Satellites reveal a 28 % drop in Ukraine&rsquo;s Nitrogen oxides emissions during the Russia-Ukraine war in 2022

Mao, Yu; Ju, Weimin; Wang, Hengmao; Liu, Liangyun; Wang, Haikun; Feng, Shuzhuang; Jia, Mengwei; Jiang, Fei

doi:https://doi.org/10.5194/egusphere-2024-3672

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https://doi.org/10.5194/egusphere-2024-3672

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06 Mar 2025

| 06 Mar 2025

Satellites reveal a 28 % drop in Ukraine’s Nitrogen oxides emissions during the Russia-Ukraine war in 2022

Yu Mao, Weimin Ju, Hengmao Wang, Liangyun Liu, Haikun Wang, Shuzhuang Feng, Mengwei Jia, and Fei Jiang

Abstract. The outbreak of the Russia–Ukraine war in 2022 brought a huge impact on the Ukrainian economic production. To quantify this effect, we invert the anthropogenic Nitrogen oxides (NO_x) emissions in Ukraine from 2019 to 2022, a key indicator of human activities, to reflect the disruption of activities in different economic sectors due to war. We found a 28 % decline in NO_x emissions during the war, if compared with the base year, which significantly exceeded the decrease caused by the 2020 COVID-19 pandemic. Eastern Ukraine experienced a 34 % decrease in NO_x emissions, whereas the other regions experienced a decrease of 24 %. The destruction of infrastructure and energy shortages severely impact the sustainable development of such social activities as industry, housing and transportation in Ukraine. These findings highlight the severe disruption of socio-economic activities due to the war, offering crucial insights into the broader implications of war on environmental and economic stability.

Received: 24 Nov 2024 – Discussion started: 06 Mar 2025

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Journal article(s) based on this preprint

30 Oct 2025

Inversion-based assessment of anthropogenic NO_x emission changes in Ukraine during the 2022–2023 war using TROPOMI satellite data

Yu Mao, Weimin Ju, Hengmao Wang, Liangyun Liu, Haikun Wang, Shuzhuang Feng, Mengwei Jia, and Fei Jiang

Atmos. Chem. Phys., 25, 14187–14204, https://doi.org/10.5194/acp-25-14187-2025,https://doi.org/10.5194/acp-25-14187-2025, 2025

Short summary

Yu Mao, Weimin Ju, Hengmao Wang, Liangyun Liu, Haikun Wang, Shuzhuang Feng, Mengwei Jia, and Fei Jiang

Interactive discussion

Status: closed

RC1:
'Comment on egusphere-2024-3672', Anonymous Referee #2, 11 Apr 2025

Review on Satellites reveal a 28% drop in Ukraine’s Nitrogen oxides emissions during the Russia-Ukraine war in 2022
by Yu Mao, Weimin Ju, Hengmao Wang , Liangyun Liu, Haikun Wang, Shuzhuang Feng, Mengwei Jia, and Fei Jiang
The manuscripts is a detailed application description of the method introduced in a previous publication Mao et al 2024 (Mao, Y., Wang, H., Jiang, F., Feng, S., Jia, M., and Ju, W.: Anthropogenic NOx emissions of China, the U.S. and Europe from 2019 to 2022 inferred from TROPOMI observations, Environ. Res. Lett., https://doi.org/10.1088/1748-9326/ad3cf9, 2024).
It shows in detail where the war in the Ukraine destroyed most of the ecomic and sozial (and human) live. The results are based on the comparison of modeled columns based on prior emissions, which are than adapted to satellite observations. The study focuses on the years 2019 (a pre-covid baseline) and the year 2022. The pandemic is included as small side note. The impact of the pandemic Ukraine’s NO_x emissions was negligible for compared to the Russian invasion.
Mayor comments
According to (https://www.temis.nl/airpollution/no2col/tropomi_no2_data_versions.php, March 2025) version 2.4 or higher of the TROPOMI NO₂ data is reommended. There was a major version change in July 2022, I am not sure the version 2.3.1 (page 4 line 124) is appropriate for the presented study focusing on 2022. I recommend to double check the results using latest version of TROPOMI NO₂ data. In this context I ask for your apologies that I did not realize in the pre-review.
Minor comments
The war has continued for 3 too long years. How did the emissions change in the second year?
page 5 line 138: The inversion is performed on a Monthly basis (due to the observation gaps of the satellite). Please elaborate in more detail what this means. Does this include that the wind fields are averaged over a month. Or where the daily observations for TROPOMI and the meteorological data used and the emission assumed to be constant for the one month.
P6/7 L188: The assumption if the dominant sector is really constant over time is not justified – Also the authors themselves state that it might not be correct. However I am afraid that large parts of the conclusions are based on this assumptions. Or is there any other way to distribute the NOx emissions among the different sources?
P13 L339: The prior emissions are stated to be overestimated by 80%. This I s quite large has this been confirmed by similar studies?
Figure 2: The Crimea peninsular in the South of Ukraine has been occupied since 2014. What causes the NOx reduction there?
Technical comments
p3 l 93: the sentence beginning with Meteorological data can be split into two, remove the word “while”.
p5 l141: “global” instead of “g lobal”

Citation: https://doi.org/10.5194/egusphere-2024-3672-RC1
- AC1: 'Reply on RC1', Yu Mao, 30 Jul 2025
  
  We thank the referee for their critical review and suggestions, which have greatly improved the quality of our manuscript.
  Please find each of the referee’s concerns addressed as point-by-point responses in the attached document.
  
  Citation: https://doi.org/10.5194/egusphere-2024-3672-AC1
RC2:
'Comment on egusphere-2024-3672', Anonymous Referee #3, 04 Jun 2025

In this manuscript Mao et al. evaluate the change in nitrogen oxide emissions in Ukraine due to the Russia-Ukraine war in 2022 using inversion methods. Overall, the discussed merit is interesting an deserves investigation. Before I can recommend publication in ACP, the authors need to address some fundamental aspects.
Major comments:

1. I would suggest to rethink your title. Your methodology relies on inversion techniques and uses TROPOMI data as an input. The phrase "Satellites reveal" is thus misleading. In addition the 28% is associated with uncertainties and I would thus remove it from the title.

2. I would expect a significant contribution of direct and indirect war related NOx emission (e.g., infrastructure fires). In your study, however, you seem to not include any war related emissions. Keeping in mind the fast changing nature of this war, what uncertainties does this introduce to your methodology and your results? To what degree do you think did war related emissions compensate the reductions reported in your study?

3. Please provide further details on the TROPOMI retrievals. The VCDs are obtained from a polar orbit meaning that the same time of day is observed. How does this impact your methodology, especially considering a shift in activities to the night?

4. To improve data quality, you perform the inversion of the anthropogenic NOx emissions on a monthly scale. Since you frequently highlight the fast changing nature of the war, it sounds that using monthly averaged data is not an optimal choice. To what degree does this influence the predicted results?

5. I was surprised to see such a coarse model resolution being used when focusing on such a small region. At the same time, TROPOMI provides data at a km scale. How can you justify such a coarse resolution knowing that other inverse modelling infrastructures provide resolutions at the km scale? How does this affect your results?
Minor comments:

- Line 13: Replace "economic production" with e.g. "society".

- Line 18: Your abstract only mentions decreases in NOx emissions, even though you document increased emissions in urban areas in West Ukraine. This can be misleading and should be mentioned in the abstract.

- The introduction would greatly benefit from a figure which shows yearly average NOx VCD from TROPOPMI for the base year as well as 2022.

- Line 196: Please elaborate on the factors 1.2 and 0.7.

- Line 244: How do you account for the population migration in your emission datasets?

- Line 271: What "drivers" are you referring to? How does military activity related to transport compensate these changes?

- Line 417: Please elaborate on what policies you are referring to.

- Fig. 1 and 2: Please fix the inconsistencies in the x-axis labels.

Citation: https://doi.org/10.5194/egusphere-2024-3672-RC2
- AC2: 'Reply on RC2', Yu Mao, 30 Jul 2025
  
  We thank the referee for their critical review and suggestions, which have greatly improved the quality of our manuscript.
  Please find each of the referee’s concerns addressed as point-by-point responses in the attached document.
  
  Citation: https://doi.org/10.5194/egusphere-2024-3672-AC2

Interactive discussion

Status: closed

RC1:
'Comment on egusphere-2024-3672', Anonymous Referee #2, 11 Apr 2025

Review on Satellites reveal a 28% drop in Ukraine’s Nitrogen oxides emissions during the Russia-Ukraine war in 2022
by Yu Mao, Weimin Ju, Hengmao Wang , Liangyun Liu, Haikun Wang, Shuzhuang Feng, Mengwei Jia, and Fei Jiang
The manuscripts is a detailed application description of the method introduced in a previous publication Mao et al 2024 (Mao, Y., Wang, H., Jiang, F., Feng, S., Jia, M., and Ju, W.: Anthropogenic NOx emissions of China, the U.S. and Europe from 2019 to 2022 inferred from TROPOMI observations, Environ. Res. Lett., https://doi.org/10.1088/1748-9326/ad3cf9, 2024).
It shows in detail where the war in the Ukraine destroyed most of the ecomic and sozial (and human) live. The results are based on the comparison of modeled columns based on prior emissions, which are than adapted to satellite observations. The study focuses on the years 2019 (a pre-covid baseline) and the year 2022. The pandemic is included as small side note. The impact of the pandemic Ukraine’s NO_x emissions was negligible for compared to the Russian invasion.
Mayor comments
According to (https://www.temis.nl/airpollution/no2col/tropomi_no2_data_versions.php, March 2025) version 2.4 or higher of the TROPOMI NO₂ data is reommended. There was a major version change in July 2022, I am not sure the version 2.3.1 (page 4 line 124) is appropriate for the presented study focusing on 2022. I recommend to double check the results using latest version of TROPOMI NO₂ data. In this context I ask for your apologies that I did not realize in the pre-review.
Minor comments
The war has continued for 3 too long years. How did the emissions change in the second year?
page 5 line 138: The inversion is performed on a Monthly basis (due to the observation gaps of the satellite). Please elaborate in more detail what this means. Does this include that the wind fields are averaged over a month. Or where the daily observations for TROPOMI and the meteorological data used and the emission assumed to be constant for the one month.
P6/7 L188: The assumption if the dominant sector is really constant over time is not justified – Also the authors themselves state that it might not be correct. However I am afraid that large parts of the conclusions are based on this assumptions. Or is there any other way to distribute the NOx emissions among the different sources?
P13 L339: The prior emissions are stated to be overestimated by 80%. This I s quite large has this been confirmed by similar studies?
Figure 2: The Crimea peninsular in the South of Ukraine has been occupied since 2014. What causes the NOx reduction there?
Technical comments
p3 l 93: the sentence beginning with Meteorological data can be split into two, remove the word “while”.
p5 l141: “global” instead of “g lobal”

Citation: https://doi.org/10.5194/egusphere-2024-3672-RC1
- AC1: 'Reply on RC1', Yu Mao, 30 Jul 2025
  
  We thank the referee for their critical review and suggestions, which have greatly improved the quality of our manuscript.
  Please find each of the referee’s concerns addressed as point-by-point responses in the attached document.
  
  Citation: https://doi.org/10.5194/egusphere-2024-3672-AC1
RC2:
'Comment on egusphere-2024-3672', Anonymous Referee #3, 04 Jun 2025

In this manuscript Mao et al. evaluate the change in nitrogen oxide emissions in Ukraine due to the Russia-Ukraine war in 2022 using inversion methods. Overall, the discussed merit is interesting an deserves investigation. Before I can recommend publication in ACP, the authors need to address some fundamental aspects.
Major comments:

1. I would suggest to rethink your title. Your methodology relies on inversion techniques and uses TROPOMI data as an input. The phrase "Satellites reveal" is thus misleading. In addition the 28% is associated with uncertainties and I would thus remove it from the title.

2. I would expect a significant contribution of direct and indirect war related NOx emission (e.g., infrastructure fires). In your study, however, you seem to not include any war related emissions. Keeping in mind the fast changing nature of this war, what uncertainties does this introduce to your methodology and your results? To what degree do you think did war related emissions compensate the reductions reported in your study?

3. Please provide further details on the TROPOMI retrievals. The VCDs are obtained from a polar orbit meaning that the same time of day is observed. How does this impact your methodology, especially considering a shift in activities to the night?

4. To improve data quality, you perform the inversion of the anthropogenic NOx emissions on a monthly scale. Since you frequently highlight the fast changing nature of the war, it sounds that using monthly averaged data is not an optimal choice. To what degree does this influence the predicted results?

5. I was surprised to see such a coarse model resolution being used when focusing on such a small region. At the same time, TROPOMI provides data at a km scale. How can you justify such a coarse resolution knowing that other inverse modelling infrastructures provide resolutions at the km scale? How does this affect your results?
Minor comments:

- Line 13: Replace "economic production" with e.g. "society".

- Line 18: Your abstract only mentions decreases in NOx emissions, even though you document increased emissions in urban areas in West Ukraine. This can be misleading and should be mentioned in the abstract.

- The introduction would greatly benefit from a figure which shows yearly average NOx VCD from TROPOPMI for the base year as well as 2022.

- Line 196: Please elaborate on the factors 1.2 and 0.7.

- Line 244: How do you account for the population migration in your emission datasets?

- Line 271: What "drivers" are you referring to? How does military activity related to transport compensate these changes?

- Line 417: Please elaborate on what policies you are referring to.

- Fig. 1 and 2: Please fix the inconsistencies in the x-axis labels.

Citation: https://doi.org/10.5194/egusphere-2024-3672-RC2
- AC2: 'Reply on RC2', Yu Mao, 30 Jul 2025
  
  We thank the referee for their critical review and suggestions, which have greatly improved the quality of our manuscript.
  Please find each of the referee’s concerns addressed as point-by-point responses in the attached document.
  
  Citation: https://doi.org/10.5194/egusphere-2024-3672-AC2

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload

AR by Yu Mao on behalf of the Authors (30 Jul 2025) Author's response Author's tracked changes Manuscript

ED: Referee Nomination & Report Request started (03 Aug 2025) by Jayanarayanan Kuttippurath

RR by Anonymous Referee #3 (20 Aug 2025)

Suggestions for revision or reasons for rejection

The revised version of the manuscript by Mao et al. substantially improved, and most of my comments have been satisfactorily addressed. However, before I can recommend publication in ACP, the authors need to address the following aspects:

Major comment:
In the revised methodology, different versions of the TROPOMI NO2 VCD data are used for different time periods. Rather than using v2.3.1, you use versions v2.4.0, v2.5.0 and v2.6.0 for different time periods. You also highlight that these versions differ substantially from each other, with improvements in the later versions compared to the previous ones. Why don't you use the same version for the entire time period? What uncertainty does this introduce to your methodology? Please provide an overview of the differences between the versions in the SI and include a discussion of the introduced uncertainties in section 4.4.

Minor comments:
- In your manuscript, you focus primarily on relative changes. The introduction would be improved by providing some context on absolute NOx emissions in Ukraine. Additionally, it would be helpful if you could provide some background information on whether Ukraine was reducing its emissions prior to 2019.
- Line 58-60: What about particle formation?
- Line 107: Please also provide the approximate grid size in km for Ukraine.
- Line 343: Change to "The war has had and continues to have direct and indirect impacts on [...]"
- Line 423: What does "SSCU" stand for? Please introduce all abbreviations properly and provide a reference for the data source.
- Table 1: Please provide a reference for the "SSCU statistics".
- Some of the wording needs to be more scientific. For example, the word "fell" (line 22) is ambiguous when describing emission reductions. Please review your manuscript accordingly.

Hide

ED: Publish subject to minor revisions (review by editor) (01 Sep 2025) by Jayanarayanan Kuttippurath

AR by Yu Mao on behalf of the Authors (08 Sep 2025) Author's response Author's tracked changes Manuscript

ED: Publish as is (23 Sep 2025) by Jayanarayanan Kuttippurath

AR by Yu Mao on behalf of the Authors (23 Sep 2025) Manuscript

Journal article(s) based on this preprint

30 Oct 2025

Inversion-based assessment of anthropogenic NO_x emission changes in Ukraine during the 2022–2023 war using TROPOMI satellite data

Yu Mao, Weimin Ju, Hengmao Wang, Liangyun Liu, Haikun Wang, Shuzhuang Feng, Mengwei Jia, and Fei Jiang

Atmos. Chem. Phys., 25, 14187–14204, https://doi.org/10.5194/acp-25-14187-2025,https://doi.org/10.5194/acp-25-14187-2025, 2025

Short summary

Yu Mao, Weimin Ju, Hengmao Wang, Liangyun Liu, Haikun Wang, Shuzhuang Feng, Mengwei Jia, and Fei Jiang

Supplement

https://doi.org/10.5194/egusphere-2024-3672-supplement

Data sets

European anthropogenic NOx emission inversion based on TROPOMI satellite observations and GEOS-Chem modeling from 2019 to 2022 Yu Mao https://zenodo.org/records/12540012

Yu Mao, Weimin Ju, Hengmao Wang, Liangyun Liu, Haikun Wang, Shuzhuang Feng, Mengwei Jia, and Fei Jiang

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The Russia-Ukraine war in 2022 severely disrupted Ukraine’s economy, with significant reductions in industrial, transportation, and residential activities. Our research used satellite data to track changes in nitrogen oxide emissions, a key indicator of human activity, during the war. We found a 28 % decline in emissions, which was twice of the decrease caused by the COVID-19 pandemic. This study highlights how modern warfare can deeply impact both the environment and economic stability.


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