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Atomic Emission Spectroscopy: Lab

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AES is a powerful analytical technique, especially effective when used with plasma sources, producing abundant spectra in characteristic emission lines. The Inductively Coupled Plasma (ICP), in particular, yields superior quantitative analytical data due to its high stability, low noise, low background, and minimal interferences under optimal experimental conditions. However, newer air-operated microwave sources are emerging as promising alternatives that could be more cost-effective than...

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Updated: Jun 4, 2025

Surface Renewal: An Advanced Micrometeorological Method for Measuring and Processing Field-Scale Energy Flux Density Data

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Published on: December 12, 2013

Using eddy covariance data to detect nuclear reactor operational status.

Vachel A Kraklow¹, E Christi Thompson², Jemma Stachelek³

¹Earth and Environmental Sciences Division, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA.

Journal of Environmental Management

|December 22, 2024

Summary

This summary is machine-generated.

This study shows that eddy-covariance towers can detect waste heat from nuclear reactors, especially latent heat. Optimal detection occurs on dry, calm, and cloudless days, enhancing remote monitoring for nuclear safeguards.

Keywords:

AmeriFlux FLUXNET Generalized additive models Latent heat Sensible heat

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Laser-heating and Radiance Spectrometry for the Study of Nuclear Materials in Conditions Simulating a Nuclear Power Plant Accident

Laser-heating and Radiance Spectrometry for the Study of Nuclear Materials in Conditions Simulating a Nuclear Power Plant Accident

Published on: December 14, 2017

Area of Science:

Environmental Science
Nuclear Engineering
Remote Sensing

Background:

Nuclear safeguards rely on monitoring reactor operations for compliance with international agreements.
Current methods using satellite/aircraft data and on-site sampling are time-consuming and labor-intensive.
Novel remote sensing techniques are needed to overcome temporal limitations in monitoring nuclear facilities.

Purpose of the Study:

To investigate the use of eddy-covariance ecosystem monitoring networks for detecting waste heat from nuclear reactors.
To determine if different reactor types (PWR vs. BWR) or heat release pathways (atmosphere vs. ocean) exhibit distinct thermal signatures.
To assess the influence of environmental factors like wind, season, and vegetation on heat flux detection.

Main Methods:

Utilized logistic generalized additive models to correlate reactor operational status (on/off) with latent and sensible heat fluxes.
Employed Dirichlet Process Means clustering to identify weather conditions favorable for detecting reactor-induced heat changes.
Analyzed data from four U.S. nuclear reactors: two pressurized water reactors and two boiling water reactors.

Main Results:

Eddy-covariance towers successfully detected waste heat flux signatures from nuclear reactors, with latent heat being particularly discernible.
The most favorable weather conditions for detecting these heat flux changes were identified as dry, calm, and cloudless days.
Environmental factors such as wind direction, seasonality, and vegetation type were evaluated for their influence on heat signatures.

Conclusions:

Remotely sensed data from eddy-covariance networks offer a viable method for monitoring nuclear reactor operations.
This approach can contribute to enhanced nuclear safeguards by providing continuous, non-invasive monitoring capabilities.
Future research can build upon these findings to refine remote sensing techniques for nuclear facility oversight.