Pretty sure this post is for you alone, @Donald Johnson.

And I'm sure Will will comment on it anyway.... SMH.

IMHO, in the electrical world, Joule Thieves live and die off that top little spike.

I have nothing to say about all the little squiggly things, but I am curious, what is the purpose of blogging about a heterodox scientific theory at a blog where few readers are capable of forming an informed opinion of its legitimacy, so that for the most part you end up arguing on the basis of authority? Why not at a science blog?

Also, are you planning on writing your book in lieu of putting your ideas through peer review, or in addition?

Are you trying to change the scientific consensus, or just hoping to affect public opinion about nuclear energy? Or do you just find this a convenient place to think out loud?

@Godoggo, that's a good question. I've been wondering when somebody would ask it.

There is an element of thinking out loud, that involves encouraging others to think for themselves about technical matters. The underlying science isn't very difficult, actually. The conversation gets dragged into the squiggly weeds by others. I'll go there, OK, but I do feel - what's the point?

(And a theory they teach in the EE Department at Stanford is scarcely heterodox. It's just that the radiation biology community doesn't use it.)

This information does need to go to a peer-reviewed journal, and it will. The impediment remains my time, and lack of access to a technical library with deep resources in the biological sciences.

More broadly, though, a theme in this blarf for more than a decade is that powerful people would rather kill us all than be less powerful. I think I'm maintaining a discussion in keeping with that theme.

@Aaron- Phew! Now I don't have to worry about spilling the beans about your plans to publish. The book's still gonna happen though, even if it means i need to come down and tend your garden. :)

BTW, I assume the Mangano mentioned in earlier comments is the Joe Mangano currently at the Radiation and Public Health Project? Noticed you passed on links to their FB group and was curious.

Ugh. Probably shouldn't have started reading other articles posted to that FB group... http://eartoearth.org/2012/07/23/cover-up-at-fukushima-workers-told-to-put-lead-over-dosimeters/

Aric: I used to wear a badge at a job I had. I was told that once it was fogged up, I was out of a job. I was young and didn't even consider using lead to save my job. I was fired for FAILURE TO LIE on some documents on safety.
Skewed data in the nuclear industry, whoda thunk, huh???

I don't know if I'll have time to comment today beyond this. Thanks for the links, Aaron. It'll be a good refresher course. And godoggo's question is a good one--I'm an amateur at this and no, I don't think that all the radiation physics/biology community are morons--they know about Poisson processes, they know about stopping power, energy needed to ionize atoms, the obvious things to calculate here, and people calculate such things. You can peak inside radiation physics and biology books at Amazon and see them doing it. You need comments from experts who could argue with you about why shot noise theory is or is not relevant to the issue. The question is not about whether the math is right--the question is why it is relevant to the problem.

Anyway, I went to the MIT opencourseware site, found the grad level class on radiation interactions in the nuclear engineering section and most of it is just standard physics, figuring out, for instance, stopping power for various particles and how many ions a particle of a given energy should create. Nobody is calculating how the fluctuations in power vary depending on the length of time used to measure it. They are more interested in calculating how long the track of an alpha particle is and how many ions are created and to me this makes sense. The MIT guy also has a section on the "bystander effect", which is about the fact that there are higher than normal mutation rates in cells which aren't directly hit by the alpha particles. I suspect that if low doses of alpha particles are more harmful than generally believed, it'll be due to some subtle biological effects, not the basic physics.

To repeat my earlier analogy, you could also apply the theory of shot noise to shot noise snipers--people firing rifles at a target, pulling the trigger when a Geiger counter goes off. The mathematics would be exactly the same--a radioactive particle is emitted, so a trigger is pulled. Just change particle energy to bullet energy and it's the same calculation. But it would be more reasonable to estimate the damage done by the number of bullets fired. So for shot noise calculations to be relevant there has to be something about the target itself which makes the shot noise calcuations relevant for cancer induction and not for damage done by bullets. And that has to be carefully argued. I don't understand the argument--as best I can tell, it has to do with the 5 ms time,but if you pick out that particular time interval (or some other) as having special biological relevance, one can just do Poisson statistics to estimate how many 1 decay events, 2 decay events and so on you'd expect to see given a dose rate and an exposure time. We did that for a particular case in the other thread. What I didn't understand is how that would refute the linear model for low dose rates.

I'm basically repeating myself,so even if I have more time later I probably have nothing to add.

I'm not sure that this is relevant to biological effects of radiation. I'm troubled by the use of P(t) in place of i(t). For an electric signal, power is assumed to be proportional to the square of i(t). So they are very different. Then, in your handwritten notes, you have the square of P_bar is proportional to the integral of the power spectral density, when this integral should give you P_bar itself. This makes the units come out right, but it seems to me that you're finding the square root of the power in a signal consisting of your P(t) squared.

This doesn't seem correct, but it's been many years since I did this kind of thing.

@Donald Johnson, I get that. I understand where you're coming from. That's my background, too.

The questions you're asking are addressed in the two papers by Spitz and Belyakov footnoted in the original post. Spitz describes how long the cells remain perturbed after a pulse of free radicals disturb the chemical equilibrium (5 ms); and Belyakov describes experiments he did in which an alpha particle traversing HERE induced chemical changes 1000 cells away.

In any event, the Wing data is so far in the shot noise limit that the values of frequency bandwidth and interaction volume size don't even enter. If the doses were actually known, it might be possible to extract these values by fitting.

@Mike B, Pbar is the noise in a random signal. The rules for calculating stuff for random signals are different. You can click through to the notes for the Stanford lecture if you like.

Aaron - I understand (I think) the lecture notes. I don't know what you mean by Pbar being the noise in a random signal. Isn't it supposed to be a measure of power in a random signal? Slide 1.5.2 defines the power spectral density (PSD), and shows that the ensemble averaged power in random signal x_T(t) is the integral of the PSD divided by 2 pi. So, assuming you have calculated the PSD correctly, what you have as Pbar-squared is the average power of the pulse train. Assuming Eo is energy, the units of this in your equation are watts-squared. Power is conventionally in watts. I think the reason you have watts-squared is that your signal is P(t), which is not equivalent to i(t) or v(t) normally assumed for signals, which must be squared to get power. So I still have the concern in my earlier comment. It seems that you're finding the power in P(t)-squared and then taking the square root. Maybe this is OK for some reason, but it's not obvious to me.

@Mike B,

Pbar is the noise power sqrt(N*delta-f) added in quadrature with the deterministic signal N. (Omitting the factor of E.)

I believe it is correct to take the square root of Pbar^2 because I was taught it's correct to do this for noise current. I recall the power being

ibar^2*R = (ibar*q*delta-f + ibar^2)*R.

Engineers then talk about the current being

ibar = sqrt(ibar*q*delta-f + ibar^2).

I would be very surprised to learn this is incorrect, as I learned it from the National Radio Astronomy Laboratory, and I thought they should know.

@Donald Johnson,

I understand what you're saying very well, and I even agree that some cancers arise in this way in a manner that is linear with dose. However: why do you believe that ALL cancers due to ionizing radiation arise in this way?

I think many cancers arise due to chemical changes in the body brought about by ionizing radiation, possibly at the level of millions or billions of cells that communicate with one another. I believe the community refers to this as "oxidative stress", and it's well established to both cause and promote cancer.

We hear so much more about the model you describe because it's possible (by counting broken pieces of DNA and so forth) to compare its predictions to results, write papers, get grants, etc. Measuring chemical stress in a large group of cells subject to a single large decay event that can't be predicted is impossible, so we don't think about it.

You and I both did the math showing >10,000 simultaneous free radicals in a single interaction volume at a very low dose rate. Is there not a possibility of synergy?

If I may, EVERY moving electron creates its own magnetic field.

Aaron - I'll wrap up my end of this dialogue with this post (probably). I don't know much about radiation or its effect on biological tissue, and I'm fine with criticism of the linear dose model. But it seemed to me you might be getting sidetracked with this shot noise argument.

I was very skeptical when I read this: "The energy delivered via radioactive decay is the integral of P(t), which correctly yields the number of decays times the energy of one decay E. The power in a random signal is not immediately calculable from P(t), which after all is perfectly random and cannot be known."

P(t) cannot be known *in advance*, but it can surely be known after the fact (by detecting and measuring it), and then its average power over some time interval should be calculable by dividing its energy (integral of its power) by the length of time. This seems self-evident to me, which doesn't me I'm right, of course.

You end up with a different result, and so I pointed out where I thought the problem was. I don't have a problem with your recent equations, but my problem is that I don't think you can treat power in the same way as current. The power spectral density equation is derived based on the assumption that the power in the signal is related to the square of the signal, which is true if the signal is i(t) or v(t), but it doesn't seem to me to be true if the signal is P(t).

I don't think random signals are fundamentally different from deterministic ones. It's just that for random signals you generally assume that you don't know the waveform in advance, but you know something about its statistical properties, so you do analysis based on those. If you know P(t), I think you can calculate its average power in the same way whether it was generated in a deterministic or a random way.

@Mike B -

You're conflating average and instantaneous power, in my opinion. You actually can't know P(t) unless you possess measurement equipment with infinite bandwidth, which doesn't exist. Furthermore, since the noise signal is random, knowledge of it in the past doesn't tell you anything about the future.

Carson's Theorem is a general result for an arbitrary pulse train. If the area of one pulse is "e", it yields ibar^2. When the area of one pulse is "E" - the energy in one radioactive decay - by analogy it yields Pbar^2.

The comment of mine you quoted simply indicates that I'm not claiming there's any more energy than calculation of the average power indicates. It's how that energy is arranged in time that creates the noise power.

Thanks for the technical conversation. I appreciate that you cared enough to read over the Stanford materials.

Folks, in this comment and the next is a look at four major papers in the TMI literature that Aaron has not talked about. They give a broader understanding of TMI that undercuts alarmist claims about the cancer effects.

--Hatch has written two studies (http://cipi.com/PDF/hatch1990%20no%20ocr.pdf) and (http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1405170/pdf/amjph00206-0049.pdf)

The Wing paper got all its data and much of its analysis from the first Hatch paper. Some differences are that Hatch grouped the data into 4 exposure groups instead of Wing’s 9; and of course, the guts of the all-important regression models used to estimate expected standardized incidence ratios are different in the two papers. Hatch did find a modest statistically significant risk associated with radiation exposure, with odds ratio of 1.11 (1.03-1.21 95% CI), a bit higher association for lung cancer, not much for other cancer types. Hatch’s group did a rather meticulous calculation of radiation doses that matched up nicely with consensus findings that TMI doses were below 1 milliSievert; Wing relied on its relative dose estimates while arguing that the absolute doses were much higher (unpersuasively, in my opinion). The takeaway from the first Hatch paper is: “Overall, the pattern of results does not provide convincing evidence that radiation releases from the Three Mile Island nuclear facility influenced cancer risk during the limitied period of follow-up.” (i. e., through 1985 as in Wing’s study.)

--The second Hatch paper, based on the same data, annoyed a lot of anti-nukes because it posited a link between TMI cancer effects and “stress” rather than the radiation itself. That’s not quite as silly as it sounds. Hatch looks at stress hormones, but more prosaic effects could flow from stress: if anxiety over the spew led people to drink and smoke more to settle their nerves, that could lead to higher cancer rates. Also, there’s the issue of “heightened monitoring.” Assumptions about increased cancer risk from the spew could cause patients and doctors to become more vigilant about looking for cancers. They might find small, non-aggressive, slow-growing cancers that would normally not have been diagnosed before they spontaneously regressed or the patient died of something else. Heightened monitoring could thus cause cancer diagnosis and incidence rates to climb even if the underlying cancer rate did not. Hatch’s evidence for all this does not seem especially persuasive to me, but they are effects that epidemiologists should watch out for as biasing factors. Wing does not seem to take account of possible increases in smoking and drinking, or heightened monitoring. The takeaway from the paper: it found a modest increase in cancer risk correlated to proximity (not to radiation exposure) with an odds ratio of 1.4 (1.3-1.6, 95%CI) which it attributed to stress, (not very convincingly to me).

Folks, two more major TMI papers from a U. of Pittsburgh study. This is a longitudinal cohort study following about 30,000 people living within 5 miles of TMI during the spew, 93 % of that population. Several papers have come out of it.

This one (http://www.ncbi.nlm.nih.gov/pubmed/21855866) looked at cancer incidence from 1982 to 1995, 13 years as opposed to Wing’s 5 years and a period encompassing a much longer latency, so likelier to contain genuine TMI cancers. It found no rise in cancer incidence at all in the cohort. Relative risk for maximum estimated TMI gamma radiation was 1.00 (0.97-1.01 95% CI) and for likely TMI gamma relative risk was 0.99 (0.94-1.03 95% CI). They looked at a bunch of cancer subtypes, and found a TMI risk for leukemia in men, but not in women. Elevated risk was also found for lung and respiratory cancers, but it correlated to background radiation, not TMI dose. The takeaway: “Increased cancer risks from low-level radiation exposure within the TMI cohort were small and mostly statistically non-significant”, but keep an eye on the leukemia in men. (Note that if you follow a lot of cancer subtypes that each have a small number of cases, you are bound to find a few statistically significant correlations just because of random statistical flukes.)

--This Pittsburgh study (http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1241392/pdf/ehp0111-000341.pdf) looked at mortality (not incidence) from cancer, heart disease and all causes from 1979 to 1998, so 19 years, encompassing much more latency than Wing’s five-year study. The Pittsburgh study did find a slightly elevated mortality rate for all cancer in men for the whole cohort, with a standardized mortality ratio (SMR) of 103.7, a difference from 100 that was not statistically significant. There was no rise at all in all-cancer SMR for women. There was a rise in respiratory and BTL cancer subtypes in men that was statistically significant. Both men and women had significantly elevated SMRs for heart disease (111 and 127) and all non-cancer deaths (108 and 116). But none of these elevated SMRs, for cancer or other causes, correlated with TMI radiation dose; getting a bigger TMI dose did not increase your mortality risk. The takeaway: “Although the surveillance within the TMI cohort provides no consistent evidence that radioactivity released during the nuclear accident has had a significant impact on the overall mortality experience of these residents, several elevations persist, and certain potential dose-response relationships cannot be definitively excluded.”

--So what does this pointillist picture of TMI in this and the previous comment add up to? To me it’s a question mark, and a rather small one. There may be some cancer risk from the TMI spew. If it’s there, it’s inconsistent, appearing in one study then disappearing in the next depending on how you design the study and slice and dice the data. Sometimes a cancer subtype pops out in a study, but in the next it’s a different subtype. When effects show up, they are sometimes correlated to radiation exposure, and sometimes not. All of these effects flicker on the border of statistical significance.

When you see effects that are very small and inconsistent as in the TMI studies, it usually means there is nothing there. A genuine effect will show up significantly and consistently, but a zero effect will often look like a small effect because statistical flukes will masquerade as minor, inconsistent correlations—that’s the nature of randomness. Whenever we see a small, inconsistent public health effect from any putative cause, we should be skeptical that it exists at all.

Again, I don’t see much to worry about in the TMI literature as a whole.

Will- FYI- just because you don't understand Aaron's theory and can't follow the math doesn't mean it's wrong.

Will Boisvert: Do those studies just count the cases in the immediate area or nation wide due to population migration?( Had I lived there I'd have got the hell out)

Will Boisvert: Is that background radiation a value of BEFORE OR AFTER the event?

So, all right, I'll play a little bit, @Will Boisvert.

Does the study by YY Han (2011) explore cancer incidence before vs. after the March, 1979 accident? The Wing study went from 1975 to 1985. What are the years covered by the Han study?

And I'm too bored to look whether you said this, but do you really not know how far the plume from TMI extended?

Really Aaron? Going with that? Would be far more interesting to explain how you and Will had been corresponding a bit before he found ATR and ever since you asked his background he's been trolling your posts with propoganda. It really helps put things in perspective...

Aaron, okay, let’s take Wing for gospel and look at the body count of excess cancer deaths in his paper as you suggest two threads ago. Then let’s look at that from a larger perspective and see how bad the risk really is.

You count 190 excess lung cancer cases in the Wing study. I don’t think it’s that many, from my calculation using OERs in table 3. ( [O-E = O (1-1/R)] from the second line of OERs.) I get 65 for a net excess.

For all cancers, from the second line I get 126 net excess cancer cases, 198 if you leave out the OERs less than one. Taking the bigger number and assuming that roughly half of cancer cases are fatal, that’s about 100 cancer fatalities over 5 years, or about 20 per year. (Assuming that all those excess cancer cases are from TMI radiation, which is not a good assumption.) So how does that danger, 20 fatalities per year from the TMI spew, look in comparison to other ordinary risks that we face? How does it stack up compared to, say, the risk of driving?

Well, in 1983, there were 42,569 traffic fatalities in the US, among a population of 234 million. (http://en.wikipedia.org/wiki/List_of_motor_vehicle_deaths_in_U.S._by_year) Assuming Wing’s study population of 160,000 had a normal share, that would be about 29 traffic fatalities per year, compared to the 20 TMI cancer fatalities per year.

All of this means that, by Wing’s data, getting caught in the TMI spew was substantially less dangerous than owning a car is.

And that’s for the people in the maximally contaminated epicenter of a spew. That scale of risk from nuclear power occurs once a generation to perhaps a few hundred thousand people. But auto fatality risks are incurred across the whole country every year. Nuclear power in the United States may be killing a few dozen people every year, but automobiles are still killing about 30,000 people every year, a thousand times as many. Add in the thousands of people whose lives are shortened by cancer and heart disease caused by air pollution from cars.

So I don’t get it. Why should we worry about the tiny risk from another TMI, when we all happily drive cars, which are a thousand times more likely to kill us? Why don’t you go on a crusade to abolish cars instead of nuclear power?

That’s why anti-nuclear alarmism is profoundly irrational, no matter whose stats you use. Like all phobias, anti-nuclear phobias are a fun-house mirror that crazily distort people’s perceptions of risk—making them shriek in terror at trivial risks while blithely wallowing in dangers that are objectively thousands of times greater.

The great danger of that irrationality now is that it may deprive us of an enormously valuable technology that provides reliable low carbon energy, abates air pollution and gives us our best chance to stop global warming. Here’s a new study that estimates that nuclear power has saved almost two million lives over previous decades just by abating air pollution. (http://pubs.acs.org/doi/abs/10.1021/es3051197?journalCode=esthag) It’s by James Hansen, the famous client scientist and another fellow, both of whom work at NASA’s Goddard Institute--just like you, Aaron. Maybe you could go talk to them about it.

Aaron, I don’t think the advanced calculus you’re using provides much insight into this issue. Simple arithmetic shows how wrong your position is.

@ Aaron,

“Do you really not know how far the plume from TMI extended?”

Beats me, Aaron. Do you have any information on where it reached and, more meaningfully, what radiation dose it delivered when it got there?

What I doubt, however, is that it reached everywhere in the United States without any dilution or any diminution in its radioactivity from decay. Was it just as intense in Maine and Florida and Montana and California as it was within ten miles of TMI in Pennsylvania? I don’t think so.

I think in places outside Wing’s 10-mile study area, if it got to any of them, the radiation must have been drastically weaker than it was in the 10-mile Wing study area. So whatever risks Wing found within his 10-mile study area must be drastically smaller outside it, plummeting further every mile we go from TMI.

We’ve established upthread that the risks from getting caught in the plume within Wing’s 10-mile study area were substantially smaller than the risks of driving a car for the people that lived there. That’s about the maximum risk anyone in the country could have faced from TMI; outside that area the risks were even smaller, drastically smaller than driving a car, and must have dropped to essentially zero before very many miles out.

Since the risks of traffic fatalities I cited obtained for the nation as a whole and its entire population, it follows that the overall risk of traffic fatalities for the nation as a whole must be colossally greater, by orders of magnitude, than the overall risk from TMI radiation.

To use a bit of math notation:

Driving risk > TMI risk in Wing study area >>>>>>>>>> TMI risk for nation as a whole. QED.

So again, hysteria over TMI radiation is very irrational when compared to everyday risks, like driving a car, that we accept without question. We don’t need exotic math to see that—just plain old arithmetic and common sense.

@ Mike Meyer,

--The U. Pittsburgh cohort study follows the whole population living within 5 miles of TMI during the spew. It follows them over time, even if they move out of the area--it has arrangements with the post office and other ways to track them. Wing's study is an ecological study, and it probably picked up some cancers in people who moved into the TMI area after the spew and thus could not have been exposed to it.

--The background radiation in a given locale does not vary over time; it's the natural radiation from space and soil.

Will Boisvert: Of course TMI no longer leaks anything since a few days after the event, and it would be IMPOSSIBLE for that background radiation to be enhanced in ANY way or appreciable amount, nor could THAT plume stretch around planet earth many times by now and STILL be a-blowin' in the wind to this VERY hour. IMHO

@Will Boisvert,

In 1980, Wahlen et al. published in Science magazine their measurements of radioxenon from the TMI plume in Albany, NY. A scientist named Armentrout also measured the plume in Portland, ME. He didn't publish his data, but he did serve as an expert witness in the lawsuit by community members against the utility.

So: if populations between Pennsylvania and Maine were exposed, why do you think that a study radius covering a ten- (or even five-) mile area really tells you much about the overall effect?

Moreover, look: you're a blathering idiot. Taking all of the scientific literature, tossing the outliers, and averaging the rest together to form an opinion is the opposite of wisdom. It's the truth, not the consensus, that matters.

By the way, when you look it up, ask yourself whether the Wahlen assessment about the dose from the plume is correct. It's based on an interesting assumption......

@Mike- "Of course TMI no longer leaks anything since a few days after the event..."

Good thing operational reactors don't occasionally burp radioactive gasses as part of their normal operation as well! If they did, the NRC would likely make that sort of information available...

Oh, wait. They do.

Crap.

@Aaron- "So: if populations between Pennsylvania and Maine were exposed, why do you think that a study radius covering a ten- (or even five-) mile area really tells you much about the overall effect?"

Simple! Because the LNT model tells us the only doses that matter are large ones, and naturally those would be contained within a short distance of the reactor!

Right, @Will? :-)

Hmm... Seems this needs to be broken out and highlighted.

Aaron: “Do you really not know how far the plume from TMI extended?”

Will: "Beats me, Aaron."

Seems to me Will just painted himself into quite the corner there... You'd think that someone making the claims he does about TMI would be at least a tiny bit familiar in passing with what happened.

SMH.

@Will Boisvert,

Your opinion is that, outside of the 10 mile area, the degree of radiation exposure would be so greatly reduced that there could have been no effect. Is this correct?

Do you also believe the linear dose model? Because if you do, the rule is that the dose creates the same number of fatalities, no matter how that dose is allocated.

Also, just for my entertainment: what's the basis for your opinion that the "exotic math" I've described doesn't apply to any aspect of radiation biology?

Do you understand the mathematics of linear energy transfer? Is that "exotic math" too? Why do you get to believe one flavor of exotic math, but not the other?

If you had any scientific training, you'd understand that all I've pointed out is that power and energy are different - and at low doses, the chemical state follows the power rather than the energy. That's scarcely exotic math. You'd also know enough to realize that the models you're defending as though they were gospel (like the DDREF) are just post-hoc rationalizations glued on to a model that fundamentally doesn't describe what's occurring in the body.

I know this because I recognize the models - I used them when I was a scientist at Bettis Atomic Power Lab. They were developed and proved describing interactions between charged particles and crystalline solids. Does that sound like a human body to you?

By the way, Hansen works at the Goddard Institute for Space Sciences, in New York City. His office is actually above the diner pictured in "Seinfeld". I work at Goddard Space Flight Center in Maryland, where we care about technical matters like shot noise. But it's a cute suggestion.

It's cool that you can read government reports and abstracts of scientific articles and spew them back at me. (By the way, who paid for the TMI studies you cite? And who didn't pay for the Wing study?) It would be cooler if you could think for yourself. A scientific education might help.

Last thing: you could have the courtesy to answer my question about the study years in the Han paper. It's right in the part you read, at the top.

Aaron, the radiation doses Wahlen measured from the passing TMI radioactive plume in Albany were incredibly tiny. (Here’s the abstract to his Science magazine report http://www.sciencemag.org/content/207/4431/639.abstract)

Here’s how tiny:

“The local gamma-ray whole-body dose from the passing radioactivity amounted to 0.004 millirem, or 0.004 percent of the annual dose from natural sources.”

The average TMI radiation dose is estimated at about 8 mrem, so according to Wahlen’s paper the plume was 2,000 times less intense by the time it reached Albany than it was at TMI proper. Thus Wahlen’s data confirm my point that TMI radiation dissipated rapidly as the plume moved away from the plant. Whatever health risks it posed must likewise have dissipated drastically. Since at the epicenter of the spew the health risks, according to Wing’s data, were substantially smaller than those from driving a car, the health risks in Albany from the plume must have been essentially zero.

How much risk would the plume have posed in Albany? According to Wahlen’s data, the cloud delivered about 20 minutes worth of natural background radiation, assuming the national average 240 mrem per year. It’s hard to see how that miniscule amount of radiation could pose any measurable health risk. As a benchmark, flying in a commercial jetliner at high altitude gives you an extra radiation dose of about 0.5 millirem per hour, or about 100 times more radiation dose in one hour than the TMI plume delivered to people in Albany. (There’s less atmosphere up there to block radiation from space.)

So if low-dose radiation is so dangerous, why aren’t anti-nuclear activists trying to ban air travel?

--I can’t find Armentrout’s data on the web. I did find this court judgment in the lawsuit that discusses his testimony, which makes for an interesting critique of his findings—the court found them unreliable and irrelevant for the purposes of estimating TMI emissions. http://www.leagle.com/xmlResult.aspx?page=37&xmldoc=1999806193F3d613_1740.xml&docbase=CSLWAR2-1986-2006&SizeDisp=7

I’m not sure we should give as much weight to unpublished data from a (paid?) expert witness for one party to a lawsuit as we do to research published in peer-reviewed journals.

--The Wahlen paper shows that TMI emissions were tiny, and the Armentrout data, as far as I can tell, don’t bolster alarmist claims about TMI emissions. I don’t think either of these sources strengthen your case about TMI risks.

--You write: “Moreover, look: you’re a blathering idiot. Taking all of the scientific literature, tossing the outliers, and averaging the rest together to form an opinion is the opposite of wisdom. It’s the truth, not the consensus, that matters.”

Aaron, I didn’t say we should toss out the Wing study, but we do need to consider the many other studies by eminent epidemiologists in peer-reviewed journals that contradict it. We should look at the whole literature, not just cherry-pick the studies that support our pre-conceived notion of the “truth” and ignore all the rest. When we do look at the TMI literature as a whole (or even just at Wing’s study) we see that TMI health risks, if they exist at all, are at most very small—much smaller than other risks we take for granted, like driving and air travel.

@Aaron- I'll save you some time and headache... Short version of the above post:

"more spew".

@Will- I think your work is done here. Please move to the exit posthaste. Your expertise is needed elsewhere. Preferably far, far away.

Clarification to my previous post:

Wahlen’s abstracts puts the local background radiation at 100 mrem, not 240 mrem, the number I quoted for the US as a whole. So the TMI dose would have delivered 21 minutes worth of the local background radiation.

21 minutes. The reactor was leaking much longer than 21 minutes. Do those numbers represent 21 minutes worth of background per milsec per interactive volume?

@Will- You've done quite a lot of typing in these posts, but if you've answered any of Aaron's questions it got lost in all the noise. Would you mind separating out the answers here? Kindly just answer the questions and leave out the noise and pontification.

1. I wonder, does it pay well trolling the internet for crimethink?

2. Look, a few years ago when we corresponded offline I offered to publish a long essay you composed on this topic on the front page here. Your essay, and your views, are strongly contrary to my views. The only condition was that you provide a short bio explaining why readers should accept your judgment as correct. I never heard from you, as apparently you prefer to express your OCD in the comments section. This is your call, but I don't feel very motivated to respond. If you want my time, explain to me why you deserve it.

3. (Have you) taken and passed an undergraduate class in statistical mechanics? Or even read a textbook on the topic?

4. Can you tell me what a Fourier transform is?

5. Does the study by YY Han (2011) explore cancer incidence before vs. after the March, 1979 accident? The Wing study went from 1975 to 1985. What are the years covered by the Han study?

6. So: if populations between Pennsylvania and Maine were exposed, why do you think that a study radius covering a ten- (or even five-) mile area really tells you much about the overall effect?

7. (Is) the Wahlen assessment about the dose from the plume is correct? It's based on an interesting assumption......

8. Your opinion is that, outside of the 10 mile area, the degree of radiation exposure would be so greatly reduced that there could have been no effect. Is this correct?

9. Do you also believe the linear dose model?

10. what's the basis for your opinion that the "exotic math" I've described doesn't apply to any aspect of radiation biology?

11. Do you understand the mathematics of linear energy transfer? Is that "exotic math" too? Why do you get to believe one flavor of exotic math, but not the other?

12. Who paid for the TMI studies you cite? And who didn't pay for the Wing study?

@Will Boisvert,

I don't know what your deal is. I do know I think less of the friend-of-a-friend-once-removed who recommended you to me as deep thinker with unconventional views.

I once had the opportunity to spend an hour with a small group of people speaking with Dr. Steven Chu. (A nuclear supporter, you might note.) As a Chinese-American (him, not me), I was interested in his opinion that the system of technical education in China is not very good. Graduate students he takes on from China, he noted, only read the abstracts and conclusions of scientific papers. They're taught by the Confucian model and don't understand that the actual scientific knowledge is contained in the middle.

Just three things, then I'm done with you. I encourage other commenters to ignore you, too, but I don't control what happens and others are free to reach their own opinions.

1) Your argument about deaths from automobiles is morally idiotic (you can choose whether to get in a car), but also arithmetically wrong. Tens of thousands of people died because of TMI, just in the few years after. The US government provides this information very plainly in the National Vital Statistics Survey, available through Google. That you don't realize this indicates that you've thought about the issue about as deeply as Ann Coulter.

2) Anyone who has watched a time-lapse weather map knows that your argument about plume dispersion is idiotic. Weather tracks can be quite coherent for dozens or hundreds of miles. For instance, after the Simon shot in 1953, there was a severe radiological incident in upstate New York due to fallout after a thunderstorm. That weather track had dissipated very little over more than 1000 miles.

3) Lastly - and please answer this - it's very nice that Wahlen et al. explained that the gamma dose from measured concentrations of radioxenon was too small to be of concern. So, can you tell me the principal decay mode of Xe-133?

You clearly lack the scientific training to be properly skeptical of what you're reading. Please stop misleading people. The situation is highly serious. And there is a nice list of questions you could answer above, if you wished to show a level of respect that might change my opinion of you.

Other readers (if there are any this far down) should note that the validity of the comparison to background - you guessed it - depends on the linear dose model. I recommend you don't trust it, for reasons I've explained.

@ Aaron,

--You write “Do you also believe the linear dose model? Because if you do, the rule is that the dose creates the same number of fatalities, no matter how that dose is allocated.”

Good point. So let’s take a stab at quantifying the number of fatalities the consensus Linear No-Threshold model might predict for the TMI spew. (Here’s the tweet if you don’t want to wade through my calculations below: for America as a whole, 1,311 fatal cancers would be a vast overestimate, 1 fatal cancer a plausible but possibly too low floor, a likely guesstimate is maybe a few dozen.)

The National Academy of Sciences BEIR VII version of LNT estimates 570 fatal cancers for every 100,000 people who each get a dose of 100 mSv.( http://dels-old.nas.edu/dels/rpt_briefs/beir_vii_final.pdf) So if we have an estimate of the average TMI dose delivered to a population per 100,000 individuals, we can multiply by that LNT risk factor to get the total fatalities predicted by LNT. (Note: BEIR VII cautions that at doses below 100 mSv there is no conclusive empirical evidence of any cancer risk from radiation.)

I don’t know of any TMI dose estimates for the US population as a whole, but let’s cobble together an overestimate from data points we do have.

The first Hatch paper that I cited above pegs an average dose estimate for the epicenter of TMI of about 0.1 mSv, a figure that’s in line with consensus estimate. Wahlen’s estimate, cited by me above, of the dose in Albany New York from the passing TMI plume is 0.004 mrem, or 0.00004 mSv. Let’s work with those numbers.

Suppose the TMI epicenter dose of 0.1 mSv applied to all 230 million Americans in 1979. According to LNT, that would yield 1,311 fatal cancers throughout the United States from TMI radiation. But that estimate is obviously far too large, because most Americans got much less than the TMI epicenter dose.

Suppose alternatively that Wahlen’s Albany dose of 0.00004 mSv is the average dose for Americans as a whole. According to LNT that would put the total number of fatal cancers caused by TMI at 0.524; rounding up that’s maybe 1 cancer death for the entire American population. That’s not a terrible estimate for the average American dose. People who lived closer to TMI than Albany would have gotten more, perhaps, but the much larger number of Americans who lived farther away from TMI than Albany is would have gotten much less than the Albany dose.

So we could bookend our LNT estimates by those two numbers. A figure of 1,311 fatal cancers from TMI radiation is definitely much too high, 1 fatal cancer is plausible but possibly too low.

We could try slightly more segmented estimates of dose-population. For example, we could guesstimate that 30 million people got an average of half the full TMI dose of 0.1 mSv (surely an overestimate, given Wahlen’s data implying rapid dissipation of the plume) and that all other Americans got the Albany dose of 0.00004 mSv or less. That would work out to about 87 fatal cancers for the whole country. If I had to guesstimate what the real number was, I would say maybe a few dozen fatalities. And those fatalities would all be conjectural, because no epidemiological study can discern such a tiny number of cancers from the background incidence.

--What’s less important than the exact number of TMI casualties predicted by LNT is the rough scale of risk that it predicts. We’re talking at most dozens of fatalities from TMI in the whole country, not millions, thousands or even hundreds.

Now you can see why it’s so important to the alarmist case to attack LNT and inflate TMI emissions by a factor of one thousand over consensus estimates. You just can’t get to an alarming body count—or even an empirically measurable one—by accepting LNT and the consensus estimates of TMI doses.

The empirical evidence—the peer-reviewed TMI epidemiological literature—roughly supports the theoretical picture painted by LNT. Taken as a whole, it finds no conclusive evidence of elevated cancer risks from TMI radiation in the maximally contaminated epicenter, just occasional small correlations that are inconsistent from one study to the next and could be random flukes. Eminent epidemiologists can disagree over whether there were any public health effects at all from TMI radiation. Even the Wing study, at the high end of estimates, calculates that in the spew’s epicenter the danger from TMI radiation was substantially smaller than the normal risk of driving.

So you can also see why it is so important to the alarmist case to attack the entire scientific field of radiation epidemiology as a swamp of “insane stupid idiocy” or a conspiracy. The epidemiology on TMI just doesn’t get you to an alaming body count either.

@ Aaron,

1) “Tens of thousands of people died because of TMI, just in the few years after. The US government provides this information very plainly in the National Vital Statistics Survey, available through Google.”

Come on, Aaron, you’re going to have to show your work here. I don’t think there’s a column in NVSS headed “Deaths from Three Mile Island Radiation.” When you make wild assertions like this, you need to present the evidence and reasoning behind them and do the calculation, preferably with links to the data so we can assess its quality. If you don’t do that, people will think there is no evidence to back up your claim and that you are talking out of your hat.

2) The Simon test in 1953 was an atomic bomb blast, which created superheated updrafts to loft the fallout into the stratosphere where it traveled on 100-mile-per hour winds. No such mechanism was present at Three Mile Island. I’m not arguing that the TMI plume did not get to Albany, just that it was about 2000 times less intense when it got there, as the Wahlen data that you cite clearly show. And at radiation levels that weak, it’s far-fetched to claim that any detectable health risk could have come of it.

Again, all the objective evidence that we have from instrumental readings of radiation levels indicate that the radioactive plume was very weak at TMI proper—a maximum dose of 1 mSv and an average dose of 0.1 mSv, per Hatch above—and extraordinarily weak and harmless at distant sites like Albany. A mountain of hard evidence confirms that conclusion, while no good evidence contradicts it..

3) The principal decay mode of Xenon-133 is beta decay, not alpha decay. So what?

--“The validity of the comparison to background…depends on the linear dose model.”

In a sense, but whatever the validity of LNT for calculating dose, the actual readings of radiation exposure at Albany were about 2000 times smaller at Albany than at TMI. Since we calculated from the Wing data you cited that TMI radiation dangers at the epicenter were smaller than those of driving a car, those at Albany must have been essentially zero.

Laboring under the false assumption that TMI, at SOME point in its existence, SOMEHOW, the reactors have stopped leaking.

@Will- Kindly take the questions in order, as that's how they were asked. Also please don't renumber them, as that gets confusing. So from the top:

2: Look, a few years ago when we corresponded offline I offered to publish a long essay you composed on this topic on the front page here. Your essay, and your views, are strongly contrary to my views. The only condition was that you provide a short bio explaining why readers should accept your judgment as correct. I never heard from you, as apparently you prefer to express your OCD in the comments section. This is your call, but I don't feel very motivated to respond. If you want my time, explain to me why you deserve it.

@Will- On a side note, I'm not merely suggesting you start with #2... Aaron wasn't joking when he said he's done with you, and that question has been left unanswered by you for *years* and really is the first thing that should be addressed. Look at it as extending an olive branch to draw him back into the discussion... Because I can tell you with absolute certainty that without it, he won't be responding. You can trust me on this bit, because the only reason he's been responding to you at all is because I've been prodding him to offline. At this point I've come around to his view that you've wasted enough of his time, so don't count on him seeing or responding to any more of your posts. Perhaps if you actually answer the questions I'll pass them along when I see him next weekend, but no promises.

@Will- Probably my favorite part of the above response: "3) The principal decay mode of Xenon-133 is beta decay, not alpha decay. So what?"

So... You just proved Aaron's point that you really don't have a clue about what you're talking about. Very eloquently, I might add. Hell, even *I* know why it's a problem!

@Will- BTW, if it's not clear, *I'm* not done with you. I'd like nothing more for you to actually answer all of Aaron's questions and get him to reenter the discussion, as I would get great pleasure watching him tear your position apart point by point. You up for that?

I guess I settle for my question to be #4.

& while WE wait, Fukushima springs ANOTHER radioactive water leak.

"More broadly, though, a theme in this blarf for more than a decade is that powerful people would rather kill us all than be less powerful. I think I'm maintaining a discussion in keeping with that theme."

Fascinating. Powerful people, methinks, are just like other people but more powerful. (So I guess by extension I'm in the Hemmingway camp on the Hemmingway/Fitzgerald argument about rich people.) Though when I think about it, I do think people who get bogged down in wealth or power or whatever don't have much time for thinking about things like whether the long term effect of what they do is likely to destroy the world. Too busy. And that's a negative thought not reflective of a can-do attitude!

@Will Boisvert- while you're working on a short bio (not sure why it's taking so long BTW, given you're a professional writer...), perhaps you could answer a question for me...

If you are indeed correct that low level exposure is nothing terribly scary (per the LNT model) and the Fukushima spew diluted quickly to minimal levels, how do you explain the 28% increase in likelihood of hyperthyroidism in US West Coast newborns since the spew?

Seems to me the obvious answer is either the doses were much higher than we were told (conspiracy!!!) or the LNT model is fundamentally flawed and Aaron's model might fit reality much, much better.

Your thoughts?

http://www.sott.net/article/260484-Almost-third-of-US-West-Coast-newborns-hit-with-thyroid-problems-after-Fukushima-nuclear-disaster

Btw, the problem with Xe-133 is that 1: it's not only a beta emmitter, but a strong one, 2: it's a gas that's heavier than air that collects in low spots, 3: it's readily absorbed into the bloodstream via the lungs, 4: it gets stored by the body in fatty tissues in the abdomen and thighs, right near reproductive organs, and 5. It has a relatively short half-life, which means any taken up by the body burns off quickly in just about the worst possible place. If you're interested, I suggest you take a l

... take a look at the MSDS sheet for Xe-133. Not good stuff for the body by a long shot.

Aric: "I have thought and smoked on this,"----Chief George. WE have made some SERIOUS charges against the nuclear industry, and TMI especially. Defrauding the public, pollution laws, endangering the public, AND possible manslaughter or possible premeditation. I, myself, have accused The NRC of collusion.(NOTICE :The word satire appears at the top of the page.)Surely in OUT little tiny revolutionary court, shouldn't WE have representation for the defendants? Otherwise why bother with evidence? Paid or not Will Boisvert IS giving it his best shot.
Being a STRICT CONSTITUTIONALIST I don't mind unequal representation UNLESS the owners of TMI are white males. ALL OTHERS, I'm satisfied with 3/5 representation.
In closing, I dare say, "Let the man speak what he will and let those who have hired him worry of his credentials".

p.s. Fukushima is still leaking, bad news for all.