For the U.S. Department of Energy Office of Legacy Management (LM) Defense-Related Uranium Mines (DRUM) team, mathematics and statistics are at the core of protecting human health and the environment.
Legacy Management Support Senior Technical Advisor Clay Carpenter, who works with the team, said the DRUM mission revolves around collecting data and determining what statistics that data can inform, to provide insight to share with partner land management agencies. More broadly, the DRUM mission is to assess the risk that abandoned uranium mines, once used for defense purposes, pose to the public and how to mitigate those risks.
“There’s really two aspects to the risks associated with the mines. There’s the openings — the physical hazards — and then there’s the human health portion, which is associated with the waste rock materials,” said Carpenter.
The waste rock materials that remain from past uranium mining are often elevated in different constituents of concern, including arsenic and radium-226, according to Carpenter. DRUM makes assessments of the risks related to human health based on very conservative assumptions with regards to those who might encounter the mines. As these mines are on public land, the DRUM team makes these assessments with outdoor recreationalists in mind.
Physical threats relate to the hazards posed by the characteristics of the mine itself. Unstable ground, fall risk, and collapsing rock all make abandoned uranium mines especially dangerous. When categorizing mines, DRUM staff rate these physical dangers on a scale that considers how dangerous a mine or feature is, and how likely members of the public would be to encounter the mine or feature.
A mine or mine feature that isn’t particularly dangerous may have a higher risk rating if it’s in an area where people spend a lot of time. Likewise, a more dangerous mine may have a lower risk rating if it’s extremely remote and rarely visited.
“We try to keep the math pretty simple when it comes to rating the physical hazards,” said Carpenter. “It’s easy to get into all the data and lose sight of what our purpose is.”
With nearly 4,000 abandoned uranium mines for assessment, keeping the math simple is important, though not always possible.
As an example, the team used linear regression analysis to see whether they could extract even more information from data they had previously collected during their many spatial surveys of gamma radiation. They made the assumption that there could be a relationship between gamma radiation readings and the concentrations of radium-226. Linear regression analysis allows the DRUM team to analyze the relation between two variables (data sets) to see if they correlate with each other.
Because radium-226 emits the relatively greatest gamma radiation activity of the uranium decay chain, the DRUM team assumed higher gamma radiation readings would generally mean higher activity concentrations of radium-226. They thought gamma radiation levels would serve as a surrogate and provide additional insight. However, since DRUM’s field sampling protocols were not designed to specifically assess this correlation, they found too many variables to confirm a well-founded correlated extrapolation. An increase or decrease in gamma radiation readings did not typically result in a corresponding increase or decrease in radium-226 concentrations. This was important for the DRUM team as this confirmed that they would need to evaluate data on radium-226 independent of data collected on gamma readings.
