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34 GROUND ENGINEERING AUGUST 2007
IntroductionA two-year research project, on cost-effective
investigation of contaminat-ed land, aimed to demonstrate that
recently developed techniques can be applied to routine commercial
investigations of contaminated land to assess and improve the
reliability of the measurements taken. These techniques also match
the expenditure on the investigation to the specific technical and
financial circumstances of that particular site. This paper aims to
show how these can be easily implemented during any commercial site
investigation (SI) and to highlight their benefits.
Research backgroundThe measurements taken to characterise areas
of potentially contaminat-ed land are important since they are one
of the key initial steps in the site regeneration process. The
measured values of contamination will be used to update the site
conceptual model and guide the assessment of risk. Scientific
considerations within an SI are usually constrained by commercial
realities, however, so the financial budget restricts the number of
field samples that can be taken and analysed. The measurements need
to be reliable as they ultimately determine the reliability of
management decisions taken.
The measurements of contaminant concentration are only really
estimates because there is always some level of uncertainty.
To understand the concept of measurement uncertainty, imagine
the measurement process of a single soil sample. A discrete mass of
soil will be taken at a sampling location at a specific depth. This
soil sample may be taken with the aim of representing an entire
sub-area of land within a site, even though the field sample may be
relatively small (
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GROUND ENGINEERING AUGUST 2007 35
a hollow metal tube into the ground, removing the tube and
collecting a portion of soil contained within the cylinder from a
specified depth, such as from the fill or natural soil beneath.
Window sampling was chosen to minimise the disturbance to the
site.
All of the soil samples removed from the 13 window sample
locations were inspected onsite by the commercial investigator to
assess the soil characteristics. A sub-set of these soil samples
(n=8 as bold in Figure 1) were selected to provide general coverage
of the site and sent to a laboratory and chemically analysed for
heavy metals.
Site investigation twoThe second investigation aimed to repeat
the approach taken in site investigation one, and was overseen by
the commercial investigator. Soil samples were taken at the same
nominal locations and depths. The samples were sent for chemical
analysis by the same external laboratory using the same analytical
suite of heavy metals.
As with most investigations it was very difficult to predict the
extent to which the measurements and interpretation of both would
differ. This study aimed to determine the differences by repeating
an investigation, but by also using the Duplicate Method to
estimate measurement uncertainty.
Applying the Duplicate Method for the estimation of measurement
uncertaintyThere is a simple method that allows an investigator to
assess how much the measurements will differ (i.e. estimate the
measurement uncertainty) for any given SI, which is called the
Duplicate Method (DM) (Figure 2).
The first investigation of this site did not implement the DM
and did not, therefore, have an estimate of the uncertainty. The
second investigation applied the DM by drilling a second borehole
in close proximity to the original sampling location. Sampling
locations where duplicate samples were taken were randomly selected
and are marked by * in Figure 1.
The distance that separated the original and duplicate samples
aimed to represent the uncertainty in relocating borehole
locations. For example, duplicate samples were taken in a pseudo
random direction of 1m away from the original location. Put simply,
it was estimated that each sampling location could be relocated to
a distance of within 1m.
Duplicate window samples were made at four of the eight original
sampling locations, with two duplicate samples taken from each. For
example, two soil samples were removed for analysis at the original
sampling location of BH6 at depths of 300mm and 1.2m, so the
duplicate samples were also taken at these depths, but 1m away.
This provided a total of eight duplicate soil samples in addition
to the samples taken during the investigation.
The eight original and eight duplicate samples were both
analysed chemically twice, as part of the balanced design (Figure
2) to provide an estimate of the analytical uncertainty.
The difference in measured concentrations between the original
and duplicate samples gives an estimate of the sampling
uncertainty, which is caused mainly by the heterogeneity of the
soil. The analytical uncertainty is estimated by the difference in
measured concentrations between the two analyses made upon the same
samples. The (total) measurement uncertainty is a sum of both the
sampling and analytical uncertainty.
ResultsBoth investigations detected elevated concentrations of
several heavy metals (Table 1). The uppermost layers of soil,
classified by the commercial inves-tigator as topsoil fill and
fill, provided the highest measured concentrations, particularly of
zinc and lead with average values of 760mg kg-1 and 3204mg kg-1
respectively. Sampling location BH1 indicated particularly high
concen-trations of several heavy metals, which is thought to be due
to remains of bomb remnants within this sub-area of the residential
garden (Table 1).
Rather than comparing just the individual measurements between
both investigations, perhaps a more useful comparison would be to
compare the classifications that are made for each contaminant.
The consultancy completed an interpretative report that
presented the 95th percentile (of the mean) value of heavy metal
concentration against their
associated threshold or site specific screen value (i.e. the
Soil Guideline Value or a value estimated from a site-specific risk
assessment).
The measurements taken by the commercial investigators were
reported to the client in a format that separated the
interpretation into the three main soil types found at the site
(i.e. the topsoil fill, the fill and the natural soil). The
measurements taken within each of these three horizons were
interpreted as being three separate averaging areas, as described
in CLR7 guidance (Department for Environment, Food & Rural
Affairs, and the Environment Agency, 2002).
To allow a comparison between the two investigations, this type
of interpretation was also undertaken for the second investigation.
For the purposes of this study, a comparison will be made using the
measurements taken in both investigations from soil taken within
the uppermost layer of material at the site, called the topsoil
fill. This is the most sensitive in terms of the pathways
identified within the human health risk assessment.
Both investigations gave the same interpretation for all of the
stated contaminants when comparing the 95th percentile of the mean
(US95) value against their respective threshold, for example SGV,
within the topsoil fill (Table 1).
This degree of agreement is good, particularly for measurements
of lead and zinc, largely because the measured values are much
greater than their screen values (i.e. the site is definitely
contaminated with these metals). For the other metals, however, the
measured concentrations are relatively close to their threshold
values, which would be expected to lead to differences in
classification between the two different surveys.
The 95th percentile of each of the nine heavy metals exceeded
their threshold value at the three different soil depths – topsoil
fill, fill and natural ground – and showed an 83% agreement between
the two separate investigations, for example, 20 of the 24
contaminant classifications. This good agreement of interpretation
was obtained despite the majority of contaminant concentrations
being relatively close to their associated threshold values, which
often means that a different classification is more likely. The two
“identical” investigations show that although the measured values
differ, as expected, the interpretations are largely
unaffected.
Estimation of measurement uncertainty for the second
investigationAs discussed, the measurement uncertainty was only
estimated during the second investigation at the site using the DM
(Figure 2). The measurement uncertainty was generally low (ranging
from ± 17% to ± 37%) (Table 2) when compared to that found in many
other SIs (> ± 80%) (Ramsey et al., 2002; Taylor et al.,
2005).
The results indicate it is the field sampling, and not the
chemical analysis, that generates the largest component of
measurement uncertainty (i.e. >94%, Table 2). Using lead as an
example, the field sampling contributed 99.9% to the measurement
uncertainty with the chemical analysis only contributing 0.1%. This
is usually the case in geochemical surveys because the uncertainty
introduced by the contaminant heterogeneity is much larger than the
variability of the laboratory analysis. This also ignores any
contribution to the uncertainty from the systematic errors, such as
possible sampling bias.
It is evident that the good agreement in
interpretations/classifications between the two SIs (Table 1) is
caused by the relatively low levels of measurement uncertainty (U%)
generated at this site (Table 2, column 2).
The benefits of estimating the measurement uncertaintyThis study
shows that there are a number of reasons why applying the MD is
beneficial. It is clear that repeating a site survey will produce
different meas-ured values, which could produce different
management decisions. Estimat-ing the measurement uncertainty gives
an indication of how much these val-ues will vary. Because the
consultant involved with the first investigation of this site did
not estimate the uncertainty, it was unable to identify or provide
information on how reliable its measurements and interpretations
were.
The uncertainty, which was easily estimated during the second
investigation, provided a quantitative indication of how repeatable
the investigation was, which was substantiated when the two
different
Sampling locationAcross-site variance
At least eight locations need to haveduplicates to give a useful
estimate
Sampling uncertainty(ssampling)
Analytical uncertainty(sanalytical)
Sample 1
Analysis 1 Analysis 2
Sample 2 (duplicate)
Analysis 1 Analysis 2
Figure 2. Illustration of the Duplicate Method (DM). The
measurement uncertainty (i.e. generated by both the field sampling
and chemical analysis) is estimated by appying the DM, which
requires duplicate samples to be taken and analysed in
duplicate.
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36 GROUND ENGINEERING AUGUST 2007
investigations were compared. This type of information could be
very useful to regulators, landowners and insurance companies who
may be interested in the reliability of an SI. Also, reporting the
estimates of measurement uncertainty could also be useful when the
site redevelopment and/or additional investigations are done by
different organisations.
The DM also indicates where the largest part of uncertainty is
being generated. If the field sampling is the main cause, then the
sampling may be modified in future investigations to reduce its
effect, by, for example, taking composite samples.
This modification of the sampling procedure to reduce
uncertainty has successfully been achieved during a supplementary
investigation at a different site used during this research
project. Expenditure may also be allocated more optimally between
the field sampling and chemical analysis to provide more acceptable
levels of uncertainty.
To reduce the costs of applying the DM, the investigator could
decide against measuring analytical duplicates, an approach that
has also been successfully applied during this research project, at
a low value site. While this substantially reduces the extra costs
necessary, it means measurement uncertainty cannot be separated
into its two components of analytical and sampling uncertainty,
unless estimates of analytical uncertainty are provided by the
laboratory. In addition, the measurements taken from analytical
duplicates can be used to assess whether the laboratory
measurements comply with the precision targets set in the
Monitoring Certification Scheme (Environment Agency, 2006).
Estimating the financial implications of measurement
uncertaintyEstimating the uncertainty also allows the investigator
to assess the financial risk of the investigation using the
Optimised Contaminated Land Investiga-tion (OCLI) method (Ramsey et
al., 2002). This decision support tool can provide an objective
assessment on whether the level of uncertainty pro-duces an
unacceptable financial risk for any given site.
As an example, the OCLI method has been applied to the SI
discussed here. OCLI uses information on the actual level of
measurement uncertainty, the costs of field sampling and laboratory
analysis, and the financial consequences of misclassifying the
site. The worst case scenario of litigation
is used in this application of the method, with a particularly
high penalty estimated at £1M. Further applications of the method
may apply to other decision-error scenarios, such as the costs of
unnecessarily remediating areas of land, erroneously classified as
contaminated because of the uncertainty.
The OCLI curve (Figure 3) shows a range of different possible
values for measurement uncertainty, one of which was estimated
during the SI (actual uncertainty = 200mg kg-1 Pb). Each
uncertainty value has a corresponding expected financial loss (£).
High levels of uncertainty are shown to cause high financial
losses, due to the resultant misclassification of the land. A
reduction in uncertainty requires an increase in measurement
expenditure however.
The judgment on whether the actual uncertainty is acceptable is
made by comparing the actual uncertainty estimated during the SI
against the uncertainty that provides the lowest possible loss,
known as the optimal. A
PAPER
Contaminant
Measurement uncertainty
(U%)
Proportion of U% from the laboratory analysis
Proportion of U% from the
field sampling
Arsenic 23 1.5 98.5
Chromium 17 1.5 98.5
Nickel 24 6.1 93.9
Lead 25 0.1 99.9
Mercury 25 0.6 99.4
Zinc 37 0.2 99.8
Copper 19 0.3 99.7
Table 2. Estimates of uncertainty from measurements estimated
using the DM. The levels of measurement uncertainty are quite low
(±17% to ±37%), compared with studies at other contaminated sites.
The measurement uncertainty is generated mainly by the field
sampling and not from the chemical analysis.
Topsoil fillFirst Investigation at Site 3 by the commercial
investigator
Location depth As Cd Cr Cu Pb Hg Ni Se Zn
BH1 100mm 45 2.4 70 234 15042 4.3 33
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GROUND ENGINEERING AUGUST 2007 37
more detailed explanation of the OCLI methodology can be found
elsewhere (Ramsey et al., 2002).
An initial inspection of the OCLI curve (Figure 3) suggests the
measurement uncertainty for lead (Pb) is not acceptable. This is
because of the large difference (a factor of 2.5) between the
actual uncertainty estimated during the investigation (200mg kg-1)
and the optimal value given by the OCLI method (497mg kg-1).
However, a closer inspection indicates the uncertainty is really
acceptable given the relatively small difference in expected loss
between optimal and actual values (£11 and £62 respectively).
Often, the difference can be substantial, >£10,000 (Boon et al.,
2007).
Interestingly, the actual uncertainty value is positioned to the
left of the optimal value on the OCLI curve (Figure 3). This means
the measurement uncertainty could actually be increased (i.e. have
less precise measurements) to reach the optimal level. The OCLI
method has again provided an objective assessment showing a much
less precise, but more rapid and less expensive measurement
strategy could be used for any supplementary surveys or at similar
sites – such as in-situ Portable X-ray Fluorescence Spectrometry
(Taylor et al., 2004).
ConclusionsThe two nominally identical investigations, conducted
at the same site, both showed elevated concentrations of heavy
metals within the soil, particularly lead and zinc. There was an
83% agreement of the 24 classifications made in each of the two
investigations (i.e. whether the US95 value exceeded the
threshold/screen value).
The measurement uncertainty was estimated during the second
investigation at the site using the DM, with marginal extra
expense. The measurement uncertainty values found were relatively
low ranging from only ±17% to ±37%.
The good agreement in measured concentrations between both
investigations, as well as the agreement in interpretation, is
consistent with what would be expected from such low values of
measurement uncertainty.
The comparison proves that estimating the uncertainty, which is
easily and relatively inexpensive to achieve, gives an indication
of how repeatable, and thus reliable, an SI actually is. The
estimates of measurement uncertainty could be used to demonstrate
to a client or regulator that the investigation was subject to
extra quality assurance procedures that increased the validity and
quality of the decisions made in the subsequent report.
The estimates of measurement uncertainty can also be used to
assess the likely financial loss that may arise due to
misclassification. For the example used here, the OCLI method
showed a low value of loss (for example, only about £60 per
location) that can be used to indicate to the investigator, and to
the client, that the measurements are of acceptable quality,
despite a particularly high potential consequence cost of £1M.
Applying the OCLI method to other SIs may show a likelihood of
substantial losses, however, which can be used to guide the
subsequent decisions taken and to design more suitable sampling
strategies at that or similar sites.
The method has also been successfully applied to five other
commercial investigations at contrasting sites, conducted by
different consultants. The research has demonstrated that the
scientific methods used to estimate the measurement uncertainty
(DM) can be easily implemented at all sites to improve the
reliability of routine commercial SIs.
The uncertainty can be estimated for different sampling methods,
such as trial pits, window sampling and hand augering. An OCLI
package was also successfully trialed with a commercial laboratory
(ALcontrol) for one of the investigations used during this project.
This allows the investigator to easily select a small proportion of
samples to be analysed in duplicate as part of the DM, which are
then reported in a user friendly format.
Observing how the consultancies conduct their investigations
showed the very different ways in which field samples are
collected, even when using
the same method (for example, trial pits). The sampling
technique can vary greatly between different companies and
individual samplers. Often, junior, member of staff are asked to
complete important field sampling, sometimes without adequate
training.
Experimental studies have repeatedly shown that uncertainty from
the field sampling is often substantially greater than the
contribution from the laboratory analysis (Ramsey and Argyraki,
1997; Ramsey et al., 2002). Currently, attention of measurement
quality is usually confined to the chemical analysis alone, but
this finding suggests that the focus should now move to field
sampling. It is worth remembering that a measurement is only as
good as the sample upon which it is made.
One barrier to implementing methods for estimating measurement
uncertainty is the extra costs that may be required. Collaborating
with other commercial organisations during this research has shown
that sample duplicates are often routinely taken during their
investigations, as directed by the British Standard guidance (BSI,
2001).
On larger projects, different consultancies are occasionally
contracted to validate the main surveys by taking a small selection
of additional samples for comparison, such as one sample for every
20 taken. But in these cases, the comparison in measured
concentrations found between the sample duplicates are only used as
qualitative indications of data reliability. This information is
better employed by using statistical software – available for free
from the University of Sussex – that provides a quantitative
estimate of the measurement uncertainty (as shown in this case
study).
The small extra cost of applying the DM, which is required to
estimate the measurement uncertainty, was shown to be
cost-effective in producing overall savings of £18,000 at one site
and £14,300 at another.
The levels of measurement uncertainty can also vary greatly
between sites. In the six assessed, the measurement uncertainty
varied from ± 25% for lead concentrations at one site and ± 158%
for arsenic at another. Using the OCLI method before a
supplementary site survey (site six) was shown to substantially
reduce (by 76%) the expected loss from £21,000 to £5,500 per
sampling location.
Applying the OCLI method before completing a supplementary
survey was shown to be particularly beneficial for the
investigation at site six. The more optimal level of measurement
uncertainty achieved during the supplementary survey, as identified
and designed using the OCLI method, was shown to reduce potential
losses by £558,000 at one site.
The OCLI method has been proven to be a commercially valuable
decision-support tool, as it is able to estimate the probable
financial cost that may arise from the uncertainty in measurements.
The information provided by these methods are useful to a range of
stakeholders associated with the management of contaminated land.
It is argued that they can be developed further, and be adopted
more widely, during routine investigations of contaminated
land.
AcknowledgementsThis research was completed as part of the
research project Cost-Effective Investigation of Contaminated Land,
funded jointly by CL:AIRE (Research Project RP4), and DTI (Contract
number STBF/004/00034C).
The full project report will shortly be available through
CL:AIRE (Contaminated Land: Applications in the Real
Environment).
References1. K.A., Boon, P.D. Taylor, M.H. Ramsey (2007).
Estimating and optimising measurement uncertainty in environmental
monitoring: an example using six contrasting contaminated land
investigations. Geostandards and Geoanalytical Research, (In
press).2. BSI (2001). Investigation of potentially contaminated
sites – Code of Practice (BS 10175:2001), British Standards
Institute, pp. 75.3. Department for Environment, Food & Rural
Affairs, and Environment Agency (2002). Assessment of risks to
human health from land contamination: An overview of the
development of soil guideline values and related research (CLR 7).
DEFRA, and EA, pp. 32.EA (Environment Agency) (2006). 4.
Performance standard for laboratories undertaking chemical testing
of soil (MCERTs – Version 3), Environment Agency, Bristol, pp. 15.5
M.H. Ramsey, A. Argyraki (1997). Estimation of measurement
uncertainty from field sampling: Implications for the
classification of contaminated land. Science of the Total
Environment, vol. 198, pp. 243-257.6. M.H. Ramsey, P.D. Taylor, and
J.C. Lee (2002). Optimized contaminated land investigation at
minimum overall cost to achieve fitness-for-purpose, Journal of
Environmental Monitoring, vol. 4, pp. 809-814.7. P.D. Taylor, M.H.
Ramsey, P.J. Potts P. J. (2004). Balancing measurement uncertainty
against financial benefits: Comparison of in situ and ex situ
analysis of contaminated land. Environmental Science &
Technology, vol. 38, pp. 6824-6831.8. P.D. Taylor, M.H. Ramsey,
P.J. Potts (2005). Spatial contaminant heterogeneity:
quantification with scale of measurement at contrasting sites.
Journal of Environmental Monitoring, vol. 7, pp. 1364-1370.
Measurement uncertainty (smeas) (mg kg-1 Pb)
Expe
cted
loss
(£)
0
20
40
60
80
100
0 100 200 300 400 500 600 700
Optimal uncertainty497mg kg-1 Pb
Actual uncertainty200mg kg-1 Pb
Figure 3. The OCLI curve for the investigation at the
residential garden and allotment.
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