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Researchers Provide New Insight into Formation of Secondary Organic Aerosols in Atmosphere; Implications for Health and Climate Models


Representation of SOA formation and growth based on the experimental observations.

Perraud et al.
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A new study led by a team from the University of California, Irvine is providing important new insight into the mechanisms by which secondary organic aerosol (SOA) particles form and grow. The work, published in the Proceedings of the National Academy of Sciences, has implications for model formulations of SOA, both outdoors and indoors, and the associated health and climate impacts predicted based on those model outputs.

Airborne particles have significant impacts on human health and can also influence the radiative balance of the atmosphere through a variety of mechanisms; the latter effects represent the largest uncertainty in calculations of climate change. A major component of these airborne particles are secondary organic aerosols (SOA), formed via the oxidation of gaseous anthropogenic (e.g., pollutants) and biogenic precursor compounds. 

The precise processes and species leading to SOA formation and growth are not fully understood; however, models used by regulators for decades have assumed that organic aerosols in pollution form liquid droplets that quickly dissolve potentially unhealthy gases. (The current models typically assume instantaneous equilibrium partitioning of semi-volatile organic compounds (SVOCs) between existing liquid airborne particles and the gas phase.)

However, these regional and global chemical models generally under-predicted SOA concentrations compared to those from field measurements, the researchers note in their paper. Despite improvements, model-measurement discrepancies of a factor of two or more remain common.

The new study, led by University of California, Irvine air chemist Barbara Finlayson-Pitts, instead found that a kinetically limited/condensation growth mechanism can provide a better fit to field data.

...the combination of experiments reported here suggests that uptake of SVOCs into ambient SOA is consistent with a kinetically limited/condensation growth mechanism. Adsorbed SVOCs become incorporated into the bulk by being buried by incoming gas molecules and do not reevaporate, at least on the timescale of the experiments. If this process proves to be a general phenomenon, then the current formation and growth of SOA is not appropriately represented in most atmospheric models that rely on instantaneous thermodynamic equilibrium of SVOCs into liquid particles. Thus, current treatments of SOA formation and growth in models for both indoor and outdoor environments and the predicted impacts based on these models may need to be revisited.

—Perraud et al.

They check in, and they don’t check out. They cannot escape. The material does not readily evaporate and may live longer and grow faster in total mass than previously thought. This is consistent with related studies showing that smog particles may be an extremely viscous tar.

—Barbara Finlayson-Pitts

The study combined α-pinene, a common ingredient in household cleaners such as Pine Sol and outdoor emissions, with oxides of nitrogen and ozone to mimic smog buildup. Atmospheric chemist Alla Zelenyuk at the Department of Energy’s Pacific Northwest National Laboratory (PNNL) evaluated millions of the artificial smog particles one-by-one using a one-of-a-kind, 900-pound instrument known as SPLAT (a single particle laser ablation time-of-flight mass spectrometer).

SPLAT lives at EMSL, DOE’s Environmental Molecular Sciences Laboratory at PNNL. The researchers also employed a 26-foot-long “aerosol flow tube” at the AirUCI unit.

The conclusions are highly significant. This paper should—and, I expect, will—have a big impact. We’ve known for nearly a decade that there’s a huge difference between what’s in the models and what’s actually in the air. Thanks to this paper, we have a much better idea of why.

—Purdue University atmospheric chemist Paul Shepson

Lead author Perraud noted that the next logical step is to straighten out the models.