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Hydrothermal Alteration of Nuclear Melt Glass, Colloid Formation, and Plutonium Mobilization at the Nevada National Security Site, U.S.A.

Mavrik ZavarinPihong ZhaoClaudia JosephJames D BeggMark A BoggsZurong DaiAnnie B Kersting
Published in: Environmental science & technology (2019)
Approximately 2.8 t of plutonium (Pu) has been deposited in the Nevada National Security Site (NNSS) subsurface as a result of underground nuclear testing. Most of this Pu is sequestered in nuclear melt glass. However, Pu migration has been observed and attributed to colloid facilitated transport. To identify the mechanisms controlling Pu mobilization, long-term (∼3 year) laboratory nuclear melt glass alteration experiments were performed at 25 to 200 °C to mimic hydrothermal conditions in the vicinity of underground nuclear tests. The clay and zeolite colloids produced in these experiments are similar to those identified in NNSS groundwater. At 200 °C, maximum Pu and colloid concentrations of 30 Bq/L and 150 mg/L, respectively, were observed. However, much lower Pu and colloid concentrations were observed at 25 and 80 °C. These data suggest that Pu concentrations above the drinking water Maximum Contaminant Levels (0.56 Bq/L) may exist during early hydrothermal conditions in the vicinity of underground nuclear tests. However, formation of colloid-associated Pu will tend to decrease with time as nuclear test cavity temperatures decrease. Furthermore, median colloid concentrations in NNSS groundwater (1.8 mg/L) suggest that the high colloid and Pu concentrations observed in our 140 and 200 °C experiments are unlikely to persist in downgradient NNSS groundwater. While our experiments did not span all groundwater and nuclear melt glass conditions that may be present at the NNSS, our results are consistent with the documented low Pu concentrations in NNSS groundwater.
Keyphrases
  • drinking water
  • health risk
  • health risk assessment
  • heavy metals
  • human health
  • public health
  • sewage sludge
  • risk assessment
  • climate change
  • machine learning
  • deep learning
  • global health
  • municipal solid waste