ASTM D5792-2002(2006) Standard Practice for Generation of Environmental Data Related to Waste Management Activities Development of Data Quality Objectives《与废物管理活动相关的环境数据形成的标准实施规程 数.pdf

《ASTM D5792-2002(2006) Standard Practice for Generation of Environmental Data Related to Waste Management Activities Development of Data Quality Objectives《与废物管理活动相关的环境数据形成的标准实施规程 数.pdf》由会员分享，可在线阅读，更多相关《ASTM D5792-2002(2006) Standard Practice for Generation of Environmental Data Related to Waste Management Activities Development of Data Quality Objectives《与废物管理活动相关的环境数据形成的标准实施规程 数.pdf（17页珍藏版）》请在麦多课文档分享上搜索。

1、Designation: D 5792 02 (Reapproved 2006)Standard Practice forGeneration of Environmental Data Related to WasteManagement Activities: Development of Data QualityObjectives1This standard is issued under the fixed designation D 5792; the number immediately following the designation indicates the year o

2、foriginal adoption or, in the case of revision, the year of last revision. A number in parentheses indicates the year of last reapproval. Asuperscript epsilon (e) indicates an editorial change since the last revision or reapproval.1. Scope1.1 This practice covers the process of development of dataqu

3、ality 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 documen

4、tsoutline 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 othe

5、r two aspects are (1) implemen-tation of the sampling and analysis strategies, see GuideD 6311 and (2) data quality assessment, see Guide D 6233.1.3 This guide should be used in concert with PracticesD 5283, D 6250, and Guide D 6044. Practice D 5283 outlinesthe quality assurance (QA) processes speci

6、fied during planningand used during implementation. Guide D 6044 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 D 6250 describes how a decisio

7、n point can 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 sh

8、ould becompleted prior to sampling and analysis activities.1.6 This practice presents extensive requirements of man-agement, designed to ensure high-quality environmental data.The words “must” and “shall” (requirements), “should” (rec-ommendation), and “may” (optional), have been selectedcarefully t

9、o reflect the importance 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 as thestandard. The values given in parentheses are for informationonly.1.8 This st

10、andard 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 regulatory limitations prior to use.2. Referenced Documents2.1

11、 ASTM Standards:2C 1215 Guide for Preparing and Interpreting Precision andBias Statements in Test Method Standards Used in theNuclear IndustryD 5283 Practice for Generation of Environmental DataRelated to Waste Management Activities: Quality Assur-ance and Quality Control Planning and Implementation

12、D 6044 Guide for Representative Sampling for Manage-ment of Waste and Contaminated MediaD 6233 Guide for Data Assessment for EnvironmentalWaste Management ActivitiesD 6250 Practice for Derivation of Decision Point and Con-fidence Limit for StatisticalTesting of Mean Concentrationin Waste Management

13、DecisionsD 6311 Guide for Generation of Environmental Data Re-lated to Waste Management Activities: Selection andOptimization of Sampling Design3. Terminology3.1 Definitions:3.1.1 bias, nthe difference between the sample value ofthe test results and an accepted reference value.3.1.1.1 DiscussionBias

14、 represents a constant error asopposed to a random error.Amethod bias can be estimated by1This practice is under the jurisdiction of ASTM Committee D34 on WasteManagement and is the direct responsibility of Subcommittee D34.01.01 onPlanning for Sampling.Current edition approved May 1, 2006. Publishe

15、d May 2006. Originallyapproved in 1995. Last previous edition approved in 2002 as D 579202.2For referenced ASTM standards, 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 Su

16、mmary page onthe ASTM website.1Copyright ASTM International, 100 Barr Harbor Drive, PO Box C700, West Conshohocken, PA 19428-2959, United States.the difference (or relative difference) between a measuredaverage and an accepted standard or reference value. The datafrom which the estimate is obtained

17、should be statisticallyanalyzed to establish bias in the presence of random error.Athorough bias investigation of a measurement procedure re-quires a statistically designed experiment to repeatedly mea-sure, under essentially the same conditions, a set of standardsor reference materials of known val

18、ue that cover the range ofapplication. Bias often varies with the range of application andshould be reported accordingly. C 12153.1.2 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

19、 fordifferent samples).3.1.2.1 DiscussionThe specified degree 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. C 12153.1.

20、3 confidence level, nthe probability, usually expressedas a percent, that a confidence interval is expected to containthe parameter of interest (see discussion of confidence inter-val).3.1.4 data quality objectives (DQOs), nqualitative andquantitative statements derived from the DQO process describ-

21、ing the decision rules and the uncertainties of the decision(s)within the context of the problem(s).3.1.4.1 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 accepta

22、ble levels of decision errors that will be usedas the basis for establishing the quantity and quality of dataneeded to support the decision. The DQOs are used to developa sampling and analysis design.3.1.5 data quality objectives process, na quality manage-ment tool based on the scientific method an

23、d developed by theU.S. Environmental Protection Agency (EPA) to facilitate theplanning of environmental data collection activities. The DQOprocess enables planners to focus their planning efforts byspecifying the use of the data (the decision), decision criteria(decision point), and decision makers

24、acceptable decisionerror rates. The products of the DQO process are the DQOs.3.1.5.1 DiscussionDQOs result from an iterative processbetween the decision makers and the technical team to developqualitative and quantitative statements that describe the prob-lem and the certainty and uncertainty that d

25、ecision makers arewilling to accept in the results derived from the environmentaldata. This acceptable level of uncertainty should then be usedas the basis for the design specifications for project datacollection and data assessment.All of the information from thefirst six steps of the DQO process a

26、re used in designing thestudy and assessing the data adequacy. EPA QA/G-43.1.6 decision error3.1.6.1 false negative error, nthis occurs when environ-mental data mislead decision maker(s) into not taking actionspecified by a decision rule when action should be taken.3.1.6.2 false positive error, nthi

27、s occurs when environ-mental data mislead decision maker(s) into taking actionspecified by a decision rule when action should not be taken.3.1.7 decision point, nthe numerical value that causes thedecision-maker to choose one of the alternative actions point(for example, compliance or noncompliance)

28、. D 62503.1.7.1 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,decision error, an estimated standard deviation, and number ofsamples. In environmental decisions, a concentra

29、tion 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 factors,actual cleanup or remediation, may have to go to a level loweror higher than this standard. This new l

30、evel of concentrationserves as a point for decision-making and is, therefore, termedthe decision point.3.1.8 decision rule, na set of directions in the form of aconditional statement that specify the following: (1) how thesample data will be compared to the decision point, (2) whichdecision will be

31、made as a result of that comparison, and (3)what subsequent action will be taken based on the decisions.3.1.9 precision, na generic concept used to describe thedispersion of a set of measured values.3.1.9.1 DiscussionMeasures frequently used to expressprecision are standard deviation, relative stand

32、ard deviation,variance, repeatability, reproducibility, confidence interval, andrange. In addition to specifying the measure and the precision,it is important that the number of repeated measurements uponwhich the estimated precision is based also be given.3.1.10 quality assurance (QA), nan integrat

33、ed system ofmanagement activities involving planning, quality control,quality assessment, reporting, and quality improvement toensure that a process or service (for example, environmentaldata) meets defined standards of quality with a stated level ofconfidence. EPA QA/G-43.1.11 quality control (QC),

34、 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 is satisfactory,adequate, dependable, and economical. EPA QA/G-43.1.12 population, nthe totality of items or

35、 units ofmaterials under consideration.3.1.13 random error, n(1) the chance variation encoun-tered in all measurement work, characterized by the randomoccurrence of deviations from the mean value; (2) an error thataffects each member of a set of data (measurements) in adifferent manner.3.1.14 risk,

36、nthe probability or an expected loss associ-ated with an adverse effect.3.1.14.1 DiscussionRisk is frequently used to describe theadverse effect on health or on economics. Health-based risk isthe probability of induced diseases in persons exposed tophysical, chemical, biological, or radiological ins

37、ults overtime. This risk probability depends on the concentration orlevel of the insult, which is expressed by a mathematical modelD 5792 02 (2006)2describing the dose and risk relationship. Risk is also associ-ated with economics when decision makers have to select oneaction from a set of available

38、 actions. Each action has acorresponding cost. The risk or expected loss is the costmultiplied by the probability of the outcome of a particularaction. Decision makers should adopt a strategy to selectactions that minimize the expected loss.3.1.15 sample standard deviation, nthe square root of thesu

39、m of the squares of the individual deviations from the sampleaverage divided by one less than the number of resultsinvolved.S 5(j 5 1nXj2 X!2n 2 1where:S = sample standard deviation,n = number of results obtained,Xj= jth individual result, andX= sample average.4. Summary of Practice4.1 This practice

40、 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 emphasized that any specificstudy scheme must be conducted in conformity with applicableagency and company guid

41、ance 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 and Use5.1 Environmental data are often required for making regu-latory and programmatic decisions. Decision

42、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 and subsequent project plan(s) to meet the DQOs,implementation and oversight of the project plan(s), andas

43、sessment 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, data collectors, and users. This practice emphasizesthe iterative nature of the process of DQO development

44、.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 as data are gathered and assessed.5.4 This practice defines the process of developing DQOs.Each step of th

45、e 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 aspectsof environmental data generation activities, and those assessingdata quality.5.6 The impacts of a suc

46、cessful 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 intended use, (3) a more resource-efficient sampling and analysis design, (4) a planned approachto data collec

47、tion 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 process is a logical sequence of seven stepsthat leads to decisions with a known level of uncertainty (Fig.1). I

48、t 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 the intended decision. The output from each step of theprocess is stated in clear and simple terms and agree

49、d 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) Developing 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 from the first five steps and end with astatement of a decision rule w