Having mathematical models that can predict the CPAM response, i.e., the monitor's output, for a defined input (airborne radioactive material concentration), it is natural to ask whether the process can be "inverted." That is, given an observed CPAM
output, is it possible to estimate the
input to the monitor?
A misleading "quantitative method" for moving-filter CPAMs A number of approaches to this inverse problem are addressed in detail in. Each method has its advantages and disadvantages, as one might expect, and a method that might work well for a fixed-filter monitor may be useless for a moving-filter monitor (or vice versa). One important conclusion from this paper is that for all practical purposes
moving-filter monitors are not usable for quantitative estimation of a time-dependent concentration. The only moving-filter method that has been used historically involves a constant-concentration, LL assumption, which leads to the RW expression: : \dot C_{RW}\,\,\, = \,\,\,\varepsilon \,k\,F_m \,\phi \,Q_0 {T \over {2\,}}\,\,\,\,\,\,\,\,\,\,\,\, \Rightarrow \,\,\,\,\,\,\hat Q_0 \,\, = \,\,{{2\,v\,\dot C_{RW} } \over {\varepsilon \,k\,F_m \,\phi \,L}} or for CW, : \dot C_{CW} \,\, = \,\,\,\varepsilon \,k\,F_m \,\phi \,Q_0 {{8R} \over {3\,\pi \,v}}\,\,\,\,\,\, \Rightarrow \,\,\,\,\,\,\hat Q_0 \,\, = \,\,{{3\,\pi \,v\,\dot C_{CW} } \over {8R\,\varepsilon \,k\,F_m \,\phi }} Thus, a concentration estimate is available
only after the transit time T has expired; in most CPAM applications this time is on the order of several (e.g., 4) hours. Whether it is reasonable to assume that the concentration will stay constant for this length of time, and to further assume that only long-lived nuclides are present, is at least debatable, and it is arguable that in many practical situations these assumptions are not realistic. For example, in power reactor leak detection applications, as mentioned in the first section of this article, CPAMs are used, and a primary nuclide of interest is 88Rb, which is far from long-lived (half-life 18 minutes). Also, in the dynamic environment of a reactor containment building the 88Rb concentration would not be expected to remain constant on a time scale of hours, as required by this measurement method. However, realistic or not, it has for decades been the practice of CPAM vendors to provide a set of curves (graphs) based on the expressions above. Such graphs have concentration on the vertical axis, and net countrate on the horizontal axis. There often is a family of curves, parameterized on the detection efficiency (or labeled as to specific nuclides). The implication in providing these graphs is that one is to observe a net countrate, at any time, enter the graph at this value, and read off the concentration that exists at that time. To the contrary, unless the time is greater than the transit time T, the nuclide of interest is long-lived, and the concentration is constant over the entire interval, this process will lead to incorrect concentration estimates.
Quantitative methods for CPAM applications As discussed in the referenced paper, there are at least 11 possible quantitative methods for estimating the concentration or quantities derived from it. The "concentration" may only be at a specific time, or it might be an average over some time interval; this averaging is perfectly acceptable in some applications. In a few cases, the time-dependent concentration itself can be estimated. These various methods involve the countrate, the
time derivative of the countrate, the time integral of the countrate, and various combinations of these. The countrate is, as mentioned above, developed from the raw detector pulses by either an analog or digital ratemeter. The integrated counts are easily obtained simply by accumulating the pulses in a "scaler" or, in more modern implementations, in software. Estimating the rate of change (time derivative) of the countrate is difficult to do with any reasonable precision, but modern
digital signal processing methods can be used to good effect. It turns out that it is very useful to find the
time integral of the concentration, as opposed to estimating the time-dependent concentration itself. It is essential to consider this choice for any CPAM application; in many cases the integrated concentration is not only more useful in a
radiological protection sense, but is also more readily accomplished, since estimating a concentration in (more or less) real-time is difficult. For example, the total activity released from a plant stack over a time interval \eta is :R_{stack} \left( \eta \right)\,\,\, = \,\,\,\int_0^\eta {Q(\tau )\,F_{stack} (\tau )\,d\tau } Then, for a fixed-filter monitor, assuming a constant stack and monitor flowrate, it can be shown that : R_{stack} \left( \eta \right)\,\,\, = \,\,\,{{F_{stack} \left[ {\dot C\left( \eta \right)\,\,\, + \,\,\,\lambda \,\int_0^\eta {\dot C\left( \tau \right)\,\,d\tau } } \right]} \over {\varepsilon \,k\,F_m \,\phi }} so that the release is a function of both the countrate and integrated counts. This approach was implemented at the
SM-1 Nuclear Power Plant in the late 1960s, for estimating the releases of episodic
containment purges, with a predominant, and strongly time-varying, nuclide of 88Rb. For a LL nuclide, the integral term vanishes, and the release depends only on the attained countrate. A similar equation applies for the occupational exposure situation, replacing the stack flowrate with a worker's breathing rate. An interesting subtlety to these calculations is that the time in the CPAM response equations is measured from the
start of a concentration transient, so that some method of detecting the resulting change in a noisy countrate must be developed. Again, this is a good application for statistical signal processing that is made possible by the use of computing power in modern CPAMs. Which of these 11 methods to use for the applications discussed previously is not especially obvious, although there are some candidate methods that logically would be used in some applications and not in others. For example, the response time of a given CPAM quantitative method may be far too slow for some applications, and perfectly reasonable for others. The methods have varying sensitivities (detection capabilities; how small a concentration or quantity of radioactivity can
reliably be detected) as well, and this must enter into the decision.
CPAM calibration The calibration of a CPAM usually includes: (1) choosing a quantitative method; (2) estimating the parameters needed to implement that method, notably the detection efficiency for specified nuclides, as well as the sampling line loss and collection efficiency factors; (3) estimating, under specified conditions, the background response of the instrument, which is needed for calculating the detection sensitivity. This sensitivity is often called the
minimum detectable concentration or MDC, assuming that a concentration is the quantity estimated by the selected quantitative method. What is of interest for the MDC is the variability (not the level) of the CPAM background countrate. This variability is measured using the
standard deviation; care must be taken to account for
bias in this estimate due to the
autocorrelation of the sequential monitor readings. The autocorrelation bias can make the calculated MDC significantly
smaller than is actually the case, which in turn makes the monitor appear to be capable of reliably detecting smaller concentrations than it in fact can. An
uncertainty analysis for the estimated quantity (concentration, release, uptake) is also part of the calibration process. Other performance characteristics can be part of this process, such as estimating response time, estimating the effect of temperature changes on the monitor response, and so on. ==Table of radiation measurement quantities==