A team of PNNL researchers are looking at how to evaluate robustness and accountability, fairness, and transparency of artificial intelligence models used to detect and quantify deceptive content online.

A Q&A with Lauren Charles, veterinarian and PNNL data scientist, on zoonotic diseases and the role biosurveillance plays in mitigating the growing threat to global health.

Examining flood occurrences associated with mesoscale convective systems and their characteristics allows researchers to explore climate-flood linkages.

Large ensemble simulations show that aerosols can significantly modify the predictability of hail and precipitation in hailstorms.

More than 30 PNNL interns contributed to the Airport Risk Assessment Model, a web-based tool that helps airport security stakeholders prioritize resource allocations.

PNNL’s data-infused approach to electron microscopes’ use in scientific experimentation will help researchers and industry interpret large data streams and drive down costs.

The most polluted U.S. communities from five years ago are still the most polluted today, 50 years after passage of the Clean Air Act amendments of 1970.

Soil texture and environmental history play a strong role in how different soils respond to moisture extremes like drought and flood.

Researchers used instruments in an airborne lab to characterize aerosol particles and their sources over the remote Eastern North Atlantic.

Seeking a deeper understanding of when, where, and why large wildfires occur using explainable artificial intelligence.

Urban pollution greatly increases the number of particles over the Amazon, mainly through new particle formation controlled by biogenic gases.

A high-resolution dataset captures a more complete view of the global distribution and characteristics of strong storms called mesoscale convective systems.

Rising greenhouse gases and declining aerosols have triggered an approximate four-day delay in rainfall over tropical land and the Sahel.

PNNL combines AI and cloud computing with damage assessment tool to predict path of wildfires and quickly evaluate the impact of natural disasters, giving first responders an upper hand.

Svitlana Volkova uses natural language processing and AI to reveal how misinformation and disinformation spread through social media.

Machine learning techniques are accelerating the development of stronger alloys for power plants, which will yield efficiency, cost, and decarbonization benefits.

Research shows that erosional nutrient fluxes linked to extreme rains may complicate efforts to reduce nutrient enrichment in the Gulf of Mexico.

New research aids the design of more efficient water purification technologies.

A research project that brings together mathematicians and atmospheric scientists has developed into a deep collaboration for improving atmospheric models.

Svitlana Volkova, chief scientist for decision intelligence and analytics at PNNL, was invited as a panelist at the SIAM International Conference on Data Mining