Fukushima Daiichi to INES Level 7 - Some Numbers on Why

During a disaster, we should certainly strive to deliver the best available information as quickly as possible. If not done correctly, public distrust increases, and while directly avoiding sensationalism, others will step up their own sensationalism. The Fukushima Daiichi disaster was not as bad as it could have been, but it may fit the INES category 7 for nuclear emergencies, which is the most severe type of accident. The Japanese government has made very strong statements indicating it will announce they've upgraded it to that level. I find the borderline between the INES categories to have at least some amount of gray area, and the major component that is required for this to be a level 7 event is vaguely worded as widespread health and environmental effects, or the use of extended countermeasures. I will address a subset of those criteria in this post.

In assessing the impacts of low-level radiation on a large population, a linear no-threshold model is used to determine the predicted increase in cancer incidence. This simply means that the cancer from low radiation doses is extrapolated from the known cancer risk from high radiation doses that we know from things like post-Hiroshima studies and other historical nuclear events. This may or may not be correct, but past studies have either confirmed this or didn't have the statistical significance to tell. Because of that reason, I find this to be a correct and reasonable application of the precautionary principle, which deals with risks we accept to our health from things that are scientific unknowns. A common value for the this correlation for the radiation is:

0.005 cancers / mSv of radiation

That is to say, we assume (for better or worse), that any given radiation dose will cause a given number of cancers by multiplying by the above number. This number will be for expected fatal cancers, and can be applied to a large population. It is "expected" because cancers have a latency period of 2-60 years between when the exposure happens and when the cancer develops. It is my contention that seriously addressing this for nuclear accidents (and informing the public) is important for recognizing the full scale of the accident based on best-available-information.

If one uses this assumption for the Fukushima Disaster, how many cancers are expected to result from the accident? I have found no estimates for this, if you know of any let me know. So I had to make my own.

Method:

1. Integrate the dose rate readings from start of the disaster to now at all cities affected by the spread of radiation - obtain total dose
   
2. Multiply this by the population of the town or area to get the expected fatal cancers in that town or area
3. Do this for all towns and areas affected by the radiation spread and sum them all up

This is fairly general and would apply for a more advanced study than what I'm able to offer here, but to be fully accurate, modeling of the transport of the radioactive particles through the environment is very important too, and completely neglected here. The concept of internal dose is a very major topic that will become more and more important as the scale of the accident comes into focus, and it is a notable and intentional omission in this post.

The above steps are actually fairly straight-forward once one has very well organized data for the dose readings. Thankfully, other productive netizens have done this work for me. See Phillip Mills's page with up-to-data readings and all kinds of goodies. This is of great help because they've provided comma separated files that make this an easy task, particularly, the task of integrating the dose rates over time at the recorded locations, as portrayed in their graphs:

Step #1: Get Dose Rates from Monitoring, Integrate to get Dose

Pragmatically, the dose rates are often given in μSv/h. That is, micro-Sievert per hour. It also so happens that dose rates are often recorded once every hour. That means the above task is accomplished by adding all the numbers. Yeah. Not that special. But sometimes they are not taken every hour, and appropriate adjustments must be made. The real world is, of course, complicated and messy. It is even more messy after a record-setting earthquake just hit your town, and that is the environment that these measurements were taken in. The following are 3 different sets of data are for the Fukushima Prefecture alone. They might look nice and well-behaved, but they are from very different kinds of information sources and do not follow the same methods at all.

Note: Doses are calculated from start of monitoring until April 8th or 9th.

These three data sets provide a wide range of monitor locations, but there are difficulties. To begin with, they are spaced out in an extremely non-uniform pattern. Some are redundant while other large areas lack readings altogether. I can help illustrate this to a limited extent by offering some maps from other crowd-sourcing radiation monitoring efforts, here I'll use RDTN and Pachube.

RDTN

Pachube

The Fukushima Prefecture border is slightly hard to make out, but just know that it goes very far into the mountains and the nuclear plants are about midway along the coast. These two projects are using most of the same sources of information that Phillip used (who I got information from). I just hope this gives a good general picture.

Although there are dozens of reported locations, I cut it down to just 15 that represent some definable population center, and when multiple readings were available I took the highest (actually doesn't matter as much as you would think). Of course, these did not add up to the entire population of Fukushima Prefecture, in fact, only about 1.4 million of the people in the prefecture of 2.0 million are represented by a radiation reading in the data set. So when I multiply the expected dose to an individual by the population, I also multiply by 2.0/1.4, doing so introduces the assumption that the average dose of the unrepresented population is the same as the represented population. This overestimates in the areas not important enough to take readings in, and underestimates in areas in the exclusion zone (although the population numbers are blown there anyway). Here is what I got.

Interpretation: Both high-population and low-population cities and towns had high doses, but long term health risk is mainly accounted for in large cities because of the large population exposed.

I could not give enough provisos for the interpretation of this chart with 10 more pages. I offer it only because I have not seen anyone else offer this information. I expect a highly modified pictures when official sources address the issue, but this is a good first shot.

Fukushima city, where the largest expected collective cancer risk lies, expects about 34 additional cancer deaths from the radiation from this accident. I just want to point out that the city has a population of 290,000 people, which brings me to my first point: We will probably never detect an increased rate of cancer incidence from the Fukushima disaster. Ever. Even if the type of cancer is narrowed down, the statistical noise will probably swamp those 34 deaths. It is still tragic, but in terms of assessing the damages, Fukushima city is the best bet for detecting long cancers caused in the general public, and it is simply not going to happen.

Finally, the larger region: People in Ibaraki Prefecture received notably a high dose and Tokyo received several times background for a while, what are the wider consequences? I applied a similar methodology for neighboring prefectures of Ibaraki and Tochigi, again from Phillip (thank you again). In order to obtain a "high" value, I took the location with the highest dose and assumed the entire population was exposed to that.

Predicted Long Term Cancer Deaths from Fukushima Daiichi Radiation Releases

	High	Predicted		Notes
Fukushima	600	120		Could be much lower due to evacuations, data done by convolution
Ibaraki	81	58.5		Probably reliably close to high number, due to population distribution
Tochigi	30	13.7		Convolution by city looks good, largest unknown is unlisted cities
Kanto plus Tohoku	126	120		Fairly reliable minimum attainable considering only metro Tokyo area
Total	861	312.2

I'll provide just a few notes about this. The same method as described for Fukushima Prefecture was applied for Ibaraki and Tochigi, which was actually easier since there is less information. Then I wanted to include effects on the larger population, so I integrated dose rates in Tokyo, but only for 3 locations. Out of those locations, they varied from 0.05 mSv to 0.06 mSv so I wasn't strongly concerned about the maximum versus average, the main problem is extrapolating this number for a larger area. It is clear from my general knowledge and sense about this (see prior posts) that the entire Kanto region (Tokyo metro, if you will) was subject to similar doses from the passing clouds. Now, it seems absurd to extrapolate to the entire Kanto and Tohoku regions on this basis, but it isn't. The Tokyo metro area is around 30 million, and even including all of Tohoku (everything North of Kanto on the main island) only adds 10 million. For the level of detail I'm concerned with, that 10 million at the same low dose really doesn't matter much to me. What I'm more concerned about is the mid-range doses delivered to prefectures not next to Fukushima, but one over (Miyagi for instance). That can affect the numbers some, but no greater scale than what is already seen on the table, and the entire reason I used Ibaraki and Tochigi in the first place is because I could see on the plots that they had high levels.

Here I leave with the final word. Chernobyl was estimated at 4,000 to 6,000 total premature deaths due to the radiation spread. Three Mile Island is estimated at around 1, just 1. Fukushima is somewhere in the middle - we already knew that. Now, I wouldn't be surprised to see a tally of 20 people or a final tally of 1,500 people. Mainly, I don't know where people were, and I don't know how much internal dose they received. These are still people's lives, and I think it allows for classification in the same stratum of accident that Chernobyl was. Aside from these, the complications of relief efforts also had a cost in human lives that I believe could be on a similar scale as the premature cancer deaths. Then there are industries and farmers who lost their livelihood and the people who are wondering when they can return home. Mainly, I want the true cost to be seriously considered by the reasonable people out there.