ASTM D5792-10(2023)

Standard Practice for Generation of Environmental Data Related to Waste Management Activities: Development of Data Quality Objectives

SIGNIFICANCE AND USE
5.1 Environmental data are often required for making regulatory and programmatic decisions. Decision makers must determine whether the levels of assurance associated with the data are sufficient in quality for their intended use.  
5.2 Data generation efforts involve three parts: development of DQOs and subsequent project plan(s) to meet the DQOs, implementation and oversight of the project plan(s), and assessment of the data quality to determine whether the DQOs were met.  
5.3 To determine the level of assurance necessary to support the decision, an iterative process must be used by decision makers, data collectors, and users. This practice emphasizes the iterative nature of the process of DQO development. Objectives may need to be reevaluated and modified as information related to the level of data quality is gained. This means that DQOs are the product of the DQO process and are subject to change as data are gathered and assessed.  
5.4 This practice defines the process of developing DQOs. Each step of the planning process is described.  
5.5 This practice emphasizes the importance of communication among those involved in developing DQOs, those planning and implementing the sampling and analysis aspects of environmental data generation activities, and those assessing data quality.  
5.6 The impacts of a successful DQO process on the project are as follows: (1) a consensus on the nature of the problem and the desired decision shared by all the decision makers, (2) data quality consistent with its intended use, (3) a more resource-efficient sampling and analysis design, (4) a planned approach to data collection and evaluation, (5) quantitative criteria for knowing when to stop sampling, and (6) known measure of risk for making an incorrect decision.
SCOPE
1.1 This practice covers the process of development of data quality objectives (DQOs) for the acquisition of environmental data. Optimization of sampling and analysis design is a part of the DQO process. This practice describes the DQO process in detail. The various strategies for design optimization are too numerous to include in this practice. Many other documents outline alternatives for optimizing sampling and analysis design. Therefore, only an overview of design optimization is included. Some design aspects are included in the practice's examples for illustration purposes.  
1.2 DQO development is the first of three parts of data generation activities. The other two aspects are (1) implementation of the sampling and analysis strategies, see Guide D6311; and (2) data quality assessment, see Guide D6233.  
1.3 This guide should be used in concert with Practices D5283, D6250, and Guide D6044. Practice D5283 outlines the quality assurance (QA) processes specified during planning and used during implementation. Guide D6044 outlines a process by which a representative sample may be obtained from a population, identifies sources that can affect representativeness, and describes the attributes of a representative sample. Practice D6250 describes how a decision point can be calculated.  
1.4 Environmental data related to waste management activities include, but are not limited to, the results from the sampling and analyses of air, soil, water, biota, process or general waste samples, or any combinations thereof.  
1.5 The DQO process is a planning process and should be completed prior to sampling and analysis activities.  
1.6 This practice presents extensive requirements of management, designed to ensure high-quality environmental data. The words “must” and “shall” (requirements), “should” (recommendation), and “may” (optional), have been selected carefully to reflect the importance placed on many of the statements in this practice. The extent to which all requirements will be met remains a matter of technical judgment.  
1.7 The values stated in SI units are to be regarded as standard. No other units of measurement are included ...

General Information

• Status: Published
• Publication Date: 31-Oct-2023
• ICS: 13.030.99 - Other standards related to wastes
• Technical Committee: D34 - Waste Management
• Drafting Committee: D34.01.01 - Planning for Sampling

Relations

• Replaces: ASTM D5792-10(2015) - Standard Practice for Generation of Environmental Data Related to Waste Management Activities: Development of Data Quality Objectives
• Effective Date: 01-Nov-2023
• Refers: ASTM D5681-23 - Standard Terminology for Waste and Waste Management
• Effective Date: 01-Nov-2023
• Refers: ASTM D5681-22e1 - Standard Terminology for Waste and Waste Management
• Effective Date: 01-May-2022
• Referred By: ASTM D7048-16 - Standard Guide for Applying Statistical Methods for Assessment and Corrective Action Environmental Monitoring Programs
• Effective Date: 01-Nov-2023
• Referred By: ASTM D6640-21 - Standard Practice for Collection and Handling of Soils Obtained in Core Barrel Samplers for Environmental Investigations
• Effective Date: 01-Nov-2023
• Referred By: ASTM D6759-16 - Standard Practice for Sampling Liquids Using Grab and Discrete Depth Samplers
• Effective Date: 01-Nov-2023
• Referred By: ASTM D6044-21 - Standard Guide for Representative Sampling for Management of Waste and Contaminated Media
• Effective Date: 01-Nov-2023
• Referred By: ASTM D7353-21 - Standard Practice for Sampling of Liquids in Waste Management Activities Using a Peristaltic Pump
• Effective Date: 01-Nov-2023
• Referred By: ASTM D7758-17 - Standard Practice for Passive Soil Gas Sampling in the Vadose Zone for Source Identification, Spatial Variability Assessment, Monitoring, and Vapor Intrusion Evaluations
• Effective Date: 01-Nov-2023
• Referred By: ASTM D6699-16 - Standard Practice for Sampling Liquids Using Bailers
• Effective Date: 01-Nov-2023
• Referred By: ASTM D5633-21 - Standard Practice for Sampling with a Scoop
• Effective Date: 01-Nov-2023
• Referred By: ASTM E2421-15(2021) - Standard Guide for Preparing Waste Management Plans for Decommissioning Nuclear Facilities
• Effective Date: 01-Nov-2023
• Referred By: ASTM D7352-18 - Standard Practice for Volatile Contaminant Logging Using a Membrane Interface Probe (MIP) in Unconsolidated Formations with Direct Push Methods
• Effective Date: 01-Nov-2023
• Referred By: ASTM D6907-22 - Standard Practice for Sampling Soils and Contaminated Media with Hand-Operated Bucket Augers
• Effective Date: 01-Nov-2023
• Referred By: ASTM D4448-01(2019) - Standard Guide for Sampling Ground-Water Monitoring Wells
• Effective Date: 01-Nov-2023

Overview

ASTM D5792-10(2023), titled Standard Practice for Generation of Environmental Data Related to Waste Management Activities: Development of Data Quality Objectives, provides a detailed framework for developing Data Quality Objectives (DQOs) in support of waste management environmental data activities. Developed by ASTM International, this standard applies to activities requiring high-quality environmental data to drive regulatory and programmatic decisions pertaining to waste management. Its principal aim is to ensure that data collected are fit for purpose, cost-effective, and support sound decision making.

This practice describes an iterative, participatory planning process that includes defining objectives, identifying data needs, and establishing criteria for data collection and quality assessment. The standard is crucial for organizations, regulatory agencies, and project teams tasked with gathering environmental data for compliance, monitoring, remediation, or risk assessment within waste management projects.

Key Topics

• Data Quality Objectives (DQOs): The standard details how to derive qualitative and quantitative statements defining the study goals, data needs, decision rules, and tolerable error levels.
• Iterative Planning Process: Emphasis is placed on the cyclical nature of defining and refining DQOs as new information becomes available during a project.
• Stakeholder Communication: Successful implementation relies on clear communication between project managers, decision makers, technical specialists, and regulatory authorities.
• Data Generation Lifecycle: DQO development is presented as the first step, followed by implementation of the associated sampling and analysis plan, and concluding with data quality assessment.
• Design Optimization Overview: While the standard covers the basics, it also refers to other documents for in-depth strategies on optimizing sampling and analysis design.
• Risk and Decision Point: The framework ensures decisions are based on data with known quality and quantified risk of error.
• Breadth of Application: Applicable to air, soil, water, biota, process, and general waste samples, reinforcing its utility across various environmental matrices.

Applications

The application of ASTM D5792-10(2023) brings significant value to organizations involved in:

• Regulatory Compliance: Ensuring that environmental data meet the required standards for regulatory approval in waste management.
• Remediation Projects: Developing DQOs for characterization and cleanup of contaminated sites to ensure actions are based on reliable data.
• Monitoring Programs: Establishing data quality requirements for ongoing environmental surveillance of waste sites, landfills, or industrial facilities.
• Risk Assessment: Supporting health and ecological risk evaluations through robust data collection plans that quantify uncertainty and support risk-based decisions.
• Resource Optimization: Streamlining sampling and analysis by targeting data necessary for decision making, avoiding unnecessary costs or effort.

By following ASTM D5792, project teams can achieve:

• Consensus among all decision makers on project goals and outcomes
• Data collection efforts that are aligned with intended use, improving relevance and reliability
• More efficient allocation of resources in environmental sampling and analysis
• Clear, quantitative criteria for determining the sufficiency and stopping points for data collection
• Transparent evaluation of risk associated with environmental decisions

Related Standards

To ensure a comprehensive approach, ASTM D5792-10(2023) references several related ASTM standards that support quality assurance, sampling design, and data assessment:

• ASTM D5283 – Quality Assurance and Quality Control Planning and Implementation for Environmental Data
• ASTM D6044 – Guide for Representative Sampling for Management of Waste and Contaminated Media
• ASTM D6250 – Practice for Derivation of Decision Point and Confidence Limit for Statistical Testing (Withdrawn 2018)
• ASTM D6311 – Guide for Selection and Optimization of Sampling Design
• ASTM D6233 – Guide for Data Assessment for Environmental Waste Management Activities (Withdrawn 2016)
• ASTM D5681 – Terminology for Waste and Waste Management

These standards work in concert to establish a comprehensive methodology for generating and evaluating environmental data related to waste management, supporting the full lifecycle from project planning through data assessment and decision-making.

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Keywords: ASTM D5792, Data Quality Objectives, environmental data, waste management, sampling design, data quality, regulatory compliance, risk assessment, environmental standards.

Standards Content (Sample)

This international standard was developed in accordance with internationally recognized principles on standardization established in the Decision on Principles for the
Development of International Standards, Guides and Recommendations issued by the World Trade Organization Technical Barriers to Trade (TBT) Committee.
Designation: D5792 − 10 (Reapproved 2023)
Standard Practice for
Generation of Environmental Data Related to Waste
Management Activities: Development of Data Quality
Objectives
This standard is issued under the fixed designation D5792; the number immediately following the designation indicates the year of
original adoption or, in the case of revision, the year of last revision. A number in parentheses indicates the year of last reapproval. A
superscript epsilon (´) indicates an editorial change since the last revision or reapproval.
1. Scope (recommendation), and “may” (optional), have been selected
carefully to reflect the importance placed on many of the
1.1 This practice covers the process of development of data
statements in this practice. The extent to which all require-
quality objectives (DQOs) for the acquisition of environmental
ments will be met remains a matter of technical judgment.
data. Optimization of sampling and analysis design is a part of
the DQO process. This practice describes the DQO process in 1.7 The values stated in SI units are to be regarded as
detail. The various strategies for design optimization are too standard. No other units of measurement are included in this
numerous to include in this practice. Many other documents standard.
outline alternatives for optimizing sampling and analysis 1.7.1 Exception—The values given in parentheses are for
design. Therefore, only an overview of design optimization is information only.
included. Some design aspects are included in the practice’s
1.8 This standard does not purport to address all of the
examples for illustration purposes.
safety concerns, if any, associated with its use. It is the
responsibility of the user of this standard to establish appro-
1.2 DQO development is the first of three parts of data
priate safety, health, and environmental practices and deter-
generation activities. The other two aspects are (1) implemen-
mine the applicability of regulatory limitations prior to use.
tation of the sampling and analysis strategies, see Guide
1.9 This international standard was developed in accor-
D6311; and (2) data quality assessment, see Guide D6233.
dance with internationally recognized principles on standard-
1.3 This guide should be used in concert with Practices
ization established in the Decision on Principles for the
D5283, D6250, and Guide D6044. Practice D5283 outlines the
Development of International Standards, Guides and Recom-
quality assurance (QA) processes specified during planning
mendations issued by the World Trade Organization Technical
and used during implementation. Guide D6044 outlines a
Barriers to Trade (TBT) Committee.
process by which a representative sample may be obtained
from a population, identifies sources that can affect
2. Referenced Documents
representativeness, and describes the attributes of a represen-
2.1 ASTM Standards:
tative sample. Practice D6250 describes how a decision point
C1215 Guide for Preparing and Interpreting Precision and
can be calculated.
Bias Statements in Test Method Standards Used in the
1.4 Environmental data related to waste management activi-
Nuclear Industry
ties include, but are not limited to, the results from the
D5283 Practice for Generation of Environmental Data Re-
sampling and analyses of air, soil, water, biota, process or
lated to Waste Management Activities: Quality Assurance
general waste samples, or any combinations thereof.
and Quality Control Planning and Implementation
1.5 The DQO process is a planning process and should be
D5681 Terminology for Waste and Waste Management
completed prior to sampling and analysis activities. D6044 Guide for Representative Sampling for Management
of Waste and Contaminated Media
1.6 This practice presents extensive requirements of
D6233 Guide for Data Assessment for Environmental Waste
management, designed to ensure high-quality environmental
Management Activities (Withdrawn 2016)
data. The words “must” and “shall” (requirements), “should”
1 2
This practice is under the jurisdiction of ASTM Committee D34 on Waste For referenced ASTM standards, visit the ASTM website, www.astm.org, or
Management and is the direct responsibility of Subcommittee D34.01.01 on contact ASTM Customer Service at [e-mail]. For Annual Book of ASTM
Planning for Sampling. Standards volume information, refer to the standard’s Document Summary page on
Current edition approved Nov. 1, 2023. Published November 2023. Originally the ASTM website.
approved in 1995. Last previous edition approved in 2015 as D5792 – 10 (2015). The last approved version of this historical standard is referenced on
DOI: 10.1520/D5792-10R23. www.astm.org.
Copyright © ASTM International, 100 Barr Harbor Drive, PO Box C700, West Conshohocken, PA 19428-2959. United States
D5792 − 10 (2023)
D6250 Practice for Derivation of Decision Point and Confi- 3.2.6.1 false negative error, n—this occurs when environ-
dence Limit for Statistical Testing of Mean Concentration mental data mislead decision maker(s) into not taking action
in Waste Management Decisions (Withdrawn 2018) specified by a decision rule when action should be taken.
D6311 Guide for Generation of Environmental Data Related
3.2.6.2 false positive error, n—this occurs when environ-
to Waste Management Activities: Selection and Optimiza-
mental data mislead decision maker(s) into taking action
tion of Sampling Design
specified by a decision rule when action should not be taken.
3.2.7 decision point, n—the numerical value that causes the
3. Terminology
decision maker to choose one of the alternative actions point
3.1 For definitions of terms used in this standard refer to
(for example, compliance or noncompliance). D6250
Terminology D5681.
3.2.7.1 Discussion—In the context of this practice, the
numerical value is calculated in the planning stage and prior to
3.2 Definitions of Terms Specific to This Standard:
the collection of the sample data, using a specified hypothesis,
3.2.1 bias, n—the difference between the sample value of
decision error, an estimated standard deviation, and number of
the test results and an accepted reference value.
samples. In environmental decisions, a concentration limit such
3.2.1.1 Discussion—Bias represents a constant error as op-
as a regulatory limit usually serves as a standard for judging
posed to a random error. A method bias can be estimated by
attainment of cleanup, remediation, or compliance objectives.
the difference (or relative difference) between a measured
Because of uncertainty in the sample data and other factors,
average and an accepted standard or reference value. The data
actual cleanup or remediation may have to go to a level lower
from which the estimate is obtained should be statistically
or higher than this standard. This new level of concentration
analyzed to establish bias in the presence of random error. A
serves as a point for decision making and is, therefore, termed
thorough bias investigation of a measurement procedure re-
the decision point.
quires a statistically designed experiment to repeatedly
measure, under essentially the same conditions, a set of 3.2.8 decision rule, n—a set of directions in the form of a
conditional statement that specify the following: (1) how the
standards or reference materials of known value that cover the
range of application. Bias often varies with the range of sample data will be compared to the decision point, (2) which
decision will be made as a result of that comparison, and (3)
application and should be reported accordingly. C1215
what subsequent action will be taken based on the decisions.
3.2.2 confidence interval, n—an interval used to bound the
3.2.9 population, n—the totality of items or units of mate-
value of a population parameter with a specified degree of
rials under consideration.
confidence (this is an interval that has different values for
different samples).
3.2.10 precision, n—a generic concept used to describe the
3.2.2.1 Discussion—The specified degree of confidence is
dispersion of a set of measured values.
usually 90, 95, or 99 %. Confidence intervals may or may not
3.2.10.1 Discussion—Measures frequently used to express
be symmetric about the mean, depending on the underlying
precision are standard deviation, relative standard deviation,
statistical distribution. For example, confidence intervals for
variance, repeatability, reproducibility, confidence interval, and
the variances are not symmetric. C1215
range. In addition to specifying the measure and the precision,
it is important that the number of repeated measurements upon
3.2.3 confidence level, n—the probability, usually expressed
which the estimated precision is based also be given.
as a percent, that a confidence interval is expected to contain
the parameter of interest (see discussion of confidence inter-
3.2.11 quality assurance (QA), n—an integrated system of
val).
management activities involving planning, quality control,
quality assessment, reporting, and quality improvement to
3.2.4 data quality objectives (DQOs), n—qualitative and
ensure that a process or service (for example, environmental
quantitative statements derived from the DQO process describ-
data) meets defined standards of quality with a stated level of
ing the decision rules and the uncertainties of the decision(s)
confidence. EPA QA/G-4
within the context of the problem(s).
3.2.4.1 Discussion—DQOs clarify the study objectives, de- 3.2.12 quality control (QC), n—the overall system of tech-
fine the most appropriate type of data to collect, determine the
nical activities whose purpose is to measure and control the
most appropriate conditions from which to collect the data, and quality of a product or service so that it meets the needs of
establish acceptable levels of decision errors that will be used
users. The aim is to provide quality that is satisfactory,
as the basis for establishing the quantity and quality of data adequate, dependable, and economical. EPA QA/G-4
needed to support the decision. The DQOs are used to develop
3.2.13 random error, n—(1) the chance variation encoun-
a sampling and analysis design.
tered in all measurement work, characterized by the random
3.2.5 data quality objectives process, n—qualitative and occurrence of deviations from the mean value; (2) an error that
quantitative statements derived from the DQO process that affects each member of a set of data (measurements) in a
clarify study objectives, define the appropriate type of data, and different manner.
specify the tolerable levels of potential decision errors that will
3.2.14 risk, n—the probability or an expected loss associ-
be used as the basis for establishing the quality and quantity of
ated with an adverse effect.
data needed to support decisions.
3.2.14.1 Discussion—Risk is frequently used to describe the
3.2.6 decision error: adverse effect on health or on economics. Health-based risk is
D5792 − 10 (2023)
the probability of induced diseases in persons exposed to
physical, chemical, biological, or radiological insults over
time. This risk probability depends on the concentration or
level of the insult, which is expressed by a mathematical model
describing the dose and risk relationship. Risk is also associ-
ated with economics when decision makers have to select one
action from a set of available actions. Each action has a
corresponding cost. The risk or expected loss is the cost
multiplied by the probability of the outcome of a particular
action. Decision makers should adopt a strategy to select
actions that minimize the expected loss.
3.2.15 sample standard deviation, n—the square root of the
sum of the squares of the individual deviations from the sample
average divided by one less than the number of results
involved.
n
¯
~X 2 X!
( j
j51
S 5
!
n 2 1
where:
S = sample standard deviation,
n = number of results obtained,
X = jth individual result, and
j
¯
X = sample average.
4. Summary of Practice
4.1 This practice describes the process of developing and
FIG. 1 DQO Process
documenting the DQO process and the resulting DQOs. This
practice also outlines the overall environmental study process
5.5 This practice emphasizes the importance of communi-
as shown in Fig. 1. It must be emphasized that any specific
cation among those involved in developing DQOs, those
study scheme must be conducted in conformity with applicable
planning and implementing the sampling and analysis aspects
agency and company guidance and procedures.
of environmental data generation activities, and those assessing
4.2 For example, the investigation of a Superfund site
data quality.
would include feasibility studies and community relation plans,
5.6 The impacts of a successful DQO process on the project
which are not a part of this practice.
are as follows: (1) a consensus on the nature of the problem and
the desired decision shared by all the decision makers, (2) data
5. Significance and Use
quality consistent with its intended use, (3) a more resource-
5.1 Environmental data are often required for making regu-
efficient sampling and analysis design, (4) a planned approach
latory and programmatic decisions. Decision makers must
to data collection and evaluation, (5) quantitative criteria for
determine whether the levels of assurance associated with the
knowing when to stop sampling, and (6) known measure of
data are sufficient in quality for their intended use.
risk for making an incorrect decision.
5.2 Data generation efforts involve three parts: development
6. Data Quality Objective Process
of DQOs and subsequent project plan(s) to meet the DQOs,
6.1 The DQO process is a logical sequence of seven steps
implementation and oversight of the project plan(s), and
that leads to decisions with a known level of uncertainty (Fig.
assessment of the data quality to determine whether the DQOs
1). It is a planning tool used to determine the type, quantity,
were met.
and adequacy of data needed to support a decision. It allows
5.3 To determine the level of assurance necessary to support
the users to collect proper, sufficient, and appropriate informa-
the decision, an iterative process must be used by decision
tion for the intended decision. The output from each step of the
makers, data collectors, and users. This practice emphasizes
process is stated in clear and simple terms and agreed upon by
the iterative nature of the process of DQO development.
all affected parties. The seven steps are as follows:
Objectives may need to be reevaluated and modified as
(1) Stating the problem,
information related to the level of data quality is gained. This
(2) Identifying possible decisions,
means that DQOs are the product of the DQO process and are
(3) Identifying inputs to decisions,
subject to change as data are gathered and assessed.
(4) Defining boundaries,
5.4 This practice defines the process of developing DQOs. (5) Developing decision rules,
Each step of the planning process is described. (6) Specifying limits on decision errors, and
D5792 − 10 (2023)
(7) Optimizing data collection design. notice. Only after the appropriate information and problem-
All outputs from steps (1) through (6) are assembled into an solving team are assembled can a clear statement of the
integrated package that describes the project objectives (the
problem be made.
problem and desired decision rules). These objectives summa-
6.2.2 Activities:
rize the outputs from the first five steps and end with a
6.2.2.1 Assembling of All Pertinent Information—The nec-
statement of a decision rule with specified levels of the
essary first action to describe a problem is to verify the
decision errors (from the sixth step). In the last step of the
conditions that indicate a problem exists. The pertinent infor-
process, various approaches to a sampling and analysis plan for
mation should be assembled during this phase of problem
the project are developed that allow the decision makers to
definition. A key source is any historical record of events at the
select a plan that balances resource allocation considerations
site where the problem is believed to exist. This enables the
(personnel, time, and capital) with the project’s technical
decision makers to understand the context of the problem. A
objectives. Taken together, the outputs from these seven steps
series of questions need to be developed concerning the
comprise the DQO process. The relationship of the DQO
problem.
process to the overall project process is shown in Fig. 2. At any
(1) What happened (or could happen) that suggests a
stage of the project or during the field implementation phase, it
may be appropriate to reiterate the DQO process, beginning problem?
with the first step based on new information. See Refs (1, 2) (2) When did it (could it) happen?
for examples of the DQO process.
(3) How did it (could it) happen?
(4) Where did it (could it) happen?
6.2 Step 1—Stating the Problem:
(5) Why did it (could it) happen?
6.2.1 Purpose—The purpose of this step is to state the
(6) How bad is (might be) the result or situation?
problem clearly and concisely. The first indication that a
(7) How fast is (might be) the situation changing?
problem (or issue) exists is often articulated poorly from a
(8) What is (could be) the impact on human health and the
technical perspective. A single event or observation is usually
cited to substantiate that a problem exists. The identity and environment?
roles of key decision makers and technical qualifications of the (9) Who was (could be) involved?
problem-solving team may not be provided with the first
(10) Who knows (should know) about the situation?
(11) Has anything been (might anything be) done to miti-
gate the problem?
4 (12) What contaminants are (could be) involved?
The boldface numbers in parentheses refer to the list of references at the end of
(13) How reliable is the information?
this practice.
(14) What regulations could or should apply?
(15) Is there any information that suggests there is not a
problem?
This list of potential information is not exhaustive, and there
may be other data applicable to the definition of the problem.
6.2.2.2 Identification of the DQO Team—Even as informa-
tion is being gathered, it is necessary to begin assembling a
team of decision makers and technical support personnel to
organize and evaluate the information. These individuals
become the core of the DQO team and may be augmented by
others as information and events dictate. The decision makers
who have either jurisdiction over the site and personnel or
financial resources that will be used in resolving the problem
usually determine the identities and roles of the DQO team
members. The DQO team is usually made up of the following
key individuals:
(1) Site Owners or Potentially Responsible Parties—These
individuals have authority to commit personnel and financial
resources to resolve the problem and have a vital interest in the
definition of the problem and possible decisions.
(2) Representatives of Regulatory Agencies—These indi-
viduals are usually responsible for enforcing the standards that
have been exceeded, leading to classifying the observations or
events as a problem. Additionally, they have an active role in
characterizing the extent of the problem, approving any pro-
posed remedial action, and concurring that the action mitigated
FIG. 2 DQOs Process and Overall Decision Process the problem.
D5792 − 10 (2023)
(3) Project Manager—This individual generally has the Preliminary milestones, timelines, and approvals should be
responsibility for overseeing resolution of the problem. This documented and concurred upon by affected decision makers.
person may represent either the regulatory agency or the The DQO team leader and technical specialists should be
potentially responsible parties. included in these discussions where possible. At a minimum,
they should be kept informed of these issues so their impact
(4) Technical Specialists—These individuals have the ex-
pertise to assess the information and data to determine the can be anticipated in the definition of the problem.
(1) Fig. 3 shows the primary components of the problem
nature and extent of the potential problem and may become key
players in the design and implementation of proposed deci- statement step. After this step is completed, the DQO team
moves on to the next step, where the process to resolve the
sions.
problem continues.
It is important that these individuals be assembled early in
(2) It is important to remember that the DQO process is an
the process and remain actively involved to foster good
iterative one. New information is collected as projects proceed.
communications and to achieve consensus among the DQO
The DQO team members associated with the problem-
team on important decision-related issues.
statement step should remain involved with the DQO process.
6.2.3 Outputs:
If new data, unavailable to the DQO team during the develop-
6.2.3.1 Statement of Problem and Context—Once the initial
ment of the problem statement, demonstrates that the statement
information and data have been collected, organized, and
is incomplete or otherwise inadequate, the problem statement
evaluated, the conclusions of the DQO team should be docu-
should be reconsidered.
mented. If it is determined that no problem exists, the conclu-
6.3 Step 2—Identifying Possible Decisions:
sion must be supported by a summary of the existing condi-
6.3.1 Purpose—The purpose of this step is to identify the
tions and the standards or regulatory conditions that apply to
possible decision(s) that will address the problem. Multiple
the problem.
decisions are required when the problem is complex. Informa-
(1) If a problem is found to exist, the reasons must be stated
tion required to make decisions and to define the domain or
clearly and concisely. Any standards or regulatory conditions
boundaries of the decision will be determined in later steps (6.4
that apply to the situation must be cited. If the initial investi-
and 6.5, respectively). Each potential decision is tested to
gation concludes that the existing conditions are the result of a
ensure that it is worth pursuing further in the process. A series
series of problems, the DQO team should attempt to define as
of one or more decisions will result in actions that resolve the
many discrete problems (or issues) as possible.
problem. The activities that lead to identifying the decision(s)
(2) The following are examples of problem statements:
are shown in Fig. 3 and discussed in 6.3.2.
(a) A former pesticide formulation facility is for sale, but
6.3.2 Activities:
it is unknown whether it meets local environmental standards
6.3.2.1 Listing of Possible Questions Leading to
for property transfer.
Decisions—All possible decisions concerning the problem
(b) An industrial site is known to be contaminated with
should be listed. Choices should not be eliminated at this time.
low levels of lead, but it is unknown whether levels are below
Possible decision statements are presented in the form of a
risk-based standards.
(c) Most of a vacant lot is believed to be uncontaminated
with PCBs (<2 ppm), but it is unknown whether abandoned,
leaky transformers in the vacant lot make it necessary to
remove any of the top layer of soil.
(d) The former industrial site has contaminated soil areas
that may be contaminating ground water, and it is necessary to
decide which type of monitoring program will satisfy local
health requirements.
(e) The city would like to use local ground water on an
athletic field near a Superfund site, but must know how this
water will impact the health of the athletes and spectators.
(3) Complex problems should be broken down into man-
ageable smaller problems that are linked together to form the
final decision. As an example, the sale of a piece of property
may involve solving the following problems:
(a) Is the site contaminated? If yes, then,
(b) Is off-site disposal required? If no, then
(c) Which of two allowable on-site treatment options
should be used?
6.2.3.2 Identification of Resources—As the nature and mag-
nitude of the problem are being documented, the decision
makers should be conferring to determine the type and amount
of resources that can be committed. Preliminary budget,
personnel assignments, and schedule should be established. FIG. 3 Stating the Problem and Identifying the Decisions
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series of questions that, when answered, result in actions that
will resolve the problem. Examples of questions related to
problems given in 6.2.3 (Step 1) are as follows:
(1) Are possible contaminants on the site below regulatory
thresholds?
(2) Must all of the surface soil be remediated to less than
5 ppm lead?
(3) Can only locations with PCB levels above 2 ppm be
remediated?
(4) Will a ground water monitoring program at the site
capable of detecting contaminants at the 5 ppm level satisfy
regulatory requirements?
(5) Will a single monitoring point on or near the athletic
field be sufficient?
6.3.3 Output—After all possible decisions that might be
made have been documented, those determined to be most
appropriate to resolve the problem should be prioritized by the
DQO team in decreasing order of level of effort (available
resources and technical challenge). Justification for the rank-
ings should be provided. The recommended sequence in which
the decisions are made should also be listed. In cases in which
a complex decision statement has been broken down into a
series of simpler decisions, the DQO team should identify
whether the individual decisions should be addressed sequen-
tially or in parallel. After the possible decisions have been
identified, the DQO team focuses on gathering the information
necessary to formulate the decision statements in Step 3 (6.4).
6.4 Step 3—Identifying Inputs to Decisions:
6.4.1 Purpose—The answers to each of the questions iden-
tified by the previous step in the DQO process must be resolved FIG. 4 Determination of Information Inputs and Study Boundaries
with data. Fig. 4 shows the key activities that lead to develop-
ment of the data requirements. This sequence of activities must
(a) What regulatory limits may be associated with the
be performed for each question. Note that the limits of the
problem or regulatory issue?
study (or boundary conditions) are determined in a parallel step
(b) Does contamination exceed regulatory limits?
identified as “define boundaries” in Fig. 1. This is another type
(c) What tests must be performed for the type of waste in
of data requirement and is discussed in 6.4.
question?
6.4.2 Activities:
(d) What are the hydrogeological considerations?
6.4.2.1 Determination of Data Requirements—At this stage
(e) What populations are at risk?
of the process, it is important to carefully examine the
(f) What are the ecological considerations?
complete set of data requirements needed to support each of the
(g) What process knowledge is available?
decisions. Each possible decision to be made should be
(h) What historical/background data (past uses or spills)
considered independently of others to ensure that no omissions
are available?
have occurred. After all possible questions concerning the
(i) What are the budget constraints?
decisions have been considered, group the data requirements
(j) What is the time schedule?
together to determine overall data needs for the project. It may
(k) What potential health, political, and social factors must
be possible to plan efficiencies in collecting and processing
be considered?
data to meet multiple needs and thereby lower overall project
(l) What is the potential for legal action?
costs or reduce the time necessary to meet important
(m) Who is the end user of the data?
milestones, or both.
(n) What data validation criteria will be used?
(1) When considering whether specific information is
(o) What, if any, limitations exist on the data collection
needed for making a decision, test the data to ensure that it is process (detection limits, matrix interferences, or no known
appropriate for the decision statement. If no use of the data can
measurement technology)?
be identified, it may be extraneous to the needs. 6.4.3 Outputs:
(2) The following list is indicative of some of the informa-
6.4.3.1 The DQO team must specify data needs for each
tion needs that may be considered for each decision. It is not problem/decision that has been identified in the first two steps.
inclusive of all important data, but it provides examples 6.4.3.2 List the types of data required. Some example data
common to many environmental problems. types include, but are not limited to, the following:
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(1) Chemical, decision makers and the technical team. The same constraint is
(2) Physical (including site hydrogeology and also placed on the extrapolation of historical or real-time data,
meteorology), or both, to future time periods.
(3) Biological, (1) The duration of new data collection activities must be
(4) Toxicological, established. In addition, the following factors should be con-
(5) Historical, sidered:
(6) Economic (time, budget, and manpower), (a) Availability and reliability of existing historical data,
(7) Demographic, (b) Access to the site or impacted area,
(8) Toxicity characteristics, and (c) Exposure potential, and
(9) Fate and transport model output. (d) Budgetary constraints.
6.4.3.3 Listing of Data Generation Activities—Determine 6.5.2.3 Definition of the Demographic Receptors—The
which data can be acquired from historical records and which DQO team must frequently define the receptor population that
new data must be obtained in the field or laboratory, or both. If may be effected. All affected populations and the mode of their
the DQO team determines that no new data are necessary to anticipated exposure should be identified. These populations
make a decision, they should document their reasoning. If new include the following:
information is necessary, activities that will be required to (1) Known/Anticipated Population(s)—Human (children,
generate inputs (data) affecting the decision should be listed. adults, age, gender, and so forth), plant/animal (wetlands,
Examples of these include, but are not limited to, the follow- endangered species, and so forth), and global;
(2) Population activity patterns; and
ing:
(1) Assembly of historical data, (3) Exposure pathway for each population.
(2) Sampling and chemical analysis, 6.5.2.4 Definition of Nontechnical Boundaries—Decision
(3) Physical testing, and makers also have to consider nontechnical boundaries that can
(4) Modeling. impact the resolution of the problem seriously. These nontech-
nical boundaries include the following:
6.4.3.4 Definition of Data Use(s)—Each set of data will be
used for some purpose. This purpose must be defined. For (1) Regulatory considerations, and
(2) Political or legal action(s).
example, will regulatory thresholds for contaminants be deter-
mined by a risk-based calculation, by reference dose, or by 6.5.3 Outputs—The results from each of the activities in this
pre-defined threshold values established by regulators? If so, step must be documented. Care must be taken to identify which
ensure that data requirements are consistent with the criteria boundary conditions apply to each decision being made. It may
against which they will be compared. Data collected at the be that similar information is needed for several decisions but
parts per million level may not be useful if they are to be different boundary conditions may apply. It is important that
compared to criteria at the parts per billion level. decision makers understand and concur on the boundaries;
otherwise, the ability to make decisions may be compromised.
6.5 Step 4—Defining Boundaries:
6.6 Step 5—Developing Decision Rules:
6.5.1 Purpose—This step of the DQO process determines
6.6.1 Purpose:
the boundaries to which the decisions will apply. Boundaries
establish limits on the data collection activities identified in
6.6.1.1 The purpose of this step is to integrate outputs from
Step 3 (6.4). These boundaries include, but are not limited to, previous steps into a set of statements that describe the logical
spatial boundaries (physical and geographical), temporal basis for choosing among alternative outcomes/results/actions.
boundaries (time periods), demographic, regulatory, political, These statements are decision rules that define the following:
and budget. The activities for this step of the DQO process are (1) How the sample data will be compared to the regulatory
shown in Fig. 4.
threshold or to the decision point,
(2) Which decision(s) will be made as a result of that
6.5.2 Activities:
comparison, and
6.5.2.1 Definition of Spatial Boundaries—Define the bound-
(3) What subsequent action(s) will be taken based on the
aries of the total area and smallest increment of concern.
decisions.
Examples of items affecting the boundary definition are as
follows: Greater details on how a decision rule is formulated can be
found in Practice D6250.
(1) Horizontal or lateral areas,
(2) Vertical boundaries (depth/height),
6.6.1.2 The formats for these rules are either “if (criterion)
(3) Discrete locations (hot spots), ., then (action)” statements or a decision tree, as shown in Fig.
(4) Media/matrix (air, soil, water, biota, and waste),
5. The decision criteria should be stated as clearly and
(5) Number of containers of waste, and concisely as possible. The rule(s) must contain both a decision
(6) Volume. point (or decision point) and an action. The decision rule is
generated through a cooperative effort among the DQO team.
6.5.2.2 Definition of Temporal Boundaries (Time Period)—
If an acceptable decision rule cannot be formulated, the process
This activity determines the time interval over which environ-
returns to the appropriate previous step of the DQO process.
mental data will be collected for use in the decision-making
process. If current or future real-time data are used to represent 6.6.1.3 Decision rules usually contain the following ele-
or model previous conditions, the basis of these assumptions or ments: measurement of interest, sample statistic, decision
models must be documented and agreed upon between the point, and a resultant action. “Measurement of interest” is the
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(2) “If the average concentration of a contaminant in a
waste is lower than the decision point for that contaminant,
then the waste is classified as ‘nonhazardous’ and there are no
special limitations placed on the disposal options.
6.6.1.7 In this illustration, the measurement of interest is
“concentration of a contaminant.” The sample statistic is the
“average concentration.” The decision point is some value to
be specified. The resultant action is “disposal according to
governing regulations.” There may be separate decision rules
for each medium, each domain (site), or other designated
collections of data.
6.6.1.8 The decision point may be an observation or occur-
rence in some cases. An example of this type of decision rule
is as follows:
(1) If soil exhibits a visible dark spot as compared to the
surrounding soil, use the portable organic monitor to screen for
organics in the dark spot.
6.6.2 Activities—The activities that must be completed to
establish a decision rule are: specification of a regulatory
threshold, agreement on acceptable false positive and false
negative error rates, estimation of a sample standard deviation,
calculation of the sample statistic and the decision point, and
specification of alternative actions as a result of the decision.
After these activities have been completed, a decision perfor-
FIG. 5 Decision Tree for Three Sequential Decision Rules (DRs)
mance curve can be graphed as in Fig. 6. Decision performance
curve is discussed in 6.7.2.5 and X1.2.8.1.
6.6.2.1 Determination of Measurement of Interest—A clear
variable or attribute to be measured. It can be concentration of
expression of the measurement (parameter) upon which the
a contaminant, volume/mass of a waste, or physical property,
decision is based must be provided.
such as flash point of a waste. “Sample statistic” is the quantity
6.6.2.2 Specification of Decision Point—The determination
computed from the sample data. It can be average value,
of the decision point for any decision is a combination of the
median, present/absent, or some other expression of quantity. If
total variability in the data acquisition process and the level of
that data are not normally distributed, statistical methods based
decision errors that decision makers will accept in the final
on other distributions or non-parametric methods can be used.
decision. The role of decision makers and decision errors is
6.6.1.4 The “decision point” is the limit against which the
discussed in 6.7 (Step 6), and the derivation of a decision point
sample statistic will be compared (see X1.2.7.5 for example).
is illustrated in Appendix X1.
Depending on whether the decision point is exceeded or not,
6.6.2.3 Specification of Sample Statistic (if Applicable)—
the specified action will result. If the decision point equals the
Prior to the statement of a decision rule, it is necessary to
regulatory threshold, the probability of a false positive error
determine how the sample statistic will be calculated and
equals the probability of a false negative error. For unequal
probabilities of the decision errors, the decision point can be
either less or greater than the regulatory threshold. The degree
to which the decision point is different from the regulatory
threshold depends on the acceptable level of uncertainty for the
decision errors that the decision makers are willing to accept.
The levels of false positive error, false negative error,
variability, and number of samples determine the decision
point. Derivation of a decision point for a given level of false
positive and false negative error is included as part of Appen-
dix X1.
6.6.1.5 The decision rule is completed by stating the “resul-
tant action” to be taken based on comparison of the sample
statistic with the decision point.
6.6.1.6 An illustration of general decision rule formats are
as follows:
(1) “If the average concentration of a contaminant in waste
is greater than the decision point for that contaminant, then the
waste will be classified as a ‘hazardous’ waste and will be
disposed of according to the governing regulations.” FIG. 6 Decision Rule Development
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expressed (units of measure). The statistical approach chosen 6.7.2.1 Specifications of Decision Errors—It should be un-
can be the mean, median, high, low, range, present/absent, and derstood that, when a decision is made based on empirical data,
so forth. The unit of measurement must correspond to those of
there is no way to reduce either type of decision error to zero.
the decision criteria, and the limit of detection (measurement)
Furthermore, there is usually a tradeoff between the two
must be lower than the decision point.
decision errors, meaning that a lower false negative error
6.6.2.4 Specification of Mode of Comparison—After the
would lead to a higher false positive error, and vice-versa (for
sample statistic is derived from historical or new sample data
a given amount of data or number of samples). Decision
and a decision point has been identified, they must be com-
makers should understand the consequences of decision errors
pared. This comparison is usually stated as greater than ., less
and the tradeoffs between a false positive error and a false
than ., or equal to. Depending on the results of the
negative error. Error rates (false positive and false negative
comparison, a specific action is indicated by the decision rule.
errors) must be specified relative to an agreed-upon concentra-
6.6.2.5 Specification of Action—When the result of the
tion regulatory threshold or health-risk level.
comparison of the sample statistic with the decision point is
6.7.2.2 Consequences of an Incorrect Decision—The ran-
known, an action will result. It should be sufficient to resolve
dom variability for empirical data is often composed of (but not
the problem. In complex situations, the action may direct
limited to) sample variability and measurement variability.
decision makers to another problem (addressed by an addi-
Taken together, they comprise the total variability in the data
tional set of DQOs) that must also be resolved. This type of
that contributes to errors in the decision under consideration.
logical pathway is described frequently as a decision tree.
Decision makers must make an a priori judgment regarding
These situations should have been identified in Step 2 (6.3).
how often they are willing to be wrong because of data
Fig. 5 shows the decision tree derived from the application of
variability. This uncertainty is the “acceptable error” in the
a set of three sequential decision rules.
decision. In the context of a decision designed to be protective
6.6.3 Outputs—An example showing the application of a
of human health and the environment, they can be wrong by
decision rule is presented in Appendix X1. Some additional
taking a prescribed action when none was necessary (false
examples of decision rules that might apply to waste problems
positive error), or they can fail to take action when it was
and possible actions discussed in 6.2 and 6.3, respectively, are
necessary (false negative error).
given as follows:
6.7.2.3 False Positive Error—If the true concentration is
6.6.3.1 If the historical record of site monitoring activities
lower than the regulatory threshold, but the decision makers
shows the absence of any regulated constituent above 1 ppm,
then the site can be left as is. conclude that the waste is hazardous because the sample
average concentration is equal to or higher than the decision
NOTE 1—A value of 1 ppm selected for this example only.
point, then a false positive error has been made. The conse-
6.6.3.2 If site characterization indicates that 20 % of the soil quence of this error is that the nonhazardous waste will be
(top 30 cm) is contaminated above 5 ppm lead, then the entire
remediated or disposed of according to stricter requirements
soil layer (1 m) must be remediated.
than what is truly needed. A false positive error is undesirable
6.6.3.3 If site characterization data show that 95 % of the because it will incur unnecessary costs and result in ineffi-
total surface area (10 cm deep) of the site contains less than
ciency.
2 ppm PCB, then only those areas exceeding that value need to
6.7.2.4 False Negative Error—If the true concentration is
be remediated.
equal to or greater than the regulatory threshold, but the
6.6.3.4 If the levels of contaminants found in the monthly
decision makers conclude that the waste is nonhazardous
ground water monitoring program total less than 1000 ppm in
because the sample average concentration is below the decision
each well, then no additional corrective action needs to be
point, then a false negative error has been made. The conse-
instituted.
quence of this error is that the waste will be disposed of by a
6.6.3.5 If no contaminant above 1 ppm is observed in a
less stringent method. This error is undesirable because this
ground water monitoring well located down gradient and
error may lead to consequences harmful to health or the
within 100 m of the site boundary during monthly monitoring
environment.
events, then additional monitoring wells will not be required.
6.7.2.5 The relationship between the probability of taking
action on a decision rule and the possible true value of the
6.7 Step 6—Specifying Limits on Decision Errors:
measurement of interest is illustrated graphically by a decision
6.7.1 Purpose—An essential part of the DQO process is to
performance curve in Fig. 7 (see example in Appendix X1).
establish the degree of uncertainty (decision errors) that
The decision performance curve depends on the decision
decision makers are prepared to accept in making a decision
concerning the problem (Refs 3-5). The purpose of this step is makers’ willingness to accept false positive and false negative
to define the acceptable decision errors based on a consider- errors, the total variability of the measurement process, the
ation of the consequences
...