Trace elements in PM<sub>2.5</sub> aerosols in East Asian outflow in the spring of 2018: Emission, transport, and source apportionment

Miyakawa, Takuma; Ito, Akinori; Zhu, Chunmao; Shimizu, Atsushi; Matsumoto, Erika; Mizuno, Yusuke; Kanaya, Yugo

doi:https://doi.org/10.5194/egusphere-2023-1336

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https://doi.org/10.5194/egusphere-2023-1336

Preprints

30 Jun 2023

| 30 Jun 2023

Trace elements in PM_2.5 aerosols in East Asian outflow in the spring of 2018: Emission, transport, and source apportionment

Takuma Miyakawa, Akinori Ito, Chunmao Zhu, Atsushi Shimizu, Erika Matsumoto, Yusuke Mizuno, and Yugo Kanaya

Abstract. Trace metals in aerosol particles impact ocean biogeochemistry. Therefore, semi-continuous measurements of the elemental composition of fine mode (PM_2.5) aerosols were conducted using an automated X-ray fluorescence analyzer on a remote island of Japan during the spring of 2018. The temporal variations of mass concentrations of geochemically important elements for this period, such as S, Pb, Cu, Si, and Fe, and their relationships with emission tracers carbon monoxide (CO) and black carbon (BC), were reported. The Integrated Massively Parallel Atmospheric Chemical Transport (IMPACT) model was used to evaluate the source apportionment of these components and was evaluated in terms of emissions and wet removal processes. Pb and Cu were found to have originated mainly from anthropogenic sources (98 % and 93 % on average, respectively) over the East Asian continent. A positive correlation of Pb and Cu with BC and CO was found during the study period, indicating that the emission sources of these metals share the region where the large CO (and BC) emission sources are located. The air masses with minimized impacts of the wet removal during the transport were extracted to elucidate the emission ratio of Pb and Cu to CO, which were, for the first time, evaluated as 152.7 and 63.1 µg g^-1, respectively, during the spring of 2018 in the East Asian outflow. The analysis of the tagged tracer simulations by the IMPACT model confirmed that BC and Si can be used as tracers for anthropogenic and dust emissions, respectively, during the observation period. The source apportionment of Fe and Mn in PM_2.5 aerosols was conducted using Si and BC tracers, which revealed that the anthropogenic contribution was 17 % and 44 % on average, respectively. Based on the air mass origins of Fe and Mn, their anthropogenic fraction varied from 2 % to 29 % and 9 % to 68 %, respectively, during the high PM_2.5 concentration periods. However, despite minor anthropogenic contributions of Fe, they can adversely affect human health and ocean biogeochemistry owing to their higher water solubility. The modeled BC, Pb, Cu, and Fe were evaluated by separately diagnosing their emission and transport. Ratios of modeled to observed concentrations for these components were analyzed in terms of the accumulated precipitation along the transport from the East Asian continent. The current model simulations were found to overestimate the emissions (based on Community Emissions Data System, CEDS v2021-02-05) of BC by 44 % and underestimate Cu by 45 %, anthropogenic Fe by 28 % in East Asia, and the wet deposition rates for BC and Pb. Overall, Cu in East Asia exhibited a different nature from BC and Pb in terms of emission sources and wet removal.

Received: 23 Jun 2023 – Discussion started: 30 Jun 2023

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27 Nov 2023

Trace elements in PM_2.5 aerosols in East Asian outflow in the spring of 2018: emission, transport, and source apportionment

Takuma Miyakawa, Akinori Ito, Chunmao Zhu, Atsushi Shimizu, Erika Matsumoto, Yusuke Mizuno, and Yugo Kanaya

Atmos. Chem. Phys., 23, 14609–14626, https://doi.org/10.5194/acp-23-14609-2023,https://doi.org/10.5194/acp-23-14609-2023, 2023

Short summary

Takuma Miyakawa et al.

Interactive discussion

Status: closed

RC1:
'Comment on egusphere-2023-1336', Anonymous Referee #1, 31 Jul 2023

General comments:
The authors present the field observation data of atmospheric trace components observed at a remote island in Japan and analyze the data using a backward trajectory technique, a three-dimensional model, and source apportionment based on a multiple linear regression method. Materials presented in the manuscript are interesting and well suited to the scope of the current journal. The manuscript is well written, and the logic is fine. The manuscript will be accepted in the journal after the authors revise the manuscript by reflecting the following general and specific comments.
1. The authors use the term “anthropogenic sources”, but it can be divided into combustion and non-combustion sources and this separation might be the key to the study. BC and CO are mainly coming from the combustion sources, and probably so as Pb, but may not the case for Cu. Could you stress more on the existence and impacts of non-combustion sources of elements for your analysis? For instance, does the IMPACT model consider non-combustion sources for Pb and Cu? If not, can it be a cause of underestimation of simulated Cu at the observation site? If the authors neglected the contribution of elements from non-combustion sources because the sizes of aerosols are larger than PM₅ and thus the out of scope of the study, please mention this in the manuscript. There may be certain levels of non-combustion origins for anthropogenic Fe, too.
Specific comments:
Abstract:
2. Ln. 10: “impact ocean biogeochemistry” -> “impact human health and ocean biogeochemistry”, because the authors mentioned the impact of human health as well, in the latter part of abstract (Ln. 26) and in the Introduction section (First paragraph, regarding reactive oxygen species).
3. Ln. 13: “S” is not mentioned in the abstract and rather “Mn” may be an important element in this study. Please consider including Mn in the sentence, at least. It is up to authors’ decision whether to exclude S from the sentence, though.
4. Ln. 20: It is relating to the major comment #1, but I am not sure whether derivations of Pb/CO and Cu/CO ratios are meaningful or not, because numerators (Pb and Cu) may come from both non-combustion and combustion sources, while denominator only comes from combustion sources.
Material and methods:
5. Ln. 119: “15 and 50 kV”. Why are two different voltage levels used in the analysis?
6. Ln. 128: Please explain more about the “uncertainties” here. What are the exact measures for the values? Are they normalized errors? Are they the uncertainties of PX-375 data against the reference data, that are measured by IC and ICP-MS? Or are they relative errors between PX-375 and IC/ICP-MS?
7. Lns. 142-145: Please include time period also. “(6600)” in Ln. 156 may be the number of trajectories but I have no idea why the total number is 6600. Time resolution of trajectory is hourly, so 24 (hrs) x 3 (layers) x 90 (days) = 6480, a little bit different from 6600, but anyway the same order. However, as written later in Lns. 157-158, L in Eq. 1 is based on 4 hourly values to match with the time resolution of PX-375, so that the orders of L may be smaller, around 1620, right?
Results and Discussion
8. Lns. 190-191, “with small Japanese emission impacts”: Fig. S2 indicates the residence time of trajectories and does not tell the impact of emissions. The impacts of emission depend on emission flux and distance from the source. Please rephrase the relevant sentence by what Fig. S2 really tells.
9. Lns. 192-193: It is not clear why the authors present Fig. S3. Probably “Notably, air masses … during the observation period.” is the reason why, but some more words may be needed to make the readers compelling. Please add some more words to explain why the fact that no correlation is found between residence time over the continent and APT in Fig. S3 is important for the analysis (and for which analysis?) of this study.
10. Lns. 243-245. “rainout” means in-cloud scavenging, right? “wet depositions” includes both in-cloud and below-cloud maybe. Do the authors intend to mean that the deposition mechanisms of BC and Pb/Cu are different? Or the same (both removed by in-cloud and/or below-cloud scavenging)? Anyway, please explain why the authors assume so? Is it because mixing-state and sizes of BC, Pb, and Cu are different (or similar) with each other?
11. Ln. 274, Fig. 4: Please reconfirm the unit of APT. It was “mm h” in Fig. 3 as well as main text, while “mm” here. Please also check it in Figs. 5, and S10, and elsewhere, if any.
12. Ln. 277: “Cu has characteristics of emission and mixing states different from BC and Pb”. This is interesting and can be an answer for my comments #1 and #10. Not only “emission” and “mixing state”, but also “size” may be an important factor to affect wet removal and thus determine the transport efficiency, but why is it not included? Is “size” already included in “emission” or “mixing state”?
13. Ln. 295: “the removal processes and emissions of Cu were not properly simulated by the IMPACT model”. I have an opposite impression from the authors for what Fig. 5 tells: constant Model/Obs ratio of Cu for different APT regions means wet removal processes of Cu in the model were rather successful! Could you explain more about the difference of wet removal calculations for BC, Pb, and Cu in the IMPACT model? (It should already be written in the description paper, Ito and Miyakawa, 2023, but please explain here again, because trends of BC/Pb and Cu are remarkably different in Fig. 5).
14. Ln. 304, Fig. S7: Are they correlations for observations or IMPACT? The panels look correlations for observation data, but from the main text, they might be the data of IMPACT, because the relevant paragraph in the main text mentions IMPACT in the beginning. Please specify.
15. Ln. 357: “dust-Mn concentrations were underestimated by the IMPACT model”. Why could it happen, although the simulated dust-Fe is successful? Dust-Mn and dust-Fe are simulated using the same total dust mass concentrations, right? I mean, dust-Mn and dust-Fe are derived from the common dust emission scheme. Is it because the Mn-content set in the model (global scale?) is very different from that in reality (Asian dust)? How are the Fe and Mn contents in the model different (or similar) from NIES CRM NO. 30 Gobi Kosa Dust, for example?
16. Lns. 383-386: I am wondering if the denominators in Fig. S11 of all studies (Jeong, Wang, and IMPACT) are the same. Are they really PM_2.5-dust only or total PM_2.5 concentrations during the dust events, which includes components other than dust. The simulation may be the former, but for observations could be the latter.
Conclusions
17. Lns. 457-458, “such as elemental concentrations and mixing states”. Probably size distribution is also important, as commented in #12. Or does the term “mixing state” include size information as well?
Supporting information
18. Sect. S1: Cl^- is used for the contribution of sea-salt particles but it is evaporative so Na⁺ may be a better indicator. Na⁺ may be difficult to be analyzed by ICP-MS or PX-375, but you have IC data, right? Why didn’t you use IC Na⁺ data for your analysis? This is also an additional comment on the main text, in Lns. 228-231, instead of Ca²⁺, non-sea-salt Ca²⁺ (derived by assuming Na⁺ as fully originated from sea-salt) can be a better indicator for dust aerosols. (Certainly, you don’t need nss-Ca²⁺, as you have already Si as a good indicator)
19. Caption of Fig. S8: what do you mean by “stacked”? (MPOA was stacked on the modeled SS).

Citation: https://doi.org/10.5194/egusphere-2023-1336-RC1
- AC1: 'Reply on RC1', Takuma Miyakawa, 06 Oct 2023
  
  Thank you very much for the constructive comments.
  Please see attached our responses.
  
  Citation: https://doi.org/10.5194/egusphere-2023-1336-AC1
RC2:
'Comment on egusphere-2023-1336', Anonymous Referee #2, 15 Aug 2023

Abstract:
Ln 10: 1^st sentence can be changed to ‘Trace metals in aerosol particles impact Earth’s radiative balance, ocean biogeochemistry, and human health.’ (as mentioned in the introduction). Also removing ‘therefore’ from the second sentence is suggested since it is not a conclusion for the previous one.
Ln 26: Using the word ‘Minor’ for anthropogenic contribution of Fe is not recommended. Maybe using the term ‘not dominant’ like used in conclusions is better.
Introduction:
Ln 42: Change ‘has been concerned’ to ‘has been a concern’.
Materials and methods:
Ln 118: Was the 4h analysis time period used so that sufficient mass could be collected on the tape, or for some other reason?
Ln 128: How were these uncertainties calculated?
Ln 146: Is there a particular reason why three-day APT was considered?
Results:
Ln 250: States that ‘Cu has different emission sources from BC, Pb and CO’. The correlations of Pb and Cu v/s BC/CO are not drastically different, so can that really imply a different emission source altogether?
Ln 690: Fig 4 and 5 give units of APT as mm, whereas Fig 3 gives it as mm h. Which is the correct one? Please check other figs in SI as well.
Ln 295: Looking at Fig 5, it seems that Cu is simulated well by the IMPACT model, due to its consistent M/O across different APT ranges, but the text states otherwise. Can you elaborate more on this?
Conclusions:
Ln 448: States that ‘emission sources of these metals share the region where the large CO (and BC) emission sources are located.’ I understand that this conclusion is based on CWT results. However, it is confusing to understand that Cu has a different emission source based on correlations but a similar region of emission. Please explain this part more clearly, and make edits to similar sentences made in the abstract and results too.
Ln 460: BC is used as a tracer for anthropogenic emissions. What is novel about this part?

Citation: https://doi.org/10.5194/egusphere-2023-1336-RC2
- AC2: 'Reply on RC2', Takuma Miyakawa, 06 Oct 2023
  
  Thank you very much for the constructive comments.
  Please see attached our responses.
  
  Citation: https://doi.org/10.5194/egusphere-2023-1336-AC2

Interactive discussion

Status: closed

RC1:
'Comment on egusphere-2023-1336', Anonymous Referee #1, 31 Jul 2023

General comments:
The authors present the field observation data of atmospheric trace components observed at a remote island in Japan and analyze the data using a backward trajectory technique, a three-dimensional model, and source apportionment based on a multiple linear regression method. Materials presented in the manuscript are interesting and well suited to the scope of the current journal. The manuscript is well written, and the logic is fine. The manuscript will be accepted in the journal after the authors revise the manuscript by reflecting the following general and specific comments.
1. The authors use the term “anthropogenic sources”, but it can be divided into combustion and non-combustion sources and this separation might be the key to the study. BC and CO are mainly coming from the combustion sources, and probably so as Pb, but may not the case for Cu. Could you stress more on the existence and impacts of non-combustion sources of elements for your analysis? For instance, does the IMPACT model consider non-combustion sources for Pb and Cu? If not, can it be a cause of underestimation of simulated Cu at the observation site? If the authors neglected the contribution of elements from non-combustion sources because the sizes of aerosols are larger than PM₅ and thus the out of scope of the study, please mention this in the manuscript. There may be certain levels of non-combustion origins for anthropogenic Fe, too.
Specific comments:
Abstract:
2. Ln. 10: “impact ocean biogeochemistry” -> “impact human health and ocean biogeochemistry”, because the authors mentioned the impact of human health as well, in the latter part of abstract (Ln. 26) and in the Introduction section (First paragraph, regarding reactive oxygen species).
3. Ln. 13: “S” is not mentioned in the abstract and rather “Mn” may be an important element in this study. Please consider including Mn in the sentence, at least. It is up to authors’ decision whether to exclude S from the sentence, though.
4. Ln. 20: It is relating to the major comment #1, but I am not sure whether derivations of Pb/CO and Cu/CO ratios are meaningful or not, because numerators (Pb and Cu) may come from both non-combustion and combustion sources, while denominator only comes from combustion sources.
Material and methods:
5. Ln. 119: “15 and 50 kV”. Why are two different voltage levels used in the analysis?
6. Ln. 128: Please explain more about the “uncertainties” here. What are the exact measures for the values? Are they normalized errors? Are they the uncertainties of PX-375 data against the reference data, that are measured by IC and ICP-MS? Or are they relative errors between PX-375 and IC/ICP-MS?
7. Lns. 142-145: Please include time period also. “(6600)” in Ln. 156 may be the number of trajectories but I have no idea why the total number is 6600. Time resolution of trajectory is hourly, so 24 (hrs) x 3 (layers) x 90 (days) = 6480, a little bit different from 6600, but anyway the same order. However, as written later in Lns. 157-158, L in Eq. 1 is based on 4 hourly values to match with the time resolution of PX-375, so that the orders of L may be smaller, around 1620, right?
Results and Discussion
8. Lns. 190-191, “with small Japanese emission impacts”: Fig. S2 indicates the residence time of trajectories and does not tell the impact of emissions. The impacts of emission depend on emission flux and distance from the source. Please rephrase the relevant sentence by what Fig. S2 really tells.
9. Lns. 192-193: It is not clear why the authors present Fig. S3. Probably “Notably, air masses … during the observation period.” is the reason why, but some more words may be needed to make the readers compelling. Please add some more words to explain why the fact that no correlation is found between residence time over the continent and APT in Fig. S3 is important for the analysis (and for which analysis?) of this study.
10. Lns. 243-245. “rainout” means in-cloud scavenging, right? “wet depositions” includes both in-cloud and below-cloud maybe. Do the authors intend to mean that the deposition mechanisms of BC and Pb/Cu are different? Or the same (both removed by in-cloud and/or below-cloud scavenging)? Anyway, please explain why the authors assume so? Is it because mixing-state and sizes of BC, Pb, and Cu are different (or similar) with each other?
11. Ln. 274, Fig. 4: Please reconfirm the unit of APT. It was “mm h” in Fig. 3 as well as main text, while “mm” here. Please also check it in Figs. 5, and S10, and elsewhere, if any.
12. Ln. 277: “Cu has characteristics of emission and mixing states different from BC and Pb”. This is interesting and can be an answer for my comments #1 and #10. Not only “emission” and “mixing state”, but also “size” may be an important factor to affect wet removal and thus determine the transport efficiency, but why is it not included? Is “size” already included in “emission” or “mixing state”?
13. Ln. 295: “the removal processes and emissions of Cu were not properly simulated by the IMPACT model”. I have an opposite impression from the authors for what Fig. 5 tells: constant Model/Obs ratio of Cu for different APT regions means wet removal processes of Cu in the model were rather successful! Could you explain more about the difference of wet removal calculations for BC, Pb, and Cu in the IMPACT model? (It should already be written in the description paper, Ito and Miyakawa, 2023, but please explain here again, because trends of BC/Pb and Cu are remarkably different in Fig. 5).
14. Ln. 304, Fig. S7: Are they correlations for observations or IMPACT? The panels look correlations for observation data, but from the main text, they might be the data of IMPACT, because the relevant paragraph in the main text mentions IMPACT in the beginning. Please specify.
15. Ln. 357: “dust-Mn concentrations were underestimated by the IMPACT model”. Why could it happen, although the simulated dust-Fe is successful? Dust-Mn and dust-Fe are simulated using the same total dust mass concentrations, right? I mean, dust-Mn and dust-Fe are derived from the common dust emission scheme. Is it because the Mn-content set in the model (global scale?) is very different from that in reality (Asian dust)? How are the Fe and Mn contents in the model different (or similar) from NIES CRM NO. 30 Gobi Kosa Dust, for example?
16. Lns. 383-386: I am wondering if the denominators in Fig. S11 of all studies (Jeong, Wang, and IMPACT) are the same. Are they really PM_2.5-dust only or total PM_2.5 concentrations during the dust events, which includes components other than dust. The simulation may be the former, but for observations could be the latter.
Conclusions
17. Lns. 457-458, “such as elemental concentrations and mixing states”. Probably size distribution is also important, as commented in #12. Or does the term “mixing state” include size information as well?
Supporting information
18. Sect. S1: Cl^- is used for the contribution of sea-salt particles but it is evaporative so Na⁺ may be a better indicator. Na⁺ may be difficult to be analyzed by ICP-MS or PX-375, but you have IC data, right? Why didn’t you use IC Na⁺ data for your analysis? This is also an additional comment on the main text, in Lns. 228-231, instead of Ca²⁺, non-sea-salt Ca²⁺ (derived by assuming Na⁺ as fully originated from sea-salt) can be a better indicator for dust aerosols. (Certainly, you don’t need nss-Ca²⁺, as you have already Si as a good indicator)
19. Caption of Fig. S8: what do you mean by “stacked”? (MPOA was stacked on the modeled SS).

Citation: https://doi.org/10.5194/egusphere-2023-1336-RC1
- AC1: 'Reply on RC1', Takuma Miyakawa, 06 Oct 2023
  
  Thank you very much for the constructive comments.
  Please see attached our responses.
  
  Citation: https://doi.org/10.5194/egusphere-2023-1336-AC1
RC2:
'Comment on egusphere-2023-1336', Anonymous Referee #2, 15 Aug 2023

Abstract:
Ln 10: 1^st sentence can be changed to ‘Trace metals in aerosol particles impact Earth’s radiative balance, ocean biogeochemistry, and human health.’ (as mentioned in the introduction). Also removing ‘therefore’ from the second sentence is suggested since it is not a conclusion for the previous one.
Ln 26: Using the word ‘Minor’ for anthropogenic contribution of Fe is not recommended. Maybe using the term ‘not dominant’ like used in conclusions is better.
Introduction:
Ln 42: Change ‘has been concerned’ to ‘has been a concern’.
Materials and methods:
Ln 118: Was the 4h analysis time period used so that sufficient mass could be collected on the tape, or for some other reason?
Ln 128: How were these uncertainties calculated?
Ln 146: Is there a particular reason why three-day APT was considered?
Results:
Ln 250: States that ‘Cu has different emission sources from BC, Pb and CO’. The correlations of Pb and Cu v/s BC/CO are not drastically different, so can that really imply a different emission source altogether?
Ln 690: Fig 4 and 5 give units of APT as mm, whereas Fig 3 gives it as mm h. Which is the correct one? Please check other figs in SI as well.
Ln 295: Looking at Fig 5, it seems that Cu is simulated well by the IMPACT model, due to its consistent M/O across different APT ranges, but the text states otherwise. Can you elaborate more on this?
Conclusions:
Ln 448: States that ‘emission sources of these metals share the region where the large CO (and BC) emission sources are located.’ I understand that this conclusion is based on CWT results. However, it is confusing to understand that Cu has a different emission source based on correlations but a similar region of emission. Please explain this part more clearly, and make edits to similar sentences made in the abstract and results too.
Ln 460: BC is used as a tracer for anthropogenic emissions. What is novel about this part?

Citation: https://doi.org/10.5194/egusphere-2023-1336-RC2
- AC2: 'Reply on RC2', Takuma Miyakawa, 06 Oct 2023
  
  Thank you very much for the constructive comments.
  Please see attached our responses.
  
  Citation: https://doi.org/10.5194/egusphere-2023-1336-AC2

Peer review completion

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

AR by Takuma Miyakawa on behalf of the Authors (06 Oct 2023) Author's response Author's tracked changes Manuscript

ED: Publish as is (13 Oct 2023) by Manabu Shiraiwa

AR by Takuma Miyakawa on behalf of the Authors (16 Oct 2023) Manuscript

Journal article(s) based on this preprint

27 Nov 2023

Trace elements in PM_2.5 aerosols in East Asian outflow in the spring of 2018: emission, transport, and source apportionment

Takuma Miyakawa, Akinori Ito, Chunmao Zhu, Atsushi Shimizu, Erika Matsumoto, Yusuke Mizuno, and Yugo Kanaya

Atmos. Chem. Phys., 23, 14609–14626, https://doi.org/10.5194/acp-23-14609-2023,https://doi.org/10.5194/acp-23-14609-2023, 2023

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This study conducted semi-continuous measurements of PM_2.5 aerosols and their elemental composition in western Japan, during spring 2018. It analyzed the emissions, transport, and wet removal of elements such as Pb, Cu, Fe, and Mn. It also assessed the accuracy of modeled concentrations and found overestimations of BC and underestimations of Cu and anthropogenic Fe in East Asia. Insights into emissions, removals, and source apportionment of trace metals in the East Asian outflow were provided.


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Trace elements in PM2.5 aerosols in East Asian outflow in the spring of 2018: Emission, transport, and source apportionment

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Trace elements in PM_2.5 aerosols in East Asian outflow in the spring of 2018: Emission, transport, and source apportionment