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Characterizing Variability and Uncertainty in Exposure Assessments Improves links to Environmental Decision-Making
Citation:
OZKAYNAK, H. A., C. H. Frey, AND B. HUBBELL. Characterizing Variability and Uncertainty in Exposure Assessments Improves links to Environmental Decision-Making. EM: AIR AND WASTE MANAGEMENT ASSOCIATION'S MAGAZINE FOR ENVIRONMENTAL MANAGERS. Air & Waste Management Association, Pittsburgh, PA, , 16-20, (2008).
Impact/Purpose:
National Exposure Research Laboratory′s (NERL′s) Atmospheric Modeling Division (AMD) conducts research in support of EPA′s mission to protect human health and the environment. AMD′s research program is engaged in developing and evaluating predictive atmospheric models on all spatial and temporal scales for forecasting the Nation′s air quality and for assessing changes in air quality and air pollutant exposures, as affected by changes in ecosystem management and regulatory decisions. AMD is responsible for providing a sound scientific and technical basis for regulatory policies based on air quality models to improve ambient air quality. The models developed by AMD are being used by EPA, NOAA, and the air pollution community in understanding and forecasting not only the magnitude of the air pollution problem, but also in developing emission control policies and regulations for air quality improvements.
Description:
Environmental Decisions often rely upon observational data or model estimates. For instance, the 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 risk information is relevant to many types of environmental decision-making, including setting standards, determining emissions controls, and mitigating exposures to pollutants.
