Researchers have developed the first coastal transport model for microplastics to understand how particles move in waterways and coastal currents.

Researchers from PNNL and Parallel Works, Inc., applied machine learning methods to predict how much oxygen and nutrients are used by microorganisms in river sediments.

Experiments focus on catalysts for off-road vehicles that optimize the use of critical minerals while lasting longer and costing less.

Lakes are important sentinels of climate change and contribute significantly to the emissions of the second most important greenhouse gas—methane.

The rate of conversion of cloud droplets to precipitation, known as the autoconversion rate, remains a major source of uncertainty in characterizing aerosol’s cloud lifetime effects and precipitation in global and regional models.

To assess the impact of observation period and gauge location, model parameters were learned on scenarios using different chunks of streamflow observations.

PNNL's E-COMP initiative is helping unleash American energy innovation with advanced theories, models, and software tools to better operate power systems that rely heavily on high-speed power electronic control.

This study presents an automated method to detect and classify open- and closed-cell mesoscale cellular convection (MCC) using long-term ground-based radar observations.

Models have identified a new source of anthropogenic emissions driving the observed weakening of the Atlantic Meridional Overturning Circulation.

Input from humans helps when deciding whether to trust the recommendations or decisions of a machine-learning system.

Less pollution and the odd shapes of snow grains as they pack together should help cut the decline of snowpack later this century.

In a new paper, researchers point to three major efforts where the biggest climate mitigation gains stand to be realized: ramping up carbon dioxide removal, reigning in non-carbon dioxide emissions and halting deforestation.

PNNL’s software tool brings together artificial intelligence and satellite imagery to help responders assess damage and take action.

A new discovery by PNNL researchers has illuminated a previously unknown key mechanism that could inform the development of new, more effective catalysts for abating NOx emissions from combustion-engines burning diesel or low carbon fuel.

Recovery Simulator and Analysis tool further strengthens PNNL’s short-term resilience planning efforts for Puerto Rico.

The Co-Optimization of Fuels & Engines initiative recently outlined six years of research in a findings and impact report.

PNNL researchers developed a new model to help power system operators and planners better evaluate how grid-forming, inverter-based resources could affect the system stability.

PNNL catalysis expert Yong Wang has assumed an associate editor role for the Chemical Engineering Journal.

A multi-institution team of researchers has developed a palladium-based catalyst that could clean emissions from natural gas engines.

PNNL is rock solid on carbon storage, building on successful demonstration in eastern Washington.