ASTM D6233-1998(2003) Standard Guide for Data Assessment for Environmental Waste Management Activities《环境废物管理活动的数据评价的标准指南》.pdf

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1、Designation: D 6233 98 (Reapproved 2003)Standard Guide forData Assessment for Environmental Waste ManagementActivities1This standard is issued under the fixed designation D 6233; the number immediately following the designation indicates the year oforiginal adoption or, in the case of revision, the

2、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 guide covers a practical strategy for examining anenvironmental project data collection effort and the r

3、esultingdata to determine if they will support the intended use. Itcovers the review of project activities to determine conform-ance with the project plan and impact on data usability. Thisguide also leads the user through a logical sequence todetermine which statistical protocols should be applied

4、to thedata.1.1.1 This guide does not establish criteria for the accep-tance or use of data but instructs the assessor/user to use thecriteria established by the project team during the planning(data quality objective process), and optimization and imple-mentation (sampling and analysis plan) process

5、.1.2 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 regulatory limitations prior to use.2. Referenced

6、Documents2.1 ASTM Standards:2D 4687 Guide for General Planning of Waste SamplingD 5088 Practice for Decontamination of Field EquipmentUsed at Nonradioactive Waste SitesD 5283 Practice for Generation of Environmental DataRelated to Waste Management Activities: Quality Assur-ance and Quality Control P

7、lanning and ImplementationActivitiesD 5792 Practice for Generation of Environmental DataRelated to Waste Management Activities: Development ofData Quality ObjectivesD 5956 Guide for Sampling Strategies for HeterogeneousWastesD 6044 Guide for Representative Sampling for Manage-ment of Waste and Conta

8、minated Media3. Terminology3.1 Definitions of Terms Specific to This Standard:3.1.1 bias, na systematic error that is consistently nega-tive or consistently positive.3.1.2 characteristic, na property of items in a sample orpopulation which can be measured, counted, or otherwiseobserved.3.1.3 composi

9、te sample, na physical combination of twoor more samples.3.1.4 confidence limit, nan upper and/or lower limit(s)within which the true value is likely to be contained with astated probability or confidence.3.1.5 continuous data, ndata where the values of theindividual samples may vary from minus infi

10、nity to plusinfinity.3.1.6 data quality objectives (DQOs), nDQOs are quali-tative and quantitative statements derived from the DQOprocess describing the decision rules and the uncertainties ofthe decision(s) within the context of the problem(s).3.1.7 data quality objective process, na quality manage

11、-ment tool based on the scientific method and developed tofacilitate the planning of environmental data collection activi-ties.3.1.8 discrete data, ndata made up of sample results thatare expressed as a simple pass/fail, yes/no, or positive/negative.1This guide is under the jurisdiction of ASTM Comm

12、ittee D34 on WasteManagement and is the direct responsibility of Subcommittee D34.01.01 onPlanning for Sampling.Current edition approved Feb. 10, 1998. Published June 1998.2For referenced ASTM standards, visit the ASTM website, www.astm.org, orcontact ASTM Customer Service at serviceastm.org. For An

13、nual Book of ASTMStandards volume information, refer to the standards Document Summary page onthe ASTM website.1Copyright ASTM International, 100 Barr Harbor Drive, PO Box C700, West Conshohocken, PA 19428-2959, United States.3.1.9 heterogeneity, nthe condition of the populationunder which all items

14、 of the population are not identical withrespect to the parameter of interest.3.1.10 homogeneity, nthe condition of the populationunder which all items of the population are identical withrespect to the parameter of interest.3.1.11 population, nthe totality of items or units underconsideration.3.1.1

15、2 representative sample, na sample collected in sucha manner that it reflects one or more characteristics of interest(as defined by the project objectives) of a population fromwhich it is collected.3.1.13 sample, na portion of material which is taken froma larger quantity for the purpose of estimati

16、ng properties orcomposition of the larger quantity.3.1.14 sampling design error, nerror which results fromthe unavoidable limitations faced when media with inherentlyvariable qualities are measured and incorrect judgement on thepart of the project team.3.1.15 subsample, na portion of a sample that i

17、s taken fortesting or for record purposes.4. Significance and Use4.1 This guide presents a logical process for determining theusability of environmental data for decision making activities.The process describes a series of steps to determine if theenviromental data were collected as planned by the p

18、rojectteam and to determine if the a priori expectations/assumptionsof the team were met.4.2 This guide identifies the technical issues pertinent to theintegrity of the environmental sample collection and analysisprocess. It guides the data assessor and data user about theappropriate action to take

19、when data fail to meet acceptablestandards of quality and reliability.4.3 The guide discusses, in practical terms, the properapplication of statistical procedures to evaluate the database. Itemphasizes the major issues to be considered and providesreferences to more thorough statistical treatments f

20、or thoseusers involved in detailed statistical assessments.4.4 This guide is intended for those who are responsible formaking decisions about environmental waste managementprojects.5. General Considerations5.1 This guide provides general guidance about applyingnumerical and other techniques to the a

21、ssessment of dataresulting form environmental data collection activities associ-ated with waste management activities.5.2 The environmental measurement process is a complexprocess requiring input from a variety of personnel to properlyaddress the numerous issues related to the integrity of thesample

22、 collection and measurement process in sufficient detail.Table 1 lists many of the topics that are common to mostenvironmental projects. A well-executed project planning ac-tivity (see Guide D 4687, Practices D 5088, D 5283, andD 5792) should consider the impact of each of these issues onthe reliabi

23、lity of the final project decision. The data assessmentprocess must then evaluate the actual performance in theseareas versus that expected by the project planners. Significantdeviations from the a priori performance level of any one orcombination of these issues may impact the reliability of thepro

24、ject decision and necessitate a reconsideration of thedecision criteria by the project decision makers.5.3 Appropriate professionals must assess the project plan-ning documents and completed project records to determine ifthe project findings match the conceptual model and decisionlogic. In areas wh

25、ere the findings dont match, the assessorsmust document and report their findings and, if possible, thepotential impact on the decision process. Items subject tonumerical confirmation are compared to the project plan andany discrepancies and their potential impact noted.5.4 Effective quality control

26、 (QC) programs are those thatempower the individuals performing the work to evaluate theirperformance and implement real-time corrections during thesampling or measurement process, or both. When qualitycontrol processes (including documentation) are properlyimplemented, they result in data sets (see

27、 Fig. 1) that aregenerated by in-control processes or out-of control processesthat were not amenable to corrective action but whose detailsare explained by the project staff conducting the work. GoodQC programs lead to reliable data that are seldom called intoTABLE 1 Information Needed to Evaluate t

28、he Integrity of theEnvironmental Sample Collection and Analysis ProcessGeneral Project Details Site HistoryProcess DescriptionWaste Generation RecordsWaste Handling/Disposal PracticesSources of ContaminationConceptual Site ModelPotential Contaminants of ConcernFate and Transport MechanismsExposure P

29、athwaysBoundaries of the Study AreaAdjacent PropertiesSampling Issues Sampling StrategySample LocationSample NumberSample MatrixSample Volume/MassDiscrete/Composite SamplesSample RepresentativenessSampling Equipment, Containers andPreservativesAnalytical Issues Laboratory Sub-samplingSample Preparat

30、ion MethodsAnalytical MethodDetection LimitsMatrix InterferencesBiasHolding TimesCalibrationQuality Control ResultsContaminationReporting RequirementsReagents/SuppliesValidation andAssessmentData Quality ObjectivesChain of CustodyAction LevelCompletenessLaboratory Audit ResultsField and Laboratory R

31、ecordsLevel of Uncertainty in Reported ValuesD 6233 98 (2003)2question during the assessment process. However, in caseswhere the absence of staff responsibility or authority toself-monitor and correct deficiencies at the working level ismissing, the burden of assuring data integrity is placed on the

32、quality assurance (QA) function. The data assessment processmust determine the location (working level or QA level) whereeffective quality control occurs (detection of error and execu-tion of corrective action) in the data collection process andfocus on how well the QC function was executed. As a ge

33、neralrule, if the QC function is not executed in real-time andthoroughly documented by the staff performing the work, themore likely the data assessor will be to find questionable data.5.5 In addition to addressing the issues listed in Table 1, thedata assessment process must search for unmeasurable

34、 factorswhose impact cannot be detected by the review of the projectrecords against expectations or numerical techniques. Theseare the types of things that are controlled by effective qualityassurance programs, standard operating procedures, documen-tation practices, and staff training. Historically

35、, efforts havebeen focused on the control of data collection errors throughdata review and the quality control process but little emphasishas been placed on the detection and evaluation of immeasur-able errors using the quality assurance process. These unmea-surable sources of error are often the gr

36、eatest source ofuncertainty in the data collected for environmental projects.Examples of unmeasurable factors are given in Table 2.5.6 Once the data assessment process has determined thedegree to which the actual data collection effort met theexpectations of the planners, the assessment process move

37、sinto the next phase to determine if the data generated by theeffort can be verified and validated and whether it passstatistical tests for useability. These issues are discussed in thenext sections.6. Sources of Sampling Error6.1 Sample collection may cause random or systematicerrors. Random error

38、affects the data by increasing the impre-cision, whereas systemic error biases the data. The dataassessment process should examine the available samplingrecords to determine if errors were introduced by impropersampling. A discussion of some of the more common sourcesof error follow.6.1.1 Random Err

39、or:FIG. 1 General Strategy for Assessment of Continuous Data SetsTABLE 2 Examples of Unmeasurable Factors Affecting theIntegrity of Environmental Data Collection EffortsBiased Sampling/Subsampling Incorrect DilutionsSampling Wrong Area or Material Incorrect DocumentationSample Switching (Mis-labelin

40、g) Matrix-Specific ArtifactsMisweighing/MisaliquotingD 6233 98 (2003)36.1.1.1 Flaws in the sampling design which result in too fewquality control samples being taken in the field can result inundetected errors in the sampling program. Adequate numbersof field QC samples (for example, field splits, c

41、o-locatedsamples, equipment rinsate blanks, and trip blanks) are neces-sary to assess inconsistencies in sample collection practices,contaminated equipment, and contamination during the ship-ment process.6.1.1.2 Variations (heterogeneity) in the media beingsampled can cause concentration and propert

42、y differencesbetween and within samples. Field sampling and laboratorysub-sampling records should be examined to determine ifheterogeneity was noted. This can explain wide variations infield and/or laboratory duplicate data.6.1.1.3 Samples from the same population (including co-located samples) can

43、be very different from each other. Forexample, one sample might be taken from a hot spot that wasnot visually obvious while the other was taken outside theperimeter of the hot spot. If data from areas of high concen-tration is contained in data sets consisting primarily of uncon-taminated material,

44、statistical outlier analysis might suggest thesample data should be omitted from consideration whenevaluating a site. This can cause serious decision errors. Priorto declaring the data point(s) to be outliers, it is important forthe assessor to examine the QC records from the analysisyielding the su

45、spect data. If the QC data indicates the systemwas in control and review of the raw sample data reveals nohandling or calculation errors, the suspect data should bediscussed in the assessors report but it should not be dis-counted. The site history and operating records may hold cluesto the possible

46、 existence of hot spots.6.2 Systematic Error:6.2.1 Flaws in the sampling design that result in sampling ofinappropriate locations can result in significant bias in the data.The samples collected from such a flawed plan will not berepresentative of the population and can result in incorrectdecisions.

47、 The assessor should review the sampling plan forsigns of potential bias and discuss their findings in the finalreport.6.2.2 Sampling tools and equipment can deselect certainparts of a sample based on the physical properties (density,particle size, multi-phasic materials, particle geometry, etc.). I

48、fthe sample is biased because of some physical characteristic,then any constituent that is distributed in the material based onthat characteristic, will be incorrectly reported. Both field andlaboratory sampling equipment can introduce this type of bias.6.2.3 Incorrect sampling procedures can cause

49、losses ofcertain constituents of a sample such as volatile organics.Failure to control the loss of of constituents that exist in thegaseous state often comprises the collection of unsaturatedmedia for volatile compound characterization. Deterioration ofthe sample can also occur after collection due to improperstorage and transportation. For example, samples left standingin sunlight or in a hot vehicle can undergo photochemicalreactions or lose volatile constituents.6.2.4 Interactions between the sample and the material ofthe sampling equipment or container, or both,