This chapter begins with an overview discussion about factors that determine the reliability of a risk assessment and a discussion of methods for characterizing and evaluating the uncertainties in a risk assessment. Next is a summary review and evaluation of the uncertainty analysis for drinking-water radon that was carried out by the Environmental Protection Agency. That is followed by the committee's consideration of the steps of the risk-assessment process described in earlier chapters and of how uncertainty and variability apply to the assessment and the extent to which they can be quantified. Particular attention is given to the importance of uncertainty across the entire process of characterizing the unmitigated risk associated with radon in drinking water and the risk reduction achieved by various technologies used to reduce radon levels in water supplies.
To identify factors that affect the reliability of radon risk assessment, the committee reviewed the scientific literature, recommendations from other National Research Council studies, and findings reported by such organizations as the International Atomic Energy Agency (IAEA), the National Council on Radiation Protection and Measurements (NCRP), and the Presidential/Congressional Commission on Risk Assessment and Risk Management. According to IAEA (1989), five factors determine the precision and accuracy, that is, the reliability, of a risk characterization: specification of the problem (scenario development), formulation of the conceptual model (the influence diagram), formulation of the computational model, measurement or estimation of parameter values, and calculation and documentation of results, including uncertainties. In such a framework, there are many sources of uncertainty and variability—including lack of data, natural-process variation, incomplete or inaccurate data, model error, and ignorance of the relevant data or model structure.
The magnitude of human exposure to toxic agents, such as radon, often must be estimated with models that range in complexity from simple heuristic extrapolations from measured trends to large-scale simulations carried out on large computers. Regardless of its complexity, any model can be thought of as a tool that produces an output, Y, such as exposure or risk, that is a function of several variables, Xi, and time, t:
The variables, Xi, represent the various inputs to the model, such as radon concentration in water, and the transfer factors between water and air. Uncertainty analysis involves the determination of the variation or range in the output-function values—that is, risk values—on the basis of the collective variation of the model inputs. In contrast, a sensitivity analysis involves the determination of the changes in model response as a result of changes in individual parameters. An approach to express the combined impact of uncertainty and variability more