Study highlights microbiome’s role in regulating xenobiotic metabolism.

Study reveals importance of accounting for micrometer scale distribution of substrates to predict carbon emissions from soil.

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.

Soil moisture influences both the activity of soil DNA viruses, as well as the composition and abundance of RNA viruses.

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

Knowing which bacteria in a community are involved with carbon cycling could help scientists predict how microbial carbon storage and release could influence future climate dynamics.

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

A new article reviews bio-orthogonal click chemistry reagents that can be used as probes to label metabolic pathways in plants and microbes.

A code comparison study demonstrates a leap in the accuracy and reliability of modeling gas hydrate for the discovery and production of natural gas.

Microbiome and soil chemistry characterization at long-term bioenergy research sites challenges idea that switchgrass increases carbon accrual in surface soils of marginal lands.

Geophysical modeling and inversion software meets DOE and nuclear industry standards for use in decision-making for radiologically contaminated sites.

To study the impact of accelerated dryland expansion and degradation on global dryland gross primary production (GPP,) PNNL and Washington State University researchers assessed GPP data from 2000-2014 and the CMIP5 aridity index (AI).

Researchers performed controlled laboratory experiments using river sediment to test organic matter thermodynamics as a mechanism of metabolic control in areas where groundwater and surface water mix.

Researchers performed a combined analysis of metabolic and gene co-expression networks to explore how the soil microbiome responds to changes in moisture and nutrient conditions.

By studying discrete functional components of the soil microbiome at high resolution, researchers obtained a more complete picture of soil diversity compared to analysis of the entire soil community.

Researchers trained deep neural networks to accurately predict microbial interactions captured by fluorescence microscopy.

Existing techniques to detect pertechnetate in the environment have drawbacks. PNNL’s redox sensor technology uses a gold probe to accurately and efficiently measure low levels of pertechnetate—and possibly other contaminants—in groundwater

A recent paper published in Water Resources Research found that the spatial variability of subsurface sediments, and seasonal fluctuations in a river’s water level, influences the behavior of a uranium contaminant plume, particularly in ...