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Research Highlights

atmospheric rivers model
Full Story | June 2015

PNNL Nabs #1 Particle Pushers
PNNL global model treatments reveal much larger climate impact from burning vegetation and biofuel emissions

New research led by Pacific Northwest National Laboratory shows, for the first time, that burning vegetation and bio-based fuel are the largest source of particle-making vapors in the atmosphere. The discovery translates to an order-of-magnitude change in the impact of secondary organic aerosols on the Earth's energy balance.

Susan Tilton
Full Story | July 2015

OSU and PNNL Scientists Develop Improved Way to Assess Cancer Risk of Pollutants
Collaboration part of a Superfund research program

Scientists at Oregon State University and PNNL have developed a faster, more accurate method to assess cancer risk from certain common environmental pollutants. They found they could analyze immediate genetic responses of skin cells of exposed mice and apply statistical approaches to determine whether those cells would eventually become cancerous.

Meng Gu
Full Story | June 2015

Gu Honored with MSA Albert Crewe Award

Former PNNL postdoc Meng Gu is the recipient of the Microscopy Society of America’s 2015 Albert Crewe Award for his contributions to the field of microscopy and microanalysis, and his outstanding work on the discovery of nickel segregation in battery materials.

Full Story | June 2015

The Reality of Problem Solving
Algorithm accounts for uncertainty to enable more accurate modeling

Today, numerical models routinely simulate physical system behaviors in scientific domains—many within DOE’s critical mission areas. However, because of incomplete knowledge about the systems being simulated, parametric uncertainty often arises, resulting in models that deviate from reality. To remedy this, PNNL’s Weixuan Li and Guang Lin from Purdue University have proposed an adaptive importance sampling algorithm that alleviates the burden caused by computationally demanding models. Using test cases, they demonstrated that the algorithm can effectively infer model parameters from any direct/indirect measurement data through uncertainty quantification, improving model accuracy and enhancing computational efficiency.

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