Groundwater ﬂow and radionuclide decay-chain transport modelling around a proposed uranium tailings pond in India L. Elango & K. Brindha & L. Kalpana & Faby Sunny & R. N. Nair & R. Murugan Abstract Extensive hydrogeological investigations followed by three-dimensional groundwater ﬂow and contaminant transport modelling were carried out around a proposed uranium tailings pond at Seripalli in Andhra Pradesh, India, to estimate its radiological impact. The hydrogeological parameters and measured groundwater level were used to model the groundwater ﬂow and contaminant transport from the uranium tailings pond using a ﬁnite-element-based model. The simulated groundwater level compares reasonably with the observed groundwater level. Subsequently, the transport of longlived radionuclides such as 238U, 234U, 230Th and 226Ra from the proposed tailings pond was modelled. The ingrowths of progenies were also considered in the modelling. It was observed that these radionuclides move very little from the tailings pond, even at the end of 10,000 y, due to their high distribution coefﬁcients and low groundwater velocities. These concentrations were translated into committed effective dose rates at different distances in the vicinity of the uranium tailings pond. The results indicated that the highest effective dose rate to members of the public along the groundwater ﬂow pathway is 2.5 times lower than the drinking water guideline of 0.1mSv/y, even after a long time period of 10,000 y. Keywords India . Radionuclide modelling . FEFLOW . Hydraulic conductivity . Distribution coefﬁcient
Received: 28 February 2011 / Accepted: 9 January 2012 Published online: 12 February 2012 * Springer-Verlag 2012 L. Elango ()) : K. Brindha : L. Kalpana : R. Murugan Department of Geology, Anna University, Chennai, 600 025, India e-mail: [email protected]
L. Elango e-mail: [email protected]
F. Sunny : R. N. Nair Environmental Assessment Division, Bhabha Atomic Research Centre, Mumbai, 400 085, India Hydrogeology Journal (2012) 20: 797–812
Introduction As the world’s energy requirement is rapidly increasing and the conventional fossil fuels are depleting steadily, nuclear energy is considered as a dependable additional energy source worldwide. In India, presently, the total installed nuclear power capacity is 2,770 MWe contributing about 2.7% of the total electrical energy produced. This is achieved through 14 nuclear power reactors operating in six states. Twelve of these reactors require natural uranium as the primary fuel. The ore for this fuel is produced from four underground uranium mines at Jaduguda, Bhatin, Narwapahar, and Turamdih, and from an open-pit mine at Bandhuhurang, all of which are located in the Singhbhum thrust belt in Jharkhand state, northern India. The excavated uranium ores are processed in the uranium mills located at Jaduguda and Turamdih. The amount of waste rocks generated by mining is relatively small and can be disposed off mostly in underground voids created by the excavation of ore. The mine water is reclaimed for use in the ore-processing mills after puriﬁcation. The mill tailings constitute the bulk of the radioactive waste generated. The coarse fractions of the mill tailings (~50%) after neutralization are used to ﬁll the mined-out stopes. The ﬁne fractions in the form of slime are neutralized and impounded in specially engineered uranium tailings ponds. The tailings ponds are enclosed by high natural hills on two-to-three sides and artiﬁcial embankments on the remaining one-to-two sides, allowing long-term tailings storage with little or no discharge into the environment. Decanted efﬂuent from the tailings ponds is treated further at treatment plants and recycled. The remaining water is discharged into the surface-water bodies if it meets the stringent discharge standards. Additional uranium reserves in India have been identiﬁed in Lambapur-Peddagattu and Thumnapalli of Andhra Pradesh, Western Kasi Hills of Meghalaya and Gogi of Karnataka. Mining of uraninite from the Lambapur and Peddagattu area of Andhra Pradesh, India is planned for the near future. After a thorough geological and hydrogeological study, it is proposed to locate the uranium tailings pond at Seripalli in Nalgonda district, Andhra Pradesh (Gupta and Sarangi 2005). Uranium tailings ponds are used not only during the life time of the mining and milling processes (usually 15–20 y), but need to be maintained for sufﬁciently DOI 10.1007/s10040-012-0834-6
longer period of time (several 100 y) as the radionuclides in that tailings may pose long-term radiological hazards if not designed and monitored properly. The principal radiation risks from uranium tailings are gamma radiation, windblown radioactive dust dispersion, and radon gas and its progenies. Uranium tailings ponds can also be major sources of surface and groundwater contamination due to leaching of radioactive and other toxic elements. Practices for the placement and disposal of uranium mill tailings have evolved to where they are today based on design, construction, management and performance of tailings dams (IAEA 2004). In many countries, the tailings ponds are designed to control the radiological hazards for at least 200 y, and if possible, up to 1,000 y (Abdelouas 2006). Stringent regulatory stipulations exist worldwide for the safe design, operation and closure of uranium tailings ponds. The long-term radiological impact assessment of uranium tailings ponds is therefore an extremely important exercise in the uranium-mining industry and is a regulatory mandate. Many modelling efforts have been made to assess the long-term impacts of contaminants released from uranium-mill tailings (BIOMOVS II, 1995; SENES 1995; Camus et al. 1998; Kalf and Dudgeon 1999; Mudd 2000; Görner et al. 2001; Zhu et al. 2001; Gómez et al. 2006; Neves and Matias 2008; Leijnse et al. 2001). Leaching of uranium and its progenies from tailings ponds is of great concern as they can contaminate groundwater, making it unsafe for human consumption. There are several factors that control the transport of naturally occurring U, Th, Ra and Rn in groundwater (Tricca et al. 2000) such as advection, physico–chemical processes of weathering, decay, α-recoil (a process by which an atom (radon) recoils in the opposite direction from the path of particle ejection following the radioactive decay of its parent atom), and sorption at the water–rock interface. Zhu et al. (2001) carried out multi-component reactive-transport modelling of natural attenuation of an acid groundwater plume at Bear Creek uranium-mill tailings site, USA, which helped to simulate radionuclide migration, including chemical interactions with the solid matrix and double-porosity effects. Modelling of radioactive reactive transport in groundwater at the Königstein uranium mine, Germany, was carried out by Nitzsche and Merkel (1999), which indicated slow migration of uranium. Nair et al. (2010) studied the long-term decay-chain transport of uranium in a heterogeneous anisotropic medium of groundwater and identiﬁed the necessity for monitoring of short-lived progeny radionuclides apart from their long-lived parents in the groundwater in the vicinity of uranium tailings ponds. Most of the studies cited in the preceding have been carried out using hydrogeological assumptions that are not strongly grounded on ﬁeld data. One of the objectives of the present study was to carry out a 2-y ﬁeld analysis of the values of hydrologic parameters around the Seripalli uranium tailings pond in Andhra Pradesh. The broader objective was to develop a three-dimensional (3D) model for groundwater ﬂow and transport of radionuclides using FEFLOW software (FEFLOW 6 2010) with a view to estimate their long-term impact on public health. Hydrogeology Journal (2012) 20: 797–812
Methodology Description of study area The study area is located in the Nalgonda district, Andhra Pradesh, about 80 km E–SE of Hyderabad. The study area consists of a well-deﬁned watershed that covers an area of about 750 km2 (Fig. 1). The southeastern side of the study area is bounded by the Nagarjuna Sagar reservoir and the southern side by the Pedda Vagu River. The northern side is bounded by the Gudipalli Vagu River. The study area has an arid to semi-arid, hot climate. Average summer (April– June) temperatures range from 30o to 46.5°C and average winter (November–January) temperatures vary from 17 to 38°C. The average annual rainfall in this area is about 1,000 mm, falling mostly during the southwest monsoon (June–September). Topography of this area ranges from 160 to 350 m above mean sea level (amsl) (Fig. 2a). The major rivers, the Pedda Vagu and Gudipalli Vagu that form two boundaries of the study area ﬂow seasonally during the southwest monsoon from July to September. The rainfall, nature of the geological formations and topography have led to a dentritic-to-subdentritic drainage pattern in this area (Fig. 1), with numerous small surfacewater bodies in the depressed parts of the undulating topography. The study area also contains a few lined canal networks for irrigation. The study area lies in the northern part of the Cuddapah basin in southern India. In the early 1990s, uranium mineralization was discovered in this area at the unconformity between the Srisailam formation of the Cuddapah super group and the basement granites, thereby establishing for the ﬁrst time the presence in India of unconformity-type uranium mineralization, which is considered as a major reserve the world over (Sinha and Saxena 2007). The principal uranium phases in this mineralization include uraninite, pitchblende, kasolite and uranophane (Singh et al. 1995)
Field study Geological investigations The geological map of this area was obtained from the Geological Survey of India (GSI) (GSI 1995). For the purpose of the present investigation, a remote-sensing technique involving the processing of LISS IV (Indian Remote Sensing System’s Linear Imaging Self-scanning Sensor IV) multispectral high-resolution images with a spatial resolution of 5.8 m was initially used to improve the geological map obtained from GSI. Furthermore, this map was updated by detailed ﬁeld studies as well as mapping of outcrops, well sections and road cuttings of the area (Fig. 2b). Also, 20 borehole logs from the Lambapur-Peddagattu regions were obtained from the Atomic Minerals Directorate (AMD) and six borehole logs were obtained from the Central Ground Water Board (CGWB) and Andhra Pradesh State Ground Water Board (APSGWB). These data were used to characterize the subsurface geology. DOI 10.1007/s10040-012-0834-6
Fig. 1 Location and drainage of the study area
Hydrogeological investigations An elaborate hydrogeological survey of the study area was carried out and several wells in the region were studied and the groundwater levels in the wells were measured. Based on the EC (electrical conductivity) and hydrogeological features, about 45 representative wells spread over the entire area were identiﬁed for periodical recording of groundwater level (Fig. 3). The groundwater levels were monitored using a water-level indicator once every 2 months from March 2008 to January 2010. VES (vertical electrical sounding) was carried out in 42 locations in the study area in March 2009. With the help of the lithological data from the wells in the study area and from the lithological logs interpreted using VES, the nature of the subsurface conﬁguration was understood and the subsurface model was prepared. In order to determine the hydraulic conductivity and storativity of the rock formations, which are essential for Hydrogeology Journal (2012) 20: 797–812
carrying out groundwater modelling, pumping tests were carried out in ﬁve locations and inﬁltration tests were carried out in four locations (Fig. 2b). Pumping test sites were selected in such a way that there would be no other pumping wells that are in use in the vicinity. Hydraulic conductivity and transmissivity results from these tests are shown in Table 1.
Modelling of groundwater flow and transport
The equation that governs groundwater ﬂow in an unconﬁned aquifer that was used for this study is as follows (Rushton 2003): @ @h @ @h @ @h @h Kx h þ Ky h þ Kz h ¼ Sy þq @x @x @y @y @z @z @t
ð1Þ DOI 10.1007/s10040-012-0834-6
Fig. 2 a Topography and b geology of the study area, with locations of pumping and inﬁltration tests
where Kx, Ky, and Kz are the hydraulic conductivities (L T–1) along x, y, and z-coordinate directions; h is the hydraulic head (L); Sy is the speciﬁc yield (dimensionless); q is the source/sink term (L T–1) and t is the time (T). This equation was used to simulate the spatial and temporal variation in hydraulic head based on ﬂow Hydrogeology Journal (2012) 20: 797–812
between the ﬁnite-element cells of the model. The seepage velocity between the cells was estimated from this equation, which formed the basis for estimating the movement of radionuclides in the groundwater. The radioactive decay of the uranium nuclide needs to be considered to estimate its spatial and temporal variation over the study area. This was carried out based on the DOI 10.1007/s10040-012-0834-6
Fig. 3 Locations of monitoring wells and rainfall stations in the study area
decay-chain transport equation for the ith member of the decay-chain, as given by Nair et al. (2010): @Ni @ @Ni @ @Ni @ ¼ þ þ Ri Dx Dy @t @x @y @x @y @z @Ni @Ni @Ni @Ni Dz v w u @z @x @y @z Ri li Ni þ Ri1 li1 Ni1
where i > 1 to M; M is the number of total nuclides in the decay-chain; Ni is the concentration of the parent in groundwater (atoms L–3 or mol); Dx, Dy, and Dz are the hydrodynamic dispersion coefﬁcients in x, y, and z directions (L2 T–1); x, y and z are the longitudinal, lateral and vertical distances from the source area (L); u, v, and w are the components of groundwater seepage velocity along x, y, and z directions (L T–1); Ri is the retardation factor and λi is the radioactive decay constant or chemical reaction rate constant of the parent radionuclide (T–1). The last term in the above equation represents the ingrowths of the progenies from the preceding parent radionuclides. Atomic concentration (atoms L–3) can be converted into radioactivity concentration (Bq L–3) by multiplying by the corresponding radioactive decay constant. Ri is the retardation factor (dimensionless) of the radionuclide i
for the linear-sorption isotherm, which is computed from the following equation: Ri ¼ 1 þ
Kdi rb q
where Kdi is the distribution coefﬁcient of nuclide ‘i’ (L3 M–1); rb is the bulk density of the aquifer material (M L–3) and q is its porosity (dimensionless). Thus the radionuclide transport model considers the relationship between the nuclides sorbed onto the aquifer material and the concentration remaining in the groundwater based on linear isotherms. Other geochemical speciation, kinetics and the mechanism of a limited number of sorption sites were not considered. Modelling of the 3D ﬂow of constant-density groundwater in anisotropic and heterogeneous porous media and the transport of radionuclides was carried out using the ﬁnite-element-based numerical modelling software, FEFLOW 6.
Model formulation Geological and hydrogeological characterization As noted in the preceding, the geology of the study area containing the proposed tailings pond was determined from detailed ﬁeld investigation, GSI maps and remote-
Table 1 Aquifer characteristics estimated from pumping tests Test numbera
Thickness of water column (b) m
Discharge (Q) m3/day
Transmissivity (T) m2/d
Hydraulic conductivity (K) m/day , K=T/b
1 2 3 4 5
Weathered Weathered Weathered Weathered Weathered
23.56 29.87 22.47 21.92 15.18
374 311 266 144 144
126.83 335.05 405.92 69.39 292.99
5.38 11.22 18.07 3.165 19.30
granite granite granite granite granite
Locations shown on Fig. 2b
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sensing data. Medium-to-coarse-grained Archean granitic rock forms the basement of this region, which is cut by numerous highly weathered and dissected dolerite dykes and quartz veins (Fig. 2b). Unconformably overlying the granitic basement is the Proterozoic Srisailam formation, the youngest member of the Cuddapah supergroup. Evidence for metamorphism of the Srisailam formation is found in exposures in the southeastern part of the study area. Uranium mineralization occurs at the unconformity between the granitic basement and quartzite of the overlying Srisailam formation at a shallow depth of about 10–15 m in Lambapur and at a greater depth of at least 50 m in Peddagattu. Hydrogeologically, the study area consists of four distinct layers. The top-most layer consists of thin soil, which is underlain successively by highly weathered rocks, moderately weathered rocks, and massive consolidated rock. The top three zones are reasonably porous and, hence, function as an aquifer. The thickness of the soil zone ranges from 1 to 12 m and is greatest in the southern and northeastern parts of the study area due to the erosional inﬂuence of the bounding rivers. The thickness of the highly weathered granite layer ranges from 11 to 33 m. The thickness is greater in the central and northwestern part of the region where the elevation is high and the soil layer is thin. The thickness of the moderately weathered granite ranges from 11 to 77 m. The soil and highly to moderately weathered rocks function as an unconﬁned aquifer in this area, with the water table ﬂuctuating between them. Numerous wells in this area supply water for domestic and agricultural purposes. Dug wells typically have depths of 1.45–20 m and diameters of 2–5 m, and tap groundwater mostly from the weathered and fractured zones. Bore wells typically have depths greater than 10 m and diameters of 15 cm. Rainfall is the major source of groundwater recharge, apart from irrigation returns. Recharge to unconﬁned aquifers is primarily by downward seepage of rainfall through the unsaturated zone. The recharge from rainfall can sometimes raise the water table elevation immediately, or as much as a month later. The groundwater levels ﬂuctuated between 0 and 12 m bgl with an average of about 6 m during the study period. During some extreme summer droughts, some wells can become completely dry.
Conceptualization and model discretization The purpose of building a conceptual model is to simplify the ﬁeld problem and organize the associated ﬁeld data so that the system can be analyzed more readily. The conceptualization includes synthesis and framing-up of data pertaining to geology, hydrogeology, hydrology and meteorology. The topographic elevations derived from 1:25,000 Survey of India topographic maps were used for representing the top surface of the modelled area (Fig. 2a). Data from geologic maps, cross-sections, VES and well logs were combined with information on hydrogeologic properties to deﬁne hydrostratigraphic units for the Hydrogeology Journal (2012) 20: 797–812
conceptual model. On the basis of lithological information derived from VES and borehole logs, the subsurface geological formations were characterized. The study region was conceptualized as a three-layer system. The topsoil is underlain by a weathered zone, which extends downward to variable depths. The weathered zone is subdivided into a highly weathered layer and a moderately weathered/fractured layer. In the model, the topsoil layer ranges in thickness from 1 to 12 m, the highly weathered layer from 11 to 33 m, and the moderately weathered/fractured layer from 11 to 77 m. The conceptual hydrostratigraphic model was discretized into four nodal slices that represent respectively the ground-surface topography, the bottom of the topsoil, the bottom of the highly weathered rock, and the bottom of the moderately weathered/fractured horizon. The remaining discretization and resultant ﬁnite-element mesh is shown in Fig. 4.
Boundary conditions Every model requires an appropriate set of boundary conditions to represent the system’s relationship with the surrounding area. The northern and southern boundaries, representing the Gudipalli Vagu and Pedda Vagu rivers were assigned as river boundaries (Fig. 4). For these boundaries, riverbed elevations and temporally varying river head values, determined from ﬁeld observations, were assigned. The southeastern boundary, where the Nagarjuna Sagar reservoir is located, was also considered to be a temporally variable head boundary based on data collected from the Public Works Department (PWD). A variable-head boundary was assigned along the western edge of the modelled area based on the groundwater levels measured in the wells located closer to this boundary.
Model input parameters Aquifer parameters The hydraulic conductivity and speciﬁc yield/speciﬁc storage values for the model hydrostratigraphic units were estimated from pumping tests in the present study, which are comparable with those described in previous studies by CGWB and APSGWD. Hydraulic conductivity values obtained from the ﬁve pumping tests carried out during this study were assigned to the study area using the Thiessen-polygon method. This method was used in preference to contouring because data points are sparse. The range of hydraulic conductivities obtained from pumping tests and speciﬁc yield based on Todd (2001) and Fetter (2000) initially considered for modelling are given in Table 2.
Initial hydraulic heads The initial hydraulic head distribution for the model was taken from March 2003, as determined from six CGWB DOI 10.1007/s10040-012-0834-6
Fig. 4 Discretization of the study area and model boundary conditions
monitoring wells (Fig. 3). The initial hydraulic head ranged from 349.5 m above mean sea level (m amsl) near the northwestern boundary of the study area to 164.5 m amsl at the southeastern part of the study area.
Groundwater recharge and abstraction The aquifer is recharged mainly by rainfall and surfacewater bodies. The rainfall distribution over the study area was estimated from four raingauge stations (shown in Fig. 3) using the Thiessen-polygon method. In addition, because recharge is a function of ground slope, the Thiessen polygons were further sub-divided based on ground slope. The recharge estimated for these different zones using the methodology of the Groundwater Resources Estimation Committee (GEC 1997) of the Ministry of Water Resources, Government of India, is given in Table 3. As the canals in the area are generally lined, recharge from them was not considered. A database of existing bore wells in the study area was created from several ﬁeld visits over the period from March 2008 to January 2010. Private electric-motor and private diesel-engine bore wells have discharge rates in the order of 250 and 60 l/min, respectively. Based on this, and also from the area of cropland being irrigated, the pumping rates were estimated.
Flow model calibration and simulation The purpose of model calibration is to tune the hydrogeological parameters so that the model can reproduce ﬁeld-measured heads and ﬂow rates. Calibration is carried out by adjusting the model parameters within the allowable range of geologically reasonable values. Initially, calibration of the model was performed under steady-state conditions. Subsequently, validation of the model was carried out under transient conditions using a time-step of 1 month. Steady-state conditions are usually taken to be the historic conditions that existed in the aquifer before signiﬁcant human development occurred, where inﬂows equal outﬂows and there is no change in aquifer storage. As model simulation was carried out commencing in March 2003, steady-state calibration of the model was done using head data from this month from six CGWB wells (Fig. 3). During the process of steady-state calibration, the hydraulic conductivity was varied within the allowable range (Table 2) during many sequential runs until a reasonable match between the observed and simulated head contours was obtained. The hydraulic conductivity values arrived at during calibration varied between 0.5 and 19.1 m/day and the speciﬁc yield varied between 0.1 and 0.22, which is comparable with values
Table 2 Hydrogeological parameter values used for modelling (parameters deﬁned in the text) Model layer number
Range of values
Kx and Ky Kz Speciﬁc yield Kx and Ky Kz Speciﬁc yield Kx and Ky Kz Speciﬁc yield
0.05–6 m/day 0.005–0.06 m/day 0.1–0.15 0.05–18 m/day 0.005–0.018 m/day 0.1–0.2 0.1 m/day 0.001 m/day 0.0–0.1
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Highly weathered rock Moderately weathered/fractured hard rock
804 Table 3 Rainfall recharge percentages used in this study Zone
Slope of surface
Percentage of monthly rainfall considered as recharge
1 2 3 4 5 6 7
Station Station Station Station Station Station Station
Steep Moderate Steep Moderate Steep Steep Moderate
Dense scrub/sheet rock Agriculture/plantation Stony waste Agriculture/plantation Dense scrub/sheet rock Dense forest Agriculture/plantation
2 10 2 10 2 2 10
1 2 2 3 3 4 4
quoted in Todd (2001) and Fetter (2000). Hydraulic conductivity along the x and y directions were assumed to be the same. Vertical hydraulic conductivity was assumed to be a tenth of the horizontal hydraulic conductivity. As the March 2003 dataset was available from only six wells, another steady-state simulation was carried out with a dataset from March 2008, derived from 33 wells. This steady-state calibration also gave a reasonable comparison between observed and simulated groundwater levels (Fig. 5a). The availability of ﬁeld measurements of hydraulic head from March 2003 to December 2009 allowed the model to be validated over this period, using the March 2003 head distribution as the initial condition. Simulation was carried out until December 2009 using a time-step of 1 month. Figure 5b shows a comparison of observed and simulated hydraulic heads for this period, illustrating broad correspondence between these two heads. Hence the model, after transient-state validation, simulates the regional and spatial groundwater levels with a reasonable level of accuracy. After transient-state calibration, the model run was then continued for a period of 20 y starting in March 2003. As an example, the regional simulated and observed hydraulic heads for the month of May 2009 are given in Fig. 6. This ﬁgure shows that both the observed and the simulated hydraulic heads for the study area indicate that in general the groundwater ﬂows towards the south east. That is, the groundwater levels in the western part of the area are relatively high compared to those in the eastern part of the area. The spacing between the groundwater contour lines in Fig. 6 indicates a more-or-less uniform hydraulic gradient over the western part of the area. The closely spaced groundwater contour lines in the southeastern part of the area indicate a steeper hydraulic gradient, which corresponds to the topography of the region (Fig. 2a). A comparison between the monthly variation in simulated and observed groundwater levels was made. This indicates that there is a reasonable match between the simulated and observed groundwater levels. The seasonal variation in observed and simulated groundwater levels at two locations is given in Fig. 7. This ﬁgure shows a reasonable match between temporal variation of observed and simulated heads. The slight difference between the observed and simulated heads can be explained by local variation in geological conditions within the model cells, groundwater levels in the wells Hydrogeology Journal (2012) 20: 797–812
possibly being inﬂuenced by pumping, and minor deviations in the hydraulic parameters, as is commonly found in groundwater modelling investigations. Since the groundwater ﬂow model simulates the groundwater levels well, with good correlation with the observed groundwater trends (Figs. 5a and b, 6 and 7), the model was used for forecasting the migration in groundwater of radionuclides from the proposed tailings pond.
Fig. 5 Comparison between observed and simulated groundwater levels at a steady state and b transient state, showing the best-ﬁt regression lines and regression coefﬁcients DOI 10.1007/s10040-012-0834-6
Fig. 6 Contours of simulated and observed regional groundwater levels in May 2009
Modelling radionuclide transport from the tailings pond Commencement of uranium mining and milling in this area will lead to storage of tailings in a pond in the northwestern part of the study area (Fig. 1). As the uranium mineralized zone is at a distance of about 42 km from the proposed location of the tailings pond (Fig. 1) and as the groundwater ﬂow is towards the mining area, there will not be any release of uranium from this mining zone to the tailings pond area. In order to consider the potential effect of this mine-waste storage on the local groundwater, the source term needs to be characterised. In the present study, the solute source term was treated as a decaying-concentration boundary condition, where the concentration of the ith species at the source area can be computed using the following equation given by Nair et al. (2010): ’i ðtÞ ¼ Ni ðtÞKli exp½ðli þ Kli Þt Hydrogeology Journal (2012) 20: 797–812
where ’i ðtÞis the release rate of the ith nuclide (atoms T–1); Ni(t) is the total of the ith nuclide (atoms) at time t; λi is the radioactive decay constant or chemical reaction rate constant of the parent radionuclide (T–1); Kli is the leach rate or fractional release rate of the ith nuclide (T–1) and t is the time (T). The exponential term in Eq. (4) represents the source depletion due to radioactive decay and leaching from the tailings pond. The leach rate from tailing ponds can be calculated as a function of the inﬁltration velocity (Nair et al. 2010). This is given in Eq. (5). Kli ¼
uS q s VRis
where ν is the inﬁltration rate of water from the tailings pond (L T–1), S is the surface area of the tailings pond (L2), V is the volume of the tailings pond (L3), θs is the porosity of the tailings (dimensionless) and Ris is the retardation factor (dimensionless) of nuclide i in the tailings pond. The ingrowths of the progenies in the DOI 10.1007/s10040-012-0834-6
Estimation of radioactivity release rates from the tailings pond
Fig. 7 Time-series of simulated and observed groundwater levels in two CGWB wells
tailings pond are evaluated using the Bateman equations (Nair et al. 2010). The following ﬁrst-order differential equation was used to calculate the number of atoms of the parent radionuclide at the source area (Nair et al. 2010): dN1 ¼ ðl1 þ Kl1 ÞN1 with initial condition N1 ðt ¼ 0Þ ¼ N10 ; dt
where N1(t) is the activity of the parent radionuclide at time t (atoms) and N10 is the number of atoms of the parent radionuclide present in the tailings pond initially (other terms as previously deﬁned). The activity of the ith daughter nuclide can be calculated using the following equation (Nair et al. 2010): dNi ¼ li1 Ni1 ðl1 þ Kli ÞNi with initial condition ð7Þ dt Ni ðt ¼ 0Þ ¼ N10 ; where Ni(t) is the number of atoms of the ith daughter radionuclide at time t (atoms) and N10 is its number of atoms present in the tailings pond initially. Hydrogeology Journal (2012) 20: 797–812
The amount of 238U in the tailings pond was calculated by determining the un-recovered 238U in the U3O8 ore (processed for an operational life of 16 y) that would be dumped to the tailings pond (UCIL 2003). The radionuclide 238U in the tailings pond is the parent radionuclide of the natural radioactive decay series with a half life of 4.5 × 109 y, which decays to form 234Th (half life= 24.1 days). 234Th decays to form 234 Pa (protactinium) (half life=1.17 min). 234 Pa results into 234U with a half life of 2.4 × 105 y. 234U decays to form 230Th (half life= 7.7 × 104 y) and 230Th decays to form 226Ra, which has a half life of 1,600 y. Because the parent radionuclide is long-lived, its daughter products are in equilibrium with their parent. The series has radon gas in the chain and the equilibrium will be disturbed from radon gas onwards. Hence, daughter radionuclides up to radium, which have a half-life of more than 1,000 y were considered in this study. However, short-lived radionuclides like 234Th (T1/2 = 24.1 day) and 234 Pa (T1/2 =1.17 min) were not considered in the series, and ingrowths of progenies only up to 226Ra were considered in the model. It is anticipated that the rate of mining of ore will be about 1,250 t/day. The average grade of uranium in the ore of this area is 0.052%. As 90% recovery is expected for natural uranium, the concentration of uranium daughters will be higher by a factor of 10 in the tailings waste relative to the unprocessed ore. The source properties including half-life, initial activity, ingestion dose and distribution coefﬁcient of the radionuclides in the tailings pond inventory are given in Table 4. In order to model the effect of leaching from the tailings pond into the watershed area, the 110 nodes in the 8 × 105 m2 of the source were treated as an array of injection wells. Each node in the source was considered to have an initial concentration of 0.129 Bq/l (238U/234U) discharging into the ﬁeld with an exponential decay-rate equivalent to the decay constant plus the leach rate of the contaminant radionuclide. The quantity of recharge of the tailings water from the pond based on the design inﬁltration velocity of 1.0 × 10–9 m/s was estimated to be 69.19 m3/day. The design inﬁltration velocity for the tailings pond was determined through inﬁltration tests carried out in this area. The concentrations of the four radionuclides, 238U, 234 U, 230Th and 226Ra, in the tailings pond were calculated as a function of time based on their initial inventories (Table 4). After 10,000 y, the 230Th was found to have the highest concentration, followed by 238U, 234U and 226Ra. This is due to the half-life periods of these four radionuclides (Table 4). Figure 8 indicates the variation in concentrations with respect to time of the four long-lived radionuclides in the tailings pond. The initial inventories of these radionuclides in the tailings pond and the distribution coefﬁcient assumed for the site are given in Table 4. The distribution coefﬁcient describes the sorption processes that control radionuclide interaction in soils, thus affecting subsequent radionuclide transport in the soil DOI 10.1007/s10040-012-0834-6
807 Table 4 Properties of radionuclides used in the model Nuclide
Initial activity in the tailings pond (Bq)
U-238 U-234 Th-230 Ra-226
4.5 × 109 2.44 × 105 7.70 × 104 1.60 × 103
2.5855 2.5855 2.5855 2.5855
× × × ×
1012 1012 1013 1013
proﬁle and radionuclide accumulation in surface soils (Vandenhove et al. 2009). Vandenhove et al. (2009) proposed that sorption is dependent on element and soiltype, and is affected by soil mineralogy (e.g. clay content and type, iron oxides and hydroxides), organic-matter content and soil geochemistry (pH, presence of colloids, presence of counter-ions), and by the experimental method used for its quantiﬁcation. Even though the experimental determination of distribution coefﬁcient is usually associated with large uncertainties, the present study was made with the distribution coefﬁcient values given in Table 4. The activity of 238U and 234U is one order of magnitude less than that of 226Ra and 230Th. The concentrations of the radionuclides in the tailings pond reduce mainly due to two processes, namely: (1) leaching into the groundwater; and (2) radioactive decay. The rate of reduction depends on the sum of the leach rate and the radioactive decay constant. Leach rate in turn is inversely proportional to the distribution coefﬁcient. 230Th, having the highest distribution coefﬁcient value among the four long-lived radionuclides, will have the lowest leach rate, and will therefore remain unchanged in the tailings pond for a longer period of time. On the other hand, 226Ra, with its low distribution coefﬁcient and consequently high leach rate and radioactive decay constant, will have low
Ingestion-dose coefﬁcient (Sv/Bq) 4.5 4.9 2.1 2.8
× × × ×
10–8 10–8 10–7 10–7
Distribution coefﬁcient for soil (ml/g) 500.0 500.0 2000.0 500.0
concentrations in the tailings pond, as its activity reduces much faster with time.
Simulation of effect of uranium tailings pond
The developed regional groundwater ﬂow model, which predicted the groundwater levels with a reasonable level of accuracy, was then used to simulate the transport of uranium and its long-lived progenies from the tailings pond for a period of 10,000 y. This length of time was considered as per the regulations in USA, Canada and Germany. This period is considered as a representative time during which the geology and climate may not change to any great extent (Roberts 1990). Over such a timescale, modelling results should be interpreted only as indicators of potential impacts and trends, rather than absolute values (Camus et al. 1998). For the long-term simulation, the values given in Table 4 were used. The monthly average values of groundwater level at the boundaries of the model domain, used for the transient-state calibration from March 2003 to December 2009, were applied to this long-term simulation of transport of radionuclides. The simulated variation in concentration of radionuclides versus distance from the edge of the tailings pond, along the direction of groundwater ﬂow
Fig. 8 Source concentrations of the long-lived radionuclides in the tailings pond as a function of time Hydrogeology Journal (2012) 20: 797–812
for a distance of 800 m after a time period of 1,000, 3,000, 5,000, 7,000 and 10,000 y are given in Fig. 9. This ﬁgure shows that the concentrations of 238U, 234U and 230Th decrease with distance and reach concentrations of less than 0.025 Bq/l within a distance of about 340 m from the edge of the tailings pond along the direction of groundwater ﬂow. In contrast, 226Ra, with its lower distribution coefﬁcient value, high concentration (almost 10 times that of 238U and 234U) at the tailings pond (Fig. 9) and shorter half-life, migrates further and the concentration increases compared to the other three radionuclides. Although, 226Ra has a shorter half-life (1,600 y) compared to the parent 238U, it is continuously being produced, thus accounting for the increased concentration at greater distances. The concentration of 226Ra peaks at a distance of around 340 m from the edge of the tailings pond after 10,000 y. However, its concentration decreases beyond that distance. With respect to time, the 226Ra concentration decrease at the source and increase along the distance as the ingrowth of the daughter products is considered, which contribute to its increase. The rate of change in the concentration of radionuclides changes at a distance of about 340 m (Fig. 9) due to the slight variation in the groundwater velocity, i.e. Darcy velocity (Fig. 10), which is because of the variation in the thickness of layers. The variation in dose of radionuclides with respect to time at a distance of 340 m from the edge of the tailings
pond, along the direction of groundwater ﬂow, was plotted (Fig. 11). It can be seen that the dose of 226Ra is higher than those of the other three radionuclides, and this is because of its high concentration at the source. As the inventories of distribution coefﬁcient values of 238U and 234 U are similar, their simulated concentrations and, thus, the dose are also the same. The concentrations of 230Th are slightly higher than those of 238U because of its relatively greater abundance at the tailings pond. However, because its distribution coefﬁcient value is very high, its concentrations are much lower than those of 226Ra. The rate of increase in the concentration of the 238U, 234U and 230 Th is much less than 226Ra due to their very high halflife. The concentration of 226Ra increases rapidly until the elapsed time of 4,000 y, due to its high concentration in the tailings pond. In order to study the possible impact of the transport of radionuclides on people, the effective dose rates were calculated. The simulated concentration of 238U was translated into effective dose rates for people who may consume the groundwater on the downstream side of the tailings pond. The effective dose rate is equal to the product of concentration, drinking water consumption rate (taken to be 2.2 l/day) and the ingestion dose coefﬁcient (Nair et al. 2010). The total annual effective dose to the public through possible use of groundwater for drinking along its ﬂow path is shown in Fig. 12, considering the
Fig. 9 Simulated concentration of radionuclides versus distance from the edge of the tailings pond, along the direction of groundwater ﬂow Hydrogeology Journal (2012) 20: 797–812
Fig. 10 Variation in simulated groundwater velocity with respect to distance from the edge of the tailings pond, along the direction of groundwater ﬂow
radioactive decay-chain transport for the long-lived radionuclides. Figure 12 also shows the maximum guideline dose rate as deﬁned by the World Health Organization (WHO). The maximum effective dose rate at an elapsed time of 10,000 y is 2.5 times lower than the WHO drinking-water guideline of 0.1 mSv/y (WHO 2004), and even lower at earlier times.
Sensitivity analysis The sensitivity analysis of the model developed for Lambapur-Peddagattu and Seripalli was carried out for two important parameters in groundwater ﬂow and contaminant transport, namely, the hydraulic conductivity
and distribution coefﬁcient. Hydraulic conductivity and distribution coefﬁcient are the two most important parameters in groundwater-ﬂow and contaminant-transport modelling. As the major goal of the radioactivetransport modelling was to understand the movement of radionuclides over a long period of 10,000 y, the results of the sensitivity analysis are presented only with respect to these two parameters. Sensitivity of the model to other model inputs such as changes in the recharge and discharge, was not investigated for the long-term simulation, as it is difﬁcult to assume their variation for a 10,000 y time period. Therefore, only the sensitivity of hydraulic conductivity and distribution coefﬁcient on the concentration of radionuclides are presented, as their values were uncertain. Any variation in hydraulic conductivity is expected to change the groundwater ﬂow-ﬁeld signiﬁcantly. This change in the ﬂow velocity will affect the contaminant concentration and, thus, the estimation of total effective dose on humans, due to consumption of groundwater. The sensitivity studies were carried out to estimate the effect on the ﬁnal output, i.e. the total effective dose due to variation in hydraulic conductivity and distribution coefﬁcient. Hydraulic conductivity values used for modelling were based on the ﬁve pumping tests and also from CGWB pumping tests. As only a few values were extrapolated over a large area, it is essential to understand the sensitivity of this parameter over the model results. The hydraulic conductivity values can vary up to a maximum of 50% from the value that was used for modelling. An increase of hydraulic conductivity by 50% is considered to be on the high side, as this value even exceeds the hydraulic conductivity values reported by
Fig. 11 Variation in simulated concentration of radionuclides with time at a distance of 340 m from the edge of the tailings pond, along the direction of groundwater ﬂow Hydrogeology Journal (2012) 20: 797–812
Fig. 12 Total effective annual dose rates from all the long-lived radionuclides at a distance of 340 m from the edge of the tailings pond, along the direction of groundwater ﬂow, compared to the WHO limit
Todd (2001) and Fetter (2000). Simulations were carried out with hydraulic conductivity increased by 50% over the entire study area. It was found that this increase had negligible inﬂuence on the concentrations of the radionuclides and the resultant effective annual dose rate (Fig. 13). Furthermore, the change in the hydraulic conductivity value also affected the ﬂow-ﬁeld arrived at after calibration.
The distribution coefﬁcient values for the hydrogeological units in the study area were not measured. Studies of similar hydrogeological units from other locations show a wide variation in distribution coefﬁcient values (Vandenhove et al. 2009; Baes et al. 1984). In order to estimate conservatively the transport of radionuclides in the area, simulation was carried out by lowering the distribution coefﬁcient values by 25% (that is Kd (U-238)=125 l/kg; Kd
Fig. 13 Effect of changes in hydraulic conductivity (K) or distribution coefﬁcient (Kd) over time on the effective dose rate through the groundwater ﬂow pathway at a distance of 340 m from the edge of the tailings pond Hydrogeology Journal (2012) 20: 797–812
(U-234)=125 l/kg; Kd (Ra-226)=125 l/kg; Kd (Th-230)= 500 l/kg). The ﬁgure of 25% was chosen because the lowest mean distribution coefﬁcient values of different types of soil was 120 l/kg as reported by Vandenhove et al. (2009). The lower the distribution coefﬁcient values, the greater the transport of the contaminants. As expected, the concentration of radionuclides along the ﬂow-path increases as a result of the reduced distribution coefﬁcient values. Even with these low distribution coefﬁcient values, the total effective dose rate from all four long-lived radionuclides was found to be 0.08 mSv/y (Fig. 13) after a time period of 10,000 y, which is still below the WHO drinking water guideline of 0.1 mSv/y. This indicates that contaminant concentration in the study area is much more sensitive to uncertainties in distribution coefﬁcient and hydraulic conductivity.
Conclusions A ﬁnite-element model was developed using FEFLOW to simulate the groundwater ﬂow and contaminant transport around a proposed uranium tailings pond at Seripalli in India. There was a reasonable level of agreement between the observed and simulated hydraulic heads. The groundwater ﬂows towards the southeast and the water table generally follows the topography. Transient simulation of hydraulic heads and movement of radionuclides was successfully carried out with the developed model. The results showed that the radiological impact of the proposed uranium tailings pond is trivial throughout the 10,000-y period of the simulation. The maximum dose that would be received by members of the public drinking the affected groundwater is predicted to be about 0.04 mSv/y after 10,000 y of transport. This value is about 2.5 times lower than the WHO drinking water guideline of 0.1 mSv/y. Sensitivity analysis on the model showed that uncertainties in distribution coefﬁcient have an effect on the predicted radionuclide transport. However, even with a low distribution coefﬁcient value, the effective dose rate was found to be less than the WHO recommended limit throughout the groundwater pathway. Acknowledgements The authors would like to thank the Board of Research in Nuclear Sciences, Department of Atomic Energy, Government of India for their ﬁnancial support (Grant no. 2007/ 36/35). The authors from Anna University also thank DST-FIST (Department of Science and Technology-Funds for Improvement in Science and Technology) (Grant no. SR/FST/ESI-106/2010) and UGC-SAP (University Grants Commission-Special Assistance Programme) (Grant no. UGC DRS II F.550/10/DRS/2007(SAP-1)) for their ﬁnancial support, which helped in creating facilities to carry out part of this work.
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