ASTM D6233-98(2003)

Standard Guide for Data Assessment for Environmental Waste Management Activities

SIGNIFICANCE AND USE
This guide presents a logical process for determining the usability of environmental data for decision making activities. The process describes a series of steps to determine if the enviromental data were collected as planned by the project team and to determine if the a priori expectations/assumptions of the team were met.
This guide identifies the technical issues pertinent to the integrity of the environmental sample collection and analysis process. It guides the data assessor and data user about the appropriate action to take when data fail to meet acceptable standards of quality and reliability.
The guide discusses, in practical terms, the proper application of statistical procedures to evaluate the database. It emphasizes the major issues to be considered and provides references to more thorough statistical treatments for those users involved in detailed statistical assessments.
This guide is intended for those who are responsible for making decisions about environmental waste management projects.
SCOPE
1.1 This guide covers a practical strategy for examining an environmental project data collection effort and the resulting data to determine if they will support the intended use. It covers the review of project activities to determine conformance with the project plan and impact on data usability. This guide also leads the user through a logical sequence to determine which statistical protocols should be applied to the data.
1.1.1 This guide does not establish criteria for the acceptance or use of data but instructs the assessor/user to use the criteria established by the project team during the planning (data quality objective process), and optimization and implementation (sampling and analysis plan) process.
1.2 This standard does not purport to address all of the safety concerns, if any, associated with its use. It is the responsibility of the user of this standard to establish appropriate safety and health practices and determine the applicability of regulatory limitations prior to use.

General Information

• Status: Historical
• Publication Date: 09-Feb-1998
• Technical Committee: D34 - Waste Management
• Drafting Committee: D34.01.01 - Planning for Sampling

Relations

• Replaces: ASTM D6233-98 - Standard Guide for Data Assessment for Environmental Waste Management Activities
• Effective Date: 10-Feb-1998
• Replaced By: ASTM D6233-98(2009) - Standard Guide for Data Assessment for Environmental Waste Management Activities (Withdrawn 2016)
• Effective Date: 01-Feb-2009
• Referred By: ASTM D6538-12(2019) - Standard Guide for Sampling Wastewater With Automatic Samplers
• Effective Date: 10-Feb-1998
• Referred By: ASTM D6759-16 - Standard Practice for Sampling Liquids Using Grab and Discrete Depth Samplers
• Effective Date: 10-Feb-1998
• Referred By: ASTM D6956-17 - Standard Guide for Demonstrating and Assessing Whether a Chemical Analytical Measurement System Provides Analytical Results Consistent with Their Intended Use
• Effective Date: 10-Feb-1998

Standards Content (Sample)

NOTICE: This standard has either been superseded and replaced by a new version or withdrawn.
Contact ASTM International (www.astm.org) for the latest information
Designation: D 6233 – 98 (Reapproved 2003)
Standard Guide for
Data Assessment for Environmental Waste Management
Activities
This standard is issued under the fixed designation D 6233; the number immediately following the designation indicates the year of
original adoption or, in the case of revision, the year of last revision. A number in parentheses indicates the year of last reapproval. A
superscript epsilon (e) indicates an editorial change since the last revision or reapproval.
1. Scope ance and Quality Control Planning and Implementation
Activities
1.1 This guide covers a practical strategy for examining an
D 5792 Practice for Generation of Environmental Data
environmental project data collection effort and the resulting
Related to Waste ManagementActivities: Development of
data to determine if they will support the intended use. It
Data Quality Objectives
covers the review of project activities to determine conform-
D 5956 Guide for Sampling Strategies for Heterogeneous
ance with the project plan and impact on data usability. This
Wastes
guide also leads the user through a logical sequence to
D 6044 Guide for Representative Sampling for Manage-
determine which statistical protocols should be applied to the
ment of Waste and Contaminated Media
data.
1.1.1 This guide does not establish criteria for the accep-
3. Terminology
tance or use of data but instructs the assessor/user to use the
3.1 Definitions of Terms Specific to This Standard:
criteria established by the project team during the planning
3.1.1 bias, n—a systematic error that is consistently nega-
(data quality objective process), and optimization and imple-
tive or consistently positive.
mentation (sampling and analysis plan) process.
3.1.2 characteristic, n—a property of items in a sample or
1.2 This standard does not purport to address all of the
population which can be measured, counted, or otherwise
safety concerns, if any, associated with its use. It is the
observed.
responsibility of the user of this standard to establish appro-
3.1.3 composite sample, n—a physical combination of two
priate safety and health practices and determine the applica-
or more samples.
bility of regulatory limitations prior to use.
3.1.4 confidence limit, n—an upper and/or lower limit(s)
2. Referenced Documents within which the true value is likely to be contained with a
2 stated probability or confidence.
2.1 ASTM Standards:
3.1.5 continuous data, n—data where the values of the
D 4687 Guide for General Planning of Waste Sampling
individual samples may vary from minus infinity to plus
D 5088 Practice for Decontamination of Field Equipment
infinity.
Used at Nonradioactive Waste Sites
3.1.6 data quality objectives (DQOs), n—DQOs are quali-
D 5283 Practice for Generation of Environmental Data
tative and quantitative statements derived from the DQO
Related to Waste Management Activities: Quality Assur-
process describing the decision rules and the uncertainties of
the decision(s) within the context of the problem(s).
3.1.7 data quality objective process, n—a quality manage-
This guide is under the jurisdiction of ASTM Committee D34 on Waste
ment tool based on the scientific method and developed to
Management and is the direct responsibility of Subcommittee D34.01.01 on
facilitate the planning of environmental data collection activi-
Planning for Sampling.
Current edition approved Feb. 10, 1998. Published June 1998.
ties.
For referenced ASTM standards, visit the ASTM website, www.astm.org, or
3.1.8 discrete data, n—data made up of sample results that
contact ASTM Customer Service at [e-mail]. For Annual Book of ASTM
are expressed as a simple pass/fail, yes/no, or positive/
Standards volume information, refer to the standard’s Document Summary page on
the ASTM website. negative.
Copyright © ASTM International, 100 Barr Harbor Drive, PO Box C700, West Conshohocken, PA 19428-2959, United States.
D 6233 – 98 (2003)
TABLE 1 Information Needed to Evaluate the Integrity of the
3.1.9 heterogeneity, n—the condition of the population
Environmental Sample Collection and Analysis Process
under which all items of the population are not identical with
General Project Details  Site History
respect to the parameter of interest.
 Process Description
3.1.10 homogeneity, n—the condition of the population
 Waste Generation Records
under which all items of the population are identical with
 Waste Handling/Disposal Practices
 Sources of Contamination
respect to the parameter of interest.
 Conceptual Site Model
3.1.11 population, n—the totality of items or units under
 Potential Contaminants of Concern
consideration.  Fate and Transport Mechanisms
 Exposure Pathways
3.1.12 representative sample, n—a sample collected in such
 Boundaries of the Study Area
a manner that it reflects one or more characteristics of interest
 Adjacent Properties
(as defined by the project objectives) of a population from
Sampling Issues  Sampling Strategy
which it is collected.
 Sample Location
3.1.13 sample, n—a portion of material which is taken from
 Sample Number
a larger quantity for the purpose of estimating properties or  Sample Matrix
 Sample Volume/Mass
composition of the larger quantity.
 Discrete/Composite Samples
3.1.14 sampling design error, n—error which results from
 Sample Representativeness
the unavoidable limitations faced when media with inherently Sampling Equipment, Containers and

Preservatives
variable qualities are measured and incorrect judgement on the
part of the project team.
Analytical Issues  Laboratory Sub-sampling
3.1.15 subsample, n—a portion of a sample that is taken for  Sample Preparation Methods
 Analytical Method
testing or for record purposes.
 Detection Limits
 Matrix Interferences
4. Significance and Use
 Bias
 Holding Times
4.1 Thisguidepresentsalogicalprocessfordeterminingthe
 Calibration
usability of environmental data for decision making activities.
 Quality Control Results
The process describes a series of steps to determine if the
 Contamination
 Reporting Requirements
enviromental data were collected as planned by the project
 Reagents/Supplies
team and to determine if the a priori expectations/assumptions
of the team were met. Validation and
 Data Quality Objectives
Assessment
4.2 This guide identifies the technical issues pertinent to the
 Chain of Custody
integrity of the environmental sample collection and analysis
 Action Level
process. It guides the data assessor and data user about the  Completeness
 Laboratory Audit Results
appropriate action to take when data fail to meet acceptable
 Field and Laboratory Records
standards of quality and reliability.
 Level of Uncertainty in Reported Values
4.3 The guide discusses, in practical terms, the proper
application of statistical procedures to evaluate the database. It
emphasizes the major issues to be considered and provides
deviations from the a priori performance level of any one or
references to more thorough statistical treatments for those
combination of these issues may impact the reliability of the
users involved in detailed statistical assessments.
project decision and necessitate a reconsideration of the
4.4 This guide is intended for those who are responsible for
decision criteria by the project decision makers.
making decisions about environmental waste management
5.3 Appropriate professionals must assess the project plan-
projects.
ning documents and completed project records to determine if
5. General Considerations
the project findings match the conceptual model and decision
logic. In areas where the findings don’t match, the assessors
5.1 This guide provides general guidance about applying
numerical and other techniques to the assessment of data must document and report their findings and, if possible, the
resulting form environmental data collection activities associ- potential impact on the decision process. Items subject to
ated with waste management activities. numerical confirmation are compared to the project plan and
5.2 The environmental measurement process is a complex any discrepancies and their potential impact noted.
process requiring input from a variety of personnel to properly 5.4 Effective quality control (QC) programs are those that
address the numerous issues related to the integrity of the empower the individuals performing the work to evaluate their
sample collection and measurement process in sufficient detail. performance and implement real-time corrections during the
Table 1 lists many of the topics that are common to most sampling or measurement process, or both. When quality
environmental projects. A well-executed project planning ac- control processes (including documentation) are properly
tivity (see Guide D 4687, Practices D 5088, D 5283, and implemented, they result in data sets (see Fig. 1) that are
D 5792) should consider the impact of each of these issues on generated by in-control processes or out-of control processes
the reliability of the final project decision.The data assessment that were not amenable to corrective action but whose details
process must then evaluate the actual performance in these are explained by the project staff conducting the work. Good
areas versus that expected by the project planners. Significant QC programs lead to reliable data that are seldom called into
D 6233 – 98 (2003)
FIG. 1 General Strategy for Assessment of Continuous Data Sets
question during the assessment process. However, in cases tation practices, and staff training. Historically, efforts have
where the absence of staff responsibility or authority to been focused on the control of data collection errors through
self-monitor and correct deficiencies at the working level is data review and the quality control process but little emphasis
missing, the burden of assuring data integrity is placed on the has been placed on the detection and evaluation of immeasur-
quality assurance (QA) function. The data assessment process able errors using the quality assurance process. These unmea-
must determine the location (working level or QAlevel) where surable sources of error are often the greatest source of
effective quality control occurs (detection of error and execu- uncertainty in the data collected for environmental projects.
tion of corrective action) in the data collection process and Examples of unmeasurable factors are given in Table 2.
focus on how well the QC function was executed.As a general 5.6 Once the data assessment process has determined the
rule, if the QC function is not executed in real-time and degree to which the actual data collection effort met the
thoroughly documented by the staff performing the work, the expectations of the planners, the assessment process moves
more likely the data assessor will be to find questionable data. into the next phase to determine if the data generated by the
5.5 In addition to addressing the issues listed in Table 1, the effort can be verified and validated and whether it pass
data assessment process must search for unmeasurable factors statistical tests for useability. These issues are discussed in the
whose impact cannot be detected by the review of the project next sections.
records against expectations or numerical techniques. These
are the types of things that are controlled by effective quality 6. Sources of Sampling Error
assurance programs, standard operating procedures, documen-
6.1 Sample collection may cause random or systematic
errors. Random error affects the data by increasing the impre-
TABLE 2 Examples of Unmeasurable Factors Affecting the cision, whereas systemic error biases the data. The data
Integrity of Environmental Data Collection Efforts
assessment process should examine the available sampling
 Biased Sampling/Subsampling  Incorrect Dilutions records to determine if errors were introduced by improper
 Sampling Wrong Area or Material  Incorrect Documentation
sampling. A discussion of some of the more common sources
 Sample Switching (Mis-labeling)  Matrix-Specific Artifacts
of error follow.
 Misweighing/Misaliquoting
6.1.1 Random Error:
D 6233 – 98 (2003)
6.1.1.1 Flawsinthesamplingdesignwhichresultintoofew 6.2.5 Inappropriate preservation of the sample can cause a
quality control samples being taken in the field can result in shift in chemical equilibria, loss of target analytes, or degra-
undetected errors in the sampling program.Adequate numbers dation, or all of these. For example, when analyzing a water
of field QC samples (for example, field splits, co-located sample for dissolved metals, addition of nitric acid to a water
samples, equipment rinsate blanks, and trip blanks) are neces- sample containing suspended solids might dissolve metals
sary to assess inconsistencies in sample collection practices, from the solids, resulting in an incorrect high concentration
contaminated equipment, and contamination during the ship- being reported. Failure to preserve water samples intended for
ment process. organic analysis may allow significant biological alteration of
the sample.
6.1.1.2 Variations (heterogeneity) in the media being
6.2.6 The time of day and prevailing weather conditions
sampled can cause concentration and property differences
between and within samples. Field sampling and laboratory whensamplesarecollectedcanaffectthesample.Forexample,
strong winds can blow dust that can contaminate the samples.
sub-sampling records should be examined to determine if
heterogeneity was noted. This can explain wide variations in Cool mornings or evening can lead to higher retention of
volatile components in near-surface soil samples compared to
field and/or laboratory duplicate data.
the samples collected in the heat of the day.
6.1.1.3 Samples from the same population (including co-
6.2.7 The above examples only serve to illustrate the need
located samples) can be very different from each other. For
for an experienced professional to review the sampling activi-
example, one sample might be taken from a hot spot that was
ties and to place the resulting analytical data in the proper
not visually obvious while the other was taken outside the
context of the sampling activity. Such assessments add mate-
perimeter of the hot spot. If data from areas of high concen-
rially to the usability of the data.
tration is contained in data sets consisting primarily of uncon-
taminatedmaterial,statisticaloutlieranalysismightsuggestthe
7. Sources of Analytical Error
sample data should be omitted from consideration when
evaluating a site. This can cause serious decision errors. Prior
7.1 Variation in the analytical process may cause random or
to declaring the data poin
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