Environmental Sampling Design - Fundamentals of Environmental Sampling and Analysis - page 47

document developed before data collection and preferably agreed to by all
stakeholders shown in Figure 3.1. This seven-step DQO process is shown in Figure 3.2.

Step 1. State the problem: define the
problem; identify the planning team;
examine budget; schedule.

Step 7. Optimize the design for obtaining
data: select resource-effective sampling
and analysis plan that meets the
performance criteria.

Step 2. Identify the decision; state
decision; identify study question; define
alternative actions.

Step 6. Specify tolerable limits on
decision errors: set acceptable limits for
decision errors relative to consequences
(health effects, costs).

Step 3. Identify the inputs to the
decision: identify information needed for
the decision (information sources, basis
for action level, sampling/analysis
method).

Step 5. Develop a decision rule: define
statistical parameter (mean, median);
specify action level; develop logic for
action.

Step 4. Define the boundaries of the
study: Specify sample characteristics;
define spatial/temporal limits, units of
decision making.

Figure 3.2 The data quality objective (DQO) process (U.S. EPA, 2000)

As it is shown in Figure 3.2, the DQO process is not a straightforward task for
beginners as well as many practitioners. Steps that are directly relevant to sampling
and analysis include Step 3 in confirming that appropriate sampling techniques and
analytical methods exist to provide the necessary data, Step 4 in defining spatial/
temporal boundaries that data must represent or where/when the sampling will be
conducted, and Step 7 in developing general sampling design and analytical plans.
To illustrate DQOs in simple terms, Popek (2003) described DQOs as a process of
asking questions and finding answers with regard to the following what, who, why,
when, where, and how:

What is the project's purpose?
What is the problem that requires data collection?
What types of data are relevant for the project?
What is the intended use of data?
What are the budget, schedule, and available resources?
What decisions and actions will be based on the collected data?
What are the consequences of a wrong decision?
What are the action levels?
What are the contaminants of concern and target analytes?
What are the acceptance criteria for the PARCC parameters?
Who are the decision-makers?
Who will collect data?
Why do we need to collect the particular kind of data and not the other?