Dr Peter Beck was a visitor to CRC CARE in June
2009, and during his visit accepted an invitation to present a
seminar entitled: “Is there contamination between my sampling
locations?”
Dr Beck has over 18 years experience in environmental science
and geotechnical engineering in the consulting and academic
environment. Project experience ranges from residential, commercial
and industrial developments and redevelopment’s, to large
mining and infrastructure projects. Specialising in the groundwater
field with a focus on impact assessment, contaminant transport and
remediation he has completed several EIS project components as well
as over 200 site assessment and remediation projects.
Peter's presentation is available for review on this page and the
abstract of his presentation is below:
The presentation examines the uni and bi variate
statistical tools available to answer the fundamental question
faced in every contaminated land assessment: “Is there
contamination between my sampling locations?”
Under the uni-variate approach one makes a
fundamental assumption on the size and shape of the area impacted
by contamination and then sets up an unbiased sampling routine that
assess the site in order to statistically demonstrate the presence
or absence of a contaminated area of the predetermined size and
shape at a given confidence interval. The second stage of this
approach then applies statistical tools to assess the concentration
data to reach a conclusion on the contamination status of the site
and finally the results represented on a plan. The first, second
and third stages of the univariate approaches are undertaken
independently. The key limitation in this approach is that the
confidence limit at which the distribution of contamination is
assessed only applies to the specific set up a assumptions made in
the design of the sampling program. Further the statistical
analysis is only valid based on the assumption that sampling was
unbiased.
The bi-vriate approach overcomes some of these key
limitations by maintaining the linkage between concentration and
location throughout the process, not requiring an assumption of
unbiased sampling and not assuming that contamination occurs in any
specified size and shape hotspot. Thus while computationally more
intensive and requiring a greater level of skill in the assessment
this approach offers a robust means of assessing the presence or
absence of contamination between sampling locations at various
confidence limits.
