The underappreciated impact of emission source profiles on the simulation of PM<sub>2.5</sub> components: New evidence from sensitivity analysis

Luo, Zhongwei; Han, Yan; Hua, Kun; Zhang, Yufen; Wu, Jianhui; Bi, Xiaohui; Dai, Qili; Liu, Baoshuang; Chen, Yang; Long, Xin; Feng, Yinchang

doi:https://doi.org/10.5194/egusphere-2022-895

Preprints

https://doi.org/10.5194/egusphere-2022-895

Preprints

13 Oct 2022

| 13 Oct 2022

The underappreciated impact of emission source profiles on the simulation of PM_2.5 components: New evidence from sensitivity analysis

Zhongwei Luo, Yan Han, Kun Hua, Yufen Zhang, Jianhui Wu, Xiaohui Bi, Qili Dai, Baoshuang Liu, Yang Chen, Xin Long, and Yinchang Feng

Abstract. The chemical transport model (CTM) is an essential tool for air quality prediction and management, widely used in air pollution control and health risk assessment. However, the current models do not perform very well in simulating PM_2.5 components. Studies suggested that the uncertainties of model chemical mechanism, source emission inventory and meteorological field can cause inaccurate simulation results. Still, the emission source profile of PM_2.5 has not been fully taken into account in current numerical simulation. This study aims to answer (1) Whether the variation of source profile adopted in chemical transport models (CTMs) has an impact on the simulation of PM_2.5 chemical components? (2) How much does it impact? (3) How does the impact work? Based on the characteristics and variation rules of chemical components in typical PM_2.5 sources, different simulation scenarios were designed and the sensitivity of components simulation results to PM_2.5 sources profile was explored. Our findings showed that the influence of source profile changes on simulated PM_2.5 concentration was insignificant, but its impact on PM_2.5 components could not be ignored. The variations of simulated components ranged from 8 % to 167 % under selected different source profiles, and simulation results of some components were sensitive to the adopted PM_2.5 source profile in CTMs. These influences are connected to the chemical mechanisms of the model since the variation of species allocations in emission sources directly affected the thermodynamic equilibrium system. We also found that the perturbation of the PM_2.5 source profile caused the variation of simulated gaseous pollutants, which indirectly indicated that the perturbation of the source profile affected the simulation of secondary PM_2.5 components. Given the vital role of air quality simulation in environment management and health risk assessment, the representativeness and timeliness of source profile should be considered.

Received: 08 Sep 2022 – Discussion started: 13 Oct 2022

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.

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Journal article(s) based on this preprint

22 Nov 2023

The effect of emission source chemical profiles on simulated PM_2.5 components: sensitivity analysis with the Community Multiscale Air Quality (CMAQ) modeling system version 5.0.2

Zhongwei Luo, Yan Han, Kun Hua, Yufen Zhang, Jianhui Wu, Xiaohui Bi, Qili Dai, Baoshuang Liu, Yang Chen, Xin Long, and Yinchang Feng

Geosci. Model Dev., 16, 6757–6771, https://doi.org/10.5194/gmd-16-6757-2023,https://doi.org/10.5194/gmd-16-6757-2023, 2023

Short summary

Zhongwei Luo, Yan Han, Kun Hua, Yufen Zhang, Jianhui Wu, Xiaohui Bi, Qili Dai, Baoshuang Liu, Yang Chen, Xin Long, and Yinchang Feng

Interactive discussion

Status: closed

RC1:
'Comment on egusphere-2022-895', Anonymous Referee #1, 25 Oct 2022
The manuscript attempts to explore the influence of adopted emission source profiles in CTMs on the simulated results of PM2.5 components by sensitivity analysis. The extent of the influence for different components were quantitatively analyzed, the impact laws and pathway were identified. The topic is interesting and their findings highlight the importance of effective utilization of emission source profiles in CTMs. Although the description of experiments is complete to allow their reproduction by fellow researchers, some explanations and discussions are not clear. I recommend its publication subject to the following amendments.

Major concerns:

What is the design basis for the perturbation of emission source profile in the sensitivity experiments?

The discussion of the results should be extended. The authors mentioned that emission source profile adopted in CTMs has a significant impact on the simulation results of PM₅ components, so how to select the appropriate source profiles in the simulation? In the section of conclusion (Line 549-551), the author concluded that “the representativeness and timeliness of the source profile should be considered”. How to understand the “representativeness” and “timeliness” here?

Minor concerns:

Line 21 and Line 27, there are two notes for CTM in one paragraph, which appear to be repetitive.

Line 57-59, the references are verbose.

Line 111-113, It is not clearly explained the role of source profiles in CTMs.

Line 257: “The detailed information on” should be “The information of…”

Line 259: “Coefficient Divergence (CD)” would be appropriate

In the supplementary material, Fig. S1, the author selected code 91041, 900162.5, 91155, 91022 and 91162 as SPECIATE source profiles for simulation. Detailed information of these source profiles need be provided by authors.
Citation: https://doi.org/10.5194/egusphere-2022-895-RC1
- AC2: 'Reply on RC1', Yinchang Feng, 21 Jan 2023
  
  Thank you very much for your consideration. Your comments and suggestions will undoubtedly help to improve the manuscript. We have developed a complete response in the attached document.
  Sincerely
  Yinchang Feng and co-authors.
  
  Citation: https://doi.org/10.5194/egusphere-2022-895-AC2
RC2:
'Comment on egusphere-2022-895', Anonymous Referee #2, 02 Nov 2022
The manuscript investigates the sensitivity of simulated PM_2.5 and its components’ concentrations to the uncertainties in the component-specified PM_2.5 source emission inventories using the CMAQ chemical transport model. The relatively-complete chemical components, including Al, Ca, Cl, EC, Fe, K, Mg, Mn, Na, OC Si, NH₄⁺, NO₃^-, SO₄^2-, and others, are taken into account in the emission inventory used. The authors showed that the influence of the relative contributions of different components to the total PM_2.5 emission (denoted as source profile changes in the manuscript) on simulated PM_2.5 concentration was insignificant, but its impact on PM_2.5 components could not be ignored. They also showed that these source profile changes caused the variations in simulated gaseous pollutants’ concentrations. While such kind of model experiment should be a welcome addition to the literature on air quality model simulation, I do have concerns that the data and methodology used in this study would be sensible (or well introduced) and the conclusions applicable to the simulations done by other chemical transport models with different chemical and physical modules. Therefore, I cannot recommend publication the current version of this manuscript in GMD.

The major issues are follows:

What is the grid resolution of the MEIC emission inventory that was used for the model simulation in this study? Is the resolution sufficiently fine for the Dom3 (4 km× 4km) simulation? What does the area marked in green in Fig. 1 refer to? No information on the regional distributions of either PM_2.5 emission sources or their simulated concentrations is provided in the manuscript. Are all the 10 monitoring sites located in the cities of Dom3? Is there any site that is located near the desert area? Were the mineral dust emissions taken into account in the simulation?

At the beginning of Sect. 2.2 it is stated that in addition to SPA and SPE, the PM₅ emission source profile database from published literature was used. Where and what are the final, merged emission source profiles used in this study? The simulated PM_2.5 and its components’ concentrations using CMAQ_SPA are compared with those using CMAQ_SPE. However, no comparison with observed PM_2.5 components’ concentrations at the monitoring sites has been made to show the advantage of the SPA over the SPE.

While the MEIC inventory includes four categories, i.e. power plants (PP), industrial processes (IN), residential emission (RE) and transport sector (TR), the SPA and SPE are shown to have different categories (perhaps more than the MEIC does). How were these chemical PM₅ emission source profiles combined to match the MECI categories? For instance, the residential emission should include not only coal burning but also straw burning, and the latter was seemly not considered in the simulations. Also, the chemical profiles for gasoline and diesel oil in the transport sector might be different.

How are the dynamic, microphysical and chemical processes of aerosols treated in the CMAQ model used for this study? Are the size distribution, mixing state, aging and solubility taken into account for different aerosol components? By which molecular form are the chemical components (Al, Ca, Cl, EC, Fe, K, Mg, Mn, Na, OC Si, NH₄⁺, NO₃^-, and SO₄^2-) emitted from the sources? Taking elemental Ca as an example, it should be emitted by CaO, CaCO₃, CaSO₄, or other compound, rather than merely by the cation Ca²⁺. The similar principle applies for anions (NO₃^- and SO₄^2-). The difference in the exiting form of these emitted aerosol components might have large impacts on the thermodynamic equilibrium of ions in liquid aerosols and clouds.

In Sect. 1 and Table S1, the deviations of PM₅ components simulated by CMAQ are presented. All these components (NH₄⁺, NO₃^-, SO₄^2-, and part of OC), except for EC and part of OC, are second aerosols, and their loadings in the atmosphere are controlled primarily by the emissions of gaseous precursors, instead of the emission of aerosols. The presentation here and associated arguments seems to be misleading as the effect of uncertainties in the gaseous emissions is not considered in this study.
Citation: https://doi.org/10.5194/egusphere-2022-895-RC2
- AC3: 'Reply on RC2', Yinchang Feng, 21 Jan 2023
  
  Dear reviewer:
  Thank you for the immensely helpful comments.
  We have uploaded a complete response in the attached document.
  Sincerely
  Yinchang Feng and co-authors.
  
  Citation: https://doi.org/10.5194/egusphere-2022-895-AC3
CEC1:
'Comment on egusphere-2022-895', Astrid Kerkweg, 11 Nov 2022

Dear authors,
from the github link you provide in your code availability section to access the WRF v3.7.1 code it is very hard to find the release code of that version. Better than a github link provide the link to the actual page, where the released versions can be downloaded as zip-files: https://www2.mmm.ucar.edu/wrf/users/download/get_source.html
Best regards, Astrid Kerkweg (Executive Editor)

Citation: https://doi.org/10.5194/egusphere-2022-895-CEC1
- AC1: 'Reply on CEC1', Yinchang Feng, 21 Jan 2023
  
  Dear Astrid Kerkweg (Executive Editor)
  We apologize for providing invalid link and bringing the inconvenience. The source code of WRF v3.7.1 has been archived at https://www2.mmm.ucar.edu/wrf/src/WRFV3.7.1.TAR.gz. We update our code availability statement accordingly.
  And sorry for response late because of our health problems (Our co-authors got COVID-19 successively).
  sincerely
  Yinchang Feng and co-authors.
  
  Citation: https://doi.org/10.5194/egusphere-2022-895-AC1

Interactive discussion

Status: closed

RC1:
'Comment on egusphere-2022-895', Anonymous Referee #1, 25 Oct 2022
The manuscript attempts to explore the influence of adopted emission source profiles in CTMs on the simulated results of PM2.5 components by sensitivity analysis. The extent of the influence for different components were quantitatively analyzed, the impact laws and pathway were identified. The topic is interesting and their findings highlight the importance of effective utilization of emission source profiles in CTMs. Although the description of experiments is complete to allow their reproduction by fellow researchers, some explanations and discussions are not clear. I recommend its publication subject to the following amendments.

Major concerns:

What is the design basis for the perturbation of emission source profile in the sensitivity experiments?

The discussion of the results should be extended. The authors mentioned that emission source profile adopted in CTMs has a significant impact on the simulation results of PM₅ components, so how to select the appropriate source profiles in the simulation? In the section of conclusion (Line 549-551), the author concluded that “the representativeness and timeliness of the source profile should be considered”. How to understand the “representativeness” and “timeliness” here?

Minor concerns:

Line 21 and Line 27, there are two notes for CTM in one paragraph, which appear to be repetitive.

Line 57-59, the references are verbose.

Line 111-113, It is not clearly explained the role of source profiles in CTMs.

Line 257: “The detailed information on” should be “The information of…”

Line 259: “Coefficient Divergence (CD)” would be appropriate

In the supplementary material, Fig. S1, the author selected code 91041, 900162.5, 91155, 91022 and 91162 as SPECIATE source profiles for simulation. Detailed information of these source profiles need be provided by authors.
Citation: https://doi.org/10.5194/egusphere-2022-895-RC1
- AC2: 'Reply on RC1', Yinchang Feng, 21 Jan 2023
  
  Thank you very much for your consideration. Your comments and suggestions will undoubtedly help to improve the manuscript. We have developed a complete response in the attached document.
  Sincerely
  Yinchang Feng and co-authors.
  
  Citation: https://doi.org/10.5194/egusphere-2022-895-AC2
RC2:
'Comment on egusphere-2022-895', Anonymous Referee #2, 02 Nov 2022
The manuscript investigates the sensitivity of simulated PM_2.5 and its components’ concentrations to the uncertainties in the component-specified PM_2.5 source emission inventories using the CMAQ chemical transport model. The relatively-complete chemical components, including Al, Ca, Cl, EC, Fe, K, Mg, Mn, Na, OC Si, NH₄⁺, NO₃^-, SO₄^2-, and others, are taken into account in the emission inventory used. The authors showed that the influence of the relative contributions of different components to the total PM_2.5 emission (denoted as source profile changes in the manuscript) on simulated PM_2.5 concentration was insignificant, but its impact on PM_2.5 components could not be ignored. They also showed that these source profile changes caused the variations in simulated gaseous pollutants’ concentrations. While such kind of model experiment should be a welcome addition to the literature on air quality model simulation, I do have concerns that the data and methodology used in this study would be sensible (or well introduced) and the conclusions applicable to the simulations done by other chemical transport models with different chemical and physical modules. Therefore, I cannot recommend publication the current version of this manuscript in GMD.

The major issues are follows:

What is the grid resolution of the MEIC emission inventory that was used for the model simulation in this study? Is the resolution sufficiently fine for the Dom3 (4 km× 4km) simulation? What does the area marked in green in Fig. 1 refer to? No information on the regional distributions of either PM_2.5 emission sources or their simulated concentrations is provided in the manuscript. Are all the 10 monitoring sites located in the cities of Dom3? Is there any site that is located near the desert area? Were the mineral dust emissions taken into account in the simulation?

At the beginning of Sect. 2.2 it is stated that in addition to SPA and SPE, the PM₅ emission source profile database from published literature was used. Where and what are the final, merged emission source profiles used in this study? The simulated PM_2.5 and its components’ concentrations using CMAQ_SPA are compared with those using CMAQ_SPE. However, no comparison with observed PM_2.5 components’ concentrations at the monitoring sites has been made to show the advantage of the SPA over the SPE.

While the MEIC inventory includes four categories, i.e. power plants (PP), industrial processes (IN), residential emission (RE) and transport sector (TR), the SPA and SPE are shown to have different categories (perhaps more than the MEIC does). How were these chemical PM₅ emission source profiles combined to match the MECI categories? For instance, the residential emission should include not only coal burning but also straw burning, and the latter was seemly not considered in the simulations. Also, the chemical profiles for gasoline and diesel oil in the transport sector might be different.

How are the dynamic, microphysical and chemical processes of aerosols treated in the CMAQ model used for this study? Are the size distribution, mixing state, aging and solubility taken into account for different aerosol components? By which molecular form are the chemical components (Al, Ca, Cl, EC, Fe, K, Mg, Mn, Na, OC Si, NH₄⁺, NO₃^-, and SO₄^2-) emitted from the sources? Taking elemental Ca as an example, it should be emitted by CaO, CaCO₃, CaSO₄, or other compound, rather than merely by the cation Ca²⁺. The similar principle applies for anions (NO₃^- and SO₄^2-). The difference in the exiting form of these emitted aerosol components might have large impacts on the thermodynamic equilibrium of ions in liquid aerosols and clouds.

In Sect. 1 and Table S1, the deviations of PM₅ components simulated by CMAQ are presented. All these components (NH₄⁺, NO₃^-, SO₄^2-, and part of OC), except for EC and part of OC, are second aerosols, and their loadings in the atmosphere are controlled primarily by the emissions of gaseous precursors, instead of the emission of aerosols. The presentation here and associated arguments seems to be misleading as the effect of uncertainties in the gaseous emissions is not considered in this study.
Citation: https://doi.org/10.5194/egusphere-2022-895-RC2
- AC3: 'Reply on RC2', Yinchang Feng, 21 Jan 2023
  
  Dear reviewer:
  Thank you for the immensely helpful comments.
  We have uploaded a complete response in the attached document.
  Sincerely
  Yinchang Feng and co-authors.
  
  Citation: https://doi.org/10.5194/egusphere-2022-895-AC3
CEC1:
'Comment on egusphere-2022-895', Astrid Kerkweg, 11 Nov 2022

Dear authors,
from the github link you provide in your code availability section to access the WRF v3.7.1 code it is very hard to find the release code of that version. Better than a github link provide the link to the actual page, where the released versions can be downloaded as zip-files: https://www2.mmm.ucar.edu/wrf/users/download/get_source.html
Best regards, Astrid Kerkweg (Executive Editor)

Citation: https://doi.org/10.5194/egusphere-2022-895-CEC1
- AC1: 'Reply on CEC1', Yinchang Feng, 21 Jan 2023
  
  Dear Astrid Kerkweg (Executive Editor)
  We apologize for providing invalid link and bringing the inconvenience. The source code of WRF v3.7.1 has been archived at https://www2.mmm.ucar.edu/wrf/src/WRFV3.7.1.TAR.gz. We update our code availability statement accordingly.
  And sorry for response late because of our health problems (Our co-authors got COVID-19 successively).
  sincerely
  Yinchang Feng and co-authors.
  
  Citation: https://doi.org/10.5194/egusphere-2022-895-AC1

Peer review completion

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

AR by Yinchang Feng on behalf of the Authors (17 Feb 2023) Author's response Author's tracked changes Manuscript

ED: Referee Nomination & Report Request started (22 Feb 2023) by Klaus Klingmüller

RR by Anonymous Referee #2 (23 Feb 2023)

RR by Anonymous Referee #1 (01 Mar 2023)

ED: Reconsider after major revisions (17 Mar 2023) by Klaus Klingmüller

Dear Authors

Thank you very much for responding to the reviewer's concerns. However, the article needs to be substantially revised to address the remaining concerns, improve the completeness/clarity, and sharpen the scope of the article before it can be reconsidered for publication in GMD.

As one reviewer points out, there is still need for clarification regarding the chemical equilibrium of ions in the model: "In the PM2.5 source profiles (Sect 2.2), the chemical components (Ca, K, Mg, Mn, Na) are expressed in the form of element. In the aerosol thermodynamic process (Sect. 5), cations (Na+, Ca2+, K+, Mg2+) are used. Are all those elemental components (Ca, K, Mg, Mn, Na) from source emissions assumed to take part in the thermodynamic process in the form of cations (Na+, Ca2+, K+, Mg2+)? If so, are there any other anions used to make an ion balance with them? Are the cations and anions in equilibrium in the source profile as well as in the SORROPIA simulation?"

Generally, please make sure that any issues that needed clarification in the online discussion are also clarified in the article.

In the following are examples of other issues that need to be addressed in the revision.

Sincerely

Klaus Klingmüller

Title: It seems inappropriate to speak of an "underappreciated" impact of the source profiles. While a realistic representation of emission sources may be challenging, the importance of source profile data is certainly appreciated. The meaning of "profile" in the context of this study should be clarified in the abstract and also in the title. A possible title might be "The effect of emission source chemical profiles on simulated PM2.5 components: sensitivity analysis with CMAQ 5.0.2".

Line 22: The claim that "current models do not perform very well in simulating PM2.5 components" is too general and does not reflect the literature.

Line 33: You highlight that the effect of changes in the source profile on the simulated PM2.5 components cannot be ignored as a major result. However, the composition of the emissions obviously affects the composition of the pollution (it is the exact relationship which is less obvious due to chemistry). In addition, the percentages given in the abstract are of limited relevance as they only apply to a single site.

The article still lacks information on how the MEIC emissions are combined with the source profiles.

Figs. 2 to 5: Please clarify that the figures present statistics of profiles from different data sources. It would be helpful to include all profiles considered - including the SPAP profiles - in Tables S3 to S11. Figs. 2 to 5 could be combined into one figure with four panels.

Article structure: Eq. (1) and its discussion should be part of Section 2.2. Please consider shortening the titles of Sections 3 to 5.

Line 281: Specify which station location is used.

Please clearly define in the main document what the numerical values represent, i.e., where appropriate, mention that you are discussing mean values and indicate the averaging period (e.g., Fig. 6, Eq. (2) etc.).

Fig. 6: Clarify the y-axis label (e.g., "Relative concentration difference")

Fig. 7 and Table 1: Please define SNA in the captions.

Table 2: The labels "Case S1" to "Case S4" are not used elsewhere, please reconsider the labelling. The choice of the factors used to enhance the components should be discussed and motivated in the main text. Note that to qualify as a model experiment description paper the article "should include the discussion of why particular choices were made in the experiment design" (cf. https://www.geoscientific-model-development.net/about/manuscript_types.html)

Line 374 and Fig. 8: The negative sensitivity coefficient of NH4p needs further explanation. For all other stations the corresponding value is positive (Table S14). Moreover, according to lines 326f, NH4p increased while PM2.5 did not change much, so a negative delta is surprising.

Fig. 8: The cyan colours are difficult to distinguish.

Line 450: Please insert the Eqs. (3) to (5) after "parameters" and adjust the following sentences accordingly.

Table 2: Please specify if these are averages. Given the variability of the simulations, it is not clear how representative these values are.

Data availability section: Please provide all input and model configuration data necessary to reproduce the results as well as all output data discussed in the article in a suitable data repository (e.g., Zenodo, see also https://www.geoscientific-model-development.net/policies/code_and_data_policy.html). For many readers, the SPAP database is behind a language barrier, please consider other ways to make it accessible (e.g., if the licence allows, provide the relevant data in another repository).

Hide

AR by Yinchang Feng on behalf of the Authors (27 Apr 2023) Author's response Author's tracked changes Manuscript

ED: Referee Nomination & Report Request started (20 May 2023) by Klaus Klingmüller

RR by Anonymous Referee #3 (07 Jul 2023)

ED: Reconsider after major revisions (25 Jul 2023) by Klaus Klingmüller

Dear authors

To make future correspondence more efficient: Please keep your replies concise. Since the review process is about your manuscript, please refrain from providing explanations, figures or tables that are not part of the revised manuscript. Also, please do not overly cite the changes you have made to the manuscript in your response, but rather simply indicate exactly where you have changed the manuscript (or supplement and data repositories).

Given the central importance of the R ratios in Table 2 (page 21) for Section 5, further clarification is required. By definition R1 >= R2 >= R3, so there is something wrong with at least the values for OHA and THA. In general, it is still not clear where the numerical values come from. In your reply you mention that they are monthly averages, but not in the manuscript. Also, are the R ratios averaged or are the concentrations used to calculate the ratios? And again, you need to discuss whether the temporal variability of the three ratios is small enough that it makes sense to consider their averages and the corresponding aerosol composition regimes.

Page 7, line 154:
As one referee pointed out, the time period you consider (October 2018) should be motivated, indicating whether choosing a different time period would affect your results.

Page 13, Fig. 3:
The percentages in the figure (and accordingly also on page 13 in lines 289f) do not agree with the values in Table S13 (for NH4+, Cl). Compared to D3_10_LOC_S1-S10.xlsx, PM2.5 is also different. In one version of the table in D3_10_LOC_S1-S10.xlsx, a factor of 100 is missing in the values for station 8.

Page 19, line 280:
The referee asks for the motivation for choosing station 8 and some information about the site.

Page 23, line 496:
There is no Fig. 9

Data availablility
- The repository https://zenodo.org/record/7865675 has to be referenced in the manuscript.
- What are the units of the values in the files "Data used/Output/D3_10_*/*/*.txt"?
- The tutorial for accessing the SPAP should included in the data repository.

Supplement, Table S27, last 2 cases:
"NaNO4" should read "NaNO3"

Kind regards

Klaus Klingmüller

Hide

AR by Yinchang Feng on behalf of the Authors (04 Sep 2023) Author's response Author's tracked changes Manuscript

ED: Referee Nomination & Report Request started (21 Sep 2023) by Klaus Klingmüller

RR by Anonymous Referee #3 (22 Sep 2023)

RR by Anonymous Referee #2 (23 Sep 2023)

ED: Publish subject to technical corrections (06 Oct 2023) by Klaus Klingmüller

AR by Yinchang Feng on behalf of the Authors (08 Oct 2023) Author's response Manuscript

Journal article(s) based on this preprint

22 Nov 2023

The effect of emission source chemical profiles on simulated PM_2.5 components: sensitivity analysis with the Community Multiscale Air Quality (CMAQ) modeling system version 5.0.2

Zhongwei Luo, Yan Han, Kun Hua, Yufen Zhang, Jianhui Wu, Xiaohui Bi, Qili Dai, Baoshuang Liu, Yang Chen, Xin Long, and Yinchang Feng

Geosci. Model Dev., 16, 6757–6771, https://doi.org/10.5194/gmd-16-6757-2023,https://doi.org/10.5194/gmd-16-6757-2023, 2023

Short summary

Zhongwei Luo, Yan Han, Kun Hua, Yufen Zhang, Jianhui Wu, Xiaohui Bi, Qili Dai, Baoshuang Liu, Yang Chen, Xin Long, and Yinchang Feng

Supplement

https://doi.org/10.5194/egusphere-2022-895-supplement

Zhongwei Luo, Yan Han, Kun Hua, Yufen Zhang, Jianhui Wu, Xiaohui Bi, Qili Dai, Baoshuang Liu, Yang Chen, Xin Long, and Yinchang Feng

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Short summary

This study explores the variation of source profile adopted in chemical transport models (CTMs) have an impact on the simulated results of chemical components in PM_2.5 based on sensitivity analysis. The impact on PM_2.5 components could not be ignored and the influence can be transmitted and linked among components. The representativeness and timeliness of the source profile should be paid enough attention in air quality simulation.

The underappreciated impact of emission source profiles on the simulation of PM_2.5 components: New evidence from sensitivity analysis

Journal article(s) based on this preprint

Interactive discussion

Interactive discussion

Peer review completion

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The underappreciated impact of emission source profiles on the simulation of PM2.5 components: New evidence from sensitivity analysis

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The underappreciated impact of emission source profiles on the simulation of PM_2.5 components: New evidence from sensitivity analysis