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Data Quality and Representativeness: Keys to Cost-Effective Site Investigation
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| A
Quick Look |
| Data quality is the function
of the data’s information content and its ability to represent the
true state of a site. |
| Data representativeness
is the measure of the degree to which samples can be used to estimate
the characteristics of the true state of a hazardous waste site. |
| Brownfields are considered
an “up-and-coming” application in which data quality and representativness
will play an important role. |
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The information value of data depends heavily upon the interaction among
sampling and analytical designs in relation to the intended use of the
data, the site-specific context surrounding that intended use, and the
associated quality control. When this concept is understood, on-site analytical
tools can play a major role in making environmental decision-making more
efficient, defensible, and cost-effective. In today’s industrial and regulatory
climate, practitioners are often required to make immediate decisions
that are based on dependable, representative data. The term “representative
data” means that there is some stability in the samples and assurance
of data density. On-site analytical techniques offer that type of decision-making
assurance to the user of the data.
Brownfields investigations require innovative approaches that are faster,
cheaper, and better than common practices. The faster approach reduces
sample turnaround times, facilitates in-field decision-making, and minimizes
deployment time of crew and equipment. The more cost-effective approach
is used to reduce analytical costs, field-labor costs, and completion
times. The better approach results in data quality that is as accurate
as that attained by fixed off-site laboratories and refined data analysis
based on the results of on-site screening. Brownfields sites are essentially
industrial sites at which people will want to take measurements, determine
the extent of contamination, and institute a plan. The sampling designs
for such sites will be dynamic in nature; therefore, the real-time analytical
capability offered by field-portable instruments will be essential in
successful sampling. Data representativeness will become increasingly
important in site characterization and remediation projects in the near
future because it supports the dynamic approach by providing real-time
feedback. With liability an important consideration at brownfields sites,
managing uncertainties and having representative data that reflect the
true site conditions is critical to property transactions. Data representativeness
can be used successfully to generate scientifically sound data that are
able to support defensible project decisions at substantial cost savings
over the cost of current practices.
Increased sampling efficiencies, fostered by the use of innovative technologies,
allow more targeted sample collection efforts that minimize the handling
of samples that provide little value in meeting site-specific data quality
objectives. Increased field analytical productivity is obtained when the
type of analysis performed is targeted so that more samples can be analyzed
each day, thereby bringing about more rapid site characterizations and
verification of cleanup. When data needs are articulated clearly, and
when a number of modern sampling and analytical options are available,
it is possible to optimize data collection so that the information produced
is accurate for its intended purpose while still being less costly than
previously possible. When applied carefully, on-site analytical methods
offer representative and decision-quality data with the added benefits
of increased sampling density and real-time availability of results.
Although traditional approaches have tended to focus heavily on the capabilities
of definitive analytical methods, the effect of sampling error on the
representativeness of monitoring and measurement activities also should
be considered. It is important to determine how data obtained from quality
assessment samples can be used to identify and control in the measurement
process sources of sampling error and uncertainties.
By increasing sampling density, made possible and cost-effective with
the use of new sampling and analytical tools, decision-makers can reduce
uncertainty and increase understanding of the true conditions of a site.
This should increase comfort among site owners, buyers, regulators, and
surrounding communities, as well as reduce the likelihood of errors and
omissions that could negatively affect the site later.
For more information see the following resources:
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Sunday, September 7, 2008
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