Impacts of summertime photochemical aging on the physicochemical properties of aerosols in a Paris suburban forest region

Yu, Chenjie; Formenti, Paola; de Brito, Joel F.; Bauville, Astrid; Bergé, Antonin; Bouzidi, Hichem; Cazaunau, Mathieu; Cirtog, Manuela; Di Biagio, Claudia; Di Antonio, Ludovico; Gaimoz, Cécile; Maisonneuve, Franck; Zapf, Pascal; Seubert, Tobias; Andersen, Simone T.; Dewald, Patrick; Türk, Gunther N. T. E.; Crowley, John N.; Kukui, Alexandre; Xue, Chaoyang; Denjean, Cyrielle; Garrouste, Olivier; Etienne, Jean-Claude; Wu, Huihui; Allan, James D.; Liu, Dantong; Wu, Yangzhou; Cantrell, Christopher; Michoud, Vincent

doi:10.5194/egusphere-2025-2667

Preprints

https://doi.org/10.5194/egusphere-2025-2667

Preprints

07 Jul 2025

| 07 Jul 2025

Impacts of summertime photochemical aging on the physicochemical properties of aerosols in a Paris suburban forest region

Chenjie Yu, Paola Formenti, Joel F. de Brito, Astrid Bauville, Antonin Bergé, Hichem Bouzidi, Mathieu Cazaunau, Manuela Cirtog, Claudia Di Biagio, Ludovico Di Antonio, Cécile Gaimoz, Franck Maisonneuve, Pascal Zapf, Tobias Seubert, Simone T. Andersen, Patrick Dewald, Gunther N. T. E. Türk, John N. Crowley, Alexandre Kukui, Chaoyang Xue, Cyrielle Denjean, Olivier Garrouste, Jean-Claude Etienne, Huihui Wu, James D. Allan, Dantong Liu, Yangzhou Wu, Christopher Cantrell, and Vincent Michoud

Abstract. Organic Aerosols (OA), which significantly affect the climate system and human health, often contain a substantial fraction of atmospherically processed species known as Oxygenated Organic Aerosol (OOA). However, the formation pathways and evolution of OOA remain poorly understood. To address this need, an experiment was conducted in a suburban forest in the Paris region to systematically study the evolution of OOA and their optical properties. Our results show that the photochemical processes drove significant increases in total submicron particle mass concentrations in the forest site, primarily via the production of OOA derived from both biogenic and anthropogenic emissions. Air mass origin critically influenced Particulate Matter (PM) pollution levels and photochemical activity: under elevated pollution and intense solar radiation during continental air mass-dominated periods, rapid formation of More-Oxidized OOA (MO-OOA) occurred. This MO-OOA dominated Brown Carbon (BrC) contributions, enhancing short-wavelength light absorption by 35 % on average after a relative ~24-hour photochemical aging process. Conversely, periods dominated by clean maritime air masses featured humid, low‑radiation conditions that yielded reduced pollution levels and an increased proportion of nitrogen‑enriched, Less‑Oxidized OOA (LO‑OOA). Suppressed photochemical activity during the clean maritime period limited MO-OOA production, resulting in a lower overall oxidation state of OA. These findings underscore the dual role of photochemistry in shaping aerosol optical properties and climate impacts, highlighting the necessity of accounting for air mass dynamics and oxidation pathways in suburban forest regions.

How to cite. Yu, C., Formenti, P., de Brito, J. F., Bauville, A., Bergé, A., Bouzidi, H., Cazaunau, M., Cirtog, M., Di Biagio, C., Di Antonio, L., Gaimoz, C., Maisonneuve, F., Zapf, P., Seubert, T., Andersen, S. T., Dewald, P., Türk, G. N. T. E., Crowley, J. N., Kukui, A., Xue, C., Denjean, C., Garrouste, O., Etienne, J.-C., Wu, H., Allan, J. D., Liu, D., Wu, Y., Cantrell, C., and Michoud, V.: Impacts of summertime photochemical aging on the physicochemical properties of aerosols in a Paris suburban forest region, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2025-2667, 2025.

Received: 06 Jun 2025 – Discussion started: 07 Jul 2025

Competing interests: At least one of the (co-)authors is a member of the editorial board of Atmospheric Chemistry and Physics.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors. Views expressed in the text are those of the authors and do not necessarily reflect the views of the publisher.

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Journal article(s) based on this preprint

21 Apr 2026

Impacts of summertime photochemical aging on the physicochemical properties of aerosols in a Paris suburban forest region

Atmos. Chem. Phys., 26, 5313–5332, https://doi.org/10.5194/acp-26-5313-2026,https://doi.org/10.5194/acp-26-5313-2026, 2026

Short summary

Interactive discussion

Status: closed

RC1:
'Comment on egusphere-2025-2667', Anonymous Referee #1, 28 Jul 2025

In their manuscript “Impacts of summertime photochemical aging on the physicochemical properties of aerosols in a Paris suburban forest region”, authors linked OA factors identified from conventional AMS-PMF approach to airmass origins and photochemical activities, and highlighted the amplified BrC absorption observed during the ACROSS project. The study is on a topic of relevance and general interest to the readers of ACP. However, I found the description of the methodology partially insufficient and partially hindered the evaluation of the results. The below comments need to be addressed first before I could comprehensively evaluate Section 3.3 onward. Therefore, I recommend a major revision and am open to review the manuscript again if needed.

Specific comments:
1. Line 46-47, “Rapid urbanization and industrial activities had led to high levels of anthropogenic aerosol emissions in developed megacities (Shi et al., 2019)”. I am unable to find much information about “anthropogenic aerosol emissions” from your referred paper. Instead, its winter section stated that “PM2.5 and O3 each had similar temporal patterns at the urban and rural sites (Fig. 5), indicating a synoptic-scale meteorological impact” instead of “anthropogenic emissions”. Please make sure the reference is correct and relevant, or modify your sentence correspondingly. One megacity (Beijing) is also insufficient to serve the purpose of representing a generalized statement (developed megacities).
2. Line 105: why near-ground measurements (5-m) can “facilitate a focused discussion on regional pollution within the boundary layer” better than the 40-m measurements, if both were available? Aren’t the lower measurements more affected by localized plumes or nearby human activities/events? If the PMF results from the below- and above-canopy measurements were consistent to each other throughout the entire campaign, then it could be less of a concern. Otherwise, it is recommended to separate the entire campaign period into episodes with vertical differences in NR-PM1 and those without to discuss separately, as shown in previous AMS-PMF study above- and below-canopy in Michigan, U.S. (Bui et al., 2021). Please clarify.
3. More contexts should be provided to Section 2.2:
3.1 HR-AMS section: Middlebrook et al. (2012) should be cited when you mention/use CDCE. How did you treat the RIE for OA since most of your analysis focused on the organics? Did you perform periodical filtered measurement for gas-phase CO2+ subtraction from your sampling and/or calculate species-dependent detection limits?
3.2 HR-AMS section: a table of IE and RIE results, and five main species detection limits should be added to the SI.
3.3 HR-AMS section: “all factors exhibited distinct temporal and spectral characteristics until a six factor, and the spectra of the six factors were consistent with source spectra in the AMS spectral database (Jeon et al., 2023)”- please elaborate quantitatively on “distinct” and “consistent” in the SI.
3.4: AE33 section: from your description, it seems that AE33 was sampling from another platform. Was the AE33 pm2.5-cycloned and Nafion dried as well? How did you treat the different collection efficiencies (line loss) of the two instruments at different platforms using different inlet systems before adding them together for a “total PM1 mass concentration”?
3.5: What is your CAPS NO2 instrument model/type?
3.6: Your net radiometer and wind monitor were mounted on the tower (40-m) while the aerosol instruments were described as near-surface. How did you account for the canopy interference to the solar radiation and wind before they reach the ground level for your subsequent analysis?
4. Much of the context in Section 3.2 should be put in the SI as an identification & justification of PMF OA-factors instead of as standalone scientific findings. Its corresponding figures are recommended to be combined with figures in Section 3.1 as an overview of campaign measurements (values reporting). For example, I see no reason why Figure 5 (diurnal of OA factors) and Figure 3 (diurnal of other species) are not combined, and why Figure 4 (TS of OA factors) are not combined into Figure 2 (TS of RH, T, bulk PM species). Having them separately is redundant and makes it harder to see general trends among different parameters.

Technical corrections:
1. Line 58: please add “Aerodyne” in front of the Aerosol Mass Spectrometer as there are more than one kind of aerosol mass spectrometer (e.g. AToFMS, PALMS) for its first appearance in your manuscript. The Aerodyne AMS/ACSM is just the most widely used commercial one.
2. Line 60-62: please specify your “Source apportionment analysis” is “Positive Matrix Factorization” because there are many ways of doing aerosol source apportionment, and the nomenclature of HOA, BBOA, COA etc is mostly for AMS-PMF analysis. You should add reference to Ulbrich et al. (2009) and Zhang et al., (2011) to this sentence for naming these OA factors.
3. Line 68: please consider rephrasing to “..., an important part of OA primarily emitted or formed through the oxidation of VOCs in the presence…”.
4. Figure 6(c) has one duplicated label (LO-OOA2).

Reference:
Bui, A. A. T et al., Transport-driven aerosol differences above and below the canopy of a mixed deciduous forest, Atmos. Chem. Phys., 21, 17031–17050, https://doi.org/10.5194/acp-21-17031-2021, 2021.
Middlebrook, A. M., et al., Evaluation of composition-dependent collection efficiencies for the Aerodyne aerosol mass spectrometer using field data, Aerosol Sci. Tech., https://doi.org/10.1080/02786826.2011.620041.
Ulbrich et al., Interpretation of organic components from Positive Matrix Factorization of aerosol mass spectrometric data, Atmos. Chem. Phys., 9, 2891-2918, 10.5194/acp-9-2891-2009, 2009.
Zhang et al., Understanding atmospheric organic aerosols via factor analysis of aerosol mass spectrometry: a review, Anal Bioanal Chem. 2011., https://doi.org/10.1007/s00216-011-5355-y.

Citation: https://doi.org/10.5194/egusphere-2025-2667-RC1
- AC1: 'Reply on RC1', Chenjie Yu, 07 Nov 2025
  
  We thank the reviewer for the valuable comments. Please find our detailed responses attached.
  
  Citation: https://doi.org/10.5194/egusphere-2025-2667-AC1
RC2:
'Comment on egusphere-2025-2667', Anonymous Referee #2, 25 Aug 2025

This manuscript presents the impacts of the photochemical process on aerosol physicochemical and optical properties in a suburban forest near Paris. The authors characterize major PM1 components and gas-phase species, identify OA factors under different air mass origins, and investigate OOA evolution and their optical properties. The topic is relevant to ACP readers, and the idea of quantifying photochemical aging is interesting in principle. However, the current methodology has significant limitations, and some conclusions are not fully supported by the presented results, as detailed below. Therefore, I do not recommend the publication of this paper in ACP.

Major Comments

1. The key analysis relies on photochemical age, but I don’t think it works for the sampling environment. The method works if one tracks the same air parcel from emission to downwind. But at this ground site, the airmass constantly changes. The method is heavily biased by the fresh plume close to the site.

Many results may be confounded by the diurnal trend of calculated photochemical age. Why does isoprene concentration increase with photochemical age?

2. OA is normalized to BC to account for dilution, but this approach only works if the species originates from the same source as BC. It may not be the case in this study. Line 220.

3. Section 3.2 on source apportionment should be revised to make the conclusions convincing:

3.1 The correlation between OA factors and tracers is generally low. For example, the correlation between LO-OOA1 and isoprene is only 0.43. The comparison matrix in Fig. S9 should be moved to the main text.

3.2 Be careful when correlating aerosol with gas species because they have different lifetimes.

3.3 Line 274. The evidence to suggest marine emissions sources needs to be strengthened.

3.4 Fig. S9 shows a stronger correlation between isoprene and MO-OOA (r = 0.65) than with LO-OOA1 (r = 0.43). Why is isoprene plotted only with LO-OOA1 in Fig. 4b?

3.5 Line 265. The reported “good correlation” between LO-OOA and MVK+MACR should be quantified explicitly by providing the r value.

Minor Comments

1. In general, more contexts should be provided for clarity beyond referencing previous literature. For example:

1.1 Line 178. Describe more about the CIMS OH measurements.

1.2 Line 103. Briefly describe the sampling site and its surroundings.

2. Line 115. Does the 0.1 L/min flow rate meet the cutoff specification for a PM2.5 cyclone, and would this low flow rate lead to potential sampling losses?

3. Eqn 5 to calculate BrC. They didn’t consider the lensing effect.

4. Line 330. It is strange that O:C does not increase. Further clarification is needed.

5. Line 349. How to define the background CO?

6. Section 3.4. Cite Washenfelder et al., GRL, 2015.

7. The manuscript uses coefficients from multiple linear regression to estimate MAC. A comparison between these estimated MAC values and directly measured MAC values from the literature should be included. The discussion should also address potential reasons for discrepancies across different factors. Additionally, interpreting correlation coefficients as contributions requires caution (i.e., variable standardization, multicollinearity, etc.). More statistical details should be provided to support this analysis.

Technical Corrections

1. A few typos were noted (e.g., “person correlation coefficient” in Line 248 and Fig. S9 caption; two “LO-OOA2” labels in Fig. 6a).

2. The use of abbreviations is occasionally inconsistent. In some cases, terms are introduced without an abbreviation where one would be expected (e.g., aerosol mass spectrometer, Line 82), while in others the full term is unnecessarily repeated (e.g., the definition of PM1, Line 142).

3. In Fig. 4a, please include labels to identify factors, such as HOA, LO-OOA1, etc., for improved readability.

Citation: https://doi.org/10.5194/egusphere-2025-2667-RC2
- AC2: 'Reply on RC2', Chenjie Yu, 07 Nov 2025
  
  We thank the reviewer for the valuable comments. Please find our detailed responses attached.
  
  Citation: https://doi.org/10.5194/egusphere-2025-2667-AC2

Interactive discussion

Status: closed

RC1:
'Comment on egusphere-2025-2667', Anonymous Referee #1, 28 Jul 2025

In their manuscript “Impacts of summertime photochemical aging on the physicochemical properties of aerosols in a Paris suburban forest region”, authors linked OA factors identified from conventional AMS-PMF approach to airmass origins and photochemical activities, and highlighted the amplified BrC absorption observed during the ACROSS project. The study is on a topic of relevance and general interest to the readers of ACP. However, I found the description of the methodology partially insufficient and partially hindered the evaluation of the results. The below comments need to be addressed first before I could comprehensively evaluate Section 3.3 onward. Therefore, I recommend a major revision and am open to review the manuscript again if needed.

Specific comments:
1. Line 46-47, “Rapid urbanization and industrial activities had led to high levels of anthropogenic aerosol emissions in developed megacities (Shi et al., 2019)”. I am unable to find much information about “anthropogenic aerosol emissions” from your referred paper. Instead, its winter section stated that “PM2.5 and O3 each had similar temporal patterns at the urban and rural sites (Fig. 5), indicating a synoptic-scale meteorological impact” instead of “anthropogenic emissions”. Please make sure the reference is correct and relevant, or modify your sentence correspondingly. One megacity (Beijing) is also insufficient to serve the purpose of representing a generalized statement (developed megacities).
2. Line 105: why near-ground measurements (5-m) can “facilitate a focused discussion on regional pollution within the boundary layer” better than the 40-m measurements, if both were available? Aren’t the lower measurements more affected by localized plumes or nearby human activities/events? If the PMF results from the below- and above-canopy measurements were consistent to each other throughout the entire campaign, then it could be less of a concern. Otherwise, it is recommended to separate the entire campaign period into episodes with vertical differences in NR-PM1 and those without to discuss separately, as shown in previous AMS-PMF study above- and below-canopy in Michigan, U.S. (Bui et al., 2021). Please clarify.
3. More contexts should be provided to Section 2.2:
3.1 HR-AMS section: Middlebrook et al. (2012) should be cited when you mention/use CDCE. How did you treat the RIE for OA since most of your analysis focused on the organics? Did you perform periodical filtered measurement for gas-phase CO2+ subtraction from your sampling and/or calculate species-dependent detection limits?
3.2 HR-AMS section: a table of IE and RIE results, and five main species detection limits should be added to the SI.
3.3 HR-AMS section: “all factors exhibited distinct temporal and spectral characteristics until a six factor, and the spectra of the six factors were consistent with source spectra in the AMS spectral database (Jeon et al., 2023)”- please elaborate quantitatively on “distinct” and “consistent” in the SI.
3.4: AE33 section: from your description, it seems that AE33 was sampling from another platform. Was the AE33 pm2.5-cycloned and Nafion dried as well? How did you treat the different collection efficiencies (line loss) of the two instruments at different platforms using different inlet systems before adding them together for a “total PM1 mass concentration”?
3.5: What is your CAPS NO2 instrument model/type?
3.6: Your net radiometer and wind monitor were mounted on the tower (40-m) while the aerosol instruments were described as near-surface. How did you account for the canopy interference to the solar radiation and wind before they reach the ground level for your subsequent analysis?
4. Much of the context in Section 3.2 should be put in the SI as an identification & justification of PMF OA-factors instead of as standalone scientific findings. Its corresponding figures are recommended to be combined with figures in Section 3.1 as an overview of campaign measurements (values reporting). For example, I see no reason why Figure 5 (diurnal of OA factors) and Figure 3 (diurnal of other species) are not combined, and why Figure 4 (TS of OA factors) are not combined into Figure 2 (TS of RH, T, bulk PM species). Having them separately is redundant and makes it harder to see general trends among different parameters.

Technical corrections:
1. Line 58: please add “Aerodyne” in front of the Aerosol Mass Spectrometer as there are more than one kind of aerosol mass spectrometer (e.g. AToFMS, PALMS) for its first appearance in your manuscript. The Aerodyne AMS/ACSM is just the most widely used commercial one.
2. Line 60-62: please specify your “Source apportionment analysis” is “Positive Matrix Factorization” because there are many ways of doing aerosol source apportionment, and the nomenclature of HOA, BBOA, COA etc is mostly for AMS-PMF analysis. You should add reference to Ulbrich et al. (2009) and Zhang et al., (2011) to this sentence for naming these OA factors.
3. Line 68: please consider rephrasing to “..., an important part of OA primarily emitted or formed through the oxidation of VOCs in the presence…”.
4. Figure 6(c) has one duplicated label (LO-OOA2).

Reference:
Bui, A. A. T et al., Transport-driven aerosol differences above and below the canopy of a mixed deciduous forest, Atmos. Chem. Phys., 21, 17031–17050, https://doi.org/10.5194/acp-21-17031-2021, 2021.
Middlebrook, A. M., et al., Evaluation of composition-dependent collection efficiencies for the Aerodyne aerosol mass spectrometer using field data, Aerosol Sci. Tech., https://doi.org/10.1080/02786826.2011.620041.
Ulbrich et al., Interpretation of organic components from Positive Matrix Factorization of aerosol mass spectrometric data, Atmos. Chem. Phys., 9, 2891-2918, 10.5194/acp-9-2891-2009, 2009.
Zhang et al., Understanding atmospheric organic aerosols via factor analysis of aerosol mass spectrometry: a review, Anal Bioanal Chem. 2011., https://doi.org/10.1007/s00216-011-5355-y.

Citation: https://doi.org/10.5194/egusphere-2025-2667-RC1
- AC1: 'Reply on RC1', Chenjie Yu, 07 Nov 2025
  
  We thank the reviewer for the valuable comments. Please find our detailed responses attached.
  
  Citation: https://doi.org/10.5194/egusphere-2025-2667-AC1
RC2:
'Comment on egusphere-2025-2667', Anonymous Referee #2, 25 Aug 2025

This manuscript presents the impacts of the photochemical process on aerosol physicochemical and optical properties in a suburban forest near Paris. The authors characterize major PM1 components and gas-phase species, identify OA factors under different air mass origins, and investigate OOA evolution and their optical properties. The topic is relevant to ACP readers, and the idea of quantifying photochemical aging is interesting in principle. However, the current methodology has significant limitations, and some conclusions are not fully supported by the presented results, as detailed below. Therefore, I do not recommend the publication of this paper in ACP.

Major Comments

1. The key analysis relies on photochemical age, but I don’t think it works for the sampling environment. The method works if one tracks the same air parcel from emission to downwind. But at this ground site, the airmass constantly changes. The method is heavily biased by the fresh plume close to the site.

Many results may be confounded by the diurnal trend of calculated photochemical age. Why does isoprene concentration increase with photochemical age?

2. OA is normalized to BC to account for dilution, but this approach only works if the species originates from the same source as BC. It may not be the case in this study. Line 220.

3. Section 3.2 on source apportionment should be revised to make the conclusions convincing:

3.1 The correlation between OA factors and tracers is generally low. For example, the correlation between LO-OOA1 and isoprene is only 0.43. The comparison matrix in Fig. S9 should be moved to the main text.

3.2 Be careful when correlating aerosol with gas species because they have different lifetimes.

3.3 Line 274. The evidence to suggest marine emissions sources needs to be strengthened.

3.4 Fig. S9 shows a stronger correlation between isoprene and MO-OOA (r = 0.65) than with LO-OOA1 (r = 0.43). Why is isoprene plotted only with LO-OOA1 in Fig. 4b?

3.5 Line 265. The reported “good correlation” between LO-OOA and MVK+MACR should be quantified explicitly by providing the r value.

Minor Comments

1. In general, more contexts should be provided for clarity beyond referencing previous literature. For example:

1.1 Line 178. Describe more about the CIMS OH measurements.

1.2 Line 103. Briefly describe the sampling site and its surroundings.

2. Line 115. Does the 0.1 L/min flow rate meet the cutoff specification for a PM2.5 cyclone, and would this low flow rate lead to potential sampling losses?

3. Eqn 5 to calculate BrC. They didn’t consider the lensing effect.

4. Line 330. It is strange that O:C does not increase. Further clarification is needed.

5. Line 349. How to define the background CO?

6. Section 3.4. Cite Washenfelder et al., GRL, 2015.

7. The manuscript uses coefficients from multiple linear regression to estimate MAC. A comparison between these estimated MAC values and directly measured MAC values from the literature should be included. The discussion should also address potential reasons for discrepancies across different factors. Additionally, interpreting correlation coefficients as contributions requires caution (i.e., variable standardization, multicollinearity, etc.). More statistical details should be provided to support this analysis.

Technical Corrections

1. A few typos were noted (e.g., “person correlation coefficient” in Line 248 and Fig. S9 caption; two “LO-OOA2” labels in Fig. 6a).

2. The use of abbreviations is occasionally inconsistent. In some cases, terms are introduced without an abbreviation where one would be expected (e.g., aerosol mass spectrometer, Line 82), while in others the full term is unnecessarily repeated (e.g., the definition of PM1, Line 142).

3. In Fig. 4a, please include labels to identify factors, such as HOA, LO-OOA1, etc., for improved readability.

Citation: https://doi.org/10.5194/egusphere-2025-2667-RC2
- AC2: 'Reply on RC2', Chenjie Yu, 07 Nov 2025
  
  We thank the reviewer for the valuable comments. Please find our detailed responses attached.
  
  Citation: https://doi.org/10.5194/egusphere-2025-2667-AC2

Peer review completion

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

AR by Chenjie Yu on behalf of the Authors (12 Nov 2025) Author's response Author's tracked changes Manuscript

ED: Referee Nomination & Report Request started (29 Nov 2025) by Roya Bahreini

RR by Anonymous Referee #1 (11 Dec 2025)

Suggestions for revision or reasons for rejection

In their revised manuscript, all of my (Reviewer#1) previously raised specific comments and technical corrections were addressed. The authors justified the main concern of using ground observation below-canopy to characterize aerosol properties influenced by regional transport, providing evidence that localized plumes had only a minor effect under a well-mixed canopy condition during the daytime. The added information solidified the methodology and enabled the subsequent evaluation of the results and conclusions, which is the focus of this review.

Specific comments:
1. I have concerns in the conclusion that rapid formation of MO-OOA contributed to the BrC increase during the daytime. We know from your introduction that 1) nitrogenous OA can contribute to the light absorption by BrC during aging, and 2) the bleaching of BrC through intense photooxidation processes is widely observed. In your results, for the strong solar radiation scenario, were you able to see any diurnal pattern of BrC vs. O:C ratio and f44? Did the absorption keep climbing with f44 throughout the day, or did they decouple later in the day? If your “MO-OOA” factor had high N/C or strong nitrate-related fragments, it might really be a secondary BrC factor that happened to be categorized into MO-OOA. I am not entirely confident with the generic conclusion that “the rapid formation of MO-OOA contributed to the BrC increase during the daytime” without having the above concerns reviewed/addressed.

In addition, I also did not see any description of BrC mass change under humid, low-radiation conditions as the contrast group. Note that your LO-OOA3 has the highest N/C among your factors. Yet your daytime photochemical indicators seemed to have a negative influence on it (e.g. Line 330-ish). Is there any discussion that could be expanded on this point? Why did LO-OOA3 get “effectively degraded” while MO-OOA were “rapidly forming”? Are the oxidants really capable of distinguishing and treating them differently in the physical world? Have you considered the possibility that the formation of your N-rich MO-OOA is actually an oxidation/conversion from your N-rich LO-OOA3?

2. Around Line 305, I suggest toning down the “explanation” of nighttime peak of LO-OOA2 to monoterpene-SOA to more of an “implication” without quantitative evidence from PBL, nighttime O3, OH, and nitrate radical data integration. Something like “The factor exhibits a nocturnal maximum and correlates best with monoterpenes among measured VOCs, implying a strong contribution from monoterpene-derived biogenic SOA, likely enhanced at night by a shallow boundary layer, lower temperatures, and NO3-initiated oxidation.” In my opinion, that very low N:C of LO-OOA2 makes “classic NO3-initiated monoterpene-SOA” unlikely as the dominant component of your factor. If you’d like to dig deeper, does the NO⁺/NO₂⁺ ratio co-vary with the factor? Does the factor ever show higher N:C in periods with high NOx and O3 at night? It might not be a focus of your current study but rather an interesting point to look into for your in-preparation nighttime above- and below-canopy differences work.

3. Around Line 315, same issue as above, your LO-OOA3 diurnal trend is so gentle that could totally be due to the change in PBL. If PBL data are available, it is recommended that you normalize your diurnal profiles of OA factors by it. I also do not think r = 0.45 and 0.4 (not r-square) can suggest anything. Your chloride is so low that I guess for the majority of your campaign period, it might be below the detection limit (yours, not DeCarlo’s). Please add a column to Table S1 to report the average +/- std of your five main AMS species. They are hard to be perceived from Figure 2. As a result, I do not really consider the subsequent discussions about sea-salt chloride etc are valid and necessary. Please clarify or revise accordingly.

4. Starting from Line 380 to 397, I am unsure of the logic behind a decreased O:C during the NE due to additional LO-OOA formation. If what you explained here was true, then the new formation of LO-OOA needs to outpace the aging of MO-OOA, in order to “dilute the overall oxidation level and lower the average bulk O:C ratio.” It is contradictory to your other results, e.g. the rapid formation of MO-OOA during the NE period, and is not backed by Figure S11 (magnitude of MO-OOA increase > LO-OOA1 increase, and LO-OOA2 to 4 changed negligibly). However, if you’d like to have more recent supportive evidence for your OOA formation from photochemical processes in urban areas, you could refer to
-Guo et al., 2024 (https://doi.org/10.1016/j.atmosenv.2024.120362) in San Antonio
-Rogers et al., 2025 (https://doi.org/10.1029/2024GL112005) in New York City
They observed the positive influences of photochemistry on OOA mass with AMS/ACSM-PMF in the 7th and 1st largest cities in the U.S.

5. I suggest avoiding putting “emissions” to any SOA (e.g. “near-ground emissions of LO-OOA2 from local vegetation”), simply because any SOA by definition is secondary, not directly emitted from a specific source. It is a minor thing but really reads counterintuitive. Sanity checks over both the manuscript and SI would be appreciated.

Technical corrections:
1. Line 68, “...formed in the oxidation of VOCs in the…”
2. Line 83, “...previous AMS-PMF analysis during…”
3. SI, Line 69, “...observed at night till the early dawn…”
4. SI, Table S1, detection limits of your specific HR-AMS should be calculated from your in situ filtered periods not adopted from DeCarlo et al. (2006). According to your response, filtered periods were performed periodically daily. You can either lump them together for a set of campaign-average MDLs or estimating MDLs for individual periods at the beginning, middle, and the end of your campaign and report a set of averages.
5. Section 3.2, Line 282-290, multiple Figure numbering in this paragraph is inaccurate.
6. Figure 6, please consider adding mass concentration next to each percentage given the total OA mass of each pie is different.
7. Line 321, I guess you’re referring to Table S2?
8. Line 341, “The MO-OOA…”

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RR by Anonymous Referee #2 (16 Dec 2025)

Suggestions for revision or reasons for rejection

The authors have substantially revised the manuscript and have addressed most of my previous minor comments and technical corrections. However, the major limitation of this study is the sampling environment and associated methodology, which remains unresolved. As a result, the measurements do not fully support the conclusions presented, and I therefore do not recommend publication of this work.
Major Comment 1 (photochemical age):
A major limitation of the current methodology is that the photochemical age inferred from NOx/NOy is not well suited for a ground-based site where air masses change continuously. This approach is appropriate in studies such as Kleinman et al. (2007), Parrish et al. (2007), and Nault et al. (2018), which rely on aircraft measurements that more closely follow evolving air parcels. Hayes et al. (2013) applied a ground-based approach but explicitly evaluated the errors introduced when an air parcel continues to receive fresh emissions during transport to the measurement site.
In the present study, although the authors state that the sampled air is clean and well mixed, this does not resolve the core issue that the measurements do not track the same air parcel from emission to downwind. Reframing the metric as “relative anthropogenic photochemical age” does not address this limitation and instead obscures the importance of mixed anthropogenic and biogenic influences emphasized in the Introduction.
Major Comment 2 (dilution correction):
If OA and BC originate from the same source, the OA/BC ratio can be used to account for dilution as air parcels mix with cleaner background air. However, in the present study, the authors state that the sampled air masses are “enriched with anthropogenic primary carbonaceous aerosols,” yet OA is not necessarily co-emitted with BC and may include contributions from secondary and non-combustion sources. The use of the OA/BC ratio for dilution correction is therefore not well justified.

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ED: Reconsider after major revisions (31 Dec 2025) by Roya Bahreini

Dear Chenjie Yu and Vincent Michoud,

Thank you for having submitted your revised manuscript. I have reviewed your revisions, responses to the reviewers, and their additional assessment. Considering all the concerns raised, I cannot accept your manuscript for publication. I agree with the 2nd reviewer's comment that photochemical age calculations based on NOx/NOy does not work when anthropogenic and biogenic air masses are mixed in, which is the case at your sampling site. This underlines a lot of your analysis (e.g., as you try to investigate the evolution of different OA factors with age). If you are willing to use another approach to investigate air mass history and aging (e.g., transport time form the model?) and provide major revisions to address this deficiency, I am happy to consider the paper again. If you decide to resubmit with major revisions, please also address the remaining comments from both reviewers and my additional questions and concerns listed below.

I appreciate your effort in submitting your best work.
Roya Bahreini

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- It’s unclear to me why the SE sector has not been considered the same way in the analysis despite a large contribution to air masses sampled in the beginning and end of the campaign.
- L272: clarify as “a relative photochemical indicator”. Also in L360, change “absolute” to “relative anthropogenic photochemical age”
How does correlation of LO-OOA1 look against isoprene+MVK+MACR?
- LO-OOA2 is suggested to be related to SOA formation from monoterpenes. However, the correlation with toluene is also relatively good. Please comment.
- L322: The statement that marine aerosol only in the form of sea salt impacted the site and not MSA-driven organics doesn’t make sense.
- LO-OOA3 factor has relatively good negative correlation with biogenic tracers but its correlation with RH or Chl are not high to suggest any real relationship. I think the explanation of this factor needs to be revised. Also, the concentration of this factor goes up at night and that’s when, regardless of air mass origin, RH is higher, so there may not be necessarily a marine source for this factor.
- L376-377: incomplete sentence
- L388: it’s important to highlight again the with O/C of LO-OOA being less than that of the background aerosols, any significant addition of LO-OOA would lower the overall O/C and hence mask the increase with aging that is expected. Note: L390-397 is duplicating the paragraph above.
- What is the source of pON? Please include references where these species have been associated with primary emissions.
- Fig 8g: due to reactivity, toluene and benzene should decrease more than CO (which is reduced mainly due to dilution) with photochemical aging, so why isn’t this trend obvious here except maybe slightly for the NE corridor.
- FigS10: what’s the interpretation of much stronger deviation for LO-OOA2 between above/below canopy measurements? I think this helps explaining the source of LO-OOA2 and a discussion on this should be added to the main text.
- Table S3. What are the uncertainties in the fitted parameters representing? Was variability in the data used in any way to constrain the fitted parameters?
- Since there were many occasions of wildfires in the region, are there any tracers from PTR-MS that can be used to track the influence of BB plumes in the data? (e.g. acetonitrile)

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AR by Chenjie Yu on behalf of the Authors (19 Mar 2026) Author's response Author's tracked changes Manuscript

ED: Publish subject to minor revisions (review by editor) (04 Apr 2026) by Roya Bahreini

AR by Chenjie Yu on behalf of the Authors (05 Apr 2026) Author's response Author's tracked changes Manuscript

ED: Publish as is (08 Apr 2026) by Roya Bahreini

AR by Chenjie Yu on behalf of the Authors (13 Apr 2026)

Journal article(s) based on this preprint

21 Apr 2026

Impacts of summertime photochemical aging on the physicochemical properties of aerosols in a Paris suburban forest region

Atmos. Chem. Phys., 26, 5313–5332, https://doi.org/10.5194/acp-26-5313-2026,https://doi.org/10.5194/acp-26-5313-2026, 2026

Short summary

Supplement

https://doi.org/10.5194/egusphere-2025-2667-supplement

Data sets

ACROSS_IMTNE_RambForest_AMS_BelowCanopy_L2 J. Brito and V. Riffault https://doi.org/10.25326/492

ACROSS_MPIC_RambForest_NOx_1min_L2 J. Crowley https://doi.org/10.25326/687

ACROSS_LPC2E_Rambforest_OH_L2 A. Kukui https://doi.org/10.25326/510

ACROSS_LISA_RambForest_AETH-Abs_PM1_1-Min_L2-station_processed L. Di Antonio and C. Di Biagio https://doi.org/10.25326/669

ACROSS_2022_RambForest_LISA_PTRMS_VOCs_Belowcanopy_10min_20220617 - 20220723 V. Michoud and H. Bouzidi https://doi.org/10.25326/685

ACROSS_CNRM_RambForest_MTO-1MIN_L2 C. Denjean https://doi.org/10.25326/437

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We presented a field measurement in a Paris suburban forest region to characterise the impacts of photochemical aging process on aerosol physical chemical properties. Photochemical production of organic aerosols increased forest fine particle mass and significantly enhanced absorption of short-wavelength sunlight. This study highlights the critical need to incorporate light absorbing carbonaceous particles formation mechanisms into models to accurately simulate their direct radiative impacts.


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