1. Introduction to the EPA 7-Step DQO Process
DQO Training Course
Day 1
Module 8
Introduction to the EPA 7-Step DQO Process
Steps 5 - 7
Presenter: Sebastian Tindall
(25 minutes)
(5 minute “stretch” break)

2. Step 5: Define Decision Rules
Step Objective:
This step combines Steps to produce the following major elements to form decision rules:
Parameter of interest
Unit of Decision Making
Action Level
Alternative Actions
Step 1: State the Problem
Step 2: Identify Decisions
Step 3: Identify Inputs
Step 4: Specify Boundaries
Step 5: Define Decision Rules
Step 6: Specify Error Tolerances
Step 7: Optimize Sample Design

3. Step 5- Define Decision Rules
Information IN Actions Information OUT
From Previous Step To Next Step
Specify the Parameter of Interest
Principal Study Question
Step 2
Information Required to Resolve Decision Statement
Step 3
Population of Interest
Step 4
If/Then Decision Rule Statements
Develop a Decision Rule
Unit of Decision Making
Step 4
Alternative Actions Step 2
Confirm the Action Level
Basis for Defining Action Level
Step 3

4. Background Major Elements of a Decision Rule:
Population Parameter of Interest
Population Parameter (ALWAYS UNKNOWN)
Sample Statistic (Used to represent the Population Parameter)
Environmental Variable
Chemical/physical attribute in the population
Levels measured (quantity)
Unit of Decision Making (Step 4)
Population (lives with Decision Unit)
Geographic Area/Volume (Population Spatial Boundry)
Timeframe (Population Temporal Boundry)
Action Level (Step 3)
Alternative Actions (Step 2)

5. Decision Rule Example
If the [true mean (as estimated by the 90% UCL of the sample mean) concentration of cadmium] within [the concrete rubble in a container truck] is > [1 mg/kg], then [the waste rubble will be considered hazardous and will be disposed of in a RCRA facility]; if not [the waste rubble will be disposed of in a municipal landfill].

6. Step 6: Specify Error Tolerances
Step Objective:
To specify the decision maker’s tolerable limits on decision errors, which are used for limiting uncertainty in the data
Since analytical data can only provide an estimate of the true condition of a site, decisions that are based on such data could potentially be in error
Step 1: State the Problem
Step 2: Identify Decisions
Step 3: Identify Inputs
Step 4: Specify Boundaries
Step 5: Define Decision Rules
Step 6: Specify Error Tolerances
Step 7: Optimize Sample Design

7. Step 6- Specify Error Tolerances
Information IN Actions Information OUT
From Previous Step To Next Step
Decision
Rules
Step 5
Determine the variability of the environmental variables
Identify the decision errors
Bounds of the Gray Region
Choose the null hypothesis
Specify the boundaries of the Gray Region
Assign probability limits on either side of the Gray Region
Decision Error Tolerances

8. Step 7: Optimize Sample Design
Step 1: State the Problem
Step Objective:
Identify the most resource-effective data collection and analysis design that satisfies the DQOs specified in the preceding 6 Steps
Step 2: Identify Decisions
Step 3: Identify Inputs
Step 4: Specify Boundaries
Step 5: Define Decision Rules
Step 6: Specify Error Tolerances
Step 7: Optimize Sample Design

9. Types of Designs Simple Random Systematic Grid with random start
Geometric Probability or “Hot Spot” Sampling
Stratified Random
Stratified Simple Random
Stratified Systematic Grid with random start
Statistical Methods for Environmental Pollution Monitoring, Richard O. Gilbert, 1987

10. Step 7- Optimize Sample Design
Information IN Actions Information OUT
From Previous Step To Next Step
Review DQO outputs from Steps 1-6 to be sure they are internally consistent
Decision Error Tolerances
Develop alternative sample designs
For each design option, select needed mathematical expressions
Gray Region
Select the optimal sample size that satisfies the DQOs for each data collection design option
Check if number of samples exceeds project resource constraints
Optimal Sample Design
Go back to Steps 1- 6 and revisit decisions.
Yes No

11. Begin with the End in Mind
DATA
Contaminant Concentrations in the Spatial Distribution of the Population
Population Frequency Distribution
Correct Equation for n (Statistical Method)
, , , 
Alternative Sample Designs
Optimal Sampling Design
How Many Samples do I Need?
The end

12. Iterative Process
Steps 1- 6
Step 7
Optimal Design

13. Judgmental Sampling Statistical Sampling Judgmental Sampling
Differences
Allows inferences about a population with a known certainty
Population inferences not plausible; no random component
Allows for decision-error analysis
No basis for decision-error analysis
Useful in all phases of environmental decision making: scoping, characterization, remediation, verification
Useful in scoping, emergency response, and waste designation

14. Summary
Going through the 7-Step DQO Process should result in a defensible and cost-effective sampling program
In order for the 7-Step DQO Process to be effective:
All steps must be performed
Inputs must be based on comprehensive scoping and maximum participation/contributions by decision makers
Sample design must be based on the severity of the consequences of decision error

15. Summary(cont.)
To succeed in a systematic planning process for environmental decision making, you need Statistical Support:
One or more qualified statisticians, experienced in environmental data collection designs and statistical data quality assessments of such designs.

16. End of Module 8 Thank you Questions? We will now take a
Third Afternoon 5-minute “Stretch” Break.
Please be back in 5 minutes