Brian O’Neill joins a Roundtable on climate security organized by the National Academies of Sciences, Engineering, and Medicine.

An analysis of land use in watersheds that supply drinking water to over a hundred United States cities identified a wide range of exposure to potential contamination.

PNNL experts perform fundamental climate science work at multiple scales to understand impacts on energy, water, food, and infrastructure systems.

PNNL researchers have uncovered a plant-derived process that leads to the formation of aerosol particles over the Amazon rainforest and potentially other forested parts of the world. 

Different sized helium cavities that form after ion irradiation are unevenly distributed in a ductile-phase toughened tungsten composite.

A PNNL team is leading the design, fabrication, and regulatory testing, and delivery of new packaging units that will be used to ship radioactive materials safely and securely.

New work developed a methodology to generate building temperature setpoint schedules for use by modelers in large-scale building energy simulations.

COVID-19 changed when and where we use electricity. Researchers showed that commonly available total demand data masked the actual changes.

Storage Targets and Release Function Inference Tool uses data to infer the operating policies of large reservoirs on a national scale.

A multisectoral analysis that combines land use data with hydrological simulations reveals wide disparities in potential source water contamination.

PNNL scientist Brian O’Neill led the compilation of an IPCC chapter about climate-related risks.

COVID-19 had multiple impacts on the electric sector and the nature of those impacts varied on the scale analyzed.

To improve the study of human-Earth interactions, a 10-year vision report by the MultiSector Dynamics community of practice encourages the use of emerging human systems datasets, embedded intelligence in modeling, and workforce diversity.

Using a novel settlement-related dataset produced more accurate population downscaling than using other, more traditional datasets.

Despite an increase in future electricity demands, virtual water trading in the U.S. electricity sector is expected to decline as renewable energy expands.