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Data Quality Objectives (DQOs)
Establishing data quality objectives (DQOs) prior to implementation of a
bioassessment program is critical to the ultimate success of the program and
its ability to correctly guide management actions. DQOs are qualitative and
quantitative statements that clarify study objectives, define the appropriate
type of data, and specify tolerable levels of potential decision errors that
will be used as the basis for establishing the quality and quantity of data
needed to support decisions (USEPA 2000c). For example, DQOs address overarching
questions such as the detection of impairment when impairment truly exists,
reliably detecting change over time, and differentiating among multiple stressors.
Therefore, the DQOs guide the design of sampling (e.g., techniques, index
period, spatial array, etc.) and analysis (e.g., data aggregation and treatment)
plans that can cost-effectively produce the kind of data needed. To a large
extent, the elements of a biological assessment program, summarized in Table
3-1, should evolve from the DQOs identified by the program. An important part
of the DQO process is developing an understanding of how uncertainties can
impact the decision-making process. The DQO process identifies what the goals
are and what the consequences may be if the decisions are made in error, and
establishes the level of confidence in making the correct decision.
Example DQO: Determine to a 90% degree of confidence whether or not the biological condition at a given site is impaired, and also whether there is more than a 20% change in condition over time among the sites.
This DQO would be identified as a management objective for determining the status of impairment of the waterbody being investigated. A different management objective for determining the cause of impairment might be:
Example DQO: Determine to a 90% degree of confidence whether or not the impaired biological condition at a given site is due to excess sedimentation.
Programs may have many reasons for conducting bioassessments. For example, if bioassessments are used to determine "hot spots" of impairment with some qualitative level of confidence, then lower levels of rigor are generally acceptable. If, however, a program uses bioassessments and biocriteria to determine compliance with aquatic life uses or other regulatory standards, higher levels of rigor are generally required. DQOs are quantified further using measurement quality objectives (MQOs) or method and data acceptance criteria (performance characteristics). Bioassessments with higher levels of rigor will generally be more precise, more sensitive to stressors, and more "accurate" in the sense of reflecting impairments when they truly exist.