Effects of Carbon Dioxide on the Mobilization of Metals from Aquifers

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Effects of Carbon Dioxide on the Mobilization of Metals from Aquifers Katerina Terzi,†,‡ Christos A. Aggelopoulos,† Ioannis Bountas,†,‡ and Christos D. Tsakiroglou*,† †

Foundation for Research and Technology Hellas, Institute of Chemical Engineering Sciences, Stadiou street, Platani, 26504 Patras, Greece ‡ Department of Chemical Engineering, University of Patras, 26504 Patras, Greece S Supporting Information *

ABSTRACT: Potential leakages of CO2 from storage sites to shallow aquifers could have adverse impacts on the quality of potable groundwater. The mineralogy of well-sorted silica sand is modified by the pH-controlled precipitation of eight metals (Cr, Mn, Fe, Co, Ni, Cu, Zn, Cd). Continuous flow tests are performed in two fixed-bed columns packed with the modified sand by coinjecting gas CO2/distilled water (2-phase column) and distilled water (1-phase column/control test) at constant influx rates for a period of two months. The concentration of dissolved metals is measured in the effluents of columns with atomic absorption spectroscopy (AAS). Mineralogical analysis of the surface of sand grains is done before and after the flow tests with scanning electron microscopy−X-ray energy dispersive spectroscopy (SEM−EDS) and X-ray photoelectron spectroscopy (XPS), whereas the precise quantitative measurement of the metal content in the sand is done with AAS. A dynamic numerical model that couples the flow and mass-transfer processes in porous media with the equilibrium and kinetically driven metal desorption processes is developed. Inverse modeling of the continuous flow test enables us to quantify and rank the selectivity of metal mobility in terms of equilibrium and kinetic desorption parameters. The continuous CO2 dissolution and water acidification causes significant mobilization and dissolution of several metals (Mn, Ni, Cu, Zn, Co), moderate mobilization of Cr, acceleration of Cd dissolution, whereas Fe remains strongly bonded on the sand grains as goethite. The parameters estimated from lab-scale column tests might be helpful for interpreting field-scale CO2 leakage scenarios and installing relevant early warning monitoring systems.

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INTRODUCTION Carbon dioxide capture and storage in geological media is a wellpromising approach for reducing anthropogenic greenhouse− gas emissions.1,2 With the choice of the appropriate reservoir for CO2 storage, and adoption of risk-based monitoring verification and accounting protocols, the risks for the human health and environment may be minimized.3 Nevertheless, there is always the risk of CO2 leakages from the deep storage reservoir toward overlying and relatively shallow aquifers of potable groundwater.4 CO2 leakage may occur due to transport via faults and fractures, through faulty well bores, or through leaky confining materials.5 The leaking supercritical CO2 is rising upward and arrives at shallow aquifer as gas phase which may be dissolved totally or in part in the groundwater. Due to CO2 dissolution, the pH decreases and this may have adverse impacts on the quality of drinking-water.4 Results show that elevated CO2 levels in freshwater aquifers can mobilize trace metals (e.g., Cd, Pb, etc.) from the solid phase and increase their concentration in groundwater to undesirable levels.6,7 Geochemical interactions between minerals and groundwater (e.g., adsorption/desorption, precipitation/dissolution) are driven by the lack of chemical equilibrium (for instance dissolution occurs when the water is not saturated with respect to some minerals) and result in modifications of the aquifer solid material and groundwater composition.8 © XXXX American Chemical Society

During the last years, significant efforts have been done on elucidating the role of CO2 leakages from storage sites on the perturbation of subsurface geochemistry and the subsequent release of metal cations from aquifer minerals.4,9 Systematic batch experiments have revealed that the CO2 influx at room temperature and pressure can mobilize significant amounts of metals from a variety of aquifer host rocks.10,11 Field-scale numerical modeling of CO2 leakage from storage sites are commonly used to predict the acidification processes and mineral alterations, and assess the risks associated with the release of contaminants and deterioration of groundwater quality.12−15 Field studies involving the controlled release of groundwater containing dissolved CO2 have recently been done to study the complex geochemical transformations that may impact the groundwater quality.16−21 There are a few lab-scale studies that analyze the metal mobilization and dissolution at continuous flow conditions by distinguishing between pH-driven and carbonate driven processes22 and monitoring the CO2 intrusion and subsequent geochemical changes from online electrical measurements.20 Received: November 12, 2013 Revised: March 6, 2014 Accepted: March 16, 2014

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dx.doi.org/10.1021/es405032d | Environ. Sci. Technol. XXXX, XXX, XXX−XXX

Environmental Science & Technology

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energy (radiation of a given wavelength) which is specific to a particular electron transition in a particular element. The relation between the measured absorbance and the analyte concentration relies on the Beer−Lambert Law. Continuous Flow Experiments. The modified sand was packed into two fixed-bed PVC (polyvinyl chloride) columns of internal diameter D = 0.03 m and length L = 0.4 m. The experimental apparatus was placed inside a thermostatic chamber to keep the temperature constant and equal to 25 °C (Supporting Information (SI) Figure S1). The columns were evacuated and saturated with distilled and deaerated water. For decoupling the water acidification and metal release from any secondary processes associated with the chemistry of synthetic or natural groundwater, distilled water was used in all experiments. In the one column (upper), both water and gas CO2 were coinjected (SI Figure S1) and in the other one (lower) only water was injected (control experiment). A multichannel peristaltic pump was used to inject water at a constant flow rate qw = 0.3 mL/min, and a gas mass flow controller was used to supply bottled CO2 at a constant flow rate, qg = 2 mL/min. The outlets of columns were kept at atmospheric pressure, and the pressure drop across each phase was monitored with differential pressure transducers and transmitted to the data acquisition card of the host computer. The water saturation, Sw, in the upper column was estimated with mass balance by measuring the extracted water volume as a function of time. Water effluent samples were collected periodically to detect the pH, the electrical conductivity and measure precisely the metal cation concentration by atomic absorption spectroscopy (AAS). The whole experiment lasted for two months and approximately 260 pore volumes of water were injected. Batch Experiments. To quantify the partition coefficient of metals as a function of pH, batch desorption experiments were performed. It is well-known that metal adsorption−desorption in soils is commonly complicated by the presence of soil organic matter and dissolved organic matter24 but this factor was overlooked by using silicate sand without organic matter. Aqueous solutions with pH in the range 3.5−5.5 were prepared by adding dropwise 1 M acetic acid in distilled and deaerated water. 50 mL of each solution was mixed with 2 g of modified sand, and the suspensions were placed in an end-over-end rotator at constant temperature T = 25 °C for 24 h until equilibrium was established. Finally, the aqueous phase was filtrated and analyzed with AAS to measure the concentration of desorbed metals.

In the present work, the effects of CO2 leakage on groundwater quality is examined with flow-through experiments and numerical modeling. Gas CO2 and distilled water are coinjected at constant influx rates in a fixed-bed column packed with watersaturated sand of well-controlled mineralogy. The single-phase flow of distilled water in an identical sand column is used as control experiment. The pH, electrical conductivity, and ion metal (Fe, Mn, Cu, Co, Ni, Cr, Zn, Cd) concentrations are measured in effluent samples collected at various times. Mineralogical characterization of the sand before and after the experiment is performed with scanning electron microscopyenergy dispersive spectroscopy (SEM-EDS), X-ray photoelectron spectroscopy (XPS), and atomic absorption spectroscopy (AAS). Batch experiments of the desorption of metals at various pH values are employed to quantify the effect of pH on their partition coefficients. A mathematical model is developed to couple the kinetics of CO2 dissolution, solution acidification, and metal desorption with the advective flow, gas/water masstransfer, and hydrodynamic dispersion. The model is fitted to experimental data sets to estimate the equilibrium and nonequilibrium parameters of metal desorption processes.

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EXPERIMENTAL METHODS Sand Modification. The mineralogy of the sand was modified uniformly by stimulating the slow and pH-controlled precipitation of metal oxides on sand grains. First, an aqueous solution of nitric salts was prepared at concentration 10−3 M in Cr(NO3)3, Co(NO3)2, Ni(NO3)2, Zn(NO3)2, Cd(NO3)2, 2 × 10−3 M in Cu(NO3)2, Mn(NO3)2, and 4 × 10−3 M in Fe(NO3)3. The selection of metal salt concentrations was based on a survey of the chemical composition of aquifers.23 Thereafter, 1 L of the aqueous solution was mixed with 700 mL of well-sorted (grain sizes∼125−250 μm) commercial silicate sand in a large flask. Periodically the suspension was stirred and left to equilibrate until finally the pH was stabilized close to 2.0. In order to stimulate the coprecipitation of metal oxides/hydroxides/ complexes, the pH was increased slowly by adding dropwise 1 M NaOH solution under continuous stirring. Then the system was left to equilibrate and the procedure was repeated for one week until the pH was stabilized in the range 7.7−8.0. Afterward, the liquid phase overlying the sand was removed and the residual solid phase was placed inside a programmable oven and left to dry for one night at 120 °C. Solid Phase Characterization. Scanning Electron Microscope equipped with X-ray Energy Dispersive Spectroscopy (SEM-EDS) was used to confirm the presence of the eight (8) metals (Cr, Mn, Fe, Co, Ni, Cu, Zn, Cd) in the surface of sand grains and estimate approximately the concentration of each metal. The EDS is based on the activation of a sample by X-rays and the analysis of their interaction. The X-ray spectrum is a fingerpring of the atomic structure of each element. The chemical state of the metals in the external (1−10 nm) surface of sand grains was identified with X-ray photoelectron spectroscopy (XPS). XPS is based on the photoelectric phenomenon: when a surface is exposed to electromagnetic radiation above a certain threshold frequency, the radiation is absorbed and electrons are emitted. Accurate quantitative analysis of the metal concentration was done by digesting the sand with dense nitric acid and analyzing the liquid phase with atomic absorption spectroscopy (AAS). In AAS, initially the sample is atomized in a flame or electro-thermal graphite tube. The electrons of the atoms in the atomizer can be promoted to higher orbitals (excited state) for a short period of time (∼ns) by absorbing a defined quantity of

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THEORETICAL APPROACH Equilibrium Desorption Model. When equilibrium is established in a mixed system of soil/aqueous solution, then the concentrations of adsorbed/dissolved metal, i, can commonly be described by the Freudlich isotherm Cpi = KdiC i ni

(1)

where Cpi is the concentration of adsorbed metal (kg/kgadsorbent), Ci is the dissolved metal (cation) concentration in aqueous solution (kg/m3), Kdi is the partition coefficient, the exponent ni is a measure of the energy heterogeneity of sorption sites, and i = Cr, Mn, Fe, Co, Ni, Cu, Zn, Cd. Assuming that there is a competition between hydrogen cations (protons) and metal cations for sorption sites on the soil surface, the partition coefficient may vary with proton molar concentration, [H+], (gmol/L) according to a power law of the form24,25 B



⎛ [H +] ⎞−mi Kdi = Kdi0⎜ +0 ⎟ ⎝ [H ] ⎠

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[H+]4 + Ka1[H+]3 + (Ka2 − [CO2 ]Ka1 − K w )[H+]2 − (K wKa1 + 2[CO2 ]Ka1Ka2)[H+] − Ka1Ka2K w

(2)

where Kdi0 is the partition coefficient at a reference proton molar concentration [H+0], and the exponent mi is governed by the molar proton/metal exchange ratio in metal equilibrium sorption on soil. Setting ni = 1 (linear sorption), eqs 1 and 2 result in ⎛ Cpi ⎞ log⎜ ⎟ = log Kdi0 − mi(pH 0 − pH) ⎝ Ci ⎠

where Kw is the water ionization constant. Model of Continuous Flow Process. Since the flow rate of CO2 is 1 order of magnitude higher than that of water we can assume that the CO2 mass-transfer from gas to aqueous phase has negligible effect on the steady-state flow regime so that the gas/ water flow velocity and water saturation are regarded as constant parameterss. Assuming that the water saturation, Sw is distributed uniformly throughout the porous medium the mass balance of dissolved CO2 for 1-dimensional flow becomes

(3)

The parameters Kdi0 and mi were estimated with linear fitting of results of batch experiments to eq 3. Two-Site Desorption Model. Under continuous flow conditions, equilibrium may be not reached rapidly enough with respect to advective transport to allow the use of local equilibrium assumption (LEA). In such cases, sorption may appear to be limited by some chemical reaction rate or physical mass-transfer resistance and metal release can be modeled by a 2site desorption model.26,27 For each metal, type 1 desorption occurs at sites governed by an equilibrium expression, whereas type 2 desorption occurs at sites governed by a nonequilibrium equation. If f i is the fraction of all type 1 sites for metal i, then the concentration of metal i, adsorbed on sites of type 1, Cpi1, will follow a modified Freudlich equation of the form Cpi1 = fi (KdiC i ni + Cpri)

ϑ2CCO2 ϑCCO2 ⎛ u w ⎞ ϑCCO2 − DL,CO2 +⎜ ⎟ ϑt ϑx 2 ⎝ φSw ⎠ ϑx ⎛k a ⎞ − ⎜ m m ⎟(Cs − CCO2) ⎝ Sw ⎠

ϑt

= ai[(1 − fi )(KdiC i ni + Cpri) − Cpi2]

where φ is the porosity, t is the time, CCO2 is the CO2 concentration in water, Cs is the CO2 solubility, uw is the superficial velocity of aqueous phase (uw=qw/A), x is the axial coordinate, DL,CO2 is the longitudinal hydrodynamic dispersion coefficient of CO2, km is the gas/water mass-transfer coefficient, am is the specific interfacial area (interfacial area/pore volume). The dispersion coefficient is approximated by 29

(4)

⎛ u ⎞β DL,CO2 = Deff,CO2 + aL⎜ w ⎟ ⎝ φSw ⎠

am =

(6)

The carbonic acid is a weak acid that dissociates in two steps 28 (8)

HCO−3 ↔ CO32 − + H+ Ka2 = 10−10.25at T = 25oC

(9)

[HCO−3 ][H+] [CO32 −][H+] Ka2 = [H 2CO3] [HCO−3 ]

Sw

Pc(Sw )dSw (14)

(15)

where Swi is the irreducible water saturation. For one-dimensional flow, the mass balance of dissolved metal i yields ⎤ ⎛ ρ ⎞ ϑ2C ϑC i ⎡ ⎢1 + ⎜ b ⎟(fi Kdin iC i n i−1)⎥ − DL,i 2i ⎥⎦ ϑt ⎢⎣ ϑx ⎝ φSw ⎠

Assuming that equilibrium is established very fast, the concentration of ionic species is governed by the two equilibrium constants Ka1 =

∫1

⎛ S − Swi ⎞−mc Pc(Sw ) = Pc0⎜ w ⎟ ⎝ 1 − Swi ⎠

(7)

H 2CO3 ↔ HCO−3 + H+ Ka1 = 10−6.37at T = 25oC

1 γgw

where γgw is the CO2/water interfacial tension, and Pc(Sw) is the drainage capillary pressure curve which is commonly described by a Corey-type model of the form 31

Equilibrium is established between the dissolved CO2 and carbonic acid, H2CO3, according to CO2(aq) + H 2O ↔ H 2CO3

(13)

where Deff,CO2 is the effective diffusivity of CO2, aL is the longitudinal dispersivity, and β is an exponent. The gas/water specific interfacial area, am, is highly dependent on the spatial distribution of fluid saturation and can be approximated by 30

(5)

where Cpi = Cpi1 + Cpi2 and ai is the corresponding desorption rate coefficient. Carbon Dioxide Dissolution. The gas CO2 dissolves in water according to the reaction CO2(g ) ↔ CO2(aq)

(12)

=0

where Cpri is the residual metal concentration remaining in the sand when the desorption ceases. The concentration of metal i on sites of type 2, Cpi2, follows the nonequilibium kinetic equation 27 ϑCpi2

(11)

=0

⎛ u ⎞ ϑC ⎛ ρ ⎞ + ⎜ w ⎟ i + ⎜ b ⎟a i[(1 − fi )(KdiC i ni + Cpri) ⎝ φSw ⎠ ϑx ⎝ φSw ⎠ − Cpi2] = 0

(10)

(16)

where ρb is the soil bulk density, and DL,i is the longitudinal hydrodynamic dispersion coefficient of metal cation i. By defining the dimensionless variables, τ = tuw/(φL), ξ = x/L, cCO2 = CCO2/Cs, ci = Ci/Ci0 (Ci0 = the theoretical concentration of

where [X] denotes the molar concentration of X in aqueous phase. Accounting for the carbon mass balance and electrical neutrality condition, and ignoring the presence of metal cations, finally we get the following algebraic equation (SI) C



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metal i in aqueous solution if all metal contained in solid phase was dissolved in the total water volume injected in the sand column, SI Table S1), cpi1 = Cpi1/Cpi0 (Cpi0 = initial concentration of metal i in solid phase, SI Table S1), cpi2 = Cpi2/Cpi0, then eqs 12, 5, and 16 are transformed to the dimensionless equations ϑcCO2 ϑτ

=−

2 1 ϑcCO2 ⎛ φDL,CO2 ⎞ ϑ cCO2 +⎜ ⎟ Sw ϑξ ⎝ Lu w ⎠ ϑξ 2

⎛ φLk mam ⎞ +⎜ ⎟(1 − cCO2) ⎝ Swu w ⎠

(17)

⎛ a φL ⎞ ⎟⎟[(1 − f )(KdiC i0 nici ni + Cpri) − c pi2Cpi0] = ⎜⎜ i i ϑτ u C ⎝ w pi0 ⎠

ϑc pi2

Figure 1. Transient variation of pH measured in the effluents of soil columns and showing a gradual and sustained decrease in the 2-phase column due to CO2 dissolution and water acidification, and fluctuations in the 1-phase column associated with the noise of pH-electrode which increases at low electrical resistivity (SI Figure S3).

(18)

⎤ ⎛ ρ ⎞ ϑc i ⎡ ⎢1 + ⎜ b ⎟fi Kdin ic i ni − 1C i0 ni − 1⎥ ⎥⎦ ϑτ ⎢⎣ ⎝ φSw ⎠ =−

2 ⎛ ρ La i ⎞ 1 ϑci ⎛ DL,i φ ⎞ ϑ c i +⎜ ⎟ ⎟ 2 −⎜ b Sw ϑξ ⎝ u w L ⎠ ∂ξ ⎝ Swu w C io ⎠

[(1 − fi )(Kdic i niC i0 ni + Cpri) − c pi2Cpio]

a low value is due to the low solubility of CO2 at atmospheric pressure and the subsequent small mass-transfer rate from gas to liquid phase. The reduction of pH (Figure 1) stimulates the release of metal cations from solid hydroxides and/or oxides, according to reactions of the form 34

(19)

The system of eqs 17−19 supplemented with eq 11 is incorporated into an inverse modeling scheme where the numerically calculated transient response of each metal concentration in the column outlet matches the experimentally measured ones in effluents so that the metal desorption parameters (Kdi0, f i, ai) are estimated. The inverse modeling was done separately for each metal, thus solving repeatedly a system of four equations to estimate three parameters instead of solving simultaneously 25 equations to estimate 24 parameters. Any potential metal interactions are expected to be embedded into the estimated parameter values and only the effect of released metal cations on the pH control is overlooked. This assumption is realistic for low metal cations concentrations and is expected to suppress the pH reduction for high metal cations concentrations. The inverse modeling was done by using the Athena Visual Studio software package (Stewart and Associates, Inc.) where the dimensionless partial differential equations were solved with finite differences, and the nonlinear fitting to experimental results was done with the aid of a Bayesian estimator.32

M(OH)x (s) + xH+ ↔ M x + + xH 2O

(20a)

M 2Ox (s) + 2xH+ ↔ 2M x + + xH 2O

(20b)

The concentrations of certain metals (Mn, Ni, Cu, Zn) in the effluents of the 2-phase column increases rapidly and may become several orders of magnitude higher than those of the effluents of 1-phase column (Figure 2b,c,d,e). Cd dissolves easily and is the only metal detected at high concentrations in the effluent of the 1-phase column, whereas the reduction of pH results in the increase of its dissolution rate in the 2-phase column (Figure 2f). For the majority of the metals (Cr, Mn, Ni, Cu, Zn, Cd) their release rate decreases gradually and their concentration tends to very low values at late times (Figure 2), mainly because of the low residual metal concentration in the solid phase. Among the two trivalent metals (Cr3+ and Fe3+), which normally dissolve at low pH values,34 only Cr was detected at respectable concentrations in the effluent of the 2-phase column (Figure 2a). Neither Fe, nor Co was detected at measurable concentrations in the effluent of the 2-phase column, though both were detected at very low concentration and for a short period of time in the effluent of the 1-phase column (SI, Figure S4). Mineralogical Variation. The aforementioned results were also confirmed with mineralogical characterization of the sand surface. SEM-EDS analyses confirmed the presence of all metals on the surface of sand grains before the experiments (Table 1, SI Figure S5). After the completion of flow tests, only Fe and Cr were detected in the sand of the 2-phase column while, with the exception of Cd, almost all other metals were detected at respectable concentrations in the sand of the 1-phase column (Table 1, SI Figure S5). XPS analysis indicated the presence of metals in the initial sand, primarily as oxides (Cr2O3, MnO2, NiO, CuO, ZnO) but also as hydroxides (FeOOH-goethite, Cd(OH)2) that have precipitated on the surface of sand grains (Table 2, SI Figure S6). The main characteristics of the sand of the 1-phase column are the following (Table 2): (i) certain

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RESULTS AND DISCUSSION Flow-through Experiment. For the sake of clarity in the following the term 2-phase column refers to the experiment of the simultaneous injection of CO2 and water and the term 1phase column refers to the control experiment. In the 2-phase column, the water saturation was stabilized very fast and no respectable change was recorded during the experiment (SI Figure S2a). At steady-state, the CO2 is expected to follow a bubble-flow dynamics reflected in the fluctuations of the measured pressure drop (SI Figure S2b) although a more systematic analysis of the two-phase flow regime over the full space of flow parameters is required.33 The CO2 dissolution in water is reflected in the increase of conductivity (SI Figure S3) and the rapid reduction of pH. The pH tends slowly to an asymptotic value (∼3.6) indicating that equilibrium has been established and the concentration of CO2 has reached its solubility limit (Figure 1). The delay in the stabilization of pH to D



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Figure 2. Transient variation of metal cations concentration measured in the effluents of the 2-phase (CO2 injection) and 1-phase (control test) soil columns and showing a significant increase at early times of CO2 injection, and a gradual decrease at long-term, tending asymptotically to zero: (a) Cr; (b) Mn; (c) Ni; (d) Cu; (e) Zn; (f) Cd.

Table 1. Elemental Analysis of Sand Grains with SEM-EDS before and after Experiment element (% w/w)

O

Na

Si

Cr

Mn

Fe

Co

Ni

Cu

Zn

Cd

initial sand 2-phase column 1-phase column

52.42 57.39 50.99

0.92

31.52 39 46.45

0.82 0.46 0.11

2.18

3.67 3.02 0.8

0.7

0.94

2.59

1.88

2.38

0.08

0.17

0.62

0.52

0.26

K 0.14

hydroxide (Cr(OH)3); (iv) the goethite (FeOOH) was stable and did not alter. The main characteristics of the sand of the 2phase column are the following (Table 2): (i) the content of almost all oxides was reduced to very low values; (ii) the residual Cu detected in sand might have been adsorbed physically in goethite (FeOOH) and/or residual manganese oxide (MnO2);34

metals were dissolved until geochemical equilibrium was established (Figure 2) and the content of corresponding oxides/hydroxides (Cr2O3, NiO, CuO, ZnO, Cd(OH)2) in the sand was reduced; (ii) one bivalent oxide (NiO) was oxidized and converted to its trivalent form (Ni2O3); (iii) one oxide (Cr2O3) was hydrolyzed and converted to its corresponding E



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Table 2. Metal Chemical States Identified in Sand Grain Surface by XPSa chemical state

Cr2O3

Cr(OH)3

MnO2

FeOOH

initial sand 1-phase column 2-phase column chemical state

++ − + Ni2O3

− ++ − CuO

++ ++ ++ ++ T ++ Cu ZnO Zn

initial sand 1-phase column 2-phase column

− + −

++ + −

− − ++

++ + −

− − T

Co

Table 3. Sorption Parameters from Batch Experiments NiO

− + T − − − Cd(OH)2 ++ + −

a

The symbols ++ , + , - , T mean strong presence, weak presence, absence, and traces, respectively.

metal (i)

mi

logKdi0

correlation coefficient r2

Cr Mn Fe Co Ni Cu Zn Cd

1.3963 1.0796 0.2575 0.448 1.1401 2.5633 1.7103 0.8079

3.3417 1.1868 0.4348 −0.5042 1.4945 5.8867 3.0484 −0.1569

0.9002 0.9687 0.798 0.5953 0.9808 0.8958 0.9061 0.8352

Cr was reduced. It seems that the type of anions (CH3COO− or HCO3−) is of secondary importance and metal desorption is mainly controlled by pH. However, a more systematic study is required by paying attention on the relative contribution fraction of pH-controlled and carbonate-controlled processes to the mobility of various metals.22 Estimation of Metal Sorption Parameters from FlowThrough Experiment. The physicochemical properties of the fluid system were obtained from literature, the transport properties of the sand columns were determined in earlier studies,29−31 the diffusion coefficients of metal cations, Dmi, were calculated from the limiting molar ionic conductivities at infinite dilution (SI Table S2) and all fixed parameters are summarized in Table 4. The exponent mi was fixed to the values estimated from batch experiments (Table 3). The parameters of the two-site sorption model (Kdi0, f i, ai) were estimated (Figure 4), separately for each metal with inverse modeling of the transient variation of metal cations concentration in the effluents of the 2-phase column (SI Figure S7). The ranking of desorption rate coefficients, ai, in descending order is Cd > Cu > Ni∼Cr > Mn∼Zn (Figure 4a) and shows the relative rate of metals release at conditions far from equilibrium. High values of f i, close to unity, indicate that the equilibrium sites are prevalent and the ranking of metals in descending order of their tendency to desorb at equilibrium is Cd > Zn > Ni∼Cr > Mn > Cu (Figure 4b). The partition coefficients, Kdi0, estimated from continuous flow experiment are in full qualitative agreement with those estimated from batch experiments (Figure 4c). The lower the Kdi0, the stronger the metal release at pH = 7 and the higher the metal concentration in aqueous phase. Therefore, the ranking of partition coefficients (pH = 7) in

(iii) FeOOH was stable whereas the concentrations of MnO2 and Cr2O3 were reduced. The aforementioned results are consistent with the SEM-EDS analyses (Table 1), the observed concentrations of metals in effluents (Figure 2), and the initial and residual concentrations of metals measured precisely with AAS (Figure 3). One metal (Cd) vanished from the sand almost totally, one metal (Fe) was geochemically trapped as goethite (Table 2) and was not mobilized almost at all from the sand of both columns, whereas the CO2 dissolution and water acidification were responsible of the significant mobilization/ dissolution of certain metals (Cu, Ni, Zn, Mn, Co), and moderate mobilization of another one metal (Cr) (Figure 3). Estimation of Metal Sorption Parameters from Batch Experiments. Considering as reference state the neutral conditions at pH0 = 7, the linear regression analysis of the results of batch tests enabled us to estimate the parameters mi, logKdi0 (Table 3). The higher the exponent mi the more sensitive the metal desorption to pH. On the other hand, the lower the logKdi0, the easier the metal desorption from the sand and its dissolution in water at neutral conditions. It seems that Cu has the weakest mobility at neutral conditions, but at the same time, its desorption capacity has the tendency to increase profoundly with the pH decreasing (Table 3). On the other hand, Co and Cd have the strongest mobility at pH0 = 7 and the minimum sensitivity to pH (Table 3). It is worth noting that when the batch test was repeated at pH = 3.5 by saturating the solution with CO2 instead of using acetic acid, the Mn, Co, Ni, Cu, Zn, Cd were almost depleted from the sand and the concentration of Fe and

Figure 3. Comparison of metal concentrations in sand before and after experiment, measured with atomic absorption spectroscopy. The metal content decreases weakly for Cr, Mn, Fe, Co, Ni, Cu, Zn and almost vanish for Cd in the sand of the 1-phase column. Respectively, the metal content remains unaltered for Fe, decreases moderately for Cr and significantly for Mn and Co, and vanishes for Ni, Cu, Zn, Cd in the sand of the 2-phase column. F



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Table 4. Physicochemical and Transport Properties k (m2)

φ −12

26.5 × 10 species

0.4

Dm ×10 (m /s) 9

(Deff)/(Dm)

2

0.62 Cr3+

Mn2+

1.8

1.3

aL (μm) 250.0 Fe3+ 1.81

β

γgw (mN/m)

1.0

70.0

Cs (kg/m3)

Co2+

Ni2+

1.436 Cu2+

1.28

1.25

1.43

km (m/s) −7

P0c (Pa)

mc

8 × 10 Zn2+

1169 Cd2+

0.055 CO2

1.40

0.74

2.0

Figure 4. Metal mobilization parameters estimated by inverse modeling of the transient variation of metal concentration in the effluent of 2-phase soil column (Figure 2): (a) the desorption rate coefficient is indicative of the metal release rate when the local equilibrium assumption is not favored; (b) the higher the fraction of equilibrium sites the stronger the tendency of the metal to desorb under equilibrium conditions; (c) the lower the partition coefficient the easier the metal release. In stochastic Bayesian estimation used, the posterior density function takes its maximum at the minimum sum of squares (prediction-observation) encountered in the permitted range of parameter values. In practice, we need not only this least-squares value (the point estimate), but also various interval estimates (which are plotted as error bars), regions of highest posterior density, and integrals over part or all of the range of parameter values.

batch tests (Figure 4c). Such differences might be attributed to the different conditions prevailing at the sand grain scale: in the batch test, the sand grains are suspended under continuous stirring in the aqueous solution; in the flow-through test, the sand grains surround the pores through which the aqueous solution is flowing. Evidently the metal desorption is expected to be faster and easier in the first case. Nevertheless, the Kdi0 values estimated from the two different types of experiments agree qualitatively. The release of metals from the minerals of the solid phase of aquifers is enhanced highly by CO2 dissolution, and depends strongly on the mineral stability, metal solubility in water and its sensitivity to pH. The flow of CO2 through an aquifer at high injection rate may lead to the equilibrium or nonequilibrium desorption of a substantial percentage of certain heavy metals from the solid phase, and unavoidably to high metal concentrations in groundwater. The CO2 injection in freshwater aquifer leads to the selective mobilization of certain metals in

descending order is Co > Cd > Fe > Mn > Ni > Zn > Cr > Cu (Figure 4c, Table 3) and shows the relative capacity of metals to desorb at neutral conditions. On the other hand, the ranking of exponents mi in descending order (Table 3) is Cu > Zn > Cr > Ni > Mn > Cd > Co > Fe and shows the relative sensitivity of metal partition coefficients to pH. In this manner, the differences observed between the metal concentrations in the effluents of 2phase and 1-phase columns are expected to be quite high for Cr (Figure 2a) and Cu (Figure 2d) and low for Cd (Figure 2f), Fe (SI Figure S4a), and Co (SI Figure S4b). Nevertheless, there is an ambiguity concerning the fate of Co in the 2-phase column given that its content in sand was reduced (Figure 3) without detecting it in effluents. On the other hand, it is evident that the mobilization of Fe was very limited (Figure 3) and this might be attributed to the strong resistance of goethite to dissolution at acidic conditions. The Kdi0 values estimated from the column test were 1 order of magnitude less than those estimated from the G



Article

metal concentrations (Table S1); measured water saturation and CO2 pressure drop over time (Figure S2); measured electrical conductivity of effluent over time (Figure S3); Fe and Co concentration in the effluent of control test over time (Figure S4); SEM image and EDS spectra of sand (Figure S5); XPS spectra (Figure S6); calculation of diffusion coefficients for metal cations (Table S2); comparison of observed vs numerically predicted metal concentrations (Figure S7); equilibrium concentration of metals dissolved from solid hydroxides (Table S3, Figure S8). This material is available free of charge via the Internet at http://pubs.acs.org.

accordance with the stability of the corresponding minerals at the prevailing acidic conditions. Because of the continuous groundwater flow and dissolved metal replenishment, theoretically, the release of a metal may continue, as long as the pH is low, until it is exhausted from the solid minerals. Preliminary studies of site characterization are usually carried out to check the suitability of a geological formation for the safe CO2 storage. During this stage, it is of key importance to make sure that hazardous heavy metals or metalloids contained in overlying freshwater aquifers will remain geochemically trapped in the host rock/soil under low acidity conditions. Of course, there is a number of other parameters that might be of high importance for the environmental impacts of CO2 on groundwater quality and deserve our attention: (i) the coupled effect of oxidation−reduction potential and pH on metal adsorption/desorption reactions in CO2 impacted systems; (ii) the dimensionless parameters governing the relative importance of reaction and mass-transfer processes with respect to convective flow (e.g., Damkohler number,8 Peclet number 29) in terms of time-scales; (iii) the ionic strength of groundwater; (iv) the flow regime of the two-phase fluid (gas/ liquid) system in porous media. 33 The equilibrium molar concentration of metal cations [Meq] in saturated solutions can be obtained from the solubility product, Ksp of insoluble hydroxides/oxides (SI Table S3) and pH. Assuming that the hydroxide/oxide dissolution is described by eq 20a, and 20b, finally it is obtained 34 −log[Meq ] = − log K sp + x(pH + log K w)

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Corresponding Author

*E-mail: [email protected]. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS This research has been cofinanced by the European Union (European Social Fund − ESF) and Greek national funds through the Operational Program “Education and Lifelong Learning” of the National Strategic Reference Framework (NSRF) - Research Funding Program: Thales. Investing in knowledge society through the European Social Fund. We thank Dr. V. Dracopoulos and Dr. L. Sygelou for performing the SEMEDS and XPS analyses, respectively. The fruitful discussions held with Prof. P. Koutsoukos are also acknowledged.

(21)

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From the solubility products of the investigated metals at 25 °C (SI Table S3) and eq 21, the concentration [Meq] was calculated for each metal (SI Figure S8). The higher the [Meq] under less acidic conditions (high pH values), the easier the dissolution of metal and the ranking of metal mobility becomes: Mn > Cd > Co > Ni > Zn > Cu > Cr > Fe. This sequence is comparable to that deduced by the values of partition coefficient, Kdi0, estimated from the continuous flow test (Cd > Mn > Ni > Zn > Cr > Cu, Figure 4c). However, the ranking of metal mobility changes when their nonequilibrium kinetic coefficient, ai (Cd > Cu > Ni∼Cr > Mn∼Zn, Figure 4a), and the percentage of equilibrium sites in solid phase (Cd > Zn > Ni∼Cr > Mn > Cu, Figure 4b) are accounted for. Therefore, the parameters estimated from continuous flow experiments enable us to give a full quantitative interpretation of the observed dynamics of metal release at conditions simulating realistically the flow regime and regional geochemistry. Such information enables us to classify the mobility of metals with respect to prevailing (equilibrium/nonequilibrium) conditions, and explain quantitatively any observed variation of the metal mobility between different geochemical environments. Analogous lab-scale experiments performed on rock or soil cores collected from the target freshwater aquifer and coupled with the numerical approach could provide all information required for the assessment of the environmental impacts of CO2 storage on the quality of freshwater. Moreover, the parameters estimated from flowthrough lab-scale tests might be helpful for the interpretation of field-scale CO2 leakage scenarios, and installation of relevant early warning monitoring systems.

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AUTHOR INFORMATION

REFERENCES

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ASSOCIATED CONTENT

S Supporting Information *

Experimental setup (Figure S1); calculation of pH as a function of dissolved CO2 concentration; calculation of characteristic H



Article

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