ASTM D5792-10(2023)
(Practice)Standard Practice for Generation of Environmental Data Related to Waste Management Activities: Development of Data Quality Objectives
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
Relations
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 service@astm.org. 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 qu
...
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