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Formation of secondary organic aerosol from wildfire emissions enhanced by long-time ageing

Nature Geoscience volume 17, pages 124–129 (2024)Cite this article

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Abstract

Wildfire smoke, consisting primarily of organic aerosols, has profound impacts on air quality, climate and human health. Wildfire organic aerosol evolves over long-time photochemical oxidation due to the formation and ageing of secondary organic aerosol, which substantially changes its magnitude and properties. However, there are large uncertainties in the long-time ageing of wildfire organic aerosol because of the distinct ageing behaviours of the complex organic emissions. Here we developed an oxidation model that simulates the ageing of wildfire organic emissions in the full volatility range on a precursor level and integrated insights from single-species ageing and wildfire emissions ageing experiments and field plume observations to constrain the long-time ageing of wildfire organic aerosol. The model captured the enhancement of organic aerosol mass (2–8 times) and oxygen-to-carbon ratio (1–4 times) in the wildfire ageing experiments. It also reconciled a long-standing discrepancy between field and laboratory observations of the magnitude of secondary organic aerosol formation. The model indicated large emissions-driven variations in precursor contributions to secondary organic aerosol, which further evolve with long-time ageing. The estimated global wildfire secondary organic aerosol production (139 ± 34 Tg per year) was much higher than previous studies omitting or under-constraining long-time ageing.

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Fig. 1: Modelling the FIREX mini-chamber experiments17.
Fig. 2: Precursor composition and contributions to SOA.
Fig. 3: Modelling SOA formation in wildfire plumes.
Fig. 4: Modelling global SOA production.

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Data availability

All data displayed in the figures are archived at https://figshare.com/projects/wildfire2023/178356.

Code Availability

Codes for the precursor-resolved 2D-VBS-MOSAIC model can be obtained from the corresponding author upon request.

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Acknowledgements

We thank J. H. Kroll and C. Y. Lim for providing the mini-chamber experiment data and for helpful data-related discussions. This work is supported by National Natural Science Foundation of China (22188102; B.Z. and S.W.) and Samsung Advanced Institute of Technology (Y.H., X.C., D.Y., B.Z. and S.W.). C.D.C. was supported by the California Air Resources Board (contract 18RD009). M.S. was supported by the US Department of Energy (DOE) Office of Science, Office of Biological and Environmental Research (BER) through the Early Career Research Program and DOE BER’s Atmospheric System Research programme. The Pacific Northwest National Laboratory is operated for DOE by Battelle Memorial Institute under contract DE-AC06-76RL01830. S.H.J. was supported by Assistance Agreement number R840008 awarded by the US Environmental Protection Agency to Colorado State University. This work has not been formally reviewed by EPA. The views expressed in this document are solely those of the authors and do not necessarily reflect those of the Agency. EPA does not endorse any products or commercial services mentioned in this publication.

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Authors and Affiliations

  1. State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, China

    Yicong He, Bin Zhao, Shuxiao Wang, Xing Chang, Dejia Yin, Boyang Feng & Jiming Hao

  2. State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing, China

    Yicong He, Bin Zhao, Shuxiao Wang, Xing Chang, Dejia Yin, Boyang Feng & Jiming Hao

  3. Université Paris-Est Créteil and Université Paris Cité, CNRS, LISA, Créteil, France

    Richard Valorso, Marie Camredon & Bernard Aumont

  4. Department of Mechanical Engineering, Colorado State University, Fort Collins, CO, USA

    Abraham Dearden & Shantanu H. Jathar

  5. Pacific Northwest National Laboratory, Richland, WA, USA

    Manish Shrivastava

  6. Carbon Neutrality Research Center, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China

    Zhe Jiang

  7. Civil and Environmental Engineering, University of California Davis, Davis, CA, USA

    Christopher D. Cappa

  8. Department of Environmental Science, Policy and Management, University of California Berkeley, Berkeley, CA, USA

    Lindsay D. Yee

  9. Department of Chemical Engineering, California Institute of Technology, Pasadena, CA, USA

    John H. Seinfeld

  10. Center for Atmospheric Particle Studies, Carnegie Mellon University, Pittsburgh, PA, USA

    Neil M. Donahue

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  1. Yicong He
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Contributions

Y.H., B.Z., S.W. and J.H. designed the study. Y.H. and B.Z. led model development. D.Y., X.C., R.V., M.C. and B.A. contributed to model development. B.F., A.D., S.H.J., L.D.Y. and J.H.S. provided experimental data. A.D. and S.H.J. helped with experimental data analysis. Y.H., B.Z. and S.W. wrote the paper with contributions from M.S., Z.J., C.D.C., N.M.D. and all other co-authors.

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Correspondence to Bin Zhao.

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Nature Geoscience thanks Marwa Majdi and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Xujia Jiang, in collaboration with the Nature Geoscience team.

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Extended data

Extended Data Fig. 1 Model Comparison with Observed Highly Aged Wildfire OA in West Africa.

The model-predicted ∆OA/∆CO is the average of the 500 Monte Carlo simulations and is compared to measurements inside highly aged biomass-burning aerosol layer in the free troposphere above western Africa38.

Extended Data Fig. 2 Same as Fig. 1 but with all I/SVOCs Lumped as Alkanes.

In comparison to simulating the specific I/SVOC precursors and their chemical classes, the increase in OA is greatly under-predicted and the O:C prediction are also affected. The box plots in (a) and (c) show distributions of each group of data points, including 20 experiments, at the 5% (lower whisker), 25% (box lower bound), 50% (box center line), 75% (box upper bound) and 95% (upper whisker) percentiles. The photochemical age is the integrated OH exposure divided by a typical atmospheric OH level (1.5 × 106 molecules cm−3). (b) and (d) show the model-measurement comparison at the end of experiments.

Extended Data Fig. 3 Predicted SOA Forming Potentials of Different Emission Sources by the Precursor-Resolved 2D-VBS-MOSAIC Model.

Initial emission from each source was aged for 48 hours and gas/particle partitioning was evaluated at a background OA of 10 µg m−3. The emission profiles for the VCP and mobile sources were based on experimental measurements.

Extended Data Fig. 4 Model Sensitivity to I/SVOC Treatment.

(a) shows the ratio between ∆OA and O:C prediction from the sensitivity case and the base case for the mini-chamber experiments. The base case treats S/L-VOCs as oxygenated aromatics and the sensitivity case treats them as polycyclic aromatics. (b) shows the averaged precursor contributions at the end of the experiments. The box plots in (a) include 20 experiments and show the 5% (lower whisker), 25% (box lower bound), 50% (box center line), 75% (box upper bound) and 95% (upper whisker) percentiles.

Extended Data Fig. 5 Model sensitivity to Heterogeneous Oxidation.

The base case (that is, as shown in Fig. 1) assumed γOH = 0.1. The box plots in (a) and (b) include 20 experiments and show the 5% (lower whisker), 25% (box lower bound), 50% (box center line), 75% (box upper bound) and 95% (upper whisker) percentiles.

Extended Data Fig. 6 FIREX and Global Emission Profiles.

(a) comparison between the averaged emission profiles from the 20 FIREX experiments and the estimated global-average profile based on Andreae6 for the species that exist in both profiles. The FIREX emission profile contains 20 data points for each species and the emission ratios are shown in mean value +/− 1.5 times standard deviation. The standard deviations of the FIREX profile have been multiplied by 1.5 to encompass the uncertainty range of the estimated global-average profile. For the Andreae profile6, the emission ratios are shown in mean value +/− one standard deviation and the number of data points varies by species; see the reference for more details. (b) the emission profiles of the different types of biomes from Andreae6, showing only the species that also exist in the FIREX profile.

Supplementary information

Supplementary Information

Supplementary Tables 1–4 and Figs. 1–12.

Supplementary Data 1

All 2D-VBS reactions represented in the form of text-based chemical reactions.

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He, Y., Zhao, B., Wang, S. et al. Formation of secondary organic aerosol from wildfire emissions enhanced by long-time ageing. Nat. Geosci. 17, 124–129 (2024). https://doi.org/10.1038/s41561-023-01355-4

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