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Evaluation of exposure of Lake Druksiai biota reference organisms using probabilistic methods/ Druksiu ezero biotos testiniu organizmu apsvitos vertinimas taikant tikimybinius metodus.

1. Introduction

Assessment of environmental impact by Ignalina NPP on radioecological changes in Lake Druksiai--which is the Ignalina NPP cooling basin--was carried out (Silumine energetika 1989; Marciulioniene et al. 1992; Mazeika 1998) taking into account the future development of the nuclear energy sector.

The environmental impact assessment report (Poveikio aplinkai ... 2006) considering Lake Druksiai biota (fauna and flora) habitats indicates the frequency of cytogenetic damage (deviations in chromosome structure and number), which--due to specific impact caused by radionuclides in Lake Druksiai--is slightly greater than the environmental level. According to ecotoxicologic investigation of Lake Druksiai water and bottom sediments, this lake can be attributed to the category of low-toxic water basins. However, long-term (1989-1996) ecotoxicologic investigations of Ignalina NPP effluents demonstrate that these effluents are more or less dangerous to hydrobionts (Poveikio aplinkai ... 2006).

An important problem was to determine the relationship between exposure dose rates of Lake Druksiai biota resulting from natural and technogenic radionuclides. One article (Nedveckaite et al. 2007) indicated that in Lake Druksiai, the dose rate of submerged hidrophytes resulting from technogenic discharged radionuclides is substantially lower as compared with the ionising radiation exposure of a natural background radionuclide ([sup.238]U, [sup.226]Ra, [sup.210]Po). It should be stressed that up to now, no such estimations were undertaken regarding other Lake Druksiai organisms.

As indicated in another article (Marciulioniene 2007), during a short time period (2-4 days), radionuclides in freshwater ecosystems are equally distributed amongst water, bottom sediments and plants. This conditions a decrease of the radionuclide activity in water, which becomes an insufficiently informative component in the evaluation of the radioecological state. The longterm contamination of the freshwater ecosystem is the best reflected by bottom sediments, which become the radionuclide accumulation medium.

Mathematical models are developed for radionuclide distribution and evaluation of biota exposure regularities in freshwater ecosystems. They were especially widely applied for evaluation of radioecological consequences and in forecasting of biota contamination and radionuclide migration in ecosystems after the Chernobyl NPP accident (Kryshev 2008).

Although mathematical simulations of radionuclide transfer in different ecosystems were used for several decades, results of simulations have numerous uncertainties. For this reason, along with the deterministic exposure evaluation, close attention is paid to the exposure analysis using probabilistic methods. For the exposure evaluation, probabilistic methods were also recently applied in Lithuania (Nedveckaite et al. 2007, 2010; Nedveckaite 2004).

Investigations based on radionuclide accumulation in hydrobionts and intended for the parameter evaluation of separate radionuclide transfer both in terrestrial (Butkus 2005, 2006, 2009; Butkus, Pliopaite-Bataitiene 2006) and freshwater ecosystems should also be mentioned (Marciulioniene et al. 1992; Cepanko et al. 2006). By combining probabilistic models and data obtained by radionuclide measurements, the most possible consequences of different decisions can be determined and various contamination scenarios can be verified. Probabilistic models enable forecasting the most probable future reality. The first step is to determine constraints of variable values (minimum and maximum). The second step is distribution of probabilistic leverages according to the maximum permissible dose rate value. In the European Union, the largest permissible exposure dose rate for biota is 10 [mu]Gy [h.sup.-1] (ERICA Assessment 2007). This standard is currently recommended as the largest permissible radiation dose rate from exposure to technogenic radionuclides.

The aim of the work is to evaluate the exposure dose rate of reference biota of the Ignalina NPP cooling basis--Lake Druksiai (within the range of ERICA computer code valid in the European Union) by applying probabilistic methods based on experimental data accumulated during 1989-2003 and taking into account ecotoxicologic investigation of Lake Druksiai water and bottom sediments.

2. Methods

Data on radionuclide activity concentrations in lake water and bottom sediments were chosen to evaluate the impact that radionuclides of natural and technogenic origin have on Lake Druksiai biota (Table 1).

ERICA computer code, which evaluates the most probable exposure dose rates of organisms by applying probabilistic methods, was used. This software that interacts with most of databases and modules referring to the experimental data of radionuclide activity concentration measurements in the environment, allows for evaluation of biota activity concentrations, and external, internal and total exposure dose rates.

The following dependences are most frequently used for estimation of internal and external absorbed dose rates:

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII.] (1)

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII.] (2)

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII.] (3)

where: [D.sup.b.sub.int] is the absorbed internal exposure dose rate of the b-th reference organism, [mu]Gy [h.sup.-1];

[C.sup.b.sub.i] is the mean activity concentration of the i-th radionuclide in the b-th reference organism (Bq [kg.sup.-1]);

[DCC.sup.b.sub.int,i] is the dose conversion factor for the internal exposure defined as the ratio between the dose rate and the activity concentration of radionuclide i in organism b ([mu]Gy [h.sup.-1])/(Bq [kg.sup.-1]);

[D.sup.b.sub.ext] is the external exposure dose rate in b-th reference organism, [mu]Gy [h.sup.-1];

[v.sub.z] is the time fraction when organism b is in habitats z;

[C.sup.ref.sub.zi] is the mean concentration of radionuclide i in the environment z (Bq [kg.sup.-1] in case of bottom sediments or Bq l-1 in case of water); [DCC.sup.j.sub.ext,zi] is the dose conversion factor for the external exposure defined as the ratio between the dose rate and the activity concentration of radionuclide i in organism b.

ERICA software (ERICA 2007), in which the Monte Carlo probabilistic simulation uses available input data distributions, was chosen to evaluate the exposure of biota reference organisms. The result of such simulation is the probabilistic distribution of the dose rate that facilitates evaluation of the most and the least probable (but possible) distribution values. In this work, due to a certain radionuclide present in the environment of the freshwater system, the finite points of the probabilistic distribution were estimated using the following dose rate D ([mu]Gy [h.sup.-1]) definition.

The value of the dose rate D not only depends on the reference organism species and the habitat of the freshwater ecosystem organism but also on the dose conversion factor (DCC), the ratio of concentrations CR (equation 4) and values of the distribution coefficient [K.sub.d] (equation 5) (D-ERICA 2007). Values of these parameters were estimated as indicated in the FASSET (Fasset 2003) database ([mu]Gy [h.sup.-1])/(Bq [kg.sup.-1]) in case of bottom sediments or Bq [l.sup.-1] in case of water.

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII.] (4)

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII.] (5)

3. Results and discussion

Investigations performed in Lake Druksiai demonstrated that the largest total dose rate (internal and external) found in tested reference organisms results from natural radionuclide activity concentrations. The following hydrobionts undergo the largest exposure: crustacean, insect larvae, vascular plants, gastropod and bivalve molluscs (Fig. 1). Out of all natural radionuclides, the reference organisms mentioned above receive the largest total dose rate from the ionizing radiation impact of [sup.238]U and its decay product [sup.226]Ra (Fig. 2).

It can be noted that the total dose rate value of vascular plants compared with that of other reference organisms is the largest and reaches 4.18 [mu]Gy [h.sup.-1] (Fig. 1); 98% of the dose rate is related to ionizing radiation of [sup.238]U and its decay products (Fig. 2). Ordinarily, the exposure to natural radionuclides is not limited.

[FIGURE 1 OMITTED]

Out of all measured technogenic radionuclides ([sup.134,137]Cs, [sup.90]Sr, [sup.54]Mn, [sup.60]Co, [sup.239]Du, [sup.3]H, [sup.14]C), the highest activity concentrations in Lake Druksiai were obtained in case of [sup.137]Cs and [sup.90]Sr. Insect larvae, crustacean, vascular plants, gastropod and bivalve molluscs undergo the largest exposure to technogenic radionuclides among all of the reference organisms (Fig. 3).

The obtained results--distributions and their statistical parameters--are presented in Figs 4 and 5. Based on the experimental data presented in Table 1, ERICA code, which applies probabilistic methods (Monte Carlo simulation), evaluates the distribution of total dose rate of reference organisms for each presented radionuclide; determines average, minimal and maximal values; and the 5th and the 95th order percentiles. The simulation results demonstrate that in Lake Druksiai, the total dose rate value of reference organisms does not exceed the largest permissible value of 10 [mu]Gy [h.sup.-1], which is indica ted in the EU recommendations for technogenic radionuclides. The distribution of the total dose rate ([mu]Gy [h.sup.-1]) resulting from ionising radiation of [sup.90]Sr, [sup.137]s and [sup.238]U estimated for vascular plants and insect larvae in Lake Druksiai is shown in Figs 4 and 5.

[FIGURE 4 OMITTED]

Various chemicals, acids and alkaline solutions, weak organic acids, heavy metals and dry materials that remain after artesian water vaporization at the Ignalina NPP, make their way to Lake Druksiai together with industrial rainwater sewerage (IRS) effluents and radionuclides. Although such wastes are attributed to comparatively low-toxicity effluents, their danger to living organisms was clearly determined. The laboratory investigation of the impact of IRS effluents on the radionuclide accumulation in water plants demonstrated that these effluents increase the [sup.137]Cs accumulation in water plants (Marciulioniene 2003). For evaluation of the radioecological state of Lake Druksiai, research data of 1988-2003 were generalised: activity concentration values of [sup.137]Cs and 90Sr in plants and especially in bottom sediments of Lake Druksiai were higher than those in the Ignalina NPP effluent channels; whereas on the contrary, activity concentration values of [sup.60]Co and [sup.54]Mn were lower in Lake Druksiai than in the Ignalina NPP effluent channels (Marciulioniene 2007).

[FIGURE 5 OMITTED]

In the distribution of [sup.137]Cs in Lake Druksiai, where the activity concentration of this radionuclide is up to 56 times higher than in plants, bottom sediments play the main role. The largest accumulation of [sup.90]Sr was determined in plants, where the activity concentration of this radionuclide was up to 3 times higher than in bottom sediments (Marciulioniene 2007). Therefore, as compared with other technogenic radionuclides, the largest exposure dose in reference organisms was identified to result from [sup.137]Cs and [sup.90]Sr (Fig. 3). Out of the technogenic radionuclides, [sup.90]Sr and [sup.137]Cs belong among the most biologically toxic radionuclides as their relatively low concentrations can disorder the main functions of organisms (Cepanko et al. 2006).

Both the bottom sediments and plants, in which average values of activity concentrations of these radionuclides in most cases were similar, play an important role in the distribution of [sup.60]Co and [sup.54]Mn (Marciulioniene 2007).

It should be noted that in the water ecosystem, uranium concentrates mostly in some plant species (Verkhovskaia et al. 1972). Accumulation of uranium by submerged plant species is 4-5 times larger as compared with air-water ecological plant species (Lavrova et al. 2003). In molluscs, uranium mostly concentrates in shells, while only small amounts are found in soft tissues. Since uranium is not a biogenic element, its accumulation in water plants under conditions of radioactive contamination can reach large values due to uranium adsorption by plant surface.

Water plants play an important role in regulation of water quality. In the case of water filtering by water plants, mechanical, biological and physical-chemical water cleaning takes place, i.e., organic, mineral and ra dioactive materials are absorbed onto stems and leaves of plants. These and radioactive elements, are accumulated in leaves and stems of water plants. Water plants, which have a large surface per weight unit, can absorb a large amount of radionuclides from the water environment (Marciulioniene et al. 1992). Due to a larger water plant surface per weight unit, since vascular plants are also attributed to submerged plants, their exposure to [sup.238]U and its decay products is the largest (Figs 1 and 2).

4. Conclusions

1. Referring to data of the radioecological research and ecotoxicologic investigations carried out in Lake Druksiai during 1989-2003, and with the help of probabilistic methods and ERICA computer code, it was determined that the dose rate values above the 95% confidence interval for reference organisms do not exceed the maximum permissible exposure dose rate level of 10 [mu]Gy[h.sup.-1], which is currently indicated in the EU recommendations for technogenic radionuclides.

2. In Lake Druksiai, the exposure dose rate received by reference organisms (vascular plants, insect larvae, crustacean, gastropod, bivalve molluscs and etc.) resulting from the impact of ionizing radiation of natural radionuclides is larger than that resulting from technogenic ones.

3. The largest exposure dose rates in reference organisms resulting from natural radionuclides were determined in the case of [sup.238]U and its decay products (insect larvae--7.2 [mu]Gy [h.sup.-1], vascular plants--41.8 [mu]Gy [h.sup.-1]). In the case of technogenic radionuclides, both [sup.137]Cs (insect larvae--5.31 x[10.sup.-2] [mu]Gy [h.sup.-1], vascular plants--2.61 x[10.sup.-2] [mu]Gy [h.sup.-1]) and [sup.90]Sr (insect larvae--1.29x10-2 [mu]Gy [h.sup.-1], vascular plants--6.16 x[10.sup.-3] [mu]Gy [h.sup.-1]) were the largest.

4. Out of all of the investigated reference organisms, the largest exposure dose rate was determined in benthic organisms (insect larvae, crustacean, gastropod and bivalve molluscs) and vascular plants. It should be noted that vascular plants undergo the largest exposure.

doi: 10.3846/16486897.2012.657775

Submitted 17 Aug. 2010; accepted 07 Apr. 2011

References

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Butkus, D.; Dimaviciene, D. 2009. Investigation of [sup.137]Cs transfer in the system ,,Soil-mushrooms-man", Journal of Environmental Engineering and Landscape Management 17(1): 44-50 (in Lithuanian). http://dx.doi.org/10.3846/1648-6897.2009.17.44-50

Butkus, D.; Konstantinova, M. 2005. Studies of [sup.137]Cs transfer in soil-fern system, Journal of Environmental Engineering and Landscape Management 13(3): 97-102. http://dx.doi.org/10.1080/16486897.2005.9636855

Butkus, D.; Pliopaite-Bataitiene, I. 2006. Investigation of relationship between pine biomass (Pinus sylvestris L.) and [sup.137]Cs activity concentration in timber, Journal of Environmental Engineering and Landscape Management 14(3): 135-140 (in Lithuanian). http://dx.doi.org/10.1080/16486897.2006.9636890

Cepanko, V.; Idzelis, R. L.; Kesminas, V.; Ladygiene, R. 2006. Radiological investigation of roach and perch from some lakes in Lithuania, Journal of Environmental Engineering and Landscape Management 14(4): 199-205. http://dx.doi.org/10.1080/16486897.2006.9636898

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FASSET. 2003. Deliverable 5. Handbook for assessment of the exposure of biota to ionizing radiation from radionuclides in the environment. Appendix 1: transfer factors and dose coefficient look-up tables. European Commission, Fifth Framework, Contract No. FIGE-CT-2000-00102 [online]. 2003. [cited 12 January 2010]

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Nedveckaite, T.; Filistovic, V.; Marciulioniene, D.; Kiponas, D.; Remeikis, V.; Beresford, N. A. 2007. Exposure of biota in the cooling pond of Ignalina NPP: hydrophytes, Journal of Environmental Radioactivity 97: 134147.

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Verkhovskaia, I. N.; Popova, E. I.; Vlasova, T. A; Esova, G. I. 1972. Radioekologicheskie issledovaniia v prirodnykh biogeocenozakh. Moskva: Nauka, 124-147 (in Russian).

Nina Prokopciuk (1), Danute Marciulioniene (2), Tatjana Nedveckaite (3), Vitoldas Filistovicius (4)

(1, 3, 4) 4Center for Physical Sciences and Technology, Savanoriu pr. 231, LT-02300 Vilnius, Lithuania (2) Nature Research Centre, Akademijos g. 2, LT-08412 Vilnius, Lithuania

E-mails: (1) nikan@one.lt (corresponding author); (2) radeko@ar.fi.lt; (3) tajana@cablenet.lt; (4) vitfil@post.skynet.lt

Nina PROKOPCIUK. Master of Ecology and Environmental Research, 2004. Doctoral student. State research institute. Centre for Physical Sciences and Technology. Nuclear and Environmental Radioactivity Research Laboratory. Publications: 4. Conferences: 3. Research interests: radioecology, radiation protection.

Danute MARCIULIONIENE. Dr Habil. Laboratory of Radioecology, Institute of Botany of Nature Research Centre. Dr Habil Biomedicine sciences (ecology and environment research), Institute of Ecology, 1994. Leader scientific Worker, 1996. Publications: 169 scientific publications. Research interests: radioecology, radiobiology, ecology, botany, ecotoxicology.

Tatjana NEDVECKAITE. Dr. State Research Institute. Centre for Physical Sciences and Technology. Senior scientific worker at the Nuclear and Environmental Radioactivity Research Laboratory. Publications: 98 scientific publications, monograph and 2 popular science books. Research interests: human and biota radiation protection, radiation safety, radioecology.

Vitold FILISTOVIC. Dr. State Research Institute. Centre for Physical Sciences and Technology. Since 1990 till present, senior scientific worker at the Nuclear and Environmental Radioactivity Research Laboratory. Scientific publications: about 50. Research interests: LS, gamma-spectroscopy, radiation protection, radiation safety, radioecology, modeling of radionuclide behaviour in the environment.
Table 1. Radionuclide activity concentrations in Lake Druksiai bottom
sediments (Bq/kg dry weight) and water (Bq/l)

Nuclide Mean S.D. Apex Distribution * Range min

Natural radionuclides Sediments

[sup.238]U 30 30 TR 10
[sup.226]Ra 48 38 LN 12
[sup.210]Pb 48 38 LN 3
[sup.210]Po 48 38 LN 12
[sup.232]Th 50 50 TR 10
[sup.40]K 504 206 LN 110

Technogenic radionuclides Sediments

[sup.54]Mn 14 28 LN 0.04
[sup.60]Co 46 45 LN 3.4
[sup.90]Sr 28 13.6 LN 8.0
[sup.134]Cs 0.7 0.7 TR 0.26
[sup.137]Cs 150 110 LN 13
[sup.239]Pu 0.9 1.2 LN 0.01

 Water

[sup.3]H 8 4.7 LN 0.05
[sup.14]C 0.012 0.012 LN 0.008

Nuclide Range max References

Natural radionuclides

[sup.238]U 150 Silumine ener-
[sup.226]Ra 120 getika 1989
[sup.210]Pb 150
[sup.210]Po 120
[sup.232]Th 100
[sup.40]K 1860

Technogenic radionuclides

[sup.54]Mn 88 Paskauskas and
[sup.60]Co 170 Mazeika 1997;
[sup.90]Sr 83 Marciulioniene
[sup.134]Cs 7.8 2007
[sup.137]Cs 440
[sup.239]Pu 2.4
[sup.3]H 20 Mazeika 2002
[sup.14]C 0.0.014

* LN--Lognormal

* TR--Triangular

Fig. 2. The total exposure dose rate of reference organisms having
received the largest exposure to natural radionuclides in Lake Druksiai

Crustacean

Th-232 <1%
Ra-226 9%
U-238 90%

Vascular plant

Th-232 <1%
Ra-226 2%
U-238 97%

Insect larvae

Th-232 <1%
Ra-226 9%
U-238 90%

Bivalve mollusc

Th-232 <1%
Ra-226 20%
U-238 79%

Note: Table made from pie chart.

Fig. 3. The total exposure dose rate of reference organisms resulting
from ionising radiation of technogenic radionuclides in Lake Druksiai

Insect larvae

H-3, Pu-239 <1%
Cs-137 40%
Cs-134 1%
C-14 2%
Mn-54 4%
Co-60 42%
Sr-90 10%

Vascular plant

H-3, Pu-239 <1%
Cs-137 40%
Cs-134 1%
C-14 2%
Mn-54 4%
Co-60 43%
Sr-90 9%

Crustacean

H-3, Pu-239 <1%
Cs-137 40%
Cs-134 1%
C-14 3%
Mn-54 4%
Co-60 41%
Sr-90 1%

Gastropod

H-3, Pu-239 <1%
Cs-137 38%
Cs-134 1%
C-14 4%
Mn-54 4%
Co-60 46%
Sr-90 6%

Note: Table made from pie chart.
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Author:Prokopciuk, Nina; Marciulioniene, Danute; Nedveckaite, Tatjana; Filistovicius, Vitoldas
Publication:Journal of Environmental Engineering and Landscape Management
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Date:Mar 1, 2012
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