Assessment of dicarbonyl contributions to secondary organic aerosols over China using RAMS-CMAQ

Li, Jialin; Zhang, Meigen; Tang, Guiqian; Sun, Yele; Wu, Fangkun; Xu, Yongfu

doi:https://doi.org/10.5194/acp-19-6481-2019

Articles | Volume 19, issue 9

https://doi.org/10.5194/acp-19-6481-2019

© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/acp-19-6481-2019

© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 19, issue 9

Research article

|

16 May 2019

Research article |

| 16 May 2019

Assessment of dicarbonyl contributions to secondary organic aerosols over China using RAMS-CMAQ

Jialin Li, Meigen Zhang, Guiqian Tang, Yele Sun, Fangkun Wu, and Yongfu Xu

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Final revised paper (published on 16 May 2019)
Supplement to the final revised paper
Preprint (discussion started on 12 Nov 2018)
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Interactive discussion

Status: closed

AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment

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- Supplement

RC1: 'Reveiw of Li et al. 2018', Anonymous Referee #1, 08 Dec 2018
RC2: 'Comments on Li et al. 2018', Anonymous Referee #2, 10 Dec 2018
- AC2: 'Response to RC2', Meigen Zhang, 03 Jan 2019
AC1: 'Response to RC1', Meigen Zhang, 03 Jan 2019

Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision

AR by Meigen Zhang on behalf of the Authors (09 Jan 2019) Author's response Manuscript

ED: Referee Nomination & Report Request started (12 Jan 2019) by Xiaohong Liu

RR by Anonymous Referee #1 (24 Jan 2019)

RR by Anonymous Referee #3 (22 Feb 2019)

Suggestions for revision or reasons for rejection

SOA remains one of the largest uncertainties in understanding atmospheric aerosols. Any new observational, laboratory, and modeling work to improve SOA understanding will benefit the aerosol community. Therefore, I recommend publication after the authors adequately address the following comments.

1) Section 3.1 (model evaluation): it is not clear to me which model configuration was evaluated. Is it the default CMAQ aerosol scheme or the one with implementation of pathway M with increased emissions and glyoxal yield? Please clarify this. In addition, is there any specific reason why different statistical measures (e.g., MB and RMSE for meteorology but FB and FE for PM2.5) were used to describe different modeled variables?

2) Comparison of modeled and MODIS measured CWP shows large difference in both quantity and location (Fig. 2). The authors simply stated that this was due to “the large uncertainties in both the model simulations and the satellite retrievals of clouds”. This gives little useful information to both modeling and retrieval communities. I suggest to make a difference (modeled – observed) map and carry out a in depth analysis on regions with the largest discrepancy, e.g., linkages to precipitation, cloud properties, etc.

3) Since default CMAQ AERO5 contains in-cloud oxidation of glyoxal and methylglyoxal, a brief description of difference between pathway M and default aqueous SOA formation is helpful.

4) Section 3.2 (the results shown in Fig. 3): it is not clear to me which model configuration was used for this analysis. Is the default CMAQ with increased emissions or CMAQ with implementation of pathway M and increased emissions?

5) Fig. 4 Suggest removing the right Y-axis and change the title of left Y-axis to SOA (ug/m^3).

6) Line 321: What is ASQT? It is not explained as well in Fig. 5.

7) Lines 329~336 According to the model results, AAQ contributed to more than 50% of total SOA, well beyond the combined contributions from other sources. Why did the authors single out ATOL + AXYL for comparison?

8) Line 334~335 Based on Table 2, AAQ (4.17 ug/m^3) contributes 63.9% (instead of 64.5%) to the total MODELED SOA (6.53 ug/m^3) in episode 2. Unless I misunderstood anything, please change the number (here and in conclusion) and explicitly states that it is to the total modeled SOA.

9) It appears that the increase of emissions of aromatic compounds and glyoxal yield from isoprene play a larger role in bringing up SOA. It would be helpful and informative to see if the implementation of such increase to the default CMAQ AERO5 would improve the model performance.

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ED: Publish subject to minor revisions (review by editor) (11 Mar 2019) by Xiaohong Liu

AR by Meigen Zhang on behalf of the Authors (20 Mar 2019) Author's response Manuscript

ED: Publish as is (07 Apr 2019) by Xiaohong Liu

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Short summary

There are large uncertainties in the sources of secondary organic aerosol (SOA). Simulations of SOA concentrations in China with aqueous SOA formation pathway updated and glyoxal simulation improved reveal that dicarbonyl-derived SOA (AAQ) can explain a significant fraction of the unaccounted SOA sources. The mean AAQ can contribute 60.6 % and 64.5 % to the total concentration of SOA in summer and fall, respectively.