Element cycling in aquatic ecosystems – modelling general and element-specific transport and accumulation mechanisms. March 2011 Lena Konovalenko Department of Systems Ecology Stockholm University Second Supervisors: Ulrik Kautsky: Swedish Nuclear Fuel and Waste Management Co (SKB) Linda Kumblad: Stockholm University Main Supervisor: Clare Bradshaw: Stockholm University 1

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 2

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: 3

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Sketches of the future final repository for spent nuclear fuel below ground. (Figure from ) Study region and selected site Aerial photo of the planned area for the spent fuel repository at Forsmark. (Figure from ) 5

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

7 Food web illustration depicting the links among Baltic Sea communities

The ecosystem perspective 8

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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 animals  adsorption of radionuclides to organic surfaces, were connected to the carbon model to account for the differences between the dynamics of carbon and the radionuclides 10

Phytoplankton Zooplankton Fish Photic zone Bentho s Macrograzers Benthic plants PM DIM Primary producer Consumers Respiratio n Faeces, 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 11

Software tools The simulations of element dynamics were executed using two software packages for intercomparison:  Matlab/Simulink with the Pandora package  Ecolego Compartment model of C N P transport in coastal ecosystem Ecolego Pandora 12

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14 Figure 2. Comparison predicted bioconcentration factor (BCF) (m 3 /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. Bioconcentration Factor BCF=Cbiota/Cwater Figure 1. Comparison predicted bioconcentration factor (BCF) (m 3 /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.

15 Figure 3. Comparison predicted bioconcentration factor (BCF) (m 3 /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 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. 16

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). 17

Questions 18