1、Designation: D6311 98 (Reapproved 2014)Standard Guide forGeneration of Environmental Data Related to WasteManagement Activities: Selection and Optimization ofSampling Design1This standard is issued under the fixed designation D6311; the number immediately following the designation indicates the year
2、 oforiginal 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 document provides practical guidance on the se-lect
3、ion and optimization of sample designs in waste manage-ment sampling activities, within the context of the require-ments established by the data quality objectives or otherplanning process.1.2 This document (1) provides guidance for selection ofsampling designs; (2) outlines techniques to optimize c
4、andidatedesigns; and (3) describes the variables that need to bebalanced in choosing the final optimized design.1.3 The contents of this guide are arranged by section asfollows:1. Scope2. Referenced Documents3. Terminology4. Significance and Use5. Summary of Guide6. Factors Affecting Sampling Design
5、 Selection6.1 Sampling Design Performance Characteristics6.2 Regulatory Considerations6.3 Project Objectives6.4 Knowledge of the Site6.5 Physical Sample Issues6.6 Communication with the Laboratory6.7 Analytical Turn Around Time6.8 Analytical Method Constraints6.9 Health and Safety6.10 Budget/Cost Co
6、nsiderations6.11 Representativeness7. Initial Design Selection8. Optimization Criteria9. Optimization Process9.2 Practical Evaluation of Design Alternatives9.3 Statistical and Cost Evaluation10. Final SelectionAnnexA1Types of Sampling DesignsA1.1 Commonly Used Sampling DesignsA1.2 Sampling Design To
7、olsA1.3 Combination Sample DesignsAppendix X1. Additional ReferencesAppendix X2. Choosing Analytical Method Based on Variance and CostAppendix X3. Calculating the Number of Samples: A Statistical Treatment1.4 This standard does not purport to address all of thesafety concerns, if any, associated wit
8、h 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 ASTM Standards:2D5956 Guide for Sampling Strategies for HeterogeneousWastesD6044 Gu
9、ide for Representative Sampling for Managementof Waste and Contaminated MediaD6051 Guide for Composite Sampling and Field Subsam-pling for Environmental Waste Management ActivitiesD6232 Guide for Selection of Sampling Equipment forWaste and Contaminated Media Data CollectionActivitiesE135 Terminolog
10、y Relating to Analytical Chemistry forMetals, Ores, and Related MaterialsE943 Terminology Relating to Biological Effects and Envi-ronmental Fate2.2 USEPA Documents:USEPA, Guidance for the Data Quality Objectives Process,EPA QA/G-4, Quality Assurance Management Staff,Washington, DC, March 199531This
11、guide 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, 2014. Published May 2014. Originallyapproved in 1998. Last previous edition approved in 2009 as D631198(2009). D
12、OI:10.1520/D6311-98R14.2For 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 Summary page onthe ASTM website.3Available from the Superintendent of
13、Documents, U.S. Government PrintingOffice, Washington, DC 20402.Copyright ASTM International, 100 Barr Harbor Drive, PO Box C700, West Conshohocken, PA 19428-2959. United States1USEPA, Data Quality Objectives Process for Superfund -Workbook, EPA 540/R-93/078 (OSWER 9355.9-01A),Office of Emergency an
14、d Remedial Response,Washington, D.C., September, 19933USEPA, Environmental Investigations Branch Standard Op-erating Procedures and Quality Assurance Manual(EISOPQAM), Region 4 - Science and Ecosystem Sup-port Division, Athens, GA, May 199632.3 There are numerous useful references available fromASTM
15、, USEPA, and private sector publishers. Appendix X1contains a list, which is by no means comprehensive, ofadditional commonly used references.3. Terminology3.1 accuracy, ncloseness of a measured value to the trueor an accepted reference or standard value. (E135)3.2 attribute, na quality of samples o
16、r a population.(D5956)3.3 characteristic, na property of items in a sample orpopulation that can be measured, counted, or otherwiseobserved. (D5956)3.3.1 DiscussionA characteristic of interest may be thecadmium concentration or ignitability of a population.3.4 composite sample, na combination of two
17、 or moresamples.3.5 confidence interval, na numerical range used to boundthe value of a population parameter with a specified degree ofconfidence (that the interval would include the true parametervalue).3.5.1 DiscussionWhen providing a confidence interval,the number of observations on which the int
18、erval is basedshould be identified.3.6 confidence level, nthe probability, usually expressedas a percent, that a confidence interval will contain theparameter of interest.3.7 data quality objectives (DQO), nqualitative and quan-titative statements derived from the DQO process describingthe decision
19、rules and the uncertainties of the decision(s)within the context of the problem(s). (D5956)3.8 data quality objective process, na quality managementtool based on the scientific method and developed by the U.S.Environmental Protection Agency to facilitate the planning ofenvironmental data collection
20、activities. (D5956)3.8.1 DiscussionThe DQO process enables planners tofocus their planning efforts by specifying the use of the data(the decision), the decision criteria (action level) and thedecision makers acceptable decision error rates. The productsof the DQO Process are the DQOs.3.9 decision ru
21、le, na set of directions in the form ofconditional statements that specifies: (1) how the sample datawill be compared to the decision point or action level, (2)which decision will be made as a result of that comparison, and(3) what subsequent action will be taken based on the deci-sions.3.10 false n
22、egative error, nan error which occurs when(environmental) data misleads the decision maker(s) into nottaking action when action should be taken.3.11 false positive error, nan error which occurs whenenvironmental data misleads the decision maker(s) into takingaction when action should not be taken.3.
23、12 heterogeneity, nthe condition of the population underwhich items of the population are not identical with respect tothe characteristic of interest. (D5956)3.13 homogeneity, nthe condition of the population underwhich all items of the population are identical with respect tothe characteristic of i
24、nterest. (D5956)3.14 representative sample, na sample collected such thatit reflects one or more characteristics of interest (as defined bythe project objectives) of a population from which it wascollected. (D5956)3.15 risk, nthe probability or likelihood that an adverseeffect will occur. (E943)3.16
25、 sample, na portion of material which is collected fortesting or for record purposes. (D5956)3.16.1 DiscussionSample is a term with numerous mean-ings. The project team member collecting physical samples(for example, from a landfill, drum or waste pipe) or analyzingsamples considers a sample to be t
26、hat unit of the populationcollected and placed in a container. In statistics, a sample isconsidered to be a subset of the population and this subset mayconsist of one or more physical samples. To minimizeconfusion, the term “physical sample” is a reference to thesample held in a sample container or
27、that portion of thepopulation which is subjected to measurement.3.17 sampling design, n(1) the sampling schemes speci-fying the point(s) for sample collection; (2) the samplingschemes and associated components for implementation of asampling event.3.17.1 DiscussionBoth of the above definitions are c
28、om-monly used within the environmental community. Therefore,both are used within this document.4. Significance and Use4.1 The intended use of this guide is to provide practicalassistance in the development of an optimized samplingdesign. This standard describes or discusses:4.1.1 Sampling design sel
29、ection criteria,4.1.2 Factors impacting the choice of a sampling design,4.1.3 Selection of a sampling design,4.1.4 Techniques for optimizing candidate designs, and4.1.5 The criteria for evaluating an optimized samplingdesign.4.2 Within a formal USEPA data generation activity, theplanning process or
30、Data Quality Objectives (DQO) develop-ment is the first step. The second and third are the implemen-tation of the sampling and analysis design and the data qualityassessment. Within the DQO planning process, the selectionand optimization of the sampling design is the last step, andtherefore, the cul
31、mination of the DQO process. The precedingsteps in the DQO planning process address:4.2.1 The problem that needs to be addressed,4.2.2 The possible decisions,4.2.3 The data input and associated activities,4.2.4 The boundaries of the study,D6311 98 (2014)24.2.5 The development of decision rules, and4
32、.2.6 The specified the limits on decision error.4.3 This guide is not intended to address the aspects of theplanning process for development of the project objectives.However, the project objectives must be outlined and commu-nicated to the design team, prior to the selection and optimi-zation of th
33、e sample design.4.4 This guide references statistical aspects of the planningand implementation process and includes an appendix for thestatistical calculation of the optimum number of samples for agiven sampling design.4.5 This guide is intended for those who are responsible formaking decisions abo
34、ut environmental waste managementactivities.5. Summary of Guide5.1 The selection and optimization process is an iterativeprocess of selecting and then evaluating the selected designalternatives and determining the most resource-effective designwhich satisfies the project objectives or DQOs. Fig. 1 i
35、llus-trates this approach.5.2 An appropriate sampling design may be implementedwithout a formal optimization, however, the following stepsare recommended. Each evaluation step typically results infewer design alternatives.5.2.1 Evaluation of the designs against the projects practi-cal considerations
36、 (for example, time, personnel, and materialresources),FIG. 1 Implement Sampling DesignD6311 98 (2014)35.2.2 Calculation of the design cost and statisticaluncertainty, and5.2.3 Choice of the sample design decision by the decisionmakers.5.3 The process steps for the evaluation can be followed inany o
37、rder. And for a small project, the entire selection andoptimization process may be conducted at the same time. Ifultimately, a design meeting the project constraints, forexample, schedule and budget, cannot be identified among thecandidate sampling designs, it may be necessary to modify theclosest c
38、andidate design or reevaluate and revise the projectobjectives.6. Factors Affecting Sampling Design Selection6.1 Sampling Design Performance Characteristics:6.1.1 The sampling design provides the structure and detailfor the sampling activity and should be chosen in light of theproject objectives. Pr
39、ior to this point, the planning processshould have addressed and defined the project needs for each ofthe sampling design characteristics, including the characteris-tics of interest, population boundaries, decision rule, accept-able decision errors and budgets. In considering all aspects ofthe proje
40、ct, the selected design should accommodate the spatialand temporal distribution of contaminants at the site, bepractical, cost effective and generate data that allow the projectobjectives to be met.6.1.2 Whenever possible, technical guidelines for measure-ment of the sources of variability and level
41、s of uncertaintyshould be established prior to developing sampling designalternatives, to ensure that it is possible to establish that theprogram objectives are met.6.1.3 Annex A1 presents an overview of some of the morecommonly used sampling designs and design tools and sum-marizes their advantages
42、 and disadvantages. Because numer-ous sampling strategies exist, this is limited to the morecommon. If the more common sampling strategies are notcost-effective or applicable to the population of interest, astatistician should be consulted to identify other strategieswhich may be more appropriate.6.
43、2 Regulatory ConsiderationsThe selection of samplingdesign, the sampling techniques and analytical methods may bedictated by current regulation, permits or consent agreements,applicable to the site. These should be reviewed to determinetheir impact on the selection process.6.3 Project ObjectivesProj
44、ect objectives are usually deter-mined by the decision makers (for example, regulatory body,consent agreement group, company management) during theinitial investigation and planning or DQO process. The deci-sion makers should have identified the population boundaries,characteristics of interest, acc
45、eptability of an average analyticalvalue, the need to locate areas of contamination or “hot spots,”the statistical needs (for example, acceptable decision errors,levels of uncertainty), and the quality control acceptancecriteria, as well as any other pertinent information.6.4 Knowledge of the SiteTh
46、e site knowledge (forexample, geography/topography, utilities, past site use) used todetermine project objectives, will also provide for a moreresource efficient sampling design, for example, divide a siteinto separate design areas for sampling or exclude an area fromsampling.6.5 Physical Sample Iss
47、uesThe physical material to besampled and its location on or within the site will usuallyimpact the sampling design and limit the choices of equipmentand methods.6.5.1 Number of Samples:6.5.1.1 The project objectives should specify the confidencelevels for decision making. Using this level of decisi
48、on error,the proximity to a threshold or action limit and the anticipatedpopulation variance, the number of samples can be calculated.The statistical parameter of interest, for example, mean or 95percentile, and type of frequency of distribution, for example,normal or log normal, will determine whic
49、h equation is used tocalculate the appropriate number of samples. Equation X3.5from Appendix X3, can be used to calculate the number ofsamples when the objective is to measure the mean for apopulation that has a normal distribution for the characteristicof interest.6.5.1.2 Appendix X3 contains statistical approaches to cal-culating the number of samples needed for estimating the meanconcentration, for simple random, statistical random, multi-stage sampling and search sampling (where the objective is todetect hot spots).6.5.2 Sample Mass or Volume:6.5.2.1
