Occupational Intakes of Radionuclides Part 1 - ICRP

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DRAFT REPORT FOR CONSULTATION
(344) It is convenient to assume that for each monitoring interval n the associated
effective dose En could be equal to 0 or positive. For a given measured quantity,
M(tk), obtained at the end of the last monitoring interval k, the associated effective
dose Ek is:
k 1
E n
Ek
M
( tk
)
z(
tk
k )
n 1 z(
tk
n )
(6.7)
where k t
is the time of measurement k (end of the last monitoring interval k);
n and
k are the time at mid-points of monitoring intervals n and k, respectively. If M(tk)
is below the decision threshold (ISO, 2011) or the result of background subtraction
is negative, then
Ek
0
.
6.4.3 Multiple Measurements
(345) Usually, the bioassay data for an intake estimate will consist of results for
different measurements performed at different times, and even from different
monitoring techniques, e.g. direct and indirect measurements.
(346) To determine the best estimate of a single intake, when the time of intake is
known, it is first necessary to calculate the predicted values, m(ti), for unit intake of
the measured quantities. It is then required to determine the best estimate of the
intake, I, such that the product I m(ti) ‘best fits’ the measurement data (ti, Mi). In cases
where multiple types of bioassay data sets are available, it is recommended to assess
the intake and dose by fitting predicted values to the different types of measurement
data simultaneously. For example, if urine and faecal data sets are available then, the
intake is assessed by fitting appropriately-weighted predicted values to both data sets
simultaneously (ISO, 2011; Doerfel et al, 2006, 2007).
(347) Numerous statistical methods for data fitting are available (IAEA, 2004a,b).
The two methods that are most widely applicable are the maximum likelihood method
(ISO, 2011; Doerfel et al, 2006) and the Bayesian approach (Miller et al, 2002;
Puncher and Birchall, 2008). Other methods such as the mean of the point estimates
and the least-squares fit can be justified on the basis of the maximum likelihood
method for certain assumptions on the error associated with the data. For example, the
least squares method can be derived from the maximum likelihood method if it is
assumed that the uncertainty on the data can be characterised by a normal distribution.
The assumed distribution (e.g. normal or lognormal) can have a dramatic influence on
the assessed intake and dose if the model is a poor fit to the data. However, as the fit
of the model to the data improves, the influence of the data uncertainties on the
assessed intake and dose reduces.
6.4.4 Chronic Exposures
(348) The amount of activity present in the body and the amount excreted daily
depend on the period of time over which the individual has been exposed. The
bioassay result obtained, e.g. the amount present in the body, in body organs, or in
excreta, will reflect the super-position of all the intakes. Intake retention functions for
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