OSHA's risk assessment assumes that silica exposures cause lung cancer
in the absence of silicosis.  OSHA's risk assessment assumes there is no
exposure threshold above 100 µg/m3 for any adverse health effect of
silica exposure.

		OSHA's risk assessment assumes a linear exposure-response relation at
low exposure levels, rather than considering possible thresholds or
J-shaped exposure-response relations.  OSHA's risk assessment fails to
model exposure estimation error.  OSHA's projection of renal disease
mortality is based on limited data from studies with large exposure
uncertainties.  It runs counter to numerous studies finding no causal
association between silica exposure and renal disease mortality.

		Finally, OSHA's risk assessment and the studies on which it is based
fail to control a number of biases that Dr. Tony Cox discussed in his
testimony last Wednesday.  These are listed on the slide.  I don't think
I have to go into them in detail because he went through them fairly
carefully last week.

		Now, I'm going to turn the microphone over, such as it is, to Sam
Boxerman, who wants to talk about technological and economic
feasibility.

		MR. BOXERMAN:  Thanks, Neil.  Yes, I'm going to briefly address the
points we've raised on technological and economic feasibility.

		The starting point, I think, is that the burden of proof in this
matter, on these issues, rests with OSHA to demonstrate the
technological and economic feasibility of the proposed standard on an
industry-by-industry basis.  Now, with regard to technological
feasibility, you've already heard presentations.  Comments have been
submitted by individual industries about the technological feasibility
of the PEL.  We support the submissions of the ones we've noted.

		But beyond that, and really the focus of the panel's consideration of
technological feasibility, is that exposure measurements at the level of
the PEL must be accurate and reliable.  You'll hear testimony.  You've
already seen comments on why we submit that it is neither accurate nor
reliable in this circumstance.

		With regard to economic feasibility, number one, I think it's pretty
well established a standard is not economically feasible if compliance
costs may threaten the existence, competitive structure, or long-term
profitability of an industry.  Likewise, that is one of OSHA's burdens
to establish economic feasibility in meeting that test.

		Now, the rule of thumb that OSHA has used to screen for economic
feasibility has involved this comparison in annualized compliance costs
to an industry's revenues profits, the 1 percent of revenue and 10
percent of profits.  In evaluating that analysis, we submit very
strongly, and you'll hear additional testimony about this, that the full
compliance cost of the proposed standard should be compared to the
industry's revenue and profit figures.

		I mean, after all, it is the full cost that facilities will actually
incur if they have to comply with proposed standard.  It's the full
costs that are going to impact a firm's profits, that will impact a
firm's financial viability.  It's the full costs that are going to
affect competitive structure in an industry, long-term profitability.

		It simply belies common sense to use anything other than
full-compliance cost.  As Lee even analyzed what OSHA has done, it has
excluded a very significant portion of the cost in its analysis.  And
Mr. Waggener and Mr. Sessions will discuss that further at length.

		With regard to the specifics of the analysis that we have done, in
comparison to OSHA, who estimated for 19 of the general industry
sectors, an incremental compliance cost in the range of $100 million.
After making adjustments and corrections to OSHA's cost model, URS has
estimated an annualized compliance cost in those same 19 general
industry sectors to be $6.1 billion on a full-cost basis and $4.7
billion on an incremental-cost basis.  That's a combination of the
engineering cost and the cost associated with provisions.

		Now, there are a number of reasons that Mr. Waggener will summarize
in his testimony.  We've listed a few of them on this slide, but I'll
leave it to his expertise to provide those explanations to you in more
detail.

		So what's the result of this gross underestimate by OSHA of the cost
associated with this standard?  We submit that it's the economic impact
assessment as a result is not credible.  It's predicated on a gross
underestimated compliance cost.  But, moreover, as we'll hear further
from Mr. Sessions, it's comparison to revenue and profits from a time
period as many as 14 years ago.  We would submit that's not
representative of how that standard is going to be implemented, if it is
implemented, going forward.

		So when the proper cost numbers are used and an appropriate
methodology is applied, we have found that the rule of thumb for a
profit and revenue is exceeded in just about every general industry
sector.  Indeed, for many industry sectors, the ratio to profits is so
high that the proposed standard can be judged to be economically
infeasible without further analysis.

		MR. KING:  I'm going to just turn back just before we conclude to the
issue of measurability.  The data that OSHA and others have presented in
this proceeding show that employers will not be able to obtain accurate
and reliable measurements when respirable crystalline silica levels are
at or below 50 µg/m3.

		OSHA relies on a March 2013 study conducted at its Salt Lake Technical
Center.  But that study is not representative of precision in results
from real world sampling and analysis.  In fact, it's not even a
representative of precision in analyses conducted at the Salt Lake
Technical Center itself.

		It fails to account for several sources of analytical error, and it
does not reflect inter-laboratory variability.  Moreover, it is far out
of sync with the variability reflected in results from the American
Industrial Hygiene Association's PAT Program, where the effects of
interfering minerals and inter-lab variability are taken into account.

		Confirmation of this point is provided by the results of a blinded
performance study of five commercial labs sponsored by the ACC
Crystalline Silica Panel.  In that study, which will be described at
greater length later this afternoon by Mr. Bailey and Dr. Lee, the
analytical results for respirable crystalline silica filter loadings
that are equivalent to eight-hour personal samples collected at exposure
levels of 25 and 50 µg/m3 clearly were not sufficiently accurate and
precise to allow reliable determinations of whether the proposed PEL and
action level were being exceeded.

		In conclusion, our view is that OSHA has not carried its burden of
showing that there's a significant risk at the level of the current
general industry PEL of 100 µg/m3, or that any such risk would be
substantially reduced by lowering the PEL to 50 µg/m3.  Nor has OSHA
shown that the proposed standard would be economically feasible in
general industry, or that worker exposures at a level of 50 µg/m3 and
below can be reliably measured within an acceptable degree of accuracy
and precision.

		With that, we're going to turn the table over to Dr. Peter Morfeld,
who will discuss the issues of epidemiology.  Peter, why don't you come
up here?

		(Background comments.)

		JUDGE SOLOMON:  Technical difficulties beyond our control.

		MR. KING:  We insist on more time to allow for this.

		(Laughter.)

		UNIDENTIFIED SPEAKER:  It's not our computer.

		DR. MORFELD:  So yes, Your Honor, ladies and gentlemen, colleagues, my
name is Peter Morfeld.  I'm head of the Institute for Occupational
Epidemiology and Risk Assessment of Evonik Industries, who is in a
public-private partnership with Cologne University.  So I'm
simultaneously at the university and lecturing there.  I'm a member of
the medical faculty.

		I'm the field for dust epidemiology for more than 20 years.  I
submitted already a detailed written testimony on 7th of February of
2014.  My comments deal mainly with the mechanism of action, lung cancer
and silicosis, lung risks and threshold, silicosis risks and thresholds,
renal disease mortality and nonmalignant respiratory diseases, measures
for a variation of health effects.  And I will summarize some of the
main points.

		First, I'd like to talk about mechanism of lung cancer.  There is one
current and very interesting paper published by Paul Borm and colleagues
in 2011 in the peer-reviewed literature, which is missing in OSHA's
documentation, which makes clear what the potential mechanism may be how
interested in silica dust can induce lung cancer.

		So this paper makes clear, by a comparison and review of all evidence
available, that the mechanism that is identified is secondary
inflammation-driven genotoxicity.  This is indirect via inflammation.
In particular, there are no crystalline silica particles found in the
nucleus of the cells.  There is nothing going on with particles in the
epithelial cells inside the lung.

		So these findings from the mechanistic point of view clearly argue in
favor of a threshold for any causal association between silica exposure
and the risk of lung cancer.

		I'd just like to note that this is widely accepted in the scientific
community.  I just cite here two longer papers Scribe (ph.) 1999 based
on MAK commission in Germany and the SCOEL 2002, which is the threshold
limit value committee of the European union.  If I understand this
correctly, OSHA appears to share this view, so in the proposal, we are
making a passage about that.  I understand this as a support of this.

		So from this point of view, I'd like to look at lung cancer
epidemiology.  Silicosis, I think this is clearly a primary effect of
excess exposures to respirable crystalline silica, resulting from
inflammation in the lungs.  This is the same principal mechanism that is
also discussed for the risk in lung cancer.

		There is absolutely no doubt that silicotics are at advanced risk of
developing lung cancer.  There is still a lot of debate how silicosis is
linked to lung cancer mechanistically.  The role is unclear.  There are
a lot of papers about this.

		It could be that silicosis is a kind of biomarker in-between, between
the exposure and the lung cancer.  It can, of course, also give us
information about this condition.

		Importantly for the discussion, when you look through all the papers
published, it appears that the subjects who did contract silicosis
appear to have no excess risk of lung cancer.  Though this was
demonstrated in a meta-analysis and, in particular, this is so when you
take smoking habits into account.  So evidence is still missing that
crystalline silica dust per se, without having developed silicosis, is
producing an excess in lung cancer risk.

		I know that there's a new Chinese study published in 2013, which is
cited in some papers as giving new information about the relationship.
I'd like to inform you that there is a major drawback with this 2013
study.  This study did not adjust for occupational confounders.

		Why is this important?  There is a case control study published on the
same study group, Chen et al., 2007, that made very clear that pottery
workers were exposed to a higher level of PRHs.  Some iron and copper
mines are also included in this new study at higher levels of PRHs --

		In this case control study, the authors showed that they showed a
clear association with crystalline silica dust exposure and the lung
cancer mortality.  But this association is gone after adjustment.

		So it is unclear to me why this paper is not really discussed in the
Liu et al. and why the authors did not address this point.  This is
something that is already under consideration among the different author
groups who have dealt with this study.

		And so, I'd like to inform you that the co-authors tried a reanalysis
to try to clarify this confounder problem.  So without having clarified
this major problem, I think it is really premature to draw any
conclusions from this study, as done in the Seened (ph.) et al. paper
that was published recently.

		One pivotal study that is really important, if you discuss lung
cancer, is the Steenland et al., 2001.  Kyle Steenland pooled 10 --
cohorts, more than 60,000 workers.  And looking at the results, he
judged that crystalline silica dust is only a weak carcinogenic in
comparison to well-established ones when looking at milligrams per cubic
meter.

		However, there is a lot of heterogeneity involved.  This heterogeneity
cannot be fully accessed statistically by doing it in tests.  You have
to look into details of comparison.  And it's easily seen that there is
a big heterogeneity within the study when you compare the aboveground
workers with the underground workers.  The underground had much less
risk in lung cancer.

		If you put this together, then this study demonstrates excess risk
convincingly only at high exposures greater than 6 mg per cubic meter
years.  Furthermore, the SMR is rather low.  It's in 20 percent excess.
This study does not account for smoking.  And, of course, this is no
silicosis information involved, so the role of silicosis is unclear.

		We have a new study now from Germany from 2012, which is of the same
size, almost 60,000 German uranium miners.  What is very interesting is
that this study can rely on a very good assessment of exposures, and
there were extensive side-by-side measurements done with original
historical equipment so that you can really see what happened in the
'50s and in the '60s.

		And after adjustment for a radon and arsenic exposures, a piecewise
linear spline function fit to the data showed a naught at 10 mg per
cubic meter years.  This means this study provided evidence of a
threshold at 10 mg per cubic meter years and supports the view that
excess lung cancer risk occur only at high crystalline silica exposures.
 This study also is not mentioned in the OSHA document.

		What is going on in the European discussions?  Based on the
considerations of mode of action, I've discussed first so the Borm paper
and others, and the clear experience of elevated lung cancer among
silicotics, but no such observations among workers without silicosis.
The German committees, the U.K. -- the HSE -- and the European committee
argued that minimizing silicosis could also minimize lung cancer risks
due to crystalline silica dust.

		OSHA presented estimates that 45 years of occupational exposure to
crystalline silica dust, at the level of 0.1 mg/m3 will increase the
risk of lung cancer by an amount between 13 of 1,000 to 60 of 1,000.  So
because these analyses ignore the concept of thresholds and, of course,
do not take into account the role of silicosis, I believe that these
estimates are unreliable.

		Now I'd like to go to silicosis.  It is clear that no other malignant
health effect is associated so clearly with crystalline silica dust
exposure and specific as silicosis.  So if you look into OSHA's
document, OSHA says that there is little quantitative data available
with which to estimate a threshold exposure.

		And they point at the paper from Eileen Kuempel from NIOSH in 2001,
who used rat-based toxicokinetic-toxicodynamic models.  I'd like to
comment that these models, of course, are based on what we call
transitional toxicology.  This kind of branch is critically discussed by
recent papers.  Can we really rely on these translations from rat
experiments into humans?

		Importantly, Steven and Patel (ph.), in their analysis, they tried to
use this model of Kuempel and showed that there is no better
relationship to lung cancer or silicosis than modeling, as it's usually
done in epidemiology with cumulative exposures.  In particular, this
study did not search for concentration thresholds.

		I'd like to discuss whether this is really the only information we
have about the potential threshold for silicosis.  I'd like to show some
slides about a new study we did in Germany about a large porcelain
cohort with workers employed at more than 100 porcelain manufacturing
plants who attended a medical screening program in 1985 up to 1987.

		Silicosis assessment was based on more than 120,000 radiographs.  We
defined the endpoint as profusion score at least 1/1 ILO 1980 on the
basis of a new consensus reason.

		So here is an overview of the dust exposures.  More than 17,000
workers were involved.  Just 40 incident cases popped up.  And what you
see, the mean of the cumulative exposures measured in milligram per
cubic meter years is at 1.5 among the controls, so the non-silicotics.
And it is, what, 9 among the incident cases, so very clear relationship
between the dust exposure and the onset of silicosis.

		If you model this, it is usually done in most of the papers by a
so-called loglinear model, you get something like this, a very steep
increase at the beginning, across the exposure scale.  And if you read
this, even at an exposure of 1 mg per cubic meter years, the log, which
is at the y-axis, the log is about 1.5.  And so you have something about
an eight-fold risk already, up to 1 mg per cubic meter years.

		Most of the analyses stop here and say, okay, we have clear evidence
of a dose response, and such curvature shows us that there is even a
large increase of rates at the low level range.  Unfortunately, this
kind of modeling forces the curvature into the results and doesn't allow
to be more flexible and to show whether this is really a steep increase
at the beginning.

		As with a couple of other papers, we also use them, restricted to
explants, one of the procedures to check for non-monotonic increase, and
to see that there is a kind of little dip at the beginning after 2
mg/m3.  Used other procedures that are also able to model non-monotonic
increases.  You see this fractional polynomial is almost the same
finding, up to 2 mg per cubic meter years.  There is no increase in risk
to see.  So this is motivation to search for a threshold.

		Now, how to do it, this is quite interesting to see that almost all
regulatory groups in the world look for thresholds and limits.  But in
epidemiology, the studies do not address this question usually.  So
there are just very rare studies around that try to estimate these
thresholds directly.  The problem is how to do it.  There are procedures
available.

		I'd like to present this very briefly to you, that this is available
and can really be used to help the regulatory agencies.  One procedure
to do is the so-called likely profile approach.  You define a grid of
potential threshold values and sequentially compress the exposure by
these potential values.  The best fit stop points at the threshold value
estimate.  So this is very abstract.

		I will run through a few slides so that you can see what this
procedure is doing.  This is a simple example, with X as exposure, from
0 to 5, with no increase in Y.  The responses are 0.  After 5, it is
increasing linearly with a slope of 1 with some error about it.

		If you look at this, this is the graph showing the points I
constructed.  If you draw a line through it, going through the origin,
you see that this is a worse fit.  Now, we compress the exposures by 1,
but do not move them to the negative side.

		This is the next picture.  We subtracted 1.  But if the result is
negative, the point sticks at 0, so this is a little bit better.  If we
subtract 3, it's getting better again.  If we subtract 5, this is where
the threshold was in our curve, it's a perfect fit.  If you do too much,
it can be worse again.  Now, this is 9.

		We can do this in a formal way, measuring the kind of variance or the
error of the model with this statistic, and where the statistics, the
error is smallest, you get the indication where the threshold is.  This
is exactly 5.

		This machinery identified the threshold we have seen before.  This can
be used now in epidemiology in the more complex situations.  What we did
is, with the German porcelain study, we applied this machinery to the
concentration, not to the cumulative exposure, to the concentration data
we have, and then rerun all the modeling and recalculated all the
cumulative exposure after having modified the concentration data.

		What you see is that you get a rather similar curve than shown before
by the illustration.  This ends up at the best estimate at around
2.5 mg/m3, clearly significant because the non-threshold model is here,
the one with the zero.  So this means it's clearly outside the
non-threshold model from the confidence interval.

		So, to summarize this, we found with this machinery, from the German
porcelain study, a threshold estimate for the concentration of
respirable crystalline silica at .25 mg/m3 with a lower 95 percent
confidence limit at .15 mg/m3.  I think that every quantitative risk
assessment of the association with crystalline silica dust exposure and
silicosis or lung cancer should allow for the existence of threshold and
really try to estimate them.

		So all other studies around don't do this, so they are not very
informative about what is really happening at the lower exposure end,
and what in particular is happening when you think about the scale in
milligram per cubic meter.  Steenland and Deddens, they also searched
for threshold for cumulative exposure in the pooled cohort study.  These
were just thresholds for the cumulative things.  They never looked into
detail into the concentration thresholds, so they are not very
informative about this, what we are really interested in.

		Therefore, I think that the risk estimates given for silicosis,
ignoring the threshold effects, the potential threshold effects, are not
very convincing.  We do not know whether these numbers are really
reliable because we do not know what will happen if all shifts are
really kept below 0.1 mg/m3.

		Renal disease mortality, just a short glance on that.  One important
study is the pooled analysis from Kyle Steenland from 2002.  There are
three studies pooled, all U.S. studies.  They saw a monotonically
increasing exposure-response relationship.  One of the studies, Attfield
and Costello, should be replaced because we have a new study, an update
of this by Vacek et al. in Vermont granite workers.  This update could
find no association, so this should be replaced, I think.

		Another point I don't understand is that this pooled study on renal
disease mortality was based on the pooled lung cancer analysis that Kyle
Steenland did when he was at IARC.  This was on the 10 cohorts, but only
three cohorts were pooled for renal disease mortality.  It's unclear
why, what about the other seven cohorts?  Why did they not get into this
pooled analysis?  This, of course, may have introduced study selection
bias.

		And if you go into another further analysis of this stuff done by
Steven and Patel in 2003 and 2004, they concluded that this amount of
data is insufficient to provide robust estimates of risk.  This is from
the original author, so it was quite surprising that OSHA now is using
these data against the warning of the principal investigator and
estimated excess risk due to renal diseases.  I think this is an
over-emphasis.

		Now, nonmalignant respiratory disease, a short look into that.  One
major study and important for OSHA's assessment is, of course, by Robert
Park et al. in California in diatomaceous earth workers.  And what I
think is critical in this study is that the authors truncated the
cumulative dust exposures at about half of the maximum.

		The high exposures showed no effects, so there were no cases, and they
were ignored.  If you do this, it means, of course, that your exposure
response estimates are biased effort.  It is better if you think you'd
like to deal with healthy worker survivor effects or things like that
with this kind of trick, then it is better to use particular procedures
which are well-developed to deal with this, like GST NIOSH.

		There are some problems with smoking, of course, because the data on
this is sparse, and which is important again, Robert Park did not switch
for the threshold effects.  It's unclear to me whether this is really
reliable at the low level of exposures.

		We know that, as I already mentioned, the Vacek et al. did an update
of the Attfield and Costello study and performed a nested case control
analysis, and they could not see any clear effects of respiratory
crystalline silica dust in the nonmalignant respiratory diseases.  So I
think because this updated study could not confirm the findings reported
before in Attfield and Costello, this ought to shed some doubt on this
relationship.

		This taught, in principle, the findings of Vacek are consistent with
the results of diatomaceous earth worker study by Checkoway et al.  They
showed that the SMR for the nonmalignant respiratory diseases were
significantly elevated in only workers who started work very early,
before 1950 when the exposures were rather high.

		If you use the unlogged model that is preferred according to Park et
al., 2002, then you see that there are definitely higher risks for
nonmalignant respiratory disease mortality only in the highest exposure
category.  So this all is compatible with a threshold.  This wasn't
checked for it.  They didn't search for a threshold, but the results we
have do not rule out the thresholds.

		There's another point, of course, that makes it difficult with a
nonmalignant respiratory diseases.  We know that general dust exposures
can cause also these kind of disease: chronic bronchitis, emphysema,
pneumoconiosis.  Recent reviews on this, given Cherry out of the U.K.,
and when we look at what the toxicological people say, they argue in
favor of a threshold effect for these endpoints.

		There is a paper by Paloo (ph.) in 2011, which was evaluated very high
in the toxicology societies.  Of course, the effects of the general dust
exposures were not taken into account simultaneously with the cross-dust
exposure.  It's unclear how much the risk estimate might change if
adjustments were made.

		So OSHA projected that 45 years of occupational exposure to
respiratory crystalline silica at a level of 0.1 mg/m3 will result in 83
excess deaths from nonmalignant respiratory diseases per 1,000 workers.
I think this is unsupported because there is no threshold estimates
taken into account.

		Some discussion and conclusion.  I would like to emphasize one point.
The epidemiological studies on which OSHA relies focused on cumulative
exposure.  This is correct as this is the driving variable for most of
the diseases.  But we are here interested in the concentration.  And
concentration thresholds have to be taken into account additionally.

		So the problem with the cumulative exposures is that the same value of
the cumulative exposure can be reached when you are exposed over a long
term to low concentration or over short term to higher concentration.
So this is a clear drawback of this metric.  In particular, if you read
from the mechanistic papers, that is very important for health effects
caused by crystalline silica exposures, if the concentrations are high
or not.

		Most of the authors of epidemiology studies perform simple calculation
to derive statements about concentrations, although they, within the
studies, only deal with cumulative exposures.  So for instance, you see
in Steenland and Ward, 2013, where they said for a comparison exposure
of the 45-year working lifetime at the current OSHA standard of 0.1 mg
per cubic meter years, resulting in 4.5 mg per cubic meter years.

		You can see that Kyle Steenland here is really changing the scale.  He
is no longer talking about milligrams per cubic meter, but he is talking
about milligram per cubic meter years as the OSHA standard.  In
principle, this calculation is right.  But because the same cumulative
exposures can occur under varying exposure intensities, this has to be
taken into account and is important for all judgments.  Then, of course,
this leads us to a serious issue then if you do such simple
recalculations.

		We know from the OSHA document that there is a lot of overexposure
going on.  So I'm not sure whether this is true.  I just take it for
granted as it is written up in the documents.  I was quite surprised
seeing that about 25 percent of all workers are calculated to have
exposures currently higher than 0.1 mg/m3.  And even about 12.5 percent
of the workers are higher than the value we identified in the German
study as the limit value for the development of silicosis, 0.25 mg/m3.

		So if you, of course, take the German study, and I try to evaluate
this situation from our results, I would say, yes, of course.  I would
expect silicosis cases under this situation.  And because the situation
was worse in former times, it is clear that we will be confronted with a
lot of diseases among the workers exposed to silicosis, exposed to
crystalline silica dust because of these overexposures.  That's very
clear.

		So if you take all this together, then of course, I think there is no
convincing epidemiological evidence that when the occupational
crystalline silica exposures are really maintained at or below a level
of 0.1 mg/m3 in every shift, that we have to expect to see any excess
risk of silicosis, lung cancer, renal diseases, or nonmalignant
respiratory diseases.  Thank you.

		MR. KING:  Thank you.  Paul Scott will now discuss measurability
issues.

		MR. SCOTT:  All right.  Today, I would like to discuss why the
analytical methods for respirable crystalline silica are inadequate to
accurately measure exposures at the proposed PEL and the proposed action
level of 25 µg/m3.

		I'd like to discuss why justification presented in the Preliminary
Economic Analysis is misguided and inaccurate.  This is primarily
because it fails to adequately characterize the various sources of
sampling and analytical error associated with the sampling analysis
methods.  The result is that OSHA has not made this a supportable
showing that silica exposures can actually measure with acceptable
precision at the concentrations of the proposed PEL and action level.

		By way of introduction, my name is Paul Scott.  I am a qualified
environmental professional with over 23 years of experience in applying
statistics in the areas of environmental and occupational health.  I
have specific experience with statistical methods for evaluating
accuracy, precision, and reliability for total and hexavalent chromium
for environment air sampling, as well as PCBs in sediment.

		The existence of sampling and analytical methods that can accurately
and precisely measure respirable crystalline silica at the proposed PEL
or action level is a first step in determining the technological
feasibility of the rule.  If the sampling analytical methods do not meet
the necessary standards of accuracy and precision, then there is
uncertainty associated with trying to determine whether our
concentrations are above or below the PEL or action level.

		If the methods do not meet the necessary standards of accuracy and
precision, then it is difficult to accurately determine the
effectiveness of engineering controls needed to meet the proposed PEL.
I just want to point out in the next couple of slides an overview of my
comments.  I'll let you take a look at those, but I'm going to discuss
each of these in detail.

		Starting with my first comment here on limits of quantification, the
limits of quantification for various x-ray diffraction and infrared
methods range from 5 to 10 µg.  These limits of quantification are
based on ideal conditions where substances that interfere with the
analysis are not present.

		Quantification in the presence of interferences increases the
potential error because additional measurements have to be made to
compensate for changes to the background under the measurement peak or
changes to the peak profile because of coinciding peaks.

		In most industrial settings, materials that interfere with the
analysis of quartz and crystallite are often present.  For x-ray
diffraction methods, the presence of iron, mica, feldspar, and other
minerals will interfere with the quartz peak and degrade detection
limits significantly.

		For infrared methods, silicates and other minerals can interfere and
affect the accuracy of analysis.  The range of reporting detection
limits in the paper by Eller et al. of 5 to 50 µg reflects some of the
variability associated with these different matrices.  But it's limited
because it's based on test data from the PAT Program and not actual air
samples collected in industrial setting.

		Depending on the setting, it can be expected that the impact of
interferences may lead to significantly higher limits of quantification
than 10 µg, depending on the concentration of particulates containing
substances known to interfere with x-ray diffraction and infrared
methods.

		My second comment is focused on use of sampling analytical error
versus precision in the PEA.  In the PEA, OSHA defines sampling
analytical error, or SAE, as a 95 percent confidence limit using the
critical value of 1.645.  This is the standard normal distribution value
for probability of 5 percent and is typically associated with a
one-sided confidence limit.

		However, unacceptable accuracy and precision measurement of plus or
minus 25 percent in 95 percent confidence indicates the need for a
two-sided confidence limit.  Also, any measure of sampling and
analytical variability should be able to evaluate whether you have an
under- or over-estimate of the true air concentration.  It is also
indicative of the need for a two-sided confidence limit.

		Using a one-sided confidence limit will result in an underestimate of
the actual variability because it only accounts for the variability
associated with values that are overestimates of the true concentration.

		Also in the PEA, precision is defined as a confidence limit using the
critical value of 1.96, which is the standard normal distribution value
for a two-sided confidence limit and is based on probability of 2.5
percent, which is half of 5 percent, for a 95 percent confidence limit.

		And in the preamble of the proposed rule, OSHA uses the SAE and not
the precision when making statements about how sensitive and precise the
sampling analysis methods are.  However, this is inappropriate because
it's only a one-sided confidence limit, and not a measure of both, of
two-sided, both underestimates and overestimates of variability.

		And my third comment is on OSHA's estimates of SAE and precision
reports concentrations at the proposed PEL and at the proposed action
limit.  Essentially, they're incorrect because they don't account for
all sources of sampling and analytical variability.

		In the PEA, OSHA defines the precision as a function of the analytical
error characterized by the coefficient of variation associated with
analytical method, this is the CV1, and the coefficient of variation
associated with sampling error, CV2.  For the CV1, relative standard
deviation from a study by the Salt Lake Technical Center, which involved
an evaluation of 10 replicate samples at quartz loadings of 21 and 40.6
µg, and CV2 of 5 percent associated with sampling pump flow variability
were used to estimate the percentage SAEs and precisions.  Our release
estimates of CV1 and CV2 are not adequate because they failed to
characterize all sources of sampling and analytical variability.

		Other sources of sampling variability, for example, include
variability between cyclones of the same type, performance of the
cyclones of different dust particle sizes for a single dust species,
with different dust species and with real-world multi-species
environments.  Also, effect of loading and cleaning on the cyclone
performance and the effect of electrostatic properties of the dust.

		There are data, however, that exist to quantify two additional areas
of sampling variability.  The first is inter-sample sampler variation
based on samplers of the same type.  In a study by Gottman and
Shrenofnat (ph.), which is also discussed in the ASTM method for
respirable dust and workplace atmospheres, they looked at variability
between samplers of the same time and estimated an average relative
standard deviation of 6 percent due to this source of error.

		In addition, the ASTM method also discussed variation using different
types of samplers in the same environment and found this to be about 5
percent.  If you estimate a modified coefficient of variation or CV2
based on these additional sources of error, you end up with a value of
9.3 percent.

		This figure presents the precision estimates associated with the Salt
Lake Technical Center evaluation as proposed action level and PEL, using
these alternative assumptions for sampling error or CV2.  Also, the line
indicates plus or minus 25 percent, NIOSH standard for precision and
accuracy.

		As can be seen from this figure, the estimated CV1 -- from Salt Lake
Technical Center 2013 evaluation is used for the corrected CV2 of 9.3
percent.  This value is 25 and 23 percent respectively for the action
level and the proposed PEL.  This is an increase in variability of about
6 percent.

		The use of the Salt Lake Technical Center evaluation to estimate
analytical error by OSHA fails to account for several sources of
analytical error that include the effective differences in particle
sizes on the analysis of silica by x-ray diffraction and infrared
methods; the effect of potential interferences on these two analytical
methods; the effect of inter-laboratory differences in sample
preparation methods, calibration standards, and the implementation of
the x-ray diffraction and infrared methods; and, finally, the effect of
inter-laboratory differences caused by differences in analysts and
sample preparation methods, as well as variability in the actual
analysis run.

		By failing to account for these additional sources of analytical
error, OSHA calculates an overly optimistic value for precision based on
artificially low value of analytical error.  To be specific, with
respect to the Salt Lake Technical Center 2013 evaluation that is
presented in the PEA, it does not account for the variability associated
with the analytical method or the differences in precision between two
methods, which have been shown to be important in the published
literature.

		Also, it doesn't account for inter-laboratory variability, which has
also been identified as major contributors to sampling analysis error.
Also, the sample size of 10 samples per loading is fairly small, given
the purpose of the study, which is to characterize analytical error for
two methods used by at least about 57 labs.

		Also, it fails to account for the variability associated with the
existence of potential interferences by using alternate sample matrices.
 The variability associated with the silica being in different matrices
can have a significant impact on the analytical error and precision as
demonstrated by Eller et al. analysis of the IH PAT data from 1990 and
the most recent analysis of 2003-2013 PAT data.

		Interestingly, the results of a similar evaluation of accuracy and
precision performed by the Salt Lake Technical Center in 2010, using
twice as many samples per loading level, are included in the OSHA docket
but not discussed in the Notice of Proposed Rulemaking or the PEA.

		While the documentation for this study is limited, it appears to
include the analysis of two sets of 10 samples of cristobalite and two
sets of 10 samples of quartz by x-ray diffraction methods at loadings of
around 20 and 40 µg.  Relative standard deviation in associated
accuracy and precision values were estimated for each set of 10 in the
same manner as the Salt Lake Technical Center evaluation that was
presented in the PEA.

		Based on what is in the docket, the estimates of the analytical error
for the study are much higher than those from the evaluation presented
in the PEA.  For quartz, the estimate of analytical error is 16.1
percent at 20 µg and 12.8 percent with 10 samples at 40.  Using those
estimates for 20 µg loading, the precision ranged from 33 percent to 36
percent, depending on the value of sampling error that is used.

		For the 40 µg loading, the precision ranges is from 27 percent to 31
percent, depending on the value of sampling error used.  These precision
estimates are higher than those presented in the PEA with a smaller Salt
Lake Technical Center evaluation, with precision at 20 µg being 14
percent higher, and the precision at 40 being 10 percent higher, for a
sampling error of 5 percent.

		These data indicate that the estimated precision at Salt Lake
Technical Center couldn't be much higher than the values estimated using
the 10 samples per loading discussed in the PEA and are well above the
25 percent level considered to be acceptable.

		Another source of data that could be used to characterize the
analytical variability is data available from the AIHA IH PAT Program
administrated by industrial hygiene professionals from the AIHA and
NIOSH over the last three years.  This program serves as a standard for
certification for silica analyses for all IH laboratories in the U.S.
and many others around the world.

		For each round of the program, samples are prepared.  The silica
loadings are embedded in different matrices on a rotating schedule and
sent to labs four times per year.  The matrices are typical of what
might exist in the samples collected from industries that monitor silica
exposures.

		These matrices include calcite, coal dust, talc dust, and a
combination of coal and talc dust.  Each laboratory analyzes a sample
based on standard methods that can include x-ray diffraction, infrared
and colorimetric methods, though very few labs have used colorimetric
methods in recent years.  Thus, the data associated with the PAT Program
can account for variability due to intra-laboratory differences over
time, and analytical method differences and differences in sample
matrices.

		The PAT data are well suited to characterize the analytical error for
analysis of silica because the program evaluates several sources of
analytical error that the Salt Lake Technical Center evaluation does
not, specifically, variability due to inter-laboratory differences,
analytical method differences, and differences in sample matrices.

		The data from the IH PAT Program are inherently more variable than
data from the Salt Lake Technical Center evaluation because of the
additional source of variability characterized by the data.  Compared to
the Salt Lake Technical Center evaluation, an analytical error estimate
for the dataset would provide a better characterization of analytical
error and results that are more representative of real world conditions.

		One of the drawbacks of the PAT Program data is that the lowest silica
loading used in the program is typically around 40 µg, which
corresponds to the proposed PEL.  However, it's been demonstrated that
relative standard deviation increases with decreasing silica loading for
x-ray diffraction and infrared methods.  Any estimate of relative
standard deviation calculated from the lower end of the silica loadings
used in these data will be lower than that, than the actual value that
might be estimated at 40 µg and certainly for 20 µg samples that
correspond to the proposed action level.

		Another drawback is that after round 158, the relative standard
deviations for labs with relative standard deviations greater than 20
percent were set equal to 20 percent.  This will buy us the overall
relative standard deviation low for any round after 158, because labs
that had relative standard deviations greater than 20 percent would be
lowered to 20 percent.

		This figure presents precision estimates for sampling error of 5
percent, calculating from the Salt Lake Technical Center 2013
evaluation, the Salt Lake Technical Center 2010 evaluation, the PAT
Program data that were included in the OSHA docket, and the PAT data
from 2003 to 2014 -- 2013, excuse me.

		The PAT data that are available to estimate for all standard deviation
associated with silica analysis in the OSHA docket are from rounds 156
to 180, and range in time from April 2004 to February 2010.  In order
to focus on mass loadings that's close to the 20 and 40 µg, as
possible, this dataset includes only samples with loadings less than 70.
 That's about 23 out of the 100 samples available in those rounds.

		Recently, Harper et al. evaluated the PAT data for 2003-2013 in a
manner similar to that used by Eller et al., 1999, who looked at the
data from 1999 and 1998.  This dataset included data from rounds 152
through 194.  They estimated a relative standard deviation for this data
that included all rounds of data and all mass loadings, ranging from 40
to 250 µg, of 20.8 percent.

		The precision estimates at the proposed action level increased from 19
percent for the Salt Lake Technical Center 2013 evaluation, to 33
percent using the 2010 data, to 37 percent for the PAT data in the PEA,
to 42 percent for the most recent PAT dataset.  A similar increase can
be seen for the proposed PEL, going from 17 percent to 27 percent to 37
percent and then finally to 42.

		It's clear from these analyses that the precision becomes poorer as
more sources of analytical error are accounted for.  The precision is
best for the data from a single laboratory, analyses pure silica samples
from one preparation and analysis method.  It is poorer for the data
from multiple sample matrices that are analyzed by multiple laboratories
using different sample preparation and analysis methods.

		On my next slide, I show a similar trend in precision using a sampling
error of 9.3 percent.  Once again, for both the Salt Lake Tech Center
2013 evaluation, the Salt Lake Tech Center 2010 evaluation, the PAT data
from the OSHA docket, and the PAT data from 2003-2013.

		Finally, another source of data that can be used to characterize the
analytical error and the overall precision are results from a respirable
crystalline silica round robin performance testing program sponsored by
the American Chemistry Council Crystalline Silica Panel.  This study
will be discussed in more detail by my colleagues after my talk.

		As an overview, filters containing three different loadings of
respirable quartz dust were sent over a period of several months to five
different AIHA-accredited laboratories for analysis as part of a blinded
testing program.  Each of these five laboratories used x-ray diffraction
method to analyze for crystalline silica.

		As part of the blinding process, the laboratories were not informed
that they were participating in the performance testing program.
Filters were submitted with a standard chain of custody forms as if they
were collected during ordinary workplace monitoring of silica exposure
by commercial customers.

		An analysis of the variance of the round robin data indicated that
there were significant differences with respect to loading level.
Because of this, the relevant standard deviations were calculated for
all the data at 20 and 40 µg loadings respectively, and separate
estimates for precision were calculated.

		The estimate of analytical error at a loading of 20 µg was 37
percent, and precision values ranged from 72 percent to 74 percent
depending on whether a value of 5 or 9.3 percent was used for sampling
error.

		At a loading of 40 µg, the estimate of analytical error was 32
percent.  The precision values ranged from 63 to 65 percent, depending
on the value of sampling error.  These estimates of precision are
substantially higher than those estimated using the Salt Lake Technical
Center 2013 evaluation for the PEA and the Salt Lake Technical Center
2010 evaluation in the two IH PAT datasets.

		This figure shows the difference between the Salt Lake Technical
Center 2013 evaluation and the estimates from the American Chemistry
Council round robin study.  Precision estimates associated with the
proposed action level and proposed PEL using the round robin study are
almost four times larger than those estimated using the Salt Lake
Technical Center 2013 dataset.

		My fourth comment discusses a miscalculation that was presented in the
PEA.  In the preamble of the proposed rule, OSHA states that most
laboratories achieve good agreement and results for samples having
filter loads just above 40 µg quartz.  This statement is based on
analysis of a number of laboratories and sample results that were within
plus or minus 25 percent of the reference value in the PAT Program for
rounds 156 to 165.

		It is unclear why OSHA limited its analysis to these data from these
rounds, which are not the most recent.  Whatever the reason, the PEA
represents the percentage of laboratories reporting values within plus
or minus 25 percent of the reference value in these rounds.

		OSHA states that for all reference values, laboratories achieve good
agreement because 80 percent are within plus or minus 25 percent of the
reference value.  In addition, OSHA states that for reference values
less than 70 µg, 81 percent of the laboratories are within 25 percent.
However, recalculation of the results from the table in the PEA
indicates that the actual percentage of the labs within plus or minus 25
percent is not 81 percent, but is actually 73 percent.

		Of the reference values of 70 µg, 83 percent of the labs were within
plus or minus 25 percent of the reference values.  This demonstrates
that precision deteriorates at lower silica loadings, and that three out
of four labs achieve good agreement results for samples with less than
70 µg of silica.

		My fifth comment discusses how past data from the PAT Program has been
used to characterize differences, method differences and
inter-laboratory variability.

		In Chapter 4 of the PEA, OSHA states that the PAT data were not
suitable for characterizing the method differences and inter-laboratory
variability because PAT sample preparation errors may contribute to the
analytical error because reference value is based on either the average
of a subset of laboratories or all the laboratories reporting for the
round, depending on the round be evaluated.

		However, the PAT data had been used to evaluate the accuracy and
precision of respirable crystalline silica method in two separate
studies that had been published in the peer-reviewed literature, Shulman
et al. in 1992 and Eller et al. in 1999.  Both papers evaluated the
accuracy and precision of the different analytical methods, as well as
inter-laboratory and intra-laboratory differences in precision, healthy
analytical methods based on the different time periods of the PAT data.

		Thus, members of the scientific community have considered the PAT
Program to be suitable and useful for evaluating the precision of
analytical methods for crystalline silica.  In addition, OSHA scientists
have also used this data to evaluate accuracy and precision in the past,
in a study by Matson (ph.) et al. in 1995.

		My sixth comment talks about use of high-flow rate sampler.  On page
443 of the PEA, OSHA states that the higher flow rate device, such as a
BGI GK 2.69, using the recommended flow rate of 4.2 L/min, can collect
more than 10 µg of dust for a one-hour sample at the proposed PEL.

		The implications of this statement is that even if the accuracy and
precision of the sampling analysis of silica does not meet the standard
of plus or minus 25 percent and a 95 percent confidence level when
silica filter loadings are 40 µg and below, there are samplers that are
capable of collecting a greater amount of silica at the same air
concentration due to the increased flow rate.

		For example, the proposed PEL, a high-flow rate sampler at 4.2 L/min
would collect 101 µg of silica for an eight-hour shift compared to 41
collected using the current Dorr-Oliver sampler at 1.7 L/min.  101 µg
silica loading is about 20 µg higher than what we collected using the
Dorr-Oliver sampler at the current PEL of 100 µg/m3 for general
industry.

		However, there are several limitations with the use of high-flow
samplers.  First, the accuracy and precision of the high-flow rate
samplers for measuring respirable crystalline silica have not been
evaluated, although performance of these samplers for collecting
respirable particulates in quartz has been evaluated relative to
low-flow samplers in several studies.

		These studies have focused on sampling efficiencies of the respective
samplers relative to the ISO/CEN particle size convention and how the
amount of mass the individual high-flow samplers collect compared to the
low-flow sampler.  No big studies evaluated the accuracy and precision
of these samplers using the methods recommended by NIOSH for sampling an
analytical method development.

		Second, the need to evaluate accuracy and precision release methods is
important because studies by Lee et al. in 2010 and 2012 indicate that
the high-flow rate samplers tend to collect a higher proportion of
larger-size particles than the lower flow rate samplers currently use.
Lee et al. in 2010 found that the BGI GK 2.69 high-flow sampler had a
large bias for particles with a large mass mean aerodynamic diameter.

		In general, all three high-flow samplers evaluated by Lee et al.,
2010, tended to have a substantial bias towards collecting more
respirable particulates than the low-flow samplers, collecting between
12 percent and 31 percent more mass than the low-flow samplers.

		Third, while the high-flow samplers collected more quartz mass than
the low-flow samplers, standard deviations associated with the mass
ratios, net mass ratios, were high, indicating a potential increase in
sampling and analysis error.

		Because respirable silica in occupational settings tend to have a
greater proportion of smaller size particles, and high-flow samplers
tend to over-sample larger particles compared to the low-flow rate
samplers, it seems likely that the high-flow samplers will collect a
greater proportion of non-silica particles that can potentially
interfere with the analysis of respirable crystalline silica using x-ray
diffraction or infrared methods.

		In addition, the collection of a larger mass of particles may lead to
overloading problems for environments with a high concentration of
particulate matter that contain a lower proportion of silica.  The
increase in the probability of interferences and the potential for
overloading will both create accuracy and precision problems.

		A further consideration is that the use of a high-flow sampler likely
will necessitate use of a pump with a higher flow rate than those
currently used by most industrial hygienists and dust samplers,
particularly if the industrial hygienist is collecting samples at a
sampling rate of 4.2 L/min versus current rate of 1.7 L/min using the
Dorr-Oliver sampler.

		For these reasons, before recommending the use of high-flow samplers,
OSHA needs to develop a better understanding of the accuracy and
precision of these samplers under real-world conditions, using particles
that may be found in the workplace.

		My concluding thoughts:  I would like to conclude by saying that the
estimates of precision presented in the PEA are overly optimistic and
inaccurate because they fail to account for a variety of sampling and
analytical errors, because only the Salt Lake Technical Center 2013
evaluation was used to define the analytical method error used to
estimate the precision for the analysis method of the proposed PEL and
action level.

		The precision estimates fail to account for inter-laboratory
variability, differences in the two methods used, the effect of
procedural interferences due to the sample matrix, or the effect of the
analyst and analysis variability over time at the same laboratory.  If
these additional sources of error are included in the precision
estimates, the accuracy and precision sampling analytical methods for
silica will not meet the plus or minus 25 percent at 0.95 standard
recommended by NIOSH for these silica loadings at 20 and 40 µg that are
associated with the proposed action level and PEL.

		In addition, the use of high-flow rate samplers may not address these
problems with precision at low silica loadings because the accuracy and
precision of the high-flow rate sampling methods are unknown, and the
high-flow rate sampling methods tend to collect a higher percent of
large particles that will tend to interfere with silica methods and may
overload the sample over an eight-hour period.  Thank you.

		MR. KING:  At this point, Kelly Bailey and Dr. Richard Lee will
describe the ACC-sponsored five commercial lab performance study.

		MR. BAILEY:  Good afternoon, Your Honor, OSHA, all you folks bearing
with us.  My name is Kelly Bailey.  I am the current chairman of the
American Chemistry Council's Crystalline Silica Panel.  I work for
Vulcan Materials Company as the corporate Director of Safety, Health and
Environment.  Vulcan is the largest U.S. producer of crushed stone, and
sand and gravel.

		I am a board-certified industrial hygienist, and I've been in the
profession for 40 years.  With me, over to my left is, Dr. Richard Lee
who is President and CEO of RJ Lee Group.  We appreciate the opportunity
that OSHA has provided the panel of witnesses to participate in these
important hearings.

		RJ Lee group is a niche laboratory specializing in asbestos, silica,
and other environmental testing.  RJ Lee Group is widely regarded as
experts in these fields.  They have been pioneers working with EPA,
OSHA, NIOSH, and MSHA in the development and improvement of methods for
the analysis of these materials.

		RJ Lee Group has worked for and collaborated with NIOSH in assisting
other countries in setting up crystalline silica analytical
laboratories.  They are also experts in the broader field of material
testing, especially with respect to the automated electron microscopic
analyses.

		Why was this done?  Why did we do this study?  Well, our testimony
describes a recent study funded by the American Chemistry Council's
Crystalline Silica Panel, hereinafter called "the panel."

		The study was designed to ascertain how accurately and precisely a
sample -- a small sample of AIHA-certified commercial laboratories
could analyze respirable crystalline silica, or RCS, masses that
correspond to the current general industry permissible exposure limit of
approximately 100 µg/m3 of air, the OSHA-proposed PEL of 50 µg/m3 of
air, and OSHA's proposed action level of 25 µg/m3.  Based on an
eight-hour work shift and a pump flow rate of 1.7 L/min, these masses
would equate to 80, 40, and 20 µg per filter, respectively.

		The objective of the study was to evaluate how labs would perform in
terms of accuracy and intra- and inter-laboratory precision at these
relevant silica loadings.  The study was limited to five laboratories
using a muffle furnace for sample preparation and x-ray diffraction for
analysis.

		The study is unique in the extent that to which it was conducted on a
blinded basis.  In a typical laboratory evaluation or performance test,
a laboratory is usually well aware that it is participating in a program
designed to evaluate the accuracy and precision that the laboratory can
achieve in analyzing samples.

		This can introduce bias into the evaluation from the very beginning.
In the study we are describing today, a conscious effort was made to
eliminate this bias by having third parties prepare and forward the
sample filters in a manner that made them look as though they were
actual air filters from an exposure-monitoring event.

		The labs were unaware that these samples were spiked with known silica
masses and that their reported results would be evaluated for accuracy
and precision.  Dr. Lee will explain the details regarding the sample
preparation that his laboratory performed for the panel.  First, I will
quickly explain how the process was carried out.

		First, polyvinyl chloride filters, PVC filters, were prepared using
Min-U-Sil-5 as the crystalline silica source.  Respirable feldspar and
respirable kaolin were selected as interfering minerals for the mixed
matrix filters prepared at a 50/50 mix with silica.  PVC filters with a
0.8 micron pore size were used to receive the deposited material at the
sample weights of 20, 40, and 80 µg.

		As I noted earlier, the five labs selected for the study used a muffle
furnace for sample preparation and XRD for analysis.  They were
geographically dispersed, as well, representing labs of different sizes.
 All were AIHA-certified for silica analyses.

		For each lab, three sets of 10 filters were prepared.  Each set
consisted of one blank filter, three pure silica one at each of the
three reference weight levels, three with silica plus feldspar, and
three with silica plus kaolin.  RJ Lee Group forwarded these filters to
Sandler Occupational Medicine Associates, or SOMA.

		SOMA added dummy plant codes and sample numbers to the filter
cassettes to make them appear to be normal field air filter samplers.
Each of the five labs got one set of 10 filters three different times
over the course of nine months.  The labs reported the results to SOMA,
which forwarded the results to Dr. Louis Anthony Cox, Jr., for
statistical analysis of the laboratory performance.

		That analysis was prepared in a blinded matter, as well, in the sense
that neither Dr. Cox nor the panel was aware of which results were
reported by which lab.  This is how the study was conducted.

		I will now turn the microphone over to Dr. Lee, to describe the
process that was used to prepare the reference filters for this study.

		DR. LEE:  Thank you, Kelly.  I, too, would like to express my
appreciation to OSHA for being able to testify at this deliberation.
The key goals of our participation in the study were to validate the
loading methodology, and to ensure that the laboratories received blind
samples.

		The methodology for depositing known quantities of materials on
filters is well understood and practiced every day in laboratories such
as ours.  A known quantity of material is weighed out and filtered,
suspended in the alcohol or other appropriate media, and filtered onto
the appropriate filter media.

		We defined recovery in the standard form as shown in this question
here, which is just the amount deposited minus the amount measured over
amount deposited and expressed a percentage.  The RJ Lee Group measured
the recoveries, that is the amount of silica deposited on the test
filters, used x-ray diffraction, using all these scores through primary
reference standards.

		We did attempt to compare the pre-imposed weights as a backup method
for evaluating the amount of material deposited.  But we found the
alcohol partially dissolved the filter, extracting about 70 µg, plus
or minus 20.  This caused a random weight loss which rendered the direct
measurement of weight unusable.

		It would have been ideal to use the 5 micron PVC filters that we
normally use in air sampling for silica.  Unlike air sampling, the 5 µm
filters proved to give very poor recoveries of the deposited material,
as the liquid media carried the respirable particles through the filter.
 Using 0.8 µm filters solved this problem.  However, we found that the
.8 micron filters did not dissolve completely in the THF used in the
chemical method for processing filters.  Consequently, the RJ Lee Group
had to eliminate from the study laboratories which used the chemical
method.

		The choice of the reference material was complicated by the
unavailability of the standard reference silica NIST 1787a.  RJ Lee
Group elected to use the Min-U-Sil 5 as the reference material.
Min-U-Sil 5 has a medium particle size, a 1.72 µm versus about 1.68 for
the NIST standard.  We used the sedimentation process on both the
Min-U-Sil 5 and on the interference materials to ensure that we did not
have oversized particles on the filter.

		As Kelly described, multiple sets of cassettes were prepared by RJ Lee
Group and sent to SOMA.  The cassettes contained pure silica or a 50/50
mixture of silica and one of the interferences.  The filters were
preconditioned, pre-weighed, and assigned a tracking number.  The
reference material was loaded on a pin on a microbalance and using an
acceptable limit of plus or minus 1 µg from the nominal weight that was
to be deposited.

		The contents of the pin were suspended in isopropyl alcohol and
deposited on a filter using vacuum filtration.  The filters are
desiccated and post-weighed.  The prepared filter was loaded in a
cassette, seated, labeled, and shipped to SOMA.  Finally, a subset of
filters were analyzed in house.  The reproducibility of the analysis was
good, and the recoveries, acceptable, averaging about 95 percent.  You
can see from the x-ray diffraction patterns that are presented
underneath, the similarity between the different replica samples.

		Finally, a set of samples was prepared, shipped to SOMA, and returned
to us, the RJ Lee Group, in a blind manner.  The estimated recovery is
because we did not have the code between the original sample and the
blind sample.  We used the nominal weights.  Using that, the estimated
recovery was about 97 percent.  We found no effect of shipping in the
weight.

		With that, I'll turn the microphone back to Mr. Bailey.

		MR. BAILEY:  So, what were the results of this five-lab performance
study?  As an industrial hygienist, I was quite surprised by the
findings.  The results I am going to share exclude those samples where
the laboratory reported the silica mass to be below the detection limit.

		Thirty-four percent of the non-blank filters, that is the filters with
silica loadings of 20 µg or greater, were reported to have silica
masses below the detection level.  Had these non-detect results been
included in Dr. Cox's analyses, the accuracy of precision value would
have been worse than those that I will show you.

		In this table, you can see that in two instances, the labs reported
silica masses on blank filters.  One of these was reported to be 52 µg
of silica.  None of the mean values for the sets of 20, 40, and 80 µg
silica loadings came within 30 percent of their respective reference
weights.

		This table includes only the pure silica filters where the lab
reported the result above the LOD, or limit of detection.  When all of
the filters were there, all the filters that were there were reported
with silica levels are examined, the next table, one finds that the
results are not much better.

		Looking at the entire set of results, whereas detected silica mass was
reported, the data indicate that these labs could not reliably
discriminate amount different reference levels.  In fact, for those
filters in the higher quartile of the reported results, all of which
should have come from the 80 µg reference level, there was a 50 percent
chance that the filter came from the 40 or the 0 blank reference level
set.

		For those filters in the lowest quartile of reported results, all of
which should have come from the 20 µg reference level set, there was a
28 percent chance that the filter came from the 40 or the 0 blank
reference level.  In effect, the results of the analyses by these five
commercial laboratories were not sufficiently accurate to reliably
distinguish between concentrations that differed by a factor of two.
For example, an 80 µg versus a 40 µg, which would be the current PEL
versus the proposed PEL.

		Moreover, the inter-laboratory variability in this study was high.
The results show that a filter with a given loading of silica could
yield a reported silica mass that varied by nearly a factor, based
solely on which lab performed the analysis.  If these results are
representative of laboratory proficiency, they indicate that
inter-laboratory variability alone would make it impossible to reliably
distinguish concentration as it differed by a factor of two.

		Intra-laboratory variability was also high.  With the exception of one
of the labs, the coefficients of variation ranged from 20 to 66 percent
at the various reference levels, indicating substantial problems with
intra-laboratory precision.  Within some of the laboratories, there was
substantial overlap among the 95 percent confidence interval around the
mean-reported silica masses for the different reference levels.  The
variability of reported results within individual labs was such that, in
some cases, the reference levels could not be reliably distinguished
from each other.

		In conclusion, based on the results of this study, obtaining reliable
silica measurements to determine regulatory compliance, even at the
current PEL, is problematic.  The results reported here would have been
even more problematic if Dr. Cox had not excluded from the analysis
those filters, more than one-third of the total, that had silica
loadings of 20 µg or more but were reported by the labs to being below
detection level.

		Based on this study, achieving the conventional 95 percent statistical
confidence of an analytical method was not reached.  Inter-laboratory
variability was so high that reported results could not be used reliably
to discriminate among filters to reflect eight-hour exposures to silica
at levels of 25, 50, and 100 µg/m3 of air.

		Within a single laboratory, there was enough variability and reported
results so that two-fold variations in exposure could not be
distinguished.  Based on the results of this small study, the panel
highly recommends that OSHA conduct a larger blinded study for all labs
intending to analyze crystalline silica before adopting exposure limits
that very likely cannot be measured reliably using current technology
and methods.

		Thank you for the opportunity to participate in this important
hearing.

		JUDGE SOLOMON:  Mr. Waggener, yeah.

		MR. WAGGENER:  Good afternoon, Your Honor, OSHA, and the audience.  My
name is Jack Waggener.  I'm a senior principal and professional
engineering manager with URS Corporation.  I'm here today to talk about
our critique of the OSHA cost model for general industry, the general
industry sectors, and our modifications that we made to that model to
make it real world in its estimate as we see it.

		I'll begin with an introduction.  The American Chemistry Council's
Crystalline Silica Panel asked and engaged URS to perform this process
for the general industry.  URS is approximately a 60,000-employee
professional engineering and scientific company that is involved in all
types of industries throughout the country, and essentially for all of
the industries in this particular category.

		Additionally with that experience, which includes mine, we have also,
for more than 25 years, owned one of the PAT-certified laboratories that
just got discussed in the discussions as to the accuracy of the test.  I
personally have more than 40 years' experience dealing in this type of
issue, beginning with the lead PEL in the 1970s, which I was highly
involved in on behalf of the industry and helping the industry getting
compliance, and actually during the generation of that PEL.  More
recently, in the 2000s, I was heavily involved in a similar fashion, as
I am today, with the hexavalent chromium rule and also in the cadmium
rule.

		URS prepared comments which are part of the docket, and that is what I
am here to talk about today primarily.  Along with that, there is an
incredible amount of Excel sheets and backup data that goes along with
all of this costing procedure, which is a very large effort on our part,
and I'm sure OSHA's part, to put it together.

		In consultation with our team at URS, we also were involved with more
than 100 different industry CIHs and engineers throughout these
particular industries that we are going to be talking about today.  To
begin the process, when the rule was proposed, we reviewed the different
documents, the PEA and so forth.  In the record, we did not have the
OSHA spreadsheets for the cost model, which we were trying to review.
We asked for that to be provided, and we appreciate the fact that you
guys promptly provided that to us.  That is what we reviewed and
critiqued.  In doing that, we found a number flaws that went along with
that, in which we made corrections.

		We'll call this the executive summary.  We will get the bottom line
first, and then talk about how we got to the bottom line.  We looked at
19 general industry sectors.  There are actually 29.  We chose the 19
because we felt like we had at least more credible data in the record to
ascertain what the actual cost would be.  I'll talk about that a little
bit more as we go through here.

		The URS annualized cost we estimated to be, for these 19 industry
sectors, over $6 billion, some 56 times more than what OSHA estimated at
approximately $100 million.  We feel like, based on the information we
did and the analysis we did, and how we chose to make changes, that we
are still conservatively low.  Even though this number far exceeds
OSHA's number, it probably is much higher than this.

		Many of the professionals in these industry categories felt like to
actually reach, and this is an important issue, the 50 versus getting to
the original 100 PEL is probably at least five times more difficult and
costly.  We found a number of flaws that created this when we tried to
make those corrections by introducing different assumptions and
different things of that nature to get to what we believe to be more
real-world corrections.

		The industries that we focused on, the 19 general industry sectors,
these are the ones that we felt like we had reasonable data to do so:
the asphalt paving products, asphalt roofing, concrete products, custom
jewelry, cut stone, fine jewelry, flat glass, iron foundries, mineral
processing, mineral wool, nonferrous sand casting foundries, non-sand
casting foundries, other ferrous sand casting foundries, other glass
products, paint, pottery, ready-mix concrete, refractories, and
structural clay.  Those would be the focus of what we'll be talking
about today.

		In the next slide, I presented the ones that we felt like we didn't
have good information to make the estimates on.  These were captive
foundries, dental laboratories, dental equipment and suppliers,
railroads, refractory repair.  There were five different porcelain
amalgam categories that were identified.

		An example of that issue, while we didn't think we had the good data
to do it, the data was not representative that was in the record, is
what I should say.  For the porcelain amalgam sectors, OSHA estimated
some more than 9,000 facilities as being out in the United States.  The
Porcelain Amalgam Institute, which represents this industry, estimates
there are 125 facilities.  There was obviously a disconnect there.  I
know the Porcelain Amalgam Institute is submitting comments to make that
correction.

		In our next slide, we break down, again, this is the URS estimate for
the cost.  It's broken down by industry category, as I just mentioned
earlier.  If you go across the top of the page, you have engineering
control costs to meet the 50 PEL.  These are emphasized.  This is the
URS estimate after we changed the model, or the assumptions and the
inputs of the model.

		The 100 PEL, and then we also had the incremental cost.  We have
ancillary provision costs.  Then we had the incremental costs and then
the full cost.  Well, of course, as you have heard previously, we
believe the full cost is the real cost that the rule will be impacting
on industry and should be part of what is really looked at here as we go
forward, not just the incremental cost.  Again, at the bottom right-hand
side of the table, you can see the total cost of them, $6.1 billion of
annualized cost that we are predicting.

		The next table is what is by the OSHA model, which is the incremental
cost, which without going into a lot of detail, it still comes up to the
$108 million versus what we had on the previous page.  Just as an
example for concrete products, the URS model cost is about 42 times
higher than what OSHA has gotten for iron foundries.  We are about 67
percent.  For structural clay, bricks, and things of that nature, we are
about 24 times higher.  We looked at each industry separately to get
what it really would approximately be relative to what was really going
on in that industry.  It goes up and down in terms of the multiples of
how much more that it would actually be.  So just briefly, for the 19
sectors, the URS estimate is about 44 times higher than the incremental
cost, as based on what OSHA got, and the URS full cost is about 56 times
higher.

		The next slide deals with the sizes of the facilities, which was taken
into account by OSHA and URS in the cost model.  I am preaching to the
choir here, I guess, but at least for the very small entities, that
represents less than 20 employees.  For the small entities, as we
analyzed, it is more than 20 or equal to 20, and up to less than 500
employees.  That is the SBA definition.  Then, we also had large
entities, which were larger than 500 employees.

		So we looked at each one of those.  That's important in looking at
what the potential cost impact is and what the economic impact to the
industry is, which Stu Sessions after me will be talking about.  The
OSHA cost model lumped, however, the very small and small entities
together.  So all the companies that were under 500 employees were all
lumped together in their analysis.

		We believe that the size, in looking more specifically also at the
less than 20, was very important.  So we did an analysis to keep those
separate and present that data separately.

		And the next slide deals with the engineering cost per facility size.
Again, this is lumping all these 19 industry sectors together,
aggregated together.  We show, in terms of millions of dollars, for that
grouping like to the left here, the $518 million for very small
facilities under URS' estimates.  OSHA had comparatively $15 million.
Then, for small entities, you can see the numbers.  It was considerably
higher, and then for OSHA, without going into all the details there.
Anyway, that shows that.

		Probably the most important one here, from my view of looking at what
we did here, or at least one of the most important things, this slide
shows what the average cost is for each facility, one individual
facility.  Not aggregated together, but what the average is based on the
models.

		And I've chosen two sectors, just as examples.  We have it for all of
them obviously.  Again, these are numbers.  We've been talking about
millions of dollars and billions of dollars.  These numbers are in
thousands of dollars.

		This is by facility.  I think the importance here is shown here.  If
you look at the iron foundries, which is the top half of the table here,
the bottom half is structural clay, you can see these are facilities
less than 20 employees.

		Actually, the average employee is like less than 10.  It is eight
employees or so forth, so you are looking at a very small business that
may be looking at an annualized cost under the URS estimate of $218,000
a year, versus under the OSHA's estimate to get into compliance with the
50 PEL, less than $3000.

		Obviously, if you think about it, I'm sure you know many small
businesses spending less than $3000, you know you can buy a pretty good
computer that goes on your desk.  That is not an unusual purchase.  But
when you start talking about having to spend $218,000 a year as a very
small mom and pop company, that is a big deal, just for reference.
Likely, you're going to go out of business.  That is a very likelihood
that that would be the case.

		Even for the small facility, it's a big margin.  Again, these are
average numbers; some are higher, some are lower.  These are iron
foundries.  The average one point, or I should say, $1,874,000 versus
about $40,000, what OSHA indicated.  Again, these are people that
average actually less than 100 employees per facility.  That is a heck
of a bite to be taking out of a small company as you go forward here.
Anyway, we recalculated these and came up with what we believe the real
numbers are, closer to what reality was than what the OSHA numbers were.

		From this point, I want to talk about that there were a number of
flaws, that at least we believe were flaws, in the OSHA model that we
have identified, and we tried to make connections to that are more
realistic.  I am going to talk about those flaws, and kind of what we
identified as flaws, some of the major flaws and corrections that we
made.

		We will start with engineering controls, and then we will go to the
ancillary provisions.  Pardon me.  Why such a large difference in the
cost?  Well, one item to begin with was how OSHA estimated the number of
controls that would be needed to be put in their cost model.

		They did not actually look at the types of facilities out there.  They
didn't look at the size of the facilities really or look at the number
of facilities.  They looked totally at whatever the over-exposed
employees were on a national basis.  Basically, through analysis, they
decided that this rounds it off, just about for all of the -- they
divided.  They said, you have four exposed workers per control.  So they
took that national average and divided it by four.

		Now, that assumes you've got four people together all the time in
these locations and so forth, which is just clearly not realistic.  We
tried to take it and move it into a different field, to where we would
look at each individual facility, the sizes, the characteristics of that
particular industry as identified by OSHA, and use that data to come up
with a different amount of a number of employees.  Not a different
number of employees that is overexposed, but a different number of
controls that would be needing to cover those facilities.

		And we took a statistical approach, a very typical standard approach
using a binomial distribution, which is commonly used.  We looked at
every industry and the sizes and every job differently because every job
has different exposures.  We generated more than 400 binomial
distributions to come up with what we believe would be the number of
controls that would be needed, spread out over the plants individually
and throughout the United States.

		I'm not going to go into statistical education here, but this is just
one of the typical curves that you would get.  This center of the curve
there at the top represents that line or the percentage of workers that
OSHA predicted to be overexposed.  The far right on the horizontal axis
demonstrates the overexposed or the total workers in that job on the
average for that size.  It's just an example.

		And you go through this, and you determine how many controls and how
many people are being exposed at certain levels and different size
facilities.  That was the way we did that.  Excruciating details with
regard to that in the record that we submitted.

		So the next slide deals with other problems that we found.  Again,
they failed to account in the model for the facility sizes, the number
of shifts that different companies operate.  They failed to recognize
the physical limitations of some of the controls.

		You've heard lots about variability of monitoring and testing.  None
of that was taken into account either when coming up with these
estimates.  As a practical matter, we cannot address only the
overexposed workers in a given job under certain situations for
controls.

		OSHA's model, we believe, frequently undersized the size of the
controls that have been put forth by OSHA.  Also, the incremental costs,
which we will talk about in more detail here, which is actually a huge
issue, the cost of LEV, local exhaust ventilation, which is just one of
many controls that are used in these types of plants.  You have also
heard mention the ISO/CEN.  The impact of the ISO/CEN was not taken into
account in the model.

		Moving forward, the first error we saw was in, again, looking at very
small and small facilities in terms of OSHA saying there are four
overexposed employees per control.  The matter of fact is that many of
these very small and small facilities have one employee where that
control would be.  And that needed to be accounted for.  And this is
very important obviously to coming up with the cost of the very small
and small facilities.

		Frankly, sometimes it's the reverse.  There are facilities, such as in
the iron foundries, that may have eight controls per employee, not the
reverse, four employees per control.  By choosing that number or
sometimes different numbers, it depends on what they were looking at,
they really underestimated what the cost of the rule would be.  So the
bottom line, we had to make corrections with regard to that.  And the
binomial distribution helped us make that -- one of the things that
helped us make that correction.

		In terms of work shifts, OSHA assumed that there would be two shifts
per facility.  The fact of the matter, most very small facilities, and
in some cases, a lot of the small facilities, they only work one shift.
And so, we had to make that adjustment in the model to account for the
cost.

		The variability in the monitoring has been spoken to before.  The
additional -- OSHA has really identified that you only need to do this
first monitoring.  You are going to find out whether or not you have
individuals that are -- that you have to have controls on or not.

		The fact of the matter is, and this is common knowledge, you know, you
do an analysis.  You go in and do sampling one time.  And the next --
and you find out that Person A is out of compliance, and Person B who is
20 feet away from him is in compliance.  You go back three months later,
and the reverse happens.  So you've got all these different people, and
you can't just say, okay, I did one sampling, and I'm okay.  I know what
the statistics are.  That's not the case.  That's not the real world.
So there's more people that need controls than just doing this one
event.  And, therefore, this four overexposed worker issue is flawed.

		And, again, kind of going along with the same thing, just from a
practical matter, you can't just address what you identify once or twice
to be overexposed workers.  If you have a very high percentage of
overexposed workers in a job, and that's a consistent thing --
consistent in a certain job area, you can't just pick and choose.
You're pretty well going to have to put in consistent controls
throughout that area, not just one control.  It may involve two or three
controls.

		And what URS did in our model, if you had more -- if the data, which
OSHA put together in a PEA, had more than 50 percent of the employees in
a given job that were overexposed, we assume that all of those workers
needed to have a control, not just half of them.  And that's more
realistic of what would go on in the real world.

		The physical limitations of controls, this is just an example that we
have given here.  The OSHA model, as I have already said, is a general
rule to assume these four workers for control.  Well, a good example of
where that does not hold is in a lot of the mobile equipment, front-end
loaders, tow motors, and so forth that operate in these facilities.

		If they are put into an enclosed cab with an air-supplied system to
protect them from the silica, there is typically one person that
operates that on a shift.  And at most, if they're running two shifts,
there's two people.  OSHA assumed there was -- that was going to cover
four people, when in fact, it typically is only going to cover one, two
at the most.  So those are -- for those types of examples, we adjusted
the model to reflect that type of thing.

		As I -- another situation I kind of alluded to earlier, iron foundry,
the muller operation project, part of the job or the mixing areas may
have one worker that is running around, taking care of five, six, seven,
eight mullers in a given shift.  Every one of those has to have a
control and a proper control.  So there you've got eight controls per
person.  So, again, just another example of why we had to make
adjustments.

		All right.  URS incorrectly used -- excuse me.  What did I just say?
URS?  Thank you.  It is getting late in the afternoon, okay.  OSHA
incorrectly used exactly the same controls to achieve the 100 PEL as
they did the 50 PEL.  And this is just not technically correct.

		In other words, it takes much more sophistication to get from -- to
50 or even to 25 action level than you are just to get to 100.  And this
is what's in the model, so there's no additional or more effective
controls to get to 50 than it is to get to 100, so this artificially
reduces the cost.

		It's clear that the four workers that are estimated to be exposed
above 50, but less than 100, it is reasonable to assume that most cases
that controls for the 100 PEL are already in place to meet the PEL.
Clearly, additional more effective controls are going to be required.

		So URS and their model increased the sizes and the efficiency of some
of the controls, which increases the cost.  It made them larger,
stronger, such things as the LEV, local exhaust ventilation, increased
the cfm to overcome and collect more of the dust or the tweaking that
may be done to those things, so that was taken into consideration.

		With regard to the model controls that were used by OSHA, URS felt
like they were considerably lower than they needed to be for many of
these industries.  We looked at this on an industry-by-industry basis in
sizes.  The capture velocity, such as for LEV, typically 100 all the way
up to maybe as much as 250 by OSHA.  For many of these industries, that
is simply not doing the job.  It would have to be sometimes two, three,
four, five times higher than that in many cases.

		OSHA -- I mean, URS did increase those to take into account that when
OSHA did their assessments, well, I should say, in the PEA, the
feasibility determinations for meeting the 50 PEL are anecdotal at best,
often based on a very few selected samples slightly below the 50 PEL,
and often after much trial and error that has taken place at that plant,
which is important to the cost aspect.

		I will just say the trial and error process, after you make these
corrections in plants and go through the process, you add a new local
exhaust ventilation.  You make a change in the table that the person's
working on.  You go back and sample again.  No matter what a great job
you do, you're going to find people that are still not in compliance.
And you have to go back and further adjust, and maybe even replace those
things.  And those are not -- those are costs that are not even built
into the URS model or the OSHA model.  So those alone could raise the
price by 25 to 50 percent.

		When OSHA looked at the -- it appears that when OSHA looked at the
data that's represented in the PEA, where they did have numbers less
than the 50, they never did a statistical analysis of it like's been
talked about, the 95 percent SAE method that's commonly used by OSHA
field personnel in doing inspections.  The result is that it's an
over-optimistic view that OSHA has taken to identify what can be
accomplished by these controls.

		OSHA's cost model did not always use all the controls recommended in
the PEA.  In the cost model, there would be things recommended that
would be necessary to meet it.  They simply think in some cases, those
are just left out of the model.  They would have been needed to reduce
the contamination or the exposure rate that was projected.

		How are we doing on time?  Okay.  All right.

		JUDGE SOLOMON:  Well, if Mr. Sessions is going to get a half an hour,
you've got about 10 minutes.

		MR. WAGGENER:  Okay.  All right.  I've got more here than I can
possibly say in that, so we'll scoot through.

		Because OSHA incorrectly calculated the incremental cost of the
proposed, OSHA subtracted the entire cost for all workers over the 100
PEL, and there were a number of people over the 100 PEL, to obtain the
incremental cost, even though the controls used by OSHA were intended to
achieve the 50 PEL.  In effect, all of these workers that are over 100,
which is more than half when you get down to the total amount that OSHA
cost, have been excluded from the cost model completely.  There is no
cost in there for those guys to reach things.  This is, to us, a huge
issue.  Stu will talk a little bit more about that.

		Anyway, we made corrections with regard to that.  The cost of the LEV
or the cfm, OSHA used a number, and we went through a process to come up
with a number that is almost double what OSHA came up with in terms of
the basis for the annualized cost for LEV, for instance.  And it's
backed up by a number of things that are in the record.

		The next thing is dealing with the ISO/CEN, which roughly we'd assume,
based on the analysis, that we would pick up an additional 20 percent of
silica, which all the PEA data is really based on the old method, not
the new method with the ISO/CEN, so we increased the number of exposed
employees.

		All right.  Moving right along, ancillary costs.  Getting out
engineering, ancillary costs.  We looked at these different items.  We
made changes to the initial exposure monitoring, periodic monitoring,
respirator use, medical surveillance training, regulated areas,
professional cleaning.

		Okay, the initial exposure monitoring, we believe OSHA underestimated
the time and cost for the CIH or CIH assistant or helper to actually
perform the task of taking samples, evaluate the results and create a
report.  URS added one day's time to that cost estimate.

		URS, excuse me, OSHA's low estimate for workers requiring this simply
used one-fourth of the workers in at-risk job.  URS has increased this
for very small facilities.  We said, you need to test all of the
employees, where we have only eight -- maybe eight different employees
on average.  And those employees are doing a lot of different things.
They're not doing one job.  They're doing two or three or four jobs,
which involved jobs that have exposure.

		For the small facilities, you would use one-half of the workers in
at-risk jobs, I should say, over a two-shift basis.  And then we adopted
OSHA's one per four method that they actually used.  We amortized
because of what actually occurs in facilities.  We amortized this over 5
years instead of 10 years because there is changes in production and
controls that go on, and it's more likely to happen over a 5-year period
than over a 10-year period.  It means you have to go back and do
monitoring.

		For the periodic monitoring, OSHA, who we believe is unrealistically
low, assumed that 15 percent of the workers would be over the action
level and that no worker would be over the PEL.  Well, based on the
discussions you've heard earlier and today, that is just simply
unrealistic.  We expect many people to be over the PEL and many more
people to be over the action level.  The proposal has that if you are
over the PEL, you will have to do quarterly sampling for that job, and
semi-annual monitoring if you're over the 25.  And there's, as I've
already said, lots of trial and error that goes into that.

		So we've increased the cost model significantly.  We assume that, for
people over the 50 PEL, at least half of the workers that are currently
over the 100 PEL that have not been able to get under 100 have not
accomplished this.  Let's just remember, this has been around a long
time.

		These companies have been working very hard to be under the 100 PEL.
They don't want to be over the 100 PEL.  And they have not been able to
get there, so it's not realistic to assume we're going to go to a level
that we can hardly measure accurately and be able to assure ourselves
that we're going to be under the 50.

		And respirator costs and use, it was somewhat the same thing, without
going into all the details.  Again, these companies, I've got an example
here, 44 percent of the iron foundry cleaning and finishing operators
are still over the 100 PEL.  And these facilities have been trying to do
it for years.  It's just unrealistic to assume you're going to be down
to a very low level and be only 10 percent over the PEL as OSHA assumed.

		Medical surveillance, again, you're going to have a lot more people we
believe that are going to have to go onto medical surveillance because
it will be at a higher level than the 25, and particularly the 50, PEL
that OSHA has estimated.  For ongoing medical, we've done somewhat the
same thing.  We've assumed that half of the workers initially over the
100 PEL would still exceed the 50 and need to have the ongoing medical.
And training had some labor issues with regard to the labor costs that
were used by OSHA.

		Regulated areas, this is where you have areas where people are over
the 50 PEL, and you have to have regulated areas.  And we'd made
different estimates than OSHA did based on the fact that we believe that
it will be much higher than what OSHA has estimated, such as having 1
visitor in very small facilities, 5 and 20 in large facilities.

		One major issue and very costly, in the PEA, OSHA estimated -- or
indicated that, to meet the 50 PEL, you would have to have professional
cleaning annually or more frequently for both the concrete products,
foundries, mineral processing, porcelain enameling, pottery, and
structural clay.  And then, in the OSHA model, I think this is just an
oversight by OSHA, the OSHA model, the engineering cost model, indicates
that they are going to do it in an ancillary cost.  But when you go to
the ancillary cost model, they didn't do it.

		So URS added that to the cost of the rule, based on industry's
estimate.  That's about a dollar per square foot of facility.  So you
have all these things adding up to be the last slide, which is like the
one I began with almost, which basically says that we are at about $6
billion for the 19 industries, some 56 times higher.  We found many
flaws, and we've tried to make real-world corrections to those to come
up with a model.

		With that, I'll turn it over to Stu, who is sitting right by me.  You
wanted to change places?

		MR. SESSIONS:  We're party to an unusual event here, which is our
attorneys are handling the tech issues that are associated with the
presentation.

		JUDGE SOLOMON:  There's a false assumption that attorneys aren't
versatile enough --

		MR. SESSIONS:  Yes, I'm quite impressed.

		MR. WAGGENER:  If we had our grandchildren here, they would've figured
it out a long time ago.

		MR. SESSIONS:  But we're not equating attorneys with our
grandchildren.

		JUDGE SOLOMON:  Is this any different than your report that you gave
the other day?

		MR. SESSIONS:  Yes, it is.  It is different, same red-colored
PowerPoint.

		I'm Stuart Sessions.  I still am the President of Environomics,
Incorporated, which is a small consulting firm in Bethesda, Maryland.
We work on economic analysis of regulatory and policy issues involving
the environment, occupational safety and health, and energy.

		Perhaps most important -- and I'm testifying on behalf of the American
Chemistry Council, which has supported my work.  Perhaps most important,
in terms of my background for this presentation, I also, for a number of
years, had management positions in the Office of Policy Analysis at the
Environment Protection Agency.  And I ran a branch there which
essentially evaluated the impact of EPA regulations on individual
plants, on industries, and on the national economy.  And then I managed
a division which, in a sense, oversaw the agency's procedures for
analysis of regulatory impacts of all sorts.

		That experience leads me to believe and to offer suggestions at a
number of points that, when OSHA has run into sort of difficulties or
inaccuracies in handling particularly regulatory analysis issues, there
are a variety of other ways that other agencies have undertaken to
pursue handling these difficulties.  And in many cases, I think our
experience working for and with other federal agencies informs our
comments and suggestions to OSHA, both about methodology and data
sources.

		In the interest of time, I'm going to skip around pretty substantially
and just sort hit some highlights of the testimony.  A first issue that
I want to talk about is a topic that had been mentioned actually several
days ago, and that both Neil in his introduction and Jack have
referenced, which is this question of what we call full versus
incremental cost.

		And I want to kind of portray exactly what we believe OSHA has done in
the current analysis in terms of estimating the costs for general
industry to comply with the proposed rule.  In looking at this, we
divide the world into two groups.  There's the group of workers who OSHA
currently assesses as being exposed between the proposed PEL and the
existing PEL, workers who are exposed at between 50 and 100 µg/m3.

		There are, according to OSHA's exposure analysis, 41,500 of them --
sorry, let me back up with another interjection.  All of these numbers
and all of my presentation -- almost all of my presentation, all of
Jack's presentation, have addressed a general industry, with the
exception of hydraulic fracturing.  At one point, I will mention
something about hydraulic fracturing.  But all that we have been saying
is general industry less hydraulic fracturing, so these numbers in
particular are less hydraulic fracturing.  They're not counted in that.

		In any case, so Group A are those exposed between the existing PEL and
the proposed PEL, and there are 41,000 of them.  Group B is all the
workers who OSHA estimates to be currently exposed above the current
PEL, and there are 81,000 of them, for a total of 122,500 workers who
OSHA estimates will need additional protection if the PEL is reduced to
50 µg/m3.

		So OSHA's cost analysis essentially estimates the cost for all 122,500
workers' exposures to be reduced below 50, and then subtracts from that
total the cost for reducing the 81,000 workers who were exposed above
the current PEL to below 50.  And OSHA's, what we call, "incremental"
cost estimate or, in some cases, they call it their "additional cost
estimate," is in fact only the cost to address the exposures of the
41,000 workers who are exposed between the proposed PEL and the current
PEL.

		And in the cost estimates that are reported by OSHA, in the preamble
or in the PEA, there is zero cost counted for the 81,000 workers who
were exposed above the current PEL.  So we contend, among other things,
that cost ought to be estimated in some manner for all 122,500 workers
OSHA reports cost.  OSHA estimates the need for controls for only the
41,000 exposed between the two PELs.

		Now, OSHA portrays their analysis as attempting to capture the
incremental costs attributable to moving the PEL down from its current
level to 50.  But I submit that some of those 81,000 workers -- let me
put it a different way, all of those 81,000 workers, their employers
would incur substantial costs due to the reduction in PEL because the
Group B workers, in order for their employers to meet the current PEL,
the employers would make efforts to reduce their exposures to just below
100.  Basically, you are in compliance with the current PEL if you get
those overexposed workers down below the current PEL.

		And so, the impact of -- the incremental impact of the reduction in
PEL will require, in fact, reductions among -- reductions in exposure
among those 81,000 workers from somewhere just below 100 to somewhere
just below 50.  So OSHA, in fact, has taken all of the costs for those
81,000 workers off the table, and arguably, the cost if OSHA were
correctly to do the incremental cost analysis that the Agency says that
it is intending to do, those 81,000 workers would have a substantial
cost.

		And, in fact, the cost per worker for those 81,000, if their exposures
were just below the current PEL, would be about the same as the cost per
worker for the 41,000 that OSHA does count.  So even in OSHA's
incremental analysis, OSHA has left out very roughly about two-thirds of
the cost that the Agency should calculate.  So that's kind of an
overarching point about the cost analysis.

		And it's actually two points.  One point is that OSHA has done the
incremental cost analysis incorrectly.  And the second point is that
when you are talking about economic impacts, which is a question of how
affordable are these costs or what impact will these costs have on the
industries that need to bear these costs, the correct notion of cost to
address, if you are worrying about economic impact, is the full cost.

		It's not just the incremental cost of reducing the PEL that these
industries will need to bear.  It's the cost of coming into compliance
with the current PEL also.  And it's the combination of those two costs,
it's the sum of those costs, which determine the economic impact of
attempting to comply with the proposed new PEL.

		Another point that I want to hit very briefly, and this is the one
time at which I will talk about all of general industry, including
hydraulic fracturing, this is just a summary of what the various
industry groups that have been evaluating this regulation have added up
as the total cost of the rule.  And so, general industry, with excepting
hydraulic fracturing and focusing in particular on the 19 of the 31
sectors that Jack mentioned, have a total cost estimate from our point
of view, from industry's point of view, of about 6.1 billion per year.

		Hydraulic fracturing, according to the estimate that the American
Petroleum Institute has prepared, estimates a cost of attempting to meet
the proposed PEL.  And API's cost estimate is very important to realize
that they don't believe that the technologies that OSHA puts on the
table for meeting the proposed PEL will, in fact, do it, and they don't
believe that those technologies are, in fact, available.  But assuming
that they were able to do it and that they were available, API estimates
those costs at about $365 million to $366 million a year.  And the
construction industry estimates the cost of the construction regulation
at $3.2 billion a year.  A total across all the industries for the
entire regulation is about $9.7 billion per year, in contrast to OSHA's
estimate, which is, if I can read the fine print up there, $686 million
per year.

		So it's not just the general industries that Jack talked about for
which industry is disagreeing sharply with OSHA's cost estimates.  It's
all of the affected industries.

		The key issue in estimating economic impacts or in assessing economic
impacts, or assessing economic feasibility of the regulation, is in a
sense comparing the compliance cost that any industry will bear against
that industry's ability to bear the cost and against, in particular,
that industry's revenues and profits.  And OSHA has taken this kind of
sensible sort of approach towards economic impact assessment for all of
the OSHA regulations that I have ever worked on.

		I don't know how long this practice has been in effect, but it's a
very sensible general approach.  And OSHA's general rule of thumb is to
get worried about affordability if the cost exceeds either 1 percent of
revenues or 10 percent of the industry's profits.  So costs below that,
there's a good case that the proposed regulation is economically
feasible.  Costs that exceed either of those two thresholds, there is
some question about economic feasibility and further analysis is needed.

		Well, to make this comparison, you need at least three pieces of data.
 You need an estimate of the cost of the regulation, you need an
estimate of the industry's revenues, and you need an estimate of the
industry's profits.  And so, Jack has talked extensively about
re-estimating the cost to the industry.

		I'll talk a bit about the process of estimating revenues and profits
for the affected industries.  And there's some amount of art and science
to estimating revenues and profits.  And there are differences of
opinion that can exist on those topics, and I'm going to talk a little
bit about some of those.

		In particular, on estimating profitability, basically OSHA took data
from a particular source on profitability for these industries, from the
years 2000 through 2006, averaged over those seven years, and said
that's the average profitability for each of these industries.  And
that's what we will run the comparison of compliance costs against.

		An easy objection or an easy criticism of what OSHA's done is
profitability for these industries now, particularly after the
recession, and profitability for these industries now, particularly
given that most of these general industries produce construction
products and the construction industry has had sort of a housing crisis
in addition to the recession to deal with, profitability now is much
different than it was for these industries between 2000 and 2006.
Certainly OSHA recognizes that and OSHA will, as OSHA staff have said,
and as they always do with the regulations, will update the data to
reflect the most current data available.  In particular, the data source
that OSHA uses is something called the Corporation Source Book.  And
that is available now through 2010, so you can update the data through
2010.

		And, in fact, in some of the numbers that I will report later, we've
updated OSHA's data through 2010.  And I call this new profit series
something called "Revised Profits."  And so, I will show some
comparisons of costs against profits as OSHA estimated them and costs
against revised profits as we've estimated them.

		But another very significant issue with regard to how OSHA chose to
measure profits is what data on profitability OSHA took from this
reference, this IRS Corporation Source Book.  And, basically, the IRS
reference includes a sample of tax returns from corporations in every
industry in the country.  And OSHA chose various data from that which is
available in the Corporation Source Book to represent profitability.

		In particular, my Point 2, I want to stress this, is essentially,
reported in this IRS documentation, OSHA data chose data for each year
for only those corporations that were profitable.  And so, OSHA, in a
sense, estimated the profitability, the profit rate, each year for the
corporations in that year were profitable.

		So in my equation under Number 2 here, essentially OSHA took total
profits for those corporate tax returns that showed positive profits,
and divided that by, quite interestingly, total revenues for all
corporations, whether they showed positive profits or not.  So that's
really kind of an unusual calculation where the numerator is profits for
one group of companies, and the denominator is revenues for a different
group of companies.  In some sense, that doesn't make sense.

		Basically, when you do the numerator and the denominator, you want
both apples in the numerator and apples in the denominator.  But,
anyway, the more important point is that the way we believe OSHA should
use this Corporation Source Book IRS data is, let's take the profits
from all corporate tax returns, whether the companies show a profit or a
loss, and let's compare that against the revenues for all the companies
in the industry, whether they show a profit or a loss.  And let's call
that the profitability of the industry.

		In particular, what's wrong with what OSHA does?  Well, it's wrong to
estimate profitability, to calculate profitability, looking only at
profitable companies.  Every year, there are some companies that are not
profitable.  And from year to year, the companies that are not
profitable for a variety of reasons tend to differ.

		So, you know, the regulation applies to all employers in the covered
industry.  It does not apply to only the profitable employers in the
industry.  And to assess the affordability of the costs, you ought to
look at the entire scope of the industry, profitable and unprofitable
companies, in judging affordability.

		And, in particular, one would think that if you're concerned about the
possibility that the regulation isn't affordable, that you would be
particularly concerned to include in the calculation the companies that
are unprofitable.  Those are the ones that are most vulnerable.  Those
are the ones you would most want to have in the calculation.

		So, basically, there are issues in terms of what OSHA has done with
regard to profitability involving the recency of the data, which can be
corrected to some degree.  You can go up to 2010, if you take the same
data source, but a big issue with regard to how OSHA is selecting data
from that source and how it's using it.

		I guess I'm not going to go through this, but the entire testimony
package will be in the record.  And I refer OSHA, in particular, to
this, which is a sample of this calculation for one particular industry
for one particular year.  And I've split the IRS data into the returns
for profitable corporations and the returns for unprofitable
corporations.  And this slide shows what OSHA did and what I would argue
they should have done.

		Another point, which is a different point, about estimating
profitability is this particular data source, the Corporation Source
Book, provides information at what's known as the three- or the
four-digit SIC level -- NAICS, excuse me.  So it provides information
for rather aggregated industries.  In fact, OSHA's cost analysis, and
much of OSHA's analysis, is more granular, and it estimates costs at the
six-digit industry level.

		When it comes, though, to comparing the cost for a particular
six-digit industry, I'll take a particular six-digit industry, ceramic
wall and floor tiles is a six-digit industry, which is within a
four-digit industry, which is clay and refractory and other non-metallic
minerals.  OSHA estimates costs for six-digit industries, but the
profitability data available via the particular chosen IRS source is
available only at the three- or the four-digit industry level.

		The net result is that OSHA represents the profits for the component
industries within a very large industry by the profitability for the
entire industry as a whole.  And you get results like for asphalt
paving, which is a very specific six-digit industry, OSHA represents the
profitability for that industry by the profitability of a larger
industry that includes petroleum refining.  And petroleum refining is,
you know, some 50 times larger than asphalt paving mixtures.  And there
is no particular reason why the profitably of asphalt paving mixture
should bear much relationship to the profitability for petroleum
refining.

		And this table just shows -- essentially its fine print, it shows
what a small percentage of the aggregate industries about which OSHA
draws profitability information, what a small percentage each of the
individual industries to which OSHA transfers that information, that
these small industries are a very small percentage of the large
industries.

		Now, this is a tough issue because profitability data is -- there are
reasons why OSHA chose the IRS source.  There are pros and cons to it.
And my bottom line suggestion on this is there are other sources of data
that go to the six-digit NAICS industry.  And OSHA ought to, in my view,
to get things more correct, do the best job it can in comparing and
combining the profitability information from the different sources.

		This issue has been discussed before, and in fact, OSHA back for the
SBREFA proceedings in 2003 used a different source for profitability
information that, in fact, does have much more granularity at the
six-digit industry level.  And OSHA backed away from that, and I think
the answer ought to be some sort of combination of attempting to make
the best judgment.

		Okay, Neil says I've got 10 minutes.  I will say one -- two rather
quick points about how OSHA estimated revenues.  The difficulty in
estimating revenues is good data on revenues by industry is available
only for every five years.  It was available for 2007, which was before
OSHA did this analysis.

		So OSHA's revenue data for 2006 was sort of generated by taking 2002
revenue data and updating it in a way that doesn't work to 2006.
Basically my answer on revenues is strong recommendation that OSHA
should wait for the 2012 data to become available.  It will become
available later this year.  And that will be far better than the
procedure that OSHA has.  Bob is shaking his head.  Maybe it won't be.
Maybe you have some insight that it won't be available later this year.

		In any case, the way OSHA has done it, taking the ratio of payroll,
which is an annually available statistic to revenue, does not work.  And
we provide a couple of charts to that effect that the ratio of payroll
to revenue is not constant over time.  It is not constant across size
categories within an industry.  And, furthermore, the ratio of payroll
to the number of establishments has declined very sharply since 2006.
And that's a reason why 2006 data certainly is not representative.

		Basically, what I want to spend my remaining time on is essentially
what do we get when we look at these comparisons of cost against profits
and revenues.  And the first table is basically comparing OSHA's cost
estimate, which is incremental costs as opposed to full costs, which
I've talked about, against OSHA's data and less than desirable estimates
of revenues and costs.

		And in the lower right corner, I apologize again, people can't see the
fine print, but basically for no industries doing this comparison does
OSHA find cost exceeding 1 percent of revenues or 10 percent of profits.
 The next comparison is, if you look at OSHA's full cost, i.e., the cost
to reduce exposures for all 122,000 workers, not just 41,000 workers.
In fact, looking at full cost on the far right-hand column, 3 of the 19
industries exceed 10 percent of profits.  So even using OSHA's own costs
and OSHA's own estimates of profits, if you switch to full cost, you
begin to see potential affordability problems as measured by profits for
3 of the 19 industries.

		When you switch to URS' cost, as you might expect, if URS is
estimating cost 60 times higher than OSHA's incremental costs -- in
fact, on this table, I've put OSHA's full cost.  You can pull out of
OSHA's spreadsheets the Agency's full cost estimates rather than the
incremental costs.  URS' cost estimate is some 30 times the OSHA's full
cost.  And in the center two columns, it's mostly yellow, which means
that for most all of the 19 industries, URS' estimated costs exceed 1
percent of profits or 10 percent of revenues.  Did I say -- yeah, 1
percent -- did I say that wrong, 1 percent of revenues, 10 percent
profits?

		And the last table I want to show is what happens when we make just
the small adjustments to profits, which I've talked about, which is
extending the profit series from 2006 to 2010, but including in the
profit calculations each year both profitable and unprofitable
industries.  The middle two columns that have yellow, let's see, well,
let's see, the third from the rightmost column is a comparison between
OSHA's full costs and redefined profits, marginally adjusted or somewhat
recalculated profits.

		And in this case, OSHA's cost -- OSHA's full cost exceeds 10 percent
of profits for getting close to half of the affected industries.  So
even a marginal adjustment of -- in the way in which profitability is
calculated, combined with looking at full costs rather than incremental
costs, leads to substantial questions about economic achievability, not
to mention what would happen if we did further adjustments to better
reflect revenues currently, which I believe on a per-facility basis are
substantially less than revenues in 2006.  And further adjusting profits
to reflect 2011 through 2013, putting them in the equation, also.  So on
balance, it's our balance that there is a substantial case that, for
most of these industries, that the proposed regulation is not
economically achievable.

		JUDGE SOLOMON:  You have five minutes.

		MR. KING:  I think -- the one last point that I think Mr. Sessions
might not have focused on quite as much is the last two columns in this
last chart show the full cost as estimated by URS as a percentage in the
one case of OSHA's profits and the other case as a percentage of the
revised profitability figures.

		And when you look at that, you see that virtually all the said
industry sectors exceed the 1 percent or 10 percent margins.  Moreover,
you find that for 10 of the 19 affected industry sectors, the URS full
cost exceeds 100 percent of the profits for the industry sector.  And we
would suggest that, where that is the case, there really isn't much need
to do further economic analysis, assuming you accept the numbers because
that's clearly going to show, we believe, economic infeasibility.

		Thank you.  I think that's the end of our presentation.

		JUDGE SOLOMON:  Do we have some idea of how many questioners?  Okay.

		MS. LINDBERG:  Your Honor, if you don't mind, I think we'd all
appreciate a short break, if our panelists don't object.

		JUDGE SOLOMON:  Okay.  We'll take five minutes.

		(Off the record at 3:55 p.m.)

		(On the record at 4:00 p.m.)

		MS. TRAHAN:  Hi.  Chris Trahan with the Building Trades.  It's
T-r-a-h-a-n.  And I have a question, I think, for Dr. Lee.  Your
laboratory analyzes crystalline silica with a limit of quantitation of
what?  What is the limit of quantitation with your lab?

		DR. LEE:  I think per AIHA, we use a reporting limit rather than a
limit of quantitation.

		MS. TRAHAN:  Okay.

		DR. LEE:  And we use that on the basis of the lowest standard.  So --
and that's 5 µg.

		MS. TRAHAN:  Okay.  And do you feel if one of your clients received
their report that a sample contained 9 µg, they would be able to feel
confident in that result?

		DR. LEE:  Depends on what lab you're using.

		MS. TRAHAN:  Your lab.  Actually, my other job, my real job, is with
an organization that does use your lab for research purposes, and you
analyze our crystalline silica samples.

		DR. LEE:  Well, I think at 9 µg, the question is whether there's any
interferences.  If there's no interferences, then you should have a very
reliable result from us.  But if there's interferences, you might have
to get up around 15 to 20.

		MS. TRAHAN:  Okay, thank you.  And I guess I'll just leave it at that,
and maybe with a request, if you can share this ability that, I think
you and several other commercial labs do have, to have good accuracy and
precision at those low levels with the rest of the world, that would be
great.

		DR. LEE:  Okay.

		MS. TRAHAN:  Thank you.

		UNIDENTIFIED SPEAKER:  Can I add just one other factor, and with
respect to R.J. Lee Lab, it's also a research laboratory versus strictly
a commercial laboratory.  So their -- you know, their processes may not
be typical that you see out there.  So that's another facet of your
question.

		MS. TRAHAN:  That's important.  I think they're a great lab, so that's
good extra information.  Thank you.

		MS. LINDBERG:  Just a quick request, since there are so many of you
over there.  If you could state your name before you start answering a
question, that would be helpful.  Thanks.

		JUDGE SOLOMON:  Right.  Ms. Seminario.

		MS. SEMINARIO:  Peg Seminario,

S-e-m-i-n-a-r-i-o.  I'm from the AFL-CIO.  A few questions for
Mr. Waggener and then for Mr. Sessions on the work that you did on
your testimony.

		Mr. Waggener, on page 20 of your written testimony, you indicated
that you conducted various surveys and discussions with industry
representatives concerning OSHA's assumptions and cost estimates in a
number of different aspects of the cost analysis.

		What kind of surveys were they?

		MR. WAGGENER:  Well, they were actual surveys sent out to the
industry.  Also, a number of meetings and conference calls with industry
that were put together.  And not -- they're professionals in the
different industries.  Does that answer your question?

		MS. SEMINARIO:  So there was a written survey that was sent out?

		MR. WAGGENER:  Yes, that was one method, yeah.

		MS. SEMINARIO:  Could you provide that for the record, in terms of the
kind of information that you were attempting to seek, the kind of
questions and information?

		MR. KING:  You're talking now about the kind of a blank survey form, I
take it, and not individual company responses.

		MS. SEMINARIO:  Right, just what were they asked?  What was the survey
instrument?  What was the information that you were trying to obtain?

		MR. WAGGENER:  We'll get back to you.  These were all done in
confidentiality and to be filled in.  And I'm -- very likely they can
be, but we'll have to get back to you on that.

		MR. SESSIONS:  The surveys were done typically by individual trade
associations rather than by the panel as a whole.  And the question --
one of the questions would be whether the individual trade
associations -- some of the individual trade associations, in fact,
were not even members of the panel.

		MS. SEMINARIO:  Okay.

		MR. SESSIONS:  The question is whether they would allow for the panel
to release those.

		MS. SEMINARIO:  Okay.  But the question I had was, looking at the
instrument, and then who was surveyed, so it was trade associations and
then not the individual employers themselves?  So where was the
information solicited from, as far as the written survey?

		MR. SESSIONS:  The trade associations issued the surveys to their
individual company members, who responded as a company.  Many of them
were facility-based rather than company-based.

		MS. SEMINARIO:  Thank you for that clarification.  And how many of the
surveys did you receive back?  How many were sent out and how many did
you receive back?

		MR. WAGGENER:  I don't remember the exact number, but I'd say close to
200.

		MS. SEMINARIO:  200.

		MR. WAGGENER:  It was a very substantial amount.

		MS. SEMINARIO:  And was this representative of the different industry
sectors that are members of the ACC Panel?  You folks represent a broad
range of different industries that are impacted by this rule.  So was it
a representative sample of responses that came back from the different
sectors?

		MR. WAGGENER:  I would say it was certainly very representative of the
different industries it actually was sent to, yeah.

		MS. SEMINARIO:  Okay.  Thank you.  As part of this survey, did you
request and did you receive any exposure monitoring data from the
facilities about their actual exposures that they were seeing in their
facilities?

		MR. WAGGENER:  I'm not -- we didn't -- I don't believe that we
actually got the actual numbers that came from the facilities.  I'd have
to get back to you on that.

		MR. SESSIONS:  It varied from survey to survey.  One survey, I know,
asked each plant what's the highest number you've seen, what's the
lowest number you've seen.  Another survey asked the plant for a given
job category for -- each of the job categories within the facility,
roughly what fraction of the exposures that you've measured are above
the current PEL versus below the current PEL.

		So some of the questions were fairly specific with regard to job
category and reference -- results across multiple samples.  I don't
think anybody asked for, could you give us your exposure data, and
turning in the actual data points, but various levels of