United States
Environmental Protection
Agency
is EPA Research and
Development
Sheboygan Confined Treatment Facility (CTF)
Bioremediation Workshop
Prepared for
Great Lakes National Program Office
Region V
U.S. Environmental Protection Agency
Prepared by
Environmental Research
Laboratory
Athens GA 30613
February 1992

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¦ £36b
pp(\	Sheboygan Confined Treatment Facility (CTF)
yV	Bioremediation Workshop
(oOJ
Cj-	On May 30, 1991, a workshop was convened by the EPA
q Environmental Research Laboratory, Athens, GA to review a
1 bioremediation pilot study to be conducted at the Sheboygan
$Q\ Confined Treatment Facility (CTF). The objectives of the
workshop were to :
1.	Conduct a scientific review of the work plan for the
pilot bioremediation study and provide recommendations for
enhancing the experimental design.
2.	Finalize a sampling plan for monitoring the CTF that
would produce a statistically supportable documentation of
the effectiveness of bioremediation.
3.	Set up a plan for conducting a scientific evaluation of
the monitoring data in order to determine loss of PCBs due
to biodegradation as compared to losses resulting from other
mechanisms.
Cn)
CO	Participants comprised representatives from Blasland and
Bouck (site contractors), Michigan State University, Wisconsin
^ Department of Natural Resources, EPA Region V, and the EPA
Environmental Research Laboratory - Athens. A complete list of
participants can be found in Appendix A.
vO
o
Dr. Mark Brown of Blasland and Bouck Engineers started the
meeting with a description of the bioremediation pilot study.
The opening remarks were followed by an open discussion of the
study. What follows is a summary of the workshop.
A CTF has been constructed on the premises of the Tecumseh
Products Company, Sheboygan Falls, WI by Blasland and Bouck
Engineering. The facility is constructed of steel sheet piling
(105 ft x 135 ft x 10 ft) lined with welded sheets of high-
density polyethylene into which are inlaid pipe grids for
detection of leaks and injection of amendment. The structure is
divided in half to permit testing of two treatments, and within
each half, approximately one-fourth the total area has been
segregated and reserved as a treatment control. In addition,
several materials are being tested as adsorbents for PCBs in the
outflow water.
Sediments were mechanically dredged from the Sheboygan River
and transported to the facility in leak-proof containers.
Although each load of sediment was not characterized before
deposition in the facility, comparability of material in each
treatment/control pair was attempted by delivering similar
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proportions of sediment from each of the dredging sites. Cells 1
and 2 were considered to have similar sediment/water
compositions; cells 3 and 4 also were considered to be similar.
However, cells 3 and 4 may be different from cells 1 and 2.
Cells 2 and 3 are approximately one third the size of cells 1 and
4, respectively. Cells 2 and 3 were meant to serve as controls
for cells 1 and 4, respectively. The requirements of mechanical
dredging preclude mixing of the sediments before deposition in
the CTF. Thus, the heterogeneous nature of the material in the
facility introduces high variance into measurements of baseline
levels of contaminants and responses to treatments.
The sampling of sediments in the CTF cells has been limited
to cell 4. Cell 4 was sampled by hand-held corers. A composite
sampling approach was used to determine PCB concentrations.
Composites were prepared from samples collected randomly from
among a set of systematic samples taken from the cell. The
composite samples were thoroughly mixed and then the mixture was
subsampled for analysis. Material from seven cores were mixed to
create seven composite samples. Only one subsample has been
analyzed per composite sample thus far. The data so derived were
utilized to estimate the mean PCB concentration in the cell, and
the sample variance was calculated to measure variability. The
mean PCB concentration of the composite samples was 325 +- 175
ppm. Cells 1, 2, and 3 were not sampled. This information was
used to project the number of samples needed to detect 50%
degradation in PCB levels.
Several areas within the river have been armored by Blasland
and Bouck Engineering to keep PCB contamination in place and
eliminate transport and dissolution as loss mechanisms for
sediment PCBs. Four areas of sediment having PCB concentrations
ranging from 25 to 270 ppm were armored, and removable sections
were installed for access to the underlying sediments. The rate
of degradation in the armored area may serve as an indication of
in situ rates and as a point of comparison for rates in the CTF.
The CTF was constructed while bench-scale experiments were
being conducted at the University of Michigan; the most promising
treatments from the bench-scale work are to be implemented and
tested in the pilot facility.
Aerobic experiments were conducted on partially
dechlorinated sediments and anaerobic experiments were performed
with contaminated sediments having low (10 ppm) and high (500
ppm) PCB concentrations. Results to date suggest that the major
component of the original contaminant was Aroclor 1248 and that
dechlorination is occurring at this contaminated area of concern.
Data collected from 1989 to 1991 support this suggestion since a .
plot of the number of ortho- versus meta- and para-chlorines
showed a shift away from the standard plot of Aroclor 1248.
Dechlorination occurred at the meta- and para-positions of highly
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chlorinated congeners which resulted in the accumulation of less
chlorinated ortho-substituted congeners. A faster rate of
dechlorination was observed in sediments with higher
concentrations of PCBs than the less contaminated sediments.
Only mono- and dichlorinated congeners were degraded in the
aerobic experiments, but the higher chlorinated congeners were
degraded in the anaerobic experiments.
The effect of amending anaerobic sediments (artificially
contaminated with Aroclor 1242) with inorganic and organic
nutrients also was investigated in bench scale experiments.
Experimental treatments tested included mineral (RAMM medium) and
secondary substrate (methanol and acetone) additions. There Was
little conclusive evidence that addition of organic substrate
plus RAMM medium enhanced rates of PCB (500 or 10 ppm)
dechlorination at an incubation temperature of 30 °C. The rate
observed of dechlorination was not enhanced significantly by
these two amendments, and an equivalent rate of dechlorination
was observed in the control vessels without addition of organic
substrates. Data from non-RAMM controls were not available.
Non-RAMM control experiments were initiated in May 1991.
Future experiments are designed to increase the
bioavailibility of sorbed PCBs. Surfactants, especially Triton
X-705, will be used to increase the solubility of PCBs. Methods
for transforming anaerobic sediments to aerobic conditions will
be studied since the products of anaerobic dechlorination may
serve as substrates for aerobic bacteria. In particular, the
rate and amount of hydrogen peroxide needed to transform
anaerobic to aerobic sediments will be investigated.
In discussions that followed the introduction by Dr. Mark
Brown, three major areas were addressed: the sampling design for
evaluating treatment options, the treatment options to be tested
this year, and the alternative treatments that could be pursued.
Concern was voiced by an EPA statistician and others that
composite samples obscured intersample variation and that the
intracomposite variance had to be established (by subsampling)
for valid comparisons of values.
A composite sampling approach probably can be used
advantageously for estimating mean PCB concentrations in the
CTFs, but its use must be treated carefully in order that
underestimation of variances or standard errors is avoided.
Underestimation could lead to declaring significant differences
where they do not really exist. It is important to realize that
there are different sources of variability in this experiment and
there has been no attempt to estimate or compare them. This
means that such composites cannot be used to fine-tune a proposed
design, as might be required when determining how many individual
samples should be combined per composite sample, how many
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composite samples should be formed, how many subsamples should be
collected from each, whether the mixing process is adequate, and
whether the laboratory-based 10-gram subsampling is acceptable.
A study to address all of these questions apparently would be
prohibitively expensive. (A note on composite sampling can be
found in Appendix B.)
Because details of the computations were not available at
the meeting, it is not possible to determine whether such
calculations have been correctly implemented. This should be
checked thoroughly inasmuch as small sample sizes have been
suggested as being adequate. The number of proposed composite
samples (five) does seem to be woefully small. The idea of using
a 50% reduction in PCB levels as the tolerable difference (i.e.,
a difference to be detected with a given confidence level) seems
to be too unrefined. It is a scientific question as to how long
a 50% reduction is likely to take, and whether it is of interest
to detect smaller changes. If smaller changes are of interest,
there will be a direct impact on the number of samples required.
If it is possible, available data should be utilized to
estimate variance components and/or standard errors, and
simulations should be conducted to investigate the proposed
sampling strategies. This modification was subsequently
incorporated into the sampling design. Subsequent to the meeting
Dr. Mark Brown proposed (Appendix C, Dr. Rudy Parrish's response
Appendix D) a sampling plan that would use 5 seven-core
composites from each of the cells. For one composite in each
cell, triplicate subsamples would be collected. The relative
standard deviations would be compared to determine whether an
alternative composite sampling design is needed.
Appendix B contains applicable expressions for the variance
of the estimate of the cell mean (Eq.4). This expression
involves four variance components. Fortunately, it is not
necessary to estimate these components individually in order to
obtain an estimate of the variance of the estimator; however, the
computation is facilitated by use of analysis of variance
calculations whenever the data are balanced. Balanced, here,
means that the same number of subsamples has been taken from each
composite. In the unbalanced case, it is not clear exactly what
the expressions should be, although they can probably be derived.
If the cores are sufficiently large relative to the subsamples,
then "S" will be large relative to "s", so that the quantity "(S-
s)/(S-l)" is approximately the same as "S/(S-1)M. If this is, in
fact, the case, then (EMSB - EMSJ can be used to produce an
estimate of the second term of Eq.4 that is associated with the
composite-related variance components. It seems that the
proposal is to calculate the variances of the cell means for
subsampling a single time versus subsampling three times, and
then compare the results. While that might be appropriate from a
general point of view, it might be better to attempt to estimate
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the variance components for: (1) between increments, (2)
measurement error, and (3) the nonlinear combination of
components due to imperfect compositing. The ANOVA-based
approach might work well here if the design can be balanced.
The unfortunate aspect of this approach is that design
questions cannot be rigorously addressed without a knowledge of
all of the individual variance components. Estimation of these
requires a more complex (and expensive) design. Using the
procedure above (with the simplifying assumption), one could
investigate effects of differing numbers of subsamples, since ns"
is a divisor in the second term of Eq.4. Similarly, one could
investigate the effects of differing numbers of subsamples on the
number of composite samples NrN. The effect of changing the
number of samples per composite, however, cannot be examined in
the same manner because "n" occurs nonlinearly.
It would also be helpful to consider unbalanced method in
terms of the variance components. Without that, it seems that a
better approach would be to utilize an equal number of subsamples
from each composite. Of course, it might also be possible to
derive appropriate expressions for the unbalanced case.
There are several features of the design of the CTFs that
could have an impact on any experimental study that is conducted
at the site. The CTFs are constructed asymmetrically to provide
four cells, each of a different size.
Statistical concerns include the following.
(1)	There might be a CTF-size effect that could interfere with
treatment-control or treatment-treatment comparisons.
(2)	The composition of cells 1 and 2 and that of 3 and 4 might
not be sufficiently similar to justify treating the smaller cells
as controls for the corresponding larger cells.
(3)	The sediments in the cells were not mixed prior to being
added, so that high spatial variability could be expected. There
are also varying sediment depths. This impacts the level of
sampling required for any given comparison or parameter
estimation.
(4)	Having only four cells limits capability to use experimental
designs that involve replication. A suggestion here is that
perhaps cells 1 and 2 and cells 3 and 4 may be considered to form
two blocks with two homogeneous experimental units (i.e., cells)
in each.
One further aspect of the design that should be mentioned is
that the effectiveness of a given amendment option will be
measured primarily on the basis of a change in concentration in a
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single cell over some time period, perhaps compared to similar
observations in a corresponding control cell. There is only one
experimental unit per option, so that any observed differences
may, in fact, be confounded with the composition of the cell
being used. There is no replication, which is needed to estimate
the experimental error for testing differences among treatments
and, thus, to make sound inferences.
It might be possible to improve on the limitation of having
only four compartments (cells) in the CTF. Cells 1 and 4 are
large enough that they could be subdivided to form a total of
eight cells in the facility. If the sediment composition in
these could be distributed more uniformly among such cells, there
would be wider latitude available for utilizing different
experimental designs involving more replication (thus, higher
power) and more treatments.
Several concerns were raised about the study's capability
to evaluate the potential for biodegradation of endogenous PCBs
and also to develop new techniques that will enhance
bioremediation of the Sheboygan River and Harbor area of concern.
These studies are currently going on in the laboratory of Dr.
Timothy Vogel at the University of Michigan.
The laboratory experiments with Sheboygan sediments were
conducted at 30aC. The justification for choosing this
temperature was probably to obtain somewhat faster dechlorination
rates to allow more rapid evaluation of the effects of different
amendments, etc. The optimum temperature for dechlorination by
the upper Hudson River microorganisms, however, is between 20 and
25*C, and para-dechlorinatinq activity is lost at 30*C. Thus,
conducting laboratory experiments with the Sheboygan sediments at
30°C does not necessarily yield any advantage (in terms of more
rapid assays), and has the obvious disadvantage that the results
are not directly transferable to the field. While the Blasland
and Bouck representatives did not have information on actual
environmental temperatures, the sediments in the CTF are
undoubtedly lower than 30°C. Average summer and winter
temperatures for different depths in the CTF should be determined
and future laboratory experiments conducted at comparable
temperatures ranges.
Blasland and Bouck Engneering has so far determined oil and
grease levels for only a few of the Sheboygan sediment samples.
Oil and grease have been shown to inhibit the aerobic degradation
of PCBs by reducing their bioavailability. The same is likely
true for anaerobic dechlorination. Results from Dr. John
Quensen's laboratory at Michigan State University suggest that
oil and grease levels above approximately 1,000 ug/g sediment
(dry weight) begin to decrease the rate of dechlorination.
Levels of 10,000 ug/g strongly inhibit dechlorination.
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Variations in oil and grease concentrations may account for
the various extents of Is situ dechlorination observed in the
Sheboygan sediments. The higher levels could limit dechlorination
within the CTF. Sediments should be sampled routinely for oil
and grease. If levels are, in fact, high enough to limit the
dechlorination process, greater overall rates of PCB
transformation may be achieved by cycling between aerobic and
anaerobic conditions. The oil and grease should be degraded
during the aerobic cycles.
Several treatment options to address the question of
availability of sediment-sorbed PCBs were discussed. Sediment
organic matter, especially sediment oil and grease, strongly sorb
PCBs, thus decreasing the rate of degradation. It was suggested
that these organic materials might be decreased during an aerobic
degradation period preceding the anaerobic treatment (e.g., 2
months aerobic followed by 8 months anaerobic). The sensitivity
of the dechlorinating microorganisms to such cycling would need
to be evaluated, but at least some of them are now known to be
spore formers. Moreover, brief (30 minute) aeration of Hudson
River inoculum did not diminish its subsequent activity under
anaerobic conditions.
The mechanics of altering the aeration status of the CTF
sediments were discussed; the plumbing arrangement for the
facility makes drainage difficult. Moreover, drainage water
would have to be treated before discharge. The possibility of
adding dissolved oxidants, including peroxides, as a means of
shifting Oxidation status was also addressed. The final
selection of treatment regimes for the CTF will be made pending
further discussions among representatives of Blasland and Bouck
Engineering, EPA scientists from the Office of Research and
Development, and EPA Region V representatives.
The aerobic degradation of PCBs is a co-metabolic process.
The responsible microorganisms are induced with biphenyl and in
some cases with mono-chlorobiphenyls. An attractive feature of
an anaerobic-aerobic biotreatment sequence is that levels of
mono-chlorobiphenyls may be achieved anaerobically that can
induce the co-metabolism of other congeners when conditions
become aerobic.
The Sheboygan sediments, however, do not appear to
accumulate the high levels of mono-chlorobiphenyls required.
Thus aerobic degradation of the dechlorination products, in the
absence of another inducer such as biphenyl, is likely to be
quite limited. A possible solution is to inoculate the sediments
with microorganisms that produce more monochlorobiphenyls, such
as upper Hudson River microorganisms. This, however, may not be
easily achieved< Drs. Vogel and Nies from the University of
Michigan attempted such an inoculation by mixing Hudson River and
Sheboygan sediments. They found this to have no effect on the
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extent of dechlorination. Similar results were obtained by Dr.
John Quensen by mixing inocula from the upper Hudson River and
Silver Lake. The Silver Lake microorganisms normally produce high
levels of 2,4,2',4'-tetrachlorobiphenyl from the dechlorination
of Aroclor 1260. Rather than dechlorinating the 2,4,2•,4'-PCB
produced by the Silver Lake microorganisms, the upper Hudson
River microorganisms apparently inhibited the activity of the
Silver Lake microorganisms.
Further attempts should be made at inoculating the Sheboygan
sediments to achieve higher levels of mono-chlorobiphenyls.
Sediments from sites other than the upper Hudson River should be
tried. More work needs to be done to further the understanding
of compatibility and incompatibility of different PCB
dechlorinating activities.
Blasland and Bouck engineers proposed several criteria
besides total PCB concentration for assessing the progress of
biotreatment in the CTF and armored areas in the river. These
included toxic equivalency, total concentration of key
dechlorination products, and average number of meta- and para-
chlorines per biphenyl. These criteria emphasize different
parameters, but are actually different ways of reducing the raw
data consisting of congener-specific PCB analysis. The first two
criteria are related to health and environmental hazards, whereas
the last two are related to the extent of anaerobic
dechlorination. All the above criteria should be considered (and
all raw data retained for further possible evaluation after the
experiment is finished).
It appears that different criteria should be applied to the
CTF and the armored areas. Armoring is essentially a technique
for reducing risk (by decreasing the chance of exposure).
Decreases in toxic and bioaccumulative congeners would further
reduce risk, and should be the major criteria applied to the
armored areas. In the case of the CTF, there is the additional
goal of reducing total PCB concentrations. This means creating
aerobic conditions at the most effective time. Therefore,
criteria related to the progress of anaerobic dechlorination are
also pertinent. The observed increase in the proportion of mono-
chlorinated biphenyls is probably most important as these
congeners may induce the aerobic co-metabolism of other PCBs
still present as indicated previously. One could go a step
further than Blasland and Bouck engineers proposed and, based on
the relative degradabilities reported in the literature for the
various congeners remaining, calculate the expected decrease in
total concentration to be expected if a switch were made to
aerobic conditions.
PCB degradation rates appear to be slightly higher In situ
than in the CTF, although, as mentioned, the values from the
armored areas do not reflect the overall variability of sediment
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rates, which may not be statistically different from the CTF
rates. Given the few openings and the small areas that are
intensively sampled at the armored sites, the data from these
areas must be interpreted cautiously.
In summary the CTF provides a valuable outside laboratory
for investigating technologies for the biological treatment of
PCBs. One should be cautioned, however, that the selection of an
appropriate sampling design is critical for the development of
sufficient data to adequately evaluate each treatment option.
Because of the size and construction of the facility, side-by-
side testing of technologies would be difficult. Current
research would suggest that several treatment options are
available for testing. These options include addition of
inorganic nutrients, organic nutrients, aerobic PCB-degrading
bacteria, and the use of alternating aerobic/anaerobic systems.
The proper selection of test conditions should result from the
careful analysis of laboratory data.
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Appendix A. List of Participants
Rebecca Adams
Technology Applications, Inc.
c/o Environmental Research Laboratory
U s EPA
Athens GA 30613-0801
404-546-3517
Rochelle Araujo
Environmental Research Laboratory
U. S. EPA
Athens GA 30613-0801
404-546-3468
Mark Brown
Blasland & Bouck Engineers, P.C.
6723 Towpath Road, Box 66
Syracuse NY 13214
315-446-9120
Dave Cowgill
Great Lakes National Program Office
230 South Dearborn
Chicago, IL 60604
312-353-3576
Bonnie Eleder
Region V, US EPA
230 South Dearborn Street
Chicago, IL 60604
312-886-4885
Terry Evanson
WI Department of Natural
Resources
101 S. Webster
PO Box 7921
Madison, WI 53707
608-266-0941
Dawn Fester
Blasland & Bouck Engineers, P.C.
6723 Towpath Road, Box 66
Syracuse NY 13214
325-446-9120
Jack Jones
Environmental Research Laboratory
U.S. EPA
Athens GA 30613-0801
404-546-3468
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Rudy Parrish
Computer Sciences Corp.
c/o Environmental Research Laboratory
U.S. EPA
Athens GA 30613-0801
404-546-3604
John Quenson
Department of Crop and Soil Sciences
Michigan State University
East Lansing MI 48823-1325
517-355-9285
John Rogers
Environmental Research Laboratory
U.S. EPA
Athens 6A 30613-0801
404-546-3592
Robin Schmidt
WI Department of Natural
Resources
101 S. Webster
PO BOX 7921
Madison, WI 53707
608-267-7569
Patricia Van Hoof
University of Georgia
c/o Environmental Research Laboratory
U.S.EPA
Athens GA 30613-0801
404-546-3349
Daniel Wubah
University of Georgia
Environmental Research Laboratory
U.S. EPA
Athens GA 30613-0801
404-546-3182
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Appendix B. A Note on Composite Sampling
Suppose r x n equal-sized portions of sediment material
(increments) are drawn randomly from a cell. From these, £
composites are formed by randomly partitioning the set of
increments into £ subsets of n increments each and physically
mixing the increments in each subset. Then, s subsamples are
drawn randomly from each composite, and £ analyses are run on
each subsample. Each composite is assumed to contain enough
material for S subsamples. (If the subsample size were the same
as the increment size, then £ = n*)
To mathematically represent the variables involved in the
composite sampling model, the following notation is defined:
= the proportion of the jth subsample from the ith
composite that comes from the 1th increment in that
composite,
¦ the concentration value associated with the portion of
the 1th increment that appears in the jth subsample from the
1th composite.
Each variable a,^ has mean na and variance cra2. The
variables 2£fjl have equal means and variances ax2 + a J, where ax2
is the between-increment variance and cth2 is the within-increment
variance subsequent to compositing and subsampling.
In different composites, the variables are uncorrelated,
but they are correlated within the same increment, in the same
subsample, and in different increments and subsamples within a
composite. Similarly, the variables 2jji are correlated within
the same increment, in the same subsample, and in different
increments and subsamples within a composite. Elder et al.
(1980) provided explicit expressions for these covariances.
The concentration value associated with the jth subsample
from the 1th composite is based on contributions from all
increments in that composite. It can be expressed as the
weighted mean
The value observed in the laboratory from the Jcth analysis,
however, is
D
(l)
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zijk ~ y±j + eijk	(2)
where is the measurement error associated with the Jcth test
on the j.th subsample from the 1th composite. The variance of ejjk
is defined as ae2. If the analysis procedure requires that
additional subsampling be done, then an additional variance
component should be incorporated.
The estimator of px for composite sampling is
* = ^ £ zij*/rst ¦	<3>
As a result of the covariance structures for the 2{jL and X,- ,• t
variables, the variance for this estimator involves four
individual variance components. Elder et al. (1980) provided
Vaz{z) =ol/(rn) + [ (S-s) / (5-1) ] [ol/n +
no\ (o2x + oI)]/(ts) + al/ (rst) .
Also, they established that the expected mean squares (between
groups, within groups, and for error) associated with the
analysis of variance for the composite sample groups are:
EMSb = (8t/n)al * [t(S-s)/(S-l)] [a2w/n + na\(al + al)) * o| (5)
EMS„ = [ tS/ (5-1) ] [a2w/n + nal(a2x + al) ] + o|	(6)
EMSb = al
(7)
Clearly, the variance of the estimator £ is equivalent to the
value EMS,/(rst). Thus, the standard error of the estimate of nx
can be obtained from a standard analysis of variance computation.
References
Brown, G. H. and N. I. Fisher. 1972. Subsampling a mixture of
sampled material. Technometrics 14(3): 663-668.
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Elder, Robert S., William 0, Thompson, and Raymond H. Myers.
1980. Properties of composite sampling procedures. Technometrics
22(2): 179-186.
Gilbert, Richard O. 1987. Statistical Methods for Environmental
Pollution Monitoring. Van Nostrand Reinhold Company, New York.
320 pp.
Parrish, R. S., G. 0. Ware, C. N. Smith, and P. A. Banks. 1990.
Composite sampling techniques for determining pesticide
concentrations. Proceedings of the KSU Conference on Applied
Statistics in Agriculture. Manhattan, KS.
Rhode, Charles A. 1976. Composite sampling. Biometrics 32, 273-
282.
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APPENDIX C
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BLASLAND & BOUCK ENGINEERS; P.C.
ENGINEERS iOEOSCIENTISTS
6723 Towpath Road, Bpx 66, Syracuse, New York 13214 (315) 448-9120
FAX: (315) 449-0017
June 13, 1991
Rudy Parrish
Environmental Research Laboratory
U.S. EPA
Athens, GA 30613
Re: Bioremediation Pilot Study Work
Plan
File: 176.06 #2
Dear Mr. Parrish:
We appreciate your review and discussion of sampling design as contained
in the Sheboygan River and Harbor Bioremediation Pilot Study Work Plan.
At the fecent workshop in Atlanta it appeared that you and I reached an
understanding that composite sampling was an acceptable approach. It was
clear, however, that your acceptance of the approach was contingent upon an
explicit assessment of the errors associated with subsampllng the composite
samples. This assessment may be useful in revising our sampling approach
if the errors are large relative to the errors associated with our current
methods. If the errors are large, we would conduct a numerical analysis to
reassess the sampling requirements balancing the number of seven-core
composite samples against the number of composite subsamples tc minimize
costs for a specified hypothesis-testing power.
We are not sure that some of the other workshop attendees are as yet
comfortable with use of composite samples for gauging the overall progress
of the experiment^). A comment which was repeatedly offered Is that our
compositing scheme obscured the real spatial variability of PCB concentrations
within the cell and that this variability needs to be explicitly considered. We
do not dispute the first contention. On the contrary, the intended purpose
of compositing was to cope with the expected high degree ot spatial
variability and the expense of analyzing large numbers of samples.
For the benefit ot others, we are providing as an attachment, an example of
how one might-approach a sampling design from error estimates for various
contributors. The approach Illustrated by Neptune et al. can be compared,
and contrasted to our approach in developing a sampling design. The
fundamental objective of both approaches was to produce a sampling design
with an acceptable power for hypothesis testing. The essential difference is
that by estimating the individual errors, the EPA authors could optimize the
number of individual samples per composite sample and the numbers of
16
Syracuse, NY • Rochester, NY • While Plains, NY • Syos.net, NY • Edison, NJ » Washington, DC ~ Columbus. OH
Boca Raton, FL • Tampa. FL * Orlando. FL

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Rudy Parrish
June 13, 1991
Page 2
composite samples per area (note that sampling errors associated with
subsampiing of composite samples Were ignored ior composites of live or
(ewer samples) to produce the most cost-effective design. In contrast, we
presumed that the spatial variability would be high and made an educated
guess as to the number of cores required per composite sample. Based
upon the actual distribution of PCB results for the composite samples, we
then back calculated the requisite number of composite samples for a
specilied power. Having obscured the actual spatial variability (in this case,
the distribution of average PCB concentrations in individual cores), we have
no basis for altering the number of cores within a composite sample. So,
for the time being, we must stay with the seven-core composites.
For the upcoming sampling, we propose to collect five seven-core composite
samples from each of the four cells. In addition, we propose to submit
triplicate subsamples of one composite sample from each of the three cells.
The relative standard deviations of each of these composites would reflect not
only our field errors in subsampiing composite samples, but also subsampiing
errors by the laboratory and subsequent analytical errors. If these relative
standard deviations are comparable to the relative standard deviations among
composite samples within a cell, we would reconsider our sampling design
based upon a numerical analysis (computer simulation ot error distributions
using random normal deviates). We would potentially recommend a revised
balance of composite samples and subsamples of ^composite samples to
minimize the costs for an acceptable hypothesis-testing power.
Please call me if you Wish to discuss this. We look forward to continued
cooperation with EPA's ARCS program.
Very truly yours,
:rs, P.O.
Manager
MPB/nlg
Attachment
cc.: Bonnie L. Eieder, Environmental Protection Agency
Robin R. Schmidt, Wisconsin Department o( Natural Resources
Dawn S. Foster, P.E., Blasland & Bouck Engineers, P.C.
John Rogers* Ph.D., Environmental Protection Agency
Chad Jafvert, Ph.D., Environmental Protection Agency
17
* ii: I « fcotirf ENGINEERS, P.C.

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APPENDIX D
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Computer Sciences Corporation
c/o U.S. Environmental Protection Agency
.* College Station Road
't'Athens, OA 30613-7799
• Phone: 404-546-3604
22 July 1991
Mark Brown, Ph.D.
Blasland & Bouck Engineers, P.O.
6723 Towpath Road, Box 68
Syracuse, NY 13214
Dear Mark:
Since talking with you a few weeks ago, I spoke briefly with John Rogers about the Sheboygan
project and your letter to me of June 13. Earlier, I provided John With summary fcommerrts pertaining to
the May 30th meeting In Atlanta; enclosed Is a copy of that memorandum tor your reference. It had been
my understanding that John Would be Communicating those and other comments to you.
The foflowtng remarks address your proposed Work, as described In your letter. I realize that you
may already have begun this work.
Your plan Indicates that you will use 6 seven-core composites from each of the cells, and for one
composite In each cell, you wilt collect tripicate subsamples. Then, compare relative standard deviations
to determine whether an alternative composite sampling design Is needed. The enclosed "Note on
Composite Sampling* contains applicable expressions for the variance of the estimate of the cell mean
(Eq.4). This expression Involves four variance components. Fortunately, It Is not necessary to estimate
these components Individually In order to obtain an estimate of the variance of the estimator, however, the
computation Is faclltated by use of analysis of variance Calculations whenever the data are balanced.
Balanced, here, means that the same number of subsamples has been taken from each composite. In the
unbalanced case, ft is not dear exactly what the expressions should be. although they can probably be
derived. H the cores are sufficiently large relative to the subsamples, then *S* wttt be large relative to*s\ so
that the quantity *(S-s)/(S-1)' Is approximately the same as *8/(S-1)\ If this Is, tot tad, the case, then (EMSB
• EMSJ can be used to produce an estimate of the second term of Eq.4 that Is associated with the
composite-related variance components. H seems that your proposal Is to calculate the variances of the
cell means for subsampilng a single time versus subsampllng three times, and then compare. While that
might be appropriate from a general point of view, it might be better to attempt to estimate the variance
components for: (1) between increments, (2) measurement error, and (3) the nonlinear combination of
components due to imperfect compositing. The ANOVA-based approach might work well here If the design
can be made balanced.
The unfortunate aspect of this is that design questions cannot be rigorously addressed without a
knowledge of all of the individual variance components. Estimation of these requires a more complex (and
expensive) design. Using the procedure above (With the simplifying assumption), one could Investigate
effects of differing numbers of subsamples, since V is a divisor in the second term of Eq.4. SlmBariy, for
the number of composite samples V. The affect of changing the number of samples per composite,
however, cannot be examined in the same manner because "n* occurs honllnearty.
Your procedure, I think, wotid be comparing two expressions based on Eq.4. it would be helpful
to consider your method In terms of the variance components. Without that, it seems to me that a better
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approach would be to LJtBize an equal number of lubsamples from each composite. Of course. It might also
be possible to derive appropriate expressions for the unbalanced case.
Hopefully, these comments w>l be of Interest and of use to you. Please let me know If you would
Rke to discuss this further.
Cordially yours,
Rudolph S. Parrlsh, Ph.D.
Statistician
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June 10,1991
To: John Rogers
Fm: Rudy Punish
Re: Sheboygan Bioremediation Meeting Held in Atlanta May 30,1991
As you requested, this It to summarfce my impressions of the Sheboygan bioremediation work discussed at the
meeting in Atlanta od May 30. these remarks are limited to statistical aspects of the experimental design
proposed for the pilot study.
Design of confined treatment facility (CTF)
There are Several features of the design of the CTFs that could have an impact on any experimental study that
is conducted at the site. The CTTFs art constructed asymmetrically to provide four cells, each of a different size.
It was indicated that cells 1 and 2 have generally the same sediment/water composition, and similarly for cells
3 and 4. Cell 2 is of a size that is in approximately a 13 ratio to cell L Similarly, the size of cell 3 is in a 13
ratio to that of cell 4. Cells 2 and 3 were meant to serve as controls for cells 1 and 4, respectively.
Statistical concerns include the following.
(1)	There might be a CTF-size effect that cotild interfere with treatment-control or treatment-treatment
comparisons.
(2)	The composition of cells 1 and 2 and that of 3 and 4 might not be sufficiently similar to justify
treating the smaller cells as controls for the corresponding larger cells.
(3)	The sediments in the cells were not mixed prior to being added, so that high spatial variability could
be expected. There are also varying sediment depths. This impacts the level of sampling required for
any given comparison or parameter estimation.
(4)	Having only 4 cells limits capability to use experimental designs that involve replication. A
suggestion here is that perhaps cells 1 and 2 and cells 3 and 4 may be considered to form two blocks
with two homogeneous experimental units (ie^ cells) in each.
Sampling designs
Tb* sampling of sediments in the OTP cells appears to have been limited to cell 4. The sampling
approach has involved composite sampling techniques wherein several samples are collected randomly
from among a set of systematic samples taken from the Cell, the samples are combined physically, and
then the mixture is subsampled. Only one subsample has been utilized per composite sample thus far.
The data so derived were utilized to estimate the mean concentration in the tell, and the sample
variance was calculated to measure variability. This information was used to project sample sizes
needed to detect 50% degradation in PCS levels.
Because details of the computations Were not available at the meeting, it is not possible to determine
whether such calculations have been Correctly implemented. This should be checked thoroughly
inasmuch as small sample sizes have beefl Suggested as being adequate. The number of proposed
composite samples (five) does seem to be Woefully too small. The idea of using a 50% reduction in
PCB levels as the tolerable difference (Le* 1 difference to be detected with a given confidence level)
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seems to be too unrefined. It is", a scientific question as to how long a 50% (eduction is likely to take,
and whether it is of interest to (feted smaller changes. If smaller changes are of interest, there will be
a direct impact on the number of. samples required.
A composite sampling approach pfpbably can be used advantageously for estimating means in the CTFs,
but its use must be treated carefully in order that underestimation of variances or standard errors is
avoided, which could lead to declaring significant differences where they do not really exist. It is
important to realize that there are different sources of variability in this experiment and there has been
bo attempt to estimate or compare them. This means that such components cannot be used to fine-tune
a proposed design, as might be required when determining how many individual samples should be
combined per composite sample, how many composite samples should be formed, how many subsamples
should be collected from each, whether the mixing process is adequate, and whether the laboratory-
based 10-gram subsampling is acceptable. A Study to address all of these questions apparently would
be prohibitively expensive. (A note on composite sampling is attached.)
If it is possible, available data should be Utilized to estimate Variance components and/or standard
errors, and simulations should be conducted to investigate the proposed sampling strategies.
Modification of the CTF
One further aspect bf the design that Should be mentioned is that the effectiveness of a given
amendment option will be measured primarily on the basis of a change in Concentration in a single cell
over some time period, perhaps Compared to similar observations in a corresponding control cell. There
essentially is only one experimental Unit per Option, so that any observed differences may, in fact, be
confounded with the composition of the cell being used. There is no real replication, Which is needed
to estimate the experimental error for testing differences among treatments and, thus, to make sound
inferences.
It might be possible to improve on the limitation of having only four compartments (cells) in the CTF.
Cells 1 and 4 are large enough that they could be subdivided to form a total of eight cells in the facility.
If the sediment composition in these could be distributed more uniformly among such cells, there would
be wider latitude available for utilizing different experimental designs involving more replication (thus,
higher power) and more treatments.
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A^Note on Composite Sampling
Suppose rxn equal-sized portions of sediment material (increment*) are drawn randomly from a cell.
From these, t composites are formed bytandomly partitioning the set of increments bto t subsets of n
increments each and physically mixing the increments in each subset, then, I subsamples are drawn randomly
from each composite, and (analyses are rati on eich subsample. Each composite is assumed to contain enough
material for S subsamples. (If the Subsample size Were the same as the increment size, then 5 ¦ n.)
To mathematically represent the variables Involved In the composite sampling model, the following
notation is defined:
0p * the proportion tf the /th subsample from the Ith Composite that comes from the /th increment in
font composite,
X9 « the concentration Value associated with the portion of the /th increment that appears b the/th
subsample from the Ith composite.
Each variable has mean /*, and variance the variables have equal means p, and variances
of 4 where o* is the between-ittaement variance and a, is the within-increment variance subsequent to
compositing and subsfcmpling.
In different composites, the variables fly ate ttncorrelated, but they are correlated within the same
bcrement, b the same subsample, and b different increments and subsamples within a composite. Similarly,
the variables X9 are correlated within the same bcrement, b the same subsample, and b different bcrements
and subsamples within a composite. Elder et el. (1980) provided explicit expressions for these covariances.
The concentration value associated with the /th subsample from the /th composite is based on
contributions from all bcrements b that composite. It can be expressed as the weighted mean
where e. is the measurement error associated with the Jtth test on the /th subsample from the Ith composite.
The variance of e* is defined as If the analysis procedure requires that additional subsampling be done,
then an additional variance component should be bcorporated.
The estimator of ^ for composite sampling Is
(1)
The value observed b the laboratory from the tth analysis, however, is

(2)
f t t

l-l M ft-i
<3)
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As a result of the covariance structures lor the and X$ Variables, the variance for this estimator involves four
individual variance components. Elder et ai. (1980) provided
(to© . aim ~ l(.Ss)t(S-molln * no] (oj ~ ®i)V(rs) ~ o]l(rst). «>
V S
Also, they established that the expected mean squares (between groups, within groups, and for error) associated
with the analysis of variance for the composite sample groups are:
EMSt * (siln)*\ ~ [KS-sW-mol/n ~ no\(p\ + oj)] «¦ o]	<*>
EMSW m ItSKS-l)][olln 4- «o*(o* + a2J] + o]	<«)
EMSs - «rj	<*>
Clearly, the variance of the estimator I is equivalent to the value EMSB/(rst). Thus, the standard error of the
estimate of /<, can be obtained from a standard analysis of variance computation.
References
Brown, O. H. and N. L Fisher. 1972. Subsampling a mixture of sampled material Technometrics 14(3): 663-668.
Elder, Robert S., William O, Thompson, and Raymond H. Myers. 1980. Properties of composite sampling
procedures. Technometrics 22(2): 179-186.
Gilbert, Richard 0.1987. Statistical Methods for Environmental Pollution Monitoring, Van Nostrand Reinhold
Company, New York. 320 pp.
Parrish, R. S., O. O. Ware, C. N. Smith, and P. A. Banks. 1990. Composite templing techniques for determining
pesticide concentrations. Proceedings of the KSU Conference on Applied Statistics in Agriculture! Manhattan, KS.
Rhode, Charles A. 1976. Composite sampling. Biometrics 32,273-281
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