Linear hypothesis

For radiation doses <0.5 Sv, there is no clinically observable iacrease ia the number of cancers above those that occur naturally (57). There are two risk hypotheses the linear and the nonlinear. The former implies that as the radiation dose decreases, the risk of cancer goes down at roughly the same rate. The latter suggests that risk of cancer actually falls much faster as radiation exposure declines. Because risk of cancer and other health effects is quite low at low radiation doses, the iacidence of cancer cannot clearly be ascribed to occupational radiation exposure. Thus, the regulations have adopted the more conservative or restrictive approach, ie, the linear hypothesis. Whereas nuclear iadustry workers are allowed to receive up to 0.05 Sv/yr, the ALARA practices result ia much lower actual radiatioa exposure. 

Zero-Threshold Linear Hypothesis—The assumption that a dose-response curve derived from data in the high dose and high dose-rate ranges may be extrapolated through the low dose and low dose range to zero, implying that, theoretically, any amount of radiation will cause some damage. 

It should be noted that there is intense controversy as to the health effects of radiation doses below about 100 mSv per year. This estimate of 15,000 annual cancer deaths from indoor radon, as well as estimates of tens of thousands of eventual cancer deaths from Chernobyl exposures, is obtained by applying the linearity hypothesis. This hypothesis has been adopted by most regulatory agencies but is strongly contested by some scientists who believe it overestimates the effects of radiation at low dose levels. Of course, if calculations based on this hypothesis overestimate the deaths from indoor radon, they also overestimate the effects of potential radiation from a waste repository. 

For nuclear waste disposal, in a site such as Yucca Mountain, if the maximally exposed individual receives the proposed annual limit of 0.15 mSv, present estimates (based on the linearity hypothesis) suggest a 0.00 1 % risk of an eventual fatal cancer. The maximum dose is reached only if the wastes are dissolved in a small volume of water, and therefore only a limited number of people would receive this dose. If this number were as high as 1000, the implied toll for Yucca Mountain neighbors would be one cancer fatality per century per repository site.19 This toll would not start for many centuries, when the waste canisters begin to fail, and it not unreasonable to expect that cancer prevention and treatment will be much improved by then. Ignoring this prospect, and assuming many repositories and some doses above the prescribed limit, it still appears that the expected toll would be well under a thousand deaths per century. 

Linear hypothesis The assumption that any radiation causes biological damage in direct proportion of dose to effect. 

The factorial design data were analyzed by use of a linear hypothesis statistical model (20). By this it is assumed that the conversion of carbon monoxide is dependent on all of the factors to the first power only. The following empirical regression equation for the percent of carbon monoxide converted resulted from this model ... 

Two models of radiation damage, illustrated in Fig. 19.17, have been proposed the linear model and the threshold model. The linear model postulates that damage from radiation is proportional to the dose, even at low levels of exposure. Thus any exposure is dangerous. The threshold model, on the other hand, assumes that no significant damage occurs below a certain exposure, called the threshold exposure. Note that if the linear model is correct, radiation exposure should be limited to a bare minimum (ideally at the natural levels). If the threshold model is correct, a certain level of radiation exposure beyond natural levels can be tolerated. Most scientists feel that since there is little evidence available to evaluate these models, it is safest to assume that the linear hypothesis is correct and to minimize radiation exposure. 

Response time is the time necessary for the conductance to reach a threshold value (usually 90%) of the difference between G( and Gj. Recovery time is the time necessary for the conductance to recover to a band expressed as a percentage fraction (usually 90%) of G(-Gi. Concerning the response of chemical sensors, the linearity hypothesis is not verified, and the response when working with gas mixtures cannot be deduced by the superimposition principle, with a simple sum of the individual response. 

Now, not only had Crum Brown provided a superior chemical argument for his alternative structure for pyrotartaric acid—for Kekule could not, in his linear hypothesis, explain Simpson s reaction route— but he also pointed to internal inconsistencies in Kekule s treatment. After citing Kekule s footnote, Crum Brown wrote ... 

The listed phenomena are only examples and there are many more in which it is possible to apply the thermodynamics of irreversible processes in a linear phenomenological setting, such as problems in the biophysics field. In cases where the knowledge of macroscopic mechanisms is limited, the use of thermodynamics in irreversible processes could be useful. However, it should be noted that this approach does not give any information about the nature of the coefficients involved and so could give rise to errors if the linearity hypothesis is not respected. So the thermodynamics of irreversible processes has a very large field of application, but its contribution is not as yet significant. 

Despite serious damage to the plant, the release of radioactive material was relatively small and presented a negligible hazard to the public. Based on the linearity hypothesis of radiation damage, the total number of radiation-induced cancer deaths likely to result from the release was estimated as 0.7. In practical terms, this means that the number of deaths will be zero or almost zero even in the unlikely event that a few radiation-related deaths were to occur, the number would be far below that which could be detected statistically in view of the normal cancer mortality rate. 

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