This paper discusses the comparison of MAX-DOAS and TROPOMI observations of NO2 and H2CO at Kinshasa. I found two aspects of the paper particularly interesting: the location of the measurements, in a very data-sparse but interesting and relevant region, and the three cases presented, which nicely show the impact of vertical profiles and line of sight on the quantitative comparisons. The paper is generally well written, with a good set of references and a good introduction. 

But, in general I found the comparisons with GEOS-Chem not very revealing/useful. The resolution of the model is very low, 2x2.5 degree, and a direct comparison with the MAX-DOAS is not even presented (maybe because of this). The model-TROPOMI comparison is limited to one location. A real model evaluation with TROPOMI would involve assessments over a larger area, addressing aspects of the emissions (choice inventory) and sectors (fires, transport, industry) contributing emissions, and evaluation of other model processes (transport, chemistry, deposition). Drawing conclusions from a time series at one location is not really possible.  A reasonal comparison is shown for Kinshasa, which is pleasing but may be coincidental. One aspect which is of interest for this paper is the NO2/H2CO profile from GEOS-Chem, and comparisons with MAX-DOAS and TROPOMI a-priori/a-posteriori profiles. This could be extended and structured differently. 

Because of this I would be in favour of publication of this work, after my comments below have been dealt with. In particular, the comparison with GEOS-Chem could be shortened and could be given a different focus.

Detailed comments:

- Abstract: The word "bias" should be used in a more balanced way to my opinion. A "bias" normally points to one of the two datasets, taking the other as reference (assuming it to be more accurate). On line 10 "shows an underestimation of TROPOMI with a median bias of -40% (s=0.26 and R=0.41) for NO2 and -26% (s=0.24 and R=0.28) for H2CO". The reader will conclude from this that TROPOMI is biased low. But the other cases show that the difference is influenced by the way the analysis is done. So, I conclude that the -40%/-26% are not so much to be attributed to TROPOMI, but also reflect the comparison approach. An alternative formulation could be "MAXDOAS is biased high by +40/+26%", which sounds like a very different conclusion. I would suggest to use a more neutral "mean difference between MAXDOAS and TROPOMI" instead of "the bias of TROPOMI"  throughout the paper.

- Abstract, line 16: "We found a bias of 16% (s= 0.42 and R = 0.80) for NO2 and bais of 61% (s= 0.05 and R = 0.24) for H2CO". Is the model or TROPOMI higher in this case? 

- Abstract, line 16: "bais"

- Fig. 1. Do these yellow lines correspond to the MAX-DOAS viewing direction? It may be useful to indicate the viewing (azimuth) direction in Fig. 12/13 as a line or arrow. Maybe Figs 12+13 could be brought to the beginning of the paper, e.g. after Fig. 1. The spatial distribution of NO2/H2CO is useful as background information before reading the rest of the paper. Would be nice to see MODIS AOD as well.

- line 107: "only MMF data selected for their consistency with corresponding MAPA results are retained." What does that mean? Which dataset(s) is (are) submitted?

- line 113: "we only considered MMF due to inconsistencies in the MAPA aerosol retrievals for our Kinshasa spectra." Please explain the "inconsistencies". 

- line 113: It would be valuable to see the results for both retrieval approaches and to know how much the MAX-DOAS results (tropospheric columns) differ between the two (e.g summarise the findings of the papers cited in line 112-113). Would it be possible to present MAPA results?

- line 115: "both algorithms." Does this refer to NO2 vs H2CO, or MMF vs MAPA? But MAPA is not used?

- line 116: "monthly climatology" Why not use the actual meteorological variables from for instance the ERA-5 reanalysis? Is the retrieval sensitive to meteorology (temperature)?

- line 124: Please add a comment on the a-priori error used: how much is the retrieval constrained by the a-priori?

- Sec 2.3: For FRM4DOAS you cite the ATBD. Likewise it would be useful to include a reference to he TROPOMI ATBDs.

- line 129: What is this "S5P-PAL" product? Please explain in one or two sentences.

- line 132: "Only pixels within a radius of 20km around the observation site" Why 20 km?

- Sec 2.4: Apart from Marais, it would be useful to add a few key references for this global GEOS-Chem (version 12). Are there other relevant studies done over Africa with the model? 

- Sec 2: I would expect a section on the intercomparison approach, dealing with aspects like profile shape, horizontal gradients, line of sight, collocation and meaning (use) of the circles in Figs 12 and 13. Instead, the cases are discussed in section 3.2.

- line 168: I was wondering how much the biomass burning season is contributing to AOD in comparison to local (dust, transportation, industry, household) contributions? Could you summarise what is known from e.g. the inventories.

- Fig. 5. "The error bars represent the standard deviation." The standard deviation of what? Is it an error bar or a measure of the spread of the values?

- line 184: "with some delay, " What would be a typical delay during daytime?

- line 187: Looking at figure 12 this first case does not seem very useful. There is clearly a strong gradient and a lot of clean area is included in the average which is not observed by the MAX-DOAS instrument. I could imagine that for H2CO a larger circle may be needed because of the larger noise level compared to NO2.

- line 195: Please provide the details. What are the units of the MAX-DOAS profile (molecules/cm^3?). How is the interpolation done? Does the interpolation conserve the column amount? What is the collection of MAX-DOAS observations from which the median is computed? Why a median instead of a mean, and does it matter?

- Case 3: Is this done in the same way as described in Dimitropoulou et al? I was wondering if the method could be visualised? For instance for one day / one overpass, showing the region like in Fig 12/13, the azimuthal viewing line and TROPOMI pixels selected. Are weights applied to the TROPOMI observations? Are pixels close to the MAXDOAS more important?

- line 203: "a coincidence test is performed" What are the criteria? What is the "surface point" of a pixel?

- line 213: Looking at the figure it seems that the retrieval is producing negative concentrations in several cases. Do you apply a clipping, or are negatives used as is?

- line 216: "motivate the application of the transformation". According to the optimal estimation theory of Rodgers averaging kernels are to be used in profile comparisons. So this would be the main motivation, rather than an observed difference in profile shape. The difference in profile shapes indicates that case 1 and 2 may differ substantially.

- Fig.6. In May-September the profiles for H2CO look quite different. Which one of the two would be more realistic? You mentioned before that the sensitivity of the MAX-DOAS rapidly decreases at 2km and above. This is also indicated by the small spread around 2 km altitude. So maybe the ground-based observation is not sensitive enough to capture elevated layers? Could this explain part of the difference ground-satellite? Please comment. 

- Fig.7. What kind of regression method is used? Does it account for satellite and ground-based retrieval errors? Please indicate in the caption that this is a case-3 comparison.

- Fig.7: "TROPOMI error bars are standard deviations. " For the monthly values I assume this is the spread in the individual column amounts used in the average. But what is shown for the daily points? Is it now the retrieval error, or still a spread in values? 

- line 277: "As for NO2, the results of the third case are shown in Figure 8" .. for H2CO.

- line 278: "The dynamic range of MAX-DOAS measurements is small compared to that of NO2 ". Does this refer to the blue-green error bars? 

"..because of the different points filtered .." this is unclear to me.

- line 282: "reduced number of TROPOMI measurements" How many measurements are used on average for NO2 and H2CO?

- Fig. 9: It would be nice if the a-priori profiles from TROPOMI and MAX-DOAS could be added in this figure as well. Like in Fig. 6 it would be good to see the season-averaged profile shape. Maybe Fig 6 and Fig 9 could be combined?

- Why is GEOS-Chem only compared with TROPOMI and not with the MAX-DOAS? Of course the resolution of the model is very low compared to TROPOMI, leading to large mismatches in the air masses probed.

- line 331: "The general underestimation of TROPOMI compared to MAX-DOAS ". It would be good to mention that you refer to the difference between case 2 and case 1 here. The "best" comparison, case3, does not show a prominent underestimation.

- line 344: "Additional uncertainties comes from clouds and aerosols". How are clouds treated/filtered in the retrieval of the MAX-DOAS? This information is not provided in the paper.

- line 363: Why refer to the raw comparison here? The best comparison is presented in Figs 10 and 11.

- Section 4.2. See my general comment above. The discussion cites a few studies and possible shortcomings. But there is not enough data to draw conclusions concerning GEOS-Chem (or TROPOMI) apart from a general reasonable agreement and similar seasonality.

- line 404: I have the feeling that the noise in individual TROPOMI H2CO retrievals is an important reason for a poor correlation with GEOS-Chem and/or MAX-DOAS. This role of the retrieval noise could be discussed in more detail.

- line 406: "The present comparisons have shown the importance of correcting the initial TROPOMI products with the profile measured over the observation site and taking into account the horizontal variability of the studied molecules." Could you generalise this finding and formulate recommendations for other sites and satellite-ground remote sensing comparisons in general? Large gradients near cities are common, and the case 3 comparison

approach could be a general recommendation for future validation work. How should previous comparisons (e.g. Verhoelst et al, Vigouroux et al) be interpreted? 

To date there are few air pollution studies being conducted in African megacities despite the fact that many of these cities suffer from poor air quality. The aim of this paper was to compare ~2 years of ground-based MAX-DOAS observations with satellite column observations over the city of Kinshasa in the DRC. In the study, the authors explore 3 different retrieval methodologies and use linear regression statistics to comment on the robustness of each method explored. A comparison between satellite retrievals (TROPOMI) and model observations (GEOS-Chem) is also presented and discussed in the manuscript.

This manuscript provides measurements of NO2 and H2CO in a region where information about these pollutants is lacking. In addition, this manuscript adds to the growing body of literature examining comparisons between satellite and ground-based measurements. I think that this paper is well written and the scientific approach taken by the authors in general is sound. I think that the paper could benefit from both an expanded discussion about the differences between retrieval cases and expanded discussion of the model/measurement comparison results. I would recommend this paper for publication after the following general and specific comments are addressed:

General comments:

Section 2: There are some missing details in the methods that should be added to or moved to this section. There should be more instrumental details presented here or Section 2.1 should reference a previous paper where the details can be found. There are also a few details noted later in the manuscript (eg. Filtering techniques/criteria, TM5 model, etc.) that should be described in Section 2 first. Finally I think it would be nice for Figures 12 and 13 to be presented in this section to better understand the NO2 and H2CO distribution, as opposed to later in the manuscript, and the oversampling technique should also be described here.

Section 3.1: This section would benefit highly from an expanded discussion of seasonal and diurnal trends (see more specific comments below). Or the authors could consider moving some of this discussion to Section 4 instead. I think the diurnal and seasonal trend analysis should also be revisited and further explored. 

Section 3.2: In general, the statistical results in section 3 are presented well. The statistical analysis and filtering methods should be clarified in more detail here.

Section 3.3: I think comparing observations with model outputs always provides some valuable insight. Ideally I would like to see a comparison of the MAX-DOAS measurements with GEOS-Chem here, but I agree because of the spatial resolution differences this wouldn’t make sense. Similarly, I think it doesn’t make sense to compare only a single (or a few) model points with TROPOMI given the coarse resolution. I think the section could largely benefit from expanding the domain of the comparison between TROPOMI and GEOS-Chem to a larger region across the DRC. Since the distribution of H2CO and NO2 are very heterogeneous this could provide insight into some of the model/measurement differences across a broader region and more statistics would be available.

Section 4.1: I would like to see an expanded discussion of the differences between Cases 1, 2 and 3 and justification for picking case 3 for NO2 and why exactly case 3 did not work as well for H2CO. For example, I would possibly argue that case 2 for NO2 showed a stronger correlation with TROPOMI and that the monthly averages also followed a more similar trend in case 2 vs case 3. I am not suggesting one method is better than the other, I just think a more detailed discussion here is needed as to why the slopes, correlations, trends may have changed in the way they did in all three cases.

Section 4.2: This is a very short discussion section mainly about model uncertainties in general. I would suggest either moving some of the points made in this section to Section 3.3 or expanding upon it with the inclusion of additional model/measurement comparisons moving forward.

Figures: Figures should be proofread and make sure the panel labels and legends are all present and readable.

Specific comments:

Line 20: Formaldehyde has strong biogenic sources and signatures as well (eg. Biomass burning, secondary formation from isoprene emissions). Consider discussing here, as you do later in the introduction, that H2CO can be a marker of both anthropogenic and biogenic sources.

Line 22: This is not true, under high NO2 conditions ozone production can decrease as ozone gets titrated in the atmosphere. I would suggest referencing more sources here (eg. Seinfeld and Pandis, 1998) and rewrite to say that VOCs and NO2 react in a non-linear manner to form ozone in the atmosphere.

Line 80: Please include more instrumental details here, such as spectrometer characteristics, optical head setup, fiber guide etc. Or reference previous manuscript where these details can be found.

Line 113: Please explain the inconsistencies here and why only MMF retrievals were considered.

Table 2: Was there a reason a scale height of 1km was initially used in the MMF reterieval for NO2 and H2CO or was this the default setting?

Line 132: Why was a radius of 20km used to select TROPOMI and GEOS-Chem for comparison? And why wasn’t an expanded range used for the satellite/model comparisons?

Line 149: The model is currently initiated with coarse (2x2.5 degree) resolution and is run for the entire globe. Given the fine resolution of TROPOMI I think it would be more suitable to run GEOS-Chem with a finer resolution and for a subset of the global domain. A finer run model setup could potentially also be used to compare with the MAX-DOAS measurements.

Figure 4: I would also like to see the monthly average measured by MODIS in this figure. And the MODIS measurements should be represented with their own y-axis since AOD is measured at 550nm. Consider moving MODIS measurements to the supplement and referring to the figure there since it is not discussed in detail in the body of the manuscript.

Line 168: Is there any contribution from biomass burning in the dry season to the increase in AOD? Or is it only from accumulation of dust?

Figure 5. Missing letter labels for the panels and it is hard to see the letters/time periods within the figures. Please describe all panels clearly in the figure description.

Lines 179 – 184: I would like a more detailed discussion here of the diurnal patterns observed or else these lines should be removed or moved to the discussion section. To me it looks like there is no diurnal pattern in H2CO in the dry season and that there is more of a clear pattern during the wet seasons. The clear increase and decrease throughout the day in the wet seasons looks like it could be either a biogenic or anthropogenic signature. The higher H2CO concentrations in the dry season seem more consistent with biomass burning dominating. In addition, the diurnal pattern in NO2 is not explored in this section and it follows a different pattern than H2CO with a maximum later in the day.

Line 203: Please provide more details on how the coincidence test was performed.

Line 206: Why were MAX-DOAS measurements averaged for a time interval of 1 hour around the TROPOMI overpass time?

Line 216: I like the discussion of Figure 6 and how it is presented as a motivation for constraining the comparison using different cases that take into account vertical and horizontal sensitivity. Do you know what might be causing the TM5 vertical profiles to be very different from what you are measuring?

Lines 210 – 217: This paragraph is redundant with the next paragraph that describes Figure 6 in much more detail. I would suggest removing these lines.

Lines 221 – 232: Here to me it looks like NO2 profiles match each other very well at 500m in all three cases, and I agree there are large differences between 500 and 3000m. However, I also notice that H2CO is highly underestimated at 500m in TM5 compared with MAX-DOAS, and then it is overestimated at higher altitudes. I think an expanded discussion here would be useful.

Table 3: Please describe the linear regression method used. Since there are many ways to statistically treat this data and given the large standard deviation (error) in both measurements, an appropriate regression method should be used to analyze the data. 

Line 279: It seems here like you have filtered more points out for H2CO versus NO2, but it seems like you describe the same filtering criteria for both pollutants earlier. Can you please explain why this is the case? And why you make the conclusion later that case 3 was worse due to this, but that wasn’t the case for NO2. In general I think the differences need more discussion and I think these lines should be moved to the discussion section and only results presented in Section 3.2.

Figure 11: It seems like there is a huge daily trend in H2CO but GEOS-Chem is not capturing this at all, do you have any idea why that is the case?

Line 327: “Clear improvements of the results when considering only TROPOMI pixel along the line of sight” – I’m not sure I am fully convinced by this conclusion. I agree that there is less overall bias with case 3 (and this assumes MAX-DOAS are the ‘true’ values), but I would argue the correlations, slopes and intercepts as well as NO2 trends may be better captured in Case 2. For example, comparing Case 2 and Case 3 for NO2 daily averages in Table 3 it seems like there is stronger correlation and less offset with Case 2. Looking at monthly averages the slope and offset also look better for case 2, and in this case TROPOMI becomes biased high at large concentrations, and very biased low at low concentrations (very negative intercept). I would like to see more justification and discussion between picking case 2 vs case 3.

Line 328: Similarly here – is there any way to probe why Case 3 did not produce as good results for H2CO? The statistics may be worse but there should still be some improvement in Case 3 if line of sight was a major influence on the retrieval comparisons. My initial thought here was that H2CO is a bit more homogeneously spread around Kinshasa (and in general across most locations) so line of sight may not be as influential as with NO2 which has more distinct emission sources.

Line 347: Could you try a stricter filter for clouds and aerosols and see if this shows improvement in the comparison at all?