Adsorption and desorption kinetics of 60Co and 137Cs in fresh water rivers

Journal of Environmental Radioactivity 149 (2015) 81e89

Contents lists available at ScienceDirect

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

Adsorption and desorption kinetics of rivers

60

Co and

137

Cs in fresh water


rez a, b, *, Lieve Sweeck b, Willy Bauwens a, May Van Hees b, Fabricio Fiengo Pe Marc Elskens c a b c

Department of Hydrology and Hydraulic Engineering, Vrije Universiteit Brussel (VUB), Pleinlaan 2, 1050, Brussels, Belgium Biosphere Impact Studies, Belgian Nuclear Research Centre SCK
CEN, Boeretang 200, BE-2400, Mol, Belgium Laboratory of Analytical and Environmental Chemistry, Vrije Universiteit Brussel (VUB), Pleinlaan 2, 1050, Brussels, Belgium

a r t i c l e i n f o

a b s t r a c t

Article history: Received 13 April 2015 Received in revised form 10 July 2015 Accepted 13 July 2015 Available online xxx

Radionuclides released in water systems e as well as heavy metals and organic toxicants e sorb to both the suspended solid particles and the bed sediments. Sorption is usually represented mathematically by the distribution coefficient. This approach implies equilibrium between phases and instantaneous fixation (release) of the pollutant onto (from) the surface of the soil particle. However, empirical evidence suggests that for some radionuclides the fixation is not achieved instantaneously and that the reversibility of the process can be slow. Here the adsorption/desorption kinetics of 60Co and 137Cs in fresh water environments were simulated experimentally and later on modelled mathematically, while the influence of the most relevant factors affecting the sorption were taken into account. The experimental results suggest that for adsorption and the desorption more than 24 h are needed to reach equilibrium, moreover, It was observed that the desorption rate constants for 60Co and 137Cs lie within ranges which are of two to three orders of magnitude lower than the adsorption rate constants. © 2015 Published by Elsevier Ltd.

Keywords: Radionuclides Distribution coefficient Adsorption Desorption Kinetics Uncertainty analysis



1. Introduction

Kd Radionuclides released in surface water systems can follow three different pathways. They can be (1) transported in solution, also called dissolved phase, (2) in suspension attached to solid particles, known as suspended solid phase, or (3) accumulated in the bed sediments. In the dissolved phase, the radionuclides follow the water flow dynamics and their residence time in the system is the same as that of the water, while in the other two phases the sediment transport processes determine the radionuclide migration, and hence the residence time is longer. The interaction between the radionuclides and the sediments is mainly governed by adsorption and desorption. Adsorption represents the transfer of a substance from the dissolved to the solid phase and desorption is the release of the substance from a particle into the water. Both are often represented by the distribution coefficient Kd (Eq. (1)) (also known as partition coefficient) under the assumption of instantaneous equilibriums and reversibility.

* Corresponding author. Boeretang 200, BE-2400, Mol, Belgium.
rez). E-mail address: [email protected] (F. Fiengo Pe http://dx.doi.org/10.1016/j.jenvrad.2015.07.010 0265-931X/© 2015 Published by Elsevier Ltd.

 ml Cs ¼ g Cw

(1)

where Cw, Cs are the concentrations of the pollutant in the dissolved phase [Bq/ml] and in the suspended solid phase [Bq/g], respectively. Here Bq represents Becquerel the quantity of radioactive material in which one nucleus decays per second. Values for Kd vary greatly as a function of aqueous and solid phase chemistry, implying that a constant single Kd value is hardly ever acceptable for an entire study site and/or period. Then, the Kd value should be modified as the chemically important environmental conditions (i.e. water ionic composition, sediment characteristics, pH, redox potential, temperature, etc.) change (Chapra, 1997; IAEA, 2009; Radovanovic and Koelmans, 1998) in order to reduce the uncertainty ranges, and improve the reliability of the predictions. Moreover, the assumptions of fast equilibrium and reversibility are not necessarily valid for all radionuclides. For some of them, the equilibrium between phases is not instantaneously achieved, and the process is hardly reversible. In these cases, besides the distribution coefficient, knowledge about the kinetics of the adsorption and desorption processes should be included. Studies related to the modelling transport of contaminants in

82

F. Fiengo P
erez et al. / Journal of Environmental Radioactivity 149 (2015) 81e89

surface waters report that the model results may underestimate the pollutant concentration in the water column, when the kinetics of the adsorptionedesorption processes are not taken into account (Liu et al., 2013). Results on the Sr and K adsorptionedesorption kinetics onto sand and gravel sized streambed sediments indicate that the adsorption process was relatively slow compared to change in the dissolved concentrations (Bencala et al., 1983). Different papers state that the inclusion of the kinetics in the modelling of radionuclide transport increases complexity and the number of parameters without a clear improvement in the results (Monte et al., 2005); especially for long term assessments. However, other studies demonstrate that the inclusion of adsorptionedesorption processes by means of a kinetic approach drastically improves the understanding and quantification of the €rman, radionuclide transport in aquatic systems (Jonsson and Wo €rman et al., 2002). 2001; Liu et al., 2013; Nyfeler et al., 1986; Wo More pragmatically, it is observed that the preference for one method over another depends mainly on the data availability and on the assessment purpose (IAEA, 2001; IAEA, 2007; Wang et al., 2014). Nevertheless, a clear advantage of the kinetic representation over the classic Kd approach is that, it can be applied both for the representation of routine or accidental releases as well as for long or short term releases. The objectives of this paper were (1) to simulate via lab experiments the adsorptionedesorption processes including the effects of the ionic composition, water pH and river's sediments characteristics on the distribution of 60Co and 137Cs between the dissolved phase and the river bed sediments, and (2) to formulate a simple and reliable adsorption and desorption model using first order kinetic laws (Ciffroy et al., 2001; Rumynin, 2011; Rumynin et al., 2005) in order to reproduce the experimental results obtained for both elements. The distribution coefficient and the adsorption and desorption rates are site specific, for this reason, the different water compositions and sediments characteristics found on the Molse NeteGrote Nete River are used in this study. The river is located in the north eastern part of Belgium and it receives low level radioactive discharges from the nuclear installations of the Mol-Dessel site and the liquid waste treatment installations of Belgoprocess 2. The formulated adsorptionedesorption model will be coupled in a near future with a hydrodynamic river, a sediment transport and an advectionediffusion model in order to quantify the fate and transport of 60Co and 137Cs in river networks. 2. Material and methods 2.1. The sampling campaign Along the Molse Nete-Grote Nete River, three sampling points were identified. The selection criteria for these locations were guided by the change of the water flow amount, the sediment transport and the distance downstream of the radionuclide discharge point into the river. At these points, water samples were taken from the water column and from the bed sediments. At the sampling points, 1 L water grab samples were taken directly from the river and kept at þ6  C until analyses. With regard to the bed sediments, around 10 kg per site was collected. The sampling was done by using (1) a van Veen grab with a capacity of 2 L and a surface cover of 260 cm2, and (2) Ekman grab with dimensions of 152  152  152 mm with a content capacity of 3.5 L. 2.2. River water characterization The river water was characterized in terms of its ionic composition, pH and conductivity. For the determination of the ionic

composition water samples were filtered by using a 0.45 mm Acrodisc® Syringe Filters with HT Tuffryn and, later on, the major anions and cations were measured by using a Dionex ion chromatography system. The pH and the electric conductivity were measured with a pH/conductivity meter PC5000H pHenomenal. The pH meter was calibrated using buffer solutions AVS TITRINORM with pH 4, 7 and 10 traceable to NIST/PTB. Regarding the electric conductivity, 0.01 mol/L KCl (match according to ISO 7880) was used for the calibration. 2.3. The bed sediment characterization Bed sediment samples were taken at the same sites as the water samples. These samples were air dried, sieved (2 mm) and kept under humidity controlled conditions during 14 days for their further physical and chemical analysis. The bed sediments were characterized in terms of their organic matter content (OMC) (ASTM, 2013), ionic composition and cation exchange capacity (CEC) (Pleysier and Juo, 1980) and texture. The particle size distribution curves were determined by sieving (diameter  50 mm) and by the sedimentation method for smaller particle sizes (ASTM, 2007). The extraction of trace metals from the sediment samples was performed in polytetrafluoroethylene digestion bottles using a CEM Mars 5 microwave oven as described in Gao et al. (2013). Trace metal analysis was performed using a sector field high resolution inductively coupled plasma mass spectrometry (Thermo Finnigan Element II). Relative precision on metal determinations was better than 7%. 2.4. The determination of the fresh water distribution coefficients Kd The Kd is affected by various environmental factors such as pH, water column composition, sediment texture, etc. (Benes et al., 1989; Ciffroy et al., 2001; Lujaniene_ et al., 2012). The effects of pH and ionic composition on the distribution coefficients of 60Co and 137 Cs were studied for bed sediment samples taken at Winkelom, Grote Nete and Hulshout. The natural pH values in the river water being in the range of 6.8e8.0, the experiments were performed at pH 6, 7 and 8. The experiments were carried out with artificial water, based on demineralized water to which major ions (cations, anions) were added in a way to resemble as closely as possible the in situ composition of the river water. The artificial water was used because it provided a better control on factors affecting the distribution coefficients, e.g. pH and ionic strength (Benes et al., 1989; Ciffroy et al., 2001). The Kd values for 60Co and 137Cs were determined following the Standard Test Method for 24-h Batch-Type Measurement of Contaminant Sorption by Soils and Sediments (ASTM, 2008). A total of 100 ml of artificial water (the details of the composition are presented in Table A.1 Appendix A of supplementary material) with a composition equal to 99 ml artificial Water (AW) and 1 ml buffer agent (HEPES (1 M) buffer agent, to keep the pH at a given level, was prepared. The pH was changed to 6, 7 and 8 by adding HCl or NaOH; 100 ml 137CsCl (1.06 MBq/ml) source was added to the mixture. This solution was divided in four, in order to have 3 replicate solutions with sediment and a control solution. 25 ml of this solution was added to each replicate containing 1.25 g of soil (solid/liquid ratio ¼ 1/20). The remaining 25 ml were measured in order to know the activity in the liquid phase before the sorption. After 24 h shaking, the samples are filtered (0.2 mm) and the remaining 137Cs amounts in the liquid phase are determined. Regarding the 60Co experiment, a similar approach was used. In this case the composition was 95 ml AW þ 1 ml HEPES (1 M) þ 4 ml 60CoCl2 (17.4 kBq/ml).

F. Fiengo P
erez et al. / Journal of Environmental Radioactivity 149 (2015) 81e89

2.5. Kinetics of adsorption and desorption reactions The procedure applied for the determination of the temporal evolution of the adsorption follows a similar methodology as aforementioned. In the case of the of 60Co experiment the compositions was 98 ml AW þ 1 ml buffer agent þ1 ml 60CoCl2 (17.4 kBq/ml). From this solution, 25 ml of were used to measure the activity before adsorption (in-solution), the rest solution was distributed in three replicates and mixed with 1.25 g sediment. Aliquots of the solution-sediment mixture were taken after 1, 3 and 6 h in order to measure e after filtrationethe activity in the liquid phase. For 137Cs the composition was 99 ml AW þ 1 ml buffer agent þ75 ml 137CsCl (1.06 MBq/ml). Similar to 60Co, the solution was distributed in one in-solution and three water sediment mixture replicates, aliquots were taken after 1, 2, 3 and 6 h. For the desorption experiment, the bottles with contaminated sediment obtained at the end of the distribution coefficient experiment were used. After the Kd experiment, most of the solution was extracted. The remaining solution was estimated as the difference between the weight of the water-sediment mixture after the experiment, and its weight after drying (40  C during 48 h). The remaining activity in the liquid phase was used to correct the activities measured during the desorption experiment. Later on, 25 ml of artificial water, with pH set initially to 6, 7 and 8, were added. The new mixture was shaken continuously during the whole experiment and aliquots of 2.5 ml were taken after 2, 6 and 24 h and, after filtration (0.2 mm), the activity of each one was measured. 2.6. Kinetic model for the adsorption and desorption reactions The adsorptionedesorption kinetic model is represented as a compartmental model with two components: one for the activity concentration in the dissolved phase and other for the representation of the activity concentration in the solid phase. The model is formulated as a system of differential equations that expresses the activity balance, once decay is included, in the dissolved and solid phases. The changes in radionuclide concentration in the liquid phase and solid phase are given by (Eq. (2)) and (Eq. (3));

vCw ¼ l$Cw  k1 $S$ðKd $Cw  Cs Þ vt

(2)

vCs ¼ l$Cs þ k1 $ðKd $Cw  Cs Þ vt

(3)

if Kd $Cw > Cs ðadsorptionÞ then k1 ¼ kws otherwise ðdesorptionÞ k1 ¼ ksw with Kd ¼

Cs Cw

where Cw, Cs are the activity concentration in the dissolved phase [Bq/ml] and in the suspended solid phase [Bq/g] respectively, l is the radioactive decay rate [1/s], Kd is the distribution coefficient between the water column and the suspended solids [ml/g], k1 represents the adsorption rate kws or the desorption rate ksw between the pollutant and the suspended solids [1/s], S is the suspended solids concentration in [g/ml]. C s [Bq/g] represents the activity concentration in the suspended solids phase in equilibrium with the activity concentration in the dissolved phase Cw. The distribution coefficient Kd is obtained from the 24 h experiment while, the sorption and desorption rates are calibrated by comparing the model simulations with empirical data obtained

83

from adsorption and desorption experiments. The right hand side of Eq. (2) has two components responsible for the activity balance in the dissolved phase compartment: the first represents the loss of radionuclides due to the radioactive decay and the last represents the loss (gain) of radionuclides due to sorption (desorption) to the solid phase. The first and the second components of the right hand side of Eq. (3) represent the activity balance in the solids phase compartment. They are equivalent to the ones in Eq. (2). Adsorption and desorption processes occur simultaneously and continuously in time; depending on the non-equilibrium conditions one of them will be the dominant process. However this fact is simplified by the proposed model where according to Eq. (2) and Eq. (3) if the concentration in the solid phase is less than the equilibrium concentration, adsorption happens, otherwise desorption will occur. The second right component of the equations also implies that the larger the difference between the ‘observed’ concentration and the equilibrium concentration, the faster the adsorption or desorption will be. The speed of transfer of radionuclides between phases will slow down as the concentrations approximate the equilibrium concentration. The system of equations Eq. (2) to Eq. (3) requires knowing the initial activity concentration in the dissolved phase and solid phase for its solution. For adsorption, the initial condition in the dissolved phase is measured from the in-solution described in section 2.5, while for the solid phase, the activity is equal to zero because the soil used is not contaminated. With regard to desorption, the definition of the initial conditions in not as straightforward as the case of adsorption. Here, according to the experiment setup, at the beginning of the experiment, there is activity in the dissolved and in the solid phases. Additionally sample manipulation (e.g. sample drying) could induce activity redistribution between both phases. Benes et al. (1989) reports that drying of sediment after preliminary adsorption probably modify the sorption properties significantly increasing the desorption. The initial conditions for desorption are optimized under two restrictions in order to obtain realistic estimations instead of merely mathematical results. The first criteria sets the starting value to the estimated initial conditions of the activity quantified after preliminary adsorption, and the second limits the maximum activities in both phases to the equilibrium conditions ruled by the predefined distribution coefficient Kd. 2.7. Initial conditions optimization, calibration and uncertainty analysis of the adsorption and desorption rate constants The initial conditions for desorption, the adsorption and the desorption rate constants are determined by coupling the kinetic model with the Differential Evolution algorithm (Price et al., 2005; Storn and Price, 1997) for supervised automatic calibration. The auto-calibration procedure is able to provide the best set of parameters, however, it is common to obtain parameter values without physical sense that minimize the difference between simulation and observation. In those cases the expert judgement makes it possible to discard or accept values based on past experiences. Once the feasible parameters are determined the model and parameter uncertainty can be quantified. The most popular approach is the Bayesian analysis based on Markov Chain Monte Carlo simulations (Beven, 2012). The Bayesian inference is computationally demanding and requires long computation time. The more recent and faster DiffeRential Evolution Adaptive Metropolis (DREAM) (Vrugt and Ter

F. Fiengo P
erez et al. / Journal of Environmental Radioactivity 149 (2015) 81e89

84

Braak, 2011; Vrugt et al., 2008) offers an alternative: the DREAM algorithm is able to explore the parameter space, identifying the most suitable set of parameters, and to quantify the model uncertainty simultaneously with less computational requirements. In this study the DREAM algorithm is used to calculate the uncertainty of the adsorption and desorption rate constants. 2.8. Data analysis and treatment All ANOVA models were built according to Doncaster and Davey (2007). The influence of pH and sampling sites on the Kdvalues were studied using a fully replicated factorial design meaning that each level of one factor is tested in combination with each level of the other one. This experimental set up allows us to test whether pH and site influence Kd additively as main effects, and whether the effect of one factor is moderated by the other in an interaction. After log transformation to ensure normality (ShapiroeWilk test) and homoscedasticity (Levene test), data were tested using a two-factor fully cross-factored model, followed by Tukey tests for multiple comparisons. Repeated-measures designs were used in kinetic experiments to determine sorption and desorption rates because repeated measurements may be correlated when made on the same treatment. Maximum likelihood based approach for parameter optimization was used to overcome this problem (Wolfinger et al., 1994). Correlation analyses were carried using Pearson correlation coefficient with associated Bonferroni probabilities. All tests were performed using XLSTAT from Addinsoft. 3. Results and discussion 3.1. Water river and bed sediments characterization As shown by Table 1, the concentrations of major anions and cations in surface waters are consistently higher at Grote Nete. It can also be noted that with the exception of nitrogenous and phosphorus nutrients, the other ions are positively correlated (Pearson correlation coefficient > 0.9, p ¼ 0.1). The CEC values are consistently higher at the Grote Nete and Hulshout, as compared to Winkelom. The difference can be explained by the differences in OMC. The sediments consists mainly of sand, and in these sediments, the CEC is essentially associated with organic matter (Pearson correlation coefficient > 0.9, p ¼ 0.05). The concentration of trace metals was determined by ICP-MS. When normalized with respect the aluminium and/or iron concentrations to account for grain size effect (Liaghati et al., 2004), the corresponding Cd-, Cu-, Pb- and ZneAl or Fe ratios appear consistently higher in sediments from Winkelom, a site probably more impacted by anthropogenic activities.

3.2. Impact of pH on the

60

Co and

137

Cs distribution coefficients

The average Kd values for 60Co and 137Cs are presented in Fig. 1. For both elements, the largest values were obtained for the sediment samples taken at the Grote Nete site. The Kd value for 60Co drastically increases as the pH increases from 6 to 8, while slightly decreasing for 137Cs. In other words, the amount of 60Co that can be released from the particulate to the dissolved phase increases with lowering pH but it is the reverse for 137Cs. Yet, the amplitude of variations and the difference between the sampling units are much more pronounced for 60Co as indicated by the results of a two way ANOVA. In this layout, pH and sampling sites are crossed because each level of one factor is found in combination with the other one. For 60Co, each factor was statistically significant (p < 0.001) but the pH effect is variable depending on the sampling site, i.e. there is a statistically significant interaction between both factors (p < 0.001). Accordingly significant mean differences were reported for all pairwise comparisons (Tukey's HSD test, p < 0.001). This indicates that for 60Co, the effect of pH on Kd strongly depends on the physico-chemical characteristics of the sampling unit. In contrast for 137Cs, the behaviour is somewhat different (Fig. 1). If the effects of pH and sampling site are significant (p < 0.001), there is no statistically significant interaction between them. Moreover, only Winkelom and the sample treatment at pH 8 behave differently from the other results, i.e. there are no statistically significant differences between the results obtained at Grote Net and Hulshout, and between the treatment at pH 6 and 7 at a a level of 5%. Finally, it should be stressed that the order of magnitude of the Kd-values obtained for both radionuclides agrees reasonably well with those reported in literature (Garnier and Roussel, 2001a, 2001b; IAEA, 2009; IAEA, 2010). To investigate possible relationships between Kd and the measured environmental factors, correlation tests were performed. The Pearson correlation matrix showed, positive associations (correlation coefficient > 0.9, p ¼ 0.1) for 137Cs between Kd and OMC, CEC, Al, V, & Cr. In the case of 60Co the Kd values were positively correlated with 59Co, 55Mn, 60Ni and 56Fe (Pearson correlation coefficient > 0.9, p ¼ 0.1). Yet no significant correlations (p  0.1) were found with the major ion concentrations in the water column. The distribution coefficients of 137Cs for the sediment sampled from Winkelom are consistently lower than these from the Grote Nete and Hulshout, and may reflect differences in CEC and the organic matter content (Table 1). The distribution coefficient of 60 Co also depends on the characteristics of the sediment. Bunker et al. (2000) reported that the levels of organic matter, iron and manganese oxides in the sediment composition may impact the mobility of this element. Complexation with organic ligands (by forming neutral or negatively charged complexes) increases the mobility of cobalt in aqueous environments (ATSDR, 2004; Garnier and Roussel, 2001b). However under normal environmental

Table 1 Bed sediment characteristics. Location

Winkelom G.Nete Hulshout Location

Winkelom G.Nete Hulshout

CEC

AWS

OMC

Sand

Clay

Silt

114

200

(meq/g)

(%)

(%)

(%)

(%)

(%)

(mg/g)

(mg/g)

(mg/g)

(mg/g)

26 43 45

0.7 1.6 2

3 4.6 5

96.9 96.9 98.4

2.2 2.1 1.5

0.8 0.9 0.1

4 2.3 2.3

0.01 0.06 0.02

15.6 12.2 8.2

2,818 6,490 6,692

56

59

60

65

(mg/g)

(mg/g)

(mg/g)

(mg/g)

(mg/g)

(mg/g)

8,547 67,573 43,384

1.7 7 3.4

3.6 6 4.4

4 3 2

756 536 383

9.4 51 19.2

Fe

Co

Ni

Cu

66

Cd

Zn

75

As

51

Hg

208

Pb

27

Al

52

55

(mg/g)

(mg/g)

(mg/g)

9.3 38.7 33

10.6 29 28.8

56.4 108.4 56.5

V

Cr

Mn

F. Fiengo P
erez et al. / Journal of Environmental Radioactivity 149 (2015) 81e89

Fig. 1. Impact of pH on the

60

Co and

137

Cs distribution coefficient at the sampling locations.

conditions (pH 4 to 8 and oxidizing conditions) and in the absence of organic ligands cobalt is mainly present as uncomplexed Co2þ and its behaviour is controlled by a strong sorption on minerals (i.e. iron and manganese oxide minerals and clay minerals) and competitive ions such as Ca2þ (Krupka, 2002). The Kd behaviour of 60Co appears to be related to that of Fe, Ni and Mn as suggested by the results of the correlation tests. The Fe and Mn levels, which may be representative for the amount of Mn and Fe oxides, are the highest at the Grote Nete location, which may explain the higher Kd values observed at this site. Moreover, since the migration behaviour of 60Co in the absence of organic ligands is controlled by cation exchange reactions, the decrease of Kd values and the concomitant increase of dissolved 60Co at lower pH might reflect a competitive effect due to the higher proton concentration (see Fig. 1). Similarly, it is possible that the decrease in the percentage of clay and the presence of the major cations (in concentration comparable to the measured in Grote Nete) observed at Hulshout (Table 1) has impacted the pH dependence of Kd, since the amplitude of response is less pronounced than what was observed for the other sites (Fig. 1). It should be noted that the largest change in Kd versus pH was observed at Winkelom, which exhibits the lowest concentrations of major cations. 3.3. Impact of 60Co and 137Cs adsorption and desorption reactions on the partition coefficient For 60Co, the fastest adsorption rates were found at the Grote Nete sampling site, where more than 80% of the added 60Co was

Fig. 2. Impact of

60

85

adsorbed after 1 h (Fig. A.2), at this location, the highest Kd values were found (Fig. 2). It is noted that at all sampling sites, the adsorption rate at pH 6 is slower than at higher pH values, probably reflecting the competitive effect of protons. The results of the repeated measures ANOVA are consistent with those obtained previously. The effects of the explanatory factors (site, pH) and their interaction (pH  site) are significant (p < 0.001). Then it can be seen that the repetition factor (contact time) has a significant impact on Kd as well as the interaction term between time and the explanatory factors (p < 0.001), i.e. the effect of pH depends on time (pH  time) while the kinetic of the sorption process is different between sampling units (site  Time). Finally it can be noted that at the three sites and for the different pH treatments, the Kd(t)-values (Kd(t)¼Cs(t)/Cw(t)) observed after 1, 3 and 6 h were significantly lower than those measured after 24 h (paired t-tests p < 0.001) demonstrating that the assumption of instantaneous equilibrium is far from being valid. The results related to the desorption experiments show that at high pH 60Co is strongly adsorbed (Fig. 3). The maximum percentage of 60Co desorption (>20%) is found at Winkelom for pH 6. The results of the repeated measures ANOVA are similar to those obtained for the sorption experiment, i.e. that both time and the explanatory factors have a significant impact (p < 0.001) on the Kdvalues. Yet a pair t-test indicates that the Kd-values obtained after 24 h in the desorption experiment exceeds significantly those observed in the adsorption experiment suggesting that the balance between both processes is longer than 24 h. These results also indicate that at low pH the adsorption process at Winkelom after

Co adsorption kinetics on the distribution coefficient.

86

F. Fiengo P
erez et al. / Journal of Environmental Radioactivity 149 (2015) 81e89

Fig. 3. Impact of

60

Co desorption kinetics on the distribution coefficient.

24 h was slower than at the other sites (<60% vs > 90%) but the desorption process was much more important (>20% vs < 5%). The Kd and the time dependent sorption obtained for 137Cs are presented in Fig. 4. It is observed that at all sites and for each time the Kd values slightly decrease as the pH increases. Larger fluctuations between repeated measures are observed at Hulshout. The adsorption rates indicate that after 1 h of contact between 137Cs and the sediment sample about 85% of the total adsorption is reached (Fig. A.4 to A.6). The results of the repeated measures ANOVA are consistent with those obtained previously for the two crossfactored model, i.e., both pH and sampling unit have a significant impact (p < 0.001) on the Kd-values but their effect are merely additive (there is no significant interaction between them at a ¼ 5%). The repetition factor (contact time) has a significant impact on Kd (p < 0.001) and there is a statistically significant interaction between sampling site and time (p < 0.001), i.e. the kinetic of the sorption process is probably influenced by the physicochemical characteristics of the bed sediments. By contrast, the pH effect does not strongly depend on time (p > 0.076). The desorption of 137Cs with time at different pHs are presented in Fig. 5. Less than 1% of the amount adsorbed is released after 24 h, which is within the margin of experimental uncertainties. Extensive studies on the behaviour of 137Cs have clearly shown that micaceous clay minerals and not organic matter strongly retain 137 Cs in soils and sediments, (Sawhney, 1972; Staunton et al., 2002; Valcke and Cremers, 1994). This could mean that, contrary to what is suggested by the observed positive correlation between Kd and organic matter, it would be the clay fraction which plays a leading role in the sorption of 137Cs.

Fig. 4. Impact of

137

3.4. Kinetic model for the adsorption and desorption reactions of Co and 137Cs

60

The previous section has clearly demonstrated that Kd is a lumped coefficient presenting large fluctuations as a function of aqueous and solid phase chemistry, and for which instantaneous equilibrium between adsorption and desorption reactions cannot be assumed. This is perfectly turned out for 60Co, probably less for 137 Cs, which showed a much lower dependence on pH and sitespecific variations in sediment conditions. There is no doubt, however, that these Kd variations may induce large unpredictability regarding the transport and fate of radionuclides in aquatic systems. To overcome this situation, a general kinetic model (Eqs. (2) and (3)) to describe the adsorption and desorption reactions of radionuclides in sediments have been designed. The modelling results for both 60Co and 137Cs agree reasonably well with the observations for the adsorption processes. However, in the case of desorption, a possible influence of the drying process is appreciated especially for 60Co, because it was observed that for the initially estimated starting conditions the model predicted systematically lower values in comparison to the empirical results. Therefore the initial conditions were optimized as described in sections 2.6 and 2.7. Once the initial conditions were corrected to take into account the extra activity artificially released during the desorption experiment set up, the model and the empirical data obtained during the desorption model for t > 0 h agree reasonably well. The initially estimated starting conditions are presented in Table A.2 Of Supplementary Material.

Cs adsorption kinetics on the distribution coefficient.

F. Fiengo P
erez et al. / Journal of Environmental Radioactivity 149 (2015) 81e89

Fig. 5. Impact of

87

137

Cs desorption kinetics on the distribution coefficient.

The agreement between model results and empirical data is illustrated in Fig. 6 for the Winkelom site. Data for the other sites are reported in Appendix A of supplementary material. For desorption figures, the value at t ¼ 0 represents the optimized activity concentration. The adsorption (kws) and desorption (ksw) rate constants at the three sites are given in Table 2. For 60Co, kws is greater than ksw by mostly two orders of magnitude. The most important changes are observed at Winkelom and for pH 6. The differences are less pronounced for the other sites and pH 7e8. These outcomes suggest that the desorption reactions are more important at low pH, where the model slightly underestimates the concentrations in the liquid phase and overestimates the concentrations in the solid phase. As previously noticed, the sorption and desorption of 137Cs is less reliant on factor pH and sites than 60Co. For example, it is possible to fit the kinetics of desorption at different pHs and sites with a single ksw value, while the reported variations for kwsare a

factor 3 only. In 137Cs, kwsexceeds ksw by about three orders of magnitude. The averaged precisions on the predicted concentrations were assessed by the root mean square errors (see e.g Ji (2007)) are given in Table 3. Fig. 7 analyzes the model fit by showing a scatter plot of predicted versus measured Kd-values for all treatments, location and times; 95% of the data lie within the diagonal bands of ±360 and ± 500 kg/L. for 60Co and 137Cs, respectively. For cobalt the largest standardized residuals (>ABS2) are observed at Grote Nete pH 8, while for cesium they are observed at Hulshout for pH between 6 and 8. 4. Conclusions The adsorption and desorption process in fresh river waters are affected by several geochemical characteristics of the water column and bed sediments. The aims of the study were to simulate the

Fig. 6. The adsorptionedesorption kinetics of 60Co in time at the Winkelom sampling site. Top panel: Adsorption model, Bottom panel: Desorption model. Circles and full line represent pH 6, squares and dot-line pH 7, diamonds and dashed line pH 7.9.

F. Fiengo P
erez et al. / Journal of Environmental Radioactivity 149 (2015) 81e89

88

Table 2 Adsorption and desorption rate values (with 95% confidence interval). Location

pH

60

137

Co

kws (1/s) Winkelom

6 7 7.9 6 7 7.9 6 7 7.9

G.Nete

Hulshout

1.7E-04 5.2E-05 3.1E-05 1.0E-04 3.8E-05 2.6E-05 5.0E-05 4.2E-05 2.6E-05

± ± ± ± ± ± ± ± ±

ksw (1/s) 1.1E-04 1.7E-05 7.0E-06 2.0E-05 1.5E-05 8.0E-06 2.4E-05 1.6E-05 8.0E-06

1.0E-05 1.3E-08 6.7E-08 2.6E-07 1.2E-07 2.3E-07 1.8E-07 1.1E-07 1.1E-07

± ± ± ± ± ± ± ± ±

Cs

kws (1/s) 3.0E-06 1.8E-08 3.0E-08 5.0E-08 8.0E-08 1.8E-07 1.0E-7 5.0E-08 4.0E-08

2.3E-05 2.4E-05 2.3E-05 1.9E-05 1.9E-05 1.8E-05 1.2E-05 1.2E-05 1.2E-05

± ± ± ± ± ± ± ± ±

ksw (1/s) 6.0E-06 6.0E-06 6.0E-06 5.0E-06 6.0E-6 4.0E-6 4.0E-06 4.0E-6 3.0E-06

¥1E-08 ¥1E-08 ¥1E-08 ¥1E-08 ¥1E-08 ¥1E-08 ¥1E-08 ¥1E-08 ¥1E-08

Table 3 Root Mean Square Errors (RMSE) corresponding to the different scenarios. pH

60

137

Co

Adsorption

Winkelom

G.Nete

Hulshout

6 7 8 6 7 8 6 7 8

Desorption

Cs

Adsorption

Desorption

Cw (Bq/ml)

Cs (Bq/g)

Cw (Bq/ml)

Cs (Bq/g)

Cw (Bq/ml)

Cs (Bq/g)

Cw (Bq/ml)

Cs (Bq/g)

19.5 6.2 2.2 2.4 4.4 2.9 13.0 8.9 5.9

401.6 128.5 37.8 35.1 38.3 58.6 274.9 170.8 86.3

11.0 0.2 0.2 0.2 1.0 2.4 1.4 0.6 0.6

282.6 2.6 52.1 15.9 17.9 48.1 25.7 14.0 13.4

8.6 8.0 8.5 7.0 7.1 8.3 16.7 19.9 17.3

260.9 271.5 332.6 115.3 148.1 176.9 259.9 369.6 332.1

1.6 1.6 1.2 0.4 0.3 0.7 0.3 0.3 0.5

34.7 41.5 26.0 35.5 29.0 9.2 67.6 26.6 45.2

Fig. 7. Scatter plot of measured vs. simulated values of the distribution coefficient.

process in laboratory and to develop a simple kinetic model that can better represent the radionuclide behaviour in rivers than a mere Kd approach frequently used. Similar to the findings of previous studies, the experimental results suggest that the adsorption and desorption kinetics of 60Co are strongly dependent on factors such as pH, contact time and solid phase chemistry. The Kd values are higher at pH 8, with regard to the adsorption rates the highest values are obtained at pH 7 except for Hulshout. In the case of 137Cs, the experimental results show that the effect of pH, time and locations are less important. Furthermore, the adsorption and desorption kinetics indicated that 137Cs is almost completely and irreversibly adsorbed on sediment particles. Even though the adsorption is faster than the desorption, the assumption of instantaneous equilibrium does not seem to be valid; our experimental results suggest that, for both adsorption and desorption, the time elapsed before equilibrium is established is

longer than 24 h. Furthermore, the desorption experiments indicate that the release of the radionuclides from the sediment into the water column is very slow. However, it must be realized that the drying of the sediments during the experimental setup could distort the desorption process especially for 60Co during 0  t < 1, and it is encourage to study this phenomena in the future. The experimental results were fit by a first order kinetic model that includes information about the distribution coefficient and the radioactive decay rate. It was observed that the desorption rate constant for 60Co is one to three orders of magnitude lower than the adsorption rate constant while for 137Cs desorption is three orders of magnitude lower. The fit between empirical and model results show that our kinetic model is able to reasonably describe the adsorption and desorption reactions. Accordingly, the kinetic model provides more accurate results than the classic Kd approach, because it takes into account the time needed to reach or restore

F. Fiengo P
erez et al. / Journal of Environmental Radioactivity 149 (2015) 81e89

the equilibrium between phases. It may be concluded that the firstorder model performs rather satisfactorily. This kinetic model can be easily implemented as an adsorption/desorption component in hydrologic transport models. Acknowledgements This work was supported by the PhD grant for first author from the Belgian Nuclear Research Centre (SCK
CEN). The authors are grateful to Jean Wannijn and Robin Nauts for their help. Appendix A. Supplementary data Supplementary data related to this article can be found at http:// dx.doi.org/10.1016/j.jenvrad.2015.07.010. References ASTM, 2007. ASTM D422-63, Standard Test Method for Particle-size Analysis of Soils. ASTM International, West Conshohocken, PA, p. 8. ASTM, 2008. ASTM D4646-03, Standard Test Method for 24-h Batch-type Measurement of Contaminant Sorption by Soils and Sediments. ASTM International, West Conshohocken, PA, p. 4. ASTM, 2013. ASTM D 2974-13, Standard Test Methods for Moisture, Ash, and Organic Matter of Peat and Other Organic Soils. ASTM International, West Conshohocken, PA, p. 4. ATSDR, 2004. Toxicological Profile for Cobalt. Agency for Toxic Substances and Disease Registry, Atlanta, GA. Bencala, K.E., Jackman, A.P., Kennedy, V.C., Avanzino, R.J., Zellweger, G.W., 1983. Kinetic analysis of strontium and potassium sorption onto sands and gravels in a natural channel. Water Resour. Res. 19 (3), 725e731.  Benes, P., Jur
ak, M., Cerník, M., 1989. Factors affecting interaction of radiocobalt with river sediments. J. Radioanal. Nucl. Chem. Artic. 132 (2), 225e239. Beven, K.J., 2012. Rainfall-runoff Modelling: the Primer. Wiley-Backwell, New York. Bunker, D.J., Smith, J.T., Livens, F.R., Hilton, J., 2000. Determination of radionuclide exchangeability in freshwater systems. Sci. Total Environ. 263 (1e3), 171e183. Chapra, S.C., 1997. Surface Water-quality Modeling. Waveland Press, Long Grove, IL. Ciffroy, P., Garnier, J.-M., Khanh Pham, M., 2001. Kinetics of the adsorption and desorption of radionuclides of Co, Mn, Cs, Fe, Ag and Cd in freshwater systems: experimental and modelling approaches. J. Environ. Radioact. 55 (1), 71e91. Doncaster, P.C., Davey, A.J.H., 2007. Analysis of Variance and Covariance: How to Choose and Construct Models for the Life Sciences. Cambridge University Press, Cambridge. Gao, Y., et al., 2013. Evolution of trace metal and organic pollutant concentrations in the Scheldt River Basin and the Belgian Coastal Zone over the last three decades. J. Mar. Syst. 128, 52e61.
ide Ce
sium 137 et environment. Garnier, L.J., Roussel, D.S., 2001a. Fiche Radionucle IRSN, France, p. 29. Garnier, L.J., Roussel, D.S., 2001b. Fiche Radionucleide Cobalt 60 et environment. IRSN, France, p. 26. IAEA, 2001. Generic Models for Use in Assessing the Impact of Discharges of Radioactive Substances to the Environment. Safety Reports Series 19. International Atomic Energy Agency, Vienna. IAEA, 2007. Testing of Models for Predicting the Behaviour of Radionuclides in Freshwater Systems and Coastal Areas, Environmental Modelling for RAdiation Safety (EMRAS) Programme. International Atomic Energy Agency, Vienna, p. 244. IAEA, 2009. Quantification of Radionuclide Transfer in Terrestrial and Freshwater

89

Environments for Radiological Assessments. IAEA TECDOC 1616. International Atomic Energy Agency, Vienna. IAEA, 2010. Handbook of Parameter Values for the Prediction of Radionuclide Transfer in Terrestrial and Freshwater Environments. Technical Reports Series 472. International Atomic Energy Agency, Vienna. Ji, Z.G., 2007. Hydrodynamics and Water Quality: Modeling Rivers, Lakes, and Estuaries. John Wiley & Sons, Inc., p. 704 €rman, A., 2001. Effect of sorption kinetics on the transport of solutes Jonsson, K., Wo in streams. Sci. Total Environ. 266 (1), 239e247. Krupka, K.,M., Serne, R.,J., 2002. Geochemical Factors Affecting the Behavior of Antimony, Cobalt, Europium, Technetium, and Uranium in Vadose Sediments. Rapport PNNL 14126. US. Department of Energy, Pacific Northwest Laboratory, Washington. Liaghati, T., Preda, M., Cox, M., 2004. Heavy metal distribution and controlling factors within coastal plain sediments, Bells Creek catchment, southeast Queensland, Australia. Environ. Int. 29 (7), 935e948. Liu, D., Lung, W.-S., Colosi, L.M., 2013. Effects of sorption kinetics on the fate and transport of pharmaceuticals in estuaries. Chemosphere 92 (8), 1001e1009.   _ G., Benes, P., Stamberg, Lujaniene, K., S ciglo, T., 2012. Kinetics of plutonium and americium sorption to natural clay. J. Environ. Radioact. 108 (0), 41e49. Monte, L., et al., 2005. Review and assessment of models for predicting the migration of radionuclides through rivers. J. Environ. Radioact. 79 (3), 273e296. Nyfeler, U.P., Santschi, P.H., Li, Y.-H., 1986. The relevance of scavenging kinetics to modeling of sediment-water interactions in natural waters. Limnol. Oceanogr. 31 (2), 277e292. Pleysier, J.L., Juo, A.S.R., 1980. A single-extraction method using silver-thiourea for measuring exchangeable cations and effective CEC in soils with variable charges. Soil Sci. 129 (4), 205e211. Price, K.V., Storn, R., Lampinen, J., 2005. Differential Evolution: a Practical Approach to Global Optimization. Springer, Berlin, Heidelberg. Radovanovic, H., Koelmans, A.A., 1998. Prediction of in-situ trace-metal distribution coefficients for suspended-solids in natural-waters. Environ. Sci. Technol. 32 (6), 753e759. Rumynin, V.G., 2011. Subsurface Solute Transport Models and Case Histories: with Applications to Radionuclide Migration, vol. 25. Springer. Rumynin, V.G., Konosavsky, P.K., Hoehn, E., 2005. Experimental and modeling study of adsorptionedesorption processes with application to a deep-well injection radioactive waste disposal site. J. Contam. Hydrol. 76 (1e2), 19e46. Sawhney, B.L., 1972. Selective sorption and fixation of cations by clay minerals: a review. Clays Clay Miner. 20, 93e100. Staunton, S., Dumat, C., Zsolnay, A., 2002. Possible role of organic matter in radiocaesium adsorption in soils. J. Environ. Radioact. 58 (2e3), 163e173. Storn, R., Price, K., 1997. Differential evolution e a simple and efficient heuristic for global optimization over continuous spaces. J. Glob. Optim. 11 (4), 341e359. Valcke, E., Cremers, A., 1994. Sorption-desorption dynamics of radiocaesium in organic matter soils. Sci. Total Environ. 157 (0), 275e283. Vrugt, J.A., Ter Braak, C.J.F., 2011. DREAM((D)): an adaptive Markov Chain Monte Carlo simulation algorithm to solve discrete, noncontinuous, and combinatorial posterior parameter estimation problems. Hydrol. Earth Syst. Sci. 15 (12), 3701e3713. Vrugt, J.A., ter Braak, C.J.F., Clark, M.P., Hyman, J.M., Robinson, B.A., 2008. Treatment of input uncertainty in hydrologic modeling: doing hydrology backward with Markov chain Monte Carlo simulation. Water Resour. Res. 44, W00B09. Wang, J., Fang, H., He, G., Huang, L., 2014. A model of Sr-90 distribution in the sea near Daya Bay Nuclear Power Plant in China. Front. Environ. Sci. Eng. 8 (6), 845e853. Wolfinger, R., Tobias, R., Sall, J., 1994. Computing Gaussian likelihoods and their derivatives for general linear mixed models. SIAM J. Sci. Comput. 15 (6), 1294e1310. €rman, A., Packman, A.I., Johansson, H., Jonsson, K., 2002. Effect of flow-induced Wo exchange in hyporheic zones on longitudinal transport of solutes in streams and rivers. Water Resour. Res. 38 (1), 2-1e2-15.