This is an interesting paper, great job. This paper is an advancement over previous papers on the topic such as Jin et al. 2020, Jin et al., 2015, Wang et al., 2021, Koplitz et al., 2022, and Nussbaumer et al. 2022 (please cite the last two, references at the end) due to the extension of the analysis to 2021 and capturing multiple continents in a single paper.

My two major concerns with this paper: 1. Some figures could be improved (primarily Figure 1 and 3 as discussed in minor suggestions). 2. Inclusion of TROPOMI data in some capacity would substantially improve this paper and very strongly believe this is not out of scope for this manuscript (as discussed in next paragraph).

One additional inclusion that would make this paper more novel, would be to include TROPOMI data in some capacity. It would be insightful to do a OMI vs. TROPOMI intercomparison for at least one of the cities investigated for multiple years. What extra detail does TROPOMI gather that OMI does not? How do the FNRs compare during the overlapping timeframes? This would also help corroborate many of the claims in the paper such as some of the OMI trends attributed to instrument drift. Previous studies (pre-dating 2019) did not have this opportunity. I understand conducting this analysis globally would be a major lift, but for 1-5 cities in the US, this should be a minor lift. I realize that some of this was addressed in Johnson et al. 2023 (Figure 5), but you now have the opportunity to do it for a longer timeframe (2018 - 2021) (not just LISTOS 2018) and a few other cities. It should be included as a case study in a new section (section 3.6)

Minor suggestions:

Line 19. Add some nuance that NOx emission reductions have been more prevalent than VOC emissions reductions. Or that NOx emission controls have been more effective at reducing NO2 concentrations, while VOC controls are important, especially in major cities, but represent a smaller fraction of overall VOC reductions. Both NOx and VOC reductions have occurred in many urban areas and it is important to acknowledge this in some capacity in both the Abstract and Discussion.

Line 53. Used “inherent” twice. Remove one of them, preferably the second one.

Line 190. Modify “NO2” —> “urban NO2”

Figure 1 and 3 are helpful to give a wider view of NO2 and HCHO, but it would also be helpful to have zoom-ins of some of the cities, such as Figure 6. I’d recommend as follows: add a completely new figure (or amend Figure 1) that would be similar to Figure 6, but only showing NO2 and HCHO for this 16-year average.  For Figure 3, I recommend only showing this at the urban scale. Too much is lost in a hemispheric image such as Figure 3. Maybe put current Figure 3 in the supplemental if you would still like to include.

Line 225. “Combustion” —> “fossil fuel combustion”

Line 233. Add somewhere in this sentence “due to biogenic emissions”. Relatedly, I don’t see HCHO/NO2 enhancement over South Asia, are you referring to Malaysia? It’s hard to tell from this image. Please clarify to a subsection of South Asia.

Figure 2. Recommend having legend on each plot individually OR have the legend be more prominent (larger), either option is OK. This plot is a bit busy and not intuitive, but don’t have any easy suggestions to amend, other than potentially having three separate panels for each city (24 total), but maybe that’d be worse.

Figure 2 and Table 1. Pittsburgh typo. Also be more descriptive with the title “USA” instead say “USA urban areas”

Line 244. Units of -0.05 and 0.15? I believe yr-1? Also slight preference to modify 0.15 to +0.15? Same comments for Table 1. I would prefer units of %/yr, but this is personal preference.

Line 246 Spatial footprint of OMI must also be playing a role here too, since HCHO in urban areas can be heterogeneous. As you know, OMI has 13 x 24 km resolution at best, often much worse.

Line 260. What does “near unity” mean in this context? Not a FNR of 1, but something else? Hard to discern.

Line 266. Why the HCHO increase hemispherically? Global temperature rising / more biogenic emissions? I think it’s too cavalier to imply that anthropogenic VOC emissions are increasing from 2005 - 2021 as alluded to in the next sentence. Some cities have done a great deal reducing local anthropogenic VOC emissions.

Figure 6. I have a slight preference if 2020 data was excluded from this spatial plot analysis. Low NO2 during 2020 was driven by stay-at-home measures and not polices, so from a policymaking perspective, I don’t think inclusion of 2020 is warranted. Section 3.5 is great, and is how I recommend the 2020 data to be discussed. Personally, I also feel that black grid boxes on this figure are not helpful. Maybe include a copy of this figure with the black boxes in the supplemental for those interested?

Discussion in Lines 428 - 440 falls flat for me because you are projecting future policy recommendations based on an old instrument (OMI). TROPOMI is better. TEMPO will be even better. This is not discussed here and should be. This is one of many reasons, why I believe that including TROPOMI data in any capacity in this paper is necessary, and not out of scope. I see that some of this discussion is in Lines 469 - 482. Maybe Lines 428 - 440 & Lines 469 - 482 need to be merged together.

References:

Koplitz, S., Simon, H., Henderson, B., Liljegren, J., Tonnesen, G., Whitehill, A., and Wells, B.: Changes in Ozone Chemical Sensitivity in the United States from 2007 to 2016, ACS Environmental Au, 2, 206–222, https://doi.org/10.1021/ACSENVIRONAU.1C00029, 2021.

Nussbaumer, C. M., Pozzer, A., Tadic, I., Röder, L., Obersteiner, F., Harder, H., Lelieveld, J., and Fischer, H.: Tropospheric ozone production and chemical regime analysis during the COVID-19 lockdown over Europe, Atmos Chem Phys, 22, 6151–6165, https://doi.org/10.5194/ACP-22-6151-2022, 2022.

This is a review of the manuscript “Insights into the long-term (2005-2021) spatiotemporal evolution of summer ozone production sensitivity in the Northern Hemisphere derived with OMI” by Johnson et al. This paper is an important contribution to ongoing efforts to identify trends in surface air quality using satellite-based observations. This study investigates trends in column HCHO, tropospheric column NO2, and the HCHO/NO2 ratio (a.k.a. FNR) as observed by the OMI satellite. Overall, this is a good paper but needs some “polishing” and clarification.

In the discussion of OMI HCHO, it should be mentioned that Anderson et al., 2017 identified uncertainties in the use of the Tropical Western Pacific as a “clean” region when post-processing the OMI HCHO VCD.

There are far more surface observations of NO2 than HCHO. Are the AQS NO2 and HCHO data co-located? If not, how is FNR calculated? Additionally, HCHO observations occur at 3, 8, 12 and 24 hr intervals and sometimes the HCHO data are only available every 6^th or 12^th day. Please provide more detail how these gaps are being handled and why only the 24hr data are used. Do you expect that afternoon OMI HCHO will be strongly correlated to 24hr avg surface observations? Is it appropriate to use 24hr average HCHO observations with 2hr avg, mid-afternoon NO2 data to calculate surface FNR?

Are HCHO observations from the Interagency Monitoring of Protected Visual Environments (IMPROVE) network, the National Air Toxics Trends Stations (NATTS), and the Photochemical Assessment Monitoring Stations (PAMS) networks used for this this study?

One issue for this study is the differentiation between urban and rural and how this component of the paper is incorporated into the study. The paper assumes that grid cells identified as “urban” are the same for every year but is this truly the case? For the 2005-2021 time series, is it possible that some “rural” areas become “urban”? If so, how will this impact the overall results.

Suburban and urban are lumped together as “rural”. Is this appropriate? In some areas, suburbs have large populations and/or are along major interstates and suffer from significant, local pollution emissions. Do the results reported here change if only truly rural areas are considered? I would expect suburban areas to be influenced by both urban and/or rural depending on meteorology. I think additional discussion is warranted.

Table 1 is confusing. Please be more specific as to what is being presented. Are the “obs.” referring to OMI or AQS sites? What is the “model”? Perhaps I missed it but what is the model referring to? CEDS? Also, which of these statistics are actually significant? A correlation coefficient (R) of -0.27 is an R^2 of 0.07, which is quite small.   

Figure 4: The y-axis changes for some of the panels. Is it possible to have a uniform Y-axis throughout?

Figure 7&8: Please choose more distinct colors to make it easier to discern between the 3 years ranges or years. The bars are very narrow and it’s difficult to clearly see the difference between orange and gold.

Figure S3: The city names on some of the panels overlaps with the “10^15” label for the Y-axis. You can overcome this by removing “10^15” from each panel and simply including this in the Y-axis label, i.e. 10^15 Molecules/cm^2”

Anderson, D. C., J. M. Nicely, G. W. Wolfe, R. J. Salawitch, T. P. Canty, R. R. Dickerson, E. C. Apel, S. Baider, T. J. Bannan, N. J. Blake, D. Chen, B. Dix, R. P. Fernandez, S. R. Hall, R. S. Hornbrook, L. G. Huey, B. Josse, P. Jockel, D. E. Kinnison, T. K. Koenig, M, Le Breton, V. Marecal, O. Morgenstern, L. D. Oman, L.L. Pan, C. Percival, D. Plummer, L. E. Revell, E. Rozanov, A. Saiz-Lopez, A. Stenke, K. Sudo, S. Tilmes, K. Ullman, R. Volkamer, A. J. Weinheimer, and G. Zang (2017), Formaldehyde in the Tropical Western Pacific: Chemical Sources and Sinks, Convective Transport, and Representation in CAM-Chem and the CCMI Models, J. of Geophys. Res. Atmos., 122(20), 11201-11226, https://doi.org/10.1002/2016JD026121