Meteorology impact on PM<sub>2.5</sub> change over a receptor region in the regional transport of air pollutants: observational study of recent emission reductions in central China

Sun, Xiaoyun; Zhao, Tianliang; Bai, Yongqing; Kong, Shaofei; Zheng, Huang; Hu, Weiyang; Ma, Xiaodan; Xiong, Jie

doi:https://doi.org/10.5194/acp-22-3579-2022

Articles | Volume 22, issue 5

https://doi.org/10.5194/acp-22-3579-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/acp-22-3579-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 22, issue 5

Research article

|

17 Mar 2022

Research article |

| 17 Mar 2022

Meteorology impact on PM_2.5 change over a receptor region in the regional transport of air pollutants: observational study of recent emission reductions in central China

Xiaoyun Sun, Tianliang Zhao, Yongqing Bai, Shaofei Kong, Huang Zheng, Weiyang Hu, Xiaodan Ma, and Jie Xiong

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Final revised paper (published on 17 Mar 2022)
Supplement to the final revised paper
Preprint (discussion started on 30 Sep 2021)
Supplement to the preprint

Interactive discussion

Status: closed

RC1:
'Comment on acp-2021-709', Anonymous Referee #1, 06 Nov 2021

The manuscript by Sun et al. analyzes the impact of meteorological factors on the changes in PM2.5 concentration in Twain-Hu Basin, China using a Kolmogorov-Zurbenko filter. They conclude that interannual and seasonal meteorology have the largest impacts on the changes in PM2.5. However, the method used in this work is not validated using synthetic data or with other methods, so the accuracy of the results is doubtful. In addition, this work focuses on a very small region, and the results may not have broader implications for national - global air pollution issues and may not fit the scope of the general ACP readership.

Abstract, please clarify why THB is selected as the studied region in this work.

L99-100, please clarify what the numbers in the subscript of KZ stand for and why using 1.7 years here.

L148-154, it is not clear to me how the authors verified this approach. How are the results compared to analyses using other methods? Could synthetic data be generated to test this approach?

Section 3.1, how are the results compared to other studies?

L196, what are the relative contributions of emissions and meteorology to the long-term changes in PM2.5 based on the analyses here?

L217-218, this can be testified by checking the trend of SO2 emissions in this region from the emission estimate. Do the emissions support your explanations here?

Citation: https://doi.org/10.5194/acp-2021-709-RC1
- AC1: 'Reply on RC1', Tianliang Zhao, 18 Jan 2022
  
  Dear Editors and Referees:
  
  Thank you very much for your careful review and constructive comments on our manuscript acp-2021-709. These comments are valuable and helpful for revising and improving our paper, as well as the important guiding significance to our research. We have accordingly made the substantial revisions. The point-to-point responses to reviewers’ comments are in supplement.
  
  Citation: https://doi.org/10.5194/acp-2021-709-AC1
RC2:
'Comment on acp-2021-709', Anonymous Referee #3, 08 Dec 2021

This study investigates the relative contribution from meteorological effect and emission changes to PM2.5 variation over the Twain-Hu Basin (THB) based on the Kolmogorov–Zurbenko (KZ) filtering of long-term air quality measurement data. It is indicated that the reduction in anthropogenic emissions was the primary cause for the long-term decline in PM2.5 concentrations and the meteorological changes moderated the PM2.5 variations in the THB. However, in terms of novelty and broad interest, this work still needs to be improved. Besides, there could be great uncertainties associated with the multiple linear regression and KZ filtering method, but the authors have not validated the method and touched on the uncertainties in the conclusion. Here list some of my main concerns.

There are many parameters used in KZ filtering and multiple linear regression. The justification and validation of the selection of them should be provided. I think the changes in data coverage or the parameter selection would largely influence the final quantitative estimation of contributions, which is suggested to be elaborated.

Another issue is the estimation of the effects of NO2 and SO2 emission reductions on PM2.5 change trends based on long-term (ðLT ) and emission-related long-term (ðemiss ) components of PM2.5, SO2 and NO2. The long-term changes in PM2.5 are also caused by the emission variation of primary components like black and organic carbon, in addition to the chemical transformation of gaseous precursors. The difference in the emission of different primary pollutants may also lead to modifications in Klt/Kemis of PM2.5. How was this impact/bias included and quantified in the present work?

Minor comments

Figure 9: Why did the contribution rates of meteorological variations show great spatial disparities at a small scale, i.e., EZ, HG and HS. It seems not very likely that the variation in synoptic weather or meteorological conditions has such a large heterogeneity at such a small spatial scale.

Citation: https://doi.org/10.5194/acp-2021-709-RC2
- AC2: 'Reply on RC2', Tianliang Zhao, 18 Jan 2022
  
  Dear Editors and Referees:
  Thank you very much for your careful review and constructive comments on our manuscript acp-2021-709. These comments are valuable and helpful for revising and improving our paper, as well as the important guiding significance to our research. We have accordingly made the substantial revisions. The point-to-point responses to reviewers’ comments are in supplement.
  
  Citation: https://doi.org/10.5194/acp-2021-709-AC2

Peer review completion

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

AR by Tianliang Zhao on behalf of the Authors (19 Jan 2022) Author's response Author's tracked changes Manuscript

ED: Referee Nomination & Report Request started (19 Jan 2022) by Leiming Zhang

RR by Anonymous Referee #3 (28 Jan 2022)

ED: Publish subject to minor revisions (review by editor) (07 Feb 2022) by Leiming Zhang

AR by Tianliang Zhao on behalf of the Authors (11 Feb 2022) Author's response Author's tracked changes Manuscript

ED: Publish as is (14 Feb 2022) by Leiming Zhang

AR by Tianliang Zhao on behalf of the Authors (16 Feb 2022) Manuscript

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Short summary

This study revealed the impact of anthropogenic emissions and meteorological conditions on PM2.5 decline in the regional transport of air pollutants over a receptor region in central China. The meteorological drivers led to upwind accelerating and downward offsetting of the effects of emission reductions over the receptor region in regional PM2.5 transport, and the contribution of gaseous precursor emissions to PM2.5 pollution was enhanced with reduced anthropogenic emissions in recent years.

Meteorology impact on PM2.5 change over a receptor region in the regional transport of air pollutants: observational study of recent emission reductions in central China

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Interactive discussion

Peer review completion

Meteorology impact on PM_2.5 change over a receptor region in the regional transport of air pollutants: observational study of recent emission reductions in central China