Measuring the ice nucleation activity of particles in the ambient atmosphere can help develop better representations for use in climate models.

PNNL contributes to 30 years of data on clouds, radiation, and other climate-making factors as part of field campaigns and analysis conducted by DOE's Atmospheric Radiation Measurement user facility.

Researchers use machine learning to speed up the analysis of aerosol datasets from mass spectrometry.

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

Combining aircraft measurements and regional modeling allowed researchers to identify the role of in-plant biochemistry in secondary organic aerosol formation.

Using unique laboratory single-particle measurements to understand some of the most uncertain processes affecting secondary organic aerosol formation.

Moving toward a deeper understanding of the influence of large marine biogenic particles on cloud ice formation by combining modeling and observational data.

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.

The rapid growth of urban nanoparticles via the condensation of organic vapors substantially alters shallow cloud formation and suppresses precipitation.

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

Simulations show a strong correlation between the water content and the viscosity of organic aerosol over the Amazon rainforest.

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.

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

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).

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 ...

DOE researchers investigated the role of microbial genetic diversity in two major subsurface biogeochemical processes: nitrification and denitrification.