Regarding the very high doses in animals, several of you did not state the obvious, that it is very expensive to test animals, and the expense of testing the many animals to observe low dose response is impractical.
Questions on uncertainties:
***Q. I have always been taught that the first step in statistical analyses
is to check your data for outliers and normalicy. It was kind of treated as
an afterthought. It is my belief/understanding that it deserves a little more
attention than it seemed to be given in the text.
A. I won't try to defend myself against charges that I am not a statistician.
Yes, first step is to look at all your data. But here are some ideas I will
throw out: Many types of contamination can vary by many orders of magnitude
within a short distance, so an apparent outlier may not be an outlier. Careful
with the word "normal." Much environmental data is log-normal. Also,
with a large site, there may be pockets of contamination, so the notion of "average"
for the site may not give an accurate picture, either.
**Q. When determining risk or site status EPA guidelines do not allow for the
use of "non-detects". Instead, protocol is to use 1/2 the detection
limit for calcualtions. Also, J-flagged values are considered "usable"
because of their "conservative nautre". As someone with more chemical
background than most, I have to ask. Why? More specifically, what proof, trends,
or justification does the EPA have for using such protocols. Analytical technology
increases at an amazing pace every year. We can now detect down to parts per
trillion in many instances. Assuming 1/2 the detection limit seems like a beaurocratic
hoop not founded in science. Any ideas on this?
A. Yes. It is a "convention" with no real scientific basis. But using
"zero" is philosophically wrong, and using the lower limit of detection
will give inflated numbers, since most of the samples really are "zero."
*Q. In the discussion about uncertainties, the Hampshire Research Institute
mentions that "Even a worker population is typically healthier and more
uniform than the average local community exposed to environmental chemicals."
Why is that the case? I thought that the opposite is right. The lower average
life expectancy of men in comparison to women is often explained with the impact
of workplace to men.
A. There are many explanations of women's longevity, including their greater
intelligence. Much is explained by their lower rate of tobacco and alcohol use,
as well as the traditional reluctance of employer's (in western countries) to
place women in dangerous jobs. Monthly iron loss may protect against heart disease.
They don't have a prostate gland. Etc., etc. Epidemiology studies always separate
the populations by gender. The "healthy worker effect" is one of the
commonest "confounding factors" in epidemiology. Workers prone to
poor health, or who perhaps developed diseases on the job, leave that job or
even the workforce, so only the healthy worker are around for the 20 or 30 years
exposure period that may be relevant.
*Q. The Ames and Gold article I found quite informative. I haven't read any
on their background. Are you familiar with their work and reputation? Their
idea of the non-linear effects of carcinogens seemed well stated. Are you aware
of the EPA moving in that direction?
A. Ames and Gold are two of the best known cancer researchers and have been
leaders for a long time. Ames is at Berkley and runs an EPA funded research
group. The EPA budged just a little, and their standard statement now reads
that "the [cancer] risk may be lower than that or may be zero."
*Q. On the various methods of treating petroleum in the risk assessment, I'll
have to disagree with you on their applicability. As you probably remember from
your work at the Corps, petroleum is on of the major contaminants the Alaska
District is cleaning up around the state. We've wrestled for many years on 'how
clean is clean?' for petroleum. I believe the indicator-surrogate method is
the best we have. And the best we have is adequate (acceptable level of uncertainty)
for determining risk and cleanup levels. Although the State of Alaska has regulations
that follow this approach (well kind of, sort of), the work done by the State
of Massachusetts and the TPH Criteria Working group is well documented and peered
reviewed. I think that using the major groupings of GRO, DRO and RRO along with
separate analysis of the carcinogens (benzene and the cPAHs) and the TEX (toluene,
ethylbenzene and xylenes) gives an accurate picture of the risk. Of course there
are uncertainties - !
and they should be stated.
A. Sounds like your up on that topic. You realize that there may be vast differences
in the chemical composition of those groups. DRO here could be very different
than DRO there. Also, there is a vast difference in their solubility. I'm not
saying that such classifications are not a practical answer to the "how
clean is clean" question, just that at any particular situation, it may
not be correct scientifically. But then, that's true of many chemicals, even
when we know the chemical.
Q. Do you think uncertainty is discussed enough with the public when a public
agency is making a decision based on statistical data and modeling? There are
uncertainties involved in scientific research, which include data gaps that
may result from missing specific measurements or from a fundamental lack of
understanding about a particular scientific phenomenon. I rarely hear anyone
mentioning these uncertainties in any kind of risk forum. What do you think?
A. The wise risk manager, when involving the public, will always mention uncertainties.
I do. The problem is that they are…uncertain. A smart antagonist can always
point out that you are not sure of this or that. The only defense is to state
that, as far as you know, the assumptions are conservative, that is, protective
of human health. Also that a "trusted agency," perhaps the EPA, recommended
you use those assumptions. That is also a good time to be humble and admit you
are just doing the best you can with the site specific data you have and the
scientific knowledge you have. This belongs in the next module on risk communications.