Aerosol Composition Trends during 2000&ndash;2020: In depth insights from model predictions and multiple worldwide observation datasets

Tsimpidi, Alexandra P.; Scholz, Susanne M. C.; Milousis, Alexandros; Mihalopoulos, Nikolaos; Karydis, Vlassis A.

doi:https://doi.org/10.5194/egusphere-2024-3590

Preprints

https://doi.org/10.5194/egusphere-2024-3590

Preprints

04 Dec 2024

| 04 Dec 2024

Aerosol Composition Trends during 2000–2020: In depth insights from model predictions and multiple worldwide observation datasets

Alexandra P. Tsimpidi, Susanne M. C. Scholz, Alexandros Milousis, Nikolaos Mihalopoulos, and Vlassis A. Karydis

Abstract. Atmospheric aerosols significantly impact Earth’s climate and air quality. In addition to their number and mass concentrations, their chemical composition influences their environmental and health effects. This study examines global trends in aerosol composition from 2000 to 2020, using the EMAC atmospheric chemistry-climate model and a variety of observational datasets. These include PM_2.5 data from regional networks and 744 PM₁ datasets from AMS field campaigns conducted at 169 sites worldwide. Results show that organic aerosol (OA) is the dominant fine aerosol component in all continental regions, particularly in areas with significant biomass burning and biogenic VOC emissions. EMAC effectively reproduces the prevalence of secondary OA but underestimates the aging of OA in some cases, revealing uncertainties in distinguishing fresh and aged SOA. While sulfate is a major aerosol component in filter-based observations, AMS and model results indicate nitrate predominates in Europe and Eastern Asia. Mineral dust also plays a critical role in specific regions, as highlighted by EMAC. The study identifies substantial declines in sulfate, nitrate, and ammonium concentrations in Europe and North America, attributed to emission controls, with varying accuracy in model predictions. In Eastern Asia, sulfate reductions due to SO₂ controls are partially captured by the model. OA trends differ between methodologies, with filter data showing slight decreases, while AMS data and model simulations suggest slight increases in PM₁ OA across Europe, North America, and Eastern Asia. This research underscores the need for integrating advanced models and diverse datasets to better understand aerosol trends and guide environmental policy.

Received: 17 Nov 2024 – Discussion started: 04 Dec 2024

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Journal article(s) based on this preprint

10 Sep 2025

Aerosol composition trends during 2000–2020: in-depth insights from model predictions and multiple worldwide near-surface observation datasets

Alexandra P. Tsimpidi, Susanne M. C. Scholz, Alexandros Milousis, Nikolaos Mihalopoulos, and Vlassis A. Karydis

Atmos. Chem. Phys., 25, 10183–10213, https://doi.org/10.5194/acp-25-10183-2025,https://doi.org/10.5194/acp-25-10183-2025, 2025

Short summary

Alexandra P. Tsimpidi, Susanne M. C. Scholz, Alexandros Milousis, Nikolaos Mihalopoulos, and Vlassis A. Karydis

Interactive discussion

Status: closed

RC1:
'Comment on egusphere-2024-3590', Anonymous Referee #1, 28 Jan 2025

The manuscript of Tsimpidi et al. “Aerosol Composition Trends during 2000 -2020: In depth insights from model predictions and multiple worldwide observation datasets” compares measured and modeled aerosol pollutants between 2000-2020 in a global scale. The paper includes a large experimental data set, and it examines extensively the pollutants trend for each area. This is an important study that should be published, after modifications.

Major comments:
My main concern is that due to the large amount of the compared data (time and space) the paper is quite long (50 pages without including references), and the reader gets tired fast. From my point of view, it is difficult to digest all this information. So, it should be somehow more concentrated and shortened. In addition, there is a lot of statistical information but in general there is little connection between all these results. Moreover, the reasons for any discrepancies between measurements and simulations should be discussed in more detail and propose modification/addition in the model to capture more accurately the measurements.
Section 4 (In depth model Evaluation) describes the comparison between measured and modeled mass concentrations. In each sub-section (4.1-4.4) the authors compare the average mass concentration (sulfate, nitrate, ammonium, OA, SOA and POA) from each campaign to the average mass concentration that the model predicts for the specific site and time. What is the time resolution of the EMAC? Hourly/daily/monthly? This should be explained in the text. The comparison between the model and the measurements should be made throughout the whole campaign and not taking on only one value from each campaign. This could be misleading for the model’s performance as for example some days (if the model resolution is every 24 hours) could be very badly simulated, leading to a high overall discrepancy.
On the contrary section 5 (Aerosol Trends) is much more meaningful for measurements and model comparison. The core of the results should be this part. Section 4 should be complementary to section 5, and I suggest that the authors should incorporate selected parts of section 4 to section 5 accordingly. The discussion should be done by area (i.e. Europe, N. America, E. Asia) so that the reader reads the “story” of each area.

Minor comments:
There are several mistakes using the words “best” and “worst” in the text. For example, lines, 618, 620, 649, 651, 810 etc. Please check the whole manuscript and make the appropriate changes. Also check the usage of the word “highest” (e.g., lines, 286, 297, 411 etc.).
Line 70: Please replace “form” with “formed”.
Line 228: “high OA fractions with regional means” please rephrase.
Line 414: PM_2.5 please use subscript for ‘2.5’.
Line 624: Please replace “underestimating” with “it underestimates”.
Line 626: Please add “it” after therefore.
Line 637: Please replace “show” with “shows”.
Line 653: Please replace “resolved” with “simulated”.
Line 678: Please delete “the”.
Line 681: Please replace “lie” with “lies”.
Line 713-714: “Ammonium tends to be overestimated during autumn and underestimated during the rest of the seasons; especially during the summer” Is there any explanation for this tendency?
Line 719: “While the good model performance” please rephrase.
Lines 727-728: “On the other hand, ammonium is overpredicted close to the deserts of Inland.
728 China (e.g., over Tibet) and over South Korea” Do you have any explanation about this behavior?
Line 750: “EMAC tends to overpredict some low OA concentrations measured by AMS” this sentence is not very clear, please rephrase.
Line 785: “.. evaporation of organic compounds upon emission…” So, vaporization is not considered by the model? Please explain.
Line 803: Please replace “are in a good” with “are in a good”.
Figure 19: There are no g and h subplots, so please make the appropriate changes in the figure caption.
Figure 21: There are no g and h subplots, so please make the appropriate changes in the figure caption.
Line 1065: PM₁ please use subscript for ‘1’.
Line 1080: Since there is little discussion about EC in the text, it should not be mentioned in the conclusions.
Figures 17, 20 and 22 should be moved to the SI, they are just the average of Figures 16, 18, 19 and 21 and they don’t add value to the paper.

Citation: https://doi.org/10.5194/egusphere-2024-3590-RC1
- AC1: 'Reply on RC1', Alexandra Tsimpidi, 22 Mar 2025
  
  The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-3590/egusphere-2024-3590-AC1-supplement.pdf
  
  Citation: https://doi.org/10.5194/egusphere-2024-3590-AC1
RC2:
'Comment on egusphere-2024-3590', Anonymous Referee #2, 29 Jan 2025
This manuscript describes the trends in global surface AMS/ACSM PM1 and PM2.5 measurements, along with a global model simulation (EMAC) for the first two decades of the 21^st century. The focus of the analysis is the aerosol composition and how it changes over time. The results are not particularly novel, but the analysis is comprehensive, the paper is clearly written, and I think this will be a useful reference for the community. I also found the discussion to be well-balanced and well-supported. I have a few comments that I believe should be addressed prior to publication. I also include a long list of more minor corrections/suggestions.

Two important aspects of terminology:
Surface: the manuscript focuses exclusively on surface concentrations. This should be made clear in the title, the abstract (line 17), and to varying degree throughout in the text.

Non-refractory PM: Throughout the manuscript, the aerosol speciation is given for “fine aerosol” but particularly when discussing the AMS/ACSM observations, what is reported is “fine non-refractory aerosol”. This should be added throughout (e.g.: line 20, line 263, 265, 272, 304, etc) so that the reader is clear that this is the total excluding BC, dust, SS, etc.

In Section 2: More detail is needed on the measurement uncertainties, detection limits, collection efficiency (in the case of the AMS/ACSM) and the variability in instrument or operation. All of these could be relevant to the comparison across observations and with the model.

Section 5: The model trends, which in many cases do not match the observations, are largely be driven by emissions. Thus, a more detailed discussion (and plots) of the trends in emissions are needed. Can the authors compare the trends in emissions with other inventories? To what degree are anthropogenic emissions consistent with CEDS or regional inventories? The paper is generally lacking substance on the “why” for model failures and a more thorough discussion and comparison of emissions could provide much needed insight here.

Minor Comments
Line 29: “with varying accuracy in model predictions” is rather vague. It would be useful to provide more concrete conclusions in the abstract. For example, it’s clear that the model underestimates the trends in inorganics in North America and Europe.

Line 66: SO2 also comes from DMS

Line 67-68: This describes the source of HNO3, but given that not all HNO3 forms particulate nitrate, there should be some mention here of the role of thermodynamic partitioning

Line 72: Fires and VCPs are also sources of VOCs

Lines 73-103: This paragraph is rather uneven. Details on policy and trends are provided for Europe, but no trends are given for North America or Asia. Can more detail on these regional trends be added? It would also be useful to insert a couple of sentences on the policy or trends context for the rest of the world.

Lines 115-129: I would suggest that the authors might want to invert the paragraph to start with the observations to be consistent with the flow of the manuscript.

Section 2: I believe that the authors are using dry PM1 and PM2.5 throughout (both in the model and observations) – this should be specified in this section.

Section 3: How is the model sampled for the comparison with observations that follow? Daily averages, 3-day averages where relevant, etc.?

Line 182: in principle POA can be “fresh” (lower O:C) or “aged” (higher O:C). I believe that aged POA is characterized as SOA in EMAC and as OOA in the observations, which is perhaps why the authors are focusing on “fresh” POA here, but the sentence should be clarified. It would also be worth clarifying that you mean only fresh POA on line 777.

Line 259: suggest text be modified to read “well represented at one site over Africa”

Figure 4: can the authors use colours to show which regions in Figure 3 correspond to the regions in the barplot?

Figure 4: what fraction of the data falls below the detection limits?

Lines 279-284: It is odd that the authors here focus on the sulfate instead of the nitrate. The sulfate concentrations are similar in Europe and North America, it is the nitrate that is considerably lower in NA. I recommend that the authors alter the text to focus on the “surprisingly low nitrate in North America”, and also ask whether this is surprising or rather consistent with the NOx emissions over Europe vs North America

Line 380: how many SPARTAN sites are used here?

Line 395: The text refers to OA, but Figure 7 shows OC. Please be consistent and if/when OA is used, describe the application of the OM:OC.

Line 418: what about crustal? It appears to be quite important in the Middle East and Africa so it seems odd that the other species add up to 100%

Line 511: which AVOC and BVOC species are included as SOA precursors?

Line 515-516: does this imply that there is no loss of carbon to the gas phase via fragmentation (i.e. HCHO)?

Section 3.4: It would be helpful to specify which (all?) emissions inventories are year-varying

Figure 9: The shades of green are quite difficult to distinguish; I suggest using more distinct shades. Also: rainbow colour bars are to be avoided, so I would strongly suggest using a different colour bar for the map (https://thenextweb.com/news/stop-using-rainbow-maps-doesnt-do-data-justice-syndication)

Lines 606-607: Why not include total dust?

Section 4: It would be helpful if the statistics include R2 to summarize the model skill in capturing the variability. This could be discussed in the text and should be included in all the tables.

Scatterplots (Figures 10, 11, 12, 13): Most of these are difficult to see (lots of wasted white space). The log-log axes are not very helpful. I would strongly recommend that the authors consider using linear scales.

Line 765: Replace “every” with “many”. The Tsigaridis et al. 2014 comparison only shows NMB, and is therefore not very relevant for showing “high concentrations”. This reference is also over 10 years old now, so it may be too strong to say that all models still perform similarly.

Figure 15: Are these differences statistically significant?

Figure 15: Could you also add BC, dust, and SS?

Figure 17: The authors might consider annotating the x-axis. There is a lot of detail in the caption that the reader has to hunt for.

Lines 1061-1063: Given that model does not reproduce the observed trends in many species, regions, it would not be a very faithful representation of the “global trends in atmospheric aerosol composition”. I suggest that the authors modify this paragraph to focus on the observed trends and then how these trends were used to test the model simulation.

Lines 1085-1086: What about dust?

Line 1107: Please correct the text: the model does not show “a major decline”
Citation: https://doi.org/10.5194/egusphere-2024-3590-RC2
- AC2: 'Reply on RC2', Alexandra Tsimpidi, 22 Mar 2025
  
  The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-3590/egusphere-2024-3590-AC2-supplement.pdf
  
  Citation: https://doi.org/10.5194/egusphere-2024-3590-AC2

Interactive discussion

Status: closed

RC1:
'Comment on egusphere-2024-3590', Anonymous Referee #1, 28 Jan 2025

The manuscript of Tsimpidi et al. “Aerosol Composition Trends during 2000 -2020: In depth insights from model predictions and multiple worldwide observation datasets” compares measured and modeled aerosol pollutants between 2000-2020 in a global scale. The paper includes a large experimental data set, and it examines extensively the pollutants trend for each area. This is an important study that should be published, after modifications.

Major comments:
My main concern is that due to the large amount of the compared data (time and space) the paper is quite long (50 pages without including references), and the reader gets tired fast. From my point of view, it is difficult to digest all this information. So, it should be somehow more concentrated and shortened. In addition, there is a lot of statistical information but in general there is little connection between all these results. Moreover, the reasons for any discrepancies between measurements and simulations should be discussed in more detail and propose modification/addition in the model to capture more accurately the measurements.
Section 4 (In depth model Evaluation) describes the comparison between measured and modeled mass concentrations. In each sub-section (4.1-4.4) the authors compare the average mass concentration (sulfate, nitrate, ammonium, OA, SOA and POA) from each campaign to the average mass concentration that the model predicts for the specific site and time. What is the time resolution of the EMAC? Hourly/daily/monthly? This should be explained in the text. The comparison between the model and the measurements should be made throughout the whole campaign and not taking on only one value from each campaign. This could be misleading for the model’s performance as for example some days (if the model resolution is every 24 hours) could be very badly simulated, leading to a high overall discrepancy.
On the contrary section 5 (Aerosol Trends) is much more meaningful for measurements and model comparison. The core of the results should be this part. Section 4 should be complementary to section 5, and I suggest that the authors should incorporate selected parts of section 4 to section 5 accordingly. The discussion should be done by area (i.e. Europe, N. America, E. Asia) so that the reader reads the “story” of each area.

Minor comments:
There are several mistakes using the words “best” and “worst” in the text. For example, lines, 618, 620, 649, 651, 810 etc. Please check the whole manuscript and make the appropriate changes. Also check the usage of the word “highest” (e.g., lines, 286, 297, 411 etc.).
Line 70: Please replace “form” with “formed”.
Line 228: “high OA fractions with regional means” please rephrase.
Line 414: PM_2.5 please use subscript for ‘2.5’.
Line 624: Please replace “underestimating” with “it underestimates”.
Line 626: Please add “it” after therefore.
Line 637: Please replace “show” with “shows”.
Line 653: Please replace “resolved” with “simulated”.
Line 678: Please delete “the”.
Line 681: Please replace “lie” with “lies”.
Line 713-714: “Ammonium tends to be overestimated during autumn and underestimated during the rest of the seasons; especially during the summer” Is there any explanation for this tendency?
Line 719: “While the good model performance” please rephrase.
Lines 727-728: “On the other hand, ammonium is overpredicted close to the deserts of Inland.
728 China (e.g., over Tibet) and over South Korea” Do you have any explanation about this behavior?
Line 750: “EMAC tends to overpredict some low OA concentrations measured by AMS” this sentence is not very clear, please rephrase.
Line 785: “.. evaporation of organic compounds upon emission…” So, vaporization is not considered by the model? Please explain.
Line 803: Please replace “are in a good” with “are in a good”.
Figure 19: There are no g and h subplots, so please make the appropriate changes in the figure caption.
Figure 21: There are no g and h subplots, so please make the appropriate changes in the figure caption.
Line 1065: PM₁ please use subscript for ‘1’.
Line 1080: Since there is little discussion about EC in the text, it should not be mentioned in the conclusions.
Figures 17, 20 and 22 should be moved to the SI, they are just the average of Figures 16, 18, 19 and 21 and they don’t add value to the paper.

Citation: https://doi.org/10.5194/egusphere-2024-3590-RC1
- AC1: 'Reply on RC1', Alexandra Tsimpidi, 22 Mar 2025
  
  The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-3590/egusphere-2024-3590-AC1-supplement.pdf
  
  Citation: https://doi.org/10.5194/egusphere-2024-3590-AC1
RC2:
'Comment on egusphere-2024-3590', Anonymous Referee #2, 29 Jan 2025
This manuscript describes the trends in global surface AMS/ACSM PM1 and PM2.5 measurements, along with a global model simulation (EMAC) for the first two decades of the 21^st century. The focus of the analysis is the aerosol composition and how it changes over time. The results are not particularly novel, but the analysis is comprehensive, the paper is clearly written, and I think this will be a useful reference for the community. I also found the discussion to be well-balanced and well-supported. I have a few comments that I believe should be addressed prior to publication. I also include a long list of more minor corrections/suggestions.

Two important aspects of terminology:
Surface: the manuscript focuses exclusively on surface concentrations. This should be made clear in the title, the abstract (line 17), and to varying degree throughout in the text.

Non-refractory PM: Throughout the manuscript, the aerosol speciation is given for “fine aerosol” but particularly when discussing the AMS/ACSM observations, what is reported is “fine non-refractory aerosol”. This should be added throughout (e.g.: line 20, line 263, 265, 272, 304, etc) so that the reader is clear that this is the total excluding BC, dust, SS, etc.

In Section 2: More detail is needed on the measurement uncertainties, detection limits, collection efficiency (in the case of the AMS/ACSM) and the variability in instrument or operation. All of these could be relevant to the comparison across observations and with the model.

Section 5: The model trends, which in many cases do not match the observations, are largely be driven by emissions. Thus, a more detailed discussion (and plots) of the trends in emissions are needed. Can the authors compare the trends in emissions with other inventories? To what degree are anthropogenic emissions consistent with CEDS or regional inventories? The paper is generally lacking substance on the “why” for model failures and a more thorough discussion and comparison of emissions could provide much needed insight here.

Minor Comments
Line 29: “with varying accuracy in model predictions” is rather vague. It would be useful to provide more concrete conclusions in the abstract. For example, it’s clear that the model underestimates the trends in inorganics in North America and Europe.

Line 66: SO2 also comes from DMS

Line 67-68: This describes the source of HNO3, but given that not all HNO3 forms particulate nitrate, there should be some mention here of the role of thermodynamic partitioning

Line 72: Fires and VCPs are also sources of VOCs

Lines 73-103: This paragraph is rather uneven. Details on policy and trends are provided for Europe, but no trends are given for North America or Asia. Can more detail on these regional trends be added? It would also be useful to insert a couple of sentences on the policy or trends context for the rest of the world.

Lines 115-129: I would suggest that the authors might want to invert the paragraph to start with the observations to be consistent with the flow of the manuscript.

Section 2: I believe that the authors are using dry PM1 and PM2.5 throughout (both in the model and observations) – this should be specified in this section.

Section 3: How is the model sampled for the comparison with observations that follow? Daily averages, 3-day averages where relevant, etc.?

Line 182: in principle POA can be “fresh” (lower O:C) or “aged” (higher O:C). I believe that aged POA is characterized as SOA in EMAC and as OOA in the observations, which is perhaps why the authors are focusing on “fresh” POA here, but the sentence should be clarified. It would also be worth clarifying that you mean only fresh POA on line 777.

Line 259: suggest text be modified to read “well represented at one site over Africa”

Figure 4: can the authors use colours to show which regions in Figure 3 correspond to the regions in the barplot?

Figure 4: what fraction of the data falls below the detection limits?

Lines 279-284: It is odd that the authors here focus on the sulfate instead of the nitrate. The sulfate concentrations are similar in Europe and North America, it is the nitrate that is considerably lower in NA. I recommend that the authors alter the text to focus on the “surprisingly low nitrate in North America”, and also ask whether this is surprising or rather consistent with the NOx emissions over Europe vs North America

Line 380: how many SPARTAN sites are used here?

Line 395: The text refers to OA, but Figure 7 shows OC. Please be consistent and if/when OA is used, describe the application of the OM:OC.

Line 418: what about crustal? It appears to be quite important in the Middle East and Africa so it seems odd that the other species add up to 100%

Line 511: which AVOC and BVOC species are included as SOA precursors?

Line 515-516: does this imply that there is no loss of carbon to the gas phase via fragmentation (i.e. HCHO)?

Section 3.4: It would be helpful to specify which (all?) emissions inventories are year-varying

Figure 9: The shades of green are quite difficult to distinguish; I suggest using more distinct shades. Also: rainbow colour bars are to be avoided, so I would strongly suggest using a different colour bar for the map (https://thenextweb.com/news/stop-using-rainbow-maps-doesnt-do-data-justice-syndication)

Lines 606-607: Why not include total dust?

Section 4: It would be helpful if the statistics include R2 to summarize the model skill in capturing the variability. This could be discussed in the text and should be included in all the tables.

Scatterplots (Figures 10, 11, 12, 13): Most of these are difficult to see (lots of wasted white space). The log-log axes are not very helpful. I would strongly recommend that the authors consider using linear scales.

Line 765: Replace “every” with “many”. The Tsigaridis et al. 2014 comparison only shows NMB, and is therefore not very relevant for showing “high concentrations”. This reference is also over 10 years old now, so it may be too strong to say that all models still perform similarly.

Figure 15: Are these differences statistically significant?

Figure 15: Could you also add BC, dust, and SS?

Figure 17: The authors might consider annotating the x-axis. There is a lot of detail in the caption that the reader has to hunt for.

Lines 1061-1063: Given that model does not reproduce the observed trends in many species, regions, it would not be a very faithful representation of the “global trends in atmospheric aerosol composition”. I suggest that the authors modify this paragraph to focus on the observed trends and then how these trends were used to test the model simulation.

Lines 1085-1086: What about dust?

Line 1107: Please correct the text: the model does not show “a major decline”
Citation: https://doi.org/10.5194/egusphere-2024-3590-RC2
- AC2: 'Reply on RC2', Alexandra Tsimpidi, 22 Mar 2025
  
  The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-3590/egusphere-2024-3590-AC2-supplement.pdf
  
  Citation: https://doi.org/10.5194/egusphere-2024-3590-AC2

Peer review completion

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

AR by Alexandra Tsimpidi on behalf of the Authors (18 Apr 2025) Author's response Author's tracked changes Manuscript

ED: Referee Nomination & Report Request started (22 Apr 2025) by Leiming Zhang

RR by Anonymous Referee #3 (25 Apr 2025)

Suggestions for revision or reasons for rejection

This manuscript report evaluations of EMAC model simulations of PM composition with a range of ground-based measurements around the globe and detection of composition trends in North America, Europe, and East Asia from both observations and EMAC model. The paper adds useful results to the discussion of PM composition trends. It is generally well written. I would recommended it be published after taking into account the following comments.

1. For the title, it may be useful to "Near-surface" because the paper only investigates the surface PM1 and PM2.5. This way distinguish this study from those looking into columnar aerosol trends (i.e., AOD).

2. Abstract: "While sulfate is a major....., AMS and model results indicate nitrate predominates in Europe and East Asia". I do not think this conclusion is consistent with what is shown in Figure 4. Clearly in Figure 4, nitrate is somewhat smaller than or comparable to sulfate in Europe and East Asia.

3. Introduction, page 2, line 4: please add "aerodynamic" before "diameters". It is important to get the definition right.

4. page 2, 2nd paragraph, line 3: should add dust as one of primary sources of aerosol.

5. page 4, EPA, EANET, SPARTAN and others throughout the paper: when those acronyms appear first, please spell them out.

6. page 5, line 6: "dry diameter", again please specify if the diameter here is aerodynamic diameter, or geometric diameter, or else.

7. page 7, Figure 1 caption: add "AMS" before "datasets".

8. in section 3.1, please specify which components are accounted as PM1 and PM2.5.

9. page 19, section 3.2 Emissions: for 2020 anthropogenic emissions, does CAMSv4.2 account for COVID lockdown's impact? If COVID impact is not factored in, will that introduce any uncertainty to your trend analysis?

10. page 20, please describe what are size ranges for dust and sea-salt in EMAC model.

11. page 20, section 3.3, "... OA is the dominant component......" how do you define "dominate"? This doesn't seem to be consistent with what Figure 8 shows.

12. page 21, figure 8: it is not clear how the ten regions are defined. Probably you could add white rectangles in the central map to show their domains.

13. Section 3.3, model evaluation: Please show evaluations of EMAC PM2.5 with filter-based PM2.5 observations(section 2.2).

14, page 25, line 2: 20-year should be 21-year, right? At least your model runs from 2020 to 2021. For the PM2.5 trends shown in this section，some ends in 2018 and some ends in 2017. It is better to say that the different years are used because of availability of the observations.

15. For Figures 11, 13, 14, and 16, can you add two panels showing total PM2.5 trends in the three continents from both observations and EMAC simulations?

16. Figure 16 caption: "2000-2018" should be "2000-2017".

Hide

RR by Anonymous Referee #4 (18 May 2025)

ED: Publish subject to minor revisions (review by editor) (20 May 2025) by Leiming Zhang

AR by Alexandra Tsimpidi on behalf of the Authors (04 Jun 2025) Author's response Author's tracked changes Manuscript

ED: Publish as is (04 Jun 2025) by Leiming Zhang

AR by Alexandra Tsimpidi on behalf of the Authors (14 Jun 2025) Manuscript

Journal article(s) based on this preprint

10 Sep 2025

Aerosol composition trends during 2000–2020: in-depth insights from model predictions and multiple worldwide near-surface observation datasets

Alexandra P. Tsimpidi, Susanne M. C. Scholz, Alexandros Milousis, Nikolaos Mihalopoulos, and Vlassis A. Karydis

Atmos. Chem. Phys., 25, 10183–10213, https://doi.org/10.5194/acp-25-10183-2025,https://doi.org/10.5194/acp-25-10183-2025, 2025

Short summary

Alexandra P. Tsimpidi, Susanne M. C. Scholz, Alexandros Milousis, Nikolaos Mihalopoulos, and Vlassis A. Karydis

Supplement

https://doi.org/10.5194/egusphere-2024-3590-supplement

Alexandra P. Tsimpidi, Susanne M. C. Scholz, Alexandros Milousis, Nikolaos Mihalopoulos, and Vlassis A. Karydis

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Aug 2025	153	14	1	168
Sep 2025	98	5	0	103

Cumulative views and downloads (calculated since 04 Dec 2024)

Month	HTML	PDF	XML	Total
Dec 2024	150	33	5	188
Jan 2025	49	30	5	84
Feb 2025	24	11	3	38
Mar 2025	31	11	2	44
Apr 2025	36	5	2	43
May 2025	35	31	1	67
Jun 2025	29	7	4	40
Jul 2025	41	15	0	56
Aug 2025	153	14	1	168
Sep 2025	98	5	0	103

Viewed (geographical distribution)

Total article views: 814 (including HTML, PDF, and XML) Thereof 814 with geography defined and 0 with unknown origin.

Country	#	Views	%

Latest update: 10 Sep 2025

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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.

Preprint (5341 KB)
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Short summary

This study examines global changes in air pollution from 2000 to 2020, focusing on fine aerosols that impact climate and health. Using models and global data, it finds organic aerosols dominate in many regions, especially with wildfires or natural emissions. Pollution from sulfate and nitrate has decreased in Europe and North America due to regulations, while trends in Asia are more complex. The findings improve understanding and support policies for cleaner air and healthier environments.


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