PNNL partners with the Department of Homeland Security, industry, and the first-responder community to improve the effectiveness and safety during biodetection responses.
RemPlex provides a global forum committed to fostering technical leadership, collaborative research, and professional development that facilitates the cost-effective remediation of complex sites.
PNNL’s pioneering CETC project with regional universities demonstrates transactive controls among multiple commercial buildings and devices for energy efficiency and grid reliability.
E4D is a 3D geophysical modeling and inversion program designed for subsurface imaging and monitoring using static and time-lapse electrical resistivity tomography (ERT), spectral induced polarization (SIP) and travel-time tomography data.
PNNL’s integrated software systems (FRAMES, MEPAS, MetView, APGEMS, CAPP) allow users to assess the environmental fate and transport of contaminants—and the potential impacts on humans and the environment—in a systematic, holistic approach.
PNNL partners with agencies and industry to identify and engage historically disadvantaged populations in regulatory decision-making, environmental assessment, and impact estimation of the consequences of complex polices and projects.
PNNL and collaborators have established a national heat pump and heat pump water heater partnership to help drive adoption of these energy-saving technologies in both residential and commercial buildings.
The Joint Global Change Research Institute conducts research to advance fundamental understanding of human and Earth systems and provide decision-relevant information for management of emerging global risks and opportunities.
PNNL is a testbed for the latest research and technologies in marine carbon dioxide removal (mCDR)—leveraging the ocean’s strength as a natural carbon sink to address pressing climate concerns.
FEMP's operations and maintenance (O&M) resources offer federal agencies technology- and management-focused guidance to improve energy and water efficiency and ensure safer and more reliable operations.
Physics-informed machine learning (PIML) is a modeling approach that harnesses the power of machine learning and big data to improve the understanding of coupled, dynamic systems.
