The authors present measurements at a street canyon in Helsinki, Finland, during late summer. They identify volatile organic compounds (VOCs) in the gas phase that originate from biogenic and anthropogenic sources using online gas chromatography-mass spectrometry (GC-MS). In addition, particle-phase measurements are performed and organic aerosols are further source-apportioned using positive matrix factorization (PMF). The particle size distribution of the different PMF factors is investigated based on the PToF data. Production rates of the oxidized compounds (OxPR) from the VOC reactions are determined using the ARCA box model. Comparison of the PMF factors to the O3, OH, and NO3 concentrations as well as specific VOC oxidation products (e.g. nopinone) highlight the benefit of performing parallel gas and particle phase observations to identify the contribution of gas phase sources to secondary aerosol pollution. This paper fits within the scope of ACP after the following comments are answered.

Main comments:

Section 3.1: Currently, the paper is missing an overview graph of the location site with info on where the coffee roastery, high traffic, restaurant locations, and other possible sources of emissions are together with the trajectory paths. Furthermore,  I believe it would make this section easier to read if the subsections were supported with graphs (even if they are in the supplement).

Section 3.2: I think we are missing here some back trajectory analysis per factor solution that would further support the origin of the sources.  Non-parametric wind regressions and pollution roses would add more confidence to the results for both the primary and secondary OA factors. For example, does the bak trajectory fit the location of the coffee roastery?

Section 3.4: There are missing VOCs to generate plots like Figures 6 and 7. I understand that there is a limited number of VOCs measured at the canyon but a discussion on the expected missing compounds would be essential. Did the authors measure carbon monoxide (CO)? If so, emissions could be estimated relative to the emissions of CO based on inventories (see e.g., McDonald et al. 2018 or Coggon et al. 2022). If CO was not measured then benzene could be used as a proxy to determine the emissions from other sources. This could help answer what is the expected influence of many OVOCs that are not measured here and could be a significant fraction of the aVOCs. Is the ARCA box model influenced by the missing VOCs in determining the OH and NO3 concentrations? A sensitivity analysis would be valuable here for the model or further discussion on how these concentrations are determined based on the model.

Minor comments:

Line 20: Please define “significantly higher”. Isn’t this due to the fact that not all anthropogenic VOCs are measured?

Line 69-71: The authors could add more references here e.g., Coggon et al., 2021, Gkatzelis et al., 2021, and others.

Line 83: This could be due to limitations from the AMS detection due to substantial fragmentation that might be important to point out here. Chemical composition could still be different but the AMS would only see fragments and no insights into the functionality of OA.

Line 154: Is a collection efficiency of 1 expected? More discussion and comparison to average values from similar urban studies would be informative here to further validate this.

Line 166-169: More discussion on the Aethalometer model would be valuable here or the supplement to avoid readers going to different papers to understand the procedure followed here. 

Line 265: I would suggest that inorganic gases are a separate section. Also, adding the standard deviation to the averages would be informative. 

Line 270-271: I would delete this sentence

Section 3.1.2: The timeseries of the number size distribution as a 3-D plot for the whole campaign would be informative to have in the supplement when discussing overall trends.

Line 276-277: I would delete this sentence.

Figure 1: I would recommend separating this figure into a 4-panel graph. On panel 1 you would have the diurnal variability of aVOCs but adding the standard deviation would provide some more insights into the variability of pollutants. On panel 2 a bar chart of the average concentration of each compound that contributes to the aVOCs from high to low. Panels 3 and 4 would be the same but for biogenic compounds. If there are important correlations to meteorology that explain the bVOC or aVOC trends this could be added to the diurnal profile of panels 1 or 3, respectively.

Line 326-328: The expected pie chart of compounds that contribute to biogenic emissions and the pie charts of the current observations would be a nice comparison graph. The expected contribution from VCPs as a pie chart is provided by Coggon et al. 2021 and could be compared to the observations too.

Line 377-394: Could the HOA factors be influenced by cooking emissions? Are there restaurants or other expected cooking sources nearby the measurement site? Could the cooking OA show similar mass spectra as traffic?

Line 404: Are there any back trajectory correlations that support these results?

Line 432-433: I would delete this sentence.

Line 464-465: Does this mean that the traffic emissions are not really only traffic but a combination of traffic and biogenic? It might be worth changing the naming then or making clear that the biogenic emissions only affect the secondary OA production which is still a significant fraction of the mass in this event.

Line 476-477: This is not only the case for traffic but for all factors. Such high particle concentrations at the lower range seem interesting. Could the authors produce a 3-D dNdlogDp timeseries plot that is usually used for "banana" plots? This could inform the readers more regarding the distribution of particles throughout the whole campaign at this urban site. Would such distribution be expected and does it compare well to previous studies?

Line 527: Replace “biogenic” with “β-pinene”.

Line 528-531: Is this true also for this period? Are the aromatics high during the event of the biogenic organic? If traffic is high why is NO low? Also, O3 will be high at higher temperatures due to more intense chemistry that could be discussed here.

Line 588: I would recommend avoiding phrases like “quite a strong correlation” with the actual statistical indicators like R and R2.

Figure 7: Is O3 currently constrained in the ARCA box model? If it wasn’t would it be captured correctly? Could this be a method to evaluate the model's performance in predicting radical concentrations?

Figure 8: I would recommend adding b-pinene to the graph since this is the precursor together with OH and O3

Line 629-632: This sentence is hard to read and hard to conclude what the authors want to highlight. Also, please provide numbers in the text.

Line 639-641: I would delete this given that the overview graph doesn’t say much about what is discussed in this section.

Line 653-654: Isn’t this because the authors are only including a fraction of the anthropogenic VOCs?

Line 665: Provide R or R2

References

Coggon, M. M., Gkatzelis, G. I., McDonald, B. C., Gilman, J. B., Schwantes, R. H., Abuhassan, N., Aikin, K. C., Arend, M. F., Berkoff, T. A., Brown, S. S., Campos, T. L., Dickerson, R. R., Gronoff, G., Hurley, J. F., Isaacman-VanWertz, G., Koss, A. R., Li, M., McKeen, S. A., Moshary, F., Peischl, J., Pospisilova, V., Ren, X., Wilson, A., Wu, Y., Trainer, M., and Warneke, C.: Volatile chemical product emissions enhance ozone and modulate urban chemistry, Proceedings of the National Academy of Sciences, 118, e2026653118, 10.1073/pnas.2026653118, 2021.

Gkatzelis, G. I., Coggon, M. M., McDonald, B. C., Peischl, J., Aikin, K. C., Gilman, J. B., Trainer, M., and Warneke, C.: Identifying Volatile Chemical Product Tracer Compounds in U.S. Cities, Environmental Science & Technology, 55, 188-199, 10.1021/acs.est.0c05467, 2021.

McDonald, B. C., de Gouw, J. A., Gilman, J. B., Jathar, S. H.,Akherati, A., Cappa, C. D., Jimenez, J. L., Lee-Taylor, J.,Hayes, P. L., McKeen, S. A., Cui, Y. Y., Kim, S. W., Gen-tner, D. R., Isaacman-VanWertz, G., Goldstein, A. H., Harley,R. A., Frost, G. J., Roberts, J. M., Ryerson, T. B., and Trainer,M.: Volatile chemical products emerging as largest petrochem-ical source of urban organic emissions, Science, 359, 760–764, https://doi.org/10.1126/science.aaq0524, 2018.

This study investigated the variation and composition of VOCs and SOA in urban street canyons using various observation instruments. The study provides some helpful information about the source apportionment and aging mechanism of SOA, which can be of considerable interest to readers. This manuscript has the potential to be published in the ACP. However, it has severe structural issues and requires a more sophisticated analysis of the results.

General comments

First, the subject of this manuscript is not clear. In Chapter 3.2, the source identification of SOA was investigated, focusing on diurnal variations. In contrast, in Chapter 3.3, the subject suddenly changed to a case study and the focus shifted to the size distribution of aerosols. In Chapter 3.4, the topic changed to the oxidation of VOCs and the generation of SOA. Chapter 3.4.1 analyzed the oxidation contributions of VOCs based on daily variability, while Chapter 3.4.2 focused again on the diurnal variability of SOA oxidation. This flow is discontinuous and disconnected, resulting in a lack of cogency. Therefore, the structure of this manuscript needs to be reorganized extensively, and some content needs to be omitted. This lack of uniformity also resulted in the abstract and conclusion being extensively long, which needs to be corrected.

Second, most of the interpretation in this manuscript relies on a PMF analysis. PMF has many advantages, but it does not guarantee unique decomposition, and the basis functions can show substantially different patterns depending on the number of basis. In addition, even if the sum of the variabilities of the basis functions explains the data satisfactorily, individual basis functions may not be scientifically significant. Multiple sources may be included in one base function, or conversely, one source may be divided into several basis functions. Therefore, individual base functions must be interpreted cautiously, and the PMF analysis must be validated. The PMF analysis in this study also included certain critical errors. For example, even though the coffee roaster is 600 m away, the CoOA explains 7% of OA on average, which is 30% of the POA caused by traffic and biomass burning (HOA-1, 2). In some periods, CoOA even constitutes 80% of the total OA. This result is not acceptable from a general point of view. Therefore, it is necessary to accurately diagnose the limitations and issues of the current PMF analysis, and these results must be presented in the paper to increase the reliability of the results.

Specific comments

1. Title: Why is an urban canyon being considered? The manuscript does not present information on the characteristics of the urban canyon.
2. Line 213: What is the concentration of OA in the two excluded periods? If PM and OA concentration information for the excluded period is also provided, it will improve readers’ understanding.
3. Chapter 3.1.1: A map of the campaign region should be provided to understand the characteristics of the region, the dispersion of nearby sources, and the vortex structure by canyon geometry.
4. Chapter 3.1.1: Presentation of diurnal variation and time series of CO concentration is helpful because CO is a primary anthropogenic pollutant and follows the characteristics of a passive tracer owing to its long lifetime.
5. Figure 2(b): What is the correlation coefficient between SV-OOA and LV-OOA-LRT? The time series look very similar. If the correlation coefficient is high, why is this so?
6. What is the correlation coefficient between bb_ff and bb_wb? HOA-1 shows a stronger correlation with bb_wb than bb_ff, although the changes in HOA-1 show a close pattern with traffic changes. It looks like bb_wb does not represent biomass burning emission.
7. Line 377: The evidence is fragile. There are no types of cars that suddenly operate only on Saturday night (or day).