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.
This study used historical data, remote sensing, and aquatic sensors to measure how far wildfire impacts propagated through the watershed after the 2022 Hermit’s Peak/Calf Canyon fire, New Mexico’s largest wildfire in history.
The Coastal Observations, Mechanisms, and Predictions Across Systems and Scales: Field, Measurements, and Experiments project established a network of observational field sites across Chesapeake Bay and western Lake Erie.
Due to their inherent variability and complexity over space and time, scientists are challenged to understand the complex interactions among soil, vegetation, and water along coastal terrestrial-aquatic interfaces.
This study characterized above- and below-ground properties to explore the spatial heterogeneity of the terrestrial aquatic interface ecosystem within the Chesapeake Bay area and evaluate the major drivers of soil respiration.
Continued studies will deepen scientists’ understanding of virus-host interactions at the molecular level and also pave the way for developing better drugs to fight emerging viruses.
In the search for rare physics events, extremely pure materials are essential. A partnership between PNNL and Ultramet has led to tungsten with low contamination from other elements.
Scientists map how transitions from day to night control gene regulatory networks in cyanobacteria, revealing key orchestrators of metabolic switching.
Researchers developed a robust, cost-effective, and easy-to-use cap-based technique for spatial proteome mapping, addressing the lack of accessible proteomics technologies for studying tissue heterogeneity and microenvironments.
This project sought to assure that research activities centered around different sampling and monitoring efforts in northwest Ohio would not disturb any historical cultural resources.