ASTM D5792-10(2015)
(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 in...
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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 2015)
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.Anumber 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-
qualityobjectives(DQOs)fortheacquisitionofenvironmental
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 and health practices and determine the applica-
generation activities. The other two aspects are (1) implemen-
bility of regulatory limitations prior to use.
tationofthesamplingandanalysisstrategies,seeGuideD6311
1.9 This international standard was developed in accor-
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,andGuideD6044.PracticeD5283outlinesthe
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 represen-
2. Referenced Documents
tativeness and describes the attributes of a representative
2.1 ASTM Standards:
sample. Practice D6250 describes how a decision point can be
C1215Guide for Preparing and Interpreting Precision and
calculated.
Bias Statements in Test Method Standards Used in the
1.4 Environmentaldatarelatedtowastemanagementactivi-
Nuclear Industry
ties include, but are not limited to, the results from the
D5283Practice for Generation of Environmental Data Re-
sampling and analyses of air, soil, water, biota, process or
lated to Waste ManagementActivities: QualityAssurance
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 D5681Terminology for Waste and Waste Management
D6044Guide for Representative Sampling for Management
completed prior to sampling and analysis activities.
of Waste and Contaminated Media
1.6 This practice presents extensive requirements of
D6233Guide for DataAssessment for EnvironmentalWaste
management, designed to ensure high-quality environmental
Management Activities
data. The words “must” and “shall” (requirements), “should”
D6250Practice for Derivation of Decision Point and Confi-
dence Limit for StatisticalTesting of Mean Concentration
This practice is under the jurisdiction of ASTM Committee D34 on Waste
Management and is the direct responsibility of Subcommittee D34.01.01 on
Planning for Sampling. For referenced ASTM standards, visit the ASTM website, www.astm.org, or
Current edition approved Sept. 1, 2015. Published September 2015. Originally contact ASTM Customer Service at service@astm.org. For Annual Book of ASTM
approved in 1995. Last previous edition approved in 2010 as D5792– 10. DOI: Standards volume information, refer to the standard’s Document Summary page on
10.1520/D5792-10R15. the ASTM website.
Copyright © ASTM International, 100 Barr Harbor Drive, PO Box C700, West Conshohocken, PA 19428-2959. United States
D5792 − 10 (2015)
in Waste Management Decisions 3.2.6.2 false positive error, n—this occurs when environ-
D6311GuideforGenerationofEnvironmentalDataRelated mental data mislead decision maker(s) into taking action
toWaste ManagementActivities: Selection and Optimiza- specified by a decision rule when action should not be taken.
tion of Sampling Design
3.2.7 decision point, n—the numerical value that causes the
decision-maker to choose one of the alternative actions point
3. Terminology
(for example, compliance or noncompliance). D6250
3.1 For definitions of terms used in this standard refer to
3.2.7.1 Discussion—In the context of this practice, the
Terminology D5681.
numericalvalueiscalculatedintheplanningstageandpriorto
the collection of the sample data, using a specified hypothesis,
3.2 Definitions of Terms Specific to This Standard:
decision error, an estimated standard deviation, and number of
3.2.1 bias, n—the difference between the sample value of
samples.Inenvironmentaldecisions,aconcentrationlimitsuch
the test results and an accepted reference value.
as a regulatory limit usually serves as a standard for judging
3.2.1.1 Discussion—Bias represents a constant error as op-
attainment of cleanup, remediation, or compliance objectives.
posed to a random error. A method bias can be estimated by
Because of uncertainty in the sample data and other factors,
the difference (or relative difference) between a measured
actual cleanup or remediation, may have to go to a level lower
average and an accepted standard or reference value. The data
or higher than this standard. This new level of concentration
from which the estimate is obtained should be statistically
serves as a point for decision-making and is, therefore, termed
analyzed to establish bias in the presence of random error.A
the decision point.
thorough bias investigation of a measurement procedure re-
quires a statistically designed experiment to repeatedly
3.2.8 decision rule, n—a set of directions in the form of a
measure, under essentially the same conditions, a set of
conditional statement that specify the following: (1) how the
standards or reference materials of known value that cover the
sample data will be compared to the decision point, (2) which
range of application. Bias often varies with the range of
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 precision, n—a generic concept used to describe the
value of a population parameter with a specified degree of
dispersion of a set of measured values.
confidence (this is an interval that has different values for
3.2.9.1 Discussion—Measures frequently used to express
different samples).
precision are standard deviation, relative standard deviation,
3.2.2.1 Discussion—The specified degree of confidence is
variance,repeatability,reproducibility,confidenceinterval,and
usually 90, 95, or 99%. Confidence intervals may or may not
range. In addition to specifying the measure and the precision,
be symmetric about the mean, depending on the underlying
it is important that the number of repeated measurements upon
statistical distribution. For example, confidence intervals for
which the estimated precision is based also be given.
the variances are not symmetric. C1215
3.2.10 quality assurance (QA), n—an integrated system of
3.2.3 confidence level, n—the probability, usually expressed
management activities involving planning, quality control,
as a percent, that a confidence interval is expected to contain
quality assessment, reporting, and quality improvement to
the parameter of interest (see discussion of confidence inter-
ensure that a process or service (for example, environmental
val).
data) meets defined standards of quality with a stated level of
3.2.4 data quality objectives (DQOs), n—qualitative and
confidence. EPA QA/G-4
quantitativestatementsderivedfromtheDQOprocessdescrib-
3.2.11 quality control (QC), n—the overall system of tech-
ing the decision rules and the uncertainties of the decision(s)
nical activities whose purpose is to measure and control the
within the context of the problem(s).
quality of a product or service so that it meets the needs of
3.2.4.1 Discussion—DQOs clarify the study objectives, de-
users. The aim is to provide quality that is satisfactory,
fine the most appropriate type of data to collect, determine the
adequate, dependable, and economical. EPA QA/G-4
mostappropriateconditionsfromwhichtocollectthedata,and
3.2.12 population, n—the totality of items or units of
establish acceptable levels of decision errors that will be used
materials under consideration.
as the basis for establishing the quantity and quality of data
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
occurrenceofdeviationsfromthemeanvalue;(2)anerrorthat
3.2.5 data quality objectives process, n—Qualitative and
affects each member of a set of data (measurements) in a
Quantitative statements derived from the DQO Process that
different manner.
clarifystudyobjectives,definetheappropriatetypeofdata,and
specifythetolerablelevelsofpotentialdecisionerrorsthatwill
3.2.14 risk, n—the probability or an expected loss associ-
be used as the basis for establishing the quality and quantityof
ated with an adverse effect.
data needed to support decisions.
3.2.14.1 Discussion—Riskisfrequentlyusedtodescribethe
3.2.6 decision error: adverse effect on health or on economics. Health-based risk is
3.2.6.1 false negative error, n—this occurs when environ- the probability of induced diseases in persons exposed to
mental data mislead decision maker(s) into not taking action physical, chemical, biological, or radiological insults over
specified by a decision rule when action should be taken. time. This risk probability depends on the concentration or
D5792 − 10 (2015)
leveloftheinsult,whichisexpressedbyamathematicalmodel 4.2 For example, the investigation of a Superfund site
describing the dose and risk relationship. Risk is also associ- wouldincludefeasibilitystudiesandcommunityrelationplans,
ated with economics when decision makers have to select one which are not a part of this practice.
action from a set of available actions. Each action has a
corresponding cost. The risk or expected loss is the cost
5. Significance and Use
multiplied by the probability of the outcome of a particular
5.1 Environmental data are often required for making regu-
action. Decision makers should adopt a strategy to select
latory and programmatic decisions. Decision makers must
actions that minimize the expected loss.
determine whether the levels of assurance associated with the
3.2.15 sample standard deviation, n—the square root of the
data are sufficient in quality for their intended use.
sumofthesquaresoftheindividualdeviationsfromthesample
5.2 Datagenerationeffortsinvolvethreeparts:development
average divided by one less than the number of results
of DQOs and subsequent project plan(s) to meet the DQOs,
involved.
implementation and oversight of the project plan(s), and
n
assessment of the data quality to determine whether the DQOs
¯
~X 2 X!
j
(
were met.
j51
S 5
!
n 21
5.3 Todeterminethelevelofassurancenecessarytosupport
the decision, an iterative process must be used by decision
where:
makers, data collectors, and users. This practice emphasizes
S = sample standard deviation,
the iterative nature of the process of DQO development.
n = number of results obtained,
Objectives may need to be reevaluated and modified as
X = jth individual result, and
j
information related to the level of data quality is gained. This
¯
X = sample average.
means that DQOs are the product of the DQO process and are
4. Summary of Practice subject to change as data are gathered and assessed.
4.1 This practice describes the process of developing and
5.4 This practice defines the process of developing DQOs.
documenting the DQO process and the resulting DQOs. This Each step of the planning process is described.
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
studyschememustbeconductedinconformitywithapplicable
planning and implementing the sampling and analysis aspects
agency and company guidance and procedures.
ofenvironmentaldatagenerationactivities,andthoseassessing
data quality.
5.6 TheimpactsofasuccessfulDQOprocessontheproject
areasfollows:(1)aconsensusonthenatureoftheproblemand
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.
6. Data Quality Objective Process
6.1 The DQO process is a logical sequence of seven steps
that leads to decisions with a known level of uncertainty (Fig.
1). It is a planning tool used to determine the type, quantity,
and adequacy of data needed to support a decision. It allows
the users to collect proper, sufficient, and appropriate informa-
tionfortheintendeddecision.Theoutputfromeachstepofthe
process is stated in clear and simple terms and agreed upon by
all affected parties. The seven steps are as follows:
(1)Stating the problem,
(2)Identifying possible decisions,
(3)Identifying inputs to decisions,
(4)Defining boundaries,
(5)Developing decision rules,
(6)Specifying limits on de
...
NOTICE: This standard has either been superseded and replaced by a new version or withdrawn.
Contact ASTM International (www.astm.org) for the latest information
Designation: D5792 − 10 (Reapproved 2015)
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 and health practices and determine the applica-
generation activities. The other two aspects are (1) implemen-
bility of regulatory limitations prior to use.
tation of the sampling and analysis strategies, see Guide D6311
1.9 This international standard was developed in accor-
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 represen-
2. Referenced Documents
tativeness and describes the attributes of a representative
2.1 ASTM Standards:
sample. Practice D6250 describes how a decision point can be
C1215 Guide for Preparing and Interpreting Precision and
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
D5681 Terminology for Waste and Waste Management
1.5 The DQO process is a planning process and should be
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
data. The words “must” and “shall” (requirements), “should”
D6250 Practice for Derivation of Decision Point and Confi-
dence Limit for Statistical Testing of Mean Concentration
This practice is under the jurisdiction of ASTM Committee D34 on Waste
Management and is the direct responsibility of Subcommittee D34.01.01 on
Planning for Sampling. For referenced ASTM standards, visit the ASTM website, www.astm.org, or
Current edition approved Sept. 1, 2015. Published September 2015. Originally contact ASTM Customer Service at service@astm.org. For Annual Book of ASTM
approved in 1995. Last previous edition approved in 2010 as D5792– 10. DOI: Standards volume information, refer to the standard’s Document Summary page on
10.1520/D5792-10R15. the ASTM website.
Copyright © ASTM International, 100 Barr Harbor Drive, PO Box C700, West Conshohocken, PA 19428-2959. United States
D5792 − 10 (2015)
in Waste Management Decisions 3.2.6.2 false positive error, n—this occurs when environ-
D6311 Guide for Generation of Environmental Data Related mental data mislead decision maker(s) into taking action
to Waste Management Activities: Selection and Optimiza- specified by a decision rule when action should not be taken.
tion of Sampling Design
3.2.7 decision point, n—the numerical value that causes the
decision-maker to choose one of the alternative actions point
3. Terminology
(for example, compliance or noncompliance). D6250
3.1 For definitions of terms used in this standard refer to
3.2.7.1 Discussion—In the context of this practice, the
Terminology D5681.
numerical value is calculated in the planning stage and prior to
the collection of the sample data, using a specified hypothesis,
3.2 Definitions of Terms Specific to This Standard:
decision error, an estimated standard deviation, and number of
3.2.1 bias, n—the difference between the sample value of
samples. In environmental decisions, a concentration limit such
the test results and an accepted reference value.
as a regulatory limit usually serves as a standard for judging
3.2.1.1 Discussion—Bias represents a constant error as op-
attainment of cleanup, remediation, or compliance objectives.
posed to a random error. A method bias can be estimated by
Because of uncertainty in the sample data and other factors,
the difference (or relative difference) between a measured
actual cleanup or remediation, may have to go to a level lower
average and an accepted standard or reference value. The data
or higher than this standard. This new level of concentration
from which the estimate is obtained should be statistically
serves as a point for decision-making and is, therefore, termed
analyzed to establish bias in the presence of random error. A
the decision point.
thorough bias investigation of a measurement procedure re-
quires a statistically designed experiment to repeatedly
3.2.8 decision rule, n—a set of directions in the form of a
measure, under essentially the same conditions, a set of
conditional statement that specify the following: (1) how the
standards or reference materials of known value that cover the
sample data will be compared to the decision point, (2) which
range of application. Bias often varies with the range of
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 precision, n—a generic concept used to describe the
value of a population parameter with a specified degree of
dispersion of a set of measured values.
confidence (this is an interval that has different values for
3.2.9.1 Discussion—Measures frequently used to express
different samples).
precision are standard deviation, relative standard deviation,
3.2.2.1 Discussion—The specified degree of confidence is
variance, repeatability, reproducibility, confidence interval, and
usually 90, 95, or 99 %. Confidence intervals may or may not
range. In addition to specifying the measure and the precision,
be symmetric about the mean, depending on the underlying
it is important that the number of repeated measurements upon
statistical distribution. For example, confidence intervals for
which the estimated precision is based also be given.
the variances are not symmetric. C1215
3.2.10 quality assurance (QA), n—an integrated system of
3.2.3 confidence level, n—the probability, usually expressed
management activities involving planning, quality control,
as a percent, that a confidence interval is expected to contain
quality assessment, reporting, and quality improvement to
the parameter of interest (see discussion of confidence inter-
ensure that a process or service (for example, environmental
val).
data) meets defined standards of quality with a stated level of
3.2.4 data quality objectives (DQOs), n—qualitative and
confidence. EPA QA/G-4
quantitative statements derived from the DQO process describ-
3.2.11 quality control (QC), n—the overall system of tech-
ing the decision rules and the uncertainties of the decision(s)
nical activities whose purpose is to measure and control the
within the context of the problem(s).
quality of a product or service so that it meets the needs of
3.2.4.1 Discussion—DQOs clarify the study objectives, de-
users. The aim is to provide quality that is satisfactory,
fine the most appropriate type of data to collect, determine the
adequate, dependable, and economical. EPA QA/G-4
most appropriate conditions from which to collect the data, and
3.2.12 population, n—the totality of items or units of
establish acceptable levels of decision errors that will be used
materials under consideration.
as the basis for establishing the quantity and quality of data
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
occurrence of deviations from the mean value; (2) an error that
3.2.5 data quality objectives process, n—Qualitative and
affects each member of a set of data (measurements) in a
Quantitative statements derived from the DQO Process that
different manner.
clarify study objectives, define the appropriate type of data, and
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
3.2.6.1 false negative error, n—this occurs when environ- the probability of induced diseases in persons exposed to
mental data mislead decision maker(s) into not taking action physical, chemical, biological, or radiological insults over
specified by a decision rule when action should be taken. time. This risk probability depends on the concentration or
D5792 − 10 (2015)
level of the insult, which is expressed by a mathematical model 4.2 For example, the investigation of a Superfund site
describing the dose and risk relationship. Risk is also associ- would include feasibility studies and community relation plans,
ated with economics when decision makers have to select one which are not a part of this practice.
action from a set of available actions. Each action has a
corresponding cost. The risk or expected loss is the cost 5. Significance and Use
multiplied by the probability of the outcome of a particular
5.1 Environmental data are often required for making regu-
action. Decision makers should adopt a strategy to select
latory and programmatic decisions. Decision makers must
actions that minimize the expected loss.
determine whether the levels of assurance associated with the
3.2.15 sample standard deviation, n—the square root of the
data are sufficient in quality for their intended use.
sum of the squares of the individual deviations from the sample
5.2 Data generation efforts involve three parts: development
average divided by one less than the number of results
of DQOs and subsequent project plan(s) to meet the DQOs,
involved.
implementation and oversight of the project plan(s), and
n
assessment of the data quality to determine whether the DQOs
¯
~X 2 X!
( j
were met.
j51
S 5
!
n 2 1
5.3 To determine the level of assurance necessary to support
the decision, an iterative process must be used by decision
where:
makers, data collectors, and users. This practice emphasizes
S = sample standard deviation,
the iterative nature of the process of DQO development.
n = number of results obtained,
Objectives may need to be reevaluated and modified as
X = jth individual result, and
j
¯ information related to the level of data quality is gained. This
X = sample average.
means that DQOs are the product of the DQO process and are
4. Summary of Practice
subject to change as data are gathered and assessed.
4.1 This practice describes the process of developing and 5.4 This practice defines the process of developing DQOs.
documenting the DQO process and the resulting DQOs. This
Each step of the planning process is described.
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
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.
6. Data Quality Objective Process
6.1 The DQO process is a logical sequence of seven steps
that leads to decisions with a known level of uncertainty (Fig.
1). It is a planning tool used to determine the type, quantity,
and adequacy of data needed to support a decision. It allows
the users to collect proper, sufficient, and appropriate informa-
tion for the intended decision. The output from each step of the
process is stated in clear and simple terms and agreed upon by
all affected parties. The seven steps are as follows:
(1) Stating the problem,
(2) Identifying possible decisions,
(3) Identifying input
...
This document is not an ASTM standard and is intended only to provide the user of an ASTM standard an indication of what changes have been made to the previous version. Because
it may not be technically possible to adequately depict all changes accurately, ASTM recommends that users consult prior editions as appropriate. In all cases only the current version
of the standard as published by ASTM is to be considered the official document.
Designation: D5792 − 10 D5792 − 10 (Reapproved 2015)
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
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 in this standard.
1.7.1 Exception—The values given in parentheses are for information only.
1.8 This standard does not purport to address all of the safety concerns, if any, associated with its use. It is the responsibility
of the user of this standard to establish appropriate safety and health practices and determine the applicability of regulatory
limitations prior to use.
2. Referenced Documents
2.1 ASTM Standards:
C1215 Guide for Preparing and Interpreting Precision and Bias Statements in Test Method Standards Used in the Nuclear
Industry
D5283 Practice for Generation of Environmental Data Related to Waste Management Activities: Quality Assurance and Quality
Control Planning and Implementation
D5681 Terminology for Waste and Waste Management
D6044 Guide for Representative Sampling for Management of Waste and Contaminated Media
This practice is under the jurisdiction of ASTM Committee D34 on Waste Management and is the direct responsibility of Subcommittee D34.01.01 on Planning for
Sampling.
Current edition approved Dec. 1, 2010Sept. 1, 2015. Published January 2011September 2015. Originally approved in 1995. Last previous edition approved in 20062010
as D5792– 02 (2006). 10. DOI: 10.1520/D5792-10.10.1520/D5792-10R15.
For referenced ASTM standards, visit the ASTM website, www.astm.org, or contact ASTM Customer Service at service@astm.org. For Annual Book of ASTM Standards
volume information, refer to the standard’s Document Summary page on the ASTM website.
Copyright © ASTM International, 100 Barr Harbor Drive, PO Box C700, West Conshohocken, PA 19428-2959. United States
D5792 − 10 (2015)
D6233 Guide for Data Assessment for Environmental Waste Management Activities
D6250 Practice for Derivation of Decision Point and Confidence Limit for Statistical Testing of Mean Concentration in Waste
Management Decisions
D6311 Guide for Generation of Environmental Data Related to Waste Management Activities: Selection and Optimization of
Sampling Design
3. Terminology
3.1 For definitions of terms used in this standard refer to Terminology D5681.
3.2 Definitions of Terms Specific to This Standard:
3.2.1 bias, n—the difference between the sample value of the test results and an accepted reference value.
3.2.1.1 Discussion—
Bias represents a constant error as opposed to a random error. A method bias can be estimated by the difference (or relative
difference) between a measured average and an accepted standard or reference value. The data from which the estimate is obtained
should be statistically analyzed to establish bias in the presence of random error. A thorough bias investigation of a measurement
procedure requires a statistically designed experiment to repeatedly measure, under essentially the same conditions, a set of
standards or reference materials of known value that cover the range of application. Bias often varies with the range of application
and should be reported accordingly. C1215
3.2.2 confidence interval, n—an interval used to bound the value of a population parameter with a specified degree of confidence
(this is an interval that has different values for different samples).
3.2.2.1 Discussion—
The specified degree of confidence is usually 90, 95, or 99 %. Confidence intervals may or may not be symmetric about the mean,
depending on the underlying statistical distribution. For example, confidence intervals for the variances are not symmetric. C1215
3.2.3 confidence level, n—the probability, usually expressed as a percent, that a confidence interval is expected to contain the
parameter of interest (see discussion of confidence interval).
3.2.4 data quality objectives (DQOs), n—qualitative and quantitative statements derived from the DQO process describing the
decision rules and the uncertainties of the decision(s) within the context of the problem(s).
3.2.4.1 Discussion—
DQOs clarify the study objectives, define the most appropriate type of data to collect, determine the most appropriate conditions
from which to collect the data, and establish acceptable levels of decision errors that will be used as the basis for establishing the
quantity and quality of data needed to support the decision. The DQOs are used to develop a sampling and analysis design.
3.2.5 data quality objectives process, n—Qualitative and Quantitative statements derived from the DQO Process that clarify
study objectives, define the appropriate type of data, and specify the tolerable levels of potential decision errors that will be used
as the basis for establishing the quality and quantity of data needed to support decisions.
3.2.6 decision error—error:
3.2.6.1 false negative error, n—this occurs when environmental data mislead decision maker(s) into not taking action specified
by a decision rule when action should be taken.
3.2.6.2 false positive error, n—this occurs when environmental data mislead decision maker(s) into taking action specified by
a decision rule when action should not be taken.
3.2.7 decision point, n—the numerical value that causes the decision-maker to choose one of the alternative actions point (for
example, compliance or noncompliance). D6250
3.2.7.1 Discussion—
In the context of this practice, the numerical value is calculated in the planning stage and prior to the collection of the sample data,
using a specified hypothesis, decision error, an estimated standard deviation, and number of samples. In environmental decisions,
a concentration limit such as a regulatory limit usually serves as a standard for judging attainment of cleanup, remediation, or
compliance objectives. Because of uncertainty in the sample data and other factors, actual cleanup or remediation, may have to
go to a level lower or higher than this standard. This new level of concentration serves as a point for decision-making and is,
therefore, termed the decision point.
D5792 − 10 (2015)
3.2.8 decision rule, n—a set of directions in the form of a conditional statement that specify the following: (1) how the sample
data will be compared to the decision point, (2) which decision will be made as a result of that comparison, and (3) what subsequent
action will be taken based on the decisions.
3.2.9 precision, n—a generic concept used to describe the dispersion of a set of measured values.
3.2.9.1 Discussion—
Measures frequently used to express precision are standard deviation, relative standard deviation, variance, repeatability,
reproducibility, confidence interval, and range. In addition to specifying the measure and the precision, it is important that the
number of repeated measurements upon which the estimated precision is based also be given.
3.2.10 quality assurance (QA), n—an integrated system of management activities involving planning, quality control, quality
assessment, reporting, and quality improvement to ensure that a process or service (for example, environmental data) meets defined
standards of quality with a stated level of confidence. EPA QA/G-4
3.2.11 quality control (QC), n—the overall system of technical activities whose purpose is to measure and control the quality
of a product or service so that it meets the needs of users. The aim is to provide quality that is satisfactory, adequate, dependable,
and economical. EPA QA/G-4
3.2.12 population, n—the totality of items or units of materials under consideration.
3.2.13 random error, n—(1) the chance variation encountered in all measurement work, characterized by the random occurrence
of deviations from the mean value; (2) an error that affects each member of a set of data (measurements) in a different manner.
3.2.14 risk, n—the probability or an expected loss associated with an adverse effect.
3.2.14.1 Discussion—
Risk is frequently used to describe the adverse effect on health or on economics. Health-based risk is 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 associated 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 documenting the DQO process and the resulting DQOs. This practice
also outlines the overall environmental study process as shown in Fig. 1. It must be emphasized that any specific study scheme
must be conducted in conformity with applicable agency and company guidance and procedures.
4.2 For example, the investigation of a Superfund site would include feasibility studies and community relation plans, which
are not a part of this practice.
5. 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
D5792 − 10 (2015)
FIG. 1 DQO Process
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.
6. Data Quality Objective Process
6.1 The DQO process is a logical sequence of seven steps that leads to decisions with a known level of uncertainty (Fig. 1).
It is a planning tool used to determine the type, quantity, and adequacy of data needed to support a decision. It allows the users
to collect proper, sufficient, and appropriate information for the intended decision. The output from each step of the process is
stated in clear and simple terms and agreed upon by all affected parties. The seven steps are as follows:
(1) Stating the problem,
(2) Identi
...
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