Work

Using eddy covariance data to detect nuclear reactor operational status

Year: 2024

Type: article

Abstract: Monitoring nuclear reactor operations is vital for nuclear safeguards as it ensures that reactors are in compliance with international legal agreements. Validating nuclear facilities and activities, including potential clandestine activities, is currently accomplished by using remotely sensed data from satellites and aircrafts and on-site sampling. However, these techniques are temporally-limited as sampling and interpretation of environmental releases frequently involve labor-intensive, on-site collections. Here, we investigate whether remotely sensed data from eddy-covariance ecosystem monitoring networks, such as AmeriFlux, can be used to detect waste heat generated from four U.S.-based nuclear reactors, two pressurized water reactors (Callaway Nuclear Generating Station and Seabrook Nuclear Power Plant), and two boiling water reactors (Columbia Nuclear Generating Station and Enrico Fermi Nuclear Generating Station). Though both pressurized and boiling water reactors release latent and sensible heat into the environment in a similar way, we evaluated whether different signatures exist among the two types of nuclear facilities. Additionally, we evaluated whether different signatures exist among those reactors that release waste heat into the atmosphere via latent heat or into the ocean via sensible heat. To do this, we used logistic generalized additive models to investigate whether reactor operational status (i.e., on versus off) can be estimated from two environmental heat fluxes, latent and sensible heat. We also evaluated whether wind direction, seasonality and vegetation type influences potential latent and sensible heat signatures from reactors. Using a Dirichlet Process Means clustering analysis, we investigated whether certain weather conditions are more conducive to detecting reactor status. Our results suggest that eddy-covariance towers can detect waste heat flux signatures from nuclear reactors, particularly latent heat. Our results also suggest that weather conditions most conducive to detecting changes in latent and sensible heat as a result of reactor status are present on relatively dry, calm, cloudless days. These results contribute to a growing body of literature utilizing new methodologies in remotely monitoring nuclear reactor operations. • Eddy-covariance data can be used to detect changes in nuclear reactor operational status via waste heat. • Used logistic generalized additive models to model relationships between reactor status, latent heat, and sensible heat. • Significant relationships exist between reactor status, latent heat, and sensible heat fluxes regardless of reactor type. • A Dirichlet Process Means clustering analysis identified unique weather patterns conducive for heat signature detection. • Weather patterns most conducive to detecting nuclear reactor heat energy fluxes occurred on dry, calm, cloudless days.

Source: Journal of Environmental Management

DOI: 10.1016/j.jenvman.2024.123569