Data Quality Objectives at Resource and Recovery Act Treatment,
Storage and Disposal Facilities (DQOs at RCRA TSDFs)

David Gossman
Gossman Consulting, Inc.

Presented at the Hazardous Waste Combustors specialty Conference, Dallas, TX, September 22-24, 1999.

ABSTRACT

EPA has provided a number of sources for guidance on the establishment of DQOs. This guidance is generally for site investigation and remediation projects. With minor modifications, the guidance is useful for establishing DQOs at commercial RCRA TSDFs. The process can help to reduce laboratory burdens as well as identify specific parameters where greater effort may be needed to maintain compliance. Two examples of how the process can be used are provided.

INTRODUCTION

EPA has provided a number of documents that provide guidance on the establishment of DQOs^,. The Department of Energy has provided a summary of EPA guidance at its website <http://etd.pnl.gov:7080/DQO/steps.htm>. ASTM has developed a standard, D5792 - Standard Practice For Generation of Environmental Data Related to Waste Management Activities: Development of Data Quality Objectives. Generally, those documents are written with an orientation toward cleanup projects. EPA, in SW-846, requires RCRA waste analysis programs to also develop DQOs. The following incorporates minor modifications into existing EPA guidance so that this process can more easily be used by commercial RCRA facilities in order to establish DQOs for laboratory determinations. Two examples of DQOs for commonly performed laboratory determinations at RCRA BIF facilities are provided.

WHY DQOs

Beyond the quick answer that it is an EPA requirement, the process of developing the DQO and the resulting DQO for use in a QA/QC plan can provide significant operational and financial benefits to a commercial RCRA facility. The DQO process develops quality control standards for the laboratory that provide needed data with a tolerable degree of uncertainty. Since the maintenance of data quality can be an enormous cost, such a program can result in significant cost savings. Furthermore, the DQO process provides defensible justification of laboratory data collection activities, thus minimizing liabilities.

STEPS IN THE PROCESS

Step One: Problem Statement
The purpose of this step is to clearly define the problem that requires environmental data so that the focus of the effort will be clear and unambiguous.

Potential Activities

• Identify members of a planning team if appropriate.
• Identify the primary decision maker if appropriate.
• Develop a concise description of the problem.
• Specify available resources and relevant deadlines for the effort if necessary.

Step Two: Decision Statement

The purpose of this step is to define the decision that will be made using data to address the issue.

Potential Activities

• State the decision.
• Categorize multiple decisions if needed.
• State the actions or outcomes that could result from the [decision].

Step Three: Identify Inputs to the Decision

The purpose of this step is to identify the informational inputs that will be required to [make the decision], and to determine which inputs require analytical data.

Potential Activities

• Identify the information that will be required to [make the decision].
• Determine the source for each item of information identified.
• Identify the information that is needed to establish the action level for the study.
• Confirm that appropriate analytical methods exist to provide the necessary data.

Step Four: Decision Boundaries

The purpose of this step is to specify the [temporal circumstances] that are covered by the decision.

Potential Activities

• Define the scale of decision making.
• Determine when to collect data.
• Determine the time frame to which the study data apply.
• Identify any practical constraints on data collection.

Step Five: Develop a Decision Rule

The purpose of this step is to integrate the outputs from previous steps into a single statement that describes the logical basis for choosing among alternative actions.

Potential Activities

• Specify the action level for the study.
• Combine the outputs of the previous DQO steps into an "if...then...." decision rule that defines the conditions that would cause the decision maker to choose among alternative actions.

Step Six: Specify Limits on Decision Errors

The purpose of this step is to specify the decision maker's acceptable limits on decision errors, which are used to establish appropriate performance goals for limiting uncertainty in the data.

Potential Activities

• Determine the possible range of the parameter of interest.
• Define decision errors and identify the potential consequences.
• Specify a range of possible parameter values where the consequences of decision errors are relatively minor (gray region).
• Assign probability values to points above or below the action level that reflect the acceptable possibility for the relative consequences for each type of decision error.
• Check the limits on decision errors to ensure that they accurately reflect the decision maker's concern about the relative consequences for each type of decision error.

Step Seven: Optimize the Design

The purpose of this step is to identify the most resource-effective analysis design for generating data that are expected to satisfy the DQOs.

Potential Activities

• Review the DQO outputs and existing data.
• Translate the information from the DQOs into a statistical hypothesis.
• Develop general analysis design alternatives.
• Select the most resource-effective design that satisfies all of the DQOs.
• Document the operational details and theoretical assumptions of the selected design.

 
 

EXAMPLE 1 - DQO FOR HEAT CONTENT DETERMINATIONS IN HAZARDOUS WASTE FUEL RECEIVED AT COMMERCIAL RCRA HAZARDOUS WASTE BURNING CEMENT FACILITIES

Problem Statement
RCRA rules and some state/local permits prohibit the use of wastes with <5000 BTU/lb as fuel for RCRA permitted Boilers and Industrial Furnaces (BIFs). Furthermore, the cement plant needs information on the heat content of the waste fuels to determine appropriate feed rates.

Decision Statement
The shipment waste testing program (part of the facility Waste Analysis Plan) seeks to provide information to aid in the prevention of receipt of shipments with less than the needed heat content. The cement plant needs heat content information in order to set fuel feed rates (heat input).

Inputs to the Decision
In addition to information and certifications from the generator, a 5000 BTU/lb threshold determination of heat content in each shipment is needed to either accept or reject the shipment. In order to set fuel feed rates (heat input), the cement kiln operator needs a quantitative evaluation of the waste fuel heat content.

Decision Boundaries
A separate determination and decision is needed for each shipment. This decision generally needs to be made within one hour of the arrival of the shipment on site. For these reasons, the analytical determination needs to be performed on site.

Decision Rule
If the measured heat content is less than 5000 BTU/lb, the shipment will be rejected. Heat content of waste fuel will be used to modify the input rate of other fuels appropriately.

Tolerable Limits on Decision Errors
There should be no greater than a 5% probability that any individual shipment is received with a heat content of <5000 BTU/lb. The kiln operator should be able to use heat content measurements of the waste fuel to adjust other fuel rates to within 2% of target levels.

Optimize the Design
In order to meet the tolerable limits on decision errors, the following procedures and QC requirements are needed:

• The procedure used shall be verified to be ± 2% relative accuracy (RA).
• Measurements will be rounded to the nearest 100 BTU/lb.
• All shipments with a measured level of <5000 BTU/lb shall be rejected.

 

 
 
 

EXAMPLE 2 - DQO FOR PCB DETERMINATIONS IN HAZARDOUS WASTE FUEL RECEIVED AT COMMERCIAL RCRA HAZARDOUS WASTE BURNING CEMENT FACILITIES

Problem Statement
The use of PCBs or PCB contaminated material as a waste fuel in cement kiln fuel programs is a violation of 40 CFR 761.60 as well as local permits and regulations. Historically, these materials have occasionally been found in wastes received for use as fuels without prior notification or manifesting.

Decision Statement
The shipment waste testing program (part of the facility Waste Analysis Plan) seeks to provide information to aid in the prevention of receipt of shipments contaminated with PCBs.

Inputs to the Decision
In addition to information and certifications from the generator, a 50 ppm threshold determination of PCB content in each shipment is needed to either accept or reject the shipment. The 50 ppm threshold is based on regulating criterion establishing the level at which a waste is considered PCB contaminated.

Decision Boundaries
A separate determination and decision is needed for each shipment. This decision generally needs to be made within one hour of the arrival of the shipment on site. For these reasons, the analytical determination needs to be performed on site.

Decision Rule
If the measured concentration of PCBs is 50 ppm or greater, then the shipment will be rejected.

Tolerable Limits on Decision Errors
There should be no greater than a 5% probability that any individual shipment is received with a PCB concentration greater than or equal to 50 ppm.

Optimize the Design
In order to meet the tolerable limits on decision errors, the following procedures and QC requirements are needed:

• The quantitation limit for each PCB aroclor shall be <40 ppm.
• All shipments with a measured level of 40 ppm or greater shall be rejected.
• The procedure used to test for PCBs shall be verified to be ± 25% relative accuracy (RA) at 40 ppm, and qualitatively accurate with <5% error rate.
• There are no specific relative accuracy requirements for PCB concentrations significantly lower than or greater than 40 ppm.

 CONCLUSION
A modified form of EPA Guidance on DQOs for cleanup projects can be readily utilized to create concise, defensible DQO for laboratory data guideline activities at commercial RCRA TSDFs. These DQOs can be used to significantly enhance the optimal relevance of generated data while simultaneously controlling costs.