Can TROPOMI NO2 satellite data be used to track the drop in and resurgence of NOx emissions in Germany between 2019–2021 using the multi-source plume method (MSPM)?

Dammers, Enrico; Tokaya, Janot; Mielke, Christian; Hausmann, Kevin; Griffin, Debora; McLinden, Chris; Eskes, Henk; Timmermans, Renske

doi:https://doi.org/10.5194/gmd-17-4983-2024

Articles | Volume 17, issue 12

https://doi.org/10.5194/gmd-17-4983-2024

© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/gmd-17-4983-2024

© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 17, issue 12

Methods for assessment of models

|

26 Jun 2024

Methods for assessment of models |

| 26 Jun 2024

Can TROPOMI NO₂ satellite data be used to track the drop in and resurgence of NO_x emissions in Germany between 2019–2021 using the multi-source plume method (MSPM)?

Enrico Dammers, Janot Tokaya, Christian Mielke, Kevin Hausmann, Debora Griffin, Chris McLinden, Henk Eskes, and Renske Timmermans

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Final revised paper (published on 26 Jun 2024)
Preprint (discussion started on 16 Dec 2022)

Interactive discussion

Status: closed

RC1:
'Comment on gmd-2022-292', Anonymous Referee #1, 03 Feb 2023
This paper presents an interesting comparison of TROPOMI-NO$_2$ satellite data and inventory data for Germany over 2019--2021. Scientifically the text is well organised, but the text needs to be tightened up and made more readable. The manuscript deserves to be published after attending to the points raised below.
General points:
The manuscript needs to be thoroughly checked for the logic and for the English grammar. Many statements are unsupported or confusing. Many commas are misplaced, making sentences difficult to read and understand. Some sentences make no sense by themselves, but do make sense if joined to the next sentence. Many citations use parentheses incorrectly. Subscripts are sometimes missed.

The authors assume an NO2 lifetime of 4 hours when deriving emissions, but shouldn’t this lifetime impact the footprint on the sources when comparing to the satellite data? With 5 m/s winds for example 4 hours of advection transports the NOx ca. 72 km, but the Gaussian smoothing used to produce Fig. 3 uses a σ of 1 grid cell, ca. 10km. Would more smoothing produce better SSIMs and more accurate inversion of the emissions?

Related to this, the TROPOMI-derived emission uncertainty is stated to be 30–40% (p21, L428), but it isn’t clear where these numbers come from. The text on p21 discusses many sources of uncertainty (including the lifetime issue), but how were these combined to make 30–40% and the error bars (whatever they are) in Fig. 8?

p2, L39–49, and also p20, L403 onwards. I missed a discussion of the known problems of real-world emissions. For example, as cited in Oikonomakis et al. (2018), several studies showed a significant discrepancy (a factor of 2–4) in the NOx emissions from light-duty diesel vehicles between two driving cycles, and Anenberg et al. (2017) and found similar issues for heavy duty vehicles, indicating inadequacy of the testing procedures to capture real-world emissions. The diesel-gate scandal was also a good example of the limitations of emissions reporting (Jonson et al., 2017).

Other points:
• p1, L12, Explain 100kt as percentage, so that the reader knows if this is a large or small number.

• p1, L14, Add a year here so that the reader knows when this recovery was ‘projected’.

• p1, L17. It sounds odd that satellites help faster ”projections”, since that term is usually reserved for future values. Re-phrase.

• p1, L18. Change meet to meets, or method to methods. Check such things throughout the manuscript.

• p2, L2. Add also a more recent reference than Crutzen 1970.

• (nit-picking I know): Say ‘largely’ primary. Some NOx is produced by lightning, and NO2 is mainly produced from ozone reactions with primary

NO.
• p2, L27. The units should be formatted correctly, not in italics. Also, be consistent. The unit on L28 has a space between g and m, whereas on L27

it doesn’t.

• p2, L30. Give reference for the statement about acidification and eutrophication.

• p2, L36. There is no such thing as the ‘Geneva Convention ...’, in this context at least. The authors mean the Air Convention or CLRTAP equivalents, e.g. https://unece.org/environment-policy/air. ‘Nations’ is also not an appropriate reference; maybe ’UN-ECE’ or similar.

• p2, L44. The word projections confused me here and elsewhere. Usually the term is used for future scenarios, e.g. for 2030 or 2050. Here I think the authors just mean emission estimates.

• p3, L60. Give info on this ‘unprecedented horizontal resolution’, or refer to appropriate section for details.

• p3, L65. Refer to appropriate section for details of code and availability.
• p3, L71. Tell the reader where this is ‘described further’.
• p3, L75 (also p7). What is Umweltbundesamt (UBA) for those not familiar with German institutions?

• p3, 1st paragraph. This section is somewhat repetitive of Sect. 1, and isn’t really ‘Methodology and Datasets’. Some is also repeated, or better placed, in Sect. 2.1.

• p4, L104. Where does this 300% number come from?

• p4, Sect. 2.1.1. Some of the text here is also more introductory material (e.g. L104 onwards) than technical description of the emission inventory.

• p4, L120. Change ‘mol’ to emissions (mol is a unit, not a quantity).

• .5, L124. NOx is a mixture of NO and NO2 , so one needs to specify the assumed molecular weight associated with your 5 kt NOx per year figure, or state as e.g. kt(N) NOx per year.

• p5, L130 onwards. Same issue with NOx units and emission amounts.

• p5, L151. What is ATBD?

• p6, L166. Say ‘well correlated with ...’; the sentence was difficult to read.

• p6, L165–173. Various statistics are given concerning bias, but are the instruments being compared with (MAX-DOAS, PANDORA) free of bias themselves? Are some of the difference due to problems with these instruments?

• p7, L205. Mangled ‘from in the naive’?

• p10. The ‘Column’ term in Eqns. (2) and (7) looks very ugly. Use a symbol, as is done for all other terms. In any case, shouldn’t this be VCD?

• p10, equations (3)–(6). Give in order of usage, thus σ1 before f (x, y), λ1 before g(y, s).

• p10, L255. Where is ai explained?
• p10, L258: ‘Following Beirle et al. (2016) we assume a lifetime of about 4 hours(±25%)’. I can’t find the terms hour or lifetime in Beirle 2016, and that paper deals mainly with the stratosphere. Why didn’t you use estimates of NO2 lifetime from LOTOS-EUROS for Germany? Does the 25% estimate really capture the uncertainty here?

• p11, L287. I didn’t find the factor 1.32 in Beirle et al. (2016) either.

• p11, L292-293: ‘The gridded NFR data .... summed to the ... grid’. Does gridded data need to be gridded? CLRTAP inventories are usually gridded

by NFR categories.

• p12, L307. Why a comma after ‘way’? This is just one example of a common problem.

• p12, L323. Again, somewhat sloppy. Fig. 4 doesn’t say anything about previous sensors, and the text doesn’t explain what the authors are thinking. If you make comparative statements, back them up.

• p12, L330. Here I also wonder about the 4-hour footprint issue mentioned above.

• p14, L345. Remove ‘the before mentioned’.

• p14, L349. How should non-Germans know where the A1 motorway is?

• p15, Fig 5. Explain letters in top-right fig. Also explain whether positive values (red color) means that the satellite has more or less emission than the inventory.

• p16, Fig 6. What are the triangles? What are the varuious letters (NEU, WW, ...)? The latter are explained in the text, but the caption should be informative.

• p16, L372. Why are 65 kt NOx added only at this stage?

• p18, Fig. 8. Again, the caption explains too little. What are the error bars? Be explicit and say slight rise in ‘reported’ emissions.

• p18, L375. Start a new paragraph frpm ‘Emissions sources ̈, so that the reader knows the subject has changed.

• p18, L380. Fig. A3 doesn’t support that the Agri emissions are spread out across the country, at least not if the text is about the >50% region.
• p18, L382. Why are ‘non-agricultural sources’ mentioned here? Only 3 sources are addressed, so many sources are excluded.

• p20, L395. When starting the discussions, be explicit that the reported emissions are for Germany.

• p20, L402. Why does Fig. 5 ‘hint’ at a small and widespread source? The values seem close to zero in most areas.

• p20, L403. Again start a new paragraph when the subject is changing.

• p21 L432. ‘improved’ - from what?

• p21, L436. ‘approach u’ typo

• p21, L437. CAMS-Europe - reference?

• p21, L440. ‘detect’ should be ‘detection’

• p21, L445. Mangled sentence.

• p21, L445. How do you know that the 4h timescale is correct for Germany as a whole in 2019?

• p21, L448. Lifetimes are not only location dependent; they depend on complex interactions between meteorology, chemistry and vegetation state (via deposition).

• p21, L453. What is meant by several %? That number seems low.

• p22, L472. What is ‘(8)’?

• p22, L484. The web-tool is mentoned, but I would hardly say it was ‘presented’.

• p23, L496. What is ‘ads’? Provide a web address.

• p23, L498. No need to use words like ‘truly’ in a scientific statement.

• p23, L502. It was discussed earlier that the resolution is not really 3.5x5.5km2, and the derived-emissions resolution are certainly not at that level.

• p22-24. Again, one sinlge paragraph over more than a whole page! Break up the text into separate topics.
• p23, L508. Reference the Green Deal.

• p23, L513. The sentence starting ‘While’ ends abruptly, making no sense. Also, rephrase ‘whole there here developed methodology’.

• p23, L518. ‘tooling’ should be ‘tools’.

• p23, L52. What is ‘link website, mode fields’?

• Fig A1. Be explicit: NOx emissions.

• p28, Fig A4. Again (as with Fig.5), explain what positive values mean.

• p30, L562. Mangled NO 2.
References
Anenberg, S.C. et al., Impacts and mitigation of excess diesel-related NOx emissions in 11 major vehicle markets, Nature, advance online publication, http://dx.doi.org/10.1038/nature22086, 2017.

Jonson, J.E. et al., Impact of excess NOx emissions from diesel cars on air quality, public health and eutrophication in Europe, Environ. Res. Lett., 12, 094 017, http://stacks.iop.org/1748-9326/12/i=9/a=094017, 2017.

Oikonomakis, E. et al., Low modeled ozone production suggests underestimation of precursor emissions (especially NOx) in Europe, Atmos. Chem. Physics, 18, 2175–2198, https://doi.org/10.5194/acp-18-2175-2018, 2018.
Citation: https://doi.org/10.5194/gmd-2022-292-RC1
- AC1: 'Reply on RC1', Enrico Dammers, 13 Jul 2023
  
  We would like to thank reviewer 1 for his/her constructive comments and suggestions.
  The detailed response can be found in the attached pdf.
  
  Citation: https://doi.org/10.5194/gmd-2022-292-AC1
RC2:
'Comment on gmd-2022-292', Anonymous Referee #2, 03 May 2023
This study compares the emissions of NOx estimated from NO2 satellite data with inventory data from 2019 to 2021, which provides a useful method to evaluate and improve NOx emission inventory. The manuscript is overall well organized, but scientifically needs clarification and more discussion regarding the uncertainties.
Major comments:
Satellite images only contain information of NO2, but NOx from the emission inventories includes NO and NO2. In emission inventory, the ratio of NOx to NO2 is different from the ratio in ambient concentration. The conversion of NO to NO2 changes the ratio. The study uses a factor of 1.32 (based on ambient concentrations) for all sources definitely leading to uncertainties.

Photochemical reactions are different among seasons and day-night. The life time of 4 hours for NO2 uniformly seems unreasonable for all days during 2019-2021. Radiation could be a good indicator for the lifetime.

The comparison of inventory and Tropomi in Figure 3 is not clearly, showing the difference is better for readers.

The abstract needs to be modified as the currently version is not clear about the method and the key results.

The discussion of uncertainties is qualitative rather than quantitative. A ranking of uncertainties from different assumptions is helpful for assessing the results when this method is used for other cases.
Citation: https://doi.org/10.5194/gmd-2022-292-RC2
- AC2: 'Reply on RC2', Enrico Dammers, 13 Jul 2023
  
  We would like to thank reviewer 2 for his/her constructive comments and suggestions.
  The detailed response can be found in the attached pdf.
  
  Citation: https://doi.org/10.5194/gmd-2022-292-AC2

Peer review completion

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

AR by Enrico Dammers on behalf of the Authors (13 Jul 2023) Author's response Author's tracked changes Manuscript

ED: Referee Nomination & Report Request started (26 Jul 2023) by Jason Williams

RR by Anonymous Referee #1 (23 Aug 2023)

Suggestions for revision or reasons for rejection

The manuscript has been substantially revised, and many issues improved (e.g. information concerning uncertainties). However, although the authors make much effort to respond to the referee comments concerning the 4h lifetime, the associated change in the manuscript is minimal and I think unsatisfactory.

In more detail, I have the following comments:

1. Although not brought by the referees originally, the title should mention Germany, since this study only deals with that country.

2. Concerning the important questions arising from the 4h lifetime, the authors have made a small adjustment in the text to say 2-5 hours, but my impression is that they are trying to avoid a proper discussion. For example, in their reply to Referee #1 they cite evidence of 12-24 hours in winter, and although this estimate was for China I would expect something like that for Germany in the (high-emitting) winter months. The discussion of footprints in the reply to Ref #2 is also useful, and helpful for readers.

Readers of the new manuscript will be unaware of the points made in this reply-to-referees, and will still be surprised I think to see the assumption of 4h without much explanation. The authors should expand this issue in the manuscript, or add much of the reply to referees in the SI.

3. p6, L167, and Ref #2 comment. The authors reply that "The MAX-DOAS and PANDORA instruments are not completely free of bias themselves but typically have much lower uncertainties than the TROPOMI-NO2 product as stated in Verhoelst et al., 2021.", but again this information is not passed to the readers of this manuscript. This should be explicit here.

4. Tools and code availability?

The footnotes on page 3, and the "Code and data availability" section gives web addresses. I thought GMD insisted on zenodo for software?

5. Typos:
p11, L294: +a few % plus/minus maybe?

p23, L471. The (Kuenen..) ref should be before the comma.

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RR by Anonymous Referee #3 (27 Sep 2023)

Suggestions for revision or reasons for rejection

The paper presents a comparison of TROPOMI-based NOx emission estimates and emission inventory data for Germany over the years 2019, 2020, and 2021, including the COVID-19 period. It provides a method to evaluate NOx emission inventories and the possibility of more recent emission information, which can be interesting for emission inventory compilers. The paper is of scientific significance and with the given information reproducible. In some parts, there is a lack of logic, too little explanation, and some misleading discussions. After addressing the comments raised below, the paper should be considered for publication.

General comments:
For some parts, there is a lack of references. See comments in the specific comment section.
Sometimes, statements are given without further or too little explanation. For example, regarding the grid size, the assumption of a 7 km plume spread, the rotation method, the COVID-19 effect, and NOx to NO2 ratios. See comments in the specific comment section.
There is quite a lot of discussion (Line 374-399) on the visibility of NOx emissions from highways in the satellite-based maps. However, this is not very well visible to the reader from the mentioned Figures (Fig. 4 and especially not in Fig. 6 showing the differences). I would recommend removing or at least weakening the statements and discussions about the NOx emissions and their changes from highways.

Specific comments:
Line 9/10: How do you know the reductions are related to the COVID-19 lockdowns, not the results of political emission reduction strategies?
Line 116: Missing reference for: “Globally, the lightning NO constitutes about 3% of the total NOx emission budget.”
Line 152/153: Since this is a more general explanation of the different TROPOMI products (NRTI, OFFL, RPRO), the given years (2019-2021 and April 2018-November 2018) are confusing. Since they are also not used/relevant for the study, I suggest to remove them. Also, delete the word “reprocessed” in combination with the offline mode in line 152.
Line 153: At this point, there is no explanation given why a reprocessed version is needed. Please add something like: “Over time, several modifications in the retrieval lead to processor updates and new product versions. Reprocessed data sets based on the latest offline version are provided at a more irregular interval.”
Line 156-159: Relevant for this study in this detail? Explanation would have been helpful in line 153 (see comment above). Maybe move some parts of it or delete it.
Line 173: Reference missing for “The negative bias can be explained by the low spatial resolution of the a-priori profiles as well as the treatment of clouds and aerosols in the retrieval.” Is this shown in van Geffen et al. (2022), otherwise, add Lange et al. (2023, https://doi.org/10.5194/amt-16-1357-2023).
Line 173: Sentence difficult to understand. What is meant by set? Where does this recommendation of 0.3 come from? I am only familiar with the cloud radiance fraction filter of 0.5, which is also the filter when the qa_value of 0.75 is applied.
Line 177: How do you know that it is COVID-19 impact, not meteorology or reducing strategy (see, for example, Goldberg et al. (2020, https://doi.org/10.1029/2020GL089269)) especially since it is not coming back in 2021? See also comment regarding line 9/10.
Line 193: I think the word “tooling” is not always used correctly; maybe better to say “Emission estimation tool”. See also line 196.
Line 195: The term “space-emissions tool” is misleading. Better would be a “satellite-based emissions tool”.
Line 195-200: Repetition this was already discussed in the introduction. Since it is not very relevant for the paper, it should be deleted in the introduction.
Line 206: Confusing: With "here" you mean the tool and not the paper? So, the tool uses the mass-balance technique, but the paper is focused on the plume based fitting method.
Figure 2 legend: “Space-emissions tool“ see above, Replace “A” with “Panel A”.
Line 222 and 223: The term “total column density” is confusing since it is also the term for the column from the surface to the top of the atmosphere, but I think this is not meant here.
Line 235: “Using this method, observations are rotated around a single point, the emission source, so that each is positioned in a similar upwind-downwind frame” How do you know your emission sources, what about area sources like highways?
Line 251/252: Change “by dividing the emission enhancement ai by the decay-rate λ;” to “by dividing the emission enhancement ai by tau”.
Line 253: What are the reasons for a grid with a resolution of 0.1 x 0.1 degrees? How does the resolution influence the results? Please provide a short statement.
Line 255: “Within our domain of interest, the chemical decay will be the dominant factor.” How do you know?
Line 261: 7 km of plume spread seems small to me, isn’t the plume spreading over a larger area quite quickly due to diffusion? It would be good to add references to other studies using similar values.
Line 265/266: Why do you need a correction factor for seasonal variability when you can get seasonal information from satellite observations?
Line 268: Please comment on whether assuming a constant lifetime over the day and for different seasons is valid.
Line 271: What is the temporal resolution of these profiles? Are there individual profiles for the different sources available, if yes, how large is the variability in the correction factor? Is it valid to use an averaged profile over all sources?
Line 286: The NOx to NO2 ratio is influenced by seasonality and time of the day. At least add something like: The NOx to NO2 concentration ratio depends on the local chemistry, influenced by ozone concentration, photolysis frequency of NO2 (solar zenith angle), and the rate constant of NO+O3 reaction (temperature).
Line 287: How is the value of +/- 0.26 determined, is it really the standard deviation, how do you know the distribution of the NOx to NO2 ratios?
Line 300: Please provide a reference, especially for the large part regarding the missing variations in the stratospheric NO2 concentration; the first part was mentioned before.
Line 325: You only discuss the wind speed, what is about wind direction, which is essential for the rotation?
Line 374: I only see elevated NOx emissions from the Rhine-Ruhr region to Hannover but not between Hannover and Magdeburg and not towards Berlin. At least not in the satellite-based maps. There are also several large power plants close to highway A2 near the Ruhr area, which is visible in your Fig. 6, so I probably don’t see the NOx emissions from the highway but from the power plants located nearby. I would recommend removing this part about the highways.
Line 394: You mention a noticeable drop in emissions around highways. Where I think this is not visible. There is probably a reduced signal in the region of the A2 highway close to the Ruhr area, but this isn't easy to disentangle since there are several power plants along the highway in this part. See comment regarding line 374. I would recommend removing the part about the highways here.
Line 396: You mention a rise in emissions from 2020 to 2021, most noticeable in large urban areas. I think this is very hard to see...maybe Berlin shows higher emissions in 2021 compared to 2020, but I don't see any clear rise in signal. Please provide examples, including values for the rising emissions.
Line 398/399: The Hannover-Rhine-Ruhr part is still visible in 2020, but especially the part with large emitters (see Fig. 6) along the highway. All this highway discussion is on very small differences and not really visible in the provided maps.
Line 415: “The Weisweiler power-plant reduces into 2019 while reducing further into 2021.” I cannot follow this statement; do you mean 2020 instead of 2019?
Figure 8 legend: “the red error bars show the uncertainty in the satellite derived” The -50 to +30% given in Table 1? But the error bars look similar in + and - directions?
This national inventory shows higher emissions in 2021 than in 2020, especially from road transport. In line 407, you stated an "upsurge in the usage of coal-fired power stations for power generation in 2021 compared to the COVID-19 year 2020". I think this is not visible here, do you have an explanation?
Line 435: What is meant by power generation, public power, or industry? Probably public power, but also for public power, your Fig 9. upper left panel shows an increase in 2021 (0.925) compared to 2020 (0.85), this is not almost back to 2019, it is in the middle between the two years.
Line 438/439: “While Road transport emissions were expected to show a recovery this is not matched by patterns in the satellite derived emissions.” Your Fig. 9 shows that road transport emissions decreased from 2019 to 2020, but satellite data show no change from 2020 to 2021. However, this reduction from 2019 to 2020 is not visible from Fig. 6, as discussed earlier. How can you explain the apparent difference between the discussed maps and Fig. 9?
Line 440/441: Not so easy to say for shipping emissions: The inventory shows a recovery. Why the difference here? However, industry emissions have continued their downward trend with no sign of recovery but are not mentioned here. In general, it is difficult to speak of a trend when it is only two years.
Figure 9 legend: “A clear downward trend is visible for most sectors.” Not really true, maybe for industry, and in general, it is difficult to speak of a trend when it is only two years. I would suggest deleting the sentence.
Line 471: This is not clear to me: emissions 2018, based on the in 2020 reported emissions
Line 552/553: How will this work with the worse resolution of OMI compared to TROPOMI?

Technical corrections:
Line 30: Change NO2 emissions to NOx emissions
Line 39/40: Two times “for example”, please rephrase the sentence
Line 45/46: Change “For example air quality applications such as forecasts” to “For example, air quality forecasts”
Line 55: x of NOx is not subscript
Line 55/56: Change “Timely verification of the inventories could potentially more rapidly identify such discrepancies.” to “Timely verification of the inventories could potentially identify such discrepancies more rapidly.”
Line 58/59: Change to “Furthermore, due to increased instrument sensitivity and spatial and temporal resolution, these satellite-based measurements ...”
Line 88: Change ”space-borne measurement” to “space-borne emission estimate”
Line 89/90: Change “In this study we focus on the plume based fitting method.” to “In this study, we focus on the results of the plume-based fitting method.”
Line 106: Change “Agricultural Soils” to “agricultural soils”
Line 107: delete “inventory”, it is doubled with the following “reported emission data sets” and “inventories” in line 108
Line 117/118: Why twice reference to EEA (2019)? The first one is enough.
Line 125: Change “as kt (NOx)” to “as kt NOx”
Line 128: Sentence not complete.
..., mostly based on the anthropogenic emissions Yienger and Levy II (1995) reported, ...
or
..., mostly based on the anthropogenic emissions (Yienger and Levy II, 1995), ...
Line 152: Space missing between offline and (OFFL)
Line 153: Delete “Finally”
Line 183: Chance section from “Meteorology” to “Wind data”
Equations 4 and 6: Functions (exp, erfc) should not be written in italics
Line 261: Change “equation” to “Equation”
Line 307: Change “Divergence” to “divergence”
Line 320: Change “seems to be” to “is”
Line 358: Change “2d” to “2D” as in line 356.
Line 369: Repetition of “emissions”, remove “emissions” after inventory.
Line 371: Change “or” to “respectively”
Line 382: Add: …such as power plants, visible in the top row without the Gaussian filter.
Line 382: “with a large spatial footprints”: remove “a”
Line 383: Change “satellite instrument” to “TROPOMI’s spatial resolution”
Figure 6 legend: Change “2020-2019 and 2021-2019” to “2020 and 2019, respectively 2021 and 2019”
You write Power-Plants and powerplants in the legend of Fig. 6. It's power plants. Please check throughout your text. Sometimes you have power-plant or powerplant.
Figure 7 legend: Change “2020-2019 and 2021-2019” to “2020 and 2019, respectively 2021 and 2019”
There are only 2 panels, change 3 to 2.
Line 423: Change “2019-2020” to “2019 to 2020”
Line 429: Somewhere, name the 5 sectors, maybe just in brackets: Only 5 sectors (public power, industry, ...) have locations ...
Line 430, 431, 432, 433: Change PublicPower, Shipping, … to public power and shipping
Line 438: Change “Road” to “road”
Line 440: 3500 km instead of kg?
Line 444: Change “in inventory emissions” to “the analyzed inventory emissions”
Line 445: Unit missing 75-100 kt NOx
Line 447: What is meant by series and sets? Maybe better both emission estimates
Line 447: Move reference to Fig. 8 forward by one sentence
Line 454: Add: Additionally, this approach…
Figure 9 legend: Change other line to solid line
Line 465: What is meant by inversions here? I think this is not the right word.
Line 466: Change “The stronger the source is, the better is the resolvability.” to “The stronger the source, the better the resolvability“
Line 552: Change “In future” to “In the future” and delete “also”

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ED: Reconsider after major revisions (17 Oct 2023) by Jason Williams

AR by Enrico Dammers on behalf of the Authors (08 Mar 2024) Author's response Author's tracked changes Manuscript

ED: Publish as is (18 Mar 2024) by Jason Williams

AR by Enrico Dammers on behalf of the Authors (22 Mar 2024)

Short summary

Nitrogen dioxide (NO_x) is produced by sources such as industry and traffic and is directly linked to negative impacts on health and the environment. The current construction of emission inventories to keep track of NO_x emissions is slow and time-consuming. Satellite measurements provide a way to quickly and independently estimate emissions. In this study, we apply a consistent methodology to derive NO_x emissions over Germany and illustrate the value of having such a method for fast projections.