Article pubs.acs.org/est
Cite This: Environ. Sci. Technol. 2018, 52, 9235−9242
A Simple Compartment Model for the Dynamical Behavior of Medically Derived 131I in a Municipal Wastewater Treatment Plant Volker Hormann*,† and Helmut W. Fischer† †
University of Bremen, Institute of Environmental Physics, Otto-Hahn-Allee 1, D-28359 Bremen, Germany
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S Supporting Information *
ABSTRACT: A compartmental model for the reactive flow of the radioisotope 131I, frequently introduced into the sewer system at varying concentrations through radiotherapy of thyroid diseases, has been developed for an existing municipal wastewater treatment plant (WWTP). It includes the transition of activity from dissolved to suspended particulate and colloid matter, and the separation of phases in sedimentation basins. It has been parametrized by experimental data obtained at key locations in the plant, and validated by measured time series of activity concentration of inflow and outflow. It can be used to predict concentrations at various locations in the WWTP, including outflow and primary sludge. It can also be reparameterized to be applied to other WWTPs based on activated sludge systems. In principle, a modification for the simulation of other nuclides is possible as well. As radioisotopes of iodine form an important part of accidental releases from nuclear power plants, they are monitored, and their environmental behavior is predicted by models. The present work can contribute to these efforts by improving predictions of radioiodine transport in the public sewer system.
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INTRODUCTION Medical 131I is introduced into municipal sewage systems on a regular basis by the waste of patients undergoing therapeutic and diagnostic treatment of thyroid diseases.1 This can be used to study the dynamical behavior of radioiodine in wastewater treatment plants (WWTP). A model for this behavior would present the opportunity to predict activity concentration processes within the WWTP, as well as releases from the WWTP. This is convenient in cases of accidental releases, e.g., in the course of nuclear accidents, where the radioiodine will be washed out from the atmosphere by rain and in many cases subsequently introduced into the sewer system. There are many models that have been developed for assessing the fate of micropollutants in a WWTP (see, e.g., the review of Pomiès et al.).2 Generally, these are compartment models concerned with organic pollutants (in some cases also metal cations), taking into account liquid, solid, and gas phases. The processes usually considered are volatilization, sorption/desorption, and biodegradation of the pollutant. Usually these models depend on a large number of parameters and assume a fixed partitioning between the solid and liquid state. While equilibrium models might be able to describe the long-term and average behavior of sewage systems, they are not suitable to describe time-dependent behavior. This is necessary for sewage plants themselves because many operational parameters can vary within hours. This is why many of these models are dynamic and also account for time-variable pollutant input. Radioactive contaminations are also time© 2018 American Chemical Society
dependent, be they of medical origin (more or less patients will contribute from day to day) or related to nuclear accidents (levels will rise drastically within hours after a radioisotope release or deposition). In both cases, concentrations can vary strongly within fractions of a day. Therefore, a realistic prediction of the short-term behavior of radioisotope concentrations within the compartments of a sewage plant clearly requires dynamical modeling. While several studies3−8 have reported 131I activity concentrations in sewage sludge or other parts of the WWTP, only one model for the radioiodine behavior seems to be available in the literature, which is the LUCIA code.9 It was developed on behalf of the Swedish Radiation Protection Agency and implemented using the proprietary risk assessment software ECOLEGO.10 This model depends on a number of biochemical parameters like COD (chemical oxygen demand) and relies on the concept of a fixed solid−liquid distribution coefficient (Kd) using literature values adapted from organic soils. It is also a stationary model assuming a constant influent water flow rate and just takes into account the total activity flow rate, neglecting the chemical form of radioiodine. 131 I is a redox-sensitive element that exhibits a complex environmental chemistry.11 In the form of iodide (which is the Received: Revised: Accepted: Published: 9235
March 22, 2018 July 18, 2018 July 29, 2018 July 30, 2018 DOI: 10.1021/acs.est.8b01553 Environ. Sci. Technol. 2018, 52, 9235−9242
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Environmental Science & Technology
Figure 1. Schematic diagram of the WWTP Seehausen (see description in text). The dashed boxes indicate splitting of the flow. The given percentages of the partial liquid flow rates are derived from average values for the year 2014 given by the WWTP operator. (Reprinted with permission from ref 1, Copyright 2017 Elsevier.)
dominant chemical form of medical 131I in the WWTP inflow)1 it has a low affinity for surfaces like hydrous ferric oxides.12 However, in an aerobic environment, it is known to be covalently bound to organic matter,13,14 a reaction that is essentially irreversible.15,16 The kinetics of this reaction depends on microbial activity.16 In a previous study,1 we found that in activated sludge, > 80% of the 131I was detected in the fraction that could be extracted with aluminum chlorohydrate (ACH), compared to less than 10% in the inflow. Thus, the application of Kd, assuming a constant partitioning between solid and liquid phase, is not valid here. This leads to a special modeling concept for radioiodine, which introduces (i) the ACH extractable iodine fraction (AEI) associated with suspended matter, colloids, and large organic molecules, and (ii) the residual fraction consisting of iodine either in dissolved form or associated with smaller molecules. These fractions replace the solid and liquid phases normally used in WWTP fate models. The advantage of this approach is that the transition of 131I between these fractions during biological treatment can be described in a first approximation by a simple factor. This factor can be estimated using activity concentrations (measured by gamma spectroscopy) in the ACH extractable fractions in samples collected at locations before and after biological treatment. Moreover, apart from allowing for time-variable activity input, this model has also the option of making the residence times in a compartment variable if the liquid inflow rate is known. Compared to other fate models, e.g., SimpleTreat,17 it does not depend on parameters usually used in activated sludge models (e.g., chemical oxygen demand), keeping the number of required parameters to a minimum. In the following sections, we describe this model and its validation using experimental data.
biological treatment, and secondary clarification). Details of the biological and chemical treatment processes as well as technical aspects important for modeling are extensively discussed in textbooks.18−20 Here, we present a compartment model for activated sludge systems based on the concept of residence times19 (see below). It is designed for simulating the 131 I activity flow in the WWTP Bremen-Seehausen, Germany, but can be easily modified to be applied to many other WWTPs as well. Wastewater Treatment Plant. The WWTP Seehausen (geographical coordinates 53.116° N, 8.715° E) is located on the left bank of the North-West German River Weser and belongs to the municipal district of Bremen-Seehausen. It is designed to handle a population equivalent of one million inhabitants; under dry weather conditions it has an average through flow of 100 000 m3 d−1 and an average water residence time of 28 h.21 About 60% of the catchment area is drained by a combined sewer system,21 meaning that wastewater and rainwater are mixed in these sewers. A simplified schematic diagram of the WWTP is given in Figure 1. After screening, grit removal and primary treatment, where the largest suspended particles and objects are removed, the wastewater path is split into two branches: about 75% of the wastewater enters the “compact facility” where it is submitted to anaerobic treatment followed by an anoxic stage and subsequently treated aerobically in an activated sludge reactor. The remaining 25% are treated aerobically in a cascade of three activated sludge basins (“cascade facility”), which is historically the older branch of the WWTP. For both branches, final (also called “secondary”) clarification is the last step, where the majority of the particulate material is removed by sedimentation and fed back to the compact and cascade facilities, respectively. Sludge digestion and further treatment are not included here because it is doubtful that these processes can be described by simple compartments and residence times.
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MODEL DESCRIPTION In every WWTP, the wastewater is treated in a series of stages (e.g., grit removal, screening, removal of primary sludge, 9236
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General Structure and Assumptions. The model describes the 131I activity dynamics as a two-component flow through a series of interconnected compartments (see Figure 1). It allows for variable water flow rates but uses a set of ratios between partial flow rates (“splitting factors”, see slashed boxes in Figure 1) that are constant except for the splitting between cascade and compact branch. The constant splitting factors have been calculated using average flow rates provided by the WWTP operator. The model does not take into account screening and grit removal, because it is to be expected that the large particles held back by this process only present a small surface area for iodine sorption and thus the loss of iodine by these processes can be neglected. We assume that the radioiodine is present in two fractions: one fraction (index P for precipitable; activity denoted by AP in Bq) can be precipitated by aluminum chlorohydrate (ACH) and contains iodine bound to suspended particles, colloids and larger molecules in solution 1. This fraction will be called “AEI fraction”. The residual fraction (index D for residual dissolved; activity AD in Bq) consists of inorganic iodine (iodide, iodate) and iodine bound to smaller organic molecules in solution. Both fractions are assumed to be conservatively transported between compartments with the wastewater. The model uses two kinds of compartments (see following sections): (i) bioreactors where biological treatment takes place and (ii) settling tanks, which themselves consist of subcompartments and where the suspended matter is partly separated and fed back to the biological treatment (“return sludge”). Each basic compartment is treated as an “ideal reactor”, which means that both incoming activity fractions are instantly distributed homogeneously inside the reactor volume. This permits us to describe the total activity in terms of a variable residence time τ = V/Q(t), where V is the compartment volume in m3 and Q(t) is the water flow rate in m3 h−1, given on an hourly basis. If qin(t) is the total activity inflow in Bq h−1, then the total activity A in Bq in the reactor is described by the following: ij Q (t ) ln2 yzz dA zz × A + qin(t ) = −jjjj + dt T1/2 z{ k V
Cs, which are also enriched in the sludge, this path is very important.23,24 Biological Treatment. In bioreactors, the treatment may be aerobic or anaerobic (for details of these processes, see, e.g,. Spellman20). For aerobic conditions, we found an activity transfer from the residual dissolved fraction to the ACHextractable fraction probably caused by microbial activity.1 The transfer will be described by a transition factor, k, which has to be determined experimentally (see Supporting Information, SI). In this model, we assume that k is a first-order reaction constant. This implies that the phase precipitated by ACH extraction provides an unlimited amount of binding sites for stable and radioactive iodine which is justified, because even stable iodine only occurs in trace concentrations (ca. 0.1 μmol L−1).25 Thus, we assume that the time evolution of the activity in Bq in such a compartment can be described by two coupled ordinary differential equations for the activities AD in the residual dissolved fraction and AP in the ACH-extractable fraction: dAD ln2 zyz ji Q (t ) zz × AD − k × AD + qD,in(t ) = −jjjj + dt T1/2 z{ k V
ij Q (t ) dAP ln2 yzz zz × AP + k × AD + qP,in(t ) = −jjjj + dt T1/2 z{ k V
(2)
qD,in(t) and qP,in(t) are the respective activity inflows in Bq h−1, and k is in h−1. For anaerobic reactors, k is set to 0, as it has been reported that under anaerobic conditions there are only limited interactions between anaerobic bacteria and iodine.26 Settling Tanks. The mathematical description of sedimentation in settling tanks (ST) is a fairly complex task and numerical modeling usually requires sophisticated techniques (for a review of one-dimensional models, see Li and Stenstrom27). As we only have information on the radioiodine content in the two aforementioned phases, we apply a very simple phenomenological model for the description of 131I activity flow rates through the sedimentation basins, which is largely based on the empirical sludge settling models in the works of Kim and Pipes28 and Giokas et al.29 We divide the ST into two compartments (see Figure 2): The first is the settling zone characterized by the sludge blanket height hR and a corresponding volume VR where the
(1)
T1/2 is the radioactive half-life of 131I (8.02 d). The 131I dynamics within the WWTP and especially the activity concentration of 131I in the effluent largely depend on the partial mass flow between these compartments. In particular, the relative amount of activity in the return sludge is important because it affects the effective overall residence time of 131I and thus the loss of 131I by radioactive decay. The primary sludge from the first settling tank and smaller fractions of the return sludge are fed into the sludge compartment (Figure 1), where the material is stored for decomposition and further treatment. In this model, the sludge compartment is merely treated as a sink, because within the scope of this project, the processes within this compartment were difficult to track experimentally. For the WWTP Bremen-Seehausen, measurements have shown that there is some enrichment of 131I activity in the sludge.22 Most of the 131I present in the sludge will decay due to its long average residence time (about 3 weeks in the Seehausen WWTP), before the digested sludge is incinerated or transported further. However, in the case of a nuclear accident, the activity concentrations in the digested sludge may not be negligible. Especially for long-lived radionuclides like
Figure 2. Schematic model of the flows within a settling tank. The qij are the fractional activity flows in Bq h−1 (D: residual dissolved iodine; P: ACH extractable iodine, including suspended particles) and the λi are the respective AEI fractions. f is the fraction of total mass inflow leaving the tank as return sludge. 9237
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Environmental Science & Technology sludge floc density is higher than in the inflow. This compartment is fed by a fraction f of the inflowing liquid within a time that is short in comparison to the average residence time of water and flocs (which is ca. 6−8 h, see SI) in this compartment. The other compartment (with volume VT) is the through flow zone which consists of the remaining liquid with a low floc density that feeds the outflow basin (Figure 1). We assume that all exchange of iodine-carrying matter between the compartments is determined by the settling process and that there is no further iodine transition caused by microbial activity from the residual dissolved fraction D to the AEI fraction P within the settling tanks. The activity flow rate qin (in Bq h−1) of the activity in the inflow to the basin can be derived from the given corresponding water flow rate Qin (in L h−1) and the activity concentration ain (in Bq L−1), assuming that the activity is conservatively transported with the inflowing liquid: qin(t ) = ain(t ) × Q in(t )
concentration in a sample from the outflow. Rearranging eq 7 we get the following: α=
Figure 2 also shows the corresponding activity flow rates for return sludge (index R) and through flow (index T). If an activity fraction λ can be found in the AEI fraction P, then the corresponding activity inflow rate is qP = λ × qin. Thus, the activity flow balances for qP and qin are as follows: (4)
qRP + qTP + qRD + qTD = qin
(5)
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MATERIALS AND METHODS Sampling and Measurement. One sample of the influent after grit removal and one sample of the WWTP effluent (24 h composite and volume 2 L, respectively) were collected daily in the WWTP Bremen-Seehausen during the period April 4− 21, 2016. The samples were taken by automatic sampling devices routinely used by the WWTP operator (timeproportional, samples taken hourly in the period from 0:00 h−24:00 h of the previous day). For the sampling period, numerical values for hourly water inflow rates and splitting of the flow between compact and cascade facilities were provided by the WWTP operating company. Each sample was submitted to gamma-ray measurement in a 2 L Marinelli beaker ca. 1 h after sampling, after addition of cellulose powder to prevent sedimentation. The measurement was carried out by high-resolution, low background gamma-ray spectroscopy (for details, see Hormann and Fischer).1 The spectra were analyzed semiautomatically with commercial software (Genie 2000, Canberra Industries). Counting times were between ca. 17 and ca. 26 h per sample, and the corresponding detection limits were ≤0.1 Bq L−1. All activities were decay corrected to the time and date of sampling. Due to the relatively fast decay of 131I, samples could not be stored for longer times without loss of signal. With two available detectors and two samples per day, no additional samples could be counted. Furthermore, splitting of samples would have aggravated the problem due to lower count rates from the individual fractions. Model Implementation. As water flow rates and activity concentrations in the inflow are time-dependent, the eqs 1 and 2 have to be solved numerically. For this purpose, the model was implemented using the Simulink toolbox within the software package MATLAB (Version R2012b, The MathWorks Inc., Natick, U.S.A.). Simulink has the advantage that mathematical operations can be easily implemented via a graphical user interface by using graphical building blocks. The time-dependent quantities required for input are (i) the activity concentrations aP and aD in the inflow and (ii) the water inflow rates Q. In this study, they are given as hourly data using an external input file. All other simulation parameters
The return water flow rate is a fraction f of the ST inflow: QR = f × Qin and the through flow rate is QT = (1 − f) × Qin. The activity flow rates qRP and qTP cannot simply be calculated by multiplying qin by λ × f and λ × (1 − f), respectively. As the activity fraction in the AEI phase is very large after biological treatment and as it is to be expected that a large amount of iodine-carrying colloids and macromolecules from this phase are associated with the settling sludge flocs, we introduce a factor α ≥ 1 which describes the enrichment of activity in the settling zone due to the higher concentration of sludge flocs. This enrichment leads to higher AEI fractions in the return sludge than in the liquid entering the ST. This has also been indicated by experimental results.1 The activity concentration in solution remains unchanged, as no further transition between the two phases caused by microbial activity is assumed. Using this approximation and the activity flow balances in eq 4 and (5), we get for the partial activity flow rates: qRP = λαf × qin qTP = λ(1 − αf ) × qin qRD = (1 − λ)f × qin qTD = (1 − λ)(1 − f ) × qin
(6)
For the WWTP model, we need an estimation of α for each ST. The quantity λΤ defined by the following: λT =
qTP qTP + qTD
=
λ(1 − αf ) λ(1 − αf ) + (1 − f )(1 − λ)
(8)
If all AEI would be removed, then clarification would be complete, meaning no ACH-extractable colloids and sludge flocs would remain in the volume VT. In this case, λΤ = 0 and thus α = 1/f, which is the maximum possible value. In the case of α = 1, there would be no clarification at all, giving λΤ = λ. In this sense the last term in eq 8 is a correction term describing incomplete clarification in the ST. For calculating the activity dynamics in the partial volumes we use eq 2 with k = 0. As the ST consists of two compartments, we have to estimate the respective residence times. This is shown in the SI. In the implementation of this model, the WWTP water inflow rate Qtot is provided by hourly data and as the ratio f is taken as fixed, the Qi are given by QR = F × f × Qtot and QT = F × (1 − f) × Qtot, where F is the ratio Qin/Qtot determined by the splitting factors of the respective branch.
(3)
qRP + qTP = λ × qin
1−f λ (1 − λ) 1 − × T f f λ(1 − λ T)
(7)
can be easily measured by comparing the activity concentration in the ACH-extractable fraction to the total activity 9238
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Figure 3. Measured 24 h averages of activity concentration in the WWTP inflow and effluent with corresponding water inflow rates (hourly values).
Figure 4. Measured activities of 24 h average samples from the effluent and corresponding simulations (24 h averages). Left: data from this study, right: data from Fischer et al.22; Simulation parameters: α = 1.93 (compact facility); α = 1.25 (cascade facility); k = 0.025 h−1 (both branches); λini = 0.08. Dotted lines: corresponding simulations with average flow rates and splitting factors.
(e.g., volumes and splitting factors) are given in the MATLAB script file that executes the Simulink model. The differential equations are solved by a fourth-order Runge−Kutta procedure with a fixed step size of 1 h. Apart from the activity concentrations, also derived quantities (e.g., λT and λR from eqs 7 can be extracted at all stages of the treatment process. Model Parametrization. The simulations are performed using dynamical input flow rates and splitting factors between the compact and cascade facilities (henceforth called “dynamical splitting factor”). The remaining constant splitting factors are calculated using average partial flow data for 2014. Using these factors, the partial flow rates are calculated for each time step. All respective data have been provided by the operating company (hanseWasser, Bremen). The volumes of biological treatment and settling tanks are taken from the technical WWTP description. The initial AEI activity fraction λini, the transition factor k, the activity enrichment factor α and the residence times τi in the partial volumes of the settling tanks have to be determined experimentally. This is shown in detail in the SI. Verification and Preadjustment for the Simulation of Time Series. The model has been verified by running the model with an input activity >0 at the first time step and input activity 0 at all other time steps and integrating the summed activity flow rates of all output channels (i.e., effluent and combined sludge). If radioactive decay is neglected, then the ratio of integrated output and input activities approaches 1 after sufficient integration time (>1500 h), i.e., the model is mathematically consistent. For the comparison of the simulations with output data, two factors have to be considered:
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• Measured input and output activity concentrations are 24 h averages. Thus, the simulated data (resolution: 1 h) have also to be averaged over 24 h for better comparison. • The history of the system before sampling is not known, meaning that within the WWTP, an unknown amount of activity is present at the time where the simulation starts. This can be partially amended by feeding a certain initial activity (pulse followed by constant activity) to the model input for a number of time steps until equilibrium is reached at the outputs. The initial activity is manually adjusted until it matches the experimental value at the end of the first day of simulation (see Figure 3). If any simulation parameters are changed, then this adjustment has to be repeated. Although this method does not account for any input activity discontinuities that might have occurred prior to measurement, it is regarded as a feasible and realistic approach.
RESULTS AND DISCUSSION Experimental Results. Results for the 24 h averaged activity concentrations in the inflow after grit removal (black bars) and the combined secondary treatment effluent (gray bars) are shown in Figure 3, together with the hourly water inflow rate (solid black line). The inflow activity data show a sharp increase on day 5, which is especially convenient for testing the dynamical response of the model. The ratio of the mean activity concentrations between inflow and effluent is 1:0.65, which is mainly due to the radioactive decay of the activity accumulated in the return sludge. This
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Environmental Science & Technology “effectiveness” concerning elimination of 131I is just valid for the given period of time and is expected to vary depending on the patterns of water inflow rates and the dynamical splitting factor (in the study of Fischer et al.,22 this ratio was 1:0.54, with data taken at the same WWTP).1 However, for an average water residence time of 28 h,21 a reduction of only about 10% would be expected due to radioactive decay alone if the 131I was transported conservatively with the water. The water inflow rate shows a diurnal pattern and two peaks which are due to rainfall events (about 60% of the catchment area is drained by a combined sewer system). Within the observation period, there is no discernible correlation between water inflow rate and average inflow activity concentration. Model Validation. For validation, the model was run using the input data shown in Figure 3. The activities Ai per time step used in eq 2 were calculated by multiplying the measured average activity concentration in Bq L−1 by the water flow in L h−1. Furthermore, the model was applied to the data set of Fischer et al.22 (samples taken in February 2008) using the same simulation parameters. The calculated data (moving average over the last 24 h) are shown in comparison with the measured effluent activity concentrations in Figure 4 (left: this study; right: data from Fischer et al.). The simulated activity concentrations in the effluent are close to the experimental data from this study. This is also true for the first half of the data set from Fischer et al., while there seems to be a time lag of unknown origin in the simulation of the last 5 days. This is possibly due to additional treatment processes (e.g., phosphorus removal) that disrupt the continuous transfer of 131 I between residual dissolved and AEI phase. However, even in this period the difference between simulation and the mean values of the measured data does not exceed 50%, and in almost all cases the simulated curve runs through the 2-σ range of the average activity concentrations. We conclude that the calibrated model provides good predictions of the 24 h averaged output concentration. Even if only average water inflow rates and splitting factors are used instead of hourly data, then the results (dotted lines in Figure 4) are still satisfactory. As only 24 h average activity concentrations have been available, the model could not be validated for the simulation of smaller time scales. Sensitivity to Parameters. The simulation parameters qualitatively analyzed here are (i) the AEI fraction λini in the influent wastewater, (ii) the enrichment factor α in the settling tank of the compact facility, and (iii) the transition factor k for the transfer of radioiodine from the residual dissolved phase to the AEI phase in the aerobic biological treatment unit of the compact facility. While the influence of the variability of λini and α on the simulation results of the effluent is moderate, the estimation of k may be most important concerning parametrization for a specific WWTP. Details can be found in the SI. Equilibrium Calculations. A possible application of the model is the evaluation of the influence of water flow (and thus, residence time) and radioactive decay on fate and removal of iodine. This is especially relevant for comparing radioiodine fate and removal in dry and rainy periods. To this end, we performed equilibrium calculations for 131I as well as for the long-lived isotope 129I (radioactive half-life: 1.57 × 107 years), feeding the model with a constant activity concentration of 1 Bq L−1 per time step (1 h) and a constant water flow rate per simulation. The range of water flow rates was
chosen according to the data from Figure 3. In most cases, equilibrium at the effluent and the combined sludge tank inflow is reached after 3000 steps. For the calculations, we assume a constant factor of 0.735 for the splitting of the flow between the two facilities, which is the average value for the given sampling period. In Figure 5, the fractions of the total
Figure 5. Calculated equilibrium fractions of 131I inflow activity in the sewage effluent that is discharged to the river (gray) and accumulated in the sludge compartment (black). Dotted lines: no radioactive decay, thin black line: loss of total activity due to radioactive decay. The additional tick mark on the x-axis indicates the average inflow rate during the sampling campaign.
nuclide inflow (i) leaving the WWTP via effluent and (ii) accumulating in the sludge compartment are depicted by gray and black lines, respectively. For reference, the dotted lines display these fractions without radioactive decay (129I). The thin black line shows the loss of total activity by radioactive decay of 131I inside the WWTP. Without decay, the effluent fraction increases with the water flow, while conversely the sludge fraction decreases. The effect of decay is most prominent at low water flow rates because the residence times decrease with increasing water flows. It also strongly affects the activity concentrations in the sludge fractions due to the longer residence time of the sludge. We now compare 129I and 131I contamination and assume that the physicochemical fractionation in the inflow is the same for both isotopes. From Figure 5 we can deduce that the percentage of the inflowing radioiodine which is expected to be found in the sludge will be substantially higher if the isotope is 129I. However, at high flow rates this discrepancy decreases, as decay starts to play a minor role. From the viewpoint of radioprotection, the activity concentrations in the sludge are of high interest. Here, the radioiodine content is increased due to the accumulation of radioiodine-carrying organic material. Figure 6 implies that, as expected, the radioactive decay of 131I strongly reduces the concentration at lower flow rates, while the effect of dilution begins to get more prominent at high flow rates. The calculated values for 131I concentration in this range are consistent with the values of about 1−4 Bq L−1 that have been measured1 in return sludge samples from the cascade and compact facilities in the same WWTP. Applicability to Nuclear Accidents and to other WWTPs. If the wastewater is contaminated in the course of a nuclear accident, then 131I speciation might be different than for medically derived radioiodine. The results presented by Xu et al.30 imply that the fraction of particle-bound 131I may be 9240
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ASSOCIATED CONTENT
S Supporting Information *
The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.est.8b01553. Model parametrization and model sensitivity to parameters; Table S2, a summary of variables and indices (PDF)
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AUTHOR INFORMATION
Corresponding Author
*Phone: +49 421218 62762; e-mail:
[email protected]. uni-bremen.de (V.H.). ORCID
Volker Hormann: 0000-0003-1216-0469
Figure 6. Calculated equilibrium activity concentrations in the sludge compartment for an inflow activity concentration of 1 Bq L−1; straight line: 131I, and dotted line: 129I.
Notes
The authors declare no competing financial interest.
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ACKNOWLEDGMENTS This work was supported by the German Federal Ministry of Education and Research (BMBF) under grant FKZ 02NUK030H. The authors are indebted to hanseWasser Bremen GmbH for providing the 24 h composite samples and the WWTP data used for the simulations. We also thank L. H. Hinders for collecting the samples and performing the gamma-ray measurements.
higher but that iodide is still the dominant fraction in solution. In this case, the model could be recalibrated by a determination of the AEI fraction in the inflow, as has been described previously.1 However, as the speciation in solution may also differ, this could in principle lead to different transition factors. As details of the iodine transfer from the residual dissolved phase to the AEI phase during biological treatment are still unknown, this is presently unclear. In its present state, the model does not take into account operative actions that may disrupt the quasi-steady state, such as the addition of coagulants if the phosphate content exceeds a certain limit or the sudden change of splitting factors. It also does not provide for different treatment methods such as rotating biological contractors or trickling filters. However, in most cases the modeling approach may be sufficient for the prediction of radioiodine dynamics. As municipal WWTPs are usually based on activated sludge treatment,31 the model can be easily modified and parametrized for other facilities as well, if tank volumes, splitting factors, and at least average flow rates are provided. Applicability to other Nuclides. In principle, this model can also be applied to other radioactive and stable nuclides. For this purpose, a similar parametrization as described in the SI would be required. However, for most nuclides, this is only possible if tracer experiments are performed or other nuclides are measured as a proxy for the nuclide in question. 137Cs for instance is another nuclide of interest, because it has been found in sludge and effluent of wastewater treatment plants after the Fukushima23,24 and Chernobyl3 accidents. In soils, it is known to be preferentially bound to clay particles32 which may be also a component of the inorganic fraction of the influent suspended matter in combined sewer systems. As this component may have different properties concerning its behavior in settling tanks, this would have to be evaluated experimentally. There are also cases where an extension of the model may be required (e.g., if the nuclide exists in a third phase that behaves dynamically different, as in the case of volatilization). Generally, the physicochemical state of the radionuclide in the different stages of treatment is an important factor because this will often strongly affect its dynamical behavior.
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REFERENCES
(1) Hormann, V.; Fischer, H. W. The physicochemical distribution of 131I in a municipal wastewater treatment plant. J. Environ. Radioact. 2017, 178, 55−62. (2) Pomiès, M.; Choubert, J.-M.; Wisniewski, C.; Coquery, M. Modelling of micropollutant removal in biological wastewater treatments: A review. Sci. Total Environ. 2013, 443, 733−748. (3) Erlandsson, B.; Bjurman, B.; Mattsson, S. Calculation of radionuclide ground deposition by means of measurements on sewage sludge. Water, Air, Soil Pollut. 1989, 45, 329−344. (4) Martin, J. E.; Fenner, F. D. Radioactivity in Municipal Sewage and Sludge. Public Health Rep. 1997, 112, 308−316. (5) Jimenez, F.; Deban, L.; Pardo, R.; López, R.; García-Talavera, M. Levels of 131I and Six Natural Radionuclides in Sludge from the Sewage Treatment Plant of Valladolid. Water, Air, Soil Pollut. 2011, 217, 515−521. (6) Rose, P. S.; Swanson, R. L.; Cochran, J. K. Medically-derived 131I in municipal sewage effluent. Water Res. 2012, 46 (17), 5663−5671. (7) McGowan, D. R.; Pratt, B. E.; Hinton, P. J.; Peet, D. J.; Crawley, M. T. Iodine-131 monitoring in sewage plant outflow. J. Radiol. Prot. 2014, 34 (1), 1−14. (8) Punt, A.; Wood, M.; Copplestone, D. Radionuclide Discharges to SewerA Field Investigation; Environment Agency Science Report: SC020150B/SR 2007. (9) Avila, R.; Cruz, I. de la.; Bergman, S.; Hasselblad, S. Radiological consequences of radionuclide releases to sewage systems from hospitals in Sweden. Swedish Radiation Protection Authority: SSI Report 2007, 10. (10) Avila, R.; Broed, R.; Pereira, A. EcolegoA Toolbox for Radioecological Risk Assessment (IAEA-CN−109(ver3)); International Atomic Energy Agency (IAEA), 2003. (11) Hou, X.; Hansen, V.; Aldahan, A.; Possnert, G.; Lind, O. C.; Lujaniene, G. A. Review on speciation of iodine-129 in the environmental and biological samples. Anal. Chim. Acta 2009, 632 (2), 181−196. (12) Chang, H.; Schwehr, K. A.; Zhang, S.; Kaplan, D. I.; Seaman, J. C.; Yeager, C.; Santschi, P.; Xu, C. Model of radioiodine speciation and partitioning in organic-rich and organic-poor soils from the Savannah River Site. J. Environ. Chem. Eng. 2014, 2 (3), 1321−1330. 9241
DOI: 10.1021/acs.est.8b01553 Environ. Sci. Technol. 2018, 52, 9235−9242
Article
Environmental Science & Technology (13) Schlegel, M. L.; Reiller, P.; Mercier-Bion, F.; Barré, N.; Moulin, V. Molecular environment of iodine in naturally iodinated humic substances: Insight from X-ray absorption spectroscopy. Geochim. Cosmochim. Acta 2006, 70 (22), 5536−5551. (14) Reiller, P.; Mercier-Bion, F.; Gimenez, N.; Barre, N.; Miserque, F. Iodination of humic acid samples from different origins. Radiochim. Acta 2006, 94 (9−11), 739−745. (15) Schwehr, K. A.; Santschi, P. H.; Kaplan, D. I.; Yeager, C. M.; Brinkmeyer, R. Organo-iodine formation in soils and aquifer sediments at ambient concentrations. Environ. Sci. Technol. 2009, 43 (19), 7258−7264. (16) Yeager, C. M.; Amachi, S.; Grandbois, R.; Kaplan, D. I.; Xu, C.; Schwehr, K. A.; Santschi, P. H. Microbial transformation of iodine: From radioisotopes to iodine deficiency. Adv. Appl. Microbiol. 2017, 101, 83−136. (17) Struijs, J. SimpleTreat 4.0, a Model to Predict Fate and Emission of Chemicals in Wastewater Treatment Plants. Background Report Describing the Equations; RIVM Report 601353005/2014. RIVM (National Institute for Public Health and the Environment): Bilthoven, the Netherlands, 2014. (18) Tchobanoglous, G.; Burton, F. L.; Stensel, H. D. Wastewater Engineering: Treatment, Disposal, Reuse, 4th ed.; McGraw-Hill: New York, 2003. (19) Gujer, W. Systems Analysis for Water Technology; Springer Science & Business Media: Berlin, 2008. (20) Spellman, F. R. Handbook of Water and Wastewater Treatment Plant Operations, 3rd ed.; CRC Press: Boca Raton, 2013. (21) hanseWasser GmbH. Umwelterklärung 2016; hansewasser GmbH, Bremen, 2015. http://www.emas.de/fileadmin/user_ upload/umwelterklaerungen/reg/DE-112-00041_hanseWasserBremen-GmbH.pdf (Accessed: 22.03.2018). (22) Fischer, H. W.; Ulbrich, S.; Pittauerova, D.; Hettwig, B. Medical radioisotopes in the environment − following the pathway from patient to river sediment. J. Environ. Radioact. 2009, 100, 1079−1085. (23) Fischer, H. W.; Yokoo, Y. Preliminary comparison of radioisotope concentration in sewage sludge after the Fukushima and Chernobyl accidents. Energy Procedia 2014, 59, 256−262. (24) Tsushima, I. Behavior of radioactive materials and safety stock of contaminated sludge. J. Environ. Sci. Health, Part A: Toxic/Hazard. Subst. Environ. Eng. 2017, 52 (2), 158−165. (25) Koppe, P.; Stozek, A. Kommunales Abwasser; 4. Auflage. Vulkan-Verlag, Essen, 1999. (26) Amachi, S.; Minami, K.; Miyasaka, I.; Fukunaga, S. Ability of anaerobic microorganisms to associate with iodine: 125I tracer experiments using laboratory strains and enriched microbial communities from subsurface formation water. Chemosphere 2010, 79, 349−354. (27) Li, B.; Stenstrom, M. K. Research advances and challenges in one-dimensional modeling of secondary settling Tanks − A critical review. Water Res. 2014, 65, 40−63. (28) Kim, Y.; Pipes, W. O. Factors influencing suspended solids concentrations in activated sludge settling tanks. J. Hazard. Mater. 1999, 67 (1), 95−109. (29) Giokas, D. L.; Kim, Y.; Paraskevas, P. A.; Paleologos, E. K.; Lekkas, T. D. A simple empirical model for activated sludge thickening in secondary clarifiers. Water Res. 2002, 36 (13), 3245− 3252. (30) Xu, S.; Zhang, L.; Freeman, S. P.; Hou, X.; Shibata, Y.; Sanderson, D.; Tanaka, A.; Cresswell, A.; Doi, T. Speciation of radiocesium and radioiodine in aerosols from Tsukuba after the Fukushima nuclear accident. Environ. Sci. Technol. 2015, 49 (2), 1017−1024. (31) Wang, F. Y.; Rudolph, V.; Zhu, Z. H. Sewage sludge technologies. In Encyclopedia of Ecology; Jorgensen, S. E., Fath, B., Eds; Elsevier: Oxford, England, 2008; pp 3227−3242. (32) Gil-García, C.; Rigol, A.; Vidal, M. New best estimates for radionuclide solid−liquid distribution coefficients in soils. Part 1. Radiostrontium and radiocaesium. J. Environ. Radioact. 2009, 100, 690−696. 9242
DOI: 10.1021/acs.est.8b01553 Environ. Sci. Technol. 2018, 52, 9235−9242
