1、Designation: D5792 10 (Reapproved 2015)Standard Practice forGeneration of Environmental Data Related to WasteManagement Activities: Development of Data QualityObjectives1This standard is issued under the fixed designation D5792; the number immediately following the designation indicates the year ofo
2、riginal adoption or, in the case of revision, the year of last revision. A number in parentheses indicates the year of last reapproval. Asuperscript epsilon () indicates an editorial change since the last revision or reapproval.1. Scope1.1 This practice covers the process of development of dataquali
3、ty objectives (DQOs) for the acquisition of environmentaldata. Optimization of sampling and analysis design is a part ofthe DQO process. This practice describes the DQO process indetail. The various strategies for design optimization are toonumerous to include in this practice. Many other documentso
4、utline alternatives for optimizing sampling and analysisdesign. Therefore, only an overview of design optimization isincluded. Some design aspects are included in the practicesexamples for illustration purposes.1.2 DQO development is the first of three parts of datageneration activities. The other t
5、wo aspects are (1) implemen-tation of the sampling and analysis strategies, see Guide D6311and (2) data quality assessment, see Guide D6233.1.3 This guide should be used in concert with PracticesD5283, D6250, and Guide D6044. Practice D5283 outlines thequality assurance (QA) processes specified duri
6、ng planningand used during implementation. Guide D6044 outlines aprocess by which a representative sample may be obtainedfrom a population, identifies sources that can affect represen-tativeness and describes the attributes of a representativesample. Practice D6250 describes how a decision point can
7、 becalculated.1.4 Environmental data related to waste management activi-ties include, but are not limited to, the results from thesampling and analyses of air, soil, water, biota, process orgeneral waste samples, or any combinations thereof.1.5 The DQO process is a planning process and should becomp
8、leted prior to sampling and analysis activities.1.6 This practice presents extensive requirements ofmanagement, designed to ensure high-quality environmentaldata. The words “must” and “shall” (requirements), “should”(recommendation), and “may” (optional), have been selectedcarefully to reflect the i
9、mportance placed on many of thestatements in this practice. The extent to which all require-ments will be met remains a matter of technical judgment.1.7 The values stated in SI units are to be regarded asstandard. No other units of measurement are included in thisstandard.1.7.1 ExceptionThe values g
10、iven in parentheses are forinformation only.1.8 This standard does not purport to address all of thesafety concerns, if any, associated with its use. It is theresponsibility of the user of this standard to establish appro-priate safety and health practices and determine the applica-bility of regulat
11、ory limitations prior to use.2. Referenced Documents2.1 ASTM Standards:2C1215 Guide for Preparing and Interpreting Precision andBias Statements in Test Method Standards Used in theNuclear IndustryD5283 Practice for Generation of Environmental Data Re-lated to Waste Management Activities: Quality Ass
12、uranceand Quality Control Planning and ImplementationD5681 Terminology for Waste and Waste ManagementD6044 Guide for Representative Sampling for Managementof Waste and Contaminated MediaD6233 Guide for Data Assessment for Environmental WasteManagement ActivitiesD6250 Practice for Derivation of Decis
13、ion Point and Confi-dence Limit for Statistical Testing of Mean Concentrationin Waste Management DecisionsD6311 Guide for Generation of Environmental Data Relatedto Waste ManagementActivities: Selection and Optimiza-tion of Sampling Design1This practice is under the jurisdiction of ASTM Committee D3
14、4 on WasteManagement and is the direct responsibility of Subcommittee D34.01.01 onPlanning for Sampling.Current edition approved Sept. 1, 2015. Published September 2015. Originallyapproved in 1995. Last previous edition approved in 2010 as D5792 10. DOI:10.1520/D5792-10R15.2For referenced ASTM stand
15、ards, visit the ASTM website, www.astm.org, orcontact ASTM Customer Service at serviceastm.org. For Annual Book of ASTMStandards volume information, refer to the standards Document Summary page onthe ASTM website.Copyright ASTM International, 100 Barr Harbor Drive, PO Box C700, West Conshohocken, PA
16、 19428-2959. United States13. Terminology3.1 For definitions of terms used in this standard refer toTerminology D5681.3.2 Definitions of Terms Specific to This Standard:3.2.1 bias, nthe difference between the sample value ofthe test results and an accepted reference value.3.2.1.1 DiscussionBias repr
17、esents a constant error as op-posed to a random error. A method bias can be estimated bythe difference (or relative difference) between a measuredaverage and an accepted standard or reference value. The datafrom which the estimate is obtained should be statisticallyanalyzed to establish bias in the
18、presence of random error.Athorough bias investigation of a measurement procedure re-quires a statistically designed experiment to repeatedlymeasure, under essentially the same conditions, a set ofstandards or reference materials of known value that cover therange of application. Bias often varies wi
19、th the range ofapplication and should be reported accordingly. C12153.2.2 confidence interval, nan interval used to bound thevalue of a population parameter with a specified degree ofconfidence (this is an interval that has different values fordifferent samples).3.2.2.1 DiscussionThe specified degre
20、e of confidence isusually 90, 95, or 99 %. Confidence intervals may or may notbe symmetric about the mean, depending on the underlyingstatistical distribution. For example, confidence intervals forthe variances are not symmetric. C12153.2.3 confidence level, nthe probability, usually expressedas a p
21、ercent, that a confidence interval is expected to containthe parameter of interest (see discussion of confidence inter-val).3.2.4 data quality objectives (DQOs), nqualitative andquantitative statements derived from the DQO process describ-ing the decision rules and the uncertainties of the decision(
22、s)within the context of the problem(s).3.2.4.1 DiscussionDQOs clarify the study objectives, de-fine the most appropriate type of data to collect, determine themost appropriate conditions from which to collect the data, andestablish acceptable levels of decision errors that will be usedas the basis f
23、or establishing the quantity and quality of dataneeded to support the decision. The DQOs are used to developa sampling and analysis design.3.2.5 data quality objectives process, nQualitative andQuantitative statements derived from the DQO Process thatclarify study objectives, define the appropriate
24、type of data, andspecify the tolerable levels of potential decision errors that willbe used as the basis for establishing the quality and quantity ofdata needed to support decisions.3.2.6 decision error:3.2.6.1 false negative error, nthis occurs when environ-mental data mislead decision maker(s) int
25、o not taking actionspecified by a decision rule when action should be taken.3.2.6.2 false positive error, nthis occurs when environ-mental data mislead decision maker(s) into taking actionspecified by a decision rule when action should not be taken.3.2.7 decision point, nthe numerical value that cau
26、ses thedecision-maker to choose one of the alternative actions point(for example, compliance or noncompliance). D62503.2.7.1 DiscussionIn the context of this practice, thenumerical value is calculated in the planning stage and prior tothe collection of the sample data, using a specified hypothesis,d
27、ecision error, an estimated standard deviation, and number ofsamples. In environmental decisions, a concentration limit suchas a regulatory limit usually serves as a standard for judgingattainment of cleanup, remediation, or compliance objectives.Because of uncertainty in the sample data and other f
28、actors,actual cleanup or remediation, may have to go to a level loweror higher than this standard. This new level of concentrationserves as a point for decision-making and is, therefore, termedthe decision point.3.2.8 decision rule, na set of directions in the form of aconditional statement that spe
29、cify the following: (1) how thesample data will be compared to the decision point, (2) whichdecision 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, na generic concept used to describe thedispersion of a set of measured
30、 values.3.2.9.1 DiscussionMeasures frequently used to expressprecision are standard deviation, relative standard deviation,variance, repeatability, reproducibility, confidence interval, andrange. In addition to specifying the measure and the precision,it is important that the number of repeated meas
31、urements uponwhich the estimated precision is based also be given.3.2.10 quality assurance (QA), nan integrated system ofmanagement activities involving planning, quality control,quality assessment, reporting, and quality improvement toensure that a process or service (for example, environmentaldata
32、) meets defined standards of quality with a stated level ofconfidence. EPA QA/G-43.2.11 quality control (QC), nthe overall system of tech-nical activities whose purpose is to measure and control thequality of a product or service so that it meets the needs ofusers. The aim is to provide quality that
33、 is satisfactory,adequate, dependable, and economical. EPA QA/G-43.2.12 population, nthe totality of items or units ofmaterials under consideration.3.2.13 random error, n(1) the chance variation encoun-tered in all measurement work, characterized by the randomoccurrence of deviations from the mean v
34、alue; (2) an error thataffects each member of a set of data (measurements) in adifferent manner.3.2.14 risk, nthe probability or an expected loss associ-ated with an adverse effect.3.2.14.1 DiscussionRisk is frequently used to describe theadverse effect on health or on economics. Health-based risk i
35、sthe probability of induced diseases in persons exposed tophysical, chemical, biological, or radiological insults overtime. This risk probability depends on the concentration orlevel of the insult, which is expressed by a mathematical modeldescribing the dose and risk relationship. Risk is also asso
36、ci-ated with economics when decision makers have to select oneaction from a set of available actions. Each action has aD5792 10 (2015)2corresponding cost. The risk or expected loss is the costmultiplied by the probability of the outcome of a particularaction. Decision makers should adopt a strategy
37、to selectactions that minimize the expected loss.3.2.15 sample standard deviation, nthe square root of thesum of the squares of the individual deviations from the sampleaverage divided by one less than the number of resultsinvolved.S 5!(j51nXj2 X!2n 2 1where:S = sample standard deviation,n = number
38、of results obtained,Xj= jth individual result, andX= sample average.4. Summary of Practice4.1 This practice describes the process of developing anddocumenting the DQO process and the resulting DQOs. Thispractice also outlines the overall environmental study processas shown in Fig. 1. It must be emph
39、asized that any specificstudy scheme must be conducted in conformity with applicableagency and company guidance and procedures.4.2 For example, the investigation of a Superfund sitewould include feasibility studies and community relation plans,which are not a part of this practice.5. Significance an
40、d Use5.1 Environmental data are often required for making regu-latory and programmatic decisions. Decision makers mustdetermine whether the levels of assurance associated with thedata are sufficient in quality for their intended use.5.2 Data generation efforts involve three parts: developmentof DQOs
41、 and subsequent project plan(s) to meet the DQOs,implementation and oversight of the project plan(s), andassessment of the data quality to determine whether the DQOswere met.5.3 To determine the level of assurance necessary to supportthe decision, an iterative process must be used by decisionmakers,
42、 data collectors, and users. This practice emphasizesthe iterative nature of the process of DQO development.Objectives may need to be reevaluated and modified asinformation related to the level of data quality is gained. Thismeans that DQOs are the product of the DQO process and aresubject to change
43、 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 communi-cation among those involved in developing DQOs, thoseplanning and implementing the sampling and analysis a
44、spectsof environmental data generation activities, and those assessingdata quality.5.6 The impacts of a successful DQO process on the projectare as follows: (1) a consensus on the nature of the problem andthe desired decision shared by all the decision makers, (2) dataquality consistent with its int
45、ended use, (3) a more resource-efficient sampling and analysis design, (4) a planned approachto data collection and evaluation, (5) quantitative criteria forknowing when to stop sampling, and (6) known measure ofrisk for making an incorrect decision.6. Data Quality Objective Process6.1 The DQO proce
46、ss is a logical sequence of seven stepsthat 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 allowsthe users to collect proper, sufficient, and appropriate informa-tion for t
47、he intended decision. The output from each step of theprocess is stated in clear and simple terms and agreed upon byall 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) Developi
48、ng decision rules,(6) Specifying limits on decision errors, and(7) Optimizing data collection design.All outputs from steps one through six are assembled into anintegrated package that describes the project objectives (theproblem and desired decision rules). These objectives summa-rize the outputs f
49、rom the first five steps and end with astatement of a decision rule with specified levels of thedecision errors (from the sixth step). In the last step of theFIG. 1 DQO ProcessD5792 10 (2015)3process, various approaches to a sampling and analysis plan forthe project are developed that allow the decision makers toselect a plan that balances resource allocation considerations(personnel, time, and capital) with the projects technicalobjectives. Taken together, the outputs from these seven stepscomprise the DQO process. The relationship of the DQOprocess to
