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

PNNL lighting research scientist Kate Hickcox will contribute her expertise to help guide industry lighting standards.

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

Four research staff from PNNL are part of an international team that earned top honors for a journal paper focused on a new algorithm-evaluation approach for buildings.

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

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

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

Through the Net Zero World Initiative, expertise from DOE national laboratories will be harnessed to put global net zero within reach.

PNNL will play a key role in advancing Connected Communities made up of efficient homes and buildings that communicate with the grid to produce energy and environmental benefits.

PNNL researchers deploying and testing novel building technologies and energy-management approaches.

The first customized resource of its kind, H-BEST analyzes the indoor environmental quality profile for buildings and helps its users identify the costs and benefits of improvements.

Modeling study suggests vigorous ventilation can cause spike in viral concentrations

U.S. Secretary of Energy Jennifer M. Granholm virtually visits PNNL, one of several national lab visits she is conducting.

Vigorous and rapid air exchanges might not always be a good thing when it comes to levels of coronavirus particles in a multiroom building, according to a new modeling study.

New approach allows nonexperts to optimize control of a building's energy systems without adding computing power or proprietary software.

PNNL’s longstanding grid and buildings capabilities are driving two projects that test transactive energy concepts on a grand scale and lay the groundwork for a more efficient U.S. energy system.

PNNL data scientists Ján Drgoňa and Draguna Vrabie are working to reduce deployment costs of advanced energy control systems for buildings.

The PNNL-developed VOLTTRON™ software platform’s advancement has benefited from a community-driven approach. The technology has been used in buildings nationwide, including most recently on a university campus.

PNNL employs deep learning strategies to advance Model Predictive Control (MPC), a highly promising solution for intelligent building operations.

PNNL’s Intelligent Load Control technology manages and adjusts electricity use in buildings when there’s peak demand on the power grid.