1. Modelling the environmental dispersion of radionuclides
Jordi Vives i Batlle Centre for Ecology and Hydrology, Lancaster, 28 April 2010 1

2. Lecture plan Dispersion models available in the ERICA Tool
Other types of dispersion models that are available
Key parameters that drive dispersion models for radioactivity in the environment
Applicability to different scenarios/circumstances (e.g. release directly to a protected site/end of pipe concentrations (e.g. mixing zones)) 2

3. What reasons to use models?
Often the receptor is not at a point of emission but is linked via an environmental pathway (dilution)
Need to predict media concentrations when (adequate) data are not available
To conduct authorisation-based assessments for the protection and conservation of species listed under the EC Birds and Habitats Directives 3

4. Part I: Dispersion modelling in ERICA4

5. IAEA SRS publication 19
Designed to minimise under-prediction (conservative generic assessment)
A default discharge period of 30 y is assumed (estimates doses for the 30th year of discharge) 5

6. Atmospheric dispersion
Gaussian plume model version depending on the relationship between building height, HB & cross-sectional area of the building influencing flow, AB
Assumes a predominant wind direction and neutral stability class
Key inputs: discharge rate Q & location of source / receptor points (H, HB, AB and x) 6

7. Atmospheric dispersion
(b)
a) H > 2.5HB (no building effects)
b) H 2.5HB & x > 2.5AB½(airflow in the wake zone)
c) H  2.5HB & x  2.5AB½(airflow in the cavity zone). Two cases:
source / receptor at same building surface
not at same surface
(c)
Not generally applicable at x > 20 km 7

8. Basic dispersion equation
Importance of Release Height 8

9. Key parameters Wind speed and direction Release height Precipitation
10 minute average from 10 m wind vane & anemometer
Release height
Precipitation
10 minute total rainfall (mm) from tipping bucket
Stability or degree of turbulence (horizontal and vertical diffusion)
Manual estimate from nomogram using time of day, amount of cloud cover and global radiation level
Atmospheric boundary layer (time-dependent)
Convective and or mechanical turbulence
Limits the vertical transport of pollutants
As you will already know emergency assessments are carried out using EAS. The met information that EAS requires to carry out an assessment is as follows:
wind direction and speed - at 10m
Rain - yes or no
Stability class estimated from nomogram or in future hope there will be an automatic calculation. Will learn more about this in the atmospheric dispersion talk in a couple of weeks. 9

10. R91 aerial dispersion model
Based on the recommendations of the Working Group on Atmospheric Dispersion (NRPB-R91, -R122, -R123, -R124)
Gaussian plume model
Meteorological conditions specified by:
Wind speed
Wind direction
Pasquill-Gifford stability classification
Implemented in PC CREAM 10

11. R91 - model limitations
Model assumes constant meteorological and topographical conditions along plume trajectory
Prediction accuracy < 100 m and > 30 km limited
Source depletion unrealistic (deposition modelling & transfer factors are uncertain)
Developed for neutral conditions
Does not include
Buildings
Complex terrain e.g. hills and valleys
Coastal effects 11

12. Surface water dispersion
Freshwater
Small lake (< 400 km2)
Large lake (≥400 km2)
Estuarine
River
Marine
Coastal
No model for open ocean waters 12

13. Surface water dispersion
Based on analytic solution of the advection diffusion equation describing transport in surface water for uniform flow conditions at steady state
Processes included:
Flow downstream as transport (advection)
Mixing processes (turbulent dispersion)
Concentration in sediment / suspended particles estimated from ERICA Kd at receptor (equilibrium)
Transportation in the direction of flow
No loss to sediment between source and receptor
In all cases water dispersion are assumed critical flow conditions, by taking the lowest in 30 years, the rate of current flow 13

14. Rivers and coastal waters
Lz = distance to achieve full vertical mixing
The river model assumes that both river discharge of radionuclides such as water harvesting is done in some of the banks, not in the midstream
The estuary model is considered an average speed of the current representative of the behaviour of the tides.
If x on the same bank side and  Lz = 7D the radionuclide
Condition for mixing is (y-y0)<<3.7x
concentration in water is assumed to be undiluted
Kd = Activity concentration on sediment (Bq kg-1) Activity concentration in seawater (Bq L-1) 14

15. Small lakes and reservoirs
Assumes a homogeneous concentration throughout the water body
Expected life time of facility is required as input 15

16. Limitations of IAEA SRS 19
Simple environmental and dosimetric models as well as sets of necessary default data:
Simplest, linear compartment models
Simple screening approach (robust but conservative)
Short source-receptor distances
More complex / higher tier assessments:
Aerial model includes only one wind direction
Coastal dispersion model not intended for open waters e.g. oil/gas marine platform discharges
Surface water models assume geometry (e.g. river cross-section) & flow characteristics (e.g. velocity, water depth) do not change significantly with distance / time
Assumes equilibrium e.g. water/sediment Kd 16

17. Part II: PC-Cream as a practical alternative for dispersion modelling17

18. Collective Dose Model - PC CREAM
Consequences of Releases to the Environment Assessment Methodology
A suite of models and data for performing radiological impact assessments of routine and continuous discharges
Marine: Compartmental model for European waters (DORIS)
Seafood concentrations => Individual doses => Collective doses.
Aerial: Radial grid R-91 atmospheric dispersion model with (PLUME) with biokinetic transfer models (FARMLAND)
Ext. & internal irradiation => foodchain transfer (animal on pasture e.g. cow & plant uptake models) => dose 18

19. Marine and aerial dispersion
Radial grid - atmospheric model
Compartmental - marine model
(continuous discharge) 19

20. Marine and aerial dispersion
Marine model (DORIS) => improvement
Has long-range geographical resolution - allows for offshore scenarios e.g. marine platform discharges
Incorporates dynamic representation of water / sediment interaction
Aerial model (PLUME) => no improvement
Still a gaussian dispersion model unsuitable for long distances (though it has been used in that way)
Also assumes constant meteorological conditions
Does not correct for plume filling the boundary layer
Must use a better model e.g. Lagrangian particle dispersion - NAME 20

21. Part III: Other alternative dispersion models21

22. Marine modelling 22

23. Geographically resolving marine models
Allow for nonequilibrium situations e.g. acute release into protected site
Advantages:
Resolves into a large geographical range
Results more accurate (if properly calibrated)
Disadvantages:
Data and CPU-hungry (small time step and grid sizes demand more computer resources)
Run time dependent on grid size & time step
Requires a more specialised type of user
Post-processing required for dose calculation (use as input to ERICA) 23

24. Model characteristics
Input requirements: Bathymetry, wind fields, tidal velocities, sediment distributions, source term
Type of output: a grid map / table of activity concentration (resolution dependent on grid size)
All use same advection/dispersion equations, differences are in grid size and time step
Types of model:
Compartmental: Give average solutions in compartments connected by fluxes. Good for long-range dispersion in regional seas.
Finite differences: Equations discretised and solved over a rectangular mesh grid. Good for short-range dispersion in coastal areas
Estuaries a special case: Deal with tides (rather than waves), density gradients, turbidity & c. 24

25. Finite differences Compartmental25

26. Readily available models
Long-range marine models (regional seas):
POSEIDON - N. Europe (similar to PC-CREAM model but redefines source term and some compartments - same sediment model based on MARINA)
MEAD (in-house model available at WSC)
Short-range marine models (coastal areas):
MIKE21 - Short time scales (DHI) - also for estuaries
Delft 3D model, developed by DELFT
TELEMAC (LNH, France) - finite element model
COASTOX (RODOS PV6 package)
Estuarine models
DIVAST ( Dr Roger Proctor)
ECoS (PML, UK) - includes bio-uptake 26

27. POSEIDON As seen previously (PC-CREAM section of the lecture)
Area of interest divided into large area boxes and transfer at boundaries is dependent on the parameters in the adjacent boxes
Contains sediment transport project (MARINA project)
Simple, quick, easy to use radionuclide transport model
Continuous discharge
Time variable discharge
Continuous leaching of an immersed solid material
Post processing for annual dose to humans is intrinsic, hence only minor coding required for determination of dose to biota 27

28. DHI MIKE21 model
Two-dimensional depth averaged model for coastal waters
Location defined on a grid - creates solution from previous time step
Hydrodynamics solved using full time-dependent non-linear equations (continuity & conservation of momentum)
Large, slow and complex when applied to an extensive region
Suitable for short term (sub annual) assessments
A post processor is required to determine biota concentrations and dose calculations 28

29. Marine Environment Advection Dispersion
Applies advection - dispersion equations over an area and time
Generates activity concentration predictions in water and sediment
Has been combined with the ERICA methodology to make realistic assessments of impact on biota
2 km grid 29

30. MEAD input data - water
Bathymetry for MEAD grid: resolution 2 km - 2 km
Residual flow field (12 month MIKE21 simulation / averaged wind conditions) 30

31. MEAD input data - sediment
Distribution of fine grained bed sediment
Distribution of suspended particles (modelled) 31

32. MEAD output - Cumbria coast
60Co in winkles
137Cs in cod / plaice
99Tc in crab
241Am in mussels
Could be used to derive CFs for use in ERICA 32

33. MEAD - long-range results
Predicted distribution of 137Cs in seawater in 2000
Predicted distribution of 137Cs in bed sediments in 2000 33

34. More complex process models
Extra modules in MIKE21
More complex water quality issues e.g. eutrophication
Wave interactions
Coastal morphology
Particle and slick tracking analysis
Sediment dynamics
ModelMaker biokinetic models
Dynamic interactions with sediment
Speciation
Dynamic uptake in biota 34

35. River and estuary modelling35

36. River and estuary models
Advantages:
Large geographical range
Consider multiple dimensions of the problem (1 - 3D)
Considers interconnected river networks
Results more accurate (if properly calibrated)
Disadvantages - same as marine models:
Data hungry
Run time dependent on grid size & time step
Requires a more specialised type of user
CPU-hungry (as time step and grid size decreases it demands more computer resources)
Post-processing required for dose calculation (use as input to ERICA) 36

37. Model characteristics
Input requirements: Bathymetry, rainfall and catchment data, sediment properties, network mapping, source term
Type of output: activity concentration in water and sediment, hydrodynamic data for river
All use same advection/dispersion equations as marine but differences in boundary conditions
Generally models solve equations to:
Give water depth and velocity over the model domain.
Calculate dilution of a tracer (activity concentration) 37

38. Common models Can be 1D, 2D or 3D models Off-the-shelf models:
1D river models: River represented by a line in downstream direction - widely used
2D models have some use where extra detail is required
3D models are rarely used unless very detailed process representation is needed
Off-the-shelf models:
MIKE11 model developed by the DHI, Water and Environment (1D model)
VERSE (developed by WSC)
MOIRA (Delft Hydraulics)
Research models:
PRAIRIE (AEA Technology)
RIVTOX & LAKECO (RODOS PV6 package) 38

39. MIKE11 - Industry standard code for river flow simulation
River represented by a line in downstream direction
River velocity is averaged over the area of flow
Cross sections are used to give water depth predictions
Can be steady flow (constant flow rate) or unsteady flow
Use of cross sections can give an estimate of inundation extent but not flood plain velocity 39

40. Aerial modelling 40

41. New-generation Gaussian plume models
Advanced models: ADMS, AERMOD
Gaussian in stable and neutral conditions
Non-Gaussian (skewed) in unstable conditions
Continuous turbulence data rather than simplified stability categories to define boundary layer
Model includes the effects on dispersion from:
Buildings
Complex terrain & coastal regions
ADMS a good choice
ADMS is a ‘ new generation’ advanced dispersion model
The difference between the traditional and ‘new generation’ models lies primarily in the methodologies for calculating the dispersion parameters within the model, i.e. sy and sz.
ADMS assumes a Gaussian distribution of concentration in the horizontal cross-wind direction and a modified Gaussian in vertical.
Plume spread depends on local wind speed and turbulence and thus on plume height.
This is in contrast to the R91 model, which assumes that plume spread is independent of height.
There is also improved physical modelling of the atmosphere...
Instead of using the Pasquill-Gifford stability classes, A to G, the stability is characterised by the boundary layer height and a turbulence scaling parameter, known as the Monin-Obukhov length (LMO). The reciprocal of the LMO (z = height above surface, usually 1 m) is used as a stability parameter:
z/L = 0 for neutral stability
z/L = for stable conditions
z/L = -5 to -1 for unstable conditions
The model also includes the effect of building downwash, complex terrain and complex coastal meteorology.
UK-ADMS published validation studies on CERC web site. 41

42. UK ADMS Modified Gaussian plume model
Gaussian in stable and neutral conditions
Skewed non-Gaussian in unstable conditions
Boundary layer based on turbulence parameters
Model includes:
Meteorological preprocessor, buildings, complex terrain
Wet deposition, gravitational settling and dry deposition
Short term fluctuations in concentration
Chemical reactions
Radioactive decay and gamma-dose
Condensed plume visibility & plume rise vs. distance
Jets and directional releases
Short to annual timescales 42

43. ADMS input Parameters Meteorological data (site specific & Met Office)
Wind speed, wind direction, date, time, latitude, boundary layer height, cloud cover
Boundary Layer Height
Height at which surface effects influence dispersion
ADMS calculates boundary layer properties for different heights based on meteorology
Monin-Obukhov Length
Measure of height at which mechanical turbulence is more significant than convection or stratification
ADMS calculates M-O length based on meteorology and ground roughness 43

44. Types of output 44

45. Terrestrial (biosphere) modelling45

46. Catchment modelling
Convert rainfall over the catchment to river flow out the catchment
Represent the processes illustrated, however in two possible ways:
Simple “black box” type model such as empirical relationship from rainfall to runoff (cannot be used to simulate changing conditions)
Complex physically based models where all processes are explicitly represented 46

47. Example Model - MIKE SHE
Integrated groundwater - surface water solution
Advanced rainfall runoff model with extensive process representation
Intense parameter demand
One of the more widely used models
A good choice when the close linkage of surface water and ground water is important to the study
Graham, D.N. and M. B. Butts (2005) Flexible, integrated watershed modelling with MIKE SHE. In Watershed Models, Eds. V.P. Singh & D.K. Frevert Pages , CRC Press. ISBN: 47

48. Conclusions
ERICA uses the IAEA SRS 19 dispersion models to work out a simple, conservative source - receptor interaction
SRS 19 have some shortcomings
PC-CREAM can be used as an alternative suite of dispersion models
There are further off-the-shelf models performing radiological impact assessments of routine and continuous discharges ranging from simple to complex
Key criteria of simplicity of use and number of parameters need to be considered 48

49. Links to alternative models49