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Assessment of routine atmospheric discharges from the Sellafield nuclear installation—Cumbria UK
Atmospheric Environment 36 (2002) 3203–3215
Assessment of routine atmospheric discharges from the Sellafield nuclear installation—Cumbria UK N. Nelson*, K.P. Kitchen, R. Maryon The Met Office, Environmental Consultancy Group, London Road, Bracknell, Berkshire RG12 2SY, UK Received 13 August 2001; received in revised form 19 December 2001; accepted 4 January 2002
Abstract The Met Office’s long-range dispersion model, the Nuclear Accident Model (NAME), has been used to assess the impact of routine atmospheric radioactive discharges from the Sellafield nuclear installation on the Cumbrian coast of England (541 25.00 31 30.20 W). The model has produced values of scaling factors for concentrations and depositions over northwest Europe. Whilst the dispersion model was designed and has been used extensively in the past for examining specific incidents of a nuclear nature, this is the first time the NAME model has been used to provide a detailed climatological impact assessment for a nuclear installation. Crown Copyright r 2002 Published by Elsevier Science Ltd. All rights reserved. Keywords: Dispersion model; Radioactive discharges; Deposition; Emissions; Boundary layer depth; Stability
1. Introduction The fate of accidental or routine emissions of radioactive material from nuclear installations is a topic of great current interest and concern. This question was tackled from a probabilistic angle in Smith and Maryon (1992). The present paper addresses the likely transports of atmospheric emissions from BNFL’s Sellafield plant in the climatological sense, using a later version of the same dispersion model, the Met Office’s medium to long-range dispersion model, the ‘nuclear accident model’—‘NAME’. Developed as a nuclear accident response model, NAME is used here to provide a detailed analysis of Sellafield’s routine emissions of radioactive material to the air. The model was run for a two-year simulation using a constant one unit per second emissions release rate for a variety of nuclides representing typical half-lives and deposition characteristics of routinely emitted material.
*Corresponding author. E-mail address: noel.nelson@metoffice.com (N. Nelson).
As the exact emission details will change each year, using a unit release for the study effectively provides a set of scaling factors for time integrated air concentrations, dry and wet depositions. These can be multiplied by typical annual emission rates to assess the long-term impact of routine emissions under any emissions scenario. The scaling factors cover the area of northwest Europe. A detailed comparison is presented of the meteorology experienced within the twoyear study period with the long-term climatological statistics. This is to ensure that the results for the two years studied are representative of the long-term climatology.
2. Model description The Met Office’s NAME model (Maryon et al., 1991) is used to simulate the medium and long-range transport and deposition of a range of airborne pollutants. The model provides estimates of instantaneous and time integrated air concentrations, together with estimates of the deposition of pollutants to the ground by both wet
1352-2310/02/$ - see front matter Crown Copyright r 2002 Published by Elsevier Science Ltd. All rights reserved. PII: S 1 3 5 2 - 2 3 1 0 ( 0 2 ) 0 0 1 8 2 - 6
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N. Nelson et al. / Atmospheric Environment 36 (2002) 3203–3215
and dry deposition processes. Wind fields and other meteorological data are generally obtained from global, regional and mesoscale versions of the Met Office’s Numerical Weather Prediction (NWP) model, the Unified Model (UM). The NAME model was developed following the Chernobyl incident. As a World Meteorological Organisation (WMO) Regional Specialist Met Centre, the Met Office has international responsibilities for providing forecasts in the event of nuclear or other large scale emergencies. In addition to nuclear accident response, current applications include forecasting the transport of volcanic ash for the WMO Volcanic Ash Advisory Centre, and studies of the sources, transport and dispersion of a wide range of airborne pollutants. Air chemistry modules are under development to facilitate the application of NAME to air quality issues. The NAME model is a Lagrangian multiple particle type (Maryon et al., 1999) simulating the release and spread of pollutants by releasing large numbers of model particles into the ‘model atmosphere’, that is, the global meteorological fields as assimilated and stored by the UM. Each of these model particles carries with it the masses or activity of a number of species, reflecting the required emission profiles. These model particles are then advected in three dimensions by the model wind field and diffused using advanced random walktype parametrizations. As the particles are transported in time and space, they may experience changes in the mass and activity they carry due to dry and wet depositions, radioactive decay and chemical transformations. These can all contribute to the depletion of airborne pollutants. Within the model these losses are applied on a particle basis, i.e. the mass of each model particle is reduced each timestep, and for deposition processes the depleted mass added to surface deposition maps. The model particles are never deposited in toto, and lost to the integration; they simply lose a proportion (large or small) of the mass they carry. Concentrations in air are computed as boundary-layer averages. This impact study was carried out on a Hewlett Packard (HP) C180 workstation running HP-UX10. The NAME model is written in standard FORTRAN77 with HP extensions. The model configuration used in this study required in the region of 0.5 Gb of memory and 2 Gb of on-line disk storage. The model computations were performed over a 5-month period, using in the region of 1.5 months CPU. 2.1. Previous model validation Like any other model NAME is not without its own sources of error. Apart from forecast error (not applicable in this study) possible sources include model
error as a consequence of numerical model design and ‘irreducible’ error caused by the variable and random nature of the atmosphere, especially turbulent and other small-scale processes, which no model can totally account for. Model error can include interpolation errors in time and space, limitations of the integration and diffusion schemes, and inaccuracies in representing the source terms. The validation of long-range dispersion models is notoriously difficult. For example, Klug et al. (1992), under the auspices of the European Commission (EC), WMO, and International Atomic Energy Authority (IAEA), attempted to use observed radioactivity following the Chernobyl disaster. However, the study suffered from the uncertainties in our knowledge of the Chernobyl source term and reflected the relatively early stage of development of many of the European dispersion models. It pointed the way to model improvement and development. It also prompted the European Tracer Experiment (ETEX, and ATMES-II), which made a more systematic attempt under the same sponsors to validate these models using controlled releases of tracer (see Girardi et al., 1998; Graziani et al., 1998; Mosca et al., 1998; Ryall and Maryon, 1997, 1998, 1999). Many of the most prominent models on the international scene participated in ETEX, and NAME was clearly in the front rank of the models, as can be seen from the validation statistics published in the references quoted. The correlation between observed and modelled plume-spread was notably strong. Reference to pages 396 and 404 of Mosca et al. (1998) indicate that the overall bias of NAME is very small, with overpredictions fewer but more marked than the underpredictions. The number of predictions within factors of 2 and 5 of the observations was ‘the highest in the ATMES II exercise’ (p. 404), that is, out of more than 40 entries from the international dispersion modelling community. NAME has also successfully been used to simulate the long-term measurements of several trace gases at the monitoring site at Mace Head on the West Coast of Ireland (Ryall et al., 1998; Derwent et al., 1998). The species analysed included the halocarbons CFC-11, CFC-12, CFC-113, methyl chloroform and carbon tetrachloride, together with trace gases methane, carbon monoxide and nitrous oxide. NAME successfully reproduced the main characteristics of the observed hourly fluctuations in the concentrations recorded at Mace Head over a two year period, and was adapted for extensive investigations into the sources and rates of emission of these substances. Because NAME has performed well in past validation studies, the level of confidence given to the scaling factors obtained in this study was reasonably high.
N. Nelson et al. / Atmospheric Environment 36 (2002) 3203–3215
2.2. Advection and diffusion A number of advection and diffusion schemes are available within the NAME model, incorporating shortrange effects on dispersion (i.e. turbulence effects within the boundary layer and plume rise). At the ranges being considered in the study, sophisticated schemes developed for near-source plumes are not required and a simple fixed timestep long-range scheme was used (Maryon et al., 1999). This advects the model particles using interpolated model winds with a small perturbation to reflect sub-grid scale diffusion. This scheme uses a diffusivity based on ambient conditions, and was found to be quite comparable with the more sophisticated schemes over long range. Any other option would have been prohibitively expensive for the prolonged integrations required for this study. 2.3. Dry deposition The basis of the parameterisation is that the flux F of pollutant to the ground is proportional to the concentration C of the pollutant within the boundary layer: F ¼ ud C
ð1Þ
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sphere to the ground. Two main processes are involved: washout, where material is ‘swept out’ by falling precipitation; and rainout, where material is absorbed directly into cloud droplets as they form by acting as cloud condensation nuclei. Rainout coefficients are dependent on the precipitation phase (whether water or ice) and on the mechanisms for droplet growth, which differ for dynamic (frontal) and convective (as generated from surface heating) clouds. Washout coefficients are also dependent on the precipitation type, for example snow flakes ‘sweep’ out aerosol more efficiently than raindrops. Enhanced removal occurs where precipitation is orographically enhanced by the seeder/feeder mechanism. This last process was omitted from the present study, which again is likely to have resulted in some over-estimate of air concentrations to the lee of the mountains E and N of Sellafield (corresponding to an underestimate of the deposition upon them). The rainfall data supplied by the Met Office UM is generally smoothed compared with reality. It is however an acceptable approximation when time integrated over a long period. The removal of material from the atmosphere by wet deposition processes is based on the depletion equation dC ¼ LC; dt
The constant of proportionality, ud ; is known as the deposition velocity. This quantity is not well understood for species such as 129I and 131I. The deposition velocity of the species is highly dependent on its chemical nature, which alters during transport. In the event, it was decided to compute both the maximum possible air concentrations and depositions to the surface at distances remote from the source. Omitting the dry deposition from the integrations (achieving the former) and applying a post-processing program, which determined the dry deposition from the time series of 15-min values of air concentration everywhere in the domain achieved latter. This should have produced conservative estimates, i.e. over-estimates, of the time-integrated air concentrations and dry deposition likely to occur at remote sites.
where r is the rainfall rate and A and B are coefficients defined for different types of precipitation (e.g. dynamic, convective, rain and snow), and the two different deposition processes, rainout and washout. All wet depositing species used the following washout and rainout coefficients in Table 1. The coefficients, based on observational data and detailed cloud modelling, were supplied by Dr. T. Choularton’s team at UMIST, who collaborated with the Met Office during the model development.
2.4. Wet deposition
2.5. Radio-active decay
For most pollutants wet deposition is the dominant means by which material is removed from the atmo-
For physically unstable materials the activity of the species is depleted by radioactive decay. The governing
ð2Þ
where t is time, and L the scavenging coefficient defined: L ¼ ArB ;
ð3Þ
Table 1 Scavenging coefficients used in the NAME model, r the rainfall rate is in mm/h
Washout Rainout
L (s1) Rain (below freezing level)
L (s1) Snow (above freezing level)
Convective
Convective
Dynamic
5 0:79
8:4 10 r 3:36 104 r0:79
Dynamic
5 0:305
8:4 105 r0:79
8:0 10 r 3:36 104 r0:79
When applied
8:0 105 r0:305
Below cloud base Within cloud
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equation is dm m ¼ ; dt aq
ð4Þ
where q is the half-life, a is a constant (1=ln 2 ¼ 1:4427), and m is the mass of material.
emitted. Those chosen for inclusion were Krypton-85 (85Kr), Caesium-137 (137Cs), Iodine-129 (129I), Iodine131 (131I) and Argon-41 (41Ar). Their properties are summarised in the Table 2 below.
4. Meteorology 3. Emissions In the NAME model the pollutant is represented by large numbers of model particles released into the model atmosphere from any number of sources. Each particle or parcel of air represents a proportion of the mass or activity of a number of pollutant species. Two emission heights were chosen for the modelling, so as to reflect the nature of emissions from the Sellafield site accurately. The heights chosen were 120 and 10 m, reflecting the range of stack heights to be found on the site. The 10 m source also incorporates the effect of any possible low level emissions. Real release rates were not used in this study, as the exact emission details will change each year. Instead, a single unit release per second of each species was incorporated (for each of the two sources), which effectively provided a set of scaling factors reflecting total annual emissions for any particular year of interest. These factors could then be applied to estimate time integrated air concentrations, dry depositions and wet depositions for any source scenario. The Met Office archives contain a limited number of years of useful NWP model data in a form suitable for use within the NAME model. Also, the NAME model is computationally expensive to run. For these reasons the emission from Sellafield for two contiguous years was simulated. The representivity of the two years chosen in relation to long-term typical values is discussed below. It was not possible to model all of the species routinely emitted from Sellafield due to memory and other computational restraints. However, a selection of species was made, which satisfactorily reflected the halflives and estimated deposition velocities and wet deposition characteristics of the range of nuclides
Weather data for the period for the two years 1 January 1995–31 December 1996 were used for this study. Data are available from various versions of the UM covering a number of geographical areas. The regional version was selected for this study. This version has the required spatial and temporal coverage and provides fields of analysed data at 3-h intervals. The resolution of the grid is approximately 50 km in both longitudinal and latitudinal directions and incorporates an area from the eastern seaboard of the USA to Eastern Europe. (The UM regional grid has recently been superceded following enhancements to the Global and Mesoscale versions of the UM, but it was the best grid for application at the time this study was carried out.) Data from the NWP model include variables such as temperature, wind speed and direction, precipitation rates, surface heat flux and so on. The correct determination of boundary layer depth is important for modelling the dispersion of airborne pollutants and the resultant deposition to the ground (Maryon and Buckland, 1994). For example, turbulent diffusion is significantly enhanced in the boundary layer, and only material in the boundary layer is subject to dry deposition. The transport, concentration and deposition of material can all be adversely affected if the boundary layer depth is incorrectly diagnosed. In NAME, fields of boundary layer depths are calculated from wind and temperature profiles, using either a Richardson number or parcel technique (Maryon et al., 1999). 4.1. Representivity of meteorology used The results obtained in this analysis were based on the meteorology that occurred over the two-year period
Table 2 Species characteristics Species 137
Cs I 131 I 41 Ar 85 Kr 129
Half life (s) 8
9.461 10 4.95 1014 6.948 105 6.57 103 3.373 108
Dry deposition velocity (m s1) 3
1.0 10 1.0 102 1.0 102 — —
Wet depositing
Comment
Yes Yes Yes No No
Long lived, reactive Long lived iodine Short lived iodine Short lived, unreactive Long lived, unreactive
N. Nelson et al. / Atmospheric Environment 36 (2002) 3203–3215
Estimates of stability category at night have been calculated from the wind speed and modified total cloud amount using empirical equations from an analysis of conditions at night measured at Cardington, Bedfordshire (Smith, 1973, 1983). Estimates of boundary layer depth in stable and unstable conditions have been calculated from equations published by Smith and Blackall (1979), which depend on estimates of sensible heat flux and friction velocity. To obtain a long-term climatology, data were extracted from the Ronaldsway (Isle of Man) and Valley (Anglesey) meteorological observation sites. These record weather giving the nearest approach to that of the Sellafield area. 4.1.1. Stability Figs. 1 and 2 show the results of the comparisons for stability. The 10-yr average extracted from both stations showed a clear predominance of stability class D (neutral) and E: The stability class D appeared to be split between day and night (approximately 40%
G
F
E
t) gh
y)
(n i
(d a
C
D
D
B
A
% Frequency
45 40 35 30 25 20 15 10 5 0
Stability Category 1996
1995
1987-1996
Fig. 1. Frequency of stabilities–Ronaldsway.
Frequency of Stabilities - Valley
G
F
ht D
(n
ig
E
)
) D
(d
ay
C
B
45 40 35 30 25 20 15 10 5 0 A
1. modified total cloud amounts from the observations describing the amount and type of each significant cloud layer, using the method given by Nielsen et al. (1981); 2. the net flux of solar radiation estimated for each hour. This is based on the date, time and location of the observation together with the modified total cloud amount, using equations based on solar radiation measurement published by Nielsen et al. (1981); 3. the number of dry days since significant rain fell. This is derived from the hourly rainfall amounts. The accumulated net solar radiation since significant rain fell is also calculated; 4. the sensible heat flux. This is calculated using a Penman–Monteith combination model for evaporation in the form suggested by Berkowicz and Prahm (1982). For this model it is necessary to estimate the surface resistance which is a function of accumulated net solar radiation.
Frequency of Stabilities - Ronaldsway
% Frequency
1995–1996. How well the results represent the impact of routine releases will in part depend on how typical the weather was during the two study years. Observations recorded from suitable locations for these two years were accordingly compared with 10-yr averages over the period 1987–1996. The meteorological variables chosen for the comparison were those which would have the most immediate bearing on the movement and dispersion of pollutants in the atmosphere. These comprised atmospheric stability, represented here by estimates of the Pasquill–Gifford stability categories, wind direction and boundary layer height. The estimates of stability category during the daytime required:
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Stability Category 1995
1996
1987 - 1996
Fig. 2. Frequency of stabilities–Valley.
and 30%, respectively). Stability class E was the next most predominant category, occurring approximately 13% of the time. The very high incidence of stability category D probably reflects the coastal location of the observing sites, although there may also have been some systematic effects from the techniques applied. It is important to bear in mind that it is the comparison which is the object of this exercise, not the absolute accuracy of the estimates: for the purpose of comparing the observations from the years 1995/6 with the decadal values the approach used is perfectly valid. The two study years were in good agreement for both stations. The daytime stability class D predominated for 1995, 1996 and the decade (for approximately 41% of the time). A nocturnal neutral stability D category occurred around 27% and 29% of the time, which was also in good agreement with the 10-year average. The two study years also show good agreement with the average in recording stability E approximately 12–14% of the time at both stations.
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Frequency of Wind Directions - Ronaldsway
30 31 -6 0 61 -9 91 0 -1 12 20 11 15 50 11 18 80 12 21 10 12 24 40 12 27 70 13 30 00 13 33 30 136 C 0 AL M
1
Wind Direction Category (Degs) 1996
1995
1987-1996
Fig. 3. Frequency of wind directions–Ronaldsway.
0 21 0 12 24 40 12 27 70 13 30 00 13 33 30 136 C 0 AL M 1-
18
21
0 15
18
1-
1-
15
0
20 -1
91
12
0
-9
-6
61
31
to
30
% Frequency
Frequency of Wind Directions - Valley 20 18 16 14 12 10 8 6 4 2 0 1
*
*
to
% Frequency
16 14 12 10 8 6 4 2 0
For 1996 a 6% increase in winds from the east to northeast occurred (13%) compared with the decadal figures, in agreement with the situation at Ronaldsway. As with Ronaldsway the percentage of calms lay between 1% and 3%.
Pollution episodes are often associated with very low wind speeds. The low percentage of calms is to be expected with stations situated in a relatively well exposed area of the country. Both stations would experience broadly similar wind exposures to the site at Sellafield. Lower category wind speeds (i.e. in the range 1–3 knots—not shown) were also investigated. They occurred between 6% and 9% of the time and showed good agreement between stations and the different years. To summarise, there is generally good agreement between the 10-yr average and the two study years. However, for both Ronaldsway and Valley, the study year of 1996 differed from the 10-yr average in that a greater number of easterly winds (61–901) occurred. Consequently winds used for the study period would impact rather more on locations west–southwest of Sellafield than would normally be expected, although in percentage terms the effect was not large.
Wind Direction Category (Degs) 1995
1996
1987-1996
Fig. 4. Frequency of wind direction–Valley.
4.1.2. Wind direction The results of the analysis of wind direction can be seen in Figs. 3 and 4. For Ronaldsway, *
*
*
*
Over the 10-yr period the most predominant wind sector lay between south and west. This compares well with the results for 1995 where the same wind sector predominates. In 1996 the most frequent wind direction was more east to northeast (61–901)—a 4.1% increase over the decadal value. The next two most frequently occurring directions lay within the same sectors for each of the study periods—for 1995, 1996 and for the ten year average. For all three cases the frequency of calm conditions in these coastal locations is relatively low, accounting for between 1% and 3% of the time. For Valley,
*
There is good agreement between the most frequently recorded winds for 1995 and the 10-yr average.
4.1.3. Boundary layer height The results for the boundary layer height are given below in Figs. 5 and 6, which show for Valley and Ronaldsway the percentage occurrence of categories of boundary layer height for the two study years and the 10-yr average. These heights were estimated as described in Section 4.1. It can be seen that for both stations and for each of the three study periods there is very good agreement. In all cases the most frequently occurring height falls in the range above 2000 m. This seems to be consistent with the high incidence of neutral/unstable conditions diagnosed in Section 4.1.1. The next most frequent height was assessed as being in the range 1200– 1400 m, the next 1000–1200 m. Note also the occasions where the boundary layer is low—indicative of potentially bad air quality episodes. In all cases these account for between 5% and 8% of the time. Such stable conditions may well have been a little more frequent at Sellafield than at Ronaldsway or Valley: this would have been reflected in the UM meteorology used in the integrations. The scheme used for the height calculations tended to produce somewhat deeper boundary layer heights than would be expected. As this analysis is concerned with the representivity, it is the comparison of results rather than the specific values themselves that are of particular interest. This bias would constitute a systematic error that would not effect the actual comparison.
N. Nelson et al. / Atmospheric Environment 36 (2002) 3203–3215 Freqency of Boundary Layer Depths - Ronaldsway % Frequency
25 20 15 10 5
0. 120 200 0. 140 400 0. 160 600 0. 1 80 -80 0. 1- 0 10 1 00 000 .1 12 -12 00 0 .1 0 14 -14 00 0 .1 0 16 -16 00 0 .1 0 18 -18 00 0 .1 0 -2 00 20 0 00 .1 +
0
Boundary Layer Height (m) 1995
1996
1987-1996
Fig. 5. Percentage frequency occurance of boundary layer depth–Ronaldsay.
0 40 0. 0 1 60 -60 0. 0 80 1-8 0. 00 10 1-1 00 00 0 . 12 1-1 00 20 0 . 14 1-1 00 40 0 . 16 1-1 00 60 0 . 18 1-1 00 80 .1 0 -2 0 20 00 00 .1 + 40
20
0.
20
10.
1-
% Frequency
Frequency of Boundary Layer Depth - Valley 20 18 16 14 12 10 8 6 4 2 0
Boundary Layer Height (m) 1995
1996
1987-1996
Fig. 6. Percentage frequency occurrence of boundary layer depth–Valley.
5. Results The monthly fields of accumulated air concentration, dry deposition and wet deposition were output for all species onto a gridded dataset of resolution 0.091 in the latitudinal direction and 0.161 in the longitudinal direction. This corresponds to a physical grid size of around 10 10 km, depending on the latitude of the grid cell (for Lagrangian models the output resolution can be defined independently of that of the input meteorology). The origin of the grid was 651 north 151 west, giving coverage of the northwest of Europe and part of the Atlantic Ocean. As only accumulations were output, the values of time-integrated air concentrations and depositions for particular months of interest could easily be calculated by subtracting the values of the previous month. In order to be able to calculate such monthly figures, the material deposited to the surface was not allowed to
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undergo radioactive decay, as would normally be the practice using the NAME model. The two-year NAME simulation of monthly timeintegrated air concentrations and estimates of deposition to the surface of all the simulated species generated a great deal of output. Apart from some sample monthly charts, only annual statistics, which constitute the estimated scaling factors, are presented here, and these only for 137Cs. The results for the other species were very similar in overall spread pattern to the 137Cs and, as a result, were not presented in this paper; they are however available from the authors. Sample monthly output for the time-integrated air concentration and dry and wet depositions to the surface are shown for January 1996, for the 120 m source, in Figs. 7–9, respectively. Figs. 10a and b illustrate the annual time-integrated boundary-layer averaged air concentrations, and show that the results for the 120 and 10 m stack emissions are almost identical for 1995. The only significant difference between the releases at the two levels is that the value of the maximum measured time integrated air concentrations predicted for the short stack release, 1.35 Unit m3, is greater than for the taller stack, 1.03 Unit m3). This is to be expected, as for each case the maximum value will lie in the immediate vicinity of the Sellafield site, where the height of the release will affect ground level concentrations. Generally, a short stack will produce relatively high near-field concentrations compared with a taller stack. For distant locations the height of the release is much less critical, the pollutant having had sufficient time in the atmosphere to be evenly mixed through the boundary layer. Figs. 11a and b show the same results as Figs. 10a and b but for 1996. These annual charts for air concentration show a broadly elliptical distribution of the isopleths around the release point, the precise shape of which is a function of wind direction. As no loss processes were applied in the course of these integrations, the fall-off of concentration reflects only transport and diffusive processes, including detrainments of material out of the boundary layer. For any realistic situation these scaling factors must accordingly be interpreted as conservative, i.e. on the high side. In view of the similarity in the results, only the 120 m release for 1996 is shown in Figs. 12 and 13, which illustrate the annual dry and wet depositions, respectively. It will be recalled these were computed by postprocessing the air concentration data. Dry deposition reflects the ambient air concentration, and so the shape of the distribution is very similar to that of Fig. 10. The wet deposition reflects the irregular rainfall patterns occurring, and is less smooth. Note that over wide areas the accumulated wet deposition is about an order of magnitude higher than the dry.
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N. Nelson et al. / Atmospheric Environment 36 (2002) 3203–3215
Fig. 7. Monthly scaling factors of time integrated air concentrations (referred to as Dosage on the charts) of 137Cs from the release at 120 m for the month of January 1996. A sample monthly chart, showing how the scaling factors differ from the annual. Note January experienced little in the way of northerly winds for the whole month, the bulk of the material having been carried to the north and west of Sellafield.
Fig. 8. Monthly scaling factors of dry deposition of 137Cs corresponding to Fig. 7. The deposition field is a function of integrated air concentration, and therefore can be expected to mimic the pattern of the time integrated air concentration field plotted in Fig. 7.
N. Nelson et al. / Atmospheric Environment 36 (2002) 3203–3215
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Fig. 9. Monthly scaling factors of wet deposition of 137Cs corresponding to Fig. 7. The wet deposition charts show the results of the spatial variability in rainfall, as represented in the UM. This is somewhat smoothed from reality, but is acceptable for long averaging periods. Wet deposition processes are very efficient at removing material from the atmosphere. Consequently the maximum deposition value is generally about an order of magnitude higher than the dry deposition figure.
Finally, Table 3 lists some results for the accumulated figures at a number of specified localities. It is interesting to note that losses from the boundary layer into the free troposphere can be quite significant, and must result, ultimately, in wider dispersal and rainout of material. An anonymous referee (to whom we owe our thanks for taking the trouble to make appropriate and we think broadly accurate estimates) found that for typical mean boundary depth values (1000 m) and wind strengths (10 m s1) simple budgetary calculations led to the conclusion that something approaching 40% of the material crossing the 1.0E-3 dosage contour disappeared in this way before the 1.0E-4 contour was reached. This is a distance of about 1400 km. Large-scale transfers into the free troposphere are effected by the numerical model’s continual recalculation of the boundary layer depth due to diurnal and along-wind changes, which will interact with the mean vertical wind velocity (see Fig. 13 of Maryon et al., 1999 for an illustration) and less importantly, the turbulent dispersion routines. This present study of course integrates the effect of the innumerable active depressions with the associated uplift of airborne material. It can also be seen that the ratio of dry to wet deposition decreases substantially with distance. This is because both dry and wet depositions reflects
the boundary layer pollutant concentrations near the source. At greater distances, the dry deposition will still reflect the (much reduced) boundary layer concentrations, as does the wet deposition washout. The wet deposition rainout, however, engages those areas of relatively undispersed and undepleted tropospheric pollutant identified in the preceding paragraph, resulting in the lower dry to wet ratio at larger distances.
6. Conclusions The NAME model has been used successfully to generate estimates of scaling factors of time-integrated air concentrations and dry and wet depositions for Sellafield’s routine emissions of radioactive material to the air. Actual concentration and deposition predictions for specified locations in the study domain may be obtained by multiplying the scaling factors by the discharge specified for any given situation. The use of the scaling factors for routine assessment assumes certain climatological weather conditions. One would not expect to observe this on a monthly timescale, as seen by the strong inter-monthly variations in the values and spatial extent of scaling factors of all fields (compare, for example, Figs. 7–9 for January 1996 with
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Fig. 10. Annual scaling factors for time integrated air concentrations (referred to as Dosage on the charts) of 137Cs for 1995: (a) release at 120 m, (b) release at 10 m. As expected, the charts show very similar spread patterns at any distance from the source in spite of the release height differences. The difference in the near field maximum concentrations occurs as a consequence of the difference in release heights. This is to be expected for time integrated air concentrations close to the source.
N. Nelson et al. / Atmospheric Environment 36 (2002) 3203–3215
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Fig. 11. As Figure 10 for 1996.
the annual charts). Consequently, the monthly values would not be used for routine assessment purposes. The annual plots, however, show much reduced noise, and the minimal variation between the two years studied give confidence in their suitability for routine assessment work.
Much of the prevailing weather for the two years studied was shown to be typical of the 10-yr average. The wind direction for 1996 in particular showed a slight bias towards easterly flows, but the effect was a fairly minor one, as can be assessed from Figs. 10 and 11.
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Fig. 12. Annual scaling factors for dry deposition of 137Cs for 1996, release at 120 m. Again, the dry deposition exhibits a similar pattern to the corresponding time integrated air concentrations, with little noise displayed.
Fig. 13. Annual scaling factors of wet deposition of 137Cs for 1996, release at 120 m. The annual wet deposition scaling factors show a fairly consistent elliptical pattern radiating from the Sellafield site, while exhibiting a little more noise than other charts. This simply reflects the levels of spatial variability of rainfall patterns not present in the wind fields.
N. Nelson et al. / Atmospheric Environment 36 (2002) 3203–3215 Table 3 Time-integrated air concentration, dry and wet depositions values for 3
Copenhagen Oslo Bonn Vienna Paris Budapest Brussels Geneva Stockholm London Manchester Glasgow Belfast Dublin
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137
Cs at various European locatities during 1995
TIAC (Unit m )
Dry deposition (Unit m2)
Wet deposition (Unit m2)
3.76 103 2.04 103 3.88 103 7.99 104 4.85 103 5.29 104 6.83 103 1.32 103 1.25 103 1.31 102 1.03 101 4.12 102 4.53 102 2.48 102
3.79 106 2.06 106 3.91 106 8.03 107 4.89 106 5.33 107 6.88 106 1.33 106 1.26 106 1.31 105 1.04 104 4.15 105 4.58 105 2.51 105
5.08 105 2.65 105 3.20 105 6.73 106 2.40 105 3.68 106 3.74 105 1.58 105 1.24 105 4.91 105 2.40 104 2.16 104 1.98 104 8.47 105
Acknowledgements This work was sponsored by British Nuclear Fuels plc, Risley, Warrington, UK.
References Berkowicz, R., Prahm, 1982. Sensible heat flux estimated from routine meteorological data by the resistance method. Journal of Applied Meteorology 21, 1845–1864. Derwent, R.G., Simmonds, P.G., O’Doherty, S., Ciais, P., Ryall, D.B., 1998. European source strengths and northern hemisphere baseline concentrations of radiatively active trace gases at Mace head, Ireland. Atmospheric Environment 32 (21), 3703–3715. Girardi, F., et al., 1998. ETEX. The european tracer experiment. JRC Environment Institute, EC EUR 18143 EN. Graziani, G., Klug, W., Mosca, S., 1998. Real-time long-range dispersion model evaluation of the ETEX first release. JRC Environment Institute, EC EUR 17754 EN. Klug, W., Graziani, G., Grippa, G., Pierce, D., Tassone, C. (Eds.), 1992. Evaluation of long range atmospheric transport models using environmental radioactivity from the Chernobyl accident. CEC, Elsevier Applied Science. Maryon, R.H., Buckland, A.T., 1994. Diffusion in a lagrangian multiple particle model: a sensitivity study. Atmospheric Environment 28 (12), 2019–2038. Maryon, R.H., Ryall, D.B., Malcolm, A.L., 1999. The NAME 4 dispersion model: science documentation. UK Met Office, Turbulence & Diffusion Note No 262.
Maryon, R.H., Smith, F.B., Conway, B.J., Goddard, D.M., 1991. The UK nuclear accident model. Progress in Nuclear Energy 26 (2), 85–104. Mosca, S., Bianconi, R., Bellasio, R., Graziani, G., Klug, W., 1998. ATMES II-Evaluation of long-range dispersion models using data of the 1st ETEX release. JRC Environment Institute, EC EUR 17756 EN. Nielsen, L.B., Prahm, L.P., Berkowicz, R., Conradsen, K., 1981. Net incoming radiation estimated from hourly global radiation and/or cloud observations. Journal of Climatology 1, 255–272. Ryall, D.B., Maryon, R.H., 1997. Validation of the UK Met Office’s NAME model against the ETEX dataset. In: Nodop, K. (Ed.), Proceedings of the ETEX symposium on long range atmospheric transport, model validation and emergency response.. Office for Official Publications of the European Communities, Luxembourg, pp. 151–154. Ryall, D.B., Maryon, R.H., 1998. Validation of the UK Met office’s NAME model against the ETEX dataset. Atmospheric Environment 32 (24), 2381–2383. Smith, F.B., 1973. NATO C.C.M.S., Air Pollution No. 14 (Chapter 17). Smith, F.B., 1983. Programme to determine boundary layer parameters, UK Met Office, Met.O.14, unpublished. Smith, F.B., Blackall, R., 1979. Mathematical Modelling of Turbulent Diffusion in Environment, Academic Press, London, pp. 201–236. Smith, F.B., Maryon, R.H., 1992. Characterisation of plumes at long range. Air pollution modelling and its application IX, Proceedings of the 19th NATO/CCMS International Technical Meeting, Ierapetra, 1991, pp. 595–604.
Assessment of routine atmospheric discharges from the Sellafield nuclear installation—Cumbria UK N. Nelson*, K.P. Kitchen, R. Maryon The Met Office, Environmental Consultancy Group, London Road, Bracknell, Berkshire RG12 2SY, UK Received 13 August 2001; received in revised form 19 December 2001; accepted 4 January 2002
Abstract The Met Office’s long-range dispersion model, the Nuclear Accident Model (NAME), has been used to assess the impact of routine atmospheric radioactive discharges from the Sellafield nuclear installation on the Cumbrian coast of England (541 25.00 31 30.20 W). The model has produced values of scaling factors for concentrations and depositions over northwest Europe. Whilst the dispersion model was designed and has been used extensively in the past for examining specific incidents of a nuclear nature, this is the first time the NAME model has been used to provide a detailed climatological impact assessment for a nuclear installation. Crown Copyright r 2002 Published by Elsevier Science Ltd. All rights reserved. Keywords: Dispersion model; Radioactive discharges; Deposition; Emissions; Boundary layer depth; Stability
1. Introduction The fate of accidental or routine emissions of radioactive material from nuclear installations is a topic of great current interest and concern. This question was tackled from a probabilistic angle in Smith and Maryon (1992). The present paper addresses the likely transports of atmospheric emissions from BNFL’s Sellafield plant in the climatological sense, using a later version of the same dispersion model, the Met Office’s medium to long-range dispersion model, the ‘nuclear accident model’—‘NAME’. Developed as a nuclear accident response model, NAME is used here to provide a detailed analysis of Sellafield’s routine emissions of radioactive material to the air. The model was run for a two-year simulation using a constant one unit per second emissions release rate for a variety of nuclides representing typical half-lives and deposition characteristics of routinely emitted material.
*Corresponding author. E-mail address: noel.nelson@metoffice.com (N. Nelson).
As the exact emission details will change each year, using a unit release for the study effectively provides a set of scaling factors for time integrated air concentrations, dry and wet depositions. These can be multiplied by typical annual emission rates to assess the long-term impact of routine emissions under any emissions scenario. The scaling factors cover the area of northwest Europe. A detailed comparison is presented of the meteorology experienced within the twoyear study period with the long-term climatological statistics. This is to ensure that the results for the two years studied are representative of the long-term climatology.
2. Model description The Met Office’s NAME model (Maryon et al., 1991) is used to simulate the medium and long-range transport and deposition of a range of airborne pollutants. The model provides estimates of instantaneous and time integrated air concentrations, together with estimates of the deposition of pollutants to the ground by both wet
1352-2310/02/$ - see front matter Crown Copyright r 2002 Published by Elsevier Science Ltd. All rights reserved. PII: S 1 3 5 2 - 2 3 1 0 ( 0 2 ) 0 0 1 8 2 - 6
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and dry deposition processes. Wind fields and other meteorological data are generally obtained from global, regional and mesoscale versions of the Met Office’s Numerical Weather Prediction (NWP) model, the Unified Model (UM). The NAME model was developed following the Chernobyl incident. As a World Meteorological Organisation (WMO) Regional Specialist Met Centre, the Met Office has international responsibilities for providing forecasts in the event of nuclear or other large scale emergencies. In addition to nuclear accident response, current applications include forecasting the transport of volcanic ash for the WMO Volcanic Ash Advisory Centre, and studies of the sources, transport and dispersion of a wide range of airborne pollutants. Air chemistry modules are under development to facilitate the application of NAME to air quality issues. The NAME model is a Lagrangian multiple particle type (Maryon et al., 1999) simulating the release and spread of pollutants by releasing large numbers of model particles into the ‘model atmosphere’, that is, the global meteorological fields as assimilated and stored by the UM. Each of these model particles carries with it the masses or activity of a number of species, reflecting the required emission profiles. These model particles are then advected in three dimensions by the model wind field and diffused using advanced random walktype parametrizations. As the particles are transported in time and space, they may experience changes in the mass and activity they carry due to dry and wet depositions, radioactive decay and chemical transformations. These can all contribute to the depletion of airborne pollutants. Within the model these losses are applied on a particle basis, i.e. the mass of each model particle is reduced each timestep, and for deposition processes the depleted mass added to surface deposition maps. The model particles are never deposited in toto, and lost to the integration; they simply lose a proportion (large or small) of the mass they carry. Concentrations in air are computed as boundary-layer averages. This impact study was carried out on a Hewlett Packard (HP) C180 workstation running HP-UX10. The NAME model is written in standard FORTRAN77 with HP extensions. The model configuration used in this study required in the region of 0.5 Gb of memory and 2 Gb of on-line disk storage. The model computations were performed over a 5-month period, using in the region of 1.5 months CPU. 2.1. Previous model validation Like any other model NAME is not without its own sources of error. Apart from forecast error (not applicable in this study) possible sources include model
error as a consequence of numerical model design and ‘irreducible’ error caused by the variable and random nature of the atmosphere, especially turbulent and other small-scale processes, which no model can totally account for. Model error can include interpolation errors in time and space, limitations of the integration and diffusion schemes, and inaccuracies in representing the source terms. The validation of long-range dispersion models is notoriously difficult. For example, Klug et al. (1992), under the auspices of the European Commission (EC), WMO, and International Atomic Energy Authority (IAEA), attempted to use observed radioactivity following the Chernobyl disaster. However, the study suffered from the uncertainties in our knowledge of the Chernobyl source term and reflected the relatively early stage of development of many of the European dispersion models. It pointed the way to model improvement and development. It also prompted the European Tracer Experiment (ETEX, and ATMES-II), which made a more systematic attempt under the same sponsors to validate these models using controlled releases of tracer (see Girardi et al., 1998; Graziani et al., 1998; Mosca et al., 1998; Ryall and Maryon, 1997, 1998, 1999). Many of the most prominent models on the international scene participated in ETEX, and NAME was clearly in the front rank of the models, as can be seen from the validation statistics published in the references quoted. The correlation between observed and modelled plume-spread was notably strong. Reference to pages 396 and 404 of Mosca et al. (1998) indicate that the overall bias of NAME is very small, with overpredictions fewer but more marked than the underpredictions. The number of predictions within factors of 2 and 5 of the observations was ‘the highest in the ATMES II exercise’ (p. 404), that is, out of more than 40 entries from the international dispersion modelling community. NAME has also successfully been used to simulate the long-term measurements of several trace gases at the monitoring site at Mace Head on the West Coast of Ireland (Ryall et al., 1998; Derwent et al., 1998). The species analysed included the halocarbons CFC-11, CFC-12, CFC-113, methyl chloroform and carbon tetrachloride, together with trace gases methane, carbon monoxide and nitrous oxide. NAME successfully reproduced the main characteristics of the observed hourly fluctuations in the concentrations recorded at Mace Head over a two year period, and was adapted for extensive investigations into the sources and rates of emission of these substances. Because NAME has performed well in past validation studies, the level of confidence given to the scaling factors obtained in this study was reasonably high.
N. Nelson et al. / Atmospheric Environment 36 (2002) 3203–3215
2.2. Advection and diffusion A number of advection and diffusion schemes are available within the NAME model, incorporating shortrange effects on dispersion (i.e. turbulence effects within the boundary layer and plume rise). At the ranges being considered in the study, sophisticated schemes developed for near-source plumes are not required and a simple fixed timestep long-range scheme was used (Maryon et al., 1999). This advects the model particles using interpolated model winds with a small perturbation to reflect sub-grid scale diffusion. This scheme uses a diffusivity based on ambient conditions, and was found to be quite comparable with the more sophisticated schemes over long range. Any other option would have been prohibitively expensive for the prolonged integrations required for this study. 2.3. Dry deposition The basis of the parameterisation is that the flux F of pollutant to the ground is proportional to the concentration C of the pollutant within the boundary layer: F ¼ ud C
ð1Þ
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sphere to the ground. Two main processes are involved: washout, where material is ‘swept out’ by falling precipitation; and rainout, where material is absorbed directly into cloud droplets as they form by acting as cloud condensation nuclei. Rainout coefficients are dependent on the precipitation phase (whether water or ice) and on the mechanisms for droplet growth, which differ for dynamic (frontal) and convective (as generated from surface heating) clouds. Washout coefficients are also dependent on the precipitation type, for example snow flakes ‘sweep’ out aerosol more efficiently than raindrops. Enhanced removal occurs where precipitation is orographically enhanced by the seeder/feeder mechanism. This last process was omitted from the present study, which again is likely to have resulted in some over-estimate of air concentrations to the lee of the mountains E and N of Sellafield (corresponding to an underestimate of the deposition upon them). The rainfall data supplied by the Met Office UM is generally smoothed compared with reality. It is however an acceptable approximation when time integrated over a long period. The removal of material from the atmosphere by wet deposition processes is based on the depletion equation dC ¼ LC; dt
The constant of proportionality, ud ; is known as the deposition velocity. This quantity is not well understood for species such as 129I and 131I. The deposition velocity of the species is highly dependent on its chemical nature, which alters during transport. In the event, it was decided to compute both the maximum possible air concentrations and depositions to the surface at distances remote from the source. Omitting the dry deposition from the integrations (achieving the former) and applying a post-processing program, which determined the dry deposition from the time series of 15-min values of air concentration everywhere in the domain achieved latter. This should have produced conservative estimates, i.e. over-estimates, of the time-integrated air concentrations and dry deposition likely to occur at remote sites.
where r is the rainfall rate and A and B are coefficients defined for different types of precipitation (e.g. dynamic, convective, rain and snow), and the two different deposition processes, rainout and washout. All wet depositing species used the following washout and rainout coefficients in Table 1. The coefficients, based on observational data and detailed cloud modelling, were supplied by Dr. T. Choularton’s team at UMIST, who collaborated with the Met Office during the model development.
2.4. Wet deposition
2.5. Radio-active decay
For most pollutants wet deposition is the dominant means by which material is removed from the atmo-
For physically unstable materials the activity of the species is depleted by radioactive decay. The governing
ð2Þ
where t is time, and L the scavenging coefficient defined: L ¼ ArB ;
ð3Þ
Table 1 Scavenging coefficients used in the NAME model, r the rainfall rate is in mm/h
Washout Rainout
L (s1) Rain (below freezing level)
L (s1) Snow (above freezing level)
Convective
Convective
Dynamic
5 0:79
8:4 10 r 3:36 104 r0:79
Dynamic
5 0:305
8:4 105 r0:79
8:0 10 r 3:36 104 r0:79
When applied
8:0 105 r0:305
Below cloud base Within cloud
N. Nelson et al. / Atmospheric Environment 36 (2002) 3203–3215
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equation is dm m ¼ ; dt aq
ð4Þ
where q is the half-life, a is a constant (1=ln 2 ¼ 1:4427), and m is the mass of material.
emitted. Those chosen for inclusion were Krypton-85 (85Kr), Caesium-137 (137Cs), Iodine-129 (129I), Iodine131 (131I) and Argon-41 (41Ar). Their properties are summarised in the Table 2 below.
4. Meteorology 3. Emissions In the NAME model the pollutant is represented by large numbers of model particles released into the model atmosphere from any number of sources. Each particle or parcel of air represents a proportion of the mass or activity of a number of pollutant species. Two emission heights were chosen for the modelling, so as to reflect the nature of emissions from the Sellafield site accurately. The heights chosen were 120 and 10 m, reflecting the range of stack heights to be found on the site. The 10 m source also incorporates the effect of any possible low level emissions. Real release rates were not used in this study, as the exact emission details will change each year. Instead, a single unit release per second of each species was incorporated (for each of the two sources), which effectively provided a set of scaling factors reflecting total annual emissions for any particular year of interest. These factors could then be applied to estimate time integrated air concentrations, dry depositions and wet depositions for any source scenario. The Met Office archives contain a limited number of years of useful NWP model data in a form suitable for use within the NAME model. Also, the NAME model is computationally expensive to run. For these reasons the emission from Sellafield for two contiguous years was simulated. The representivity of the two years chosen in relation to long-term typical values is discussed below. It was not possible to model all of the species routinely emitted from Sellafield due to memory and other computational restraints. However, a selection of species was made, which satisfactorily reflected the halflives and estimated deposition velocities and wet deposition characteristics of the range of nuclides
Weather data for the period for the two years 1 January 1995–31 December 1996 were used for this study. Data are available from various versions of the UM covering a number of geographical areas. The regional version was selected for this study. This version has the required spatial and temporal coverage and provides fields of analysed data at 3-h intervals. The resolution of the grid is approximately 50 km in both longitudinal and latitudinal directions and incorporates an area from the eastern seaboard of the USA to Eastern Europe. (The UM regional grid has recently been superceded following enhancements to the Global and Mesoscale versions of the UM, but it was the best grid for application at the time this study was carried out.) Data from the NWP model include variables such as temperature, wind speed and direction, precipitation rates, surface heat flux and so on. The correct determination of boundary layer depth is important for modelling the dispersion of airborne pollutants and the resultant deposition to the ground (Maryon and Buckland, 1994). For example, turbulent diffusion is significantly enhanced in the boundary layer, and only material in the boundary layer is subject to dry deposition. The transport, concentration and deposition of material can all be adversely affected if the boundary layer depth is incorrectly diagnosed. In NAME, fields of boundary layer depths are calculated from wind and temperature profiles, using either a Richardson number or parcel technique (Maryon et al., 1999). 4.1. Representivity of meteorology used The results obtained in this analysis were based on the meteorology that occurred over the two-year period
Table 2 Species characteristics Species 137
Cs I 131 I 41 Ar 85 Kr 129
Half life (s) 8
9.461 10 4.95 1014 6.948 105 6.57 103 3.373 108
Dry deposition velocity (m s1) 3
1.0 10 1.0 102 1.0 102 — —
Wet depositing
Comment
Yes Yes Yes No No
Long lived, reactive Long lived iodine Short lived iodine Short lived, unreactive Long lived, unreactive
N. Nelson et al. / Atmospheric Environment 36 (2002) 3203–3215
Estimates of stability category at night have been calculated from the wind speed and modified total cloud amount using empirical equations from an analysis of conditions at night measured at Cardington, Bedfordshire (Smith, 1973, 1983). Estimates of boundary layer depth in stable and unstable conditions have been calculated from equations published by Smith and Blackall (1979), which depend on estimates of sensible heat flux and friction velocity. To obtain a long-term climatology, data were extracted from the Ronaldsway (Isle of Man) and Valley (Anglesey) meteorological observation sites. These record weather giving the nearest approach to that of the Sellafield area. 4.1.1. Stability Figs. 1 and 2 show the results of the comparisons for stability. The 10-yr average extracted from both stations showed a clear predominance of stability class D (neutral) and E: The stability class D appeared to be split between day and night (approximately 40%
G
F
E
t) gh
y)
(n i
(d a
C
D
D
B
A
% Frequency
45 40 35 30 25 20 15 10 5 0
Stability Category 1996
1995
1987-1996
Fig. 1. Frequency of stabilities–Ronaldsway.
Frequency of Stabilities - Valley
G
F
ht D
(n
ig
E
)
) D
(d
ay
C
B
45 40 35 30 25 20 15 10 5 0 A
1. modified total cloud amounts from the observations describing the amount and type of each significant cloud layer, using the method given by Nielsen et al. (1981); 2. the net flux of solar radiation estimated for each hour. This is based on the date, time and location of the observation together with the modified total cloud amount, using equations based on solar radiation measurement published by Nielsen et al. (1981); 3. the number of dry days since significant rain fell. This is derived from the hourly rainfall amounts. The accumulated net solar radiation since significant rain fell is also calculated; 4. the sensible heat flux. This is calculated using a Penman–Monteith combination model for evaporation in the form suggested by Berkowicz and Prahm (1982). For this model it is necessary to estimate the surface resistance which is a function of accumulated net solar radiation.
Frequency of Stabilities - Ronaldsway
% Frequency
1995–1996. How well the results represent the impact of routine releases will in part depend on how typical the weather was during the two study years. Observations recorded from suitable locations for these two years were accordingly compared with 10-yr averages over the period 1987–1996. The meteorological variables chosen for the comparison were those which would have the most immediate bearing on the movement and dispersion of pollutants in the atmosphere. These comprised atmospheric stability, represented here by estimates of the Pasquill–Gifford stability categories, wind direction and boundary layer height. The estimates of stability category during the daytime required:
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Stability Category 1995
1996
1987 - 1996
Fig. 2. Frequency of stabilities–Valley.
and 30%, respectively). Stability class E was the next most predominant category, occurring approximately 13% of the time. The very high incidence of stability category D probably reflects the coastal location of the observing sites, although there may also have been some systematic effects from the techniques applied. It is important to bear in mind that it is the comparison which is the object of this exercise, not the absolute accuracy of the estimates: for the purpose of comparing the observations from the years 1995/6 with the decadal values the approach used is perfectly valid. The two study years were in good agreement for both stations. The daytime stability class D predominated for 1995, 1996 and the decade (for approximately 41% of the time). A nocturnal neutral stability D category occurred around 27% and 29% of the time, which was also in good agreement with the 10-year average. The two study years also show good agreement with the average in recording stability E approximately 12–14% of the time at both stations.
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Frequency of Wind Directions - Ronaldsway
30 31 -6 0 61 -9 91 0 -1 12 20 11 15 50 11 18 80 12 21 10 12 24 40 12 27 70 13 30 00 13 33 30 136 C 0 AL M
1
Wind Direction Category (Degs) 1996
1995
1987-1996
Fig. 3. Frequency of wind directions–Ronaldsway.
0 21 0 12 24 40 12 27 70 13 30 00 13 33 30 136 C 0 AL M 1-
18
21
0 15
18
1-
1-
15
0
20 -1
91
12
0
-9
-6
61
31
to
30
% Frequency
Frequency of Wind Directions - Valley 20 18 16 14 12 10 8 6 4 2 0 1
*
*
to
% Frequency
16 14 12 10 8 6 4 2 0
For 1996 a 6% increase in winds from the east to northeast occurred (13%) compared with the decadal figures, in agreement with the situation at Ronaldsway. As with Ronaldsway the percentage of calms lay between 1% and 3%.
Pollution episodes are often associated with very low wind speeds. The low percentage of calms is to be expected with stations situated in a relatively well exposed area of the country. Both stations would experience broadly similar wind exposures to the site at Sellafield. Lower category wind speeds (i.e. in the range 1–3 knots—not shown) were also investigated. They occurred between 6% and 9% of the time and showed good agreement between stations and the different years. To summarise, there is generally good agreement between the 10-yr average and the two study years. However, for both Ronaldsway and Valley, the study year of 1996 differed from the 10-yr average in that a greater number of easterly winds (61–901) occurred. Consequently winds used for the study period would impact rather more on locations west–southwest of Sellafield than would normally be expected, although in percentage terms the effect was not large.
Wind Direction Category (Degs) 1995
1996
1987-1996
Fig. 4. Frequency of wind direction–Valley.
4.1.2. Wind direction The results of the analysis of wind direction can be seen in Figs. 3 and 4. For Ronaldsway, *
*
*
*
Over the 10-yr period the most predominant wind sector lay between south and west. This compares well with the results for 1995 where the same wind sector predominates. In 1996 the most frequent wind direction was more east to northeast (61–901)—a 4.1% increase over the decadal value. The next two most frequently occurring directions lay within the same sectors for each of the study periods—for 1995, 1996 and for the ten year average. For all three cases the frequency of calm conditions in these coastal locations is relatively low, accounting for between 1% and 3% of the time. For Valley,
*
There is good agreement between the most frequently recorded winds for 1995 and the 10-yr average.
4.1.3. Boundary layer height The results for the boundary layer height are given below in Figs. 5 and 6, which show for Valley and Ronaldsway the percentage occurrence of categories of boundary layer height for the two study years and the 10-yr average. These heights were estimated as described in Section 4.1. It can be seen that for both stations and for each of the three study periods there is very good agreement. In all cases the most frequently occurring height falls in the range above 2000 m. This seems to be consistent with the high incidence of neutral/unstable conditions diagnosed in Section 4.1.1. The next most frequent height was assessed as being in the range 1200– 1400 m, the next 1000–1200 m. Note also the occasions where the boundary layer is low—indicative of potentially bad air quality episodes. In all cases these account for between 5% and 8% of the time. Such stable conditions may well have been a little more frequent at Sellafield than at Ronaldsway or Valley: this would have been reflected in the UM meteorology used in the integrations. The scheme used for the height calculations tended to produce somewhat deeper boundary layer heights than would be expected. As this analysis is concerned with the representivity, it is the comparison of results rather than the specific values themselves that are of particular interest. This bias would constitute a systematic error that would not effect the actual comparison.
N. Nelson et al. / Atmospheric Environment 36 (2002) 3203–3215 Freqency of Boundary Layer Depths - Ronaldsway % Frequency
25 20 15 10 5
0. 120 200 0. 140 400 0. 160 600 0. 1 80 -80 0. 1- 0 10 1 00 000 .1 12 -12 00 0 .1 0 14 -14 00 0 .1 0 16 -16 00 0 .1 0 18 -18 00 0 .1 0 -2 00 20 0 00 .1 +
0
Boundary Layer Height (m) 1995
1996
1987-1996
Fig. 5. Percentage frequency occurance of boundary layer depth–Ronaldsay.
0 40 0. 0 1 60 -60 0. 0 80 1-8 0. 00 10 1-1 00 00 0 . 12 1-1 00 20 0 . 14 1-1 00 40 0 . 16 1-1 00 60 0 . 18 1-1 00 80 .1 0 -2 0 20 00 00 .1 + 40
20
0.
20
10.
1-
% Frequency
Frequency of Boundary Layer Depth - Valley 20 18 16 14 12 10 8 6 4 2 0
Boundary Layer Height (m) 1995
1996
1987-1996
Fig. 6. Percentage frequency occurrence of boundary layer depth–Valley.
5. Results The monthly fields of accumulated air concentration, dry deposition and wet deposition were output for all species onto a gridded dataset of resolution 0.091 in the latitudinal direction and 0.161 in the longitudinal direction. This corresponds to a physical grid size of around 10 10 km, depending on the latitude of the grid cell (for Lagrangian models the output resolution can be defined independently of that of the input meteorology). The origin of the grid was 651 north 151 west, giving coverage of the northwest of Europe and part of the Atlantic Ocean. As only accumulations were output, the values of time-integrated air concentrations and depositions for particular months of interest could easily be calculated by subtracting the values of the previous month. In order to be able to calculate such monthly figures, the material deposited to the surface was not allowed to
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undergo radioactive decay, as would normally be the practice using the NAME model. The two-year NAME simulation of monthly timeintegrated air concentrations and estimates of deposition to the surface of all the simulated species generated a great deal of output. Apart from some sample monthly charts, only annual statistics, which constitute the estimated scaling factors, are presented here, and these only for 137Cs. The results for the other species were very similar in overall spread pattern to the 137Cs and, as a result, were not presented in this paper; they are however available from the authors. Sample monthly output for the time-integrated air concentration and dry and wet depositions to the surface are shown for January 1996, for the 120 m source, in Figs. 7–9, respectively. Figs. 10a and b illustrate the annual time-integrated boundary-layer averaged air concentrations, and show that the results for the 120 and 10 m stack emissions are almost identical for 1995. The only significant difference between the releases at the two levels is that the value of the maximum measured time integrated air concentrations predicted for the short stack release, 1.35 Unit m3, is greater than for the taller stack, 1.03 Unit m3). This is to be expected, as for each case the maximum value will lie in the immediate vicinity of the Sellafield site, where the height of the release will affect ground level concentrations. Generally, a short stack will produce relatively high near-field concentrations compared with a taller stack. For distant locations the height of the release is much less critical, the pollutant having had sufficient time in the atmosphere to be evenly mixed through the boundary layer. Figs. 11a and b show the same results as Figs. 10a and b but for 1996. These annual charts for air concentration show a broadly elliptical distribution of the isopleths around the release point, the precise shape of which is a function of wind direction. As no loss processes were applied in the course of these integrations, the fall-off of concentration reflects only transport and diffusive processes, including detrainments of material out of the boundary layer. For any realistic situation these scaling factors must accordingly be interpreted as conservative, i.e. on the high side. In view of the similarity in the results, only the 120 m release for 1996 is shown in Figs. 12 and 13, which illustrate the annual dry and wet depositions, respectively. It will be recalled these were computed by postprocessing the air concentration data. Dry deposition reflects the ambient air concentration, and so the shape of the distribution is very similar to that of Fig. 10. The wet deposition reflects the irregular rainfall patterns occurring, and is less smooth. Note that over wide areas the accumulated wet deposition is about an order of magnitude higher than the dry.
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N. Nelson et al. / Atmospheric Environment 36 (2002) 3203–3215
Fig. 7. Monthly scaling factors of time integrated air concentrations (referred to as Dosage on the charts) of 137Cs from the release at 120 m for the month of January 1996. A sample monthly chart, showing how the scaling factors differ from the annual. Note January experienced little in the way of northerly winds for the whole month, the bulk of the material having been carried to the north and west of Sellafield.
Fig. 8. Monthly scaling factors of dry deposition of 137Cs corresponding to Fig. 7. The deposition field is a function of integrated air concentration, and therefore can be expected to mimic the pattern of the time integrated air concentration field plotted in Fig. 7.
N. Nelson et al. / Atmospheric Environment 36 (2002) 3203–3215
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Fig. 9. Monthly scaling factors of wet deposition of 137Cs corresponding to Fig. 7. The wet deposition charts show the results of the spatial variability in rainfall, as represented in the UM. This is somewhat smoothed from reality, but is acceptable for long averaging periods. Wet deposition processes are very efficient at removing material from the atmosphere. Consequently the maximum deposition value is generally about an order of magnitude higher than the dry deposition figure.
Finally, Table 3 lists some results for the accumulated figures at a number of specified localities. It is interesting to note that losses from the boundary layer into the free troposphere can be quite significant, and must result, ultimately, in wider dispersal and rainout of material. An anonymous referee (to whom we owe our thanks for taking the trouble to make appropriate and we think broadly accurate estimates) found that for typical mean boundary depth values (1000 m) and wind strengths (10 m s1) simple budgetary calculations led to the conclusion that something approaching 40% of the material crossing the 1.0E-3 dosage contour disappeared in this way before the 1.0E-4 contour was reached. This is a distance of about 1400 km. Large-scale transfers into the free troposphere are effected by the numerical model’s continual recalculation of the boundary layer depth due to diurnal and along-wind changes, which will interact with the mean vertical wind velocity (see Fig. 13 of Maryon et al., 1999 for an illustration) and less importantly, the turbulent dispersion routines. This present study of course integrates the effect of the innumerable active depressions with the associated uplift of airborne material. It can also be seen that the ratio of dry to wet deposition decreases substantially with distance. This is because both dry and wet depositions reflects
the boundary layer pollutant concentrations near the source. At greater distances, the dry deposition will still reflect the (much reduced) boundary layer concentrations, as does the wet deposition washout. The wet deposition rainout, however, engages those areas of relatively undispersed and undepleted tropospheric pollutant identified in the preceding paragraph, resulting in the lower dry to wet ratio at larger distances.
6. Conclusions The NAME model has been used successfully to generate estimates of scaling factors of time-integrated air concentrations and dry and wet depositions for Sellafield’s routine emissions of radioactive material to the air. Actual concentration and deposition predictions for specified locations in the study domain may be obtained by multiplying the scaling factors by the discharge specified for any given situation. The use of the scaling factors for routine assessment assumes certain climatological weather conditions. One would not expect to observe this on a monthly timescale, as seen by the strong inter-monthly variations in the values and spatial extent of scaling factors of all fields (compare, for example, Figs. 7–9 for January 1996 with
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Fig. 10. Annual scaling factors for time integrated air concentrations (referred to as Dosage on the charts) of 137Cs for 1995: (a) release at 120 m, (b) release at 10 m. As expected, the charts show very similar spread patterns at any distance from the source in spite of the release height differences. The difference in the near field maximum concentrations occurs as a consequence of the difference in release heights. This is to be expected for time integrated air concentrations close to the source.
N. Nelson et al. / Atmospheric Environment 36 (2002) 3203–3215
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Fig. 11. As Figure 10 for 1996.
the annual charts). Consequently, the monthly values would not be used for routine assessment purposes. The annual plots, however, show much reduced noise, and the minimal variation between the two years studied give confidence in their suitability for routine assessment work.
Much of the prevailing weather for the two years studied was shown to be typical of the 10-yr average. The wind direction for 1996 in particular showed a slight bias towards easterly flows, but the effect was a fairly minor one, as can be assessed from Figs. 10 and 11.
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Fig. 12. Annual scaling factors for dry deposition of 137Cs for 1996, release at 120 m. Again, the dry deposition exhibits a similar pattern to the corresponding time integrated air concentrations, with little noise displayed.
Fig. 13. Annual scaling factors of wet deposition of 137Cs for 1996, release at 120 m. The annual wet deposition scaling factors show a fairly consistent elliptical pattern radiating from the Sellafield site, while exhibiting a little more noise than other charts. This simply reflects the levels of spatial variability of rainfall patterns not present in the wind fields.
N. Nelson et al. / Atmospheric Environment 36 (2002) 3203–3215 Table 3 Time-integrated air concentration, dry and wet depositions values for 3
Copenhagen Oslo Bonn Vienna Paris Budapest Brussels Geneva Stockholm London Manchester Glasgow Belfast Dublin
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137
Cs at various European locatities during 1995
TIAC (Unit m )
Dry deposition (Unit m2)
Wet deposition (Unit m2)
3.76 103 2.04 103 3.88 103 7.99 104 4.85 103 5.29 104 6.83 103 1.32 103 1.25 103 1.31 102 1.03 101 4.12 102 4.53 102 2.48 102
3.79 106 2.06 106 3.91 106 8.03 107 4.89 106 5.33 107 6.88 106 1.33 106 1.26 106 1.31 105 1.04 104 4.15 105 4.58 105 2.51 105
5.08 105 2.65 105 3.20 105 6.73 106 2.40 105 3.68 106 3.74 105 1.58 105 1.24 105 4.91 105 2.40 104 2.16 104 1.98 104 8.47 105
Acknowledgements This work was sponsored by British Nuclear Fuels plc, Risley, Warrington, UK.
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