Element cycling in aquatic ecosystems – modelling general and

element-specific transport and accumulation mechanisms.

March 2011

Lena Konovalenko

Department of Systems EcologyStockholm University

Second Supervisors:Ulrik Kautsky: Swedish Nuclear Fuel and Waste Management Co (SKB)Linda Kumblad: Stockholm University

Main Supervisor:Clare Bradshaw: Stockholm University

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Objectives

• Development of dynamic compartment model for radionuclide transport in a coastal marine ecosystem (Baltic Sea)

• Prediction of radionuclide concentrations in the various parts of marine ecosystem

Project

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Physical transport compartment models are the most commonly used; they are briefly represented and discussed in Monte, (2009) and SKB (2004).

Biogeochemical models have been developed and reported in [Dittrich et al., 2009], but they have a high degree of complexity and demand huge amount of input parameters.

Biological models describe radionuclide transport through the biotic environment considering processes

Many models have been verified for the isotopes Cs-137 and Sr-90, and less research has carried out for other isotopes.

Methodological approaches for modelling of radionuclide migration in aquatic ecosystems:

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Sketches of the future final repository for spent nuclear fuel below ground. (Figure from www.skb.se )

Study region and selected site

Aerial photo of the planned area for the spent fuel repository at Forsmark. (Figure from www.skb.se )

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The partitioning of the Forsmark coastal area into subbasins (SBs) with labelling of the major basins. 11.5 km2

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Food web illustration depicting the links among Baltic Sea communities

The ecosystem perspective

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Element /radionuclide model

The radionuclide flow was assumed to follow the flow of organic matter,

but radionuclide-specific mechanisms such as:

radionuclide uptake by plants excretion of radionuclides by animalsadsorption of radionuclides to organic surfaces,

were connected to the carbon model to account for the differences between the dynamics of carbon and the radionuclides

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Phytoplankton Zooplankton

Fish

Photic zone

Benthos

Macrograzers

Benthic plants

PM

DIM

Primary producer

Consumers RespirationFaeces, excretion and excess

Element intake and uptake

Element adsorbtion to surface from DIM Air exchange

Water exchange

The arrows in the figure illustrate the flow of the contaminating element

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Software toolsThe simulations of element dynamics were executed using two software packages for intercomparison:

Matlab/Simulink with the Pandora package

Ecolego http://ecolego.facilia.se/ecolego/show/Downloads

Compartment model of C N P transport in coastal ecosystem

EcolegoPandora

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Cl

Sr

Np

Pa

Ca

Ra

U

Cs

Ho

Nb

I

Se

Sn

Ac Ni

Pd

Ag

Po

Zr

Am

Pb

Sm

Pu

Cm Th

Tc

1.0E-04

1.0E-03

1.0E-02

1.0E-01

1.0E+00

1.0E+01

1.0E+02

1.0E+03

95% percentile 50 % percentile5% percentileMean BE genericMean BE site data

Element

Ben

tho

s B

CF

m3/

kgC

Figure 2. Comparison predicted bioconcentration factor (BCF) (m3/kgC) by probabilistic simulation for marine benthos (median 50%, 5% and 95% percentiles) and experimental mean BCF data for marine filter feeders with minimum and maximum values.

Cl

Pa Sr

Np

Ca

Ra U Cs

Ho

Am

Pb

Nb

Se I

Po

Sn

Ag

Ac Ni

Pd

Zr

Pu

Th

Sm

Cm Tc

1.0E-04

1.0E-03

1.0E-02

1.0E-01

1.0E+00

1.0E+01

1.0E+02

1.0E+03

1.0E+04

95% percentile 50 % percentile5% percentileMean BE genericMean BE site data

Element

Gra

zers

BC

F m

3/kg

C

Bioconcentration Factor BCF=Cbiota/Cwater

Figure 1. Comparison predicted bioconcentration factor (BCF) (m3/kgC) by probabilistic simulation for marine grazers/macrograzers (median 50%, 5% and 95% percentiles) and experimental mean BCF data for marine crustaceans with minimum and maximum values.

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Cl

Pa

Sr

Np

Ca

Ra U Cs

Ho

Nb

I

Se

Sn

Ac Ni

Po

Pd

Ag

Zr

Am Pu

Pb

Sm

Cm Th

Tc

1.0E-03

1.0E-02

1.0E-01

1.0E+00

1.0E+01

1.0E+02

1.0E+03

1.0E+04

95% percentile 50 % percentile5% percentileMean BE genericMean BE site data

Element

Fis

h B

CF

m3/

kgC

Tc

Cl

Sr

Ca

Pa

Ho

U

Np

Sn

Nb

Se

Ac I

Pd

Ra

Sm

Ni

Cs

Po

Ag

Zr

Pb

Pu

Am

Cm Th

1.00E-06

1.00E-04

1.00E-02

1.00E+00

1.00E+02

1.00E+04

1.00E+06

95% percentile 50 % percentile5% percentileERICA genericSKB 2008 site dataIAEA generic

Element

Zo

op

lan

kto

n B

CF

L/ k

g w

w

Figure 3. Comparison predicted bioconcentration factor (BCF) (m3/kgC) by probabilistic simulation for marine fish (median 50%, 5% and 95% percentiles) and experimental mean BCF data for marine fish with minimum and maximum values.

Figure 4. Comparison predicted bioconcentration factor (BCF) (L/kg ww) by probabilistic simulation for marine zooplankton (median 50%, 5% and 95% percentile) and experimental available BCF data from four different sources: ERICA – data base, SKB 2010 – /report TR-10-08/, SKB 2008 – /report TR-08-09, App. 22/, IAEA – /report IAEA 1985/ .

Mesocosm study October 2008 - 9

• Other researchers within the research group at Systems Ecology have for the last 5 years been done, such as using experimental ecosystems (mesocosms) to study the transport and fate of environmental contaminants, including radionuclides, in Baltic Sea ecosystems.

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The data from these experiments is complex and multidimensional and often complicated to interpret.

Models used:a) to evaluate the experimental results; b) to test how these results may be altered under different

scenarios (e.g. increased temperature, presence of a toxin, presence or absence of a predator etc).

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Questions

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