UPDATE OF AQUATRIT MODEL FOR TRITIUM DYNAMIC TRANSFER IN AQUATIC FOODCHAIN

1. UPDATE OF AQUATRIT MODEL FOR TRITIUM DYNAMIC TRANSFER IN AQUATIC FOODCHAIN Anca Melintescu PhD “Horia Hulubei” National Institute for Physics and Nuclear Engineering, Bucharest-Magurele, ROMANIA ancameli@ifin.nipne.ro, melianca@yahoo.com 2nd Meeting of the EMRAS II Working Group 7, “Tritium”, Chatou, France, 28–29, September 2009

2. Dynamic approach of tritium transfer in aquatic food chain Presentsituation and EMRAS experience • Tritium in aquatic environments started in ’70s in USA with a series of experiments and few modelling trials; • The old data base has been analyzed in order to start the Romanian model; The report is now available (before it was NRG property); • Tritium does not interfere with sediments in a significant proportion • Tritium dynamics (as HTO) in water can be well modeled with hydrological models • In EMRAS, the aquatic WG exercised a case for Loire River. • The performance of models describing tritium dynamics, as HTO, in river is satisfactory; • It remains to describe tritium transfer in food chain Comparison between different models and measurements used to predict tritium concentration in Loire River

3. EMRAS experience Mussel scenario • Intermediate case between routine and accidental situation; • It considers H-3 in aquatic media; • Coordinated by AECL Canada; • Two phases: uptake and depuration – abrupt change of environmental tritium concentration; mussel specie – Elliptio complanata

4. Uptake – all models underestimate the • OBT concentration at the beginning; • Potential explanations – stomach • content and/or complex metabolism • (two biokinetic rates: slow and fast) • Depuration - OBT concentrations did not • reach steady state over the 117-day • period of this study; • Fast dynamics at the beginning (stomach • content influence) not observed; • NIRS model with two biokinetic rates • better succeeds, not models with a single • biokinetic rate; • Lesson: • Complex models are not recommended for radiological assessment due to paucity of input data or excessive cost to obtain. • A compromise must be obtained for an operational model but the problem is to have a control on model predictive power (uncertainty included). • The EMRAS mussel scenario was very helpful in order to assess and improve the model predictive power.

5. Romanian approach for mussel scenario – AQUATRIT • Mussel uptake and depuration can be reproduced using an appropriate metabolic rate • and stomach content; • Metabolic rates for mussels vary with age and specie. AQUATRIT results with and without stomach content

6. AQUATRIT – the Romanian approach Initially, it was a contract with NRG, The Netherlands (2002); Model description: body HTO is in fast equilibrium with surrounding water (few hours) → it could be considered full equilibrium; as for mammals, a part of body organically bound hydrogen is metabolically derived from free hydrogen in body water → this fraction can be higher, because aquatic organisms appeared earlier at evolution scale; Scanning of the old literature shows that the specific activity of OBH is decreasing among aquatic organism, considering the evolution scale SAR of aquatic animals The specific activity (SA) of tritium = the ratio between the tritium activity and the mass of hydrogen corresponding to the specific form. The specific activity ratio (SAR) = SA OBT in the animal divided by SA of HTO in media water; - it is close to 0.25, if the source is only HTO in water

7. AQUATRIT - considers a simplification of food web, selecting specific aquatic organisms - Initial version published in 2005 (D. Galeriu, R. Heling and A. Melintescu, “The Dynamic of Tritium- Including OBT- In the Aquatic Food Chain”, Fusion Science and Technology, Vol. 48, Number 1 – July/August 2005, P. 779-782); - tritium in the body water of the animal is considered in equilibrium with the tritiated water in the aquatic environment CHTO = CW*(1-Dryf)*0.001 CHTO - HTO concentration in organism [Bq kg-1 fw] CW - the HTO concentration in water medium [Bq m-3] 0.001 - unit transformation m3 L-1 - starts with autotrophic level - phytoplankton. - We deduced a specific equation for OBT in phytoplankton and successfully tested with experimental data: modlight * modtemp µ=µo* Co,phpl – OBT concentration in phytoplankton [Bq kg-1fw]; µ - growth rate of phytoplankton [d-1]. Dryf - dry mass fraction of aquatic organism, tipycal value 0.07 CW - HTO concentration in water [Bq m-3]

8. Dynamics of OBT in consumers • We considered the transfer from water (direct metabolisation of free H(T)) and transfer from food: Corg,x - the OBT concentration in animal x (Bq kg-1 fw); Cf,x - the OBT concentration in food of animal x (Bq kg-1 fw); ax - the transfer coefficient from the HTO in the water to OBT in the animal x; bX - the transfer coefficient from OBT in food to OBT in the animal x; K05,x - the loss rate of OBT from animal x (d-1) • For a proper mass balance we have: Cprey,I - the OBT concentration in prey I Pprey,i - the preference for pray I

9. Simplified pelagic food chain It remains to establish the biological loss rate K05 for the aquatic organisms. For some aquatic organism K05is taken fromexperimental data reported in literature. Zooplankton: K05=(0.715-0.13log(V))+(0.033-0.008log(V))* 1.06(T-20) V(m3) - zooplankton volume 10-104 K05 = 0.19 - 0.7 d-1 (average 0.3) Mollusks and crustaceans show large variability → Must be adapted to different cases Mollusks - near 0.02 at a mass of 1 g and 0.005 d-1 at about 30 g of soft tissue crustaceans - average 0.007 d-1

10. Loss rate for Mytilus edulis at 10 °C – data taken from literature Mass dependence of OBT loss rate for Mytilus edulis Temperature dependences show adaptation to climate for two types of mollusks

11. Fish biological loss rate • Has two component due to: - growth dilution (relative growth rate RGR); E - the net assimilation efficiency; C - daily consumption rate (g/g per day); Rtot - the total metabolic rate (g/g/ per day), including activity - metabolic loss rate (R) • C and Rtot depend on body mass W and ambient temperature T. • The daily consumption rate depends also on the food availability (abundance, competition) and is a fraction “c” of the maximum, potential one Cmax- can be obtained only in optimal, laboratory conditions. • In field condition, primary production depends on trophic level and is highly seasonal; • Considering the specie dependent generic expressions for consumption and metabolic loss we can write: Ca, Cb, Ra, Rb - determine the weight effect on metabolism and maximum consumption rate; A - the activity multiplier on metabolism; Fc , Fr - give the temperature effect on consumption and metabolism

12. Biological loss rate for fish: Temperature dependence for the biological loss rate The mass dependence of biological loss rate (T=10 ºC, cE = 0.3) There are data on model constants for many fish

13. Model extension for benthic case • Macrophyte - food for benthic planktivorous fish • Use the same equation as for phytoplankton (autotrophic) → approximation: ba=0.01*1.07(T-8)modlight0.31(d-1) T - water temperature in C ZOOBENTHOS • Loss rate 0.1 (d-1) at 20 °C – assessed by us as a compromise between components: • Larvae - Chironoma - 0.06-0.2 • Small mollusks and crustacean - 0.01-0.05 • Use the temperature dependence as for Tridacna

14. Model application to Danube ecosystem • We exercised for pike, carp – fish commonly found in Danube river K05, pike= 0.15*0.192*M-0.18 *((34-temp)/10)2.206*exp(2.206*(1-(34-temp)/10)) K05,preyfish=0.3*0.105*M-0.27*foodave*((36-temp)/9)1.54*exp(1.54*(1-(36-temp)/9)) K05, carp= foodave*0.028*M-0.25*((32-temp)/6)0.89*exp(0.89*(1-(32-temp)/6)) foodave – moderator which takes into account food availability fluctuation foodave = 0.7 for winter time = 1.9 for summer time • The specific food for a benthic-pelagic fish comprises 0.3 zooplankton, 0.4 zoobenthos, 0.3 benthic alga

15. Flowchart of the model

16. Accidental release in Danube – HTO concentration in water

17. Accidental release to Danube – OBT concentration in aquatic food chain

18. Danube debit SEASONAL variability of dose Across seasons, Danube debit varies, as well as temperature and light.

19. Potential extension of the model more fish data and selection of few generic fish classes search for experimental data on OBT in fish 2 compartment or stomach + 1 compartment (mollusks) understanding more on zoo benthos clarifying dry matter Dry matter fractions (IAEA TECDOC, 2009)

20. CONCLUSIONS • Publications must be released; • The need for experimental data for OBT loss rate in large fish to test the modelling predictions; • Model can be simplified for generic class of fish.

21. Thank you!