Trends in secondary inorganic aerosol  pollution in China and its responses to  emission controls of precursors in wintertime

Meng, Fanlei; Zhang, Yibo; Kang, Jiahui; Heal, Mathew R.; Reis, Stefan; Wang, Mengru; Liu, Lei; Wang, Kai; Yu, Shaocai; Li, Pengfei; Wei, Jing; Hou, Yong; Zhang, Ying; Liu, Xuejun; Cui, Zhenling; Xu, Wen; Zhang, Fusuo

doi:https://doi.org/10.5194/acp-22-6291-2022

Articles | Volume 22, issue 9

https://doi.org/10.5194/acp-22-6291-2022

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

https://doi.org/10.5194/acp-22-6291-2022

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

Articles | Volume 22, issue 9

Research article

|

16 May 2022

Research article |

| 16 May 2022

Trends in secondary inorganic aerosol pollution in China and its responses to emission controls of precursors in wintertime

Fanlei Meng, Yibo Zhang, Jiahui Kang, Mathew R. Heal, Stefan Reis, Mengru Wang, Lei Liu, Kai Wang, Shaocai Yu, Pengfei Li, Jing Wei, Yong Hou, Ying Zhang, Xuejun Liu, Zhenling Cui, Wen Xu, and Fusuo Zhang

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Final revised paper (published on 16 May 2022)
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Preprint (discussion started on 27 Oct 2021)
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Interactive discussion

Status: closed

RC1:
'Comment on acp-2021-716', Anonymous Referee #1, 23 Nov 2021

The study examined annual trends in PM_2.5 chemical components based on a meta-analysis and the efficiencies of NH₃ and acid gas emission reductions on PM_2.5 mitigation. The authors also looked at hazy vs non-hazy days yet the abstract doesn’t mention them – could this be addressed?

The CMAQ model run undertakes a 50% reduction in NH3 but only for January – very little comment is made of why this month was chosen and how this relates to an annual average. Comment on whether 50% reduction is a realistic target for the Chinese Government.

The authors spend a lot of time undertaking a meta analysis of the literature in order to put a database of secondary PM measurements together and this seems to have been done thoroughly, although I am not suitably familiar enough with the methods to comment further.

I don’t think the CMAQ model has been evaluated for Jan 2010 using measurements of PM or PM components, although there was some evaluation of met. parameters - temperature looked good RH and especially Wind Speed were quite poor (Fig s4) – note R was 0.5 on the wind speed graph but 0.64 in the text?. There was a comparison between the CMAQ and STET model (defined as ‘observations’) but these were just two maps side by side. I’m not sure whether the STET model comparison is for the same period.

No evaluation of CMAQ modelled components was made either, which makes one wonder whether it did predict well in Jan 2010. Without this the conclusions are weakened somewhat. I think to have more confidence in the results more should be made of the evaluation against PM2.5 and if possible PM components.

It would have been useful for the authors to undertake a comparison of the CMAQ model predictions, associated with changing COVID emissions, and the actual measured changes.

The measurements of PM2.5 were taken using TEOM’s although no mention was made of the associated problems under reading PM associated with nitrate and operational temperature, which common to these instruments. This is especially important since the paper focuses on SIA

Results

As a general comment a lot of analysis has been made between Hazy and non-Hazy days, but the conclusions and abstract don’t seem to reflect this.

For the trend analysis (fig 2) suggests a 19% reduction of PM2.5 between period 1 and 3 on non-hazy days although all of the box plots are for different numbers of sites and so it would be hard to say whether this is true? also the concentrations seemed to increase in period 2? Are these trends significant?

Since the measurements are combined into periods the true trends are difficult to interpret. I think a description of a PM2.5 timeseries for a site throughout the period would be beneficial. With some comment on things like seasonality and reasons for the measurement trends. Most trends are ascribed to Government policy, although with the changes that have taken place in China, this may well be too simple

The authors mention the results in Fig 2a (page 11) and b,c,d, (page 16) which makes it hard for the reader. Consider revising the diagrams.

The authors spend quite a long time stating that PM2.5 on hazy days is greater than on non-hazy days which seems fairly obvious given that the meta analysis chose data in this way.

It says that SIA is a major influencing factor for haze pollution, yet in Fig 4 B (b) the proportion of total PM2.5 is about the same as non Hazy day 40% vs 36% respectively, suggesting that SIA goes up but so do other components of PM.

There is very little mention of the other components of PM2.5, OC,EC and the ‘other’ components, all of which are important – plus no model evaluation of these.

I hope these comments are useful

Citation: https://doi.org/10.5194/acp-2021-716-RC1
- AC1: 'Reply on RC1', Wen Xu, 15 Mar 2022
  
  The comment was uploaded in the form of a supplement: https://acp.copernicus.org/preprints/acp-2021-716/acp-2021-716-AC1-supplement.pdf
  
  Citation: https://doi.org/10.5194/acp-2021-716-AC1
RC2:
'Comment on acp-2021-716', Anonymous Referee #2, 13 Dec 2021

The comment was uploaded in the form of a supplement: https://acp.copernicus.org/preprints/acp-2021-716/acp-2021-716-RC2-supplement.pdf

Citation: https://doi.org/10.5194/acp-2021-716-RC2
- AC2: 'Reply on RC2', Wen Xu, 15 Mar 2022
  
  The comment was uploaded in the form of a supplement: https://acp.copernicus.org/preprints/acp-2021-716/acp-2021-716-AC2-supplement.pdf
  
  Citation: https://doi.org/10.5194/acp-2021-716-AC2

Peer review completion

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

AR by Wen Xu on behalf of the Authors (15 Mar 2022) Author's response

EF by Sarah Buchmann (16 Mar 2022) Manuscript

EF by Sarah Buchmann (16 Mar 2022) Author's tracked changes

ED: Referee Nomination & Report Request started (16 Mar 2022) by James Allan

RR by Anonymous Referee #3 (29 Mar 2022)

Suggestions for revision or reasons for rejection

The author analyzed the long-term trends of PM2.5 chemical components and their drivers using numerical models. Some issues still need to be addressed although the author have made great efforts according to previous comments. I suggest major revision for the manuscript prior to be finally published in ACP.

1. In the introduction, the author spent lots of length to describe the air pollutants control measures for SO2, NOx and NH3 during different stages in the past decade. Since the aims of this manuscript is to evaluate the changes of SIA as responses to stringent measures, more results of SIA variations should be cited and summarized. Indeed, extensive researches have reported on this topic. Also, the author should compare the trends in PM2.5 and components in this paper with previous studies.
2. Considering that the author divided 2000-2019 into three periods, e.g., period I (2000-2012), period II (2013-2016) and period III (2017-2019), annual trend used in the analysis might not be appropriate. Annual trend often refers to year-to-year variations. Measurements during three periods covered different seasons (winter, summer etc.) and sites (urban, suburban or rural), these actually influenced the conclusions because it’s well known that more polluted air quality frequently occurred during wintertime in urban site. The author used PM2.5 at a long-term monitoring site during 2012-2020 to verify the decreasing trend summarized from meta-analysis. I supposed that this evidence could only support that the decreasing trend was reliable. The quantitative results, e.g., decreased by 8.2% from period I to period III, was still to be evaluated. It is a bit confused that the author collected publications covering four-season measurements to summary the trends from period I to period II, however, only January was chosen to do simulations. The author explained that severe haze pollution often occurred in January. The effectiveness of precursors controlling measures could be season-dependent. That’s another uncertainty for this study.
3. The author attributed all the variations of PM2.5 and chemical components to changes of gaseous precursors resulting from control measures. The reasons were somewhat pale and inadequate. In fact, the responses of SIA to precursors were complex and sometimes non-linear. Previous studies have concluded that enhanced atmospheric oxidation capacity, faster deposition of total inorganic nitrate and the changes of atmospheric circulation could be possible drivers. That’s likely why PM2.5 showed no significant trend from period I to period II despite control measures were implemented since 2013. Please cite more relevant publications, and then rephrase and expand the related explanations throughout the study. In current revised manuscript, the results and discussion were flat.
4. For model simulations, the author fixed meteorology in 2020 to exclude the impacts of meteorology. Thus, the CMAQ simulation before and during the COVID-lockdown didn’t represent the actual results during these periods. In Figure S6, the author compared the CMAQ results with ground observations for PM2.5, SO2 and NO2. This is not reasonable. The author should firstly do simulations using real meteorology to evaluate the performance of CMAQ model, and then do controlled experiments using fix meteorology. Also for Figure S3-S5, the author only assessed the simulation results in January 2010 with observations. Indeed, they should do these year by year using real modelling results.
5. The author compared the model results between 50% reductions in NH3 emissions and 50% reductions in acid gases, concluding that reducing acid gases is more effective. Did the author do sensitivity cases with reductions of 50% NH3 and 50% acid gases, which might be more close to the facts. Another issue is quantifying how much the precursors should be decreased to fulfill air quality targets.
6. In Figure 4, all measurements were averaged to derive the two pie charts. The author only filtered the data for meta-analysis using the measurements at sites that include both PM2.5 and SIA, we noticed that many of these re-filtered measurements didn’t include mental species (Na+, Mg2+, Ca2+, F-), which called “Other”, accounting for 36.8-37.4% during non-haze and haze days. The inconsistency among measurements used for averaging species caused large uncertainties to the conclusions. Studies simultaneously measured all species could be more reliable and scientific, at less for the pie charts.
7. The author concluded that increased SIA formation is the major driving factor for haze pollution, which was obviously true consistent with previous studies. Due to the limitations of collecting datasets from publications instead of long-term filed measurements, the contribution of SIA slightly increased from 36% during non-haze days to 40% during haze days. The concentrations of SIA and other PM2.5 components synchronously increased from non-haze to haze days. Thus, it is not appropriate and convincing to draw this conclusion solely based on this study.
8. The first reviewer mentioned that the results in Figure 2a and b,c,d crossed several pages, and the interruption makes it hard to read. In the response, the author only added more detail figure caption to Figure 2. Indeed, the reviewer suggested to recombine the figures, rephrase the sentences or rearrange the paragraphs, making them more coherent in the context.
9. In Figure 7, S3, S4 and S7, the south China Sea were missed in maps. This is really less rigorous.
10. The author added more citations in the revised manuscript, which were not shown in the Reference.

Referee Report: PDF

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RR by Anonymous Referee #4 (04 Apr 2022)

ED: Publish subject to minor revisions (review by editor) (09 Apr 2022) by James Allan

AR by Wen Xu on behalf of the Authors (19 Apr 2022) Author's response Author's tracked changes Manuscript

ED: Publish as is (22 Apr 2022) by James Allan

AR by Wen Xu on behalf of the Authors (25 Apr 2022) Manuscript

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Short summary

PM_2.5 pollution is a pressing environmental issue threatening human health and food security globally. We combined a meta-analysis of nationwide measurements and air quality modeling to identify efficiency gains by striking a balance between controlling NH₃ and acid gas emissions. Persistent secondary inorganic aerosol pollution in China is limited by acid gas emissions, while an additional control on NH₃ emissions would become more important as reductions in SO₂ and NO_x emissions progress.