Do houses in Alaska have ceiling fans


, and the EPA has long recognized that the linear model is invalid, but decided to use it anyway. In their 2003 risk estimates

The BEIR VI committee adopted the linear no-threshold assumption based on our current understanding of the mechanisms of radon-induced lung cancer, but recognized that this understanding is incomplete and that therefore the evidence for this assumption is not conclusive.

In fact, the linear model is not based on “current understanding of the mechanisms of radon-induced lung cancer,” rather it is based on a voluntary adoption of a philosophical position that is traditionally used in risk estimation where there is no evidence of harm at the doses received.

Indeed, in the previously referenced position statement by the Health Physics Society, the authors explicitly state that qualitative assessments should NOT be made (since no risk is observed when this is done), and that, instead, a subjective philosophically-based risk assessment should be made exclusively based on the assumption of a linear model.

The assumed model has never been shown to be correct, and is scientifically accepted as incorrect. The Health Physics Society at the University of Michigan made the following observation:

There is, however, substantial scientific evidence that this model is an oversimplification of the dose-response relationship and results in an overestimation of health risks in the low dose range.

The statement continues with:

Radiogenic health effects (primarily cancer) are observed in humans only at doses in excess of 10 rem delivered at high dose rates. Below this dose, estimation of adverse health effects is speculative. Risk estimates that are used to predict health effect in exposed individuals or populations are based on epidemiological studies of well-defined populations (e. g. the Japanese survivors of the atomic bombings in 1945 and medical patients) exposed to relatively high doses delivered at high dose rates. Epidemiological studies have not demonstrated adverse health effects in individuals exposed to small doses (less the 10 rem) delivered in a period of many years.

This position is repeated throughout legitimate science, including US Governmental Laboratories such as the University of California, Lawrence Berkley National Laboratory which concluded:

Despite being widely accepted as a guideline in setting standards for protecting public health, the linearity hypothesis is not firmly established as an expression of scientific knowledge.

More recently, (2017), other authors have been more succint:.

The linear no-threshold model of radiogenic cancer is false. Because it fails empirically against superior empiricism, it becomes misinformation and opinion, not knowledge, but it is still advocated because it lends contentment to believers. We can no longer tolerate the universal application of LNT misinformation when, as people of science, we are about helping, not harming,humanity.

Therein lies the crux of the “controversy:” On one side of the controversy are political lobbyists who want to show a risk to help out the multi-billion dollar radon testing and mitigation industry arbitrarily using invalid risk models, and on the other side of the “controversy” we have scientists saying “There is no evidence of risk.”

The EPA, being a political organization, ignored the science behind the biological effects and used an unsupported assumption that the health effects from the radon could be extrapolated in a linear fashion from the lowest radon concentration in the study (2,720,000 pCi/l-hour) to those levels found in homes. This linear "dose-response" assumption was made even though there was considerable uncertainty for the validity of the extrapolation at lower levels of SLRDs.

The lowest radon concentration in the BEIR IV study (2,720,000 pCi/l-hour) was typically received by the miners over a five year period . Yet the EPA and NRC take this five year exposure and spread it out over the course of 70 years, and assume that an individual will spend 18 hours per day in their home, 365 days per year for 70 years. They also assume the “home” is situated in an underground mine, and that the occupants smoke cigarettes in their underground home.

This equates to an accumulative radon concentration of about 6 pCi/l in the home. It is not known, at this time, if it is valid to assume that only the accumulative dose, rather than the dose rate is the sole factor for determining risk. In its model, the EPA, eliminated the "dose-rate-effectiveness factor" from the quality factor which is usually attributed for alpha particles.

Although it is a generally held industrial hygiene principal, that fractionation of the dose over a long period of time lessens the overall effect, this concept is not always accepted for carcinogens (tumor initiators), such as ionizing radiation. For carcinogens, fractionation of the dose may actually increase the overall risk .

The NRC understood the limitations of the study and concluded ...

In summary, a number of sources of uncertainty may substantially affect the committee's risk projections; the magnitude of uncertainty associated with each of these sources cannot readily be quantified. Accordingly, the committee acknowledges that the total uncertainty in its risk projections is large.

A later study (referred to as the Cohen Study), which is one of the largest studies, incorporated about 33% of the counties in the U.S. and looked at the issue of the linear, no-threshold dose-risk relationship used by the EPA. In this study, a least squares linear regression of lung cancer rates vs. mean radon levels gave a negative correlation between death and exposure levels. In other words, the higher the radon level in the county, the lower the death rate from lung cancer was for the community. The result was not due to questionable interpretation of shaky statistics; each of the studies showed a negative correlation with slopes of not less than seven standard deviations (and sometimes greater than 10 standard deviations) greater than zero.

This study, known as an "ecological" epidemiological study, looks at relationships between exposure groups and mortality rates. Ecological epidemiological studies carry less weight than studies based on individuals where the actual exposures are known and the study cohort is compared to an unexposed group. In an ecological study, the person who dies may not have been the person who was exposed to the insult. Additionally, ecological studies tend to be more susceptible to confounders. Nevertheless, the author of the Cohen Study maintained that in a study on linear no-threshold relationships, this limitation is not considered to be applicable since the mortality rate depends directly on the average exposure.

Several other independent studies also looked at mortality rates vs. mean radon concentrations and have found similar negative correlations.

Indeed, buried deep within the US EPA documents, and worded in a very complex way, the EPA recognizes that as residential radon concentrations go up, the cancer rates go down. However, the casual reader, thumbing through the EPA risk discussion, would not likely recognize this admission since the EPA made the statement in a purposely very convoluted and confusing manner wherein they state:

Unlike what was found with the more limited BEIR IV and ICRP analyses, the BEIR VI committee was able to conclude that the ERR per WLM increased with decreasing exposure rate or with increasing exposure duration (holding cumulative exposure constant).

We begin to see that the “numbers game” works best when we use language and syntax that no rational member of the general public can actually understand.

At least five U.S. State sponsored projects have performed similar ecological epidemiological studies; four have concluded that low levels of radon, such as those found in the average home, are not harmful (show a negative correlation) and the sixth study indicated a very slight (less than one standard deviation) positive slope, indicating some risk at low level.

Epidemiological studies in other radiological settings have similarly observed evidence contrary to fear-based public health policies. For example, an epidemiological study was conducted for a total of 95,673 workers in combined nuclear industry facilities (Hanford, Oak Ridge, and Rocky Flats, three for sites in the UK, and one in Canada).

60% of the workers, whose radiological exposures were very well defined, received doses greater than 10 mSv (1 rem). The comprehensive results for all cancers taken together showed a very slight decrease in cancer rate with increasing dose. As with all studies, statistical issues can be raised, regarding the results, however, unquestionably was the fact that even at these doses, a clear-cut unambiguous risk was not observed, and the risk appeared to decrease with increasing dose, or decreasing dose rate.

In a US Government Publication concerning radon, the following statement is made:

Currently there is very little information about...the health effects associated with exposures to radon at levels believed to be commonly encountered by the public. The only human data available for predicting the risks to the public are studies examining the health effects of exposure to radon and its progeny in underground miners. This information would be appropriate for predicting the risks to the public if everyone was a miner, everyone lived in mines, and a large fraction of the general population smoked cigarettes.

Clearly, then, the models used for the estimation of risk are inappropriate since the average American, Canadian, or Western European is not a miner, we do not live in mines, and we do not have similar exposures. The same document then goes on to state:

Depending on the set of assumptions used, the estimated values for lung cancer rates from environmental exposure to radon currently range from quite small to as large as 25% of the total annual lung cancer deaths in the United States.

Ongoing studies that employ more realistic models fail to find any evidence that the risk of death from cancer induced by residential radon exposure is even noteworthy. Furthermore, in modern science, it has been generally assumed that virtually all effects of ionizing radiation result in detrimental effects. However, over the past decades, reports in scientific literature seem to suggest that that low-dose ionizing radiation is not only a harmless agent but often has a beneficial or “hormetic” effect.

The same uncertainties plague chronic-exposure risk assessments for other forms of low level radiation; and the concept of ALARA (As Low As Reasonably Achievable) is used to control those forms of radiation. The definition of ALARA is found in the United States Code of Federal Regulations, and is essentially defined as follows:

A policy of reducing personal and environmental radiation exposures to the lowest level commensurate with sound economics, available technology, and good operating procedures.

,. Very low radon concentrations are commonly seen in very tight buildings and high levels are often seen in the leakiest of houses.

Therefore, the second most important factor in radon entry into buildings is the DP. Several studies have shown that a very strong correlation between DP and radon concentration exists. All things being equal, the greater the pressure differential, the higher the radon level.

Since most commercial buildings fitted with industrial heating, ventilation and air conditioning (HVAC) systems are designed to keep the structure at positive pressure, excessive radon levels in commercial buildings in the U.S. are rare even in "high radon" areas. Typically, the most successful radon reduction techniques are those which address the driving forces of the pressure differential.

Weather can also effect the DP. Generally speaking, when the outside air is cold and the interior of the building is warm, the DP is greater. When the wind blows, the DP is greater. Additionally, when the water table rises, such as following a recent rain, the soil gas pressure rises, increasing the DP. Other meteorological factors such as snow cover can also effect the radon concentrations in a building by creating a "cap" under which the radon can accumulate.

In the U.S., Britain and Sweden, the majority of the radon which enters a building is from the presence of radon in the soil gas. However, there are two other significant sources of radon- well water and building materials. For structures, which are serviced by well water, a significant contribution of indoor radon can be from the radon in well water. Worldwide , the average concentration of radon in surface water is about 10 pCi/l. In the U.S., the average private well-water contains about 750 pCi/l. Levels exceeding 20,000 pCi/l are not uncommon and this author has seen references to levels exceeding 1.6 million pCi/l (0.16 µCi/l).

Due to radon's very high Henry's Law Constant, radon will quickly evolve from water when it is aspirated or exposed to the air. For this reason, processed city water is rarely seen as a contributing factor to the overall radon concentration in a building, since essentially all the radon has left the water in the predistribution processing. However, in well water, the water is not subject to the chlorination and aspiration processes and can be a significant contributor to the building's burden of radon. It is commonly quoted that a water radon concentration of between 6,000 and 10,000 pCi/l will increase the airborne radon concentration in a building by 1 pCi/l.

In a few isolated cases, decorative stone and other building materials have also been identified as being the single largest significant contributors to indoor radon concentrations. The building construction material called "granite" is usually a similar material called granodiorite. The granodiorite has been shown in some cases to be the sole source of radon in a structure. However, no studies have ever demonstrated that the radon contributed by these materials pose an health hazard.


The error is due to the large fluctuations seen in radon concentrations at any point in time. The result of a “radon test” can change dramatically when any of the following parameters change:

A cycling air conditioner goes on or off
A cycling forced air furnace goes on (or off)
Barometric pressure fluctuations
Differences in indoor to outdoor temperatures
External doors in the structure are opened or closed
Internal doors in the structure are opened or closed
Macro-airborne particle changes (such as dust from a dirt road)
Phases of the moon
Recent rain
Relative humidity
Snow cover
Soil porosity at the time of the test
Solar loading on the structure
The amount of radon exposed in the underlying soil
Time of day
Time of year
Ultrafine airborne particle loading (such as burning a candle)
Water table levels
Wind direction changes
Wind speeds change
Windows in the structure are opened or closed

As such, the short term “radon” measurements have a huge error associated with them in extrapolating the long term concentrations. Depending on the type of device used by the home inspector, the result may only integrate the last 12 hours of a multiple day test.

To illustrate the sampling error of the short-term methods employed today let’s imagine a single family, single structure two storey home with a partial dug-out basement, on city water, and with a forced air heating system.

Imagine our home has an actual “true” annual radon concentration of 47 pCi/l. Now, let’s hire an home inspector to randomly test the property 21 times over the course of the year. The inspector produces 21 separate “lab reports” with the following results (expressed in pCi/l):

If we now analyze the validity of the results, we find that there is no statistically significant difference between the results of the tests. That is, each of the test results are “valid” and are all within the upper and lower 95% confidence intervals for the short term method employed. Therefore, based on the short term method used in real estate transactions, an house with a yearly “radon” concentration of say 47 pCi/l (as that given above) can give a reading of anywhere between 91 pCi/l and 2 pCi/l and still be "correct."

(We purposely selected 47 pCi/l since that allows us to illustrate the variance without the use of decimal points. For example, if the house contained 4.7 pCi/L, the range of readings would be from 0.2 to 9.1; but as explained below, the number to the right of the decimal point is meaningless.)

Since the number to the left of the decimal lacks confidence, how could the number to the right of the decimal have meaning? Imagine an agricultural inspector was asked to estimate the annual average weight of cows in a barn by measuring the cows over a three day period using the same precision as “radon” methodologies. Although during the three day period, there were 100 cows, the rancher moves cattle in and out, some of the cows are calves whose weight will increase rapidly over the next few months, and some are full grown, some are dairy and some are breeders, etc. But using the “radon method” of estimation, inspector reports that the annual average weight of the cows was EXACTLY 125,234.2392 pounds.

The inspector simply has no chance of being correct with such precision. Since the number to the left of the decimal point has low precision, to try to pretend that ANY number to the right of the decimal has any meaning is nonsense. At best, and with valid confidence and precision, one could estimate the annual weight of the cows as follows “You have about 60 tons of cows.”

And so it is with radon readings. When we hear someone describe their “average” radon concentration as some value followed by a decimal point and another value, (such as “five POINT two “) it is meaningless. When someone has a reading of, say, 5.2 pCi/l it means just one thing: The actual yearly average radon concentration in the house is probably somewhere between 0.01 pCi/l and twenty pCi/l.

The methods used cannot, with confidence, distinguish an annual exposure concentration of 91 pCi/l from a reading of 2 pCi/l let alone a reading of say 3.6 versus 4.2 pCi/l This misplaced trust in magical laboratory reports is what we call the “CSI” effect.