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Modelling the environmental dispersion of radionuclides Jordi Vives i Batlle Centre for Ecology and Hydrology, Lancaster, 1 st – 3 rd April 2014.

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Presentation on theme: "Modelling the environmental dispersion of radionuclides Jordi Vives i Batlle Centre for Ecology and Hydrology, Lancaster, 1 st – 3 rd April 2014."— Presentation transcript:

1 Modelling the environmental dispersion of radionuclides Jordi Vives i Batlle Centre for Ecology and Hydrology, Lancaster, 1 st – 3 rd April 2014

2 What happens if do not have media concentrations? Need method of predicting from release rates over a dilution pathway between source and receptor If have dispersion model can run and input predictions If not then ERICA has some screening level models built-in to enable this in Tiers 1 and 2

3 1 - Dispersion modelling in ERICA

4 Designed to minimise under-prediction (conservative generic assessment): Under no circumstances would doses be underestimated by more than a factor of ten. A default discharge period of 30 y is assumed (estimates doses for the 30 th year of discharge) Models - atmospheric, freshwater (lakes and rivers) and coastal water models available Taken from IAEA SRS Publication 19 SRS-19 is linked to ERICA help file

5 Gaussian plume model version depending on the relationship between building height & cross-sectional area of the building influencing flow Assumes a predominant wind direction and neutral stability class (=doesnt enhance or inhibit turbulence) Atmospheric dispersion Key inputs: discharge rate Q & location of source / receptor points

6 Importance of Release Height Basic dispersion equation Effective stack height

7 (a) (b) (c) Conditions for the plume a) H > 2.5H B (building height): No building effects b) H 2.5H B & x > 2.5A B ½ (cross-sectional area of building): Airflow in the wake zone c) H 2.5H B & x 2.5A B ½ : Airflow in the cavity zone. Two cases: source / receptor at same building surface not at same surface Not generally applicable at > 20 km from stack

8 Wind speed and direction 10 minute average from 10 m wind vane & anemometer Release height Precipitation 10 minute total rainfall (mm) 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 Key parameters

9 Output Radionuclide activity concentrations in air (C,H,S & P) or soil (everything else)

10 Freshwater Small lake (< 400 km 2 ) Large lake (400 km 2 ) Estuarine River Marine Coastal Estuarine No model for open ocean waters Surface water dispersion

11 Based on analytical 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 K d at receptor (equilibrium) Transportation in the direction of flow No loss to sediment between source and receptor In all cases water dispersion assumes critical flow conditions, by taking the lowest in 30 years, instead of the rate of current flow The only difference between RNs in predicted water concentrations as material disperses is decay by their different radiological half-lives. Processes and assumptions

12 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. Some restrictions related to short receptor discharge point distances (mixing zone) and length discharge pipe and angle to shoreline receptor For 10s of km maximum Condition for mixing is x > 7D and (y-y 0 )<< 3.7x concentration in sediment is assumed to be concentration in water x K d L z = distance to achieve full vertical mixing K d = Activity concentration on sediment (Bq kg -1 ) xxxxxActivity concentration in seawater (Bq L -1 ) Rivers and coastal waters

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

14 Surface area >400 km 2 As a rough rule a lake can be considered to be large when the opposite side of the lake is not visible to a person standing on a 30 m high shore. Large Lake Some restrictions related to length discharge pipe and angle to shoreline receptor, short receptor discharge point distances (mixing zone) Estimates concentration along shoreline and along plume centre line.

15 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 Equilibrium between liquid and solid phases - K d 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) which do not change significantly with distance / time End of pipe mixing zones require hydrodynamic models Limitations of IAEA SRS 19

16 2 : PC CREAM as a practical alternative for dispersion modelling

17 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 Collective dose model PC CREAM

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

19 Gaussian plume model Meteorological conditions specified by: Wind speed, Wind direction, Pasquill-Gifford stability classification Implemented in PC CREAM and CROM Model assumes constant meteorological and topographical conditions along plume trajectory Prediction accuracy 20 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 R91 aerial dispersion model

20 Marine model (DORIS) => improvement Has long-range geographical resolution Incorporates dynamic representation of water / sediment interaction Aerial model (PLUME) => no improvement Still a gaussian dispersion model unsuitable for long distances > 20 km Also assumes constant meteorological conditions Does not correct for plume filling the boundary layer Degree of improvement of the models


22 Include deviations from idealised Gaussian plume model Include turbulence data rather than simplified stability categories to define boundary layer Include particulate vs gases and chemical interactions Model includes the effects on dispersion from: Complex buildings Complex terrain & coastal regions Advanced models: ADMS, AERMOD Gaussian in stable and neutral conditions Non-Gaussian (skewed) in unstable conditions New-generation plume models

23 Modified Gaussian plume model Gaussian in stable and neutral conditions Skewed non-Gaussian in unstable conditions Boundary layer based on turbulence parameters UK ADMS Model includes: Meteorological pre-processor, 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


25 Allow for non-equilibrium 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 specialist users Post-processing required for dose calculation (use as input to ERICA) Geographically-resolving marine models

26 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, etc. Model characteristics

27 Finite differences Compartmental Model characteristics

28 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 Some commonly available models

29 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 DHI MIKE 21 model

30 Has been combined with the ERICA methodology to make realistic assessments of impact on biota Marine Environmental Advection Dispersion (MEAD) Runs on a 2-km 2-dimensional grid Input: bathymetry, wind field, sediment distribution maps Applies advection - dispersion equations over an area and time Generates long-range radioactivity predictions in water and sediment

31 Extra modules for extra processes More complex issues (eutrophication) Wave interactions Coastal morphology Particle and slick tracking analysis Sediment dynamics ModelMaker biokinetic models Dynamic interactions with the sediments Speciation Dynamic uptake in biota More complex process models


33 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) River and estuary models

34 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) Model characteristics

35 Can be 1D, 2D or 3D 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) Common models

36 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 Example - MIKE 11

37 Convert rainfall over the catchment to river flow out the catchment Represent the processes illustrated, however in two possible ways: empirical relationship from rainfall to runoff (cannot be used to simulate changing conditions) Complex physically based models where all processes are explicitly represented Example: DHI MIKE-SHE, HP1 (HYDRUS + PHREEQC) SVAT modelling Catchment modelling

38 ERICA uses the IAEA SRS 19 dispersion models to work out a simple, conservative source - receptor interaction SRS 19 has some shortcomings PC-CREAM can be used as an alternative to the SRS-19 marine model 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 – must match complexity to need Conclusions

39 U ncertainty associated with the application of aquatic SRS models : Models generally conservative. From factor of 2 to 10 difference with respect to a dynamic model. U ncertainty associated with the application of a Gaussian plume model for continuous releases: About a factor of 4 or 10 for a flat and complex terrain respectively. At distances < 2.5 times the square root of the frontal area of the building, the model provides conservative results. For distances of about 2.5 the above, the model tends to underpredict for wind speeds above 5-m s -1. Effect of using different models

40 For aerial, PC-Cream is no improvement to SRS 19 For marine, PC cream has a dynamic compartment model Effect of using such a fully dynamic model: In periods where concentrations in compartments increase, dynamic model estimates of transfer will be lower than for equilibrium model (build-up effect) In period where environmental concentrations decrease, dynamic model estimates higher than equilibrium model (memory effect) Diffcult to generalise, but differences could be up to a factor of 10. Effect of using different models (2)

41 Summary of key points SRS19 modelPC CreamOrther models MarineDORISMarine + point in coast + Large compartment box model + compartmental models for large areas + requires very few parameters + Dynamic transfer to water and sediments + Grid models for fine resolutions (small areas) - no offshore dispersion - requires more parameters + Dynamic / time-variable discharges - very simple equilibrium model (Kd based) - Does not work well at fine resolution - parameter hungry (bathimetry, gridding, etc) River, lake, reservoirN/ARiver, lake, reservoir + very simple 1D model + 2D - 3D models - only models riverbanks + Full representation of hydrodynamics - Simple average flow conditions + Can deal with tides, concentration gradients - very simple equilibrium model (Kd based) + Dynamic / time-variable discharges - Simple linear river + Complex river networks - parameter hungry (bathymetry, gridding, etc.) AerialPLUMEAERMOD, ADMS, etc. + limited range 100 m to 20 km - Same as SRS19 + Non Gaussian for unstable conditions + constant meteorology + Buildings and terrain + Gaussian plume, still conditions + Solute modelling + Complex meteorology

42 Links to alternative models

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