5 SUMMARY OF RESULTS AND DISCUSSION
5.1 Dose calculation results
The same source term and the probability of release scenario were postulated for all national studies presented in this report. These data are the result of probabilistic safety assessments of level 1 and 2 which are publicly available in the open domain [18, 29]. A one-year set of meteorological data was taken from the data base comprising the results of real measurements at the site of an operating NPP. These meteorological data were used as an input for most (see the 3rd bullet of Section 4.2.1) of the national studies in this report. However, in some cases this information was subject to preliminary processing prior to the main part of the calculation.
Other environmental data (landscape, population distribution etc.) used for the assessment are not related to any existing site. This set of input has been developed by the participants of this exercise assuming locations of the reactor site and populated areas which could provide sufficient information for further comparison of different assessment tools and methods.
Several participants of the exercise used commercially available computer tools for their calculations; other participants applied methodologies and tools developed in their institutions or relied on the generic estimation methods. Simplified methods could not provide as many details in the results as could be obtained by sophisticated computer tools; they normally produce conservative estimates of doses which can be interpreted relatively easy due to the transparency of the algorithms used. Authors have found these approaches to be applicable for providing a generalized prospective assessment of radiological impacts from the potential accidents identified through safety analysis.
Detailed results of calculations provided by national experts are presented in Annexes I to X to this publication. This section summarises the results of dose estimations. Figures 14 to 31 below show comparisons of the results of each participant’s assessment for those using a deterministic method and Figures 32 to 87 for those using a probabilistic method. From the comparison of the deterministic methodologies, the following conclusions can be drawn:
The differences and similarities can be most clearly seen in the comparison for cloud-shine dose (Figure 28) since the results depend only on the time-integrated activity concentration in air, the time spent indoors and outdoors, the shielding effect of buildings and the dose coefficients for cloud-shine. In comparison to the dose from deposited activity and inhalation, cloud-shine dose depends on fewer parameters and so there is less scope for participants to choose different values. The dose from deposited activity, in addition to depending on all the parameters values and models used for dispersion, also depends on the choice of values for deposition velocities. Similarly, inhalation dose also depends on the value used for breathing rate and the choice of lung clearance type for the inhalation dose coefficient.
There are two broad groups of results:
o The two groups differ from each other at most distances by about an order of magnitude.
o Within each group there is reasonably close agreement.
o The difference between the two groups is probably due to the choice of stability category which reflects whether a conservative (stable conditions – for example Category F) or best-estimate (neutral conditions – for example Category D) approach was adopted.
o Some participants (India and Russia) performed calculations for six stability categories; results for Category D and F are shown in the plots below.
o The participant from the UK performed the analysis on a best-estimate basis for comparison with the UK’s severe accident targets.
The comparison of inhalation dose (Figures 26 and 27 for adult and infant respectively) also shows a clear grouping into two groups indicating that similar assumptions have been made by participants on the parameters listed in the first bullet above.
The comparison of deposition dose (or ground-shine) (Figures 29 to 31 for integration periods of 1, 2, and 7 days respectively) reflects both the assumptions made for dispersion and the deposition velocities assumed for particulate and elemental iodine as well as the atmospheric dispersion assumptions discussed above.
o The upper group is broadly consistent, although the results from the participant from India seem somewhat anomalous at long distances.
o The low values for the UK assessment compared with the assessment from Russia (both Stability Category D) – about a factor of 5 lower at short distances falling to about a factor 9 lower at 50 km – probably reflects different assumptions on deposition velocity (the UK participant used lower values consistent with its selection of a best-estimate approach), and possibly also reflects modelling of the plume depletion from deposition which will have a larger effect at longer distances.
The results for thyroid dose show a similar pattern.
Some results show a peak dose at 2.5 km (the exact location may lie between 1.0 and 3.5 km as these were the distances selected); this probably reflects:
o The assumptions made about the effective release height; i.e. whether any plume rise effects were accounted for due to the heat associated with the release increasing the effective height of the release or whether any building wake effects were accounted for reducing the effective height.
o The stability category selected – for a given effective release height, the peak ground-level concentration will occur further downwind for more stable conditions.
Other differences in the results may be due to different assumptions on release duration, boundary layer heights, and terrain (as discussed above).
With regard to comparison of the probabilistic methodologies, it is more difficult to draw conclusions because there are fewer assessments and less overlap between the assessments of the different participants; however, the following conclusions are drawn:
There is generally greater consistency between the assessments when comparing the same percentiles (apart from the assessment from Argentina which is discussed below) than for the deterministic assessments.
o This is because the main reason for the large variation in the deterministic results is due to the level of conservatism used (e.g. D5 weather for a best-estimate approach and F2 weather for a conservative approach).
o For the probabilistic assessments, on the other hand, the level of conservatism is reflected in the percentile – although choices of parameter values such as deposition velocities may also affect the results – and comparing assessments for the same percentiles is comparing assessments with the same level of conservatism.
o Differences in approach may then be reflected in which percentile is used for the national assessment.
The results from the participants from Germany and the UK are very close as might be expected as they used the same computer code (COSYMA) albeit different versions.
o The small differences between these two assessments that are observed probably result from the different sampling regimes used, namely cyclic or stratified (as discussed in Annex X).
The results from the Argentinian participant are approximately an order of magnitude higher than those of the other participants other than those for the maximum dose.
o This is probably a consequence of different approaches for processing the results from the different meteorological sequences. For example, COSYMA – as used by the German and UK participants – calculates results for each radius and for each sector (direction) defined (in the UK case 72) for each of the 144 meteorological sequences sampled; it then determines the percentiles for each radius from the 144·72 results.
This gives the same results whichever way the wind is blowing and so no account is taken of the actual windrose. The Argentinian assessment, on the other hand, selects the direction with the maximum result for each radius and calculates the percentiles from the one per sample results.
o When the maximum value is required rather than a percentile, COSYMA finds the maximum value from the 144·72 results for each radius which then essentially becomes the same as the Argentinian approach – both approaches are finding the maximum for each radius; this explains why there is much better agreement among the participants for the maximum than there is for the other percentiles.
o The maximum value from the sampling is not really a statistically meaningful quantity as the values will depend on the sample taken and the size of the sample. The maximum will generally increase with increasing size of the sample whereas the other percentiles should converge on a value.
A different approach was used by the participant from Ukraine:
o In this approach, the doses were calculated at actual locations for the areas of habitation (the distance and the bearing).
o Using this approach requires a much larger sample; this is because for many meteorological sequences, the wind direction will be away from the habitation and lead to no dose at all – in fact, the Ukrainian assessment used every sequence, i.e. 8760 samples as opposed to the 144 used by the German and UK participants.
o When using the actual locations, the percentiles of dose for some locations further away might give higher doses than locations closer in if the wind blows in that direction more often and with more stable weather.
o The assessment from Ukraine (Annex IX) shows the windrose (inverted to show the direction to which the wind blows) overlaid on the location of the population centres;
this shows that the frequency with which the wind blows towards the populations at 2.5 km and 3.5 km is much lower than that for the other populations.
o This approach does use the windrose and the population distribution.
o This is why the results from the Ukrainian participant show a variable pattern with distance when compared with the monotonically decreasing pattern shown in the results from the other participants for percentiles other than the maximum (Figure 36 to Figure 39, Figure 44 to Figure 55, Figure 60 to Figure 63).
o Comparing the results from Ukraine with those from the UK, for example, for total dose and 2-day integration time (Figure 36 to Figure 39) shows that sampling and not considering wind direction is giving a reasonably good representation of the full dataset (with a more uniform population distribution and windrose the comparison would have been even better).
Another difference between the assessments may be how – or whether – the release is split into different phases to reproduce the release profile.
Figures 88 and 89 show a comparison of deterministic and probabilistic results using the UK results as an example; the following conclusions are drawn:
The best-estimate or typical weather condition (D5) approach roughly corresponds to the 99th percentile – in other words, making the deterministic assumption that the exposed person is directly downwind of the release is roughly equivalent to using the 99th percentile in a probabilistic assessment.
o This case study considers 72 sectors giving a 1 72⁄ chance of wind blowing in a given direction. The weather most likely will be typical. From the meteorological data file used in this exercise ~37% of the hourly data lines were category D.
o Fractions of weather records attributed to other categories are the following: ~9%
attributed to category B, ~19% to category C, ~15% and ~18% to categories E and F.
These categories are less likely and would correspond to a higher percentile.
The conservative weather condition (F2) approach roughly corresponds to the maximum values from the probabilistic assessment; this is again as might be expected although it also shows that F2 does not give the maximum dose at distances close to the release point for elevated releases as is the case here.
o For short distances, the probabilistic maximum dose is much higher than for a deterministic assessment using F2 weather conditions; this is because, as discussed above, it may take some distance for the plume to ground in stable conditions (Category E and F) and unstable weather (Category A or B) may give higher air concentrations close to the release.
o For the probabilistic assessments, the meteorological sequence that gives the maximum value at each distance may be different; the meteorological sequence that gives the maximum dose may be different at different distances and the maximum dose possible may be higher at distances further downwind in some cases.
FIG.14. Total Effective Dose (adult, 1-day integration for deposited dose)
FIG.15. Total Effective Dose (infant, 1-day integration for deposited dose)
FIG.16. Total Effective Dose (adult, 2-day integration for deposited dose)
FIG.17. Total Effective Dose (infant, 2-day integration for deposited dose)
FIG.18. Total Effective Dose (adult, 7-day integration for deposited dose)
FIG.19. Total Effective Dose (infant, 7-day integration for deposited dose)
FIG.20. Thyroid Dose (adult, 1-day integration for deposited dose)
FIG.21. Thyroid Dose (infant, 1-day integration for deposited dose)
FIG.22. Thyroid Dose (adult, 2-day
integration for deposited dose) FIG.23. Thyroid Dose (infant, 2-day integration for deposited dose)
FIG.24. Thyroid Dose (adult, 7-day integration for deposited dose)
FIG.25. Thyroid Dose (infant, 7-day integration for deposited dose)
FIG.26. Inhalation Dose (adult) FIG.27. Inhalation Dose (infant)
FIG.28. Cloud shine dose FIG.29. Deposited Dose (1-day integration)
FIG.30. Deposited Dose (2-day integration) FIG.31. Deposited Dose (7-day integration)
FIG.32. Total Effective Dose, 90th percentile (adult, 1-day integration for deposited dose)
FIG.33. Total Effective Dose, 95th percentile (adult, 1-day integration for deposited dose)
FIG.34. Total Effective Dose, 99th percentile
(adult, 1-day integration for deposited dose) FIG.35. Total Effective Dose, maximum (adult, 1-day integration for deposited dose)
FIG.36. Total Effective Dose, 90th percentile (adult, 2-day integration for deposited dose)
FIG.37. Total Effective Dose, 95th percentile (adult, 2-day integration for deposited dose)
FIG.38. Total Effective Dose, 99th percentile (adult, 2-day integration for deposited dose)
FIG.39. Total Effective Dose, maximum (adult, 2-day integration for deposited dose)
FIG.40. Total Effective Dose, 90th percentile (adult, 7-day integration for deposited dose)
FIG.41. Total Effective Dose, 95th percentile (adult, 7-day integration for deposited dose)
FIG.42. Total Effective Dose, 99th percentile
(adult, 7-day integration for deposited dose) FIG.43. Total Effective Dose, maximum (adult, 7-day integration for deposited dose)
FIG.44. Total Effective Dose, 90th percentile (infant, 2-day integration for deposited dose)
FIG.45. Total Effective Dose, 95th percentile (infant, 2-day integration for deposited dose)
FIG.46. Total Effective Dose, 99th percentile (infant, 2-day integration for deposited dose)
FIG.47. Total Effective Dose, maximum (infant, 2-day integration for deposited dose)
FIG.48. Thyroid Dose, 90th percentile (adult, 1-day integration for deposited dose, Ukraine results for 2 days added to aid comparison)
FIG.49. Thyroid Dose, 95th percentile (adult, 1-day integration for deposited dose, Ukraine results for 2 days added to aid comparison)
FIG.50. Thyroid Dose, 99th percentile (adult, 1-day integration for deposited dose, Ukraine results for 2 days shown to aid comparison)
FIG.51. Thyroid Dose, maximum (adult, 1-day integration for deposited dose, Ukraine results for 2 days shown to aid comparison)
FIG.52. Thyroid Dose, 90th percentile
(adult, 2-day integration for deposited dose) FIG.53. Thyroid Dose, 95 th percentile (adult, 2-day integration for deposited dose)
FIG.54. Thyroid Dose, 99th percentile (adult, 2-day integration for deposited dose)
FIG.55. Thyroid Dose, maximum (adult, 2-day integration for deposited dose)
FIG.56. Thyroid Dose, 90th percentile (adult, 7-day integration for deposited dose)
FIG.57. Thyroid Dose, 95th percentile (adult, 7-day integration for deposited dose)
FIG.58. Thyroid Dose, 99th percentile (adult, 7-day integration for deposited dose)
FIG.59. Thyroid Dose, maximum (adult, 7-day integration for deposited dose)
FIG.60. Thyroid Dose, 90th percentile (infant, 1-day integration for deposited dose, Ukraine results for 2 days shown to aid comparison)
FIG.61. Thyroid Dose, 95th percentile (infant, 1-day integration for deposited dose, Ukraine results for 2 days shown to aid comparison)
FIG.62. Thyroid Dose, 99th percentile (infant, 1-day integration for deposited dose, Ukraine results for 2 days shown to aid comparison)
FIG.63. Thyroid Dose, maximum (infant, 1-day integration for deposited dose, Ukraine results for 2 days shown to aid comparison)
FIG.64. Inhalation Dose, 90th percentile (adult)
FIG.65. Inhalation Dose, 95th percentile (adult)
FIG.66. Inhalation Dose, 99th percentile (adult)
FIG.67. Inhalation Dose, maximum (adult)
FIG.68. Inhalation Dose, 90th percentile (infant)
FIG.69. Inhalation Dose, 95th percentile (infant)
FIG.70. Inhalation Dose, 99 th percentile (infant)
FIG.71. Inhalation Dose, maximum (infant)
FIG.72. Cloud-shine Dose, 90 th percentile FIG.73. Cloud-shine Dose, 95 th percentile
FIG.74. Cloud-shine Dose, 99 th percentile FIG.75. Cloud-shine Dose, maximum
FIG.76. Deposited Dose, 90 th percentile
(1-day integration) FIG.77. Deposited Dose, 95 th percentile (1-day integration)
FIG.78. Deposited Dose, 99 th percentile
(1-day integration) FIG.79. Deposited Dose, maximum (1-day
integration)
FIG.80. Deposited Dose, 90 th percentile (2-day integration)
FIG.81. Deposited Dose, 95 th percentile (2-day integration)
FIG.82. Deposited Dose, 99 th percentile (2-day integration)
FIG.83. Deposited Dose, maximum (2-day integration)
FIG.84. Deposited Dose, 90th percentile
(7-day integration) FIG.85. Deposited Dose, 95th percentile (7-day integration)
FIG.86. Deposited Dose, 99th percentile
(7-day integration) FIG.87. Deposited Dose, maximum (7-day
integration)
FIG.88. Comparison of probabilistic and deterministic assessments for total effective dose (adult, 1-day integration)
FIG.89. Comparison of probabilistic and deterministic assessments for total effective dose (adult, 7-day integration)