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Novel insights into aerosol behavior could improve air-quality forecasting
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Add as preferred source New research from Purdue University demonstrates how organic aerosol molecules can influence one another's behavior in ways that existing atmospheric models do not fully capture.
A team led by Alexander Laskin, professor in the James Tarpo Jr. and Margaret Tarpo Department of Chemistry, discovered that neighboring molecules in aerosol mixtures tend to "hold on" to each other, dramatically increasing the time it takes for them to evaporate and escape from the atmosphere. The findings are published in the journal Proceedings of the National Academy of Sciences .
Existing atmospheric models estimate evaporation based on an individual chemical's volatility—how easily it escapes into the air—rather than accounting for how chemicals actually exist, surrounded by hundreds of other molecules.
Using a novel mass spectrometry technique developed in Laskin's lab, the research team tracked the behavior of more than 1,500 individual chemical species across 33 complex mixtures representative of real biomass-burning smoke and urban haze. Across this massive data set, the pattern was clear: The surrounding molecular neighborhood, rather than a chemical's intrinsic properties, often dictates how easily it escapes.
To illustrate this "matrix effect," Laskin cites how scents cling to different types of materials.
"Spray perfume onto a glass plate and the scent disappears quickly. Spray the same perfume onto a thick wool sweater and the smell lingers for days because the fabric traps the fragrance molecules," Laskin said. "An aerosol mixture acts like a wool sweater, holding molecules much more strongly than if they were alone."
When neighboring molecules hold on to each other, evaporation slows and creates volatility suppression of three to five orders of magnitude, which Laskin identified as "unexpectedly large."
"As a result, current models often assume smoke disappears too quickly and therefore underestimate how long it remains in the atmosphere," Laskin said.
Ultimately, this research answers real-world questions: "How long does wildfire smoke stay in the air? Where will it travel? And how will it affect our health, weather and climate?"
According to Laskin, "This research lays the foundation for a new generation of machine learning models that can predict the volatility of complex environmental mixtures by accounting for interactions among thousands of molecules rather than treating each compound independently."
Looking forward, the team aims to build machine learning models that account for interactions among thousands of molecules simultaneously, providing a stronger foundation to improve air-quality forecasts, assess public health risks and guide environmental mitigation policies.
Laskin's team included Qiaorong Xie, a postdoctoral researcher who executed the study, and graduate students Abigail Smith, Sara Botero Carrizosa and Steven Sharpe, who contributed to data acquisition and assisted with data set curation.
Qiaorong Xie et al, Matrix effects reshape organic aerosol volatility and atmospheric persistence, Proceedings of the National Academy of Sciences (2026). DOI: 10.1073/pnas.2614944123
Journal information: Proceedings of the National Academy of Sciences
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