In search of representativeness: evolving the environmental data quality model.
Environmental regulatory policy states a goal of "sound science." The practice of good science is founded on the systematic identification and management of uncertainties; i.e., knowledge gaps that compromise our ability to make accurate predictions. Predicting the consequences of decisions about risk and risk reduction at contaminated sites requires an accurate model of the nature and extent of site contamination, which in turn requires measuring contaminant concentrations in complex environmental matrices. Perfecting analytical tests to perform those measurements has consumed tremendous regulatory attention for the past 20-30 years. Yet, despite great improvements in environmental analytical capability, complaints about inadequate data quality still abound. This paper argues that the first generation data quality model that equated environmental data quality with analytical quality was a useful starting point, but it is insufficient because it is blind to the repercussions of multifaceted issues collectively termed "representativeness." To achieve policy goals of "sound science" in environmental restoration projects, the environmental data quality model must be updated to recognize and manage the uncertainties involved in generating representative data from heterogeneous environmental matrices.
Crumbling
D M
DM
U.S. EPA Technology Innovation Office, 1200 Pennsylvania Avenue, NW, Washington, DC 20460, USA. crumbling.deana@epa.gov
D004784:Environmental Monitoring / Q000592:standards*; D006256:Hazardous Waste; D006801:Humans; D017146:Models, Organizational; D009937:Organizational Objectives; D011640:Public Policy; D011786:Quality Control; D012107:Research Design / Q000592:standards*; D017749:Total Quality Management; D014481:United States; D014484:United States Environmental Protection Agency / Q000458:organization & administration*
D006256:Hazardous Waste