The effects of plant traits and phylogeny on soil-to-plant transfer of 99Tc

Journal of Environmental Radioactivity 101 (2010) 757e766

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Journal of Environmental Radioactivity journal homepage: www.elsevier.com/locate/jenvrad

The effects of plant traits and phylogeny on soil-to-plant transfer of

99

Tc

N.J. Willey a, *, S. Tang b, A. McEwen a, S. Hicks a a b

Centre for Research in Plant Science, University of the West of England, Coldharbour Lane, Frenchay, Bristol, BS16 1QY, UK Agro-Environmental Protection Institute of the Ministry of Agriculture, No. 31 Fukang Road, Nankai District, Tianjing City 300191, PR China

a r t i c l e i n f o

a b s t r a c t

Article history: Received 29 January 2010 Received in revised form 21 April 2010 Accepted 21 April 2010

Assessments of the behaviour of 99Tc in terrestrial environments necessitate predicting soil-to-plant transfer. An experiment with 116 plant taxa showed that 99Tc transfer to plants was positively related to plant dry weight but negatively related to % dry matter and age at exposure. Activities of 99Tc analysed by hierarchical ANOVA coded with an angiosperm phylogeny revealed significant effects, with 55% of the variance between species explained at the Ordinal level and above. Monocots had significantly lower transfer of 99Tc than Eudicots, within which Caryophyllales > Solanales > Malvales > Brassicales > Asterales > Fabales. There was a significant phylogenetic signal in soil-to-plant transfer of 99Tc. This phylogenetic signal is used to suggest that, for example, a nominal Tc Transfer Factor of 5 could be adjusted to 2.3 for Monocots and 5.3 for Eudicots. Ó 2010 Elsevier Ltd. All rights reserved.

Keywords: Technetium Phylogeny Soil-to-plant transfer

1. Introduction Technetium-99 is a long-lived, low-energy b--emitting radioisotope (half-life ¼ 2.11 105 y). It has a fission yield from 235U of 6.2% e comparable to that of 137Cs (6.1%) and 90Sr (5.75%) and is therefore an important component of nuclear waste produced from fission of 235U. Discharges of 99Tc to the aqueous environment from sites processing spent nuclear fuel, such as the Sellafield complex in the UK with a peak discharge of 200 TBq 99Tc in 1995 (Leonard et al., 1997), provoked environmental concern (Brown et al., 1999) and have decreased in recent years (Bryan et al., 2008). An increasing proportion of the large amount of 99Tc produced by nuclear installations is earmarked for terrestrial nuclear waste repositories. In aerobic conditions the highly mobile and biologically available 99 99 TcO Tc (Bennett and Willey, 4 is the dominant ionic species of 2003). In leakage from a repository storing waste from 235U fission, 99TcO 4 is likely to be amongst the most mobile radiation sources. Fission of 235U is an expanding global energy source and there is great pressure to begin long-term storage of increasing amounts of nuclear waste in terrestrial repositories. Predicting the behaviour of 99Tc in terrestrial environments is part of assessments of nuclear waste repositories (Echevarria et al., 2003) and also important to the capacity to react to any accidental releases of 99Tc from an expanding global nuclear industry. Here we analyse inter-

* Corresponding author. Tel.: þ44 117 3282314; fax: þ44 117 3282904. E-mail address: [email protected] (N.J. Willey). 0265-931X/$ e see front matter Ó 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.jenvrad.2010.04.019

species differences in 99Tc transfer from soil-to-plants to aid assessments of 99Tc behaviour in the environment. Technetium-99 released into soils has the potential to contaminate food chains, although its radiological effects are normally only considered to be significant at very high activities (Gerber et al., 1989). In a review of data generated prior to 1983, Coughtrey et al. (1983) concluded that transfer of 99Tc from soil-to-plant was affected by soil factors, and by intra-species and inter-species effects. In subsequent years knowledge of the soil and intra-species effects impacting on 99Tc transfer has become extensive, but there is still no systematic understanding of inter-species factors affecting plant uptake of 99Tc. The 99Tc is bioavailable in aerobic soils and plant uptake substantial (Echevarria et al., 1997). Plant uptake from aerobic soils is, in fact, so rapid that there is significant potential for quantitative removal of 99Tc from soil by plants (Bennett and Willey, 2003). In anaerobic soils 99Tc occurs mostly as insoluble forms (Ishii et al., 2004). Soil oxygen status is, therefore, vital to predicting 99Tc behaviour in soils but other soil factors, in particular NO 3 concentration also influence soil-to-plant transfer of 99 Tc (Echevarria et al., 1998; Krijger et al., 2000). Intra-species factors such as physiological processes that redistribute 99Tc (Mousny et al., 1979; Lembrechts and Desmet, 1985; Tagami and Uchida, 2005) and that produce a variety of molecules incorporating 99Tc (e.g. Krijger et al., 1999) have been described in detail. The implications of advances in soil science and molecular biology to soil and intra-species effects have been reviewed (Bennett and Willey, 2003) but there have been few analyses of inter-species effects. A phylogenetic perspective has been useful to understanding inter-species effects in soil-to-plant transfer of inorganic

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contaminants including metals (Broadley et al., 2001), 137Cs (Broadley et al., 1999; Willey et al., 2005), 36Cl (Willey and Fawcett, 2005), 90Sr (Willey and Fawcett, 2006a), 109Ru (Willey and Fawcett, 2006b), 35S (Willey and Wilkins, 2006) and 60Co (Willey and Wilkins, 2008), but has not yet been reported for 99Tc. Such an analysis of inter-species effects on 99Tc transfer could complement established knowledge of soil and intra-species effects on the soilto-plant transfer of 99Tc and other radioisotopes. Previous analyses of the effects of phylogeny on element concentrations in plants were based in significant part on data compiled from the literature. These data were derived under a range of conditions and a variety of other variables other than element concentration were reported. The effects on soil-to-plant transfer values of different conditions used in different experiments were minimised statistically but comparisons of phylognetic effects with, for example, those of simple plant traits were not possible because the same variables were infrequently reported in different datasets. Further, much data did not enable Concentration Factors (CFs) or Transfer Factors (TFs) to be calculated. It might, therefore, be useful to; a) analyse a phylogenetic signal generated under a single set of conditions, b) compare the effects of simple plant traits with those of phylogeny, and c) estimate the effects on CFs and TFs. Here we generate a dataset for soil-to-plant transfer of 99Tc to meet these needs. The understanding of the evolutionary history (phylogeny) of many groups of organisms has been transformed in the last decade both by the recognition of gene sequences as taxonomic traits and the development of algorithms to reconstruct phylogenies. This has provided new phylogenies for flowering plants (Soltis et al., 1999; APG, 2003) that have been very useful to understanding variation in a number of plant traits, including the concentrations to which a variety of elements are accumulated. It is widely recognized that, even when an element is equally available to them, different plant species do not take it up to the same concentration (Marschner, 1995; White and Broadley, 2003; Watanabe et al., 2007). It is only recently, however, that it has been established that a proportion of inter-species difference in the uptake of elements can be ascribed to phylogeny (Broadley et al., 2004; Watanabe et al., 2007). This is potentially useful for understanding element behaviour in the soilplant system for three reasons. First, it describes plant groups which have higher or lower than average uptake, thus avoiding having to measure the behaviour of each species before predictions can be made. Second, it reveals that species are not independent units for element concentration e which then might have to be accounted for in statistical analyses. And third, it indicates whereabouts on the phylogeny difference is located, which can be helpful in the search for mechanisms controlling concentrations in plants. We hypothesised that there is a significant phylogenetic signal in the soil-to-plant transfer of 99Tc. To test this hypothesis we analysed the effects of simple plant traits and phylogeny on 99Tc activities in 116 taxa of flowering plants. We show that, from acute exposures on a single soil type, there are some simple plant traits and phylogenetic categories that can be used to predict a significant proportion of the inter-species differences in 99Tc activities in plants. The role that phylogeny might play in refining predictions of soil-to-plant TFs for 99Tc is discussed. 2. Materials and methods 2.1. Plant culture Five replicate pots of each of 116 angiosperm species/varieties (Table 1) were grown in randomized block designs in two separate experimental phases, 69 species in the first phase, 49 species in the second and 2 in both. In the first phase (ten experiments which generated datasets 1e10) plants were grown in 12-cm diameter pots in Levington’s F2S compost (Fison’s, Ipswich, UK) in a greenhouse with about 22  C 16 h light/15  C 8 h night. Each pot had about 280 g F2S fresh weight at Field Capacity (about 175 g dry weight). In the second phase (datasets 11e21) conditions

were the same but Levington’s F1 compost (Fison’s, Ipswich, UK) with 20% added grit was used.Pots were free draining, were watered on demand and were not fertilised. Plants were grown in compost because all the species would not have grown as well on a single soil type. Species/varieties were chosen that were amenable to rapid growth in pots, i.e. they were predominantly herbaceous, but maintaining as wide a phylogenetic spread as practically possible. Species/varieties were chosen to provide a spread across the angiosperm phylogeny and to include representatives from China. Five replicate pots of Beta vulgaris and Lactuca sativa were included in both phases as markers in order to enable data from the two phases to be amalgamated. Five replicate pots of Saponaria vaccaria, Daucus carota, Taraxicum officinale, Salvia officinales, Aubretia deltoides, Cistus ladanifer, Hypericum olympicum and Fragaria vesca were grown in both of two different datasets (Table 1) to provide link species between datasets within the experimental phases. 2.2. Radiolabelling plants For each of the 21 datasets, plants were radiolabelled in a randomized block design in an arena supplied with supplementary light at about 350 mE m1 s1 PAR with 8 h night by adding 100 mL of 609 kBq 99Tc L1 solution to the surface of each pot. For almost all pots there was excess solution caught by saucers (which was mostly rapidly reabsorbed) so we assume approximate saturation of the substrate and homogenous distribution of 99Tc at 348 Bq 99Tc g1 (60.9 kBq total per pot/175 g substrate). Whole green shoots, which included all leaves and stems, were harvested 1 cm above substrate surface 48 h after 99Tc application. The 48 h exposure simulated acute exposure and minimised radiological exposure during the use of large numbers of plants. For a few species there were only 4 replicates suitable for harvest (see Table 1). Plants were radiolabelled when mature and in almost all cases before any flowering had occurred. All the species/varieties were therefore of a similar ‘physiological’ age although their absolute ages (i.e. days post-germination) varied (Table 1). Samples were weighed fresh immediately after harvest and weighed dry after at least 48 h at 80  C. This experimental regime provided values for the simple plant traits of fresh weight (FWt), dry weight (DWt), % dry matter (%DM) and absolute age at harvest. To measure 99Tc activities in plants, plant material was ground in an Ultraturax grinder and 0.1 g digested in 5 mL conc nitric acid for 12 h at room temperature, then at 80  C for 1 h or until brown fuming ceased. Five mL H2O2 was added and the samples kept at 80  C for 1.5 h or until clear with no bubbling to minimise quenching during scintillation counting. A 0.1 mL aliquot of digest solution was added to 10 mL scintillant and counted for 99Tc b-emissions with appropriate background and counting efficiency corrections. 2.3. Data analysis Three-way unbalanced ANOVA of all concentration values was carried out using Systat 11.0 for Windows (Systat Software Inc, USA) with phases, datasets and species as factors. The two phases of experiment, the first of which was of plants grown specifically for 99Tc labelling and the second of which was of plants grown alongside another experiment (Willey et al., 2005), produced significantly different mean 99Tc activity concentrations for the B. vulgaris and L. sativa ‘marker’ species. All plant activity concentration values in phase 1 were therefore multiplied by a factor of 0.79 which was derived from the mean concentrations in marker species of phase 1 divided by mean concentration in marker species of phase 2. Residual Maximum Likelihood (REML) fitting of a mixed-model linear regression (Willey and Wilkins, 2008) was then used to adjust means for species/varieties across the 21 datasets. REML was run in the statistical package Genstat for Windows Release 11.1 (VAG International, Oxford, UK) (Thompson and Welham, 2001). In the REML analysis, species/variety was used as the ‘fixed’ factor and dataset as the ‘random’ factor. We adjusted by 0.79 for the experimental phases because only one ‘random’ factor is possible in the REML. The REML procedure adjusts for the differences due to dataset using values from species the datasets have in common to estimate relative means for the species/varieties. Following the REML procedure, Genstat was used to run a seven-way unbalanced hierarchical ANOVA with factors coded using a phylogeny of flowering plants (Soltis et al., 1999) (see Table 1). This principle is similar to a simpler ANOVA but with factors, i.e. the taxomomic groups, nested within each other. The ANOVA was performed on REML-adjusted ln values because these were most nearly normally distributed. The relationship between the Linnean hierarchy and phylogenetic groups above the Ordinal level is contentious, so here we use ‘Class’, ‘Subclass’, ‘Group’ and ‘Superorder’ in a nominal sense only. All correlations, regressions, and normality statistics were performed using SigmaStat 3.01.1 (SPSS Inc., USA).

3. Results 3.1.

99

Tc activities in plants

Three-way unbalanced ANOVA of 99Tc activities in plants at harvest indicated that there were highly significant effects of experimental phase (F ¼ 16.8, P < 0.001), dataset (F ¼ 14.1,

Table 1 99 Tc in 116 species/cultivars of flowering plant from 21 datasets according the phylogeny of Soltis et al. (1999) Nature 402, 402e404. (REML-adjusted ¼ 99Tc from residual maximum likelihood analysis of absolute values across 21 datasets, ’Concentration Factor’ ¼ Bq g-1 DWt Plant/Bq g-1 DWt soil [60.9 kBq/175 g substrate ¼ 348 Bq/g], Mean % 99Tc ¼ mean % removed of 60.9 kBq added per pot, n ¼ number of replicate pots). ’Class’

’Subclass’

’Group’

’Superorder’

Order

MAGNOLIIDS

Magnoliids Moncots

" NonCommelinoids

" "

Laurales Asparagales Liliales

Commelinoids

"

Arecales Commelinales

EUDICOTS

Basal

"

Ranunculales

Proteales Caryophyllales

Pink Sweet William Asian Pokeweed Bloodwort Hybrid Dock Bloodwort Small Headed Knotweed Beet Beet Quinoa Goosefoot Fat Hen Lamb’s Quarters Spinach

Laurus nobilis Asparagus plumosus nanus Allium cepa Allium schoenoprasum Canna indica Phoenix canariensis Commelina coelestis Commelina communis Tradescantia x andersonia Carex comans Carex pendula Triticum durum Lolium perenne Ranunculus acris citrinus Nigella damascena Papaver somniferum Papaver rhoeas Platanus orientalis Amaranthus cruentus Amaranthus paniculatus Amaranthus tricolour Celosia argentea Celosia cristata Melandrium apricum Saponaria vaccaria Gypsophila elegans Gypsophila oldhamiana Lychnis chalcedonica Lychnis senno Dianthus deltoides Dianthus spp Dianthus barbatus Phytolacca acinosa Rumex sanguineous Rumex patienta x R tianschanicus Rumex hastatus Polygonum microcephalum Beta vulgaris Beta vulgaris var Lutiancai Chenopodium quinoa Chenopodium spp Chenopodium album centrorubrum Chenopodium amaramticolor Spinacia oleraceae

REMLAdjusted

Concentration Factor

Mean % 99Tc

Age

Dataset (n)

0.24 0.31 0.57 0.58 4.00 0.20 4.01 5.79 4.96 1.17 2.25 7.85 4.85 0.80 2.79 8.73 2.34 2.31 15.12 4.82 9.11 15.62 19.07 9.77 3.94 6.91 8.01 8.08 8.76 1.48

0.20 0.23 0.61 0.59 3.78 0.16 3.39 5.31 4.14 0.86 1.43 7.61 3.10 0.89 1.79 9.39 1.48 0.63 11.02 3.35 6.42 11.25 12.94 5.26 3.90 5.16 5.31 6.62 6.23 1.25

0.7 0.9 1.6 1.7 11.5 0.6 11.5 16.6 14.2 3.4 6.4 22.5 13.9 2.3 8.0 25.0 6.7 6.6 43.4 13.8 26.1 44.8 54.7 28.0 11.3 19.8 23.0 23.2 25.1 4.3

0.1 0.7 1.8 1.1 5.8 0.1 7.0 14.1 4.4 0.5 4.7 17.7 2.7 1.0 8.2 26.4 3.4 4.4 35.6 27.3 33.0 49.1 36.0 13.4 2.55, 3.21 7.4 26.2 4.3 15.6 3.4

155 98 49 54 46 155 47 47 61 118 98 25 51 109 47 41 65 55 39 46 46 48 63 79 40 40 55 55 50 56

10 (4) 6 (5) 2 (5) 3 (5) 3 (5) 10 (5) 3 (5) 3 (5) 5 (5) 10 (5) 6 (5) 1 (4) 20 (4) 9 (5) 20 (5) 2 (5) 21 (5) 4 (5) 12 (5) 13 (5) 13 (5) 13 (5) 21 (5) 16 (5) 8,9 (10) 8 (5) 14 (5) 10 (5) 15 (5) 4 (5)

9.53 7.22 11.19 5.62 15.57

6.82 5.10 8.17 5.70 9.87

27.4 20.7 32.1 16.1 44.7

20.2 20.6 40.6 12.7 30.8

48 55 81 55 34

13 (5) 14 (5) 16 (5) 4 (5) 11 (5)

7.39 0.46 6.32 10.41 21.38 13.98 13.63

4.66 0.33 5.37 7.32 14.73 10.18 9.39

21.2 1.3 18.1 29.9 61.3 40.1 39.1

31.0 2.7 11.3 29.5 34.2 34.2 39.4

57 82 33 46 34 52 51

15 (5) 16 (5) 1,19 (9) 19 (5) 17 (5) 20 (5) 20 (5)

12.45 12.79

8.76 9.21

35.7 36.7

33.4 25.8

36 40

18 (5) 18 (5)

N.J. Willey et al. / Journal of Environmental Radioactivity 101 (2010) 757e766

Poales

Bay Asparagus fern Onion Chives Indian Shot Canary Date Palm Blue Spiderwort Asiatic Dayflower Garden Spiderwort Bronze Sedge Pendulous Sedge Durum Wheat Rye Grass Buttercup Love-in-a-mist Opium Poppy Field Poppy Oriental Plane Grain Amaranth Purple Amaranth Joseph’s Coat Mfungu Cockscomb Catch-fly Soapwort Baby’s Breath Gypsophilia Cross of Jerusalem Jianhang Shahua Maiden Pink

Mean

(continued on next page)

759

’Class’

760

Table 1 (continued) ’Subclass’

’Group’

’Superorder’

Order

Core Eudicots

Asterids

Euasterid 2

Apiales Asterales

Gentianales Solanales

Lamiales

Rosids

Basal

Vitales Saxifragales Myrtales

Eurosid 2

Malvales

Brassicales

REMLAdjusted

Concentration Factor

Mean % 99Tc

Age

Dataset (n)

Daucus carota Helianthus annuus Helianthus edulis Lactuca sativa ’Little Gem’ Lactuca sativa Taraxacum officinale Calendula officinalis Pyrethrum pulchrum Bellis perennis Cichorum intybus Ageratum houstonianum

2.56 6.63 12.07 4.21 3.53 3.76 7.38 8.99 5.40 4.70 5.11

2.05 5.70 11.94 3.00 2.29 3.49 8.08 6.30 3.67 5.05 4.62

7.4 19.0 34.6 12.1 10.1 10.8 21.2 25.8 15.5 13.5 14.7

10.6, 8.9 10.4 17.7 11.4 8.1 3.15, 6.39 9.0 27.0 13.0 4.2 2.7

49 25 25 34, 33 33 33 33 39 59 33 40

2,3 (10) 1 (5) 1 (5) 1,11 (10) 11 (5) 7,8 (10) 7 (5) 12 (5) 15 (5) 7 (5) 8 (5)

Cynara scolymus Silybum marianum Carthamus tintorius Tragopogon porrifolius Artemisia annua Exacum affine Lycopersicon esculentum Nicotiana glauca Nicotiana sylvestris Nicotiana tabaccum Ipomoea purpurea Borago officinalis Myosotis alpestris Digitalis purpurea Digitalis viridiflora Mentha spicata Micromeria Emperor’s Mint Monarda citriodora Nepeta cataria Ocimum basilicum Origanum vulgare Salvia officinalis Satureja montana Thymus vulgaris Parthenocissus tricuspidata Liquidambar styraciflua Astilbe chinensis pumila Callistemon citrinus Oenothera pallida Alyssum maritimum Alyssum montanum Aubrieta deltoides Brassica oleracea Brassica juncea folia Brassica juncea napiformis Brassica juncea tumida Brassica campestris Cheiranthus Blood Red Cheiranthus cheiri Isatis tinctoria Raphanus sativus Cistus ladanifer Malva sylvestris Malva sinensis Pentapetes phoenicea

1.82 8.01 4.85 0.56 8.33 1.20 6.91 10.54 11.72 7.06 7.33 6.93 3.52 7.45 0.88 3.91 3.04 7.35 7.37 7.79 3.72 1.29 1.57 1.88 0.61 0.23 2.80 0.53 1.56 9.49 3.75 3.61 6.14 7.09 7.00 7.68 13.98 4.37 3.77 8.76 8.58 0.64 6.27 13.77 6.63

1.52 5.70 3.53 0.49 5.99 0.93 6.82 11.25 11.47 5.47 7.24 7.03 3.13 7.17 0.67 3.42 3.46 6.55 6.49 8.67 3.97 1.28 1.80 2.16 0.49 0.20 2.18 0.39 1.57 9.97 3.13 3.06 5.87 4.85 4.53 5.47 9.30 3.86 4.01 6.82 9.12 0.53 6.75 9.97 4.31

5.2 23.0 13.9 1.6 23.9 3.5 19.8 30.3 33.6 20.3 21.0 19.9 10.1 21.4 2.5 11.2 8.7 21.1 21.2 22.4 10.7 3.7 4.5 5.4 1.8 0.7 8.0 1.5 4.5 27.2 10.8 10.4 17.6 20.3 20.1 22.0 40.1 12.5 10.8 25.1 24.6 1.8 18.0 39.5 19.0

1.2 30.5 18.3 0.5 18.4 0.6 20.4 16.4 14.0 23.4 24.3 26.2 5.7 11.2 1.7 3.0 2.2 2.8 4.4 7.9 2.5 2.75, 2.19 0.6 1.2 0.3 0.1 2.3 0.4 4.2 26.5 11.3 7.8,1.2 18.7 25.4 23.5 22.7 44.3 3.5 2.0 8.8 14.5 0.87,0.21 18.2 38.3 24.8

40 34 41 55 53 96 33 42 42 40 33 33 48 47 63 62 46 46 40 33 46 54 46 46 61 63 62 62 55 42 47 46 33 40 34 34 34 40 33 40 33 62 41 34 65

8 (5) 11 (5) 12 (1) 10 (5) 14 (5) 7 (5) 1 (5) 2 (5) 2 (4) 2 (4) 1 (4) 1 (5) 3 (5) 3 (5) 5 (5) 6 (5) 9 (4) 9 (5) 8 (5) 7 (5) 9 (6) 3,4 (10) 9 (5) 9 (5) 5 (5) 5 (5) 5 (5) 5 (5) 4 (5) 2 (5) 3 (5) 9,10 (10) 1 (3) 18 (5) 11 (5) 11 (5) 11 (5) 8 (5) 7 (5) 8 (5) 7 (5) 5,6 (10) 2 (5) 17 (5) 21 (5)

N.J. Willey et al. / Journal of Environmental Radioactivity 101 (2010) 757e766

Euasterid 1

Carrot Sunflower Cucumber-leafed Sunflower Lettuce Little Gem Lettuce Dandelion Pot Marigold Pyrethrum Daisy Chicory Ageratum "Blue Mink" Globe Artichoke Holy Thistle Safflower Salsify Sweet Annie Persian Violet Tomato "Moneymaker" Yellow Tobacco Tree Wood Tobacco Tobacco Convolvulus Borage Forget-me-not Foxglove Green-flowered Foxglove Spearmint Emperor’s Mint Bergamot Catmint Basil Marjoram Sage Winter Savory Thyme Boston Ivy Sweet Gum Astilbe Lemon-scented Bottlebrush Evening Primrose "Innocence" Alyssum "Rosie O’Day" Yellow Alyssum Aubrietia Cauliflower "All Year Round" Kai choy Kai choy Kai choy Mustard Wallflower Wild wallflower Woad Radish Rock Rose Mallow "Zebrina" Chinese Mallow Nooin Flower

Mean

Eurosid 1

Sapindales

Curcurbitales

Rosales

Fabales

Malpighiales

Melianthus Rue Creeping St John’s Wort Wild Pansy Soya Bean Soya Bean Soya Bean Coffee Weed Huang Qi Huang Qi Huang Qi Red Clover White Clover Wild strawberry White Mulberry Watermelon

Melianthus major Ruta graveolens Hypericum olympicum Viola tricolor Glycine max xiangxi no 3 Glycine max xiangxi no 119 Glycine max aijiaohan Cassia occidentalis Astragalus sinicus zhezi no 84 Astragalus sinicus zhezi no 5 Astragalus sinicus shangde Trifolium pratense Trifolium repens Fragaria vesca Morus alba Citrullus lanatus

0.95 1.58 0.61 4.16 3.88 6.01 7.94 14.95 3.51 3.83 3.63 2.18 3.85 2.10 6.13 4.88

0.57 1.52 0.59 3.82 2.36 3.97 5.64 10.59 2.75 2.53 2.44 2.32 4.10 1.77 4.90 5.16

2.7 4.5 1.8 11.9 11.1 17.2 22.8 42.9 10.1 11.0 10.4 6.3 11.0 6.0 17.6 14.0

1.6 2.1 0.13, 0.79 7.2 10.8 20.0 43.4 36.0 9.9 10.5 9.4 10.8 11.9 1.37, 2.47 13.0 10.0

97 55 62 46 35 35 35 47 47 47 47 41 41 55 57 16

6 (5) 4 (5) 6,7 (10) 3 (5) 17 (5) 17 (5) 17 (5) 19 (5) 19 (5) 19 (5) 19 (5) 2 (5) 2 (5) 4,5 (10) 4 (5) 2 (5)

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761

P < 0.001) and species/variety (F ¼ 13.59, P < 0.001). Using the marker species B. vulgaris and L. sativa, an adjustment of 0.79 for all phase 1 activity concentration values gave similar mean values for marker species in both phases. All subsequent analyses therefore used phase-adjusted values. REML adjustment using dataset as the random factor and species/variety as the fixed factor on phaseadjusted data gave relative mean activities for 116 species/varieties which ranged from 1.86 to 2.69 (Table 1). In the REML analysis natural logs of activities are used and hence e(relative mean) gives estimated REML-adjusted means in original units (kBq g1) (Table 1). These estimated means take account of the effect of dataset, which ANOVA showed was significant, on 99Tc activities in plants to give an estimate of activities that might be obtained if all the species could have been grown and labelled simultaneously. As we generated all 21 datasets under similar conditions, REML adjustments, although significant, were relatively minor (Table 1). REML-adjusted mean 99Tc activities in the 116 species were not normally distributed (W-statistic of 0.951 from ShapiroeWilk test in Sigmaplot 11.0) and ln-transformation was the transformation we found that came closest to producing normally distributed data. The frequency distribution of none of the measured traits passed tests of normality and again ln-transformation produced the most nearly normal frequency distributions. We carried out subsequent parametric analyses using the ln-transformed data because the tests we used are relatively robust to the assumption of normality. 3.2.

99

Tc activity and plant traits

As expected for a healthy cohort of plants there was a strong positive correlation between lnFWt and lnDWt at harvest (r ¼ 0.909, P < 0.001). Simple linear regression of ln-transformed data revealed that ln99Tc Bq g1 DWt at harvest was dependent positively and significantly on lnFWt (Fig. 1a) but negatively and significantly on ln% DM and ln_age (Fig. 1b,c). The strong correlation between lnDWt and lnFWt meant that very similar relationships to those described in Fig. 1 for ln99Tc Bq g1 DWt were found for ln99Tc Bq g1 FWt. The ln%99Tc removed was most strongly dependent on ln99Tc Bq g1 DWt (Fig. 2a), although there was also significant dependence on lnFWt and ln% DM (Fig. 2b,c). The highest mean % of added 99Tc removed to shoots was 49% by Celosia argentea (Table 1) and many species removed greater than 30% to shoots. Calculating a ‘Concentration Factor’ (CF) based on the initial 99 Tc Bq g1 DWt in substrate and 99Tc Bq g1 DWt plant at harvest showed that most species had mean CFs greater than 1, with the highest being 61 for Chenopodium quinoa (Table 1), and more than 40 species had CFs of >20. Overall, therefore, both 99Tc activities in plants and % removal of added 99Tc were dependent, after acute exposure, to a significant extent on easily measured plant traits. It is notable, however, that there is still unexplained variation in 99Tc concentrations in plants and that the significant effects of species/ variety is not accounted for in the above relationships. 3.3.

99

Tc concentration ratios and plant phylogeny

A seven-way ANOVA coded using the angiosperm phylogeny of Soltis et al. (1999) revealed a significant effect of phylogeny on activity concentrations to which plants took up 99Tc (Table 2). There were significant effects at all levels of the phylogeny (Table 2). At the level of the ‘Class’ Monocots had significantly lower mean CFs than Eudicots (Fig. 3a). There were also significant effects at the level of the ‘Superorder’, with the basal Eudicots, which in Table 1 are composed primarily of species on the Caryophyllid clade, having mean 99Tc CFs significantly higher than any of the other clades (Fig. 3b). These phylogenetic effects can be visualised as a phylogenetic signal in Tc CFs (Fig. 4). At the Ordinal level the

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Fig. 1. Relationships between plant traits and 99Tc concentrations in 607 individuals of 116 species of angiosperm (5 replicates of most species, 10 replicates of some) acutely exposed for 48 h to 348 Bq99Tc g1 substrate including results for simple linear regression.

Fig. 2. Relationships between plant traits and % 99Tc removed by 607 individuals of 116 species of angiosperm (5 replicates of most species, 10 replicates of some) acutely exposed for 48 h to 348 Bq99Tc g1 substrate including results for simple linear regression.

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763

Table 2 Results of ANOVA on relative mean concentrations of 99Tc in 116 angiosperm species/varieties coded using the phylogeny of Soltis et al. (1999). (SS ¼ Sum of Squares, VR ¼ Variance Ratio). df ’Class’ ’Subclass’ ’Group’ ’Superorder’ Order Family Genus Residual Total

SS

1 2 1 4 15 16 42 34

13.38 6.62 1.60 15.73 29.95 19.74 24.92 9.96

115

121.92

Cum. SS

VR

P Value

11.00 16.41 17.72 30.62 55.19 71.39 91.83 100.00

45.67 11.29 5.45 13.42 6.81 4.21 2.02

<0.001 <0.001 0.026 <0.001 <0.001 <0.001 0.018

following Orders, in decreasing rank, had mean 99Tc CFs significantly higher than other Orders: Caryophyllales (Beets and relatives), Solanales (potato and relatives), Malvales (cotton and relatives), Brassicales (cabbage and relatives), Asterales (sunflowers and relatives) and Fabales (legumes and relatives). For the three

Fig. 4. Running mean (n ¼ 10) of soil-to-plant Concentration Ratios for 99Tc for 116 species acute exposure for 48 h to 348 Bq99Tc g1 in substrate in order of APG II (2003). Concentration Factors calculated using initial soil concentrations of 99Tc and final plant concentrations of 99Tc at harvest.

species in the database for which 3 cultivars were grown, a separate ANOVA detected significant differences between species but no significant differences between varieties, although inter-varietal differences for Glycine max (soya bean) were greater than for the other two species (Fig. 5). 3.4. Predicting inter-species differences in

99

Tc activity

Using the relationships in Fig. 1., which we tested as having no co-linearity, FWt, % DM and age were found, in multiple regression, to be significant, predictive independent variables for 99Tc activity concentration (R2 ¼ 0.407, F ¼ 127, P < 0.001). In addition, and again using only categories tested to have no co-linearity, it was found that ‘Class’, ‘Group’ and ‘Order’ used as dummy variables (i.e. categorical data coded with integers) in a multiple regression were significant predictors of 99Tc concentrations in plants (R2 ¼ 0.147, F ¼ 28, P < 0.001). Together plant traits and phylogenetic categories

Fig. 3. Geometric mean and 96% Confidence Intervals for Tc soil-plant Concentration factors in 116 angiosperms at different taxonomic levels based on the phylogeny of Soltis et al. (1999). Concentration factors calculated using initial soil concentrations of 99 Tc and final plant concentrations of 99Tc at harvest (a): ‘Classes’, 1 ¼ Monocots (n ¼ 12), 2 ¼ Eudicots (103). (b): ‘Superorders’, 1 ¼ Non-Commelinids (4), 3 ¼ Commelinids (8), 4 ¼ Basal Eudicots (31), Asterids (36), Rosids (35).

Fig. 5. Differences between cultivars of Brassica napus, Glycine max and Amaranthus sinicus in 99Tc activities following acute exposure for 48 h to 348 Bq99Tc g1 in substrate.

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used in a single multiple regression, which thus combined the effects of plant traits and phylogeny, predicted a highly significant proportion (R2 ¼ 0.497, F ¼ 98.5, P < 0.001) of variation in 99Tc concentrations in the 607 individual plants used in experiments (Table 3). Given that all plants were in substrates with the same initial 99Tc activities, these predictors of 99Tc concentrations in plants also predict CFs for Tc based on initial activities to exactly the same extent. 4. Discussion Table 1 is, taxonomically, the most wide-ranging comparison of Tc uptake by flowering plants yet reported. Overall, analyses of its data indicate that, in an acute soil contamination event, mean 99Tc activities will; a) be significantly different between species, b) not be normally distributed but approximately ln-normally distributed, and c) likely be related to FWt (Fig. 1a), % DM (Fig. 1b), and plant age at exposure (Fig. 1c). Inter-species differences are also likely to be affected by phylogeny, with significant differences arising from a number of categories above the species level (Table 2), and there will probably be smaller differences between cultivars than between higher taxonomic categories. Such acute exposures might be of direct radiological importance e 99Tc is highly mobile if it contaminates an aerobic soil and exposure from below through a fluctuating water-table, such as might occur with a leak from a nuclear repository, might produce acute exposures. In lysimeters with salt-stone waste buried in 1984 at Savannah River laboratory, 99 Tc from the waste was detectable in vegetation after upwards movement through the soil after only a few years (Murphy and Johnson, 1993). In addition, given that plants take up a high proportion of many elements when mature but before they initiate flowering (Marschner, 1995), the differences in 99Tc activities following acute exposures in Table 1 might be related to those resulting from chronic exposures. To predict absolute activities of 99Tc in plants in the field soil factors such as oxygen status and NO 3 concentration have to be taken into account. Translocation within plants can also affect absolute 99Tc activities in stem and leaf for example (Mousny et al., 1979; Lembrechts and Desmet, 1985). It is likely that 99Tc partitioning between stem and leaf differed between species in the data reported here. Such variables are clearly crucial in predicting absolute 99Tc activities in plants and plant parts. However, in order to predict 99Tc movement from soil to any plant species it is necessary to complement predictions that use soil and intraspecies effects with predictions of inter-species effects. It seems clear that simple plant traits might be used to predict a substantial 99

proportion of the differences between species in 99Tc concentrations. There are numerous established methods for quantifying plant growth via relatively easily measured variables (Hunt et al., 2002) that might aid predictions of 99Tc concentrations in the many species in which uptake has never been measured. However, even these methods might be time-consuming to enact in numerous plant species at short notice in a contamination event. The regression analyses reported here suggest that, as a general prediction for plants with unknown uptake, big, young plants with a low % DM will become contaminated at the highest 99Tc activity after an acute exposure whilst small, old plants with a high % DM the lowest activity. This is consistent with previous reports of 99Tc movements into fleshy leaves (Lembrechts and Desmet, 1985). Measuring simple plant traits on the same species used for the phylogenetic analysis indicates that plant traits probably explain a greater amount of the variation in 99Tc activity than does phylogeny. However, the ANOVA of REML values suggests that phylogenetic categories, which are predefined and necessitate no measurements at all, also provide general predictions of relative 99 Tc uptake. After a contamination event the significant differences between, for example, Monocots and Eudicots or various Orders, could provide general predictions for plants with unknown uptake. They might also be a useful guide to the design of species-sampling regimes to monitor contamination. Importantly, they are also an instantly available way to refine predictions based on plant traits or measured uptake. Thus we predict that large, young plants with a low %DM on the dicot, and especially the Caryophyllid, clade might become contaminated at the highest 99Tc activities, and that old Monocots with high %DM will become the least contaminated. Some such plants are included in Table 1 but there are numerous genera on the Monocot and Caryophyllid clades with species that might provide an independent test of this prediction. The prediction is, however, consistent with some previously reported patterns of 99Tc uptake. For example, Landa et al. (1975) noted that there was less root-to-shoot transfer of 99Tc in Monocots than Dicots (mostly on the Eudicot clade) and Cataldo et al. (1984) noted high 99Tc TFs in Artemesia and Centaurea (Eudicot clade). In studies of edible plant parts there are clearly intra-species translocation effects but also generally higher 99Tc activities in Eudicot species (Yanagisawa and Muramatsu, 1993, 1997). Thus, predictions based on phylogeny might be useful when a quick response to 99Tc release is necessary and there are no direct measurements of plant uptake available for the species involved. For most soils and most plants there are no data for 99Tc and quick predictions of which species are likely to have the highest and lowest 99Tc might be useful, at the very least,

Table 3 Results of multiple linear regression analysis for ln.kBq99Tc g-1 DWt for 607 plants of 116 species/varieties (%DM ¼ % dry matter, DWt ¼ dry weight). Regression Equation: ln.kBq99Tc g-1DWt ¼ 14.207 þ (0.252*ln.DWt)  (0.815*ln.%DM)  (1.181* ln.Age) þ (1.379*Class)  (0.941*Group) þ (0.117*Order) R ¼ 0.705: R2 ¼ 0.497: Adj R2 ¼ 0.492 Coefficient Std. Error Constant ln.DWt ln.%DM ln.Age Class Group Order

14.207 0.252 0.815 1.181 1.379 0.941 0.117

0.621 0.0462 0.118 0.152 0.159 0.116 0.0197

t

p

22.872 5.46 6.917 7.746 8.685 8.088 5.949

<0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001

Analysis of Variance DF

SS

MS

F

P

Regression Residual

6 596

375.084 379.31

62.514 0.635

98.447

<0.001

Total

602

754.395

1.253

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for refining sampling or monitoring regimes. Much radioecology research after Chernobyl emphasised the importance to impact assessments of understanding uptake of radionuclides by a range of flora (Bell and Shaw, 2005), but for most countries, and especially for those without a history of releases of radioactivity to the environment such as China, there is still a great paucity of such data for 99 Tc. The data here includes many species grown in China where the conclusions made might be particularly useful. The effects reported here of simple plant traits and phylogeny on relative activities of 99Tc in plants might be helpful in refining recommended values of 99Tc TF from soil-to-plant. The CFs reported here were not measured under the long-term ‘equilibrium’ conditions under which TFs are measured so cannot be used directly as surrogates for field TFs, i.e. they are not assessment ready. Neither can they take account of the effects of different chemical species or of ageing on TFs. However, some of the effects on CFs might be relevant to refining estimates of TFs. For example, recommended soil-to-plant TFs for 99Tc have had, in general, a mean of 5 (IAEA, 1982) and a range of 0.1e10 (IAEA, 1994). These recommendations are currently being reviewed and somewhat different values, e.g. a median of 8.1 (Frissel and van Bergeijk, 1989) have been recommended. The mean CF calculated for experiments reported here was 17. As previously noted, experimentally derived CFs/TFs for Tc tend to be higher than those derived at ‘equilibrium’ in the field (Tagami and Uchida, 2005) and thus a mean of 17 is not unexpected. If we assume that the effects reported here are maintained under TF conditions and a recommended mean TF is taken, it can be quantitatively refined using the effects we report here on the mean CF of 17. For example, if a recommended mean TF of 5 is used then, for the Monocots which here had a mean CF of 8.12 as compared to the overall mean of 17, a mean TF of 2.39 can be calculated. For Eudicots (mean here 18.17) the mean TF would be 5.3. For the two highest clades here (basal eudicots and eurosids 2 with CFs of 25.5 and 18.1) the global values would be 7.5 and 5.4 respectively. Similar calculations could be made for whatever means/medians or ranges were deemed appropriate and thus the effects of phylogeny might be used to refine predicted TFs. Clearly, the assumptions would need to be tested but it is notable that previous reports of phylogenetic effects for other elements have been based in part on reported TFs. The data reported here suggest that such refinements, if valid, should be based on geometric rather than arithmetic means. Phylogenetic effects have now been reported in the concentrations to which angiosperms take up numerous elements (Watanabe et al., 2007), and the 99Tc data reported here lends further support to the assertion that plant uptake of many radioisotopes differs between clades of the angiosperm phylogeny. With 55% of the variance in 99Tc concentrations at the level of Order and above, the phylogenetic effect on 99Tc concentrations is greater than that on N (3.3%) and P (6.8%) (Broadley et al., 2004), Cs (15%) (Willey et al., 2005), Pb (20%), Cr (23%), Cu (24%) (Broadley et al., 2001), Na (23%) (Broadley et al., 2004), and Cd (27%) (Broadley et al., 2001), similar in magnitude to that for Zn (44%), Ni (46%) (Broadley et al., 2001), and K (49%) (Broadley et al., 2004), but less than that on Ca (63%) (Broadley et al., 2003). If there is no effect of phylogeny on inter-species differences, variance occurs primarily at the species level e as is approximately the case for N and P (Broadley et al., 2004). The analyses reported here therefore provide evidence that for 99Tc plant species do not behave independently but that their behaviour is linked through phylogeny. This means that in statistical analysis of 99Tc behaviour in the soil-plant system, as with that of numerous elements, care must be taken not to assume that plant species behave independently. Phylogenetic effects on 99 Tc uptake indicate that, in general, plants on the Monocot clades take up 99Tc to lower concentrations than those on the Eudicot

765

clades. This is also the case for Ca (Broadley et al., 2003), 36Cl (Willey and Fawcett, 2005), 137Cs (Willey et al., 2005), 90Sr (Willey and Fawcett, 2006a), 109Ru (Willey and Fawcett, 2006b) and 60Co (Willey and Wilkins, 2008), and seems to confirm a general pattern of low shoot concentration of many radioisotopes on the Monocot clades. This effect can be visualised if CF for species are plotted using APG II in linear order (Haston et al., 2007) (Fig. 4). This illustrates that species in, and closely related to, the Caryophyllales have high uptake of 99Tc to shoots relative to other Orders of plants, as they do for 137Cs (Willey et al., 2005) and 60Co (Willey and Wilkins, 2008). This strengthens the assertion that plants on the Caryophyllid clades might merit particular radioecological attention. Phylogenetic effects might also provide some useful perspectives on the search for mechanisms controlling 99Tc uptake by plants. The uptake of 99Tc occurs at least partly through anion transporters (Krijger et al., 2000), but also through mass flow and the use of TcO 4 as a counter ion for cation uptake (Tagami and Uchida, 2005). There is a wide range of uptake behaviour in the data reported here and not all species might have 99Tc uptake dominated by the same mechanisms. The fact that big, young plants with low %DM, which are likely to have high transpiration rates reached the highest 99Tc concentrations, is however consistent with an important role of mass flow. Such observations might provide clues to the mechanisms causing differences between plant species in 99Tc uptake. They might also mean there are differences between acute exposures to young plants (in which transpiration is likely to be important) and chronic exposure to older plants (in which effects chemical reduction in chloroplasts and ontogeny might be more important) which are worth clarifying. The high availability of 99Tc to plants from aerobic soils suggests that 99Tc contaminated soils might be amenable to phytoremediation. Table 1 shows that a significant % of added 99Tc can be removed in a short period of time and there are a number of reports of single crops removing a significant % of contaminating 99Tc in the field (Bennett & Willey, 2003). A number of phytoremediation researchers note that a significant barrier to phytoremediation is matching species to sites (Willey, 2007) e species with high contaminant uptake are of no use if they cannot grow under the necessary site conditions. A method for selecting species which enables them to be matched to sites is desirable. The plant characteristics and taxonomic categories describe groups of plants with high uptake e particular species with the necessary characteristics that are suited to site conditions might be selected from these groups for 99Tc phytoremediation. 5. Conclusion As increasing amounts of 99Tc are earmarked for storage in terrestrial waste repositories it has become increasingly important to be able to predict the movement of 99Tc in terrestrial ecosystems. Experience from Chernobyl-derived radioactive contaminants demonstrated that understanding radionuclide uptake in a greater range of species than a few staple crops was necessary for rigorous radiological assessments. Thus, although 99Tc has primarily been of radiological importance in aquatic ecosystems and is a b-emitter of low energy, understanding 99Tc uptake by a wide range of terrestrial species might enable radioecologists to refine safety assessments of terrestrial nuclear waste repositories. Further, nuclear industries are now expanding in countries such as China in which there are less data on uptake of radionuclides by indigenous plant species/varieties than in countries affected by, for example, Chernobyl fall-out. The relationships reported here not only suggest hypotheses for further testing, in particular the effects following chronic exposure, but in the absence of direct measurements for

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the vast majority of plant species indicate how general predictions of differences in 99Tc concentrations between plants growing on the same contaminated soil might be made. This will help safety assessments of nuclear waste repositories and the selection of plants for phytoremediation of 99Tc. Acknowledgements We would like to thank the Leverhulme Trust for funding Dr Shirong Tang’s visit to the UK and Dr Andrew Meade of Warwick HRI, UK for writing the Genstat programme. References APG (Angiosperm Phylogeny Group), 2003. An update of the Angiosperm Phylogeny Group classification for the orders and families of flowering plants: aPG II. Bot. J. Linn Soc. 141, 399e436. Bell, J.N., Shaw, G., 2005. Ecological lessons from the Chernobyl accident. Environ. Int. 31, 771e777. Bennett, R., Willey, N., 2003. Soil availability, plant uptake mechanisms and soil to plant transfer of 99Tc e A review. J. Environ. Radioac 65, 215e231. Broadley, M.R., Willey, N.J., Mead, A., 1999. A method to assess taxonomic variation in shoot caesium concentrations among flowering plants. Environ. Pollut. 106, 341e349. Broadley, M.R., Willey, N.J., Wilkins, J., Baker, A.J.M., Mead, A., White, P.J., 2001. Phylogenetic variation in heavy metal accumulation in angiosperms. New Phytol. 152, 9e27. Broadley, M.R., Bowen, H.C., Cotterill, H.L., Hammond, J.P., Meacham, M.C., Mead, A., White, P.J., 2003. Variation in the shoot calcium content of angiosperms. J. Exp. Bot. 54, 1e16. Broadley, M.R., Bowen, H.C., Cotterill, H.L., Hammond, J.P., Meacham, M.C., Mead, A., White, P.J., 2004. Phylogenetic variation in the shoot mineral concentration of angiosperms. J. Exp. Bot. 55, 321e336. Brown, J.E., Kolstad, A.K., Brungot, A.L., Lind, B., Rudjord, A.L., Strand, P., Føyn, L., 1999. Levels of 99Tc in seawater and biota samples from Norwegian coastal waters and adjacent seas. Mar Pollut. Bull. 38, 560e571. Bryan, S.E., MacDonald, P., Hill, R., Wilson, R.C., 2008. Sea to land transfer of anthropogenic radionuclides to the North Wales coast. Part 1. External gamma radiation and radionuclides in intertidal sediments and air. J. Environ. Radioac 99, 7e19. Cataldo, D.A., Garland, T.R., Wildung, R.E., 1984. Plant root absorption and metabolic fate of technetium in plants. In: Desmet, G., Mytteneare, C. (Eds.), Technetium in the Environment. Elsevier, London, pp. 265e280. Coughtrey, P.J., Jackson, D., Thorne, M.C., 1983. Radionuclide Distribution and Transport in Terrestrial and Aquatic Ecosystems, a Critical Review of Data, vol. 3. AA Balkema, Rotterdam. 99  Echevarria, G., Vong, P.C., Morel, J.L., 1998. Effect of NO 3 on the fate of (TcO4 )-Tcsystem in the soil-plant system. J. Environ. Radioac 38, 163e171. Echevarria, G., Vong, P.C., Leclerc-Cessac, E., Morel, J.L., 1997. Bioavailability of Technetium-99 as affected by plant species and growth, application form, and soil incubation. J. Environ. Qual. 26, 947e956. Echevarria, G., Morel, J.L., Florentin, L., Leclerc-Cessac, E., 2003. Influence of climatic 99 conditions and soil type on (TcO 4 ) - Tc uptake by rye grass. J. Environ. Radioac 70, 85e97. Frissel, M.L., van Bergeijk, K.E., 1989. Mean Transfer Values Derived by Simple Statistical Regression Analysis, Sixth Report of IUR Working Group on Soil-toPlant Transfer Factors. RIVM, Bilthoven.

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