Characterizing Variability and Uncertainty in Exposure Assessments Improves Links to Environmental Decision-Making

Environmental decisions often rely upon observational data or model estimates. For instance, evaluation of human health or ecological risks often includes information on pollutant emission rates, environmental concentrations, exposures, and exposure/dose-response data. Whether measured or modeled, each of these elements of information has certain underlying limitations. In addition to basic accuracy and precision issues, spatial and temporal representativeness of data and their applicability to different population or receptor groups of interest are important concerns. Understanding the various sources of variability and uncertainty in exposure and risk information is relevant to many types of environmental decision-making, including setting standards, determining emissions controls, and mitigating exposures to pollutants. Moreover, critical gaps in knowledge can be determined based on evaluation of uncertainties, so that priorities for collecting new data can be specified to improve confidence in future assessments. Risk managers typically consider likely impacts of alternative regulatory or risk management choices by performing risk comparisons, examining risk-risk trade-offs, or comparing risks to benchmarks. Incorporating variability and uncertainty surrounding the estimates of human exposures is key to making sound decisions and maximizing the benefits attained from such decisions.