Formation and Evolution of VOCs, Black Carbon, and SOA in Wildfire Plumes
DOI:
https://doi.org/10.54097/b2mmd794Keywords:
Volatile Organic Compounds; Black Carbon; Secondary Organic Aerosol; Wildfire Emissions.Abstract
Wildfires are major sources of atmospheric gases and particles that influence air quality, radiative forcing, and human health on local to global scales. This review synthesizes laboratory, field, and modeling evidence on the formation of volatile organic compounds (VOCs) and black carbon (BC) during combustion and the subsequent evolution of these emissions into secondary organic aerosol (SOA). Combustion regime and fuel composition govern VOC speciation and BC yields; multipathway oxidation (OH, O₃, NO₃, and aqueous processes) produces SOA within hours to days; and vertical transport, including pyrocumulonimbus lofting, can extend particle lifetimes to months, enhancing their climatic relevance. Heterogeneous BC–SOA mixing states modulate optical properties, hygroscopicity, and atmospheric aging, thereby shaping radiative impacts and long-range transport. This research highlights key uncertainties in S/IVOC emissions, nighttime and aqueous chemistry, BC coating heterogeneity, and stratospheric smoke persistence, and outlines priorities for improving air-quality forecasts, climate projections, and health-risk assessments in the context of a warming world where wildfire frequency is expected to rise.
Downloads
References
[1] Andela N, et al. A human-driven decline in global burned area. Science, 2017, 356(6345): 1356-1362.
[2] Wu C, et al. Reduced global fire activity due to human demography slows global warming by enhanced land carbon uptake. Proceedings of the National Academy of Sciences of the United States of America, 2022, 119(20): 1-12.
[3] Kampf S K, et al. Increasing wildfire impacts on snowpack in the western U.S. Proceedings of the National Academy of Sciences of the United States of America, 2022, 119(39): 1-7.
[4] Bourgeois I, et al. Large contribution of biomass burning emissions to ozone throughout the global remote troposphere. Proceedings of the National Academy of Sciences of the United States of America, 2021, 118(52): 1-10.
[5] Fierce L, et al. Radiative absorption enhancements by black carbon controlled by particle-to-particle heterogeneity in composition. Proceedings of the National Academy of Sciences of the United States of America, 2020, 117(10): 5196-5203.
[6] Liu J C, et al. Wildfire-specific fine particulate matter and risk of hospital admissions in urban and rural counties. Epidemiology, 2017, 28(1): 77-85.
[7] Robinson A L, et al. Rethinking organic aerosols: semivolatile emissions and photochemical aging. Science, 2007, 315(5816): 1259-1262.
[8] Zhang H, et al. Monoterpenes are the largest source of summertime organic aerosol in the southeastern United States. Proceedings of the National Academy of Sciences of the United States of America, 2018, 115(9): 2038-2043.
[9] Baboomian V J, et al. Sunlight can convert atmospheric aerosols into a glassy solid state and modify their environmental impacts. Proceedings of the National Academy of Sciences of the United States of America, 2022, 119(43): 1-10.
[10] Dickinson G N, et al. Health risk implications of volatile organic compounds in wildfire smoke during the 2019 FIREX-AQ campaign and beyond. GeoHealth, 2022, 6(8): e2021GH000546.
[11] Perraud V, et al. Nonequilibrium atmospheric secondary organic aerosol formation and growth. Proceedings of the National Academy of Sciences of the United States of America, 2012, 109(8): 2836-2841.
[12] Kodros J K, et al. Rapid dark aging of biomass burning as an overlooked source of oxidized organic aerosol. Proceedings of the National Academy of Sciences of the United States of America, 2020, 117(52): 33028-33033.
[13] Ahern A T, et al. Production of secondary organic aerosol during aging of biomass burning smoke from fresh fuels and its relationship to VOC precursors. Journal of Geophysical Research Atmospheres, 2019, 124(6): 3583-3606.
[14] Forrister H, et al. Evolution of brown carbon in wildfire plumes. Geophysical Research Letters, 2015, 42(11): 4623-4630.
[15] Solomon S, et al. On the stratospheric chemistry of midlatitude wildfire smoke. Proceedings of the National Academy of Sciences of the United States of America, 2022, 119(10): 1-9.
[16] Uecker T M, et al. The post-wildfire impact of burn severity and age on black carbon snow deposition and implications for snow water resources, Cascade Range, Washington. Journal of Hydrometeorology, 2020, 21(8): 1777-1792.
[17] Higgins J P T, et al. A re-evaluation of random-effects meta-analysis. Journal of the Royal Statistical Society. Series A (Statistics in Society), 2009, 172(1): 137-159.
[18] Wallach J D, et al. Sex based subgroup differences in randomized controlled trials: empirical evidence from Cochrane meta-analyses. BMJ: British Medical Journal, 2016, 355: i5826.
[19] Evangelou E, et al. Uncertainty in heterogeneity estimates in meta-analyses. BMJ: British Medical Journal, 2007, 335(7626): 914-916.
[20] Madansky A. The fitting of straight lines when both variables are subject to error. Journal of the American Statistical Association, 1959, 54(285): 173-205.
[21] Sen P K. Estimates of the regression coefficient based on Kendall’s tau. Journal of the American Statistical Association, 1968, 63(324): 1379-1389.
[22] Egger M, et al. Bias in meta-analysis detected by a simple, graphical test. BMJ: British Medical Journal, 1997, 315(7109): 629-634.
[23] Duval S, Tweedie R. A nonparametric trim and fill method of accounting for publication bias in meta-analysis. Journal of the American Statistical Association, 2000, 95(449): 89-98.
[24] Mathur M B, VanderWeele T J. Sensitivity analysis for publication bias in meta-analyses. Journal of the Royal Statistical Society. Series C (Applied Statistics), 2020, 69(5): 1091-1119.
[25] Lau J, et al. Evidence based medicine: the case of the misleading funnel plot. BMJ: British Medical Journal, 2006, 333(7568): 597-600.
[26] Efron B. Bootstrap methods: another look at the jackknife. The Annals of Statistics, 1979, 7(1): 1-26.
[27] Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society. Series B (Methodological), 1995, 57(1): 289-300.
Downloads
Published
Issue
Section
License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.








