Figure 2 - uploaded by Jeffrey J Whicker
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Two hypothetical release scenarios that result in an air concentration of 1 Bq m 3 (y-axis). Fig. 2a illustrates the single release scenario (release from t 10 to 21 min), and Fig. 2b shows the release scenario with two discrete releases (t 0 to 11 min and from 15 to 23 min).
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Continuous air monitors (CAMs) sample air and alarm when concentration levels of radioactivity in air exceed preset alarm levels. The air concentrations through time are calculated based on accumulation sampling techniques. Accumulation air sampling is the process in which radioactive aerosol is continually deposited onto a collection medium and a...
Contexts in source publication
Context 1
... evaluate the capabilities of three methods to calculate con- centrations, two release scenarios were conceived that resulted in a hypothetical concentration of 1 Bq m 3 (Fig. 2). The first simu- lated release scenario was a single 10-min release, and the second simulated release scenario was a multiple release consisting of an initial 11-min release followed by an 8-min release 5 min later. As the air was sampled, radioactivity would accumulate on the filters and be measured. The examples show the progression ...
Context 2
... number of counts in each count interval for a single release, as illustrated in Fig. 2a, is ...
Context 3
... results from a multiple re- lease scenario, as illustrated in Fig. 2b, are provided in Table 2, and the time-dependent nature of the measured concentrations is shown in Fig. 4a. The response patterns are similar to a single release scenario. As before, con- centrations calculated using Method 1 show relatively quick response, but similar to the single release scenario, the standard de- viations of the ...
Context 4
... values estimated from Fig. 7 in the Hayes and Beekman (2002) data are provided in Ta- ble 3. These data points (partic- ularly N 1 ) correspond to N g,i , N g,i1 , N g,i2 . . . in Table A-2 Table 3 correspond to the deci- sion threshold, and were calcu- lated as 1.645 times the standard deviation of the measured concentration (i.e., L c,i 1.645 i ). This provides approximately a 5% probability of falsely con- cluding activity was present on the filter when it was not. ...
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Citations
... The alarm set point of a CAM is an important instrument-related factor that impacts the level of worker protection. The alarm set point is dependent on numerous factors, but especially the background radiation levels and the ability of the internal software to compensate for background and its fluctuations without excessive false alarm rates (Hayes and Beekman 2002;Hayes 2003;Zhengyong and Whicker 2008). Related, the length of the counting interval also impacts the ability of an instrument to statistically distinguish target counts from background counts during accumulation counting (ISO 2005). ...
... While longer count times allow greater sensitivity to a release, longer count times also result in longer times to alarm and subsequently greater exposure times for workers. Thus, a proper balance between the length of the counting interval and the alarm level is required (Zhengyong and Whicker 2008). ...
... Alarm time is the parameter describing the measurement counting interval of the CAM. Faster alarm counting cycles result in faster alarms, but the uncertainty of the counting is greater and there is a greater chance of falsely concluding the air concentration had exceeded target air concentration when there was little or no material actually released (Zhengyong and Whicker 2008). For the Monte Carlo simulations, the distribution shapes for alarm time were assumed to be triangular with a most likely time of 1 min (range 0.5-1.5 min) for the general condition and 0.5 min (range 0.25-0.75 ...
Effective continuous air monitor (CAM) programs can eliminate or significantly reduce the amount of inhaled radioactive material following an accidental release. Numerous factors impact the levels of protection CAM programs provide to the workers during these releases. These factors range from those related to the capability of the CAM instrument (e.g., CAM alarm set point and length of counting intervals) to those related to CAM placement in the room relative to dispersion rates and patterns of the released material in a room. While the impact of many of these factors on alarm sensitivity has been investigated in isolation, there are no methods for holistic evaluations of CAM programs relative to radiation protection goals (RPGs) or the contribution of the factors, either individually or combined, toward limiting worker dose. In this study, worker exposure was predicted using CAM response models developed to evaluate protection levels for continuous and acute releases. Monte Carlo simulations of 10,000 releases were performed using various combinations of model parameter values, with associated uncertainty distributions, to assess the expected ability of a CAM program to meet RPGs, and, further, to assess the relative influence of each factor toward lowering worker exposure. Results showed that improvements to CAM instrument capability combined with better ventilation and CAM placement improve worker protection nonlinearly and that these improvements are critical to meet RPGs. The sensitivity analysis showed that ventilation-driven dilution had the greatest impact on exposure reduction with the selected counting interval for alarm decisions and the alarm set point as secondarily important.
... The artificial count is often very low; in consequence it should be compensated from the natural radioactivity. Different techniques exist; deferred analysis or algorithm compensations are the most used (McFarland et al. 1992;Domnikov et al. 2001;Shevchenko et al. 2006;Zhengyong and Whicker 2008). ...
An alpha spectrometer including a semi-conductor detector is commonly used for measurements of the emergent alpha particles from an air filter, on which was sampled a radioactive aerosol. The alpha spectrometry and the detection efficiency are necessary input information for real radioactivity measurements. The MCNPX code based on the Monte Carlo method has been applied to simulate the detection process in order to obtain spectrum peaks and determine the detection efficiency for modeled geometry. First simulations with MCNPX have been carried out in order to validate the alpha particle energy spectrometry of an electrodeposited solid source and an initial simulated filter model. Furthermore, to improve our first filter model, the real spatial distribution of radioactive aerosols across the filter thickness, found experimentally, is taken into account in a multi-layer filter model. Such an alpha particle distribution allows achieving an adequate simulation of the filter. Comparison between measured and simulated alpha spectra highlights the good agreement in spectral parameters and in detection efficiency even under different aerosol spatial distributions inside the volume of the filter.
A surprisingly large amount of variance reduction has been observed when filtering International Organization for Standardization (ISO) "ISO Method" continuous particulate air monitor (CPAM) airborne radioactivity concentration estimates with a simple three-point moving average. This processing has relatively little lag relative to the amount of variance reduction obtained. The key factor producing this effect is the specific autocorrelation structure of the estimated concentrations, which are based on taking first differences of integrated-count data; this scheme results in successive count differences that contain a common count value between them. The observed variance reduction factor has also been derived analytically.
Responders need tools to rapidly detect and identify airborne alpha radioactivity during consequence management scenarios. Traditional continuous air monitoring systems used for this purpose compute the net counts in various energy windows to determine the presence of specified isotopes, such as ²³⁵U, ²³⁹Pu, and ²⁴¹Am. These calculations rely on having a well-calibrated detector, which is challenging in low-background environments. Here, an alternative approach of using artificial neural networks to classify alpha spectra is presented. Two network architectures, fully connected and convolutional neural networks (CNNs), were trained to classify alpha spectra into four categories: background and background plus the three isotopes above. Sources were injected into measured background at various fractions of the derived response level (DRL) corresponding to early-phase Protective Action Guides. The convolutional network identifies all sources at 1% of the DRL with average probability of detection of 95% and false alarm probability of 1%. Further, the network identifies sources ranging between 0.25% and 1% of the DRL with higher than 80% probability of detection and lower than 7% false alarm probability. Most significantly, the network performance improves in low-count background conditions, increasing its minimum probability of detection to 93% and reducing the false alarm probabilities to lower than 0.25%. These results show that, once trained on datasets representing a range of detection scenarios, artificial neural networks can accurately identify alpha isotopes of interest without the need for detector calibration.
A calculation for estimating concentrations of long-lived airborne particulate radioactivity using fixed-filter continuous air monitors is given in an ISO standard. The method uses counts integrated over relatively long time intervals, rather than the 'instantaneous' count rates that in digital systems are evaluated using much shorter time intervals and some form of variance-reduction filtering. This article presents three ways of deriving and interpreting this calculation, based on previously published mathematical models that were derived from first principles. The method is also extended here to apply for short-lived activity. Some statistical properties of the estimator are discussed, including its time-dependent variance and the presence of strong autocorrelation in the concentration estimates. An interactive simulation was used to examine the performance of the concentration estimation, using physically plausible concentration time-dependence profiles; example plots are provided. The conclusion of these studies is that the method, as modified herein, can perform remarkably well in providing periodic average-concentration estimates for both long- and short-lived activity, and it should be considered an appropriate method in those situations where the tracking of a time-dependent concentration is deemed necessary.
In the nuclear industry, workers may be exposed to artificial radioactive aerosols. These aerosols are generally composed of particles with a diameter measuring between 0.1 μm and 10 μm. To protect workers in nuclear facilities, monitors that continuously measure radioactivity in the air are used. The main function of the monitor is to provide real-time measurement of activity concentration. Measurement of aerosol activity concentration can be affected by a number of factors specific to the aerosols and the instrument. The first part of the article will present the general operating principles of continuous air monitors (CAMs) and inherent measurement difficulties, as well as the main standard tests. The second section describes the experimental ICARE facility The ICARE facility generates standard artificial and natural radioactive aerosols for calibrating continuous air monitors under real operating conditions.
This paper describes an algorithm intended for use in the U.S. Navy's next-generation air particle detector designed for measuring 60 Co air contamination. The algorithm measures both alpha and beta activity from an air filter utilizing passivated implanted planar silicon detectors for spectrometry of both particle types and is designed to compensate for radon progeny to discriminate this from the beta emissions of 60 Co. This is done by correlating the specific alpha emissions with their beta emission parents, or their beta emission progeny, as appropriate. In addition, the algorithm is unique in that by using region of interest (ROI) windows, it is less sensitive to spectral smearing due to dust or humidity effects on the particle depositions or more specifically to variable energy loss of alpha particles to the detector from deposited material on the filter. A weakness of this approach is that thoron B (212Pb) does not have a detectable alpha parent and the next alpha progeny must decay through an isotope ( 212Bi) with a half-life of 60.6 min. This causes predictions of the 212Pb activity to lag in time to some extent. Mitigation of this effect is realized by using a first-order correction utilizing appropriate mathematical equations to account for the physics of this buildup and decay. This paper concludes by demonstrating that the beta assay value is a linear superposition of the alpha ROI values from the three dominant alpha peaks. Initial estimates on the coefficients of the alpha ROI values are derived with final values recommended to be determined from operational measurements.
Equations are derived that provide the numerical algorithms necessary for the calculations of both concentration (such as #DAC) and exposure (such as #DAC-h) within continuous air monitors (CAMs) employing collection media. Both calculations utilize measured counts over certain CAM counting intervals. The relationship to similar, although oft misinterpreted, equations given in International Organization for Standardization Standard 11929-5:2005 is detailed.
