April - June 2007 - Kasetsart University

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Kasetsart J. (Nat. Sci.) 41 : 274 - 281 (2007) Soil-to-Plant Transfer of Radiocaesium in Thailand Thitika Thammavech and Teerasak Veerapaspong* ABSTRACT Soil-to-plant transfer factors (TF) of radiocaesium-137 were estimated by considering soil properties of 51 provinces in Thailand, and by using the model of Absalom. According to our study, the Absalom model could estimate average TF values to be 0.0852 ± 0.0475. Compared with average measured TF values which was 0.1289 ± 0.0529, it was found that calculated TF values decreased with increasing pH, clay contents and exchangeable K + . The corresponding calculated TF values increased with increasing organic matter contents and NH 4 + concentrations. Statistical analysis showed that Relative Euclidean Difference (RED) was 0.238, reliability index (k) was 0.661 and geometrically intuitive reliability index (k g) was 1.97, which confirmed that the Absalom model was reasonably accurate. Calculated TF values by the Absalom model were in good agreement with the measured ones. However, calculated TF values were found to be significantly different from the measured ones for some provinces in Thailand. The parameters used in the Absalom model needed to be modified to suitably match soil properties in Thailand. Key words: transfer factor, Absalom model, radiocaesium; soil properties INTRODUCTION Radionuclides produced by nuclear explosion and nuclear facilities have the potential to be released into the atmosphere. These nuclides are part of the fallout which is deposited on the ground and reach human bodies via food chain (Eisenbud, 1973). Among deposited radionuclides, radiocaesium ( 137 Cs, half life is 30 years) is the dominant fission product which has a high relative mobility in the soil–plant system, long term bioavailability, high radiotoxicity, continuing to cycle through the soil plant-animal system, and is longlived. The plant uptake of deposited 137 Cs from soil, commonly expressed as soil to plant transfer factor (TF) is widely used while Department of Physics, Faculty of Science, Kasetsart University, Bangkok 10900, Thailand. * Corresponding author, e-mail: fscitsv@ku.ac.th calculating the radiological humus dose via the ingestion pathway. Absalom et al. (2001) presented a model which predicted the radiocaesium soil to plant transfer factor (TF) on the basis of easily measured soil characteristics (pH, clay content, organic matter content, exchangeable K + + and NH4 concentration). In the present work, data of soil properties and 137Cs activity concentrations in soil and grass of some selected provinces in Thailand were collected and were used as input parameters to calculate transfer factor (TF) in the Absalom model. Finally, the calculated TF values were compared with the measured TF values to test whether the Absalom model could be applied to the soil characteristics in Thailand. Received date : 06/11/06 Accepted date : 01/02/07
MATERIALS AND METHODS Model descriptions Absalom et al. (1999) presented a semimechanistic model, which predicted activity concentrations of 137 Cs in plants. The model utilized as input soil characteristic parameters including clay content and exchangeable K + . In 2001, Absalom et al. (2001) developed the model which accounted for the effect of organic matter on 137 Cs adsorption by soil and uptake by plants. Therefore, radiocaesium bioavailability is strongly influenced by soil properties such as pH, clay content, organic matter and exchangeable K + (Cremers et al., 1988). This model can be applied to mineral and organic soils simultaneously to provide a more generally applicable simulation of 137 Cs dynamics. The model of Absalom et al. (2001) assumed that 137 Cs adsorption occurred exclusively on both clay and humus surfaces, however, fixation only occured on clay, and the radiocaesium adsorbed on the organic fraction was not subject to fixation. The relationship between adsorbed and solution of 137 Cs was described by a labile 137 Cs distribution coefficient (k dl, dm 3 kg -1 ) which was estimated as a function of clay content and exchangeable K + . Plant uptake of radiocaesium was described by a concentration factor (CF, Bq kg -1 plant/Bq dm -3 soil solution) which was related to solution K + concentration ([m K], moles dm -3 ). Data sources According to input parameters, the data referred to six different regions in Thailand. Samples were collected from several provinces in the north, northeast, east, west, middle and south of Thailand. Each soil sample consisted of subsamples collected from an area of 100 m 2 . The samples were taken from 0 to 10 cm upper soil layer. Specific soil parameters in each province were available for comparison with 137 Cs concentration in the grass samples. Kasetsart J. (Nat. Sci.) 41(2) 275 Five independent soil properties (pH, clay content, organic matter, exchangeable K + and + NH4 concentration) and initial 137Cs activity in soil were required as the model input parameters in the Absalom model assuming certain days after a deposition of 137Cs in soil for the prediction of TF values in the selected regions. Organic matter (OM) content was calculated as OM = organic carbon × 1.724 (Nelson and Sommers, 1982). The five values (pH, clay content, organic matter, exchangeable K + + and NH4 concentration) in Table 1 (LLD, 1988) are used as the input parameters to calculate transfer factor of soil-to-plant (here, it was grass) in the model. The soil and grass were dried and homogenized before being analysed. 137Cs activities in soil and grass, measured by a Hyperpure Germanium gamma-ray detector (HPGe), are also shown in Table 1 (Itthipoonthanakorn). RESULTS AND DISCUSSION Since the Absalom model takes into account the time-dependent changes in TF due to radiocaesium fixation, the calculations were performed assuming 365 days after uniform deposition of a certain amount of 137 Cs (Bq m -2 ) in soil. The same parameters as in the model were used in the calculations. Predicted and observed 137 Cs transfer factor (TF) values for grass are given in Table 2 and Figure 1. Calculated TF values of 137 Cs from soil to grass grown in tropical Thailand are shown in Figures 2-6 compared to different functions of soil properties. It can be seen from Figures 2-4 that the calculated TF values decrease with increasing pH, clay content and exchangeable K + . The corresponding calculated TF values increase with increasing organic matter content and NH 4 + concentration, as shown in Figures 5-6.
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April - June 2007 Volume 41 Number
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KASETSART JOURNAL NATURAL SCIENCE T
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Kasetsart J. (Nat. Sci.) 41 : 205 -
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harvesting time which started from
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y Chen and Paull (2000). Figure 1 a
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pineapple fruit allowed the accumul
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Kasetsart J. (Nat. Sci.) 41(2) 215
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Cluster analysis Cluster analysis u
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Table 3 (Continued) 1 2 3 4 5 6 7 8
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Page 25 and 26: CONCLUSION AFLP markers classified
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Page 29 and 30: difference of soil texture used in
Page 31 and 32: under wet soil condition planting.
Page 33 and 34: tropics. Following the expansion, w
Page 35 and 36: sealed in a plastic box with 1 cm w
Page 37 and 38: moisture content increment after so
Page 39 and 40: Frequency Frequency 30 20 10 0 20.0
Page 41 and 42: School, Kasetsart University. The a
Page 43 and 44: for combining ability of lines (Lam
Page 45 and 46: selective mass sibbing within indiv
Page 47 and 48: Although most of S 2-interfamily hy
Page 49 and 50: composite sets as used in this stud
Page 53 and 54: days. The numbers of calli producin
Page 55 and 56: the report of Ching (1982) showing
Page 57 and 58: clearly amplified bands generated b
Page 59 and 60: caused by either the recombination
Page 61 and 62: Kuhlmann, V. and B. Foroughi-Wehr.
Page 63 and 64: Xanthomonas fuscans subsp. aurantif
Page 67 and 68: was collected and washed with 70% e
Page 69 and 70: expected size was amplified with 35
Page 71 and 72: eaction technique has been used for
Page 73: Schaad, N.W., D. Opgenorth and P. G
Page 79 and 80: calculated TF value calculated TF v
Page 81 and 82: sincere gratitude and deep apprecia
Page 83 and 84: 1989). In monogastrics, the inclusi
Page 85 and 86: of all LLS samples in this study we
Page 87 and 88: Melbourne, Parkville, Victoria. Goe
Page 89 and 90: considered responsible for low prod
Page 93 and 94: mineral supplementation suggested b
Page 95 and 96: Body weight chane (kg/head) 32 30 2
Page 97 and 98: CONCLUSION AND RECOMMENDATION With
Page 99 and 100: SAS. (Statistical Analysis System).
Page 101 and 102: genes covering a variety of desirab
Page 103 and 104: mg/l NAA and 0.5 mg/l zeatin) incub
Page 105 and 106: 5. Purification by various sucrose
Page 107 and 108: as the culture period increased. So
Page 109 and 110: culture, pp. 1-20. In L.C. Fowke an
Page 113 and 114: GC. Analytical methods Total carboh
Page 115 and 116: ash. The crude hot water polysaccha
Page 117 and 118: spirulan (H-SP), and a desulfated c
Page 121 and 122: cells often displayed an increased
Page 123 and 124: LITERATURE CITED Bell, T.A. and D.V
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for alternative sources of supply.
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BR2.1.2 formed the same clade with
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supplement with glutamate (Iida et
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optimal for S. limacinum BR2.1.2 fo
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fluorescent lamp at 1.5 kLux develo
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Wisconsin, USA). Total extracted RN
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RESULTS Cloning and sequencing of m
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Expression of rmIL-2 and purificati
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other strains of mice have differen
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dose interleukin-2 therapy promotes
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The chitosano-oligosaccharides are
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enzyme and cells were maximized by
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of crude chitosanases. Fortunately,
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Relative activity (%) 100 80 60 40
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Uchida, K. Tateishi, O. Shida and K
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MATERIALS AND METHODS 1. Materials
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without coating and coated with pec
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in Table 5-7. It was found that the
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the cultures at 10,200 × g for 15
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incubation and reached the final pH
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y lysine- and tyrosine-decarboxylas
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during the stages of ripening as su
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As the 25 companies were divided in
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All the data comes up with informat
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as piece “A” or straight as pie
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et al. (1998); Zeng (2000); and Tal
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p 1, i * Kasetsart J. (Nat. Sci.) 4
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c ≤ c2 jq2 j n q p k p * 2, j , ,
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algorithm would consider RM 5, prio
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the maximum deviation of about 10%.
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Stock Quantities Using Heuristic Op
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which dynamically and automatically
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2. Framework architecture and compo
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Risk (VaR) calculation which was im
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Throughput (job/sec) 7 6 5 4 3 2 1
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Speed up 40 35 30 25 20 15 10 5 0 F
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and discovery, resource selection a
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April - June 2007 - Kasetsart University

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