1、Designation: D6044 96 (Reapproved 2015)Standard Guide forRepresentative Sampling for Management of Waste andContaminated Media1This standard is issued under the fixed designation D6044; the number immediately following the designation indicates the year oforiginal adoption or, in the case of revisio
2、n, 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 guide covers the definition of representativeness inenvironmental sampling, identifies sources tha
3、t can affectrepresentativeness (especially bias), and describes the attri-butes that a representative sample or a representative set ofsamples should possess. For convenience, the term “represen-tative sample” is used in this guide to denote both a represen-tative sample and a representative set of
4、samples, unlessotherwise qualified in the text.1.2 This guide outlines a process by which a representativesample may be obtained from a population. The purpose of therepresentative sample is to provide information about a statis-tical parameter(s) (such as mean) of the population regardingsome chara
5、cteristic(s) (such as concentration) of its constitu-ent(s) (such as lead). This process includes the followingstages: (1) minimization of sampling bias and optimization ofprecision while taking the physical samples, (2) minimizationof measurement bias and optimization of precision whenanalyzing the
6、 physical samples to obtain data, and (3) minimi-zation of statistical bias when making inference from thesample data to the population. While both bias and precisionare covered in this guide, major emphasis is given to biasreduction.1.3 This guide describes the attributes of a representativesample
7、and presents a general methodology for obtainingrepresentative samples. It does not, however, provide specificor comprehensive sampling procedures. It is the users respon-sibility to ensure that proper and adequate procedures are used.1.4 The assessment of the representativeness of a sample isnot co
8、vered in this guide since it is not possible to ever knowthe true value of the population.1.5 Since the purpose of each sampling event is unique, thisguide does not attempt to give a step by step account of how todevelop a sampling design that results in the collection ofrepresentative samples.1.6 A
9、ppendix X1 contains two case studies, which discussthe factors for obtaining representative samples.1.7 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 heal
10、th practices and determine the applica-bility of regulatory limitations prior to use.2. Referenced Documents2.1 ASTM Standards:2D3370 Practices for Sampling Water from Closed ConduitsD4448 Guide for Sampling Ground-Water Monitoring WellsD4547 Guide for Sampling Waste and Soils for VolatileOrganic Co
11、mpoundsD4700 Guide for Soil Sampling from the Vadose ZoneD4823 Guide for Core Sampling Submerged, Unconsoli-dated SedimentsD5088 Practice for Decontamination of Field EquipmentUsed at Waste SitesD5792 Practice for Generation of Environmental Data Re-lated to Waste Management Activities: Development
12、ofData Quality ObjectivesD5956 Guide for Sampling Strategies for HeterogeneousWastesD6051 Guide for Composite Sampling and Field Subsam-pling for Environmental Waste Management Activities3. Terminology3.1 analytical unit, nthe actual amount of the samplematerial analyzed in the laboratory.3.2 bias,
13、na systematic positive or negative deviation ofthe sample or estimated value from the true population value.3.2.1 DiscussionThis guide discusses three sources ofbiassampling bias, measurement bias, and statistical bias.There is a sampling bias when the value inherent in thephysical samples is system
14、atically different from what isinherent in the population.1This 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 Sept. 1, 2015. Published September 2015. Originallyappr
15、oved in 1996. Last previous edition approved in 2009 as D6044 96 (2009).DOI: 10.1520/D6044-96R15.2For 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 Docum
16、ent Summary page onthe ASTM website.Copyright ASTM International, 100 Barr Harbor Drive, PO Box C700, West Conshohocken, PA 19428-2959. United States1There is a measurement bias when the measurement processproduces a sample value systematically different from thatinherent in the sample itself, altho
17、ugh the physical sample isitself unbiased. Measurement bias can also include any sys-tematic difference between the original sample and the sampleanalyzed, when the analyzed sample may have been altereddue to improper procedures such as improper sample preser-vation or preparation, or both.There is
18、a statistical bias when, in the absence of samplingbias and measurement bias, the statistical procedure produces abiased estimate of the population value.Sampling bias is considered the most important factoraffecting inference from the samples to the population.3.3 biased sampling, nthe taking of a
19、sample(s) with priorknowledge that the sampling result will be biased relative tothe true value of the population.3.3.1 DiscussionThis is the taking of a sample(s) based onavailable information or knowledge, especially in terms ofvisible signs or knowledge of contamination. This kind ofsampling is u
20、sed to detect the presence of localized contami-nation or to identify the source of a contamination. Thesampling results are not intended for generalization to theentire population. This is one form of authoritative sampling(see judgment sampling.)3.4 characteristic, na property of items in a sample
21、 orpopulation that can be measured, counted, or otherwiseobserved, such as viscosity, flash point, or concentration.3.5 composite sample, na combination of two or moresamples.3.6 constituent, nan element, component, or ingredient ofthe population.3.6.1 DiscussionIf a population contains several cont
22、ami-nants (such as acetone, lead, and chromium), these contami-nants are called the constituents of the population.3.7 Data Quality Objectives, DQOs, nqualitative andquantitative statements derived from a DQO process describingthe decision rules and the uncertainties of the decision(s)within the con
23、text of the problem(s) (see Practice D5792).3.8 Data Quality Objective Processa quality managementtool based on the Scientific Method and developed by the U.S.Environmental Protection Agency to facilitate the planning ofenvironmental data collection activities. The DQO processenables planners to foc
24、us their planning efforts by specifyingthe use of data (the decision), the decision criteria (actionlevel), and the decision makers acceptable decision error rates.The products of the DQO process are the DQOs (see PracticeD5792).3.9 error, nthe random or systematic deviation of theobserved sample va
25、lue from its true value (see bias andsampling error).3.10 heterogeneity, nthe condition or degree of the popu-lation under which all items of the population are not identicalwith respect to the characteristic(s) of interest.3.10.1 DiscussionAlthough the ultimate interest is in thestatistical paramet
26、er such as the mean concentration of aconstituent of the population, heterogeneity relates to thepresence of differences in the characteristics (for example,concentration) of the units in the population. It is due to thepresence of fundamental heterogeneity (or fundamental error)3in the population t
27、hat sampling variance arises. Degree ofsampling variance defines the degree of precision in estimatingthe population parameter using the sample data. The smallerthe sampling variance is, the more precise the estimate is. Seealso sampling error.3.11 homogeneity, nthe condition of the population under
28、which all items of the population are identical with respect tothe characteristic(s) of interest.3.12 judgment sampling, ntaking of a sample(s) based onjudgment that it will more or less represent the averagecondition of the population.3.12.1 DiscussionThe sampling location(s) is selectedbecause it
29、is judged to be representative of the averagecondition of the population. It can be effective when thepopulation is relatively homogeneous or when the professionaljudgment is good. It may or may not introduce bias. It is auseful sampling approach when precision is not a concern.Thisis one form of au
30、thoritative sampling (see biased sampling.)3.13 population, nthe totality of items or units of materi-als under consideration.3.14 representative sample, na sample collected in such amanner that it reflects one or more characteristics of interest (asdefined by the project objectives) of a population
31、 from whichit is collected.3.14.1 DiscussionA representative sample can be a singlesample, a collection of samples, or one or more compositesamples. A single sample can be representative only when thepopulation is highly homogeneous.3.15 representative sampling, nthe process of obtaining arepresenta
32、tive sample or a representative set of samples.3.16 representative set of samples, na set of samples thatcollectively reflect one or more characteristics of interest of apopulation from which they were collected. See representativesample.3.17 sample, na portion of material that is taken fortesting o
33、r for record purposes.3.17.1 DiscussionSample is a term with numerous mean-ings. The scientist collecting physical samples (for example,from a landfill, drum, or monitoring well) or analyzing samplesconsiders a sample to be that unit of the population that wascollected and placed in a container. A s
34、tatistician considers asample to be a subset of the population, and this subset mayconsist of one or more physical samples. To minimizeconfusion, the term sample, as used in this guide, is a referenceto either a physical sample held in a sample container, or thatportion of the population that is sub
35、jected to in situmeasurements, or a set of physical samples. See representativesample.3Pitard, F. F., “Pierre Gys Sampling Theory and Sampling Practice:Heterogeneity, Sampling Correctness and Statistical Process Control,” 2nd ed.,CRC Press Publishers, 1993.D6044 96 (2015)23.17.1.1 The term sample si
36、ze also means different things tothe scientist and the statistician.To avoid confusion, terms suchas sample mass/sample volume and number of samples areused instead of sample size.3.18 sampling errorthe systematic and random deviationsof the sample value from that of the population. The systematicer
37、ror is the sampling bias. The random error is the samplingvariance.3.18.1 DiscussionBefore the physical samples are taken,potential sampling variance comes from the inherent popula-tion heterogeneity (sometimes called the “fundamental error,”see heterogeneity). In the physical sampling stage, additi
38、onalcontributors to sampling variance include random errors incollecting the samples.After the samples are collected, anothercontributor is the random error in the measurement process. Ineach of these stages, systematic errors can occur as well, butthey are the sources of bias, not sampling variance
39、.3.18.1.1 Sampling variance is often used to refer to the totalvariance from the various sources.3.19 stratum, na subgroup of the population separated inspace or time, or both, from the remainder of the population,being internally similar with respect to a target characteristic ofinterest, and diffe
40、rent from adjacent strata of the population.3.19.1 DiscussionA landfill may display spatially sepa-rated strata, such as old cells containing different wastes thannew cells. A waste pipe may discharge into temporally sepa-rated strata of different constituents or concentrations, or both,if night-shi
41、ft production varies from the day shift. In this guide,strata refer mostly to the stratification in the concentrations ofthe same constituent(s).3.20 subsample, na portion of the original sample that istaken for testing or for record purposes.4. Significance and Use4.1 Representative samples are def
42、ined in the context of thestudy objectives.4.2 This guide defines the meaning of a representativesample, as well as the attributes the sample(s) needs to have inorder to provide a valid inference from the sample data to thepopulation.4.3 This guide also provides a process to identify thesources of e
43、rror (both systematic and random) so that an effortcan be made to control or minimize these errors. These sourcesinclude sampling error, measurement error, and statistical bias.4.4 When the objective is limited to the taking of arepresentative (physical) sample or a representative set of(physical) s
44、amples, only potential sampling errors need to beconsidered. When the objective is to make an inference fromthe sample data to the population, additional measurementerror and statistical bias need to be considered.4.5 This guide does not apply to the cases where the takingof a nonrepresentative samp
45、le(s) is prescribed by the studyobjective. In that case, sampling approaches such as judgmentsampling or biased sampling can be taken. These approachesare not within the scope of this guide.4.6 Following this guide does not guarantee that represen-tative samples will be obtained. But failure to foll
46、ow this guidewill likely result in obtaining sample data that are either biasedor imprecise, or both. Following this guide should increase thelevel of confidence in making the inference from the sampledata to the population.4.7 This guide can be used in conjunction with the DQOprocess (see Practice
47、D5792).4.8 This guide is intended for those who manage, design,and implement sampling and analytical plans for waste man-agement and contaminated media.5. Representative Samples5.1 Samples are taken to infer about some statistical param-eter(s) of the population regarding some characteristic(s) of i
48、tsconstituent(s) of interest. This is discussed in the followingsections.5.2 SamplesWhen a representative sample consists of asingle physical sample, it is a sample that by itself reflects thecharacteristics of interest of the population. On the other hand,when a representative sample consists of a
49、set of physicalsamples, the samples collectively reflect some characteristicsof the population, though the samples individually may not berepresentative. In most cases, more than one physical sample isnecessary to characterize the population, because the popula-tion in environmental sampling is usually heterogeneous.5.3 Constituents and CharacteristicsA population canpossess many constituents, each with many characteristics.Usually it is only a subset of these constituents and character-istics that are of interest in the context of the stated probl
