the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 23 Jun 2026
Global budgets of atmospheric primary and secondary organic aerosols constrained by full-volatility-range organic emissions
Abstract. Organic aerosol (OA) constitutes a major fraction of tropospheric submicron particulate matter, with primary (POA) and secondary (SOA) components exhibiting different physicochemical properties and health impacts. The POA and SOA budgets are however highly uncertain, and the results from different model studies are confusing because of more or less consideration of the volatility distributions of organic precursor emissions and inconsistent attributions of model tracers in model-observation comparisons. Here we develop an OA simulation framework in GEOS-Chem that resolves the full volatility spectrum of organic precursor emissions from anthropogenic sources and open biomass burning. The model reasonably reproduces the observed OC, POA, and SOA concentrations from comprehensive surface, shipborne, and airborne datasets, providing a consistent global validation. The model simulations suggest greater POA (0.5 Tg) and SOA burdens (2.0 Tg) and potentially stronger and more widespread impacts of the OA components on air quality, health, and radiation than previous estimates, led by both of the emission updates and the revised OA scheme. The simulated global SOA production is about 106 Tg in 2018, 46 % of which is contributed by open biomass burning. The results demonstrate distinct regional variations in the dominant source types and population exposure distributions of POA and SOA, highlighting the needs for SOA mitigation, multi-sector control measures, and clean energy replacements for long-term health-oriented air quality improvements globally. The model results are sensitive to the emissions and wet-deposition parameterization, calling for more measurement constraints on local emission factors and the deposition fluxes of OA and its components.
Competing interests: At least one of the (co-)authors is a member of the editorial board of Atmospheric Chemistry and Physics.
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 paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors. Views expressed in the text are those of the authors and do not necessarily reflect the views of the publisher.- Preprint
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Status: open (until 04 Aug 2026)
- RC1: 'Comment on egusphere-2026-3160', Anonymous Referee #1, 14 Jul 2026 reply
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This paper investigates the impact of full-volatility-range organic emissions on the global budgets of organic aerosols using the GEOS-Chem atmospheric chemistry transport model. The authors highlight the importance of incorporating a wide volatility spectrum of organic precursor emissions from anthropogenic sources and open biomass burning on global SOA production. This approach leads to greater POA and SOA burdens compared to the previous model configuration. Overall, the proposed developments appear to improve the model's agreement with a range of observational datasets used for the present study, compared to the previous organic aerosol representation.
General comments:
The manuscript provides a detailed description of the emissions used for this study. I acknowledge the authors' effort in presenting all of the different sensitivity simulations; however, the aim of this strategy is not clearly explained until the end of the manuscript, which makes it challenging for the reader. The discussion of the results is well-organized, though some repetitions exist at the beginning of certain sections. Aside from these minor concerns, the discussion of the modeling methods could have been more explicit in some cases to better justify the conclusions of this study, e.g., regarding whether the assumptions used are driven by observational/laboratory constraints, computational/modeling limitations, or both. Therefore, I recommend a revision to address such issues before the submission is accepted.
Major comments:
The authors present their new scheme as a "full-volatility-range" representation. However, it appears to be more of a hybrid volatility-based scheme that combines traditional VBS theory and empirical SOA yield parameterizations. The latter is also supported by the authors' statement in the Conclusions that "the SOA scheme remains a volatility-lumped and yield-based approach." Stating this explicitly in the Methods, rather than in the Conclusion of the manuscript, would improve the transparency of the methodology section and help the reader better understand these developments.
The authors also state in the Methods that "Under low-NOx conditions, the IVOC-SOA is treated as nonvolatile mass with a constant SOA yield of 73%." As far as I understand, this suggests that a constant yield is applied for every IVOC category in this case, which is a strong assumption. IVOC oxidation is parameterized completely differently between high- and low-NOx conditions; i.e., under high-NOx conditions, oxidation products are distributed among separate volatility bins, while under low-NOx conditions, SOA-IVOCs are parameterized as nonvolatile with a fixed yield. It would be very useful for the reader if the authors discussed more explicitly why they use different parameterizations under high- and low-NOx conditions and what the added value of this assumption is compared to other modeling studies. I would also propose further discussing the implications for SOA partitioning/evaporation under different NOx conditions in the model (and what thresholds are used).
Minor comments:
Abstract: The transition to "population exposure" and "air quality improvements" seems quite abrupt, as this is not the focus of this study. I suggest moving this sentence to the conclusion/discussion section, rather than the abstract, which should focus on the actual developments the paper presents.
Lines 61-62: It would be helpful to briefly refer to why an overestimation appears for the nonvolatile POA assumption.
Section 1: An outlook paragraph presenting what each of the following sections discusses will be helpful to the reader.
Section 2.2: A table presenting the global annual emissions of S/LVOCs and IVOCs from anthropogenic sources and biomass burning sources used for each scheme (i.e., the simple and complex OA schemes, etc.) would be useful.
Lines 129-133: Here, the authors refer to heterogeneous reactions for IEPOX and glyoxal/methylglyoxal, but they also include a sentence about neglecting isoprene SOA formation under low-NOx conditions. Please explain briefly why low-NOx isoprene SOA formation is neglected and whether this impacts heterogeneous SOA production. If this is not the case, it would be better to move this sentence earlier to improve the flow to avoid confusion.
Line 137: Please provide information about the enthalpy of vaporization used for all components.
Lines 148-149: Please clarify whether POA and SOA are allowed to re-evaporate, particularly under low-NOx conditions.
Lines 204-212: The authors appear to attempt to justify why they compare observations from 2011–2022 with a single model simulation as the reference year. Although this approach includes several assumptions, it is common practice in such modeling studies, focusing mostly on the "climatological" evaluation of the model rather than interannual variability. However, it is unclear from the discussion whether only one year of observation (that of the model simulation) is used, or if the model is run with "average-year" emissions for comparison with observations from several nearby years. Note, however, that since the authors state that biomass burning contributes 46% to total SOA formation, this can vary significantly between years due to climate variability (e.g., El Niño). The earlier statement, therefore, appears more valid for anthropogenic emissions than for biomass burning emissions. Moreover, in Sect. 4, lines 403-404, the authors mention: "Significant changes in SOA production are expected in the future because of the reduction of anthropogenic emissions driven by tightened regional air quality standards (e.g., in China) and the intensified wildfires." This appears somewhat inconsistent with the earlier claim that using a single year would counteract potential biases. Overall, I recommend explicitly stating your methodology for the model evaluation and simply discussing the limitations of using a single reference year.
Lines 355-356: Since this is not a climate study, it is better to move this sentence to the discussion section.
Lines 356-357: The authors imply that roughly 31% of the total SOA burden in the model comes from anthropogenic SOA. How does this finding compare with other studies? Please discuss.
Lines 360-361: Such a sentence should also be placed in the introduction to justify the later choice of an additional sensitivity simulation focusing on the wet removal efficiency. Otherwise, this comes very abruptly in the text.
Section 2.4: Does the model separately track the different species calculated by VBS? How many (additional) species does the model use for the different sensitivity simulations? How much does the computational cost increase depending on the simulation setup?
Section 4: Please briefly discuss which of the assumptions used in the model have the greatest impact on the reported increases in POA and SOA burdens.
Technical Comments:
Line 33: validation → evaluation
Line 45: "Immediately?" Under what ambient atmospheric conditions? Please remove or rewrite by explaining in more detail.
Line 400: Better split into a new paragraph.
Table S1: What do OPOAs stand for? Also add some explanation for other species’ names.
Manuscript: The names of the main simulations, i.e., "Default" and "Base," are somewhat confusing since they both imply “reference” simulations. You can consider adopting more descriptive names/abbreviations or explicitly note their definitions throughout the manuscript (esp. figures/tables) to avoid confusion.