representative cities, emphasizing their unique characteristics relevant to the study

objectives, such as population density, industrial activities, and geographical features.

Response: The reason for which I selected as investigation test cases European megacities Berlin, London, Madrid, and Paris was related to the high COVID-19 incidence and mortality during the first COVID-19 wave in spring 2020, explained by the Figure 2, which shows that the geopotential at 500 mb positive anomaly occurrence over the investigated cities during the first COVID-19 wave in the south-Western part of Europe, that favored accumulation of virus-laden aerosols near the ground and COVID-19 disease transmission, and explains the associated existing correlations between urban air pollution episodes and intensity of COVID-19 waves incidence and mortality.

Thank You very much for your question.

# Comment 3: . Consider providing more information about the time frame of the study period (March 2020 to March 2022). Explain why this time frame was chosen and how it aligns with the research objectives. Additionally, discuss any notable events or trends during this period

that may have influenced the study outcomes.

Response: The selected time frame research period is related of the higher intensity of COVID-19 waves and the availability of data, being associated also with lockdown periods and retrictions.

Thank you so much.

# Comment 4: Consider providing additional context or discussion regarding the potential mechanisms underlying the observed positive impact of aerosol loading on COVID-19 transmission.

Exploring potential pathways or mechanisms through which aerosol particles interact with

viral pathogens could strengthen the interpretation of the findings.

Response: Experimental studies found the presence of SARS-COV-2 virions in the outdoor samples of aerosol particles, especially in densely populated cities.

Seefor eg. Yile Tao, Xiaole Zhang, Guangyu Qiu, Martin Spillmann, Zheng Ji, Jing Wang,

SARS-CoV-2 and other airborne respiratory viruses in outdoor aerosols in three Swiss cities before and during the first wave of the COVID-19 pandemic, Environment International,

Volume 164,2022,107266, https://doi.org/10.1016/j.envint.2022.107266.

Thanks!

# Comment 5: It would be beneficial to discuss any limitations or assumptions associated with the analysis, particularly regarding the interpretation of correlations between air pollutants,

climate parameters, and COVID-19 transmission. Acknowledging potential confounding

factors or uncertainties would improve the robustness of the conclusions.

Response: In Section 4 (Strengths and Limitations) was shown that “ this study did not consider spatiotemporal human mobility distribution in the studied metropolitan areas, as well as other sociodemographic factors exerting a stronger influence over environmental features. Aso have not been considered factors like as vaccination rate, demographic characteristics regarding age, sex and comorbidities as well as the quality and interventions of health services”.

However the limited space of the paper did not allow me to write more.

Thanks a lot.

Regarding Comments on the Quality of English Language

Minor editing of English language required

Response: I check the text and modified it, and also I asked an English native person to check the language in my paper.

Thanks a lot.

Thank you very much for your comments!

Wish you all the best.

Prof. Dr. Maria Zoran

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

The manuscript explores the impact of air pollution on epidemics, and the article is logically organized and has some research implications. However, there are shortcomings in the innovativeness of the study, the results and discussion do not contain novel insights, other specific issues are as follows:

1. In lines 348-354, the authors did not provide a detailed explanation of the limitations of the study and did not consider what are the reasons for population movement? How will the follow-up address this issue? Please explain

2. 2. In Introduction, the author needs to further summarize the description and references. For example, in lines 29-30: "The global severity of COVID-19 infectious disease has been associated with various urban characteristics, including exposure to ambient air pollutants.", what is the basis for this statement?

3. Some abbreviations and formats need attention, for example, the first mention of COVID-19 needs to be in full.

4. In lines 140-161, the authors do not provide any websites for the data sources, please provide shorter details about the Data Sets used in this study.

5. The manuscript has a large number of formatting errors, paragraphs, indentations, such as in lines 140-161, lines 185-230.

Comments on the Quality of English Language

English language level and writing of articles must be improved

Author Response

Comments from Reviewer# 2:

Dear Colleague Reviewer,

I am very thankful for all your comments. I have addressed all your points of view and I believe that based on your comments, this paper was significantly improved.

Please, see my point-by-point answers below.

# Comment 1: 1. In lines 348-354, the authors did not provide a detailed explanation of the limitations of the study and did not consider what are the reasons for population movement? How will the follow-up address this issue? Please explain

Response: Regarding the content of 348-358 lines

“As limitations, this study did not consider spatiotemporal human mobility distribution in the studied metropolitan areas. Besides air pollution and climate influencing factors and relative risks, which varied across time and space, must be considered sociodemographic factors exerting a stronger influence over environmental features. Aso have not been considered factors like as vaccination rate, demographic characteristics regarding age, sex and comorbidities as well as the quality and interventions of health services”

human mobility distribution- means urban transport …

I had not free available data for urban transport in the selected cities.

Thanks a lot.

# Comment 2 : 2. In Introduction, the author needs to further summarize the description and references. For example, in lines 29-30: "The global severity of COVID-19 infectious disease has been associated with various urban characteristics, including exposure to ambient air pollutants.", what is the basis for this statement?

Response: Urban characteristics are very diverse (size, form, landscape, density of population, mobility, micro and macroclimate, environmental pollution, social and cultural, etc).

At your suggestion, in the corrected manuscript text I rephrased: The global severity of Coronavirus (COVID-19) infectious disease attributed to SARS-CoV-2 pathogens has been associated with various urban characteristics (size, form, landscape, density of population, mobility, micro and macroclimate, socioeconomic, environmental pollution) including exposure to ambient air pollutants.

Hope is O.K

Thanks!

# Comment 3: Some abbreviations and formats need attention, for example, the first mention of COVID-19 needs to be in full.

Response: You are perfectly right. I introduced ” Coronavirus (COVID-19) infectious disease attributed to SARS-CoV-2 pathogens” in the first phrase.

Thanks!

# Comment 4: In lines 140-161, the authors do not provide any websites for the data sources, please provide shorter details about the Data Sets used in this study.

Response: As other reviewers considered I used Reference section for this [31-36]

Worldometer Info. 2023. Available online: https://www.worldometers.info/ (accessed on 20 January 2024).
WHO, 2024. https://covid19.who.int/WHO-COVID-19-global-data.csv (accessed on 25 January 2024).
Giovanni, 2024. https://giovanni.gsfc.nasa.gov/giovanni/ (accessed on 25 January 2024).
MERRA, 2024. http://www.soda-pro.com/web-services/meteo-data/merra (accessed on 25 January 2024).
Copernicus, https://cds.climate.copernicus.eu/ (accessed on 20 January 2024).
AQICN, 2024. https://aqicn.org/city/ (accessed on 20 January 2024).

Thanks a lot.

# Comment 5: The manuscript has a large number of formatting errors, paragraphs, indentations, such as in lines 140-161, lines 185-230.

Response: I checked the text.

Thanks a lot.

Comments on the Quality of English Language

English language level and writing of articles must be improved.

Response: I modified the text and I asked an English native person to check the language in my paper.

Thanks a lot.

However, at your suggestion I improved my article.

Hope this new variant is O.K.

Thank you!

Wish you all the best.

Prof.Dr. Maria Zoran

Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

Dear Authors, please, consider the following recommendations below and using them to improve the article

1. In the introduction it will be useful to include information about your predecessors studying contribution of climatic, meteorological, and geophysical agents to the spread of the acute respiratory infection pandemic. In particular, the results of the study of association between temperature, humidity, atmospheric pressure, solar activity, cyclonic activity, and the spread of pneumonia, influenza, and enteritis in the northern hemisphere, including Berlin, was performed by C. M. Richter (C. M. Richter. The simultaneous and cyclic appearance of epidemics of pneumonia, grip and enteritis on the Northern hemisphere and their synchronism with solar cyclonic activity// Jour. A. M. A. Dec. 19, 1911.pp. 1964-1967).

2. Also, it would be worthwhile to elaborate on the identification of similarities and differences in the results of the authors' own studies and those of other studies close to them. It would also be useful to discuss the contribution of the UV and vitamin D to epidemic seasonality.

3. In the section of “ Materials and Methods” you give the terrain height only for Madrid. It would be useful to include a characteristic such as altitude for each study area.

4. If the descriptive statistical analysis was performed, it would be appropriate to present these results in tables, as well as regression analysis data. In this work, it would be more appropriate to apply multiple regression to identify the main contributions of the studied indicators to the morbidity and mortality from Covid-19.

5. In the section of “Results and Discussion”, please, provide data from statistical studies, at least the average monthly values of the parameters being studied. Also, a table with the results of multiple regression, reflecting the main contributions of the studied parameters to the morbidity and mortality of Covid-19, would be more informative than Table 1 with correlation coefficients. The regression model can also include the surface area, height location above sea level, population size of the study metropolitan regions.

6. Line 283: “During the period 2015–2019, AOD levels recorded different variations, as seen in Figure 3.” At the same time, Figure 3 shows variations in AOD from 2019 to 2021. What does it mean?

7. To better understand, the relationship between AOD indicators with the dynamics of new cases of morbidity and mortality from Covid-19, it is better to present separate graphs for selected metropolitan areas, which would present data on morbidity and mortality, indices of the AOD, particles contents in atmosphere in one time scale for each metropolitan area.

8. It is also important to discuss in the article the contribution of vitamin D and solar activity to the spread of the Сovid-19 epidemic in the study areas.

9. To identify the contribution of air pollution by dust particles to the spread of Covid-19, it would be useful to compare the prevalence of epidemics in megacities and rural settlements.

Comments for author File: Comments.pdf

Author Response

Dear Colleague Reviewer,

I am very thankful for all your comments. I have addressed all your points of view and I believe that based on your comments, this paper was significantly improved.

Please, see my point-by-point answers below:

Dear Authors, please, consider the following recommendations below and using them to

improve the article

# Comment 1: In the introduction it will be useful to include information about your predecessors studying contribution of climatic, meteorological, and geophysical agents to the spread of the acute respiratory infection pandemic. In particular, the results of the study of association between temperature, humidity, atmospheric pressure, solar activity, cyclonic activity, and the spread of pneumonia, influenza, and enteritis in the northern hemisphere, including Berlin, was

performed by C. M. Richter (C. M. Richter. The simultaneous and cyclic appearance of

epidemics of pneumonia, grip and enteritis on the Northern hemisphere and their

synchronism with solar cyclonic activity// Jour. A. M. A. Dec. 19, 1911.pp. 1964-1967).

Response: I improved this paper by added information regarding previous several studies including the finding of Dr. C.M.Richter as follows:

Starting from the last century pioneering climate research, Richter C.M. in 1911 [27] found correlations between climate conditions (solar activity, atmospheric pressure, and air quality), and the prevalence of viral respiratory infections (pneumonia, influenza, enteroviruses) which caused seasonal epidemics and pandemics with their transmission influenced by climate conditions in large cities in United States of America (Chicago and San Francisco). Some previous studies explored the connections between seasonality of meteorological conditions associated with extreme climate events and seasonal large-scale outbreaks of different viral infections such as SARS (severe acute respiratory syndrome) in 2002-2003, influenza H1N1 in 2009, MERS (Middle East Respiratory Syndrome) in 2012-2015, and new waves of SARS-CoV-2 (COVID-19) in 2019, with severe impacts on human excess of lethality and morbidity, and significant economic disruption. Existing scientific literature provided information on seasonal variability in bacterial and fungal diversity of the near-surface atmosphere which can target the human immune system through damage of innate immune recognition receptors that respond to unique pathogen-associated molecular patterns [28-33]. Recent advances in toxicological studies of associated mechanisms with airway disease attributed to air pollutants considered epigenetic alteration of genes by combustion-related pollutants and how polymorphisms in genes involved in antioxidant pathways and airway inflammation can modify responses to air pollution exposures [34-39].

With corresponding new references:

Richter C.M.The simultaneous and cyclic appearance of epidemics of pneumonia, grip and enteritis on the Northern hemisphere and their synchronism with solar activity cycles. JAMA. 1911, LVII(25):1964-1967. doi:10.1001/jama.1911.04260120154002.
Bosch BJ, van der Zee R, de Hana CAM, Rottier PJM. The coronavirus spike protein is a class I virus fusion protein: structural and functional characterization of the fusion core complex. J Virol 2003, https://doi.org/10.1128/jvi.77.16.8801-8811.2003.
Bowers, R.M., Clements, N., Emerson, J.B., Wiedinmyer, C., Hannigan, M.P., Fierer, N. seasonal variability in bacterial and fungal diversity of the near-surface atmosphere. Sci. Technol. 2013, 47, 12097-12106.
Cáliz J., Triadó-Margarit X., Camarero L., Casamayor E.O. A long-term survey unveils strong seasonal patterns in the airborne microbiome coupled to general and regional atmospheric circulations. Natl. Acad. Sci. U. S. A., 2018, 115, 12229-12234
Cao, Jiang W., Wang B., Fang J., Lang J., Tian G., Jiang J., Zhu T. InhalableMicroorganisms in Beijing’s PM_2.5 and PM₁₀ Pollutants during a Severe Smog Event. Environ. Sci. Technol. 2014, 48, 3, 1499-1507.
Jones, A.M., Harrison, R.M. The effects of meteorological factors on atmospheric bioaerosol concentrations — a review. Total Environ. 2004, 326 (1–3), 151–180.
Khan M.F., Hamid A.H. , Bari M.A., Tajudin A.B, Latif M.T. , et al. Airborne particles in he city center of Kuala Lumpur: Origin, potential driving factors, and deposition flux in human respiratory airways, Science of The Total Environment ., 2019, 650, Part 1, 1195-1206.
Kelly F. J. and Fussell J. C. Air pollution and airway disease. Clinical & Experimental Allergy 2011,41, 1059–1071
Khan M.F., Hamid A.H. , Bari M.A., Tajudin A.B, Latif M.T. , et al. Airborne particles in he city center of Kuala Lumpur: Origin, potential driving factors, and deposition flux in human respiratory airways, Science of The Total Environment ., 2019, 650, Part 1, 1195-1206.
Lepeule J., Bind M.A.C., Baccarelli A.A., Koutrakis P., et al. Epigenetic influences on associations between air pollutants and lung function in elderly men: The normative aging study. Environmental Health Perspectives2014, 122, 566-572.
Ciencewicki, Jaspers I. Air Pollution and Respiratory Viral Infection. Inhal Toxicology 2007, 19(14), 1135-46, doi: 10.1080/08958370701665434
Cui Y, Zhang Z, Froines J, Zhao J, Wang H, Yu S, et al. Air pollution and case fatality of SARS in the People’s Republic of China: an ecologic study. Environ Health 2003, 2(1):15.
Innocente, Squizzato S., VisinF.,Facca C., Rampazzo G., et al. Influence of seasonality, air mass origin and particulate matter chemical composition on airborne bacterial community structure in the Po Valley, Italy. Science of The Total Environment , 2017, 593–594, 677-687.

Hope is O.K

Thanks a lot.

# Comment 2 : Also, it would be worthwhile to elaborate on the identification of similarities and

differences in the results of the authors' own studies and those of other studies close to them.

It would also be useful to discuss the contribution of the UV and vitamin D to epidemic

seasonality.

Response: I added in Introduction Section the following text:

Also, solar radiation through its ultraviolet electromagnetic band regions UVB (280–315 nm) and UVA (315–400 nm), is the primary virucidal agent in the environment [40-43]. Is well recognized to be an important variable, which may affect the transmission and the outcomes of the COVID-19 disease through reduction of SARS-CoV-2 pathogens diffusion and the virus inactivation during specific time periods of exposure [44-45]. Through vitamin D synthesis in the human body, solar radiation plays an essential role in the increasing of the innate and adaptative immune systems defense, and by this the reducing risk, severity and mortality of the respiratory viral tract diseases like as COVID-19 and influenza [46-49].

With corresponding references:

Herman, J., Biegel, B., Huang, L. Inactivation times from 290 to 315 nm UVB in sunlight for SARS coronaviruses CoV and CoV-2 using OMI satellite data for the sunlit Earth. Air Qual. Atmos. Health. 2020, https://doi.org/10.1007/s11869-020- 00927- 2.
Giese, A.C. Living with Our Sun’s Ultraviolet Rays. Plenum Press, New York (Chapter 3), 1976, 33–
Heßling, M., H¨ones, K., Vatter, P., Lingenfelder, C. Ultraviolet irradiation doses for coronavirus inactivation - review and analysis of coronavirus photoinactivation studies. GMS Hyg Infect Control 2020, https://doi.org/10.3205/dgkh000343, 15 Doc08.
Sagripanti, J.L., Lytle, C.D. Estimated inactivation of coronaviruses by solar radiation with special reference to COVID-19. Photobiol. 2020, 96, 731–737. https://doi.org/10.1111/php.13293.
Luo, X., Liao, Q., Shen, Y., Li, H., Cheng, L.Vitamin D deficiency is associated with COVID-19 incidence and disease severity in Chinese people. Nutr. 2021, 151, 98–103. https://doi.org/10.1093/jn/nxaa332. Lytle, C.D., Sagripanti, J.-L., 2005. Predicted inactivation of viruses of relevance to biodefense by solar radiation. J
Calder, P.C., Carr, A.C., Gombart, A.F., Eggersdorfer, M. Optimal nutritional status for a well-functioning immune system an important factor to protect against viral infections. Nutrients 2020, 12, 1– https://doi.org/10.3390/nu12041181
Castillo, M.E., Entrenas Costa, L.M., Vaquero Barrios, J.M., Alcal´a Díaz, J.F., Lopez Miranda, J., Bouillon, R., Quesada Gomez, J.M. Effect of calcifediol treatment and best available therapy versus best available therapy on intensive care unit admission and mortality among patients hospitalized for COVID-19: a pilot randomized clinical study. Steroid Biochem. Mol. Biol. 2020, 203, 105751 https://doi. org/10.1016/j. jsbmb.2020.105751.
Schuit, et al. Airborne SARS-CoV-2 is rapidly inactivated by simulated sunlight. Infect. Dis. 2020, 222, 564–571.
Jayawardena, R., Jeyakumar, D.T., T, V., Misra, A. Impact of the vitamin D deficiency on COVID-19 infection and mortality in Asian countries. Diabetes, Metab. Syndrome: Clin. Res. Rev. 2021, 15 (3), 757–
Zhang H., Wang J., Liang Z., Wu Y. Non-linear effects of meteorological factors on COVID-19: An analysis of 440 counties in the Americas. Heliyon 2024, 10 (10), e31160, https://doi.org/10.1016/j.heliyon.2024.e31160.

Hope is O.K

Thanks!

# Comment 3: In the section of “ Materials and Methods” you give the terrain height only for Madrid. It would be useful to include a characteristic such as altitude for each study area.

Response: You are right, I added some new information with blue color on the manuscript with corrections

Thanks!

# Comment 4: If the descriptive statistical analysis was performed, it would be appropriate to present these results in tables, as well as regression analysis data. In this work, it would be more

appropriate to apply multiple regression to identify the main contributions of the studied

indicators to the morbidity and mortality from Covid-19.

Response: You are perfect right, the results can be presented in tables, but this needs several tables.

At Section 2.2 Methods I added 2 phrases:

However, at your suggestion I improved my article.

Response:

Thanks a lot.

# Comment 5: 5. In the section of “Results and Discussion”, please, provide data from statistical studies, at least the average monthly values of the parameters being studied. Also, a table with the results of multiple regression, reflecting the main contributions of the studied parameters to

the morbidity and mortality of Covid-19, would be more informative than Table 1 with

correlation coefficients. The regression model can also include the surface area, height

location above sea level, population size of the study metropolitan regions.

Response: In my analysis I inserted 2 new tables with corresponding explanations:

As can be seen in Table 2, during the investigated COVID-19 period March 2020 till March 2022, London metropolis recorded the greatest AOD value (0.247 ± 0.161), followed by Paris (0.213 ± 0.145), Berlin (0.184 ± 0.089), and Madrid (0.124 ± 0.102). This can explain the highest rates of COVID-19 mortality and incidence and mortality presented respectively in Figure 4, and Figure 5.

Table 2. Mean of the daily average air pollutants concentrations, and mean daily AOD levels for the selected metropolitan areas during March 2020 and March 2022.

Mean Daily Average Variable

London

Paris

Madrid

Berlin

PM2.5 (µg/m³)

(Particulate matter 2.5 µm)

(18.55 ± 9.61)

In the range

(2 - 68)

(24.80 ± 11.61)

In the range

(4 – 75)

(14.55 ± 9.61)

In the range

(2 – 68)

(24.80 ± 11.61)

In the range

(4 – 75)

PM10 (Particulate matter 10 µm) (µg/m³)

(45.02 ± 20.44)

In the range

(11 – 154)

(50.37 ± 21.19)

In the range

(9 – 145)

(45.01 ± 20.45)

In the range

(11 – 154)

(50.37 ± 21.19)

In the range

(9 – 145)

O₃ (Ozone) (µg/m³)

(22.34 ± 11.06)

In the range

(1 – 73)

(26.01 ± 11.6)

In the range

(1 -71)

(22.35 ± 11.06)

In the range

(0 – 73)

(26.0 ± 11.05)

In the range

(0 – 71)

NO₂ (Nitrogen dioxide) (µg/m³)

(16.60 ± 8.78)In the range

(0 – 52)

(19.88 ± 11.34)

In the range

(2 – 70)

(16.60 ± 8.78)

In the range

(1 – 52)

(19.88 v 11.35)

In the range

(2 – 70)

AOD

(0.247 ± 0.161)

In the range

(0.096 – 0.664)

(0.213 ± 0.145)

In the range

(0.090 – 0.699)

(0.124 ± 0.102)

In the range

(0.038 – 0.547)

(0.184 ± 0.089)

In the range

(0.054 -0.44)

As can be seen in Table 2, the greatest COVID-19 incidence (DNC) and mortality cases have been registered in London metropolis with the highest population density (8,285.39 inhabitants/km²), followed by Madrid metropolis with a population density (5,113.55 inhabitants/km²), and Paris metropolitan area with a population density (4,542.90 inhabitants/km²). The lowest values of COVID-19 incidence (DNC) and mortality cases have been recorded by Berlin metropolis, which has also the lowest population density (3,910.82 inhabitants/km²). The trend analysis found a high linear correlation (R² = 0.9591). It means that during pandemics population density is an effective factor for viral diseases spreading in the large metropolitan areas.

Table 3. Summary of population density and total COVID-19 incidence DNC and total COVID-19 deaths DND for the selected metropolitan areas during March 2020 and March 2022.

Metropolis

Berlin

Paris

Madrid

London

Population Density

(Inhabitants/km²)

3,910.82

4,542.90

5,113.55

8,285.39

Total COVID-19 cases (DNC)

during March 2020- March 2022

905,272

1,155,528

2,246,443

3,278,230

Total COVID-19 deaths (DND)

during March 2020- March 2022

4,381

25,312

30,284

30,321

Thanks a lot.

# Comment 6: Line 283: “During the period 2015–2019, AOD levels recorded different variations, as seen in Figure 3.” At the same time, Figure 3 shows variations in AOD from 2019 to 2021. What does it mean?

Response: Sorry for misunderstanding.

I modified the text as: Despite of COVID-19 outbreak in spring 2020, and the subsequent restrictions on mobility and physical contacts associated also with extreme collapse of international tourism, comparative with the same time window (March-May) for pre-pandemic (2015–2019) period, during lockdown period (March 2020–May 2020), AOD levels recorded different variations as can be seen in Figure 3.

Thanks a lot.

# Comment 7: To better understand, the relationship between AOD indicators with the dynamics of new cases of morbidity and mortality from Covid-19, it is better to present separate graphs for

selected metropolitan areas, which would present data on morbidity and mortality, indices

of the AOD, particles contents in atmosphere in one time scale for each metropolitan area.

Response: I added more 4 new graphs for temporal patterns of DNC, DND, and the main air pollutants in the selected metropolitan areas.

Thanks a lot.

# Comment 8: It is also important to discuss in the article the contribution of vitamin D and solar activity to the spread of the Сovid-19 epidemic in the study areas.

Response: I included also Figure 10 for temporal pattern of surface solar irradiance (SI) for all investigated metropolitan areas. Also, I added some new comments ….

Thanks a lot.

# Comment 9: To identify the contribution of air pollution by dust particles to the spread of Covid-19, it would be useful to compare the prevalence of epidemics in megacities and rural settlements.

Response: You are right, but for the rural settlements was difficult to get datasets for both COVID-19 incidence and mortality as well as for air pollutants concentrations.

Thanks a lot.

However, at your suggestion I improved my article.

Hope this new variant is O.K.

Thank you!

Wish you all the best.

Prof. Dr. Maria Zoran

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

Quality of the Figure 1 needs to be improved by adding longitude and latitude degree and legend.

Comments on the Quality of English Language

Minor editing of English language required

Author Response

Dear Colleague Reviewer,

I am very thankful for all your positive comments. I have addressed all your points of view and I believe that based on your comments, this paper was significantly improved.

# Comment 1:

Quality of the Figure 1 needs to be improved by adding longitude and latitude degree and legend.

Response: I am very sorry, but I could not find a similar figure with geographical coordinates. I tried to get from Google maps, but is difficult to insert latitude and longitude numbers and legend because of the image size to be visible.

Thank you very much.

# Comment 2: Minor editing of English language required

Response: I check the text and improved it, and also, I asked again an English native person to check the language in my paper.

Thank You very much

Thank you very much for your positive comments!

Wish you all the best.

Have a nice day.

Prof. Dr. Maria Zoran

Reviewer 2 Report

Comments and Suggestions for Authors

作者对意见和建议进行了有保留的修改

The author has made reservations about the comments and suggestions

Author Response

DearColleague Reviewer,

Thank you very much for your positive comments!

Wish you all the best.

Have a nice day.

Prof. Dr. Maria Zoran

Reviewer 3 Report

Comments and Suggestions for Authors

Dear Authors, thank you for taking into account some of the recommendations to improve the article. However, the article requires further work to improve it.

1. First of all, remove duplicate Figures and also correct the numbering of Figures in the text, which confuses the understanding of what the article is about.

2. Please correctly characterise what the AOD reflects: line 103-105: ‘As Aerosol Optical Depth -AOD is sensitive to multiple air pollutants in the lower atmospheric system, including sulfur, black carbon, and organic components, it is an appropriate parameter to study the transmission of viruses through the airborne pathway.’ - AOD characterises the optical density of the lower atmosphere as an indirect indicator of its pollution level, but not the transmission of viruses through the airborne pathway.

3. In Section "2. Materials and Methods": "2.1 Study test metropolitan areas"

A) Maybe one should remove Fig. 1, as the localisation of the metropolitan areas characterised is shown in Fig. 6?

B) It would be better to present information on population size in the compared metropolitan areas as a separate line in Table 3, above the line on population density.

In subsection "2.2. Data Sets" for all indicators used, enter the units of measurement. Pay particular attention to ‘COVID-19 viral infection incidence and mortality’, which should be presented in normalised units relative to the population in the selected metropolitan regions (per 1000 or per 100 000 or per 1000 000 of the respective population). This is essential when comparing territorial morbidity and mortality (Berlin, Paris, Madrid, London) - otherwise you may misinterpret the data. DNC and DND only make sense when characterising a single territory or identifying the relationship between them and environmental factors.

In subsection "2.3 Methods": I strongly recommend that you apply multiple regression and non-linear regression analysis to prioritise the contributions of environmental parameters to COVID-19 morbidity and mortality.

Your arguments why this cannot be done - (line 237-239: ‘In order to investigate the mutual influence between the number of COVID-19 daily new cases (DNC) and COVID-19 daily cases new deaths (DND) and the climate variables, we must consider two elements: firstly, the phenomena involved are strongly nonlinear and secondly the data are quite sparse’) - are not convincing:

Firstly, because there are appropriate methods (see, e.g., C. Ireland. Multiple regression and non-linear regression analysis. In book: Experimental statistics for agriculture and horticulture. October 2010. DOI: 10.1079/9781845935375.0244):

This chapter introduces the regression techniques for dealing with multiple variables. The multiple linear regression model is described and the testing of the significance, goodness-of-fit and assumptions of multiple linear regression are discussed. The regression analysis of non-linearly related data and the application of the data transformations to 'straighten' the data are shown. The curvilinear regression analysis is also described, as well as the truly non-linear regression analysis

And secondly, your data cannot be called scarce: daily readings during March 2020 to March 2022 imply about 720 measurement points (N) for more than 10 independent variables, including wind direction (By the way, why is this parameter not reflected in the results? It may be important for estimating the direction of aerosol transport).

Spearman rank correlation shows only the relationships but not the prioritization of the contributions of the independent variables to the dependent DNC and DND.

4. In section ‘3. Results and Discussion’ it would be more logical to restructure the presentation of the material:

4.1 First characterise and compare morbidity and mortality in the selected areas (Table 3 transform in Table 1). To do this, enter in Table 3 (now Table1) the row with population size, the rows with weighted morbidity (per 1,000 or per 100,000 of the corresponding population) and mortality (per 1,000,000 of the corresponding population) and compare with density. Then you will see that density is higher in London than in Madrid, and morbidity and mortality are lower than in Madrid.

4.2 You keep Table 2 as its number, but additionally enter the statistical characteristics of the other indicators, including temperature, humidity, prevailing wind direction, etc. At the same time, you MUST compare the significance of differences in these average values for all territories. Otherwise you will not be able to talk about differences between them.

4.3 Table 1 can be transform into Table 3, but supplemented with correlations with AOD, wind direction (degrees) and etc..

4.4 If non-linear regression analysis will be done, enter Table 4 in addition.

4.5 Correct the captions for Figures 2-5. Indicate what January 2020 corresponds to: 0 ? Further, it will be very useful to supplement these Figures with daily indices SI (Then Figure 10 - not needed), temperature, humidity, etc., especially since there are higher correlation coefficients between these, DNC and DND than with Particles. Why is the NO2 indicator introduced only for Berlin (Figure 5)? Then show its dynamics in other Figures as well. Please, supplement and harmonise Figures 2-5.

4.6 Provide the reference from where Fig. 6. It appears that Figure 6 is modified by you, but taken from the article ‘Zoran A.M., Savastru S.R.,.Savastru M.D, et al. Impacts of exposure to air pollution, radon and climate drivers on the COVID 705 19 pandemic in Bucharest, Romania: A time series study. Environ.Res. 2022, 212, 113437. ?. Then refer to this article in the caption of the Figure 6 and annotate it with the comments given in the article. Otherwise, this Figure 6 is not understandable.

4.7 Please explain what is the point of Figure Figure 7? - AOD data ends in 2021; the data of DNC and DND conjugate with Covid-19, with particulate and ozone data ends in 2022 and DNC and DND (Figures 8,9) ends in 2023 ? It would be useful to compare the monthly averages of DNC and DND to 2023 with dynamics of the every monthly variations of AOD.

4.8 Please clarify the meaning of the sentence ‘The elevated values in Table 1 of Spearman rank correlation coefficients between the daily COVID-19 incidence DNC cases and DND deaths, associated with daily average at the ground level O3 concentrations and NO2, show the significance of addressing air pollution as a pressing public health issue, especially in controlling NO2 and O3 together’. Indices of NO2 and O3 have different effects on DNC and DND: NO2 has a positive effect and ozone has a negative effect. Could it be that ozone is related to SI and has a positive effect, reducing the virulence of COVID-19?

4.9 In your discussion, compare your findings with other results from similar studies. For example: Biqing Chen, Xue-Jun Zhu et al. Roles of meteorological conditions in COVID-19 transmission on a worldwide scale. Preprint - March 2020 DOI: 10.1101/2020.03.16.2003716. https://www.researchgate.net/publication/340069893.

In this study the temperature, wind speed, and relative humidity combined together could best predict the epidemic situation. The meteorological model could well predict the outbreak around the world with a high correlation (r 2 > 0.6) with the real data. Using this model, Authors further predicted the possible epidemic situation in the future days for several high-latitude cities with potential outbreak. This model could provide more information for government's future decisions on COVID-19 outbreak control.

Dear authors, if you transform the structure of the section ‘3. Results and Discussion’, you will have to change the interpretation of the results. I wish you success.

Comments for author File: Comments.pdf

Author Response

Dear Colleague Reviewer,

I am very thankful for all your comments. I have addressed all your points of view and I believe that based on your comments, this paper was significantly improved.

Please, see my point-by-point answers below.

Top of Form

Comments and Suggestions for Authors

Dear Authors, thank you for taking into account some of the recommendations to improve the article. However, the article requires further work to improve it.

# Comment 1: First of all, remove duplicate Figures and also correct the numbering of Figures in the text, which confuses the understanding of what the article is about.

Response: I think this article has not duplicate figures, maybe you consider the comparative DNC and DND figures to be similar with figures with air pollutants.

Thanks a lot.

# Comment 2: Please correctly characterize what the AOD reflects: line 103-105: ‘As Aerosol Optical Depth -AOD is sensitive to multiple air pollutants in the lower atmospheric system, including sulfur, black carbon, and organic components, it is an appropriate parameter to study the transmission of viruses through the airborne pathway.’ - AOD characterizes the optical density of the lower atmosphere as an indirect indicator of its pollution level, but not the transmission of viruses through the airborne pathway.

Response: I resolved this issue.

Thanks a lot.

# Comment 3: In Section "2. Materials and Methods": "2.1 Study test metropolitan areas"

Maybe one should remove Fig. 1, as the localization of the metropolitan areas characterized is shown in Fig. 6?

Response: These figures are just different, the first is with geographic location of the metropolitan areas, while the next is regarding composite anomaly chart of geopotential 500 mb over Europe during the first COVID-19 wave.

Thanks a lot.

It would be better to present information on population size in the compared metropolitan areas as a separate line in Table 3, above the line on population density.

Response: I resolved this issue.

Thanks a lot.

In subsection "2.2. Data Sets" for all indicators used, enter the units of measurement. Pay particular attention to ‘COVID-19 viral infection incidence and mortality’, which should be presented in normalized units relative to the population in the selected metropolitan regions (per 1000 or per 100 000 or per 1000 000 of the respective population). This is essential when comparing territorial morbidity and mortality (Berlin, Paris, Madrid, London) - otherwise you may misinterpret the data. DNC and DND only make sense when characterizing a single territory or identifying the relationship between them and environmental factors.

Response: I resolved this issue.

Thanks a lot.

Response: You are perfectly right, but this article will be published in a special issue on air pollution, so this was the main reason to analyze in more details air pollutants and not meteorological factors. So, this article is not focused on climate parameters, the title is more connected with air pollution. However, I considered the most important parameters which are high correlated with COVID-19 incidence and mortality (PBL height, air temperature at 2 height, RH, SI-surface solar irradiance), wind speed intensity has a lower correlation with COVID-19 cases.

Thanks a lot.

Spearman rank correlation shows only the relationships but not the prioritization of the contributions of the independent variables to the dependent DNC and DND.

Response: Maybe you are right, but similar techniques have been applied by many other authors in the published papers. This can be done in another article.

Thanks a lot.

# Comment 4: In section ‘3. Results and Discussion’ it would be more logical to restructure the presentation of the material:

Response: I resolved this issue.

Thanks a lot.

Response:

Thanks a lot.

4.3 Table 1 can be transform into Table 3, but supplemented with correlations with AOD, wind direction (degrees) and etc..

Response: At your suggestion I made the changes.

Thanks a lot.

4.4 If non-linear regression analysis will be done, enter Table 4 in addition.

Response: I added new comments.

Thanks a lot.

Response: I did. I added a new graph with the same components like in the previous figures.

Thanks a lot.

Response: I used that image or Europe and indeed for my previous published paper for Bucharest, but in order to show the amplitude of atmospheric anomaly I mentioned the towns to be investigated. I think is not a problem.

Thanks a lot.

Response: During 2020 and 2021 have been adopted much more restrictions regarding pandemic safety, after that few restrictions have been considered. I fact my study concerns January 2020- March 2022 period. For this reason, I considered AOD variation during pre-pandemic period till end 2021, very close of March 2022. Figures 8 and 9 ends in 2023, to be clear the intensity of first pandemic period 2020-2022.

Thanks a lot.

Response: I inserted a new paragraph.

Thanks a lot.

Response: I did. I introduced a new citation recommended by you.

Thanks a lot.

Dear authors, if you transform the structure of the section ‘3. Results and Discussion’, you will have to change the interpretation of the results. I wish you success.

Response: This paper provides a very good model for COVID-19 trend analysis.

Thanks a lot for all your comments that helped me to improve the paper.

However, this research field is very complex and can be written a lot of new papers.

Have an excellent day,

Professor Dr. Maria Zoran

Author Response File: Author Response.pdf

Round 3

Reviewer 3 Report

Comments and Suggestions for Authors

Dear Authors,

Thank you for your co-operation and your efforts to improve the article.

Unfortunately, my main recommendations on the presentation of factual material and statistical processing of data were not taken into account. Therefore, I consider further review of the article to be inappropriate.

I wish you good health, good luck and success in your future scientific research

Comments on the Quality of English Language

Minor editing of English language required

Author Response

Dear Reviewer,

According to all your comments I consider that my article was really improved.

I did some small improvements in the main text.

However I considered your comments and in Section 2.3, I introduced as new reference [63]

"this study used descriptive statistical analysis, rank-correlation non-parametric test coefficients, Spearman rank correlation, and linear regression analysis considering the regression analysis of non-linearly related data "[63]. Indeed the data used are not sparse.

[63] Ireland C.. Multiple regression and non-linear regression analysis. 2010, In book: Experimental statistics for agriculture and horticulture. October 2010. DOI: 10.1079/9781845935375.0244)

Thank you very much for your observations.

Have an excellent day,

Maria Zoran

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