A multi-omics analysis provides the framework for gaining insights into the structure and function of microbial communities across multiple habitats on a planetary scale

STEM Education programs at DOE and national labs offer environmental management internships; applications are due soon for summer 2023.

A review article led by researcher Jade Holliman explores the different classes of metamaterials, from the underlying fundamental science to potential applications.

Data-driven experiments can help determine best practices for Random Forest machine learning to predict water quality, particularly nitrate levels.

A review article examines a decade of work on the atom-scale process of turning carbon dioxide into stable minerals.

PNNL joint appointee Auroop Ganguly uses data science to study the impacts of climate change.

A new web-based tool provides easy-to-understand progress metrics and other data about groundwater cleanup sites overseen by the DOE Office of Environmental Management.

Biochemical preservation and mineral association retain carbon in grassland soils, resulting in consistent molecular composition across ecosystems.

Across the United States, water moving between the river and riverbed sediments does not overcome localized processes that govern organic matter chemistry.

A new study shows how organic molecules respond to global change varies according to functional traits and ecological processes.

Updated flexible software generates and optimizes monitoring programs for detecting potential leaks from geological carbon storage with an enhanced user experience.

A novel combination of temperature and four-dimensional geophysical sensors revealed otherwise unseeable hydrologic dynamics below riverbed sediments.

PNNL scientists have proposed an "adaptive site management" cleanup strategy for the Hanford Site's Central Plateau that incorporates a structured, flexible approach to environmental remediation.

ICON science is a Department of Energy-developed framework to enhance scientific outcomes via more intentional design of research efforts across all domains of science.

New data analysis approach gives researchers deeper understanding of microbial ecology and evolution.

Improving predictability of water quality and nutrient cycling in large river systems.

Developing conceptual models for microbial-environmental–ecosystem interactions is key to enhancing the ability of models to predict future ecosystem function.

Research from the PNNL-led WHONDRS consortium reveals chemistry and spatial gradients of metabolomes from river water and sediments around the globe.

Detailed characterization of molecules in, under, and along the Columbia River reveal potential mechanisms for biogeochemical cycling.

Researchers used deep learning methods to estimate the subsurface permeability of a watershed from stream discharge measurements.