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DOE OSTI2025

A Review of Variables Impacting the Indoor Inhalation Radon Equilibrium Factor (FEQ)

With radon and its daughter products estimated as the second leading cause of lung cancer in the United States, it is imperative to understand their relative equilibrium inside commercial, community, and residential dwellings. The radon indoor inhalation fractional equilibrium factor (F eq ) quantifies the disequilibrium between radon and its progeny in indoor air, and recent advances have shown how air exchange rates (ACH) influence F eq . These numerically derived ACH-dependent F eq values are incorporated into the U.S. EPA's Radon Vapor Intrusion Screening Level (RVISL) calculator, which assists risk assessors in evaluating radon exposure. To advance the risk assessment science of actinon (Rn-219), thoron (Rn-220), and radon (Rn-222), the impact of variables such as indoor aerosol concentration and composition, outdoor air quality, household-specific characteristics, and environmental/meteorological conditions on the F eq must be examined. The primary objective of this research is to analyze these additional variables to determine the usefulness of incorporating such adjustment factors into the RVISL calculator and to identify areas of future research. Studies regarding the influence of these parameters are presented along with recommendations regarding the adjustment of the numerically derived F eq value. For example, elevated indoor aerosol concentrations, such as those originating from outdoor PM 2.5 or cigarette smoke, increase the abundance of accumulation- mode particles indoors, which in turn raises F eq values by facilitating the attachment of radon progeny to these aerosols. Smoking increases both the bronchial dose and F eq , while regions with high smog levels demonstrate the impact of regional air quality on F eq . In contrast, air cleaning systems and purifiers have been shown to reduce the F eq , suggesting that these systems could help mitigate radon exposure. Additionally, higher F eq values are typically observed during winter when ventilation rates are lower. This paper presents adjustment factors that may be applied to the RVISL F eq , emphasizing the need for further research to refine these variables and ensure accurate risk assessments in diverse environments. Applying these adjustment factors will minimize calculator over- and underestimations, providing a more accurate representation of the real-world risk associated with radon.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Upper bounds for 21st-century surface air temperatures in the Western United States

The last decade has seen a large number of severe heatwaves that were unprecedented in the observational record, highlighting challenges associated with observationally-based statistical quantification of the likelihood and magnitude of future extreme temperatures. An alternative to such probabilistic assessments is identification of upper bounds that quantify the hottest surface air temperatures that can possibly be achieved by the end of the 21st century. Theory, simulations, and observational analyses support the existence of a finite upper bound for surface air temperature; however, estimates for future upper-bound values that are realistic and usable for planning remain unavailable. Here, we combine atmospheric theory with large ensembles of dynamically downscaled projections to estimate historical and end-of-century upper bounds for surface air temperatures. A number of physical mechanisms can influence upper bounds, and at the end of the 21st century, estimates based on mechanisms that yield more moderate upper-bounds produce values around 60∘C for much of the Western United States and in excess of 80∘C for the hottest parts of the domain. Even cooler high-altitude locations have end-of-century upper bounds over 50∘C. Although these upper-bound estimates might seem implausibly large, increases in the upper bounds over the 21st century are similar to increases in dynamically downscaled peak surface temperatures after adjusting those downscaled temperatures to eliminate the possibly biased model trends in surface specific humidity. While upper bound estimates are high relative to historical observations, they nonetheless suggest that heatwave intensity risk is bounded, with uncertainty dominated by projections of surface and upper-level humidity.

Risser, Mark D↗

DOE OSTI2024

Estimating the Contributions to Human Error Probability from the Convolution of the Distribution of Time Available and Time Required

As part of their duties, Human Reliability Analysis must often evaluate if crews in nuclear power plants (NPPs) can complete tasks associated with a human-failure event within time limits. For example, the time required in NPP scenarios is determined by systematic and structured walkthroughs, feasibility studies, recorded times from training exercises, and interviews with experienced operators and experts. Typically, a point estimate is derived for the estimate (mean, maximum, or 95th percentile of time required). Using point-estimate values can mask the risk associated with variability among crews, plant conditions and set-up, environmental conditions, and other impact factors under which these actions are executed. While point estimates for time required and time available have served the industry well, without considering the uncertainty they could lead to biased understanding about the risk. The Integrated Human Event Analysis System - General Methodology (IDHEAS-G) model (developed by the US Nuclear Regulatory Commission, NRC) for human error probability calculates human error probability by summing two probabilities: insufficient time and cognitive error. As such, the model takes a more holistic approach by considering the full distributions for time required and time available to calculate the human error probability because the time available to complete the task is insufficient. In this study, we expand on the work of the NRC and discuss methods for estimating these time considerations. For example, for the time required, the impact of Performance Influencing Factors (PIFs) on the distribution was divided into impacts that are aleatory in nature, such as crew-to-crew variability, and those that are epistemic (i.e., the PIFs). Starting with the factors that introduce aleatory uncertainty, a first-order distribution was developed from a large set of time required (i.e., NPP task completion times) data for the range of operator actions that occur in the NPP control room under simulated accident conditions. The first-order distribution can then be adjusted to account for epistemic uncertainty using research associated with the impact of applicable PIFs on the time required. We also develop guidance for analysts to address the probability distributions for the time available. The guidance we developed on how to estimate time required and time available distributions is based on the identification of pertinent research and data, data analyses, and expert knowledge elicitation.

human error probability, human performance, time e↗