Incorporation of lumped IVOC emissions into the ORACLE model (V1.1): A multi-product framework for assessing global SOA formation from internal combustion engines

Scholz, Susanne M. C.; Karydis, Vlassis A.; Gkatzelis, Georgios I.; Fuchs, Hendrik; Pandis, Spyros N.; Tsimpidi, Alexandra P.

doi:10.5194/egusphere-2025-2510

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https://doi.org/10.5194/egusphere-2025-2510

Preprints

03 Jul 2025

| 03 Jul 2025

Incorporation of lumped IVOC emissions into the ORACLE model (V1.1): A multi-product framework for assessing global SOA formation from internal combustion engines

Susanne M. C. Scholz, Vlassis A. Karydis, Georgios I. Gkatzelis, Hendrik Fuchs, Spyros N. Pandis, and Alexandra P. Tsimpidi

Abstract. Secondary organic aerosol (SOA) is a major component of particulate matter but is often underpredicted in chemistry climate models. Recent advances in measuring and resolving the chemically complex structure of intermediate volatile organic compounds (IVOC) have shown that IVOCs, despite their high SOA yields, have long been underrepresented in models. These compounds are key precursors of SOA from emissions in the road transport sector and significantly influence SOA formation. Understanding vehicle emissions, their chemistry, and their SOA-forming potential is essential for accurately estimating their contributions to atmospheric SOA and global organic aerosol loads. To improve this understanding, we have updated the organic module ORACLE in the global chemistry climate model EMAC. The existing IVOC representation was based on scaled organic carbon (OC) emissions and a highly parameterized volatility basis set (VBS) which underestimated global IVOC emissions and oversimplified their chemistry. Here, we replaced this approach with a lumped species framework, in which experimental data for gasoline and diesel emissions were grouped into seven lumped species based on their chemical properties and hydroxylation potentials. These species were linked to adjusted emission inventories for regional diesel and gasoline consumption. A 10-year simulation with the updated ORACLE-IVOC model resulted in significant changes. The global atmospheric burden of road transport IVOC-derived SOA (SOA-iv) increased by 1 order of magnitude, from 0.014 Tg to 0.13 Tg. The composition of road transport organic aerosol (OA) shifted, with SOA-iv contributing 2.5 to 13 times more than the primary organic aerosol (POA) and SOA derived from semi-volatile organic compounds combined. In the results using the previous model, this ratio was between 0.4 and 1.1. The geographical distribution of OA also changed. Regions rich in gasoline relative to diesel emissions experienced higher concentration increases, and remote areas experienced elevated concentrations due to more efficient long-range transport of the new lumped IVOC species. Overall, these changes led to a significant increase in the contribution of road transport to total anthropogenic SOA-iv from an average value of 3 % to 35 %.

Received: 29 May 2025 – Discussion started: 03 Jul 2025

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Journal article(s) based on this preprint

18 Dec 2025

Incorporation of lumped IVOC emissions into the ORACLE model (V1.1): a multi-product framework for assessing global SOA formation from internal combustion engines

Susanne M. C. Scholz, Vlassis A. Karydis, Georgios I. Gkatzelis, Hendrik Fuchs, Spyros N. Pandis, and Alexandra P. Tsimpidi

Geosci. Model Dev., 18, 10119–10142, https://doi.org/10.5194/gmd-18-10119-2025,https://doi.org/10.5194/gmd-18-10119-2025, 2025

Short summary

Susanne M. C. Scholz, Vlassis A. Karydis, Georgios I. Gkatzelis, Hendrik Fuchs, Spyros N. Pandis, and Alexandra P. Tsimpidi

Interactive discussion

Status: closed

RC1:
'Comment on egusphere-2025-2510', Anonymous Referee #1, 12 Aug 2025
In this study, the authors have updated the organic module ORACLE in the global model EMAC by adding the SOA aging process from seven lumped IVOC precursor groups. A new road transport IVOC emission inventory with chemical speciation was also established and input into the updated model. However, given that there is already sufficient research basis on IVOC emission factors by vehicle type and emission standard, the newly established road transport IVOC emission inventory appears to be rather coarse. Moreover, the authors did not demonstrate the superiority of the new model compared to previous models, such as its better alignment with the observations. Overall, this study needs establishment of a more detailed emission inventory, and careful consideration of scenario design to more clearly discuss the significance of the new mechanism and the new inventory, to draw more scientifically robust and reliable conclusions.
Line 17-19: Are IVOC emissions based on scaled OC definitely underestimating IVOC emissions? What if IVOC emissions are overestimated in some regions or sectors?

Introduction: The introduction section needs to be reorganized. Specifically, some general knowledge about IVOCs could be simplified, while the importance of road transport sector and the limitations of current IVOCs oxidation mechanisms in models should be emphasized to better demonstrate the novelty of this study.

Line 63-66: More quantitative information is needed here, e.g., the proportion of road transport IVOCs to total IVOCs, to prove the importance of road transport IVOCs.

Line 66-71: These statements describe the general characteristics of IVOCs, which could be stated earlier. For example, in the second paragraph of introduction.

Line 73-80: Too many technical details about IVOC measurements could distract the readers from the main focus of this research.

Line 81: The saturation concentration should be between 10³ and 10⁶, as you have defined in Line 49.

Line 111-114: The example of Sartelet et al. (2018) is confusing. Why do you emphasize good agreement with chamber experiment measurements rather than observations, since it is a chemical transport modeling study. Line 113-114 is also confusing.

Line 114-120: Lu et al. (2020) has realized the species lumping by chemical structure and volatility in modeling. Therefore, you need to further elaborate on the necessity of applying Manavi and Pandis (2022) in models. For example, what’s the limitation of Lu et al. (2020) in modelling SOA; what aspects can Manavi and Pandis (2022) improve compared to Lu et al. (2020); why a new chemical lumping of IVOCs and the elimination of volatility lumping, as in Manavi and Pandis (2022), are needed compared to the approach of Lu et al. (2020)

Line 128-133: Why can’t the new module be applied to other sectors? How will other sectors incorporated into this module if other sectors emit precursors of similar chemical structure as road transport sector?

Line 155-156: How do you decide the volatility distribution for OC? Are there supporting measurements?

Section 2.2: The two modules have incorporated two different IVOC emission inputs. In this way, the simulation results from the two scenarios not only reflect the contribution of new SOA mechanisms, but also reflect the contribution of the new IVOC emission inventory. We can’t distinguish how much the new SOA mechanism has contributed to the improvement in SOA modelling.

Please add a brief summary of the scenario settings in this study, including the SOA mechanism, I/S/LVOC and VOC emission input of road transport and other sectors, and other settings in EMAC to help readers better understand the simulation results.

Figure 2: Is fragmentation considered in this mechanism? Fragmentation is considered an important atmospheric process of SOA in previous literatures.

Line 270-271: Road transport VOC emissions generate from not only gasoline/diesel exhausts and road asphalt, but also gasoline evaporation. Assuming that the VOC emissions are solely from exhausts would lead to overestimation of IVOCs.

Section 2.3.1: Why is country-level data compiled to form regional shares of diesel and gasoline? How about directly applying country-level data to improve spatial resolution?

Section 2.3.2: Here the authors distinguished IVOC/VOC by fuel, which has neglected the different IVOC/VOC characteristics by vehicle type and emission standard. Given that the previous measurements you have mentioned, and the road transport emission factor papers (e.g., DOI: 10.5194/acp-17-12709-2017; 10.1016/j.scitotenv.2022.158312; 10.1016/j.envpol.2022.119887) have refined IVOC EF by standard and vehicle types, I think it is necessary to further differentiate the IVOC/VOC across different vehicle types and emission standards, at least in regions with more comprehensive statistical data, such as the United States, Europe, and China. Moreover, there have been global and regional emission inventories quantifying road transport IVOCs directly by emission factors (DOI: 10.1016/j.oneear.2022.03.015; 10.1021/acs.est.3c04106), which might be a better method than applying IVOC/VOC ratio, but there is limited discussion about why the authors have chosen IVOC/VOC ratio rather than emission factors.

Line 349-367: We have already known that IVOCs calculated by empirical ratios are unreliable and bear large uncertainties, so I don’t think it’s necessary to compare the new emission inventory with this 1.5´OC emission inventory in such detail. Please consider eliminating the analysis and moving Figure 5 to SI to simply demonstrate the emission differences in two modelling scenarios.

Figure 8: The graphs indicated by (a) and (b) are reversed in the caption.

Results: Each subsection in section 4 has compared the difference between ORACLE-IVOC and ORACLE-base. However, there is no comparison between simulation results and observations, so it’s hard to confirm the superiority of ORACLE-IVOC compared to ORACLE-base.

Line 584-586: Please elaborate more on how the updated framework improves the long-range transport of IVOC in the methodology.

Section 4.3: If the total amount and the chemical composition of IVOC emissions of the other sectors are the same in ORACLE-IVOC and ORACLE-base, I think neither of the scenarios could provide a scientifically sound understanding of contribution of road transport IVOC to total SOA-iv.
Citation: https://doi.org/10.5194/egusphere-2025-2510-RC1
- AC2: 'Reply on RC1', Alexandra Tsimpidi, 07 Oct 2025
  
  The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2025/egusphere-2025-2510/egusphere-2025-2510-AC2-supplement.pdf
  
  Citation: https://doi.org/10.5194/egusphere-2025-2510-AC2
RC2:
'Comment on egusphere-2025-2510', Anonymous Referee #2, 30 Aug 2025

(I apologize to the authors, reviewer, and editor for my late review; I appreciate your patinece.)
In this study, the authors present an update to the chemistry module ORACLE by incorporating IVOC emissions from the road transport sector. It turns out the update made significant changes to the simulation OA (e.g., Fig 9). I think this is a high-quality study; the work is well written. I think it fits GMD relatively well. And it is of general interest to the community. I only offer two minor substantive comments below. These are non-blocking and I therefore support the publication of this work. For the record, I found Reviewer 1's comments to be worthy of consideration and I encourage the authors to address them.

L124: Not sure if the “, but” is needed here? And maybe perspectives (plural) at the end of the sentence?
L138: Can you say more why you chose not to discuss SOA formation? It’s not readily clear to me that it is not related. You are not saying ultimate SOA load in both versions will be the same, right? Maybe L323 is related? Planned future work?
L617: Uhh. Didn’t you say you didn’t want to discuss SOA formation? Am I confused?
Code/data: Not sure if this aligns with GMD’s policies, but I defer to the editor. As a curious reviewer, I couldn’t see the code and I couldn’t see underlying data. The underlying data isn’t even cited in this statement. Nbd on my end, but just pointing it out to the editor.

Citation: https://doi.org/10.5194/egusphere-2025-2510-RC2
- AC1: 'Reply on RC2', Alexandra Tsimpidi, 07 Oct 2025
  
  The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2025/egusphere-2025-2510/egusphere-2025-2510-AC1-supplement.pdf
  
  Citation: https://doi.org/10.5194/egusphere-2025-2510-AC1

Interactive discussion

Status: closed

RC1:
'Comment on egusphere-2025-2510', Anonymous Referee #1, 12 Aug 2025
In this study, the authors have updated the organic module ORACLE in the global model EMAC by adding the SOA aging process from seven lumped IVOC precursor groups. A new road transport IVOC emission inventory with chemical speciation was also established and input into the updated model. However, given that there is already sufficient research basis on IVOC emission factors by vehicle type and emission standard, the newly established road transport IVOC emission inventory appears to be rather coarse. Moreover, the authors did not demonstrate the superiority of the new model compared to previous models, such as its better alignment with the observations. Overall, this study needs establishment of a more detailed emission inventory, and careful consideration of scenario design to more clearly discuss the significance of the new mechanism and the new inventory, to draw more scientifically robust and reliable conclusions.
Line 17-19: Are IVOC emissions based on scaled OC definitely underestimating IVOC emissions? What if IVOC emissions are overestimated in some regions or sectors?

Introduction: The introduction section needs to be reorganized. Specifically, some general knowledge about IVOCs could be simplified, while the importance of road transport sector and the limitations of current IVOCs oxidation mechanisms in models should be emphasized to better demonstrate the novelty of this study.

Line 63-66: More quantitative information is needed here, e.g., the proportion of road transport IVOCs to total IVOCs, to prove the importance of road transport IVOCs.

Line 66-71: These statements describe the general characteristics of IVOCs, which could be stated earlier. For example, in the second paragraph of introduction.

Line 73-80: Too many technical details about IVOC measurements could distract the readers from the main focus of this research.

Line 81: The saturation concentration should be between 10³ and 10⁶, as you have defined in Line 49.

Line 111-114: The example of Sartelet et al. (2018) is confusing. Why do you emphasize good agreement with chamber experiment measurements rather than observations, since it is a chemical transport modeling study. Line 113-114 is also confusing.

Line 114-120: Lu et al. (2020) has realized the species lumping by chemical structure and volatility in modeling. Therefore, you need to further elaborate on the necessity of applying Manavi and Pandis (2022) in models. For example, what’s the limitation of Lu et al. (2020) in modelling SOA; what aspects can Manavi and Pandis (2022) improve compared to Lu et al. (2020); why a new chemical lumping of IVOCs and the elimination of volatility lumping, as in Manavi and Pandis (2022), are needed compared to the approach of Lu et al. (2020)

Line 128-133: Why can’t the new module be applied to other sectors? How will other sectors incorporated into this module if other sectors emit precursors of similar chemical structure as road transport sector?

Line 155-156: How do you decide the volatility distribution for OC? Are there supporting measurements?

Section 2.2: The two modules have incorporated two different IVOC emission inputs. In this way, the simulation results from the two scenarios not only reflect the contribution of new SOA mechanisms, but also reflect the contribution of the new IVOC emission inventory. We can’t distinguish how much the new SOA mechanism has contributed to the improvement in SOA modelling.

Please add a brief summary of the scenario settings in this study, including the SOA mechanism, I/S/LVOC and VOC emission input of road transport and other sectors, and other settings in EMAC to help readers better understand the simulation results.

Figure 2: Is fragmentation considered in this mechanism? Fragmentation is considered an important atmospheric process of SOA in previous literatures.

Line 270-271: Road transport VOC emissions generate from not only gasoline/diesel exhausts and road asphalt, but also gasoline evaporation. Assuming that the VOC emissions are solely from exhausts would lead to overestimation of IVOCs.

Section 2.3.1: Why is country-level data compiled to form regional shares of diesel and gasoline? How about directly applying country-level data to improve spatial resolution?

Section 2.3.2: Here the authors distinguished IVOC/VOC by fuel, which has neglected the different IVOC/VOC characteristics by vehicle type and emission standard. Given that the previous measurements you have mentioned, and the road transport emission factor papers (e.g., DOI: 10.5194/acp-17-12709-2017; 10.1016/j.scitotenv.2022.158312; 10.1016/j.envpol.2022.119887) have refined IVOC EF by standard and vehicle types, I think it is necessary to further differentiate the IVOC/VOC across different vehicle types and emission standards, at least in regions with more comprehensive statistical data, such as the United States, Europe, and China. Moreover, there have been global and regional emission inventories quantifying road transport IVOCs directly by emission factors (DOI: 10.1016/j.oneear.2022.03.015; 10.1021/acs.est.3c04106), which might be a better method than applying IVOC/VOC ratio, but there is limited discussion about why the authors have chosen IVOC/VOC ratio rather than emission factors.

Line 349-367: We have already known that IVOCs calculated by empirical ratios are unreliable and bear large uncertainties, so I don’t think it’s necessary to compare the new emission inventory with this 1.5´OC emission inventory in such detail. Please consider eliminating the analysis and moving Figure 5 to SI to simply demonstrate the emission differences in two modelling scenarios.

Figure 8: The graphs indicated by (a) and (b) are reversed in the caption.

Results: Each subsection in section 4 has compared the difference between ORACLE-IVOC and ORACLE-base. However, there is no comparison between simulation results and observations, so it’s hard to confirm the superiority of ORACLE-IVOC compared to ORACLE-base.

Line 584-586: Please elaborate more on how the updated framework improves the long-range transport of IVOC in the methodology.

Section 4.3: If the total amount and the chemical composition of IVOC emissions of the other sectors are the same in ORACLE-IVOC and ORACLE-base, I think neither of the scenarios could provide a scientifically sound understanding of contribution of road transport IVOC to total SOA-iv.
Citation: https://doi.org/10.5194/egusphere-2025-2510-RC1
- AC2: 'Reply on RC1', Alexandra Tsimpidi, 07 Oct 2025
  
  The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2025/egusphere-2025-2510/egusphere-2025-2510-AC2-supplement.pdf
  
  Citation: https://doi.org/10.5194/egusphere-2025-2510-AC2
RC2:
'Comment on egusphere-2025-2510', Anonymous Referee #2, 30 Aug 2025

(I apologize to the authors, reviewer, and editor for my late review; I appreciate your patinece.)
In this study, the authors present an update to the chemistry module ORACLE by incorporating IVOC emissions from the road transport sector. It turns out the update made significant changes to the simulation OA (e.g., Fig 9). I think this is a high-quality study; the work is well written. I think it fits GMD relatively well. And it is of general interest to the community. I only offer two minor substantive comments below. These are non-blocking and I therefore support the publication of this work. For the record, I found Reviewer 1's comments to be worthy of consideration and I encourage the authors to address them.

L124: Not sure if the “, but” is needed here? And maybe perspectives (plural) at the end of the sentence?
L138: Can you say more why you chose not to discuss SOA formation? It’s not readily clear to me that it is not related. You are not saying ultimate SOA load in both versions will be the same, right? Maybe L323 is related? Planned future work?
L617: Uhh. Didn’t you say you didn’t want to discuss SOA formation? Am I confused?
Code/data: Not sure if this aligns with GMD’s policies, but I defer to the editor. As a curious reviewer, I couldn’t see the code and I couldn’t see underlying data. The underlying data isn’t even cited in this statement. Nbd on my end, but just pointing it out to the editor.

Citation: https://doi.org/10.5194/egusphere-2025-2510-RC2
- AC1: 'Reply on RC2', Alexandra Tsimpidi, 07 Oct 2025
  
  The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2025/egusphere-2025-2510/egusphere-2025-2510-AC1-supplement.pdf
  
  Citation: https://doi.org/10.5194/egusphere-2025-2510-AC1

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 (30 Oct 2025) Author's response Author's tracked changes Manuscript

ED: Publish as is (25 Nov 2025) by Jason Williams

AR by Alexandra Tsimpidi on behalf of the Authors (03 Dec 2025)

Journal article(s) based on this preprint

18 Dec 2025

Incorporation of lumped IVOC emissions into the ORACLE model (V1.1): a multi-product framework for assessing global SOA formation from internal combustion engines

Susanne M. C. Scholz, Vlassis A. Karydis, Georgios I. Gkatzelis, Hendrik Fuchs, Spyros N. Pandis, and Alexandra P. Tsimpidi

Geosci. Model Dev., 18, 10119–10142, https://doi.org/10.5194/gmd-18-10119-2025,https://doi.org/10.5194/gmd-18-10119-2025, 2025

Short summary

Susanne M. C. Scholz, Vlassis A. Karydis, Georgios I. Gkatzelis, Hendrik Fuchs, Spyros N. Pandis, and Alexandra P. Tsimpidi

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https://doi.org/10.5194/egusphere-2025-2510-supplement

Susanne M. C. Scholz, Vlassis A. Karydis, Georgios I. Gkatzelis, Hendrik Fuchs, Spyros N. Pandis, and Alexandra P. Tsimpidi

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We studied how pollution from cars and trucks contributes to tiny airborne particles that affect air quality and climate. These particles, called secondary organic aerosols, were often underestimated in global models. By improving how certain overlooked emissions from fuel use are represented in our model, we found that their impact is much larger than previously thought. Our results suggest that road traffic plays a far greater role in global air pollution than earlier estimates showed.


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