ASTM D5792-02(2006)
(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
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.
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.
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.
This practice defines the process of developing DQOs. Each step of the planning process is described.
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.
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 D 6311 and (2) data quality assessment, see Guide D 6233.
1.3 This guide should be used in concert with Practices D 5283, D 6250, and Guide D 6044. Practice D 5283 outlines the quality assurance (QA) processes specified during planning and used during implementation. Guide D 6044 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 D 6250 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 the standard. The values given in parentheses are for information only.
1.8 This stan...
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Designation:D5792–02 (Reapproved 2006)
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 statements in this practice. The extent to which all require-
ments will be met remains a matter of technical judgment.
1.1 This practice covers the process of development of data
1.7 The values stated in SI units are to be regarded as the
qualityobjectives(DQOs)fortheacquisitionofenvironmental
standard. The values given in parentheses are for information
data. Optimization of sampling and analysis design is a part of
only.
the DQO process. This practice describes the DQO process in
1.8 This standard does not purport to address all of the
detail. The various strategies for design optimization are too
safety concerns, if any, associated with its use. It is the
numerous to include in this practice. Many other documents
responsibility of the user of this standard to establish appro-
outline alternatives for optimizing sampling and analysis
priate safety and health practices and determine the applica-
design. Therefore, only an overview of design optimization is
bility of regulatory limitations prior to use.
included. Some design aspects are included in the practice’s
examples for illustration purposes.
2. Referenced Documents
1.2 DQO development is the first of three parts of data
2.1 ASTM Standards:
generation activities. The other two aspects are (1) implemen-
C1215 Guide for Preparing and Interpreting Precision and
tationofthesamplingandanalysisstrategies,seeGuideD6311
Bias Statements in Test Method Standards Used in the
and (2) data quality assessment, see Guide D6233.
Nuclear Industry
1.3 This guide should be used in concert with Practices
D5283 Practice for Generation of Environmental Data Re-
D5283,D6250,andGuideD6044.PracticeD5283outlinesthe
lated to Waste Management Activities: Quality Assurance
quality assurance (QA) processes specified during planning
and Quality Control Planning and Implementation
and used during implementation. Guide D6044 outlines a
D6044 GuideforRepresentativeSamplingforManagement
process by which a representative sample may be obtained
of Waste and Contaminated Media
from a population, identifies sources that can affect represen-
D6233 Guide for Data Assessment for Environmental
tativeness and describes the attributes of a representative
Waste Management Activities
sample. Practice D6250 describes how a decision point can be
D6250 Practice for Derivation of Decision Point and Con-
calculated.
fidenceLimitforStatisticalTestingofMeanConcentration
1.4 Environmentaldatarelatedtowastemanagementactivi-
in Waste Management Decisions
ties include, but are not limited to, the results from the
D6311 Guide for Generation of Environmental Data Re-
sampling and analyses of air, soil, water, biota, process or
lated to Waste Management Activities: Selection and
general waste samples, or any combinations thereof.
Optimization of Sampling Design
1.5 The DQO process is a planning process and should be
completed prior to sampling and analysis activities.
3. Terminology
1.6 This practice presents extensive requirements of man-
3.1 Definitions:
agement, designed to ensure high-quality environmental data.
3.1.1 bias, n—the difference between the sample value of
The words “must” and “shall” (requirements), “should” (rec-
the test results and an accepted reference value.
ommendation), and “may” (optional), have been selected
3.1.1.1 Discussion—Bias represents a constant error as
carefully to reflect the importance placed on many of the
opposedtoarandomerror.Amethodbiascanbeestimatedby
the difference (or relative difference) between a measured
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 May 1, 2006. Published May 2006. Originally contact ASTM Customer Service at service@astm.org. For Annual Book of ASTM
approved in 1995. Last previous edition approved in 2002 as D5792–02. DOI: Standards volume information, refer to the standard’s Document Summary page on
10.1520/D5792-02R06. the ASTM website.
Copyright © ASTM International, 100 Barr Harbor Drive, PO Box C700, West Conshohocken, PA 19428-2959, United States.
D5792–02 (2006)
average and an accepted standard or reference value. The data 3.1.7 decision point, n—the numerical value that causes the
from which the estimate is obtained should be statistically decision-maker to choose one of the alternative actions point
analyzed to establish bias in the presence of random error.A (for example, compliance or noncompliance). D6250
thorough bias investigation of a measurement procedure re-
3.1.7.1 Discussion—In the context of this practice, the
quires a statistically designed experiment to repeatedly mea-
numericalvalueiscalculatedintheplanningstageandpriorto
sure, under essentially the same conditions, a set of standards
the collection of the sample data, using a specified hypothesis,
or reference materials of known value that cover the range of
decision error, an estimated standard deviation, and number of
application. Biasoftenvarieswiththerangeofapplicationand
samples.Inenvironmentaldecisions,aconcentrationlimitsuch
should be reported accordingly. C1215
as a regulatory limit usually serves as a standard for judging
3.1.2 confidence interval, n—an interval used to bound the attainment of cleanup, remediation, or compliance objectives.
value of a population parameter with a specified degree of Because of uncertainty in the sample data and other factors,
confidence (this is an interval that has different values for actual cleanup or remediation, may have to go to a level lower
different samples). or higher than this standard. This new level of concentration
serves as a point for decision-making and is, therefore, termed
3.1.2.1 Discussion—The specified degree of confidence is
the decision point.
usually 90, 95, or 99%. Confidence intervals may or may not
be symmetric about the mean, depending on the underlying 3.1.8 decision rule, n—a set of directions in the form of a
statistical distribution. For example, confidence intervals for conditional statement that specify the following: (1) how the
the variances are not symmetric. C1215 sample data will be compared to the decision point, (2) which
decision will be made as a result of that comparison, and (3)
3.1.3 confidencelevel,n—theprobability,usuallyexpressed
what subsequent action will be taken based on the decisions.
as a percent, that a confidence interval is expected to contain
the parameter of interest (see discussion of confidence inter- 3.1.9 precision, n—a generic concept used to describe the
val). dispersion of a set of measured values.
3.1.4 data quality objectives (DQOs), n—qualitative and 3.1.9.1 Discussion—Measures frequently used to express
quantitativestatementsderivedfromtheDQOprocessdescrib- precision are standard deviation, relative standard deviation,
ing the decision rules and the uncertainties of the decision(s) variance,repeatability,reproducibility,confidenceinterval,and
within the context of the problem(s). range. In addition to specifying the measure and the precision,
itisimportantthatthenumberofrepeatedmeasurementsupon
3.1.4.1 Discussion—DQOs clarify the study objectives, de-
which the estimated precision is based also be given.
fine the most appropriate type of data to collect, determine the
mostappropriateconditionsfromwhichtocollectthedata,and 3.1.10 quality assurance (QA), n—an integrated system of
establish acceptable levels of decision errors that will be used management activities involving planning, quality control,
as the basis for establishing the quantity and quality of data quality assessment, reporting, and quality improvement to
needed to support the decision.The DQOs are used to develop ensure that a process or service (for example, environmental
a sampling and analysis design. data) meets defined standards of quality with a stated level of
confidence. EPA QA/G-4
3.1.5 data quality objectives process, n—a quality manage-
ment tool based on the scientific method and developed by the 3.1.11 quality control (QC), n—the overall system of tech-
U.S. Environmental Protection Agency (EPA) to facilitate the nical activities whose purpose is to measure and control the
planning of environmental data collection activities.The DQO quality of a product or service so that it meets the needs of
process enables planners to focus their planning efforts by users. The aim is to provide quality that is satisfactory,
specifying the use of the data (the decision), decision criteria adequate, dependable, and economical. EPA QA/G-4
(decision point), and decision maker’s acceptable decision
3.1.12 population, n—the totality of items or units of
error rates. The products of the DQO process are the DQOs.
materials under consideration.
3.1.5.1 Discussion—DQOs result from an iterative process
3.1.13 random error, n—(1) the chance variation encoun-
betweenthedecisionmakersandthetechnicalteamtodevelop
tered in all measurement work, characterized by the random
qualitative and quantitative statements that describe the prob-
occurrenceofdeviationsfromthemeanvalue;(2)anerrorthat
lem and the certainty and uncertainty that decision makers are
affects each member of a set of data (measurements) in a
willing to accept in the results derived from the environmental
different manner.
data. This acceptable level of uncertainty should then be used
3.1.14 risk, n—the probability or an expected loss associ-
as the basis for the design specifications for project data
ated with an adverse effect.
collectionanddataassessment.Alloftheinformationfromthe
3.1.14.1 Discussion—Riskisfrequentlyusedtodescribethe
first six steps of the DQO process are used in designing the
adverse effect on health or on economics. Health-based risk is
study and assessing the data adequacy. EPA QA/G-4
the probability of induced diseases in persons exposed to
3.1.6 decision error
physical, chemical, biological, or radiological insults over
3.1.6.1 false negative error, n—this occurs when environ-
time. This risk probability depends on the concentration or
mental data mislead decision maker(s) into not taking action
leveloftheinsult,whichisexpressedbyamathematicalmodel
specified by a decision rule when action should be taken.
describing the dose and risk relationship. Risk is also associ-
3.1.6.2 false positive error, n—this occurs when environ- ated with economics when decision makers have to select one
mental data mislead decision maker(s) into taking action action from a set of available actions. Each action has a
specified by a decision rule when action should not be taken. corresponding cost. The risk or expected loss is the cost
D5792–02 (2006)
multiplied by the probability of the outcome of a particular determine whether the levels of assurance associated with the
action. Decision makers should adopt a strategy to select data are sufficient in quality for their intended use.
actions that minimize the expected loss. 5.2 Datagenerationeffortsinvolvethreeparts:development
3.1.15 sample standard deviation, n—the square root of the of DQOs and subsequent project plan(s) to meet the DQOs,
sumofthesquaresoftheindividualdeviationsfromthesample implementation and oversight of the project plan(s), and
average divided by one less than the number of results assessment of the data quality to determine whether the DQOs
involved. were met.
5.3 Todeterminethelevelofassurancenecessarytosupport
n
¯
the decision, an iterative process must be used by decision
~X 2X!
(
j
j 51
Œ
S 5 makers, data collectors, and users. This practice emphasizes
n 21
the iterative nature of the process of DQO development.
where: Objectives may need to be reevaluated and modified as
S = sample standard deviation, information related to the level of data quality is gained. This
n = number of results obtained,
means that DQOs are the product of the DQO process and are
X = jth individual result, and
subject to change as data are gathered and assessed.
j
¯
X = sample average.
5.4 This practice defines the process of developing DQOs.
Each step of the planning process is described.
4. Summary of Practice
5.5 This practice emphasizes the importance of communi-
4.1 This practice describes the process of developing and
cation among those involved in developing DQOs, those
documenting the DQO process and the resulting DQOs. This
planning and implementing the sampling and analysis aspects
practice also outlines the overall environmental study process
ofenvironmentaldatagenerationactivities,andthoseassessing
as shown in Fig. 1. It must be emphasized that any specific
data quality.
studyschememustbeconductedinconformitywithapplicable
5.6 TheimpactsofasuccessfulDQOprocessontheproject
agency and company guidance and procedures.
areasfollows:(1)aconsensusonthenatureoftheproblemand
4.2 For example, the investigation of a Superfund site
thedesireddecisionsharedbyallthedecisionmakers,(2)data
wouldincludefeasibilitystudiesandcommunityrelationplans,
quality consistent with its intended use, (3) a more resource-
which are not a part of this practice.
efficient sampling and analysis design, (4) a planned approach
to data collection and evaluation, (5) quantitative criteria for
5. Significance and Use
knowing when to stop sampling, and (6) known measure of
5.1 Environmental data are often required for making regu-
risk for making an incorrect decision.
latory and programmatic decisions. Decision makers must
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 decision errors, and
...
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