Strange Glow
Page 36
Now let’s look at this health question in a slightly different way and make another comparison. Cesium is a manmade radioisotope, and people tend to worry more about manmade contaminants. But there is also a lot of natural radioactivity in food; not just in seafood, but in all foods. Again using the bluefin as an example, we’ll now turn our attention to the natural radioactivity that the scientists found in the bluefin samples.
Potassium-40 (K-40) is a natural radioisotope, not manmade, and it was also found in all the bluefin samples. We already know that potassium is enriched in muscle. There was an average of 347 Bq per kg of K-40 radioactivity in the bluefin muscle (35 times the cesium radioactivity levels). Humans have less natural K-40 radioactivity in their muscles (<100 Bq per kg) than the fish, owing to a lower overall potassium concentration in our muscles compared to bluefin tuna.12 Our body burden of natural K-40 contributes about 0.15 mSv per year to our annual effective background dose. (You’ll recall total annual background effective doses for Americans are typically about 3.0 mSv per year, so about 5% of our annual background dose is due to the K-40.)
All the potassium on Earth has a radioactive component because 0.012% of natural potassium is the radioisotope K-40 (the rest being mostly the stable isotopes K-39 and K-41). Therefore, all of our bodies contain K-40 radioactivity. No point worrying about it. We can’t get the radioactivity out of the natural potassium and we can’t live without potassium. In fact, the human body is so dependent upon potassium that it keeps its potassium levels tightly regulated. Too much or too little and cardiac death soon follows.
The body doesn’t store potassium so it needs a consistent dietary input to fulfill its needs. Fish and meat (i.e., animal muscle) are good sources of dietary potassium. For vegetarians, certain vegetables and fruits, particularly bananas, are a major source of their dietary potassium. So aren’t we getting a lot of radiation dose from K-40 radioactivity by eating fish or bananas? And what additional radiation dose are we going to receive if we start eating more fish or start putting more bananas on our cereal every morning? Oddly, the answer is none! But how can that be?
Despite what you may have heard, you get no additional radiation dose from eating those naturally radioactive bananas. Why? Because the body must keep a constant level of potassium, so when you eat 10 excess milligrams of potassium, you pee out 10 milligrams of potassium. Hence, the body burden for K-40 remains steady and unchanged no matter how many bananas you eat, and thus your annual effective dose from K-40 also remains unchanged.13
This physiological balancing act of potassium results in an interesting paradox for our cesium-contaminated bluefin. Being a natural radioisotope, K-40 provides a uniform background dose in our bodies that cannot be changed by altering our potassium consumption, so the K-40 in the bluefin has no effect on our annual radiation dose. Strangely, even though there is 35 times more K-40 radioactivity than cesium radioactivity in the bluefin, only the much smaller amount of cesium radioactivity can actually increase our radiation dose if we ingest the tuna. This is because cesium is more radioactive than K-40, molecule for molecule, and cesium is an additional radioisotope that would not be part of our natural environment, were it not for atomic bomb testing and nuclear reactor leaks. Thus, this manmade radioisotope adds to our natural radioactivity.
At the end of the day, however, effective dose is effective dose. So what is the effective dose delivered from eating the cesium-containing bluefin, and how does it compare to what we’re getting from natural potassium simply by virtue of having potassium-filled tissues? If every week for one year, someone were to eat one pound (0.45 kg) of bluefin with 100 Bq per kg of cesium (i.e., 10 times what Madigan and Fisher found in their fish samples), that person would have received an effective dose of 0.001 mSv from the cesium.14 Compare that to the 0.15 mSv annual effective dose we receive from our bodies’ natural potassium (a 150-fold higher dose than from the cesium). And further compare it to the total background effective dose that we normally receive in a year, which is 3.0 mSv (3,000 times more than the tuna cesium dose).15 So, the radiation doses we receive from eating these supposedly radioactive tuna are trivial in comparison to the background radiation doses that we all receive, apart from the tuna.
At these extremely low levels of dose, cancer risk is strictly theoretical. No one has ever proved that there is any cancer risk from radiation doses that are this low. Nevertheless, since we’re playing the conservative assumption game, let’s assume there are, in fact, risks, and calculate the lifetime fatal cancer risk from the cesium in the tuna based on the atomic bomb survivor data:
0.001 mSv (effective dose) × 0.005% per mSv =
0.000005% (or odds of 1 in 20,000,000)
This means that the NNH would be 20,000,000 people. Expressed in words, 20 million people would have to eat a pound (0.45 kg) of the contaminated bluefin every week for a full year before we might expect one of them to come down with cancer because of the radioactive cesium that it contains. There are about 14,000,000 people living in Tokyo, so even if everyone in Tokyo each ate 52 pounds of contaminated bluefin tuna over the course of a year, we wouldn’t expect even one of them to contract cancer from the experience. Now compare this with the annual baseline cancer rate for Tokyo, which is 150,000 cases per year.16
Let’s try one more comparison for good measure. We’ll now look at the risk in yet another way. As we’ve discussed before, cancer is, unfortunately, a common disease. We can expect about 25% of people living today to ultimately die of cancer. Thus, the lifetime risk for any one individual dying from cancer, all else being equal, is also about 25%. This is the baseline risk of cancer death. The 0.000005% cancer risk calculated above adds to this baseline risk. So the added risk of cancer death after a year’s worth of daily bluefin consumption moves from 25% up to 25.000005%.
Now we’ve looked at the risk from eating the cesium-contaminated tuna in three different ways: (1) we’ve compared the cesium dose to annual background doses; (2) we’ve compared dose from ingesting the manmade cesium to the dose we receive from a natural radioisotope (K-40); and (3) we’ve compared the expected increase in cancer incidence from eating the cesium-contaminated bluefin to the baseline cancer incidence. Although one of these three comparisons may speak to you more than the others, it doesn’t matter which one you choose because all three are just different characterizations of the same risk level.
As for the benefits of eating the bluefin sushi; well, that’s another matter. Bluefin certainly doesn’t contain any nutritional benefit that cannot be obtained from other foods. But, nevertheless, based on the prices people are willing to pay for it, a good number of diners must place a very high value on its flavor. Even at just $50 per pound, the 365-day bluefin diet we described above would have an annual cost of $2,600. Armed with all this information, the consumer can do her own risk-benefit analysis (and a cost-benefit analysis as well). Then it’s decision time. To eat, or not to eat? That is the question.
NOT SO FAST!
We need to wait a minute here; some of us are still skeptical. All these doses from ingested radioisotopes are calculated with statistical models because the dose from ingested radioisotopes can’t be measured directly in tissue. Suppose these models are way off? Suppose the dose (and thus the risks) are actually much higher than the models predict? These are good questions.
What these questions really are getting at is how much uncertainty there is in the dose modeling. Uncertainty is the bane of all risk assessments. What if our model parameters, input data, and assumptions are not accurate or valid? As we all know, for all computer-based models, garbage in means garbage out.
The answer is that most dosimetric models have not, unfortunately, been rigorously validated. There are just too many radioisotopes and too many exposure scenarios to consider. Also, it isn’t usually possible to ask people to voluntarily eat or breathe radioactivity just to validate a dosimetric model. Nevertheless, some scientists have made serious efforts to assess the accuracy of t
heir models in real-life situations. Three examples address situations that we’ve already discussed.
The first example is a little gruesome. It has to do with our old friend Robley Evans, the MIT scientist who labored mightily to estimate the bone doses for people who ingested radium, and Eben Byers, the business magnate who had a penchant for Radithor. In 1965, 33 years after poor Mr. Byers had passed away from his Radithor habit, Evans secured permission to recover Byers’s skeleton from its coffin in a Pittsburgh mausoleum to measure the actual amount of radium in his bones.17 Evans measured the radium radioactivity and then returned the skeleton to its coffin, where it remains to this day.
He measured a total of 225,000 Bq (6.1 μCi) of radium radioactivity in the skeleton. He then compared the actual radium measurements from Byers’s skeleton to the amount of radium that his model had predicted to be in the bones based on Byers’s own account of how much Radithor he said he had drunk over his lifetime. The results showed that there was actually twice as much radium in Byers’s bones than had been predicted by the model. But the question remained: Was the model wrong by twofold, or had Byers underreported his Radithor consumption by twofold? That is, what was the source of the uncertainty, a weak model or poor input data? You can see why uncertainty is so hard to nail down.
We’ll look at another example, this one from our radon story. As we have already discussed, radon is one of the few radioisotopes for which we don’t typically use effective dose (in mSv) as a dose metric. Why? Because all the exposure data comes in the form of air concentration levels in mines (i.e., in working levels; WL), and we want to limit people’s lung dose by limiting the radon concentration in their home air. Therefore, epidemiologists find it more reliable to directly assess risk in terms of radon air concentrations, rather than go back and forth converting WLs to modeled lung radiation doses with dosimetry models that might introduce uncertainty. Nevertheless, it would be interesting to see whether the dosimetric models result in lung cancer risk levels comparable to the risks levels calculated directly from WLs.
We won’t go into all the details here, but the findings for radon in the lungs were similar to those for radium in the bones. The WL risk levels were within twofold of the risk levels predicted by modeling the dose to the lungs.18 But the question still remains: Are the WL risks off by twofold or is the model’s mSv dose off by twofold? Who’s to say?
The final example comes from the tuna. Madigan and Fisher were able to use their fish tissue concentration model to back calculate the cesium levels that the bluefin tuna should have had while they were in Japanese waters, back in March 2011, in order for them to have had the tissue concentrations that were later measured in California waters in August 2011 (i.e., 10 Bq per kg). Their model indicated that the cesium concentrations for the fish while in Japanese waters would have been about 150 Bq per kg. Compare this to what the Japanese government reported as the actual cesium concentration in bluefin caught in local waters at the time: 170 Bq per kg. This amounts to a discrepancy between modeled and the measured values of about 13%. Not bad. But again, is the model off by 13% or are the measurements off by 13%? Tough call.
So validation of dosimetric models is difficult and, even when successful, it’s hard to interpret what the information means in terms of the accuracy of the model. Nevertheless, enough validation studies have been completed for the major dosimetric models to suggest that they may be off some, but they are not off by orders of magnitude (i.e., multiples of ten). Rather, if they are off at all, it’s likely they’re off by no more than two to threefold, a discrepancy that we can usually live with. What would a threefold underestimation of cesium dose mean for the bluefin risk estimate we calculated above?
It could mean that the baseline cancer rate for the tuna eaters might actually be 25.000015%, rather than the 25.000005% we had predicted. Or it may mean that one person out of the 14,000,000 people in Tokyo might contract cancer, rather than none. If these small differences in risk are going to be a game changer for the risk-benefit analysis you just completed, you’d better reconsider eating that tuna. But in most cases, such small differences are not going to upset the cart. The added risk from the cesium is still pretty low, even if the dosimetric model isn’t perfect.
THE UPSIDE OF DEATH AND TAXES
In previous chapters, we’ve alluded to uncertainty a few times. But this is the first instance where we’ve discussed uncertainty in a methodical way, and the first time we’ve acknowledged that uncertainty can be a systemic problem for risk assessment. We’re going to see more about uncertainty going forward, but let’s just recognize here that uncertainly comes in two flavors. The first flavor is the known unknowns. We may not be certain how much Radithor Mr. Byers actually drank, but at least we know that we don’t know it. That’s why Mr. Beyers’s Radithor consumption rate is a known unknown.
For known unknowns, we can usually estimate how big a problem we have on our hands.19 Technically speaking, we can put bounds on the magnitude of the uncertainty. In the case of Mr. Byers, he might have reported just half of what he actually drank, but he probably did not drink much less than that; otherwise he wouldn’t have suffered the health problems that he did.
In contrast, the real problem for risk assessment is the unknown unknowns; that is, the things that we don’t even know that we don’t know. For example, suppose we are wrong about DNA damage being required for cancer induction. Let’s say, out of the blue, some scientist conclusively proves to everyone’s satisfaction that the gravitational pull from the moon—that same force that drives the ocean’s tides—causes cancer in humans even though it doesn’t produce DNA damage. That certainly would be a game changer across the board in our understanding of the mechanism of environmental cancer induction. And we might then have to go back and rethink the cancer risk from those cell phones; after all, we had considered the plausibility of their causing cancer suspect because radio waves don’t damage DNA. But who would have thunk it? And it’s precisely the “who would have thunk it” stuff that can cause the big problems. We must keep an eye out for these unknown unknowns.
Let’s leave uncertainty to rest for a while. We’ll get back to it shortly. But we’ve introduced the uncertainty concept now so we don’t get too cocky with our risk percentages, our NNTs, our NNHs, and all that stuff. 20 There’s only so far we can go with these risk metrics if the fundamental premises on which they are based are shaky. In situations where we have a lot of experience and tons of data collected over time, and the conditions in the future seem little different from those in the past, we can be fairly confident that such metrics are covering all the bases. In these cases, we can feel assured that our risk-benefit analyses are valid. But when data are scarce, the situation is new or different, or our experience is limited, uncertainty rears its ugly head. Do we really know that all swans are white, or have we just not looked at enough swans to be aware that black swans exist?21 Hard to say. Are death and taxes, as Benjamin Franklin claimed, the only truly certain things in life?
THE LATEST BUZZ
The year 2011 is gone. The risk of eating bluefin back then is of historical interest, but what is the current situation? Scientists have asked that same thing.
In 2013, Eric Norman, a physicist at the University of California at Berkeley, was perplexed by ongoing reports on the Internet and elsewhere saying that foods were still contaminated with unsafe levels of radioactivity, two years after the Fukushima accident. Was this even possible? He thought not. When he went looking for data that supported these claims he could find none, so he decided to gather his own.
Norman had his students collect samples of plants, milk, fish, seawater, and salt from various locations in the Pacific Ocean, particularly coastal regions. Included were samples of seafood products from Hawaii, the Philippines, all over the California coastal area, and even Japan. Norman then recruited the assistance of Al Smith and Keenan Thomas, fellow faculty members and experts on radioactivity counting, to assess the samples. They
looked for radioactivity in everything, but focused particularly on seaweed samples because seaweed concentrates potassium, and thus cesium. The search found nothing.
“We looked very hard,” said Norman. Nevertheless, none of his samples showed any radiation that could be linked to Fukushima. This is likely due to two major factors. First, much of the radiation has simply decayed away and is continuing to do so. But the second factor is probably the more important. That is, the radioactivity has been diluted to such a large extent after mixing in the sea for two years that it is no longer readily detectible. “There’s a lot of water in the Pacific,” says Norman. “Whatever gets dumped in the ocean will get diluted by enormous factors.”22
The window of risk from Fukushima radiation in seafood has apparently closed. The large amount of radioactivity dumped into the Pacific in March 2011 has decayed and dissipated to trace levels. Any ongoing leaks from the Fukushima nuclear power plant site are apparently too minimal to result in significant levels of radioactivity in the world’s seafood.
How do we know that future radiation leaks won’t change things for the worse? We don’t. But we have a watchdog on duty. He is Ken Buesseler, a marine chemist at the Woods Hole Oceanographic Institute on Cape Cod, Massachusetts. Buesseler directs the Center for Marine and Environmental Radioactivity (CMER), which actively monitors radioactivity in the ocean. He fills a void that no government organization currently occupies, and he does it all on a shoestring budget funded largely by public donations. According to Buesseler, “Whether or not you agree that levels of radiation along the Pacific coast of North America are too low to impact fisheries and marine life, we can all agree that radiation should be monitored.” He analyzes seawater samples sent to him by the public from coastal communities along the Pacific coast and, in the interest of transparency, he posts the results for all to see on the CMER website.23 If any new source of radioactivity crops up in the Pacific, either from Fukushima or elsewhere, Buesseler’s team should be able to detect it and warn us of the threat. So far he’s found only trace amounts, but he intends to keep looking, as long as crowd funding continues to support his efforts.24