Is it tackling the right questions?  And also the methods, is it
tackling those questions in correct ways?  So I'll tell you what I think
about that, and that will include touching on some of the topics that
those of you who've been here for a while have already heard.

		So I, too, will comment briefly on some biases, the threshold issue,
how well uncertainties have been characterized, and I especially want to
underscore the topic of causality.  So those -- that's the meat in the
sandwich.  That's the key part of what I want to cover.

		And then I'll switch to conclusions and recommendations.  My
understanding of at least part of the purpose of this hearing is to say
how good is the current preliminary QRA?  Out of that flow
recommendations, saying how might it be done better?  And so I'll close
by touching on those.

		Let me start with what I take to be the goals for a good quantitative
risk assessment, and here I'm drawing heavily on the NRC volume, Science
and Decisions, which emphasizes that a good quantitative risk assessment
is, from the outset, designed to support better risk management
decisions.

		So here, the risk management decision of interest is should we
change -- lower the standard for crystalline silica in order to protect
worker health?  Is that a desirable decision, or will it not produce the
effects that we want it to produce?

		So we want to support improved risk management decisions by revealing
the probable consequences of different choices.  If we do this, what are
the likely consequences?

		And at the same time, we're all imperfect human beings.  We need to
acknowledge there is some uncertainty left.  So if we think we know what
will happen if we do or do not change the current standard, how sure are
we that that will happen?  And an honest characterization of that
uncertainty is important to guiding decisions.

		My approach to reviewing the preliminary QRA was to ask, would this
document -- should this document convince an open-minded skeptic?  So
here I mean a skeptic who starts off saying, well, I don't know whether
this is right or not, but let's walk through and see what we think.  So
would this convince an open-minded skeptic knowledge about risk
assessment?

		And although the NRC suggests quite an elaborate process for
quantitative risk assessment, the three things that I want to focus on
are, first, decisions, on the right hand side, pulling the risk
assessment.  Are we going to be able to reduce uncertainty about the
likely consequences of different decisions enough to be useful?  That's
the sort of key point.

		And on the left hand side, have we formulated the right questions?
Have we scoped the risk assessment so that it answers those
decision-relevant questions?

		And in the middle, we have the familiar building blocks of
quantitative risk assessment and risk characterization and uncertainty
characterization and so forth, but with the purpose of supporting better
decisions.  So that's the framework that I'll be following.

		And for those of you who were planning to sleep in the post-lunch
period, let me just state my main conclusion, that I think my best
assessment is that the Preliminary Quantitative Risk Assessment is not
ready for use.  It's not ready for prime time, in some sense.  And
here's why.  And then I want to develop these points over the next half
hour.

		The answers and estimates -- risk estimates that the preliminary QRA
delivers are not trustworthy.  You shouldn't bet money on them.  They
might not be right.  They might not be wrong, but there's -- but
they're not trustworthy.

		Specifically, there are what I take to be important statistical errors
in modeling and inference.  Now, when anybody -- when a statistician
accuses anybody else of making an error, you're entitled to know, error
with respect to what standard?  What's the gold standard that entitles
you to say this is wrong or this is right?

		So I'll be referring to principles such as, if there is substantial
measurement error in exposure estimates, then that measurement error
should be modeled, that I think are uncontroversial.  I think that
people with any preference for the outcome -- yes, we should reduce the
standard, or no, we shouldn't -- would agree or should agree to these
principles.  They're widely accepted in statistics.

		I will touch upon numerous biases that I think have not been
adequately controlled.  And these biases lead to false positives.  It's
a natural question to say, well, couldn't they lead also to false
negatives, that is, failing to find something that's there?

		But for reasons that I'll elaborate on, mainly having to do with the
fact that uncertainties are ignored which artificially contract
confidence bands, the only effect of that is to increase false
positives.  It never increases false negatives.

		I'll talk about exposure measurement error.  And I heard that being
discussed before lunch.  I think it's an important topic.  But I'll talk
about it briefly because it's already been touched on.

		I do want to look at the possibility of thresholds, and I find that
the question of is there a threshold and how do we model exposure
measurement error are closely related.

		Okay, and then I want to talk about how uncertainties should be
characterized to give policymakers an honest understanding, an accurate
understanding of where the reigning uncertainties lie.  I think that has
not yet been well done, not only because exposure measurement error has
not been well characterized, but because modeling uncertainties have
been ignored, by and large.

		And then, thirdly, I'm going to make a case that what we really care
about here is a cause and effect relationship.  If we reduce exposure,
will that cause harm to workers to go down?

		And that crucial question, which is the one that ought to be of
primary interest to policymakers and risk managers, has not been
addressed at all in the Preliminary Quantitative Risk Assessment, except
for in a couple of passages where OSHA says, we think this is causal.
But they provide no information about why they think that, no rational
basis for following or refuting their reasoning.

		Almost -- it's almost the case that there is no discussion of
causation other than such opinions, and it is the case that no formal
causal analysis or causal modeling has been presented.  And this, to me,
is crucial.  It's causal relationship that we want.

		Okay, so let me talk about the scope of the Preliminary Quantitative
Risk Assessment.  By the scope, I mean, what questions does it set out
to address?  As I read the preliminary QRA, it seems to me that it
presents evidence of statistically significant exposure response
associations.

		I think we can and should quibble over how good is that evidence, how
strong are the associations, do we believe that they're really there?
But the basic setup is to say, let's document all the reasons that we
think that in numerous studies there's an association, a positive
association and a nonrandom association between exposure and response.

		That's the basic scope of the study.  And after going through hundreds
of pages of examination, the study concludes that reducing exposure
further would most likely reduce risk further.  And there's an
implication for policymakers that this is a good thing to do.  We want
to reduce risk.

		I want to point out to you that there is no logical bridge from "here
is an association" to "therefore, if you reduce exposure, health risks
will go down."  This is deriving a causal conclusion from non-causal,
i.e., association-based data, and that, there's no legitimate basis for
doing.  You need to use causal analysis if you're going to draw valid
causal conclusions.

		Key questions for evaluating -- excuse me, evaluating any
quantitative risk assessment are, first, are the presented results
valid?  Do we believe them?  And what I mean by valid, in this case, is
do the results flow from the data?  Do they follow from the data, or are
they interpretations imposed on top of the data, where somebody could
equally well have reached a different conclusion from the same data?

		Secondly, do the premises imply the stated conclusions?  And I've
already told you my conviction that from non-causal analyses, valid
causal conclusions do not follow.

		Thirdly, how shall we determine the answer?  And I'll just take the
shorthand of saying, we ought to apply appropriate methods.  They're
there; let's use them.

		And, fourthly, how confidant can we be in the answer?  So I'll look at
those things.

		But let me begin -- I think everybody here -- most likely -- I'm
not going to ask for a show of hands.  But I would expect everyone to
agree that association is not necessarily causation, although if
causation is there, you might expect to see association.

		Let me remind you with a simple example, one of hundreds, of why this
distinction matters.  Here I've plotted on the x-axis the cell phones
per capita in different countries, and on the y-axis, the cardiovascular
mortality rate per capita in those same countries.

		And you can see that there's a roughly linear, non-threshold, clearly
statistically significant association between cell phones per capita and
heart disease per capita -- I'm sorry, cardiovascular mortality per
capita.  And yet I suspect that few of us would be convinced by this
association that reducing cell phones in a country would reduce
cardiovascular disease, right?

		So here we have association without causation.  We might have
excellent goodness of fit, and we do.  We might have a high R squared,
and we do.  What we're missing is, what would happen on the y-axis, the
health outcome, if we were to change the x-axis, if we were to force a
change by reducing exposure to cell phones?  That's the question that we
want to know the answer to.

		Common sense says, well, nothing would happen.  What I want to
emphasize is that there is nothing in the statistics and nothing in the
data that provides warrant for drawing a causal conclusion from
non-causal data.

		And yet what OSHA has done, in effect -- and in fairness, not just
OSHA but many papers on which it relies, is exactly analogous to saying,
well, look, look at this nice, strong relationship.  Therefore, if we
reduce exposure, health effects, health risks will go down.  It's an
unwarranted conclusion.

		And this is true in key studies.  This is true of Attfield and
Costello, 2004.  It's true for lung cancer studies and for non-lung
cancer studies.  There's a real problem here, even in IARC's analysis,
which is the question, "How much of this association is causal?" is
never explicitly addressed or answered using reproducible, generally
acceptable methods.

		There is sort of an implicit assumption that the answer is 100
percent, the whole thing is causal, but it might be that 0 percent of it
is causal.  It simply hasn't been addressed.

		So, in terms of scoping, I would say, the scope didn't include -- the
scope of the preliminary QRA, excuse me, didn't include the question of
greatest interest to policymakers, one that should be of greatest
interest to policymakers, which is what would happen to health if we
were to change exposure standards?

		Now, there are many non-causal sources of association.  I want to
touch on a couple of them.  I notice that Dr. Park is in the room.  I
was struck, in reading the Park et al. paper from the early 2000s, 2002,
I think, that age categories of about 5 years were used, bins, to put
people into groups.

		It occurs to me that there's a phenomenon called residual confounding,
which is, if within a five-year bin -- older people tend to have higher
risks of lung cancer, for example, or any other disease, and if within
that same five-year bin the older people also tend to have had greater
exposure, then there will be confounding within the bin that is not
controlled for by creating different bins.

		So in other words, the words say we controlled for age by using
five-year age buckets, but in fact there can be residual confounding
that needs to be tested for and controlled for.  That would be one
possible source of an association that is not a causal association.
It's just that people who are older both have more exposure and have
higher risk.  But that doesn't mean that one causes the other.

		Coincident trends -- over time, risks have come down and exposures
have come down.  This is another possible source of association without
necessarily causation.  Modeling assumptions are a very popular and
often hard-to-spot source of association.  I'll touch on that in a
moment.

		Model interpretation -- I show you here a picture from Steenland et
al., from 2001, which the authors suggest is "reasonably
monotonic" -- their phrase -- and which they interpret as not
providing evidence for threshold.

		And what I'll say is if you look carefully at the graph, you'll see
that, in fact, there is a negative slope at low exposures, and again at
higher exposures.  Whether that's reasonably monotonic, I don't know,
but it's not monotonic.  It goes down before it goes up, although on
this scale that's a little bit hard to see.  You have to look carefully.

		But I'll also point out to you that if you thought there was a
threshold, it would be hard to imagine a dataset that gave stronger
support.  I mean, you could see there's really no increase in risk until
exposure gets sufficiently large.

		So I think there's a lot of just interpretation that gets put on
graphs and data in the OSHA key papers that somebody else could have
interpreted differently, and perhaps no less validly.

		Let me turn to the meatiest slide that I have here today.  I don't
want to go through all of these in equal detail because some of them
have been touched on already.  But I do want to look at, not questions
of scope, but questions of method.

		So a question of scope is did we ask and answer the right question.  I
think the right question is a causal question, which has not been
addressed.  I think the question of method is, have we used correct
methods to at least answer the questions that we formulated?  And here I
think there are significant problems.

		So, the overall theme here is one that was touched on more than once
this morning, which is that selection of studies and data and models and
methods and assumptions can create uncontrolled false positive
associations.

		Now, again, it's fair to say -- well, couldn't they also create
uncontrolled false negative?  And in the abstract, perhaps the answer is
yes.  But in the concrete, I think there's a steady push towards false
positives, as I'll now explain.

		On the study selection side, there has been a fair amount of
discussion already about whether the studies that OSHA thinks are most
credible, are most credible.

		Now, I'm familiar with the concept of deferring to regulators on
topics that they should be deferred to on.  But I do think it's fair to
say that if OSHA, or any regulator, sets out a set of principles that
they're going to use for choosing among studies, then they should abide
by those principles and not pick and choose some over others.

		In my written testimony, I have elaborated what seems to me to be
cherry-picking by OSHA where, for example, they preferred and used and
gave great weight to the Attfield and Costello 2004 study, even though
that study didn't meet the criteria that they had specified in terms of,
for example, having good measurement -- sorry, exposure estimation
data.

		And they rejected other studies, prominently Pam Vacek's study, that
to some people, at least using the criteria that OSHA had specified,
might seem to have been at least as good and probably better.

		My role as a commenter is not to say which is the right study, but it
is to underline that if criteria are going to be developed and used,
then they should be applied consistently.  Failing to do that creates
the potential for study selection bias.  And I think that potential is
manifested in the preliminary QRA.

		Data selection biases, this is another vexed topic that a lot of
people have spent a lot of time talking about.  The question is -- or a
question, which I'm not going to answer, is, is it legitimate to throw
out data, let's say at the high exposure levels, not only because it
doesn't show what you expect it to show, for example, because risk comes
down at high exposures and that just doesn't seem to make sense, but is
it legitimate to throw out that data if you have good reason to suspect
that that high exposure data isn't very trustworthy?

		Well, without being able to answer that question, is it a good idea to
throw out data, what I can say is if you are willing to throw out data,
then the significance levels that are quoted for whatever results you
come up with must be adjusted to reflect the fact that you've taken more
flexibility in your approach to data handling than the tests assume.

		The tests of significance don't provide for selectively excluding data
in order to get a coherent story.  And if one is willing to do that,
then one ought to pay the price of reducing p-values accordingly.

		On the data selection front, Park et al. excluded some high exposure
data.  Attfield and Costello excluded some high exposure data.  And they
did so not in an ad hoc way, and not simply to get a good fit, but they
did so because they thought there was a lot of exposure measurement
error at the high exposure levels.  And that may well be true.

		But if it is true, not only does one have to adjust the p-value for
tests after throwing out some data, just to keep the statistical
accounting honest, but then one ought to use methods and models that
explicitly take account of, model exposure measurement error in the data
that remains.

		It is a big, important factor and needs to be taken in account.  It's
not hard to do.  And it hasn't been done, either in the OSHA preliminary
QRA or in the key studies on which that QRA rests, including Attfield
and Costello, and Park and Rice and Steenland.  Not one of them used
correct methods, and these are not difficult methods.

		So that's on data selection.  On model selection and multiple
comparisons, I think we've all noticed that there's a lot of choice in
the modeling that's been done.  So different exposure metrics have been
tried, sometimes lag, sometimes not, sometimes cumulative, sometimes
something else.

		Different lag lengths have been tried, with inconsistent results
coming from different studies.  Different model forms, whether loglinear
or linear relative risk or something else have been tried.

		This multiple testing in order to see whether something looks
significant and coherent and easy to explain again carries with it a
necessary price if one is going to do valid statistical inference.

		And the necessary price is to understand that the reported
significance levels, the reported p-values are no longer valid in the
presence of multiple testing and need to be adjusted.

		The model form -- I've shown here, on the right-hand side, I've drawn
a picture in which there is no relationship between x and y.  What I did
is I just randomly sampled x values, and independently I randomly
sampled y values, and I created a random scatter plot.  But then I fit a
model to that data.  My model was risk as proportional to exposure, plus
some error term.

		And if you go home and do that, using your favorite statistics, what
you will find is that there is a statistically significant positive
relationship between these two independent variables.  How can that
possibly be?

		Easy.  If risk is proportional to exposure, that means when exposure
is zero, risk has to be zero.  So I've pinned down one end of the line
to go through (0,0).  And risk is never negative, and exposure is never
negative, and so all of the other values are positive.

		Well, if you pin one end of the line down at (0,0) and all the other
data points are positive, you can be guaranteed that that line will
slope upward.

		I show you this simple example because it illustrates in textbook form
the problem of model specification error, making up a model that might
seem intuitively plausible to you, but it has not been validated, and in
fact it leads to biased results.

		OSHA, as far as I know, has not used this particular model, but
neither -- and when I say OSHA, again, I mean here, the papers on which
OSHA relies, they've not presented responsible model diagnostics in
keeping with sort of good housekeeping practices for epidemiology and
statistics.  They haven't shown residual plots.  They haven't said, yes,
this model describes the data generating process pretty well.

		They have used goodness-of-fit comparisons, which are almost
meaningless.  Goodness of fit is a very weak criterion.  But there
hasn't been any thorough justification for models.

		And sometimes, going back to the original papers, it's been impossible
for me to figure out exactly how the models were chosen at all, even
after carefully reading the underlying papers, because they contain such
elusive words as "adjustments were made and a final model was selected
based on visual evaluation."  I can't reproduce that.  I don't
understand what it means.

		The result of picking any model, including the best-fitting one, and
then assuming that it's correct is to underestimate confidence
intervals.  They're too narrow because model uncertainty has been
ignored.

		This issue of ignoring model uncertainty is a problem for Rice et al.,
2001; for Steenland et al., 2001; Attfield and Costello.  None of the
key papers that I looked at, nor QRAs just -- sorry, nor OSHA's
preliminary QRA, in its discussion, provided a thorough treatment or any
treatment of model uncertainty and its effects on what the confidence
intervals for its risk estimates should be.  It's a grave omission.

		Okay.  Proceeding now more quickly, model over-fitting refers to using
the same model -- or I'm sorry, the same data, both to fit a model and
then to assess the goodness of fit.  It's well known that that leads to
an overly optimistic assessment of how good the model is.

		Biases due to residual confounding, we've already touched on, so I
won't say more about them.  Model uncertainty bias have -- I'll -- I
won't say more about it now.  Biases from exposure estimation errors,
because they were discussed this morning, even though I think they're
very important, I'll save for a final discussion of thresholds.

		Because to me, the question of is there a threshold there interacts
strongly with the question of have we modeled uncertainties in exposure.
 So I'll come back to that.

		JUDGE SOLOMON:  So you're at uncertainty characterization?  So could
you do this in about five minutes, and then we could have some questions
from the peers and then the public and then OSHA?

		DR. COX:  Yes.  I think that'll fit very well.

		MR. KING:  He's not at uncertainty characterization quite yet.

		DR. COX:  Yes.  Okay.  So the next question is are these complaints
nitpicking?  Is this -- no study is perfect.  Is this merely
highlighting imperfections, or are they essential?  And I think the
answer is that they are essential.  And in the interest of time, I won't
double-click on each of these things, but I'll give you my bottom line.

		If you ask how will risk change when exposure is changed, and what's
the ratio of change in risk to change in exposure, I believe that the
bottom line is that the answer is unknown.  There's nothing in the
preliminary QRA that provides a credible answer to that question, for
reasons similar to my cell phone example.

		Let me move on to the question of thresholds.  OSHA's analysis and the
supporting studies, I think, do not show that -- well, it's hard to
show a negative, but they don't show the absence of a threshold even at
relevant exposure levels, for example 100 µg/m3 or above, for the
cancer and non-cancer endpoints of interest, nor do they make absence of
a threshold plausible.

		A big reason for this is that they don't adequately model errors and
uncertainties in exposure estimates.  And those errors and uncertainties
can hide a threshold if it's there.

		I will say that there are, in my judgment, good reasons to expect a
threshold on biological grounds, and epidemiological data are not
inconsistent with there being a threshold.  Again, I'm showing the
picture of Steenland et al., 1991, which is not inconsistent with there
being a threshold.

		But OSHA did look quite hard at this question for lung cancer.  They
looked at using an incorrect or an inappropriate technical tool of Monte
Carlo estimate -- sorry, Monte Carlo simulation.  Monte Carlo
simulation does not address the problem that a correctly specified model
must include an error term for exposure.

		What it does is to replace that problem, fitting a curve when exposure
is uncertain, with another problem, fitting a curve when exposure is
soon to be known, and then it repeats that many, many times.  It draws
many know exposures from a distribution of exposures.

		That's not the same thing as correctly modeling the effects of
exposure measurement error.  So although they spent quite a lot of
effort there -- I mentioned Berkson error, for example -- but I don't
think that they correctly addressed the question.

		So now, and indeed I'm at uncertainty characterization.  I can be
swift.  Model uncertainty has not been quantified.  There's no base in
model averaging.  There are no other techniques here that correctly
characterize model uncertainty or its effects.

		Uncertainty due to errors in exposure estimates has not been
quantified.  An effort to do that for the special case of cancer
endpoints was made, but used again an inappropriate tool.  It's one that
doesn't solve the problem.

		Uncertainty about the causal fraction of associations has not been
quantified.  There's sort of an implicit assumption that it's 100
percent, but there's no explicit discussion of what the fraction is or
what the basis is for assessing the fraction.  And that, to me, is a
huge hole in the analysis.  You can't -- you basically can't answer the
question of concern to policymakers without filling that hole.

		And then uncertainty due to various biases including residual
confounding has not been quantified.

		So, in conclusion, on uncertainty characterization, the preliminary
QRA results, to me, have no known relation to the real world.  We don't
know how exposure and response to causal relation in the real world
looks, based on what's been done in the preliminary QRA.  That's why
it's not ready for use, in my judgment.

		So let me then wrap up with the following recommendations.  Fix what's
broken.  Fill the holes that are missing where they really matter.  And
that's basically the five things that I've talked about.

		So we ought to correct for biases.  We ought to do causal analysis,
very importantly.  We ought to model exposure errors.  We ought to
reexamine the question of thresholds taking exposure errors into
account.  And we ought to present an uncertainty analysis that includes
model uncertainty as well as exposure uncertainty, and explain whether,
when that's done, there's any evidence of an association remaining.
Thank you.

		JUDGE SOLOMON:  Thank you.  Ms. Ryder, do you want to enter your
appearance?

		MS. RYDER:  Sure.  My name is Anne Ryder from the Solicitor's Office.

		JUDGE SOLOMON:  Okay.  Now, any of the peer reviewers, do you want to
ask some questions?  Okay, so let's do this in order.  Why don't you --
you haven't asked any questions yet.  Would you go forward and state
your name, please?

		DR. MILLER:  My name is Brian Miller.  I am from the Institute of
Occupational Medicine in Edinburgh.  I'm an epidemiologist and
statistician.

		Dr. Cox, you're very critical of the analyses that have been done.  I
look at the epidemiology of silica and silicosis over many decades, and
I think everything that we've done can be criticized on the basis that
you have criticized the current work.

		Are you unconvinced that silica causes silicosis?  And have we been
wasting our time in bringing the limits down and protecting people from
silica?

		DR. COX:  No, I'm not unconvinced that silica causes silicosis.  And I
don't know whether or for how long we might have been wasting time.  If,
indeed, we're already below a threshold, and have been for some time,
then recent reductions won't have purchased any more -- I'm sorry,
recent reductions of exposure won't have purchased any improvements in
health.

		JUDGE SOLOMON:  Okay.  So we have two other people raised your hands?
Go ahead, doctor.  Dr. Ginsberg, if you want to get into --

		DR. CRUMP:  Kenny Crump's my name.

		JUDGE SOLOMON:  -- position, maybe that will save a few seconds.

		DR. CRUMP:  Good afternoon, Dr. Cox.  Good to see you again.

		DR. COX:  Good to see you.

		DR. CRUMP:  I have several questions.  I'm not sure I've prioritized
these appropriately.  You were very emphatic that OSHA should use causal
studies and causal analyses.  Can you tell OSHA of any causal studies of
silica that they should have used but did not use?

		DR. COX:  I think OSHA could look at a paper from around 2007 of
Brown's, on some of the issues and causal analysis, but I think that the
crystalline silica area has been behind other particulate matter areas,
such as PM2.5 epidemiology, in not using causal analysis methods.  So
no, I can't point to a good study that they should have included but
didn't.

		DR. CRUMP:  Thank you.  Well, you referred several times to the graph
of Steenland, and you said that this is not inconsistent with a
threshold.

		DR. COX:  Yes.

		DR. CRUMP:  Couldn't you say that about any study?

		DR. COX:  Perhaps I should have clarified and said it's not
inconsistent with a threshold at or above the current PEL.  And no, you
couldn't say that about just any study.

		DR. CRUMP:  You could not?

		DR. COX:  No.  I think that since it's impossible to prove a negative,
empirically, you can't say if you go low enough for any substance, might
not there be a threshold there?  You could never rule out that
possibility.  And I will grant you that.

		But here I'm making a stronger statement, which is not somewhere
between here and zero, but somewhere between here and infinity, we may
already be below the threshold.  This is not a small hypothetical
threshold that I'm complaining about; it's a threshold that we may
already be below in practice.

		And I think that there are plenty of chemicals for which the
hypothesis of a threshold exists at or above current standards could be
ruled out because you see people getting sick at current levels.

		DR. CRUMP:  Well, would you say the other side of that question is
also true, is any data are not inconsistent with a no threshold?

		DR. COX:  I think until exposure errors are well modeled, the data as
interpreted via the current set of models are consistent with many
hypotheses, including both that there is a threshold and that there
isn't.  I don't think that the data, as interpreted through models that
don't take into account exposure measurement error, tell us what the
answer is.

		DR. CRUMP:  Okay.  In that graph that we were talking about,
Steenland, you graphed the x-axis in a log scale.

		DR. COX:  Or I didn't, but he did, yes.

		DR. CRUMP:  Okay.  But that does tend to make most any graph look like
it has a threshold, does it not?

		DR. COX:  No.  If you take an exponential relation and graph it on a
log scale, you'll get a straight line.

		DR. CRUMP:  Okay.  Do I have time for another question?

		JUDGE SOLOMON:  Depends on Dr. Ginsberg.

		DR. GINSBERG:  No, go ahead.

		DR. CRUMP:  In your written testimony, you testified, mentioned
several times a review by Health Canada.

		DR. COX:  Yes.

		DR. CRUMP:  And they demonstrate that a threshold approach to risk
assessment is appropriate for silica-induced lung cancers.  And then
later you interpret that as findings of Health Canada previously
discussed showing that a threshold is likely inappropriate on biological
grounds.  Does the threshold approach necessarily imply a threshold?

		DR. COX:  I'm not sure.  Those are Health Canada's words, but I think,
in fact, Health Canada did assert the plausibility of a threshold, as
well as advocating a threshold approach.  So I would defer to them for
exactly what they mean, but I think they asserted both.

		DR. CRUMP:  It is true that EPA, for example, would often use this
so-called approach without necessarily implying that there is a
threshold.  Is that right?

		DR. COX:  Well, again, if the proposition is that EPA would often
apply Canada's threshold approach, I would defer to the people who are
doing that work.  I'm not really sure.

		DR. CRUMP:  And would you agree that this approach -- one more,
please sir, that this approach would not be appropriate for OSHA because
it's not risk-based?  And OSHA is -- must -- requires estimates of
risk.

		DR. COX:  No.  I don't think I would agree with that, because the
whole question about what is the risk turns on is there a threshold?

		DR. CRUMP:  Thank you.

		DR. COX:  Thank you.

		JUDGE SOLOMON:  Thank you.

		DR. GINSBERG:  Okay, my question is going to --

		JUDGE SOLOMON:  State your name.

		DR. GINSBERG:  I'm sorry.  Gary Ginsberg.  I'm on the peer review
panel.  Two directions for my questions: one is I'm a little dumbfounded
about the concern over causality, given all the animal evidence that --
and, again, I can understand your cell phone example, and your baby
aspirin examples, where there's an obvious explanation for the
correlation.

		And you may bring up radon exposure or asbestos co-exposures or
smoking co-exposures to explain some of the associations seen in some of
the epidemiology.  What you didn't talk about are the confounders that
may have gotten in the way.

		But we have animal studies and we have cell cultural studies that show
the effects of crystalline silica on biological systems, which help
explain -- and usually toxicologists use the animal evidence in a
causal way to support the epidemiology.

		So my first question is what do you make of the animal evidence, and
why is it you do have such a strong focus on causality as an issue when
we have such a strong -- as IARC pointed out, and as there's a lot of
animal data to support the cause and effect of silica on lung pathology?

		And then the second direction is, because I don't know that much about
measurement error and its implications on statistics, is it possible
that if the corrections were made for measurement error, that the
regression slopes would be strengthened, because my understanding about
errors tend to bias towards the null and weakened associations.

		DR. COX:  So let me address those in reverse order.  The proposition
that omitted or unmodeled measurement error biases result specifically
in regression coefficients towards the null is correct when there's only
one explanatory variable, or one independent variable.

		It is incorrect, as I think I heard before lunch, in multi-variant
models.  And here we're dealing with multi-variant models.  So the
correct answer, to me, is not to speculate about what would happen if we
were to correct -- make the needed corrections; it's let's make the
needed corrections and see what happens.  A priori, and at an abstract
level, it could go in either direction.

		What is -- what can only go in one direction is that when ignoring
uncertainty or failing to model uncertainty artificially reduces
confidence intervals, there is a bias towards false positives.

		Now, for your question about the animal data, to me, the animal data
are very coherent with human data in suggesting that, indeed, high
concentrations of various poorly soluble particulates can lead to
chronic inflammation, unresolved inflammation of the lung, and to a high
ROS lung environment that is conducive to the onset of fibrosis and
other lung diseases.

		However, the animal data are also very coherent, let's say insistent,
on the fact that such processes are threshold or threshold-like
processes.  One particle will be cleared, where many particles might
lead to a lung overburden or a unresolved inflammation situation.

		The relevant causality that I am emphasizing so much, and that took
you aback so much, has to do with is there a causal relation between
reductions in the current PEL and reductions in occupational risk.
That's the causal relationship of interest.

		I don't think that it's usefully informed by animal models, which show
why exposing the lung to high doses causes lung disease.  I think we
understand that.  It doesn't illuminate the question of what happens at
ambient levels at currently permitted levels, or at lower than currently
permitted levels.

		JUDGE SOLOMON:  Before you ask the next question, are there any
members of the public who want to ask a question?  Okay, I see one hand.
 Would you please get in line?  And it's Dr. Park again.  Go ahead,
Dr. Ginsberg.

		DR. GINSBERG:  Yes, so are you aware that there is an animal-based
model system which incorporates dose-response data on the critical
loading dose for silica in the lung, which projects to a human threshold
for, again, critical loading capacity in the lung before you start
getting inflammation and lack of clearance that is well below the PEL?

		And, in fact, if you factor variability in, that study's projection is
that it's about 10 times below the PEL where some workers would have an
impact that would lead to inflammation.

		DR. COX:  Are you referring the Kempel et al. study?

		DR. GINSBERG:  Exactly.

		DR. COX:  Yes, I'm quite familiar with it.  And here the single most
important word in your question is the word "project," how that
projection is done.  And the Kempel et al. study did not account for
exposure measurement error in estimating human parameters from which to
extrapolate from animal data.

		So, again, I think we're right together, that high dose data in animal
and humans is firmly in support of inducing diseases at high doses.  But
I don't think that that's informative about what will happen to health
when we reduce exposures below current levels.

		DR. GINSBERG:  Yes, I brought up that study because it has both low
dose and high dose.  The short time frames were low-level loading, where
they didn't even -- did not see a no effect level at the lowest
loadings.  So I think it's worth taking a harder look at that --

		DR. COX:  Again, low compared to other doses used in that study, but
not low compared to what many people are exposed to.

		JUDGE SOLOMON:  State your name.

		DR. PARK:  Robert Park from NIOSH in Cincinnati.  It's ludicrous to
hear someone question causality.  There's 100 years of research in
occupational medicine, in exposure assessment.  People here even in
industry would agree that silica they say causes silicosis, which causes
lung cancer.

		There's some debate about whether the middle step is required.
There's no question that there's excess lung cancer in silica-exposed
populations.  We look at literature, and we identify what we call good
studies.  Good studies are ones that look at confounding, asbestos,
whatever.  We make judgments.  If there's data that allows one to
control for confounding, that's part of the analysis.

		If there is confounding that we can't control for, we evaluate it.  We
ask how bad could it be?  There's a lot of empirical judgment from
people who know these populations, know these exposures, know these
industries, who can make very good judgments about that.  We aren't
stupid.  So I don't know where you're coming from.

		JUDGE SOLOMON:  Okay, let me -- two things.

		DR. PARK:  Number two.

		JUDGE SOLOMON:  No, wait a minute.  Stop at number one.  First of all,
you were nodding with -- just for the record, the witness was nodding
along when Dr. Park was making some of the statements.

		And, number two, is that a question mark at the end of number one?

		DR. PARK:  Yes.

		JUDGE SOLOMON:  Okay.  You can answer the question, Dr. Cox.

		DR. COX:  Okay.  I was nodding as I ticked off the various points:
we're not stupid, people have looked at this for a long time, we make
judgments, and so forth.  However, it has been abundantly shown -- are
you familiar with the Wason selection task?

		DR. PARK:  No.

		DR. COX:  No.  It has been abundantly shown by psychologists who have
studied the capacity of expert scientists and statisticians to make
judgments about causality, that we typically have very high confidence
and very low -- or not as high as we would like -- accuracy in our
judgments.  Judgment is not a substitute for rigorous analysis.

		I do not question that at sufficiently high exposures, there are real
effects.  I do question that those effects can be extrapolated without
careful causal analysis, to the specific context that's of interest to
us today, which is will further reductions in exposure bring about
further health benefits to workers?

		I think that answering that question soundly requires a great deal
more than judgment.

		JUDGE SOLOMON:  Okay.  How long is this going to be, Dr. Park, the
next question?

		DR. PARK:  Two more, pretty brief.

		JUDGE SOLOMON:  Okay.  Here's the problem.  We have questions that are
going to come from OSHA, and then we have another two-hour presentation.
 So we're supposed to end at 5:00.  Apparently -- we could go on.  I
mean, if these are that crucial, we could go on.  So if you have two
more questions, how long is it going to take?

		DR. PARK:  Two minutes.

		JUDGE SOLOMON:  Okay, go ahead.

		DR. PARK:  Okay, on the question of model selection, again, it's not a
simple-minded "Let's pick the best one."  There's judgment involved.
There's biology.  There's prior observation in other studies.  Linear
exposure responses actually have a lot of prior support.  We don't just
pick the one with the best estimate.  You might want to respond to that.

		Finally, on the question of exposure error measurement and confidence
intervals, that's true, but we're actually not interested in confidence
intervals.  We've already established causality.  We need to estimate
what is our best guess for the exposure response.  What's our best
estimate?

		It doesn't matter if the confidence interval is super tiny or somewhat
wider.  We don't have any choice.

		DR. COX:	So, I would disagree with you that causality has been
established at relevant exposure levels, and I would disagree with you
with confidence intervals don't matter.

		DR. PARK:  Okay.

		JUDGE SOLOMON:  Okay.  So I assume OSHA has questions.

		MR. PERRY:  Yes, we do, Your Honor.  If I may, Tiffany DeFoe has a
question for Dr. Cox.

		MS. DeFOE:  Thank you.

		Dr. Cox, I'd like to get a better grasp on your thoughts about the
sort of uncertainty in error analysis that you describe, and what they
might mean for OSHA decision making.

		So earlier in your statement you made a general statement that until
exposure errors are well modeled, many datasets might be consistent with
a threshold effect or with no threshold effect.

		So specifying that a little bit to say that if you believe that until
the exposure errors are well modeled in this case, the current data are
consistent with a threshold or no threshold in the area of interest, is
that something you'd agree with?

		DR. COX:  If I understand you correctly, yes.  And just to be clear,
I'm referring here to models based on epidemiological data.

		MS. DeFOE:  Yes, thank you.  And when you discuss the procedures that
you say we should do to account for types of error in exposure, are you
suggesting simulation studies that we -- okay.

		DR. COX:  No.  Here is an incorrect procedure.  Use simulation to draw
a number of imputed exposures from some uncertainty distribution.  Then
fit a model that does not account for exposure measurement error to the
drawn values.  Repeat it many times and see what distribution of
regression coefficients you get.  That would not be a correct procedure.

		Here is the heart of the matter.  The basic approach to modeling, when
exposure error is ignored, is to say, we can regress risk against
exposure and come up with some coefficient.  How big is that
coefficient?  Right, that's the basic idea.

		The basic idea that would be correct is to regress risk against
estimated exposure plus error.  So in other words, you put an error term
right into the exposure of your model.  That calls for a different
fitting procedure and will produce different results.

		So what I'm saying is we have to do that second thing, because doing
the first thing a number of times, for example, in Monte Carlo
simulation, doesn't solve the basic problem, that you haven't correctly
specified the model to take exposure estimation error into account.

		MS. DeFOE:  Okay, let me make sure I understand.  So you're suggesting
that we should model error --

		DR. COX:  Correct.

		MS. DeFOE: -- of various types and should, based on different
assumptions and information about the nature of the error, fit a series
of different models to see the best model form?

		DR. COX:  That's going a little bit further than I did.  I would --
all that I'm insisting on, if you will -- actually, I'm not in a
position to insist, but all I'm strongly advocating is that the models
that are fit should include errors in the independent variables.

		So repetition of a model that doesn't have errors in exposure or other
independent variables won't solve the problem of correctly estimating
slope coefficients for a model that does have error in the exposure
variable.

		So what I'm saying is use those models that include error in the
exposure variable.  And then whether you do a series of them or one, I
haven't gone that far.

		MS. DeFOE:  I'm going to pass the microphone on.  I might come back if
I manage to reformulate my question correctly.

		DR. COX:  Great, thank you.

		JUDGE SOLOMON:  So who is next, Mr. Perry?

		MR. PERRY:  Next will be Janet Carter from our Office of Chemical
Hazards - Metals.

		MS. CARTER:  Good afternoon, Dr. Cox.  I appreciate the opportunity
to talk to you today and ask questions.  I wanted to get a little more
at your view on causality.  I think it's very interesting.  You
certainly state in your testimony and today that causation -- or
association is not causation, and I think that that's understood.

		You talk about some animal data being coherent with human data and
that it tends to lead the conclusion of a threshold response.  How do
you relate that to some of the in vitro studies which show direct acting
mechanism of genotoxicity to epithelial cells?  How would you evaluate
that in this causality model?

		DR. COX:  Yes.  I think I would be very slow to try to extrapolate
from in vitro and cellular level responses.  The fact that you can see a
NF-κB pathway activated at the cellular level essentially indicates
that this is a living organism that's responding to stimuli.  I mean,
these are highly conserved pathways, and they'll light up in response to
any number of shocks.

		Extrapolating that -- from that to a whole organism, I think, is not
prudent for lung cancer or other lung diseases, because I think that the
lung response is primarily a tissue-level response.  And what I mean by
that is that you have cross-talk between different cell populations.

		You do have your alveolar epithelial cells, but you also have T-cells
that have infiltrated the lung, for example, that play an important
mediating role.  You have interleukin-8 signaling to a variety of cells
within the lung.

		To understand the onset and the dynamics and the possible threshold in
the lung's response, I think it's fruitful to take a tissue-level view
in which multiple cell populations help to regulate each other in
feedback loops.  And that's not going to come out of a cell-level
perspective.

		MS. CARTER:  But there have been cellular studies looking at tissue
responses, and actually taking tissue constructs and doing the same
thing, and seeing that evaluation.  So that would seem to fit within
your paradigm, would it not?

		DR. COX:  I guess we'd have to talk about specifics.  I think that the
paradigm in which chronic unresolved inflammation of the lung is a first
step towards increased risk of various lung diseases has a lot of
support for it.  So inflammation plays an essential role.

		And as long as we have enough of the lung modeled to sensibly discuss
inflammation and its sequelae, I think we're in good shape.

		MS. CARTER:  It does, but there are expert authoritative bodies who
have not discounted a direct genotoxic mechanism.

		DR. COX:  Sure.  And again, as --

		MS. CARTER:  And certainly -- I mean, I don't think anybody would
argue the case for inflammation, absolutely not.

		DR. COX:  Right.  And again, I think it's hard to prove a negative,
especially at relatively low doses, but I think the best supported model
is the one we've been talking about, where in fact, without inflammation
you don't see excess risk.  I think there's good science that needs to
be done to clarify that.

		JUDGE SOLOMON:  You have any other questions?  Mr. Perry?

		MS. CARTER:  I think I'll leave it there.  No.

		MR. PERRY:  Yes, Dr. Cox, I just have a couple.  On Page 15, I think
you talked about this idea of residual confounding, but I don't see --
I don't really see a citation to that.  Is this something that's been
raised or discussed in the literature as a --

		DR. COX:  I think residual confounding has been raised and discussed
in the statistics literature, but I don't know that it's -- in fact, I
have an impression that it has not, just reflecting on the papers that
I've read, been raised specifically in the crystalline silica
literature.

		MR. PERRY:  Oh, I -- yes, I wasn't asking if it was specific in the
silica literature, but --

		DR. COX:  But in the general --

		MR. PERRY: -- in the context of epidemiology --

		DR. COX:  Yes.  It's well known.

		MR. PERRY:  Literature in general.

		DR. COX:  Yes.

		MR. PERRY:  If you could --

		DR. COX:  Provide reference?

		MR. PERRY:  -- provide some information to the record, I would invite
you to submit whatever papers you think is appropriate to illustrate
that phenomenon.

		Also, just so I'm clear, you also, on that same -- starting on that
same page, you say, "Quantitative risk assessment such as what OSHA has
done, they routinely use a large excess of false positive errors."

		DR. COX:  Right.

		MR. PERRY:  So, and then you go on to -- I'm not familiar with the
papers you're citing here, but you quote one of them.  I think it's --
looks like Ioannidis, 2005?

		DR. COX:  Oh yes.

		MR. PERRY:  As saying, "Most published research findings are wrong."

		DR. COX:  That's actually the title of his paper, yes.

		MR. PERRY:  That's the title of the paper, okay.  Wow.  I mean, the
implications are rather staggering to me, but my question here is
how -- first of all, I'd like to know really how common this is.  If
you've got -- again, I would invite you to submit any additional
studies of anyone who's actually analyzed scientific literature and
identified false positive studies and --

		DR. COX:  And indeed they have.  And actually I've cited those.

		MR. PERRY:  Okay.  So they -- you -- those are the ones you --

		DR. COX:  But there's also a -- there's a January editorial in
Science by Marcia McNutt, the new editor of science, that touches on the
related topic of non-reproducibility of studies.

		MR. PERRY:  Okay.  I'm wondering, as studies are done over time, as we
have in the case of crystalline silica, more and more studies look at
different working populations, studies find associations in different
situations.  More and more of them accumulate.  Doesn't -- wouldn't
that reduce the odds that really this can all be explained through a
false positive bias?

		DR. COX:  I wish that were true, but I don't think it is.  I think
doing the same basic thing a thousand times will tend to produce the
same result a thousand times, but that's not evidence that the result is
correct unless the methods and analysis are correct.  It shows
consistency, but not correctness.

		MR. PERRY:  So as I understand it, you basically think there's a good
possibility that the entire literature base, with respect to lung cancer
now, I'm talking about, is wrong?

		DR. COX:  You mean with respect to lung cancer in the context of
crystalline silica?

		MR. PERRY:  Yes, sir.

		DR. COX:  I think that consistent with the findings of Lauer and
Ioannidis and others, I think that it's very possible and plausible that
there is a consistent pattern of false positives in the literature base,
yes.  And that implies, yes, they are wrong.  False positives are false.

		MR. PERRY:  Okay.  Thank you.

		DR. COX:  Thank you.

		JUDGE SOLOMON:  Okay, we're right at 2:02 right now, so --

		DR. COX:  Okay.

		MS. RYDER:  Yes, I think we still have a few more questions, if that
would be okay.

		JUDGE SOLOMON:  Well, here's what we could do.  We have -- we're
probably going to have to take a few minutes to, you know, change the
cast of characters up here.  They've got approximately two hours, but
that includes the questions from the peers and from the public, and you
get to ask questions again of their witnesses.  They come back on Friday
this week.

		MS. RYDER:  The peer reviewers won't be here.

		JUDGE SOLOMON:  So -- the peer reviewers will not be here, right.  So
we probably better make sure that we cover, at a minimum --

		MR. PERRY:  Yes.  I mean, this is a very important witness for us to
ask.  We only have a few more questions left.

		JUDGE SOLOMON:  Okay, go ahead.

		MR. PERRY:  I think Ms. Ryder has a few.

		MS. RYDER:  Sure.  B.J., you can go first if you want.

		MR. PERRY:  Okay.  Then this is Brian Albrecht from Salt Lake City
Laboratory.

		MR. ALBRECHT:  Thank you.

		Dr. Cox, in your written submissions to the docket, you wrote a paper
called "Statistical Assessment of Performance Tests for the Analysis of
Respirable Crystalline Silica Quartz by Commercial Laboratories Using
XRD."  I was a little surprised that you did not discuss any of this
paper or this data today.  Would you mind just summarizing very quickly
what the findings were and how they affected your testimony?

		MR. KING:  Excuse me.  That will be discussed by witnesses on our
panel next Wednesday, in depth.

		MR. ALBRECHT:  Oh.  Well, I'll save my questions till then.

		DR. COX:  Thank you.

		MR. PERRY:  Fine.  Well, Dr. Cox, will you be on that panel?

		MR. KING:  No.  He wasn't --

		JUDGE SOLOMON:  In that case, answer the question.

		MR. KING:  All right, go ahead.

		DR. COX:  I don't think it really played any role.

		MR. ALBRECHT:  So it had no bearing on your testimony today?

		DR. COX:  I think that's correct.  My testimony is based first on my
reading of the OSHA preliminary QRA, primarily on that, and secondly on
reading key papers that support that.  And I think that's really the
basis of my testimony.

		MR. ALBRECHT:  Okay, thank you.

		DR. COX:  Thank you.

		JUDGE SOLOMON:  Ms. Ryder.

		MR. PERRY:  Ms. Ryder.

		MS. RYDER:  Okay, I just have one question, Dr. Cox.  You talked a
little bit earlier about the false positives that are --

		DR. COX:  Yes.

		MS. RYDER: -- present with a lot of the studies on lung cancer.
And, but I believe, in your comment you didn't say that there are any
of those same false positives with studies dealing with silicosis and
silica exposure.  Is that correct?

		DR. COX:  I don't think I opined on that.  So -- and I really haven't
looked carefully at the question.  I do take it as given that silica at
sufficiently high and prolonged exposures causes silicosis.  I've not
really examined that literature.

		MS. RYDER:  So you don't think that those studies have the same issues
that some of the lung cancer studies have?

		DR. COX:  I don't really know.

		(Off microphone conversation.)

		DR. COX:  Yes.  Neil is pointing out that there's a different question
about whether those issues arise in the context of discovering
thresholds and performing risk assessment.  However, the easiest answer
is that I haven't looked at the question of the connection between
silica and silicosis, nor do I think that the -- well, here's what I
really think.

		MS. RYDER:  That would be great.

		DR. COX:  You can't tell the difference between the results in the
OSHA preliminary QRA and the results that should be expected if there
isn't really a relationship there, but you apply these methods and
modeling to just purely random data.

		So if you walk up to the preliminary QRA and say -- and ask, does it
do a good job of showing that these conclusions are likely to be
correct, I would say no.  It sort of assumes that these conclusions are
correct, but it doesn't show it.

		Is that true for silica and silicosis?  I expect that the evidence is
much stronger for silica and silicosis.  But I haven't reviewed it, so I
can't testify to it.

		MS. RYDER:  Okay.  Are you planning on doing that before the -- in
the posting and comment period?

		DR. COX:  No.  I -- no.  I was really -- my comments are based on
the preliminary QRA and just trying to evaluate that.  And unless asked,
I'm not planning to do anything further.  Thank you.

		MS. RYDER:  All right.

		JUDGE SOLOMON:  Is that it?

		MS. RYDER:  I --

		MR. PERRY:  I believe that's it, Your Honor.

		JUDGE SOLOMON:  Okay, so it's --

		MR. PERRY:  Thank you, Dr. Cox.

		JUDGE SOLOMON:  It's 2:07.  Thank you very much.

		MS. RYDER:  Before we go off the record, could I get a copy of your
written remarks to add to the docket?

		DR. COX:  They should be in the docket already.

		MR. KING:  His written testimony is -- full written testimony is
already in the docket.

		MS. RYDER:  Can I have his slides for them?

		MR. KING:  We could leave a copy of these slides for you.

		MS. RYDER:  Okay.

		MR. KING:  Yes?

		MS. RYDER:  Right, sure.

		MR. PERRY:  Great.

		JUDGE SOLOMON:  Do you have a copy?

		MS. RYDER:  Yes, but I already put it in --

		JUDGE SOLOMON:  So do you want to mark it?

		MS. RYDER:  Yes.  I think this is going to be marked as Exhibit 7.

		MR. KING:  Exhibit 7?

		JUDGE SOLOMON:  And without objection, I will enter it into evidence.

(Whereupon, the document referred to as Hearing Exhibit 7 was marked and
received in evidence.)

		JUDGE SOLOMON:  Okay, we're going to go off the record now.  We'll
come back in five minutes.  In the meantime, we will have a change of
the cast of characters here, and we'll have a brief discussion about how
we're going to handle the remainder of the day.

		(Off the record.)

		(On the record.)

 		JUDGE SOLOMON:  We have the Chamber of Commerce, Panel Number 2 this
afternoon.  As I said earlier, they had been allocated two hours, but
included in that allocation would be questions from the peer reviewers,
questions from the public, and then questions from OSHA.  So we're
probably going to have a presentation, hopefully of about an hour to an
hour and 15.  Is that okay?

		MR. CHAJET:  We'll give it our best shot, Your Honor.

		JUDGE SOLOMON:  Okay.  All right, so do you want to introduce your
panel, please?

		MR. CHAJET:  I do.  Thank you very much.  Good afternoon, everyone.
Testifying for the Chamber this afternoon is Dr. Thomas Hall.
Dr. Hall has more than 30 years of experience in industrial hygiene and
occupational health and epidemiology, primarily dealing with exposure
assessment.  He served on a faculty at the University of Oklahoma.

		He's published research on exposures to airborne particulates such as
asbestos, diesel exhaust, hazardous air pollutants, crystalline silica,
and respirable coalmine dust.  Additionally, Dr. Hall has designed and
implemented large field studies to assess asbestos exposure variability
in silica exposures, mercury exposures, and lead exposures.

		We also have on our panel, testifying today, Robert Lieckfield.
Mr. Lieckfield is a Certified Industrial Hygienist.  He is perhaps one
of the most well known CIHs in the country.  He directs Bureau Veritas
HSE Lab in Novi, Michigan and Zurich, Illinois, and Atlanta.

		He does industrial hygiene consulting around the world, project
management involving multi-disciplinary teams, and he consults for the
industry, the government, and academia.  Most importantly,
Mr. Lieckfield is very familiar with measurement and variability in
error and has audited analytical labs around the nation and led the AIHA
Lab Accreditation Program.

		In addition, we have testifying today Dr. William Bunn, who's sitting
behind me.  Dr. Bunn is a recently retired Vice President of health,
Safety and Security and Productivity and now conserves as a consultant
to Navistar.

		Along with his extensive experience in the corporate sector, Dr. Bunn
is a faculty member at Northwestern Illinois, University of Cincinnati,
and former faculty member at Duke, Yale, and the University of Colorado.

		And finally today, Dr. Gerhard Knutson will be providing testimony
based on his 35 years of experience as a consultant, evaluating and
designing industrial ventilation and air pollution systems and
conducting industrial hygiene studies for private industry, commercial
facilities, and for government clients.

		Dr. Gerhard is well known to all of you that have been involved in
EPA or prior OSHA ventilation issues and has testified on both the
government and design engineering controls around the world.  With that
I turn it over to our first witness.

		DR. HALL:  Thank you, Henry.  They picked me to go first because I've
been in the longest and forget the most.

		JUDGE SOLOMON:  Okay.  Would you state your name again, please?

		DR. HALL:  Yes.  My name is Thomas Hall.  You need my location?  I
live in Oklahoma, as a result of being a former professor of the
University of Oklahoma.

		I'm going to talk about a series of issues that deal specifically
with -- the first one is the change in convention from the ACGIH to the
ACGIH/ISO/CEN particle penetration conventions for determining or for
defining respirable dust.

		I have a few brief comments on that, then I will go on to discuss what
the Occupational Safety and Health Administration has talked about with
some issues associated with the sampler performance and sampling for
crystalline silica aerosols, specifically focusing on the cyclones that
they have recommended be used, so they can -- to collect, excuse me, a
larger fraction of dust than can be collected typically with a sampler,
and separated like a cyclone 1.7 -- excuse me, a Dorr-Oliver cyclone,
operating at 1.7, because there are issues with that particular system
when you get down to aerosol concentration ranges of crystalline silica
at 15 µg/m3 and below.

		Relatively recently, at least in my history, there has been -- there
was a move, has been a move, and the OSHA proposal for quartz has
incorporated this move, is the adoption of a new particle penetration
convention.

		For the first 25, 20-some-odd years of my professional life, the ACGIH
particle penetration convention for defining respirable dust was kind of
the ruling convention.

		And more recently there has been a move to harmonize this
understanding, at least our understanding in the United States, of what
is respirable dust.

		And with those in other places like in Europe, the British had a
different standard.  The French and Europeans had a somewhat different
standard.  Those have all been kind of combined now into one specific
convention, which when in comparison to the previous ACGIH convention,
in the review by Soderholm, a NIA scientist, demonstrated that there are
some significant advantages to this association, or this harmonization,
excuse me, and there are some built-in -- there is one built-in
difference that can make a significant difference when interpreting
exposure monitoring results collected under this new convention.

		And that is that there's approximately a 30 percent positive bias
built into the movement from the previous convention into this one.  And
this positive bias is built around the issue of what kind of particles
it collects.

		The adoption of the ACGIH/ISO/CEN particle penetration convention will
result in this 30 percent increase, as I just stated, in mass collected
by the proposed samplers, the OSHA proposed samplers.  This would
produce a difference in exposure results from the current exposure
results from historical measurements that have been used in the risk
assessments.

		That means the exposure measurements that are collected now really
don't represent necessarily the same exposures or risk that have been
characterized historically.

		The adoption of the new definition of respirable dust will not be
founded on the respirable fraction of dust that is thought to result in
disease risk, but will permit collection of dust more heavily weighted
to larger particles that are not thought to be part of the regulated
risk historically.

		The proposed imprecise definition, what is respirable dust, combined
with the device over-sampling of large, non-respirable particles will
result in an overestimation of respirable dust concentrations under
measurements taken under this convention.

		To move on with -- under this new convention, and under the issue of
having to collect enough quartz for analytical purposes, OSHA
recommended that a series of samplers that were called high volume
cyclone samplers be utilized or can be utilized, such that the sampler
can collect enough dust to feed into the analytical system that they can
give them a reliable analytical result.

		At low levels of quartz exposure surrounding the action level, the use
of a -- or the Dorr-Oliver in a 1.7 flow rate per minute -- 1.7,
excuse me, cubic centimeters per minute -- 1.7 liters, excuse me, per
minute, flow rate results in relatively small concentrations of quartz
on the filter for analysis.

		And it puts you into a range where the analysis is fraught with a lot
of uncertainty.  The use of these new samplers -- and there's a series
of them, but the one that was prominent in the discussion in the OSHA
PEA was a sampler called the GK 2.69.

		One of the reasons it was kind of a prominent in the OSHA PEA is that
it operates -- when they were looking at their analysis, at between 4.1
and 4.2 L/min.

		This would suggest that using this sampler at a bare minimum, if the
results are similar to what could be obtained in a -- or from a cyclone
operated at 1.7, with obvious scale-ups for volume, that this device
would just provide more dust for analysis.

		But recognized experts in the field of industrial hygiene have
demonstrated that there are potentially large contributions to the
errors associated with the sampling analytical process that could be
attributed to specifically the use of a high-volume sampler.

		Now, while there are, you know, a series of errors that basically are
well known, that is the errors associated with the analytical process,
and the errors that are potentially associated with the pump,
recently -- well recently, in the last 15 years have become relatively
minor given that most pumps are controlled by a computer microprocessor,
and the flow rate varies not significantly really.

		The potential errors, though, associated with the sampler, that is
this, specifically this GK 2.69 cyclone, have been demonstrated by Lee
and Stacey and Chen and others to be fairly significant and be dependent
upon the aerosol that you're sampling.

		In samples that have a relatively low GM, that is geometric mean --
or mean diameter, excuse me, and a relatively small GSD, the GK 2.69
sampling at about 4.2 L/min had some bias.  The bias was between 10 and
20 percent, a positive bias.

		But as the change in aerosol concentration that you're sampling, the
change in aerosol characteristics, as that changes, then that bias can
increase rather dramatically.  In aerosols greater than -- having a
geometric mean of 10 microns or greater, that bias can jump up to 20 to
60 percent.

		And that bias impacts the total variability that's seen in the
sampling analytical system.  This would mean if you were sampling an
aerosol with that kind of a bias associated with it, that you could
actually be looking at concentrations as low as 25 to 30 µg, but
interpreting those concentrations as being up around the PEL of 50 µg.

		The conclusion based on a review of these samplers is that these
samplers and respirable dust definition issues can and will adversely
impact results of anybody analyzing samples collected by the cyclone
samplers proposed by OSHA.

		OSHA has not demonstrated in the proposed rule and their analysis,
have demonstrated these samplers are actually feasible, excuse me, the
sampler, GK 2.69, and other high volume samplers, are actually feasible
in obtaining results that will allow for a fairly precise estimation of
the error and bias associated with this sampling such that you can
basically provide regulatory base samples.

		I had the single -- I had the opportunity of doing one other review
for the client, and that is a review of a specific paper that was
proposed or put into the PEA that dealt with a study done by Esswein,
looking at fracking operations and the exposures to quartz associated
with those operations.

		While this is an interesting study, it was purported by OSHA that this
was a baseline study of the industry.  And although I think that's a
kind of significant extension, it's not what the authors claim.  The
authors did not make such a claim, and I don't think that claim could be
made given that there was a small number of sites that were visited,
they were not selected randomly from a large population, and they were
basically sites that volunteered to be studied.

		In this study there was a -- really, a relatively small number of
samples were collected from the monitored population.  There were like
15 different occupations that were available for sampling for quartz.
Only 7 of those had four or more samples collected, and those four or
more samples were collected from basically 11 different collection sites
so that not every site had samples collected for every occupation they
demonstrated.

		And, in fact, almost half of the air samples were collected from one
occupation.  That was one occupation that was heavily exposed,
potentially heavily exposed; that's a sand-mover operate -- occupation,
excuse me.

		They also had issues of combining measurements from different sites,
resulting in what appeared to be population description parameters that
were -- I would say they were inaccurate and misleading.

		The problem being is that at one of them there was, in one particular
occupation, there was a geometric standard deviation -- a reported
geometric standard deviation of 7 to 8, and that is, actually in my
30-some-odd years, 35-some-odd years of working in this business, I have
never seen a GSD of quite that magnitude.

		Three to four is -- I think was discussed earlier as being an
uncontrolled environment, while two is being controlled.  And when I
have seen that kind of variation, when I've seen it, it has been
typically from a combination of combining various occupational groups,
sub-occupational groups into one occupation that really have different
levels of exposure.  And it distorts what you can obtain from the
information.

		During the study that was conducted by Esswein et al., they used this
sampler that I discussed earlier, GK 2.69, available from BGI
Incorporated.

		And the thing that I found that would have been really helpful for the
study to understand their results, given that there was this bias
associated with the particular methodology they used, if they had
collected side-by-side samples, this would have been really, I think,
helpful in understanding, will this new cyclone, the high volume
cyclone, provide a sampling that will be effective for the industrial
hygienists in the field to then gather samples sufficiently large enough
they can be analyzed?

		And that ends my comments on the two areas of the OSHA PEA that I
looked at.

		JUDGE SOLOMON:  Next.

		MR. LIECKFIELD:  Hello.  My name is Bob Lieckfield, and I've got 37
years of analytical experience.  And the only point that I am testifying
to is the ability of the current analytical methods to measure
crystalline silica at the levels required under the proposed PEL.

		And with the 1.7 Dorr-Oliver, we're looking at levels of 10 µg at the
action level and 40 µg at the PEL.  If we use the high-flow sampler,
which Dr. Hall just talked about, we're at 25 µg for the action level
and 100 µg for the PEL.

		My position, at this point, is it's not technologically feasible to
measure at those levels.  There is literature.  Dr. Stacey from the
Health and Safety Executive did a paper in 2007, where he demonstrated
that the -- trying to measure at those levels would be fraught with
error.

		The accuracy that I'm speaking about is plus/minus 25 percent at those
concentration levels.  They're -- the x-ray method -- and I'll focus
on the x-ray method.  To summarize, there's x-ray, infrared, and the
colorimetric method.

		We won't talk about the colorimetric method.  It is rarely, if at all,
used.  In my experience the main method is x-ray diffraction, which
allows you a direct measurement of polymorphs, α-quartz, cristobalite,
and tridymite.

		The proposed standard seems to imply, to me at least, that x-ray would
be the method of choice, because there is a standard for quartz,
cristobalite, and tridymite.  And I have some comments about how that
would work, through the measurement process.

		The infrared method is -- there is not a direct way to measure
α-quartz and cristobalite.  You're doing it by a mathematical
manipulation, which would be secondary to a direct measurement.  The
infrared method is also prone to interferences.  It's very good in coal
mines because it's a characterized dust.

		In general industry, the question would come up, what type of
interferences might an individual site expect, which then pushes the
technology more to x-ray diffraction.

		So we'll focus on the x-ray method.  The other -- the last point of
that is OSHA only has a method for x-ray.  They did not show an FTI
here, so that's why I'm focusing more on the x-ray method, but I don't
believe either method is capable of measuring accuracy at plus/minus 25
percent at the levels required of the action limit or the proposed PEL.

		To substantiate this, we look at the AIHA PAT samples, which is an
industry standard for performance testing.  They are more real-world
samples.  They give a better measurement of what an individual lab can
produce with a particular method.

		That's a better measure to look at than an internally generated
precision and accuracy.  If you take a pure standard, which is what OSHA
put in their PEA, spiked samples in the lab, known concentrations, you
can tighten up that accuracy, or it would appear that you can.

		So my comments are more in the real world when you're producing data.
And I come from a production lab, so we're producing, you know, 50 to
100 data points a day for silica.  The industry requires or demands to
fit this, for employers and consultants, whoever is taking silica
measurements, that the number they're going to get is representative of
the sampled environment.

		But if we look at the PAT samples, the error at -- and the lowest
they have is 40 µg, so we're not at the concentration levels that we
still need to measure, but between -- greater than 40, the error bar
around there is plus/minus 30-some percent.

		Specific numbers would be at from 40 to 50 µg.  It's plus/minus 35
percent.  And this is two standard deviations, which is the OSHA
requirement for accuracy.  From 50 to 80 µg is plus/minus 32.  From 80
to 100 it's plus/minus 31, and 100 to 200 is plus/minus 29.

		I indicate that I probably should have said it in reverse, but if as
you -- as loadings go higher, your error starts to decrease.
Similarly, as loadings go lower, your error is going to increase.  So
below 40, we don't know what the precision and accuracy of the
analytical methods are.

		We can surmise that it has to be somewhere in the neighborhood of 30
percent or more, which then does not hit the -- OSHA's precision
requirements for a valid analytical method.

		And not to -- I'm going to summarize the data that was in the PEA on
the OSHA labs PAT performance.  The numbers I just gave you was on
the -- excuse me, the community of industrial hygiene labs within the
AIHA PAT Program.

		But if we look specifically at the OSHA lab -- and this is not saying
anything that the OSHA lab does not employ the method.  This is all
about the method itself, not the people running it.  That they had 81
percent of their results were within plus/minus 25 percent of the
reference value; 38 percent were -- excuse me, let me rephrase.

		Forty-five percent of the data was below -- outside of that standard,
reference standard for AIHA.  And 65 percent was an overestimate -- 45
percent underestimate, 65 percent of the data would be overestimated,
when then puts things into -- for compliance testing, a bit dicey,
because there would be always some questions as to whether an employer
was in compliance or not.

		And if -- for just general studies with industry, they would not have
a good idea of that either, given the methods that we're using.

		What I am proposing to OSHA before -- in consideration of the
analytical testing, is to do further studies on the methods that'll be
used for compliance testing.  Define the variability across the
laboratories.  What is the source?  How can we improve it?

		Dr. Eller of NIOSH did that back in 1999 and made recommendations.
That was 14 years ago.  Probably most of those recommendations have been
incorporated.  I think we need to look at that again and hone in on
improving that method to meet that plus/minus 25 percent.

		The limits of detection and limits of quantitation, the lab industry
has various ways to go about doing that.  I think we need to define,
especially for silica, a set way of doing LOD and LOQ, because that is a
variable, and also collect data, assemble data in the area, in the range
that we're actually needing to measure, which would be somewhere between
10 and 50 µg of silica.  And that ends my comments.

		JUDGE SOLOMON:  Okay.  Who is next?

		DR. KNUTSON:  I'm Gerhard Knutson.  I'm the President and senior
consultant in a small consulting company, Knutson Ventilation out of
Minneapolis.  I welcome the opportunity to speak before OSHA and the
peer reviewers, especially since I get to come south in order to be able
to enjoy your winter as opposed to our winter.

		My testimony today is going to be focused in one small area.  I'm
going to be looking only at Appendix A.  Appendix A has to do -- the
Silica PEA has to do with hydraulic fracturing.

		And I'm even going to narrow it down a little bit further than that.
I'm going to look at one item in that, and that is specifically I'm
looking at feasible engineering controls.

		And the bottom line, the conclusion I draw, is that in Appendix A of
the Silica PEA, OSHA did not establish feasible engineering controls
capable of reaching or even approaching the proposed permissible
exposure limit.  And some of the things I'll follow will kind of
substantiate why I drew that conclusion.

		First of all, I think we have to back off and we have to ask what is
hydraulic fracturing?  And there's two aspects to it.  One of the
aspects is what goes on underground?  And this is the magic where you
have very high-pressure slurry that's got a proppant that forces the
shale to fracture.  It takes the fracture in the shale and opens it up
and puts a proppant in there to prop it open, so that natural gas can be
extracted from the well.

		We're not going to talk about that magic, although that sounds like an
awful interesting topic.  I'm only going to look at what is above
ground.  And the aboveground portion of it is the handling of the
proppant.

		And for today's talk, I'm only going to assume that we're looking at
crystalline and free silica as the proppant, sand.  And that's what most
people are using most of the times anyways.

		So when we look at it, we have to look at what goes on above ground.
And the things that go on above ground is, the sand gets shipped in.
It's not manufactured or it's not classified.  It's not -- nothing's
done with the sand at that hydraulic fracturing site.

		It gets brought in by car -- or trucks, rather, pneumatic conveying
trucks.  And the pneumatic conveying trucks then convey this sand over
to big hoppers.

		The big hoppers discharge down onto conveyor belts, which move over to
other conveyor belts, which move down to a hopper that feeds an auger,
that feeds a mixer.

		And the mixer now takes the proppant, adds water to it, adds the magic
that goes into the mystery that really happens underground, some
chemicals that are added in order to make the process work better.  And
then it goes out in a sealed pipe.  Once it leaves the mixer, there's no
realistic exposure to silica at all.

		So the question is, where does the exposure occur and what has to be
done about it?  Well, NIOSH, in its review of several hydraulic
fracturing sites, came up with seven different sources of airborne dust,
airborne silica.

		Remember, this proppant is almost all silica, so when we talk about
dust and we talk about silica and we talk about proppant, we're talking
about the same thing.  So it -- not always respirable, because some of
this stuff is pretty big.  But at any rate, what we have to worry about
is where these sources are.

		I don't disagree with the sources that NIOSH identified.  I might have
gone a little bit differently and taken some of their sources and broken
them down to multiple sources within the source, but they generally
found the sources that were critical.

		In order to understand hydraulic fracturing, I think you have to go a
little bit further than just thinking about conveyors.  Let's look at
the characteristic of the material that's moved.

		The sand that gets brought in to the operation has been worked on
before it gets there.  Specifically, it comes out the quarry, it gets
crushed.  It gets washed.  It goes over to a dryer so that it becomes
bone dry.  And then it goes through a classification process that gets
the particle size of the proppant into a very narrow band.

		And there's multiple bands of proppant that are used in the hydraulic
fracturing operation because of the magic that goes on underground.  But
it's very critical that you don't mix them.

		So now we take this proppant, and we put it into big hoppers.  Since
in a given well site you may end up using one proppant in the beginning
and one proppant in the end, you've got different hoppers for different
size materials.  And now you just charge that on.

		But I think we also have to sit back and say how big is this
operation?  And people in the hydraulic fracturing have a very nice unit
that they use.  They talk about sacks.  A sack, a 1 cubic foot, a
100-pound sack of sand.  They don't have any sacks but they talk about
sacks.

		And when you look at hydraulic fracturing, you're talking about
production rates in the order of 200 sacks per minute.  Two 100-pound
sacks, 20,000 pounds, 10 tons per minute, 600 tons per hour; this is a
big operation.  You're moving an awful lot of material.  We'll come back
to that.

		In the PEA, OSHA looked at several different research papers that have
been published and tried to use those to show that it is technologically
feasible to meet the standard.  And I'm going to look at two of those in
particular.

		One was a study -- and both of these are applied, not to the most
obvious source, because that's the transport of the sand through
pneumatic conveying, but through the sand handling at the sand movers,
at the conveyor belts, and at the hopper feeding the mixer.

		And in there, they looked at one study that was a stone-crushing
operation in Iran.  Let's think a little bit about what this operation
was and what happened.  Rocks were manually charged into a crusher, that
went into a grinder, that went down into a conveyor system that went
over to a series of screens, that got bagged out and shipped off to be
processed by some other plant somewhere else.

		The materials that were used were high grade silica -- 97 to 100
percent silica, and low grade, which was 80 percent silica, still pretty
high silica content.  The operation, on a daily basis, typically had 6
to 8 tons per day.  Compare that to hydraulic fracturing, that's one
minute of operation, corresponds to their daily production.

		The approach that was used, I fully support.  The idea was to take a
look at these operations -- many of them are typical operations, at the
crushing facility, go to the industrial ventilation manual, look up the
ventilation sketches, the VS prints, see what they suggest, apply those
prints to the ventilation system and make it work.

		And that's precisely what this research team did.  I've had
correspondence with them, and they've identified which VS prints they
used.  It's in my report.  Many of the prints were for canopies,
canopies over the crusher, for enclosures around screens.  So these are
listed, and now the system was put into effect.

		And another paper, not the one that was cited by OSHA but a second
paper by the same authors that talked about the other end of the
ventilation system, the air pollution aspects of it, talked about
volumetric flows.  You can go back and look at the flows for it.

		And a moderate flow is 15,000, 20,000 ACFM type of operations, not
horrendously large but a fair amount of air.  And so when we look at
what happened here, we had a marked reduction in exposures.  Well, you
start out with a rather primitive operation that doesn't have any
control in it, and what happens?  Reasonable controls give us very good
reductions, in the neighborhood of 99 percent reduction.

		I mean, that's a phenomenal reduction.  On the other hand, when you
look at it, the reduction that was observed in this operation did not
bring the exposures into compliance with the current permissible
exposure limit.

		Not only did they not do that, but when you go -- and this was a
factor of two to four -- well, about two times the amount of the
current exposure limit, but if you look at where the operators are in
the crushing operation versus where the operators are in hydraulic
fracturing, they're in different locations.

		In the crushing operation, the operators were moving material but not
spending a whole lot of time next to a crusher.  I've been in a lot of
crushing operations.  Nobody in their right mind, except an engineer
trying to figure out how to control it, is going to stand near the
crusher.

		And that's where the real high exposures -- in the hydraulic
fracturing, remember, we're moving 600 tons an hour, 10 tons a minute,
300 pounds a second.  If you aren't watching what happens, you overflow
the bin, you spill on the ground, and we're not talking about a couple
of pounds.  We're talking 300 pounds a second falling on the ground.

		You can't not be there in order to make sure that operation exists.
So even though the operator exposures were only twice the permissible
exposure limit, at this marked -- the system did a great job of
reducing exposures.

		When you look at the area samples that are more representative of the
kinds of exposures that hydraulic fracturing operators would have if
they were in there, now you're talking about 100 times.  That's a
substantial excess of the permissible exposure limit.

		You're not talking fractions of a milligram; you're talking about
milligrams.  So it's a very substantial exposure that existed at the
stations, which is more consistent with what would have happened had
this been similar to hydraulic fracturing.

		The problem is, none of the equipment matched.  None of the controls
that were used are applicable for hydraulic fracturing operations.
Still, the exposures were very high.  And, obviously, the production
rate was miniscule.

		The second operation that was cited by OSHA is also a crushing
operation.  This is stone crushing, again, and now the rocks that are
coming in are a little different grade.  They're lower silica, and the
silica samples that were reported in the study were anywhere from 0 to
27 percent of both quartz and cristobalite.  The 27 percent was the sum
of the two.

		And so the quantity of crystalline silica in the rock material was
less.  The approach that was used -- well, let's go back and talk about
the operation.  The operation manually charged these rocks into
crushers, brought it down to a grinder, and then took the material that
came out of that, and that was their end product.

		They have a beautiful picture in the report that really explains
what's going on.  They have a woman with a basket of rocks on her head
walking up to the crusher, another woman standing at the crusher with a
basket of rocks waiting to dump it into the crusher, and another woman
walking out of what appears to be a tunnel -- appears, because there's
so much dust in the air you can't see what's going on, that would have
been directly under the crusher, again with a basket on her head full of
crystalline silica.

		Now, I don't know how many sacks per hour or per minute this
corresponds to when you've got people carrying them on head baskets, but
I guarantee you it's not anywhere near 200 sacks per minute, probably
closer, at best, to 1 or 2 sacks per minute, because one of the pictures
showed several men and children -- because child labor was a problem in
the area, that were carrying this material and hand dumping it in there.

		How this can be related to hydraulic fracturing that is mechanically
moving 20 sacks per minute through a very carefully controlled system,
I'm at a loss.  The production rates would have been a fraction of it.
The equipment that was used was completely a disjoint from any of the
equipment that is used in hydraulic fracturing.

		The process that was used was misting and using of water.  And the
study wasn't clear on one other thing.  It used water techniques, but it
didn't talk about exactly what it did.  There was a picture of a water
spray in a chute that looked like it was capturing the material coming
out of the crusher and wetting it.

		And you could see an accumulation of mud on the side of the chute.
They also did some misting.  Well, go back to hydraulic fracturing.
What have we got?  We have sand that has been dried, classified, sized
very carefully.

		And if you add water to that, if you wet the material, you are really
in trouble, because the material in hydraulic fracturing, the media that
is being used, the material itself, almost flows like water.

		It's dry, it's crystalline, and the hopper bottoms aren't real steep.
You don't have to have a 60-degree hopper bottom.  You can get by with a
45-degree hopper bottom, because it flows so well.  Add a little bit of
water to it, and now you have to go inside and muck it out in order to
get the material to move.

		It sticks onto to the belt, it sticks in the hopper, it screws up the
augers.  So you can't wet the material.  You can use misting in the air.
 And the concept that misting in the air is that you will take the small
particles, and it meets the larger particles, and through Brownian
motion or impaction or whatever mechanism, the particles get to be
bigger.

		Now they're bigger and they fall out.  If you take a small, respirable
particle and you engulf it in a droplet of water, now it's no longer
respirable.  That makes a big difference.  So it does have some
advantage.

		In the conclusion that they had, they didn't differentiate between the
benefit of wetting the material and the benefit of the misting.  And
that's a major flaw in the application of this.  Again, when you look at
the end result, the end result was exposures above the permissible
exposure limit, both the current and the proposed.

		So we've got a situation now that we have two controls, questionably
applicable.  Assuming that they are applicable and pushing the number
through, now what OSHA does is it takes the two controls and applies it
to the same operation.  Not a bad thing to do.  Obviously it's going to
improve the control.

		It should be better than both of it, but there is a logical and
scientifically incorrect assumption made, and that assumption is that
they're independent.

		Any of you that have anything to do with probability recognize what an
independent event is.  The problem is these two controls are not
independent.  They control the same error at some times, and you end up
double dipping.

		And you have -- what you're essentially doing is you are taking
credit for the dust that is controlled by the local exhaust ventilation
and the dust that's controlled by the misting simultaneously.  And that
doesn't happen.

		And in my report I made up a problem and put some numbers on it, ran
some numbers, and showed how doing it one way gives you one number and
the other one gives you about a 30 percent difference in the efficiency.

		The approach that was used by OSHA, by assuming the independence of
control efficiency, is wrong.  One minus the efficiency is not equal to
the product of the quantities one minus the efficiencies.  It's going to
be much less than that.  And the more product items you have in there,
the greater the difference.

		So where do we stand?  We have a situation where I think if we had a
bunch of experts get together and discuss what does it mean to have to
show the requirement to demonstrate feasible engineering controls to
meet the permissible exposure limit or meet a proposed permissible
exposure limit, you'd have some -- quite a bit of disagreement as to
what all that means.

		But I think you would have unaniminity that it's not a numbers game.
We're not playing with numbers, that you really have to sit back and
look at it.  I think you'd get agreement that it is not the
juxtaposition of control measures that did not work, in industries that
are unrelated to hydraulic fracturing, with equipment that's unrelated
to hydraulic fracturing, and draw any conclusion about feasibility in
hydraulic fracturing.

		And as you can see, I firmly believe they did not establish that there
are feasible engineering controls that either are known or can be
reasonably expected to achieve the proposed permissible exposure limit.

		JUDGE SOLOMON:  So, Dr. Knutson, we're at about 3:05.  How much time?

		DR. KNUTSON:  Zero.

		JUDGE SOLOMON:  Pardon?

		DR. KNUTSON:  That was my last statement.

		JUDGE SOLOMON:  Oh.

		DR. KNUTSON:  Your timing is excellent.

		JUDGE SOLOMON:  You have one more witness?

		MR. CHAJET:  We have one more witness, Dr. Bunn.

		JUDGE SOLOMON:  So we'll play a little musical chairs here.  If you'll
move forward.  State your name, please.

		DR. BUNN:  My name is Dr. William Bunn.  I've served as medical
director for multiple industries from 1982 to 2013.  That included
Bristol-Myers, Manville, ExxonMobil, and Navistar International.

		My responsibility during this period of time for over 100,000 people,
about a third of those were exposed to significant levels of crystalline
silica.  There were many exposures above the PEL in these groups;
however, with effective programs, I never saw or was advised of a single
case of silica-related disease during my tenure as medical directors.

		I provide comments today on behalf of the U.S. Chamber of Commerce in
an effort to help focus the silica rulemaking on the reality of
conditions in the U.S. workplaces, separate from statistical modeling
and other approaches.

		In my experience, I do not believe that we necessarily need to change
the rules.  My interest in lung disease and lung cancer began with my
fellowship, sponsored actually by the National Cancer Society and NIOSH
at Duke University and University of North Carolina.

		I was privileged to see a number of folks with silicosis during that
period of time and treat them, so it's very -- I recognize it's a very
significant disease, and took care of a lot of folks.

		My first role as a medical director, having previously served as a
consulting director, actually, for EPA and IEHS, was a large corporation
where we dealt with factory ceramic fibers, roofing, mining, paper
products, forest products, all were areas where there was significant
amount of crystalline silica exposure.

		Of note were the mining operations, and this corporate medical
director role included diatomaceous earth, milling, mining, and
calcining.  And diatomaceous earth needs a small amount of crystalline
silica when we start it at 1 to 5 percent.

		However, as we go through the milling and the heating of the
substance, we come out with primarily cristobalite.  It starts as
quartz, goes to cristobalite.  It's mainly used for things you'd
recognize.  It's actually filtration in swimming pools, filtration for
pharmaceuticals and beverages and other things.

		While I was there, I was privileged to be a part of a Harvey Checkoway
study.  And you're going to laugh at me.  This was at a plant that's
been studied for many, many years in California.  Many questions have
risen with unions and management.

		That study finished up in the late '80s.  It was updated in the '90s,
and we have data past 2000.  I talked to Dr. Checkoway.  We put
together a table, which I've included in my comments.  I would ask that
you take a quick look at it.  And it comes from his study of mortality
among workers in the diatomaceous earth industry.  This is a study
that's been significant in IARC and other findings.

		Basically, what we found, as we look back, is that for folks who
entered the cohort after the 1960s, we didn't have any further cases of
silicosis.  So if you look at the table, we had large numbers of
silicosis in other cohorts of workers.  When we got to the '60s, we
didn't have further cases.  And we followed these cases out to past
2000.

		So what that suggests is something happened.  We know what happened,
is we changed the ventilation, we enforced protection, respiratory
protection, and we put in great programs in cooperation with the unions
there.

		In the same role, I was in charge of fiberglass operations.
Fiberglass is obviously made from sand, so we had significant exposures
to folks bringing in the sand and folks putting it there.  I never saw a
case of silicosis in this area.  Because of the concerns about fibers,
we actually saw every patient, did PFTs, three-year chest x-rays on
every single individual in a fiberglass plant.  So these people were
very closely monitored.

		Forest products may sound interesting, but I was -- you know, people
who are working with trees, there's a lot -- particularly in the south,
where we exceeded the PEL on a number of cases and we used masks, and
that's somewhat surprising.

		My next role was a petrochemical company.  We didn't really see --
this was really before fracking, but we didn't see anything even though
we did have some levels close to the PEL.  We used personal protective
equipment during that time.

		My final employer of the last 18 years was Navistar.  Our primary
exposure was in foundries.  This is where the engine parts were made.
Basically what happens is you build a mold.  The mold is built with
crystalline silica and other components.  You pour the molten metal into
the mold.  Then the challenge comes is to get -- after it cools, is to
get the mold away from the metal part.

		This is very difficult.  The chipping part is something we really have
to do by hand.  You have shaking, but there's really a hands-on piece of
that.  What we determined over the years is that we couldn't meet the
PEL in most cases.  We could get very close.

		To give you an example, it took about a 60-mile-an-hour face wind in
ventilation in the area to get close to the -- so we used a combination
of ventilation and personal protective equipment.  We monitored
everything closely.  We worked with our unions there.  And in over 18
years we saw no cases of silicosis and no workers' compensation cases.

		So what can I say?  I can say that over a 30-year career, responsible
for 100,000 employees, many exposed well above the PEL, I saw no new
cases of silicosis and none reported or were litigated.

		I think one of the common protection was a good combination of
engineering controls and personal protective equipment.  I would raise
the point that to put together good protective programs, we need to
consider protective equipment, respiratory protection, other changes,
wetting.

		It's a critical component is this respiratory issue.  We've looked at
it over years.  It's improved a lot.  We have much better respiratory
protection than we had in the past.  And we really haven't had problems
with making that a part of the process.  It's not the only answer, but
many cases, it's the part of the answer that we have to have, for
fracking and for other things.

		So everything's improving, and I think that's important.  Thus, I
think it's important that OSHA consider PPA as primary control,
establish a focus on education in workforce for the approximately large
number of employees that actually exceed the PEL.

		We haven't seen a particular evidence of anything of -- in our plant,
with effective programs, we've not seen silicosis.  We've looked some,
at actually, at the cancers as well.  So I guess the bottom line is that
in my 30 years of experience using engineering and personal protection
controls in good cooperation with the workforce, silicosis can be
prevented.

		JUDGE SOLOMON:  Is that your presentation?

		MR. CHAJET:  It is, Your Honor, for today.

		JUDGE SOLOMON:  The peer reviewers, are any of you -- do any of you
want to ask questions?  Dr. Ginsberg.  Okay, now while he is going up,
in the audience, are there any of the public who want to ask questions?
There are a number of hands.  Let's start at the first row which is over
here to my right, your left.  You'll be the next person.  You want to
get in line?

		MR. CHAJET:  Your Honor, I do want to move into the record the vials
of silica.  And I want to put a photograph up on the screen.

		JUDGE SOLOMON:  Okay.  So let's do this by the numbers.  Ms. Ryder?

		MS. RYDER:  Sure.

		JUDGE SOLOMON:  You have those exhibits and the --

		MS. RYDER:  Not that I know of.  Were they in the PowerPoint
presentation?

		JUDGE SOLOMON:  There are the vials right there, and --

		MS. RYDER:  Oh, these.

		JUDGE SOLOMON:  -- I asked if there was an envelope.  If you have an
envelope, maybe you can mark the envelope, put the vials in the
envelope.

		MS. RYDER:  Okay.

		JUDGE SOLOMON:  I mean, this is a public hearing.  It's not like the
documents are going to be sealed or anything, but --

		MS. RYDER:  Do you want to identify these?

		JUDGE SOLOMON:  Yes.  That's a good idea.

		MR. CHAJET:  Yes.  Those are three sealed vials or bottles that
contain the amount of silica or dust that would be needed to fill the
air in a room approximately 20 by 30 by 20, a couple of different vials,
depending on what the PEL is.

		Here you've got the material in teaspoons, demonstrating it.  We
wanted to put that in the record to show the amount of silica that is
the equivalent of the PEL in a large room, as compared to the railcars
full of silica sand or the silica sand that you see on the beach.

		JUDGE SOLOMON:  There are three vials.  How do you differentiate among
the vials?

		MR. CHAJET:  Let me ask Bob to identify them.

		JUDGE SOLOMON:  Is there some way to mark them so that we can know?

		MS. RYDER:  Yes.  They're labeled.

		UNIDENTIFIED SPEAKER:  They are labeled.

		MR. CHAJET:  Yes, they're marked.

		MS. RYDER:  Just to clarify, is this respirable crystalline silica?

		UNIDENTIFIED SPEAKER:  Yes, it is.

		MS. RYDER:  Okay.  All right, so I think these will be marked as
Exhibit 8 --

		JUDGE SOLOMON:  You want to read what's on them?

		MS. RYDER:  -- for 5.6, nine, Exhibit 9 for 12.7, and Exhibit 10 for
25.5.

		JUDGE SOLOMON:  Okay.  And there's no objection, so those are admitted
into evidence.

(Whereupon, the objects referred to as Hearing Exhibits 8 through 10
were marked and received in evidence.)

		JUDGE SOLOMON:  Each one of those, I guess, will get an envelope that
will be marked, is that --

		MS. RYDER:  Yes.  And they'll be available for inspection in the
docket office.

		MR. CHAJET:  And we'll give you a copy of the slide.

		JUDGE SOLOMON:  Okay.  Now, to the photograph, what is the photograph?

		MR. CHAJET:  The photograph is the amount of silica reflected in those
vials, but on a teaspoon measuring device.

		JUDGE SOLOMON:  Do you have a copy for the record?

		MR. CHAJET:  Not with me, but I will provide one.

		MS. RYDER:  Okay.  I will reserve Hearing Exhibit 11 for that
document.

		JUDGE SOLOMON:  Okay.  Dr. Ginsberg, you have a question.

		DR. GINSBERG:  Yes.  I have two questions for -- is it Dr. Bunn?

		DR. BUNN:  Yes.

		DR. GINSBERG:  Sorry.  I didn't catch your credentials.  And one
question for Mr. Hall; is that right?

		UNIDENTIFIED SPEAKER:  Dr. Hall.

		DR. GINSBERG:  Dr., I'm sorry.

		DR. HALL:  That's okay.

		DR. GINSBERG:  I didn't pay close enough attention during the intros.
All right, so Dr. Bunn, we heard something about exceedances earlier
today, about how they might explain current problems with silicosis that
you wouldn't expect at the PEL, but exceedances above the PEL.

		So without knowing really the industrial workplace like you do, when
there is an exceedance at say your facility, and OSHA comes in and finds
it and maybe gives a fine or a warning or whatever they do, in 30 --
and that was supposedly in about 30 percent of the cases across a
variety of industries, we saw some exceedances -- what happened?  How
quickly are those exceedances fixed, and is that, tend to be an ongoing
problem that would lead to a long-term exposure above 0.1?  Or when OSHA
finds a problem, does that tend to get addressed, say at your
facilities?

		And then regarding the PPE question, I'm not sure exactly why, maybe
you could educate us, why wouldn't -- why hasn't OSHA historically used
PPE rather than engineering controls to regulate these kinds of
exposures?  And what might be the issues in a facility that isn't as
tightly managed, medically managed, and --

		JUDGE SOLOMON:  So, are these for Dr. Bunn or for both?

		DR. GINSBERG:  Yes, these two are.  What are the issues with PPE and
what -- and how well do these inspections then get you down to the PEL?
 And then the question for Dr. Hall is, well, we have an issue with
needing to get more dust on a sampler so you can accurately measure
quartz.

		So you don't want -- so you want to have, on the one hand, the high
flow, the high volume, but you're saying that there's an inaccuracy
there, a potential positive bias, so what do you recommend OSHA do to
get more sensitivity going forward?

		JUDGE SOLOMON:  Okay.  I'll start with Dr. Bunn.

		DR. BUNN:  Yes.  The first question on OSHA inspections is that
generally we do regular -- OSHA notwithstanding, and as OSHA comes in,
we usually have crystalline silica measurements.

		In areas where we have difficulty, we actually sit down with OSHA and
reach an agreement, which really key to sort of three questions.  One is
that, what can we do with engineering controls?  At our foundries we
really, there's just literally -- and OSHA agrees, there's no feasible
way to get there except for the use of respiratory protection.

		And I would emphasize, this isn't the whole plant.  This is at certain
specific, restricted areas, and they could be easily observed.

		So generally -- I'm trying to -- generally we haven't had fines.
What we've had is meetings to sit down and work together.  And I think
that's worked out quite well.  So that's -- so the easy answer is we're
working with OSHA all the time because we know we have areas are going
to be high, and we're working with them to show them that we've done the
best thing we can.

		And it's sometimes a challenge, because engineering equipment breaks
down, too.  And people may not wear their masks, people with -- so
we've really got to have a good inspection and maintenance system and
convince them of that.

		So the answer is, when we have an exceedance, we fix it if it's at all
possible, and if not, we work with OSHA.  And NIOSH has been in a lot of
our plants, too, like in Franklin, where we've had NIOSH in to help us
out, in a cooperative effort to get to the lowest possible level, and
then to use respiratory protection along with those.

		Question -- so that's what happens in our plants.  And there's always
a question, but the question always is well, that's a big -- well,
we've purchased a lot of small companies.  Some have had 20 people.
And, you know, this kind of approach could be used, I would say, in most
facilities.

		The second question is a tough one for me.  You know, as a physician
we wear a mask in operating rooms, and people who have infectious
disease.  I worked in the pharmaceutical industry.  We had, could wear
them with animals, because of allergies and other issues.

		Yes, actually I worked at EPA for a while, and we had this -- so I'm
very, you know, I'm used to wearing a mask in a lot of different roles.
I think there is a role for us, for OSHA to sit down and look at it.

		I mean, we have primary preference for engineering controls.  I don't
know if we want to change that, but what I do think is we really don't
give much credit for respiratory controls, the way things are written
now.  We really ought to look at an integrated approach that -- there's
sort of three pieces to it.

		There's engineering, there's respiratory controls, and there's other
safety controls and prudent cleaning.  There's a lot of other things we
do in our plants to make sure we keep the operation clean.

		So that's -- you want my -- my easy answer is, I don't really know
why we've not used them.  Perhaps and -- if we look back, there's some
bad examples.  You know, it's 40 years of respiratory improvement.
That's an important issue.  It's a lot better than it used to be.  It
used to be less comfortable.

		You know, I think there are employers who try to use respiratory
control, perhaps, instead of looking at engineering.  Ventilation
systems have changed a lot, too.  So they're always there now, pretty
much.  You know, we look at construction, whatever, but in our industry.

		So I guess the answer is yes, I think now may be the time to put more
respiratory protection into the approach.

		JUDGE SOLOMON:  Dr. Hall, you remember the question?

		DR. HALL:  Barely, but I remember -- I would like to comment further
on what Dr. Bunn said about --

		JUDGE SOLOMON:  Why don't you answer the question first, and then
we'll go on.

		DR. HALL:  What would I expect OSHA to do, specifically, about the
biases associated with the high volume samplers?

		DR. GINSBERG:  No.  I'm sorry.  What would you want them to do, given
that they need to collect more dust to be more sensitive, yet they --
you talk about this bias.  So what would you want them to do to fix the
problem?

		DR. HALL:  First up, they need to collect more dust.  Provided they
change the PEL and the AL to a lower level of 50 and 25 µg, they need
to collect more dust to reach a level of quartz.

		And still, reaching the action level, even with high volume samplers,
is going to be dicey at best.  But what they could do is, specifically,
is characterize high volume samplers much more effectively.

		They haven't really characterized the GK 2.69, although they
recommended this, specifically looking at or making a requirement to
look at the environment you're going to use that sampler in, and to know
what the characteristics of the aerosol are, so that you can understand
the biases associated with the data that you grab.

		JUDGE SOLOMON:  Anything else, Dr. Ginsberg?

		DR. GINSBERG:  Yes.  I just wonder if part of the logic of lowering
the PEL is that if we acknowledge that there's always going to be
exceedances, sort of as a halo above the PEL, of -- you know, whether
they're temporary or whether they're systematic at one plant or another,
if you're lowering the target, you know, you always have, you know, the
bull's-eye and the halo effect.  That your exceedances then would also
shift downward so that you've got less problem at the high end.  Is
there a logic to that?

		DR. BUNN:  Well, I'll answer, part from a -- I work, currently, with
four plants that crush rock.  And the answer is kind of maybe, maybe
not, because they're -- when they have exceedances, and they have them
on a continuous basis, really, they have them when the equipment breaks
down because of the material they're handling.

		They only occur in certain parts of the crushing operation.  Now, at
this facility, they're all wearing respiratory protection whenever the
plant is operating.  So, and they're wearing respiratory protection that
meets the requirements of reducing their exposures.  And they have
little or no complaints.  These are union shops, too.  They have little
or no complaints from the folks.

		I think, in my career, respiratory protection devices have changed
significantly, while we have, as a professional organization and
occupation, haven't really looked at how those changes have impacted how
we make recommendations and why we make the recommendations for control
that we make.

		JUDGE SOLOMON:  Thank you, Dr. Ginsberg.  You want to come forward
please?  State your name for the record.

		MS. TRAHAN:  Hi.  Chris Trahan with the Building Trades.

		JUDGE SOLOMON:  Can you spell your last name?

		MS. TRAHAN:  T-r-a-h-a-n.  Actually, I have a question for
Dr. Lieckfield.  Did I say your name right?

		MR. LIECKFIELD:  Close enough.

		MS. TRAHAN:  Sorry.

		UNIDENTIFIED SPEAKER:  Close enough.

		MR. LIECKFIELD:  I'll answer.

		MS. TRAHAN:  You said something that I found interesting.  You said
that the lab -- and correct me if I'm misstating what you said, the lab
industry has a way of defining LOD and LOQ -- the limit of detection
and limit of quantitation.

		MR. LIECKFIELD:  Yes.

		MS. TRAHAN:  But you didn't say what your industry -- the lab
industry, how you do that.  Can you explain that?

		MR. LIECKFIELD:  The point is, there is no standard way to define
LOD/LOQ across the industry.  Some use -- now I'm talking about the lab
industry, not individual labs.

		MS. TRAHAN:  Okay.

		MR. LIECKFIELD:  Some use a signal-to-noise for an LOD, and then 10
times signal-to-noise for an LOQ.  Other labs will use a low calibration
standard, something they can see --

		MS. TRAHAN:  Okay.

		MR. LIECKFIELD:  -- that they will call an LOD, but it's not
necessarily the analytical method LOD.

		MS. TRAHAN:  Now, how does your lab define LOQ?

		MR. LIECKFIELD:  We define it, for our routine work, as low standard,
something we can see.

		MS. TRAHAN:  So, is there accuracy and precision?  What is the
accuracy and precision for silica analysis at the LOQ?

		MR. LIECKFIELD:  Well, I'll answer the LOD question; it's plus/minus
50 percent, is what we would expect as a pass/fail at the LOD.

		MS. TRAHAN:  So what about the LOQ, because that's a higher level
obviously?

		MR. LIECKFIELD:  My whole point is, getting the industry to make sure
we're consistent in how we measure that.

		MS. TRAHAN:  Okay.

		MR. LIECKFIELD:  We do not measure an LOQ, we being Bureau Veritas, we
do not use the concept of LOQ.  We sort of mix it together, low
standard.

		MS. TRAHAN:  For a reporting limit?

		MR. LIECKFIELD:  For a reporting limit.

		MS. TRAHAN:  Now, how would you define a reporting limit, then?

		MR. LIECKFIELD:  It goes along with getting the industry to define all
these terms.

		MS. TRAHAN:  So it may be inconsistent from lab to lab?  How does your
lab --

		MR. LIECKFIELD:  It is inconsistent from lab to lab, yes.

		MS. TRAHAN:  Can you explain how your lab does that?  I'm just trying
to figure out if, for example, your lab has accuracy and precision at
your reported reporting limit.  Did I --

		MR. LIECKFIELD:  At the reporting limit -- and we use 5 µg as a
reporting limit, something we can see, with a low standard, 50 percent
of the time, meaning it's -- it can go from 5 to 7½ and down below 5.

		MS. TRAHAN:  Okay.

		MR. LIECKFIELD:  That is our reporting limit.  That is neither an LOD
nor an LOQ.

		JUDGE SOLOMON:  Since we have a short break, will the people who had
their hands up in the next row please get in line.  Go ahead.

		(Off microphone conversation.)

		MS. TRAHAN:  And one other thing that I think you said early in your
comments was that you had to be able to accurately measure 10 µg in a
sample if you're using a 1.7 L/min flow rate, in order to accurately
measure at the action level.  Is that correct?

		MR. LIECKFIELD:  Correct.  That's correct.

		MS. TRAHAN:  Okay.  Thank you.

		JUDGE SOLOMON:  Okay, next.

		(Off microphone conversation.)

		JUDGE SOLOMON:  We know you, but you have to state your name, and
spell your last name.

		MR. HEARL:  Hi.  I'm Frank Hearl, and I'm the Chief of Staff for NIOSH
here in Washington, D.C.

		JUDGE SOLOMON:  Spell your last name for us.

		MR. HEARL:  H-e-a-r-l.

		JUDGE SOLOMON:  Thank you.

		MR. HEARL:  Okay, thank you.  Yes, I have some follow-up questions for
Dr. Lieckfield.  Actually, she was kind of hitting on some of my
points.  I'd like to start out, though, with just a slightly different
set of things.

		At Bureau Veritas, you do quite a bit of silica analysis, and I was
wondering, when you do -- when you get a sample in your lab, do you
always examine those samples for oversize particles?

		MR. LIECKFIELD:  No, we don't.

		MR. HEARL:  You don't?

		MR. LIECKFIELD:  No.  And I should clarify, he's a doctor, he's a
doctor, he's a doctor.  I'm not a doctor.

		MR. HEARL:  Okay, got you.

		UNIDENTIFIED SPEAKER:  I thought he was.

		MR. HEARL:  Okay, so there is no check for the oversize particle
thing?

		MR. LIECKFIELD:  Correct.

		MR. HEARL:  Okay.

		MR. LIECKFIELD:  But we're also not doing compliance --

		MR. HEARL:  Right.

		MR. LIECKFIELD:  -- results.

		MR. HEARL:  Okay.  So -- and then, on this other point about the
sampling, normally a person would sample under -- using the current 10
mm Dorr-Oliver cyclone at 1.7 L/min for a full shift, 480 minutes, that
works out to be about 0.8 m3.

		So if you were to need to quantify something at 25 µg/m3, and you
collected 0.8 m3, would that not be 0.2 or 20 -- or 10, 20 µg, not 10?

		MR. LIECKFIELD:  If it's -- you have the same problem at 20 as you do
at 10.

		MR. HEARL:  But it's twice as much on the filter as what you were
saying you needed to get the 25.  Because 0.8 times 25 is 20.

		MR. LIECKFIELD:  Perhaps I misspoke.

		MR. HEARL:  Okay.

		MR. LIECKFIELD:  At the -- we're talking action level?  Or are we
talking --

		MR. HEARL:  Whatever level it is.  I mean, whatever --

		MR. LIECKFIELD:  I was speaking action level and PEL.

		MR. HEARL:  Okay.  I mean, at the PEL, it would be 50 µg times 0.8,
and that would give you the number of µg you'd need on the filter.  If
you're wanting to quantify 25 µg/m3, and you sample 0.8 m3, that should
be 20 µg.

		MR. LIECKFIELD:  Okay.  The math -- I will accept your math.

		MR. HEARL:  Okay.

		MR. LIECKFIELD:  But at the end of the day, the lab industry and the
current published methods do not have any data underneath 50 µg.  And
at 50, OSHA is plus/minus 26 percent, NIOSH is plus/minus 18 percent
without measuring bias, because accuracy is precision plus bias,
absolute value of the bias.

		JUDGE SOLOMON:  Dr. Schwartz, you want to move down?  People in the
next row who had your hands up, could you get -- move up?

		MR. HEARL:  One other thing, at least.  I went to the Bureau Veritas
site, and where it cites how you're going to supply services, like for
the new OSHA proposed PEL, and it says that the evaluation of exposure
limits for crystalline silica, the reporting limit, and then you've
got -- or LOQ -- in parentheses, actually on that, for quartz and
cristobalite is 5 µg per filter, and the limit for tridymite is 10 µg.

		So is that consistent with the way you describe reporting limit?
Because you have LOQ in parentheses at your website.

		MR. LIECKFIELD:  There's a little bit of poetic license there.

		MR. HEARL:  Okay.

		MR. LIECKFIELD:  But at the same time, what we can measure -- there's
no -- I have no issues with the ability of the current methods to
measure down at the 5 and 10 µg level.  That's really not at all my
point.

		The point is, you cannot measure it accurately enough to meet the OSHA
requirements of plus/minus 25 percent.  It's always been that way.  I
mean, it's -- there's always been that variability.  All analytical
methods, as you know, have increased variability down near the LOQ.

		MR. HEARL:  Sure.  That's -- I mean, that's the reason why they also
give extra credit, basically, in terms of how far you have to exceed the
limit before they cite.

		MR. LIECKFIELD:  Right.  But my old position, from the commercial
industry side, we are not doing compliance --

		MR. HEARL:  Okay.

		MR. LIECKFIELD: -- or we're not providing analytical data for
compliance sampling.  We know what the variability or the overall method
precision is, and we can accept that.  When you get into compliance, I
believe OSHA themselves say that it has to be plus/minus 25 percent.

		There is no data to support that under 50 µg that I've seen.

		MR. HEARL:  Okay.  Thank you, Your Honor.

		MS. KEY-SCHWARTZ:  I'm Rosa Key-Schwartz, from NIOSH in Cincinnati.  I
have--

		JUDGE SOLOMON:  I have to -- I know how to spell your name, but the
Court Reporter may not.

		MS. KEY-SCHWARTZ:  K-e-y, hyphen,

S-c-h-w-a-r-t-z.  I have three questions for analysis.  The PAT Program
provides analytical interferences for the laboratories to prove their
ability to analyze in the presence of mineral or interferences; however,
would you please speak to the article published in the Synergist in 2000
by Edwards, which states that proficiency testing data should not be
used for assessing the performance of an individual analytical method?

		MR. LIECKFIELD:  I can say I just disagree with that statement.

		MS. KEY-SCHWARTZ:  All right.

		MR. LIECKFIELD:  Because -- that was simple, Rosa, data coming from a
laboratory, we all -- the lab industry generates their own performance
data by making pure spikes, which will tell -- give you probably the
optimal precision and accuracy.

		The PAT Program adds a bit of mystery to it because you don't know
what your -- you know their performance samples, but you don't know
what concentration, so it adds a little mystery to the laboratory staff.

		We're -- to me, that -- the PAT samples are real world.  It's just
as if you were getting a client sample, in our case.  And it's measuring
that performance.  So I think it is very key.

		I guess I would ask it back the other way, why did Dr. Edwards think
it wasn't appropriate?

		MS. KEY-SCHWARTZ:  Do you get to ask me a question?

		MR. LIECKFIELD:  No.

		MS. KEY-SCHWARTZ:  Well, I would just briefly say that in the NIOSH
methods, we have validation data for that method down to 20 µg, so I
want to speak to that, and then --

		JUDGE SOLOMON:  Do you have another question?

		MS. KEY-SCHWARTZ: -- and then move on to the next question, thank
you.  You talked about the performance having been studied by NIOSH, and
you suggested it be studied again, which I think is a good idea.

		Then the colorimetric methods are known to be significantly more
variable, and I just wondered if you knew that, the fact that the
colorimetric methods have not been reported at all for PAT samples the
past few rounds, and so we expect the variability to significantly
decrease in the PAT data.

		MR. LIECKFIELD:  I don't know that, Rosa, specifically.  On the
colorimetric labs, the last metric I looked when the PAT data was
assembled was three or labs were still using colorimetric.  I didn't --

		MS. KEY-SCHWARTZ:  Okay, they were -- I just wondered if you were
aware that they were not reported at all in the past few rounds --

		MR. LIECKFIELD:  No.

		MS. KEY-SCHWARTZ:  -- so we would expect to see a difference in
variability.  I think that's a very positive statement that you made
about wanting to study that again.  Thank you.

		MR. LIECKFIELD:  That's the purpose of --

		MS. KEY-SCHWARTZ:  Okay.

		MR. LIECKFIELD:  My whole position is that we need, as an industry, to
understand the analytical method, now at the levels that are -- would
be required.

		MS. KEY-SCHWARTZ:  All right.  Thank you.  And my last question is
that the National Institute of Standards and Technology has provided
standard reference materials that are deposited on filters down to 10
µg.  The quartz series is 2950 and the cristobalite series is 2960.
Does your lab employ these calibration standards?

		MR. LIECKFIELD:  Not as reference standards.  We use standard
reference material to create the calibration standards.

		MS. KEY-SCHWARTZ:  All right.  Thank you.

		JUDGE SOLOMON:  Next.

		DR. SIVIN:  Dr. Darius Sivin, United Auto Workers Health and Safety
Department.  My office is in Washington, D.C.  Question is for
Dr. Bunn.

		JUDGE SOLOMON:  You have to spell your last name today.

		DR. SIVIN:  S-i-v-i-n.  Dr. Bunn, you stated that you're unaware of
any cases of silica-related disease in folks who entered the various
workplaces that you worked in since the 1960s; is that correct?

		DR. BUNN:  I started in the 1980s, but the 1960s was the --

		DR. SIVIN:  But the folks in the -- the folks you're referencing
started in the 1960s and later; is that correct?

		DR. BUNN:  Some of them did.  I'd have to look.

		DR. SIVIN:  Okay.  The reason I ask is that are you aware, in the
Checkoway study that you cited, there was a statistical increase in lung
cancer among folks entering the cohort in 1970 through 1979?

		DR. BUNN:  I have to go look carefully at that.  Harvey and I went
through this, and I didn't think we got that.  But I'll look at it.

		DR. SIVIN:  Are you further aware that there was a statistically
significant increase in nonmalignant respiratory disease, both in folks
who entered the cohort in 1970 through 1979, and in folks who entered
the cohort in 1980 through 1987?

		DR. BUNN:  We didn't look at nonmalignant respiratory disease as
opposed to fibrotic disease, obviously.  And, yes, I'll stop at that
area.

		DR. SIVIN:  Okay.  I'd like to ask you a few further questions about
your experience as medical director at these corporations.  It is well
known that many chronic occupational conditions don't show up on OSHA
logs or workers' compensation claims, and that many are misdiagnosed by
family physicians.

		Did you ever undertake a systematic analysis of your employers'
sickness and accident claims or health insurance claims for both active
and retired workers to determine whether silica-related or other
occupationally related conditions showed up there?

		DR. BUNN:  We did -- two things we do.  I was in charge of the health
plan also, and so we analyzed regularly for major trends.  So I'm not
going to say you couldn't miss an individual, but see, respiratory