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)

Step 5: Define Decision Rules Step Objective: This step combines Steps 1 - 4 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

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

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)

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].

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

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

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

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

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

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

Iterative Process Steps 1- 6 Step 7 Optimal Design

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

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

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.

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