Journal of Contaminant Hydrology 161 (2014) 35–48

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Leaching potential of pervious concrete and immobilization of Cu, Pb and Zn using pervious concrete U. Solpuker a,⁎, J. Sheets a, Y. Kim b, F.W. Schwartz a,1 a b

School of Earth Sciences, The Ohio State University, 125 S. Oval Mall, Columbus, OH 43210, USA Korea Institute of Geoscience and Mineral Resources (KIGAM), 92 Gwahang-no, Yuseong-gu, Daejeon 305-350, Republic of Korea

a r t i c l e

i n f o

Article history: Received 10 October 2013 Received in revised form 19 March 2014 Accepted 21 March 2014 Available online 30 March 2014 Keywords: Pervious concrete Urban runoff PHREEQC Trace metal Leaching Immobilization

a b s t r a c t This paper investigates the leaching potential of pervious concrete and its capacity for immobilizing Cu, Pb and Zn, which are common contaminants in urban runoff. Batch experiments showed that the leachability of Cu, Pb and Zn increased when pH b 8. According to PHREEQC equilibrium modeling, the leaching of major ions and trace metals was mainly controlled by the dissolution/precipitation and surface complexation reactions, respectively. A 1-D reactive transport experiment was undertaken to better understand how pervious concrete might function to attenuate contaminant migration. A porous concrete block was sprayed with low pH water (pH = 4.3 ± 0.1) for 190 h. The effluent was highly alkaline (pH ~ 10 to 12). In the first 50 h, specific conductance and trace-metal were high but declined towards steady state values. PHREEQC modeling showed that mixing of interstitial alkaline matrix waters with capillary pore water was required in order to produce the observed water chemistry. The interstitial pore solutions seem responsible for the high pH values and relatively high concentrations of trace metals and major cations in the early stages of the experiment. Finally, pervious concrete was sprayed with a synthetic contaminated urban runoff (10 ppb Cu, Pb and Zn) with a pH of 4.3 ± 0.1 for 135 h. It was found that Pb immobilization was greater than either Cu or Zn. Zn is the most mobile among three and also has the highest variation in the observed degree of immobilization. © 2014 Elsevier B.V. All rights reserved.

1. Introduction Urbanization around the world has greatly expanded the impermeable surface cover of cities (e.g. parking lots, roadways, roof tops etc.) and the urban infrastructure (e.g., storm sewers), which are particularly efficient in transporting contaminated stormwater to urban rivers and streams, and eventually to coastal environments. The types of pollutants (e.g. trace metals, nutrients and hydrocarbons) in urban stormwater runoff are extremely varied and the pollutant loadings are significantly dependent on land use and rainfall ⁎ Corresponding author. Tel.: +1 614 2926193; fax: +1 614 2927688. E-mail addresses: [email protected] (U. Solpuker), [email protected] (J. Sheets), [email protected] (Y. Kim), [email protected] (F.W. Schwartz). 1 Tel.: +1 614 2926196; fax: +1 614 2927688.

http://dx.doi.org/10.1016/j.jconhyd.2014.03.002 0169-7722/© 2014 Elsevier B.V. All rights reserved.

(Davis et al., 2001; Herngren et al., 2005; Pitt et al., 2004). Among the sources of trace metals (e.g., lead, copper, cadmium, and zinc) in urban runoff are sidings on buildings, automobile frames and bodies, particulates from vehicle brake and tire wear, and atmospheric pollution (Boving et al., 2008; Davis et al., 2001; Herngren et al., 2005; Pitt et al., 2004; Sansalone and Buchberger, 1997). Furthermore, polycyclic aromatic hydrocarbons (PAHs) are ubiquitous from sources that include automobile exhaust, lubricating oils, gasoline, tire particles, erosion of street asphalt, atmospheric deposition and coal-tar based seal coats for asphalt parking lots (Mahler et al., 2005). Problems of flooding and contamination associated with urban runoff are now widely known and understood. City managers are now taking steps to reduce these problems through engineered systems that are now part of the urban landscape. For example, pervious pavements are one of the

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popular Best Management Practices (BMPs) to reduce flash flooding due fast runoff from impervious surfaces and are commonly used in parking lots, sidewalks and pathways, low traffic areas etc. (Boving et al., 2008). They minimize stormwater-related flooding by allowing the infiltration of surface runoff through the pavement (asphalt or concrete) (Tennis et al., 2004). Pervious pavements can be constructed from the same materials as conventional concrete except that the fine fraction of the aggregate is eliminated, the size distribution of coarse fraction is narrowed, and just enough cementitious paste is used to coat the coarse aggregates while keeping the interconnectivity of the voids (Tennis et al., 2004). Pervious pavements are placed over a 15 to 30 cm thick layer of permeable subbase made up of either a 2.5 cm maximum size aggregate that is separated from the underlying soil with a non-woven geotextile that prevents the soil particles from migrating into the subbase but allows water to drain into the soil. Providing a capability for infiltration of storm water through permeable pavements to the subsurface reduces the peak flows along stream and drainage channels and reduces the risk of floods (Boving et al., 2008; Pratt, 1999; Tennis et al., 2004). Furthermore, these pavements are effective in reducing the pollutant loads in stormwater runoff (Boving et al., 2008; Kwiatkowski et al., 2007; Legret and Colandini, 1999; Legret et al., 1996, 1999). Legret et al. (1996) concluded that runoff waters passing through porous pavements contain markedly lower pollutant loads than those from a reference catchment due to the accumulation of metallic micro-pollutants from runoff waters on the surface of the pervious pavement. Nevertheless, there is still a question as to whether pervious pavements actually improve the quality of urban streams or simply shift the contamination problem to groundwater (Kwiatkowski et al., 2007). Over the past several years, we have been working to develop a series of passive and semi-passive technologies that are capable of reducing the loading of metals and organic contaminants from parking lots. The work reported in this paper elucidates the trace metal geochemistry of pervious pavements. Our hypothesis is that pervious concrete pavements can serve as an important element of a system designed to reduce contaminant loading. More specifically, we set out to describe both the trace metal retention potential of pervious pavements but also their potential to leach from concrete. The investigative approach involves batch and 1-D column experiments and model studies with PHREEQC (batch and 1-D reactive transport) to evaluate the efficacy of using permeable and reactive pavements to control trace metal contamination associated with urban runoff.

aggregate for about 1 min to coat the surface of aggregates with cement. The rest of the Portland cement and water were added to the concrete batch, and mixed manually for about 3 min. After pausing for 2 min, the concrete batch was mixed for an additional 3 min. The batch was placed into a cylindrical low-density polyethylene (LDPE) mold in three layers. After placing the first layer, the surface of the mixture was struck 25 times with a rod to squeeze the mixture. The mold was then placed on a vibration table and shaken at high speed for 10 s. The same procedure was applied for the last two layers and a piece of wet cloth was laid over the top of the mold. The cloth was kept wet but not allowed to drip water. The sample was stored at room temperature in a closed humid environment for 28 days. The final length and the diameter of the concrete sample were 9.5 cm and 8.6 cm, respectively. The sample had a porosity of 10% measured using the Archimedes Principle (Montes et al., 2005). 2.1. Chemical analysis and phase characterizations A qualitative assessment of the mineralogical assemblage of pervious concrete and hardened Portland cement was carried out at 28 days using X-ray powder diffraction (XRD) of ground-up samples. The analysis was performed with a Scintag XDS-2000 X-ray diffractometer (CuKα radiation) at the Institute for Materials Research of the Ohio State University. This sample of pervious concrete was mainly composed of calcite and dolomite as suggested by their sharp peaks. Portlandite and quartz are among the minor phases (Fig. 1a). In addition, the XRD spectrum of the hardened Portland cement indicates the presence of ettringite, hydrotalcite, monocarboaluminate and calcium silicate hydrate (CSH) (Fig. 1b). The CSH peak overlapped with the calcite peak. The total elemental analysis of Portland cement and pervious concrete was determined by Elemental Analysis, Inc. (Lexington, KY) using proton induced X-ray emission (PIXE) analysis where proton beams are used as excitation of the atoms in the samples to produce characteristic X-rays (Table 1). Statistical error associated with each elemental is also given in Table 1. Crushed pervious concrete was homogenized, providing a subsample of about 20 g. That subsample was further pulverized to b 200 mesh in size. After the final homogenization, an aliquot of approximately 1 g was taken from the sieved portion and used for PIXE analysis. Scanning electron microscope (SEM) images of the pervious concrete surfaces were prepared for samples collected from the end of the 1-D column, after a solution spiked with Cu, Pb and Zn was sprayed on the pervious concrete block for 135 h. Energy dispersive spectroscopy (EDS) analysis at 25 keV used a Bruker QUANTAX SEM at the School of Earth Sciences of the Ohio State University.

2. Materials and methods 2.2. Experiments A pervious concrete block was prepared from Portland cement type 1 and limestone aggregate No .8 (9.5 mm to 2.36 mm) (ASTM Standard C 33, 2013), a common limestone aggregate manufactured in Columbus, Ohio. An aggregate to cement ratio of 4.5 and water to cement ratio of 0.38 (by weight) was used in the concrete batch. About 10% (by weight) of Portland cement was mixed with limestone

Ultra-pure water (Millipore, 18.2 MΩ·cm at 25 °C) and trace metal grade nitric acid (Fisher Scientific) were used in the experiments. LDPE bottles and FEP tubing were used in the experiments. Eluates were filtered through 0.45 μm membranes and acidified. New pipette tips were for each sample preparation. Conductivity standards and pH buffers were used

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Fig. 1. XRD pattern for pervious concrete (a) and hardened Portland cement at 28 days (b) (c: calcite, d: dolomite, q: quartz, p: portlandite, e: ettringite, m: monocarboaluminate, ht: hydrotalcite, csh: CSH).

to calibrate the conductivity meter (Oakton RS-232) and the pH meter (IQ Scientific Instruments), respectively before each use. Calibration standards and spiked solutions of Pb, Zn and Cu were prepared by using certified solutions (Inorganic Ventures) of known concentrations. A certified reference material (NIST 1643e) was analyzed and compared to reference values for accuracy. The percent errors for Ca, Al, Mg, Na, K, Fe, Cu, Zn, and Pb were 7%, 1%, 5%, 1%, 2%, 5%, 7%, 6%, and 2%, respectively. Internal standards, check standards and blank samples were run periodically to check for the drift in the analysis. Percent relative standard deviation (% RSD) was 3% or lower for most of the elements except K and Na which had an RSD of 14% and 4%, respectively. 2.2.1. Batch experiments The leaching behavior of the pervious concrete was tested following the European Standard CEN/TS 14429 (2004). This test is used to determine the influence of pH on the leachability of inorganic constituents from a material, inferred from the leachant composition at different pH values and the acid neutralizing capacity of the material. Crushed pervious concrete samples were sieved to a grain size of less than 1 mm and placed into LDPE bottles. Ultra-pure water was added to provide a liquid to solid ratio of 10 ± 0.2 (L/kg). Testing was performed at eight different pH values across a range from 4 to 12 at room temperature. Predetermined quantities of trace metal grade nitric acid solutions were added to the homogenized samples to provide the desired pH values. After the addition of acid, samples were agitated on a shaking table. Equilibrium of the suspension was considered to have been reached when the difference between each pH measurement

of the suspension at 44 and 48 h of agitation differed by less than 0.3 pH units. The eluates were filtered through a 0.45 μm membrane, acidified with nitric acid and analyzed for cations with a Thermo Finnigan Element 2 ICP-Sector Field-MS. 2.2.2. 1-D column experiment The pervious concrete block was placed in a LDPE column with a spray nozzle at the top and a sampling port at the bottom. Ultrapure water was acidified to a pH of 4.3 ± 0.1 Table 1 PIXE analysis of Portland cement and pervious concrete. Element

Portland cement (mg/kg)

Pervious concrete (mg/kg)

Ca Mg Al Si S Fe Cl K Ti Cr Mn Ni Cu Zn Br Rb Sr Zr Pb

319,580 ± 3200 14,840 ± 610 21,740 ± 450 77,110 ± 770 16,480 ± 280 16,270 ± 160 2300 ± 120 6930 ± 220 1,880 ± 110 106.5 ± 11.3 167.6 ± 8.3 21.0 ± 2.6 58.4 ± 2.7 170.9 ± 3.8 65.3 ± 3.7 47.6 ± 4.7 384.0 ± 11.6 810.011.8 33.2 ± 6.4

250,160 ± 2500 50,650 ± 580 6350 ± 160 23,890 ± 240 2990 ± 80 5520 ± 60 410.7 ± 42.8 1420 ± 80 384 ± 38.5 18.98 ± 5.4 140.8 ± 4.9 5.0 ± 1.5 10.8 ± 1.3 25.7 ± 1.5 10.1 ± 1.4 12.4 ± 2.1 134.7 ± 5.3 BDLa BDLa

a

Below Detection Limit (BDL).

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Table 2 Thermodynamic data and the initial quantities of phases for calculations with PHREEQC. Phase

Reaction

Log K

Initial quantity (mol/kg dry matter)

Calcitea Dolomitea CSHb Quartza Portlanditea Monocarboaluminatec Ettringited Hydrotalcitee Fe-monosulfatef CZSHg Cr-monosulfateh Pb(OH)2a Cu(OH)2a Brucitea Fe(OH)3(am)a Boehmitea

CaCO3 = Ca+2 + CO−2 3 CaMg(CO3)2 = Ca+2 + Mg+2 + 2CO−2 3 Ca1.7SiO3.7:1.7H2O = 1.7Ca+2 + H2SiO−2 + 1.4OH− 4 SiO2 + 2H2O = H4SiO4 Ca(OH)2 + 2H+ = Ca+2 + 2H2O −2 Ca4Al2(CO3)(OH)12:5H2O = 4Ca+2 + 2Al(OH)− + 4OH− + 5H2O 4 + CO3 −2 Ca6(Al(OH)6)2(SO4)3:26H2O = 6Ca+2 + 2Al(OH)− + 26H2O 4 + 3SO4 Mg2Al(CO3)0.5(OH)6:H2O + 6H+ = 2 Mg+2 + Al+3 + 0.5CO−2 + 7H2O 3 Ca4Fe2(SO4)(OH)12:6H2O + 12H+ = 4Ca+2 + 2Fe+3 + SO− 4 + 18H2O Ca4Si4ZnO12(OH)2:4H2O = 4Ca+2 + Zn(OH)2 + 4H2SiO−2 4 −2 Ca4Al2O6(CrO4):15H2O = 4Ca+2 + 2Al(OH)− + 4OH− + 9H2O 4 + CrO4 + +2 Pb(OH)2 + 2H = Pb + 2H2O Cu(OH)2 + 2H+ = Cu+2 + 2H2O + +2 Mg(OH)2 + 2H = Mg + 2H2O Fe(OH)3 + 3 H+ = Fe+3 + 3H2O AlOOH + 3H+ = Al+3 + 2H2O

−8.48 −17.09 −11.28 −4.00 22.804 −31.47 −44.90 25.43 66.05 −40.00 −30.33 8.15 8.674 17.10 4.89 8.578

3.49 2.08 0.53 0.33 0.17 0.08 0.015 0.026 0.05 3.3 × 10−5 3.5 × 10−4 6.6 × 10−6 1.7 × 10−5 – – –

a b c d e f g h

MINTEQ.V4 database (Allison et al., 1990). Rothstein et al. (2002). Lothenbach and Winnefeld (2006). Perkins and Palmer (1999). Johnson and Glasser (2003). Blanc et al. (2010). Tommaseo and Kersten (2002). Perkins and Palmer (2001).

using trace metal grade nitric acid and sprayed over the concrete sample homogeneously with an intensity of 2.7 cm3/min for approximately 190 h. The experiment was continued for an additional 135 h using a solution with 10 ppb Zn, Cu and Pb and a pH of 4.3 ± 0.1. Spiked solution was prepared daily from concentrated standards and pH was adjusted by adding diluted trace metal grade nitric acid. A peristaltic pump maintained the flow of solutions into and out of the pervious concrete block. Specific conductance and pH were measured immediately after sampling. Eluates were sampled periodically and stored in LDPE bottles, filtered through a 0.45 μm membrane and acidified with trace metal grade nitric acid for cation analysis using Thermo Finnigan Element 2 ICP-Sector Field-MS. 2.3. Modeling The geochemical code PHREEQC (version 2.18.3) (Parkhurst and Appelo, 1999) was used to model the leaching behavior of pervious concrete in batch and 1-D column experiments. The MINTEQ.V4 thermodynamic database was used in simulations but equilibrium constants of some of the particular mineral phases associated with concrete were compiled from different sources. 2.3.1. Batch modeling Geochemical modeling including surface complexation was used to simulate the leaching behavior of pervious concrete at different pH values. The Davies equation was used for calculating activity coefficients of ion complexes. It was assumed that equilibrium would be attained between the minerals and the solutions. The dissolution of CSH, quartz, dolomite and portlandite is assumed to be kinetically controlled. The kinetic reaction for the CSH is assumed to be a

function of the activity of hydrogen and the departure from equilibrium. The rate equation used for CSH is  0:2 RCSH ¼ kCSH : aHþ :ð1−SRCSH Þ:ðmCSH =moCSH Þ

ð1Þ

where a is the activity of hydrogen ion, k is the rate constant (10−8 mol/L·s), mo is the initial moles of CSH, m is the moles of CSH at a given time and SR is the saturation ratio: SR ¼ IAP=K

ð2Þ

where IAP is the ion activity product and K is the equilibrium constant. The rate equations for quartz, dolomite and portlandite are given in Table 4. Trace metal bearing phases such as Fe-monosulfate, Cr-monosulfate, zinc-bearing calcium silicate hydrate (CZSH), Cu(OH)2 and Pb(OH)2 were included in the model in addition to phases identified with the XRD analyses. CSH has a variable composition and exhibits increasingly incongruent solubility as its Ca/Si ratio increases (Kersten, 1996; Kulik and Kersten, 2001; Stronach and Glasser, 1997; Walker et al., 2007). To overcome this complication, Rothstein et al. (2002) used a simple approach to formulate an IAP from the most abundant ions, fixing the ratios to maintain charge balance.

Table 3 Parameters used in the surface complexation model. Surface parameters Adsorbent Binding site (weak) Binding site (strong) Specific surface area Mass

Hydrous ferric oxide 2.31 sites/nm2 0.05775 sites/nm2 600 m2/g 0.1 g

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Fig. 2. The conceptual model for pervious concrete used in 1-D reactive-transport modeling.

Assuming the CSH to have a Ca/Si ration of 1.7 and using the most abundant calcium and silicon ions in the pore solution 2 − 1.4 result in IAP = [Ca2 +]1.7[H2SiO− . 4 ][OH ] Table 2 lists the phases and their initial quantities used in the modeling. The initial quantities of each phase were calculated based on establishing a mass balance between the available quantities of all elements determined by the chemical analysis of the pervious concrete (Table 1) and the total quantities of elements used in the reconstructed mineralogy of the pervious concrete. The error was ≤2% for all the elements considered except calcium which has 16% error. Surface complexation is modeled based on the diffusedouble layer (DDL) theory of Dzombak and Morel (1990) for complexation of trace metal ions on hydrous ferric oxides. PHREEQC calculated the diffuse-double layer composition according to the method of Borkovec and Westall (1983). Table 3 shows the parameters used in the surface modeling. Although, there are other phases that can be used as adsorbents such as CSH, only hydrous ferric oxides were considered as the adsorbent due to database restrictions. 2.3.2. 1-D reactive-transport modeling Two types of porosity, namely capillary and matrix porosities, were assumed present in the pervious concrete. Capillary and matrix porosities are connected to each other but the former has significantly greater influence on

transport processes (Galindez et al., 2006). The acidic influent (pH ≈ 4.3) flows predominantly through capillary pores and reacts kinetically with concrete phases present along the interconnected pore system. The alkaline pore water found in the matrix is assumed at or near equilibrium with concrete phases. PHREEQC was configured to represent mobile and immobile “stagnant” zones, which corresponded to the capillary and matrix porosities, respectively. The pervious concrete block was discretized as ten cells of 0.95 cm each. The time step for each advective shift is 101 s. Dispersion is ignored in this model. One stagnant cell is associated with each mobile cell. During 1-D transport, mixing of the mobile and immobile pore waters is simulated explicitly with MIX function in PHREEQC. During mixing, it is assumed that only portlandite and CSH dissolve kinetically in the alkaline immobile pore water whereas all other concrete phases react kinetically in the mobile pore water (Fig. 2). The general rate law for the kinetic reactions is defined in Table 4 as:    Rp ¼ kp mp =mop : 1−SRp

ð3Þ

where kp is the reaction constant (mol/L/s), mp is the quantity of the phase at a given time (mol), mop is the phase quantity

Table 4 Rate laws used in simulations. Phase

Dissolution rate laws (20 °C)

Calcitea Dolomiteb Quartzc CSH Portlandite Ettringite Monocarboaluminate Hydrotalcite Monosulfate-Cr Monosulfate-Fe CZSH Cu(OH)2 Pb(OH)2 NaOH KOH

(1.0 × 10−4.32 (aH+) + 1.0 × 10−7.59 (aCO2) + 1.0 × 10−9.94 (aH2O)) · (1–100.67xSICalcite) · (mCalcite/moCalcite)0.67 (1.0 × 10−4.92 (aH+)0.5 + 1.0 × 10−6.47 (aCO2)0.5 + 1.0 × 10−10.76 − 1.0 × 10−4.85 (aHCO3−)) · (1-SRDolomite) · (mDolomite/moDolomite)0.67 1.0 × 10−13.7 (1-SRQuartz) · (mQuartz/moQuartz) 1.0 × 10−8 (a+0.2 ) · (1-SRCSH) · (mCSH/moCSH) H 1.0 × 10−7.25 (1-SRPortlandite) · (mPortlandite/moPortlandite) −8.75 1.0 × 10 (1-SREttringite) · (mEttringite/moEttringite) 1.0 × 10−8.75 (1-SRMonocarboaluminate) · (mMonocarboaluminate/moMonocarboaluminate) 1.0 × 10−9.25 (1-SRHydrotalcite) · (mHydrotalcite/moHydrotalcite) 1.0 × 10−11.25 (1-SRMonosulfate-Cr) · (mMonosulfate-Cr/moMonosulfate-Cr) 1.0 × 10−10.5 (1-SRMonosulfate-Fe) · (mMonosulfate-Fe/moMonosulfate-Fe) 1.0 × 10−10.5 (1-SRCZSH) · (mCZSH/moCZSH) 1.0 × 10−12.0 (1-SRCu(OH)2) · (mCu(OH)2/moCu(OH)2) 1.0 × 10−12.7 (1-SRPb(OH)2) · (mPb(OH)2/moPb(OH)2) 1.0 × 10−6.75 (1-SRNaOH) · (mNaOH/moNaOH) 1.0 × 10-6.75 (1-SRKOH) · (mKOH/moKOH)

Rate constants: aPlummer et al. (1978), bBusenberg and Plummer (1982) (rate constant from dolomite sample B at 20 °C), cVan Lier et al. (1960); SIp = log (SRp).

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U. Solpuker et al. / Journal of Contaminant Hydrology 161 (2014) 35–48 Table 5 Initial conditions and concentrations of elements assumed for the pore waters. T (°C)

20.0

pH

12.8

Elements

Concentration (mM)

Al C Ca Cr Cu Fe K Mg Na Pb S Si Zn

6.80 1.30 0.34 5.50 2.60 3.60 3.00 2.50 1.30 4.10 1.30 3.50 2.00

× × × × × × × × × × × × ×

10−2 10−2 101 10−3 10−5 10−3 101 10−4 101 10−6 10−1 10−1 10−5

at time zero (mol) and SRp is the saturation ratio of the phase. The kinetic reaction constants for concrete phases were estimated by fitting the simulated aqueous concentrations to the experimental results (Table 4). The initial conditions and composition of the pore waters are given in Table 5. 3. Results 3.1. Batch experiments and modeling 3.1.1. Acid neutralizing capacity of pervious concrete The acid neutralizing capacity (ANC) of the pervious concrete was measured following the European Standard CEN/TS 14429 (2004). ANC is a measure of the quantity of acid needed to maintain the pH of the solution at some desired value. The pervious concrete in this study has an ANC profile that is typical of concrete slabs (Schiopu et al., 2007). It also has a large acid neutralizing capacity due to the large quantities of calcite, dolomite and portlandite phases that can donate carbonate and hydroxide ions to the solution (Fig. 3). The pH of solution decreases sharply from pH 12 to

about pH 6 with a small addition of acid (b 2 mol H+/kg). However, the amount of acid significantly increases to about 9 mol H+/kg in order to reduce the pH from 6 to 5. 3.1.2. Major ion leaching from pervious concrete Fig. 4 shows the experimentally measured concentration of selected major ions in an aqueous solution in equilibrium with the pervious concrete at a particular pH (black circles). The solid lines represent model results from PHREEQC where the chemical equilibrium between the concrete phases and the solution was sought for all elements except Si, Ca and Mg. Dissolution and precipitation of CSH, quartz, portlandite and dolomite were modeled kinetically to describe the behavior of these elements. The gray lines represent the associated phase contribution for a given element where positive and negative values indicate precipitation and dissolution, respectively. These lines also assist in showing how the presence or absence of each phase can affect the overall concentration of an element. For example, it is the equilibration of Al-rich phases, hydrotalcite and boehmite, with the solution that reduces the Al concentration at different pH values. Similarly, eliminating brucite causes Mg concentrations to increase at high pH values. Within the given pH range, Si concentrations in solution decrease as the pHs of the solutions decrease. Si concentration measured at pH b 4 is about two orders of magnitude higher than the one measured at pH N 12 (Fig. 4a). For Si, the model fit shown assumes kinetically controlled dissolution of CSH and quartz. Additionally, amorphous SiO2 and CZSH are allowed to precipitate from the solution. The modeling shows that observed concentration profile is related mostly to the dissolution of calcium silicate hydrates (CSH) under more acidic conditions (Fig. 4a). Only at low pH values is amorphous silica in equilibrium with the solution and only a portion of CZSH dissolved to reach equilibrium with the solution at high pH values. Measured Ca concentrations are relatively constant in the range from pH 6 to pH 12. At low pH values, Ca concentrations are an order of magnitude higher (Fig. 4b). Modeling results suggest that calcite, dolomite and portlandite are the main phases that control the concentration of Ca. The model shows

Fig. 3. Acid neutralizing capacity (ANC) of pervious concrete.

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that calcite is no longer a stable phase at low pH values, which causes an increase in the Ca concentration. Dolomite and portlandite dissolve kinetically at all pH values but dolomite reaches equilibrium at pH 5 to 7. Mg concentrations remain relatively constant between pH 7 and pH 10 but in general, Mg concentrations increase as the pH of the solution decreases (Fig. 4c). At high pH values (pH N 10), the concentration of Mg is three to four orders magnitude lower. According to the model, this decrease is associated with equilibrium constraints provided by brucite and hydrotalcite with the solution. Dolomite dissolves at all pH values but hydrotalcite is not at equilibrium with the solutions at pH ≤ 7. Finally, as shown in Fig. 4d, Al concentrations are relatively low over a pH range between 6 and 10, but is much higher under acidic (pH b 6) and alkaline (pH N 10) conditions. The modeling suggests that Al concentrations in the solutions are controlled by the combination of Al-bearing phases (e.g. ettringite, hydrotalcite, monocarboaluminate and boehmite). Boehmite is the only phase that is in equilibrium with the solutions at relatively low pH values (pH b 7), while ettringite and monocarboaluminate reach equilibrium only under highly alkaline conditions (pH N 11). Additionally, hydrotalcite reaches equilibrium across 3 pH units between pH 9 and pH 11.

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3.1.3. Trace metal leaching from pervious concrete Fig. 5 shows the concentrations of trace metals in solutions at equilibrium with respect to pervious concrete (black circles). In numerous PHREEQC modeling trials, we examined the potential constraints exerted by a variety of possible solid phases that contain these trace metals, and the controls exerted by differing initial concentrations. The purpose of the modeling was to find a stable phase that could explain the observed concentration constraints at equilibrium. Thus, in addition to 100% of initial phase quantities used in modeling (Table 1), 10% of the initial amount of the phase was also considered where necessary, assuming that the rest of the solid phases might potentially occur in the matrix and be unavailable for reaction. Model results show the outcomes of both precipitation/ dissolution equilibrium reactions (labeled ppt./diss. in the figure legends) and surface complexation reactions (labeled as DDL). In the case of measured Fe concentrations, there is a trend in declining concentrations from pH 4 to pH 7. At higher pH values (N7), concentrations again increased (Fig. 5a). This behavior was modeled in terms of the dissolution/precipitation reaction of 100% Fe-monosulfate and amorphous Fe(OH)3. Fe-monosulfate reaches equilibrium only at high pH values (pH N 11) and amorphous hydrous ferric oxide precipitates across the range pH 4 to pH 11.

Fig. 4. Major ion concentrations of solutions equilibrated with pervious concrete. sample (L/S = 10) at various pH values (black circles). Solid black lines: model, dotted black lines: model without the specified phase, dashed black lines: background equivalent concentration (BEC), gray lines: phase contributions. For each phase, the positive and negative values indicate precipitation and dissolution, respectively.

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Fig. 5. Trace metal concentrations for solutions equilibrated with pervious concrete sample (L/S = 10) at various pH values (black circles). Dashed black lines: background equivalent concentration (BEC), (a) Solid black line: model, gray lines: phase contributions. For each phase, the positive and negative values indicate precipitation and dissolution, respectively.

Unlike Fe, the geochemical behavior of the other trace metals (Cu, Pb, Zn, Cr) as a function of pH cannot be explained with dissolution/precipitation processes alone. The approach we followed was to consider surface complexation in addition to the dissolution/precipitation processes. The measured data show the concentration of Cu to be relatively low between pH 7 and pH 10 with a slight tendency for concentrations to increase with increasing pH. Below pH 6,

concentrations of Cu increase significantly. The dissolution/ precipitation processes alone (dotted and dash/dotted lines Fig. 5b) do not explain the observed Cu behavior because Cu(OH)2 was not stable at pH b 9 and as a result the modeled Cu concentrations fit poorly with the measured values assuming both 100% and 10% for the initial phase. Adding the possibility for surface complexation of Cu provides a possible explanation of the concentration behavior below pH b 7

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(dashed line, Fig. 5b). However, this process ended up with Cu concentrations less than the observed values in the pH range 6 to 10 (Fig. 5b). Pb concentrations exhibited the typical saddle-shaped behavior of the other metals with concentrations increasing as a function of pH at pH b 6 and pH N 11. Pb concentrations were below detection (10−10 mol/L) between pH 6 and pH 11. Modeling showed that Pb(OH)2 was stable only between pH 9 and pH 11 and that equilibrium dissolution/ precipitation processes again could not explain the Pb behavior (see dotted and dash/dotted lines Fig. 5c). Modeling suggested that lower concentrations of Pb at pHs b 11 could be simulated only by assuming that 10% of the initial phase was involved in the reactions (solid line, Fig. 5c). Measured Zn concentrations are highest at low pH values (pH 4 to pH 5) and decline up to 3 orders of magnitude as pH increases. In modeling, Zn-bearing CSH (CZSH) was used as the zinc phase to constrain dissolution/precipitation processes because it tended to be stable at high pH values. This phase was unstable at lower pH values and leaving only surface complexation as the only possibility for lowering the concentration of Zn. The solid line in Fig. 5d represents our best fit of measured data with the model with CZHS as the mineral phase at 10% of the initial phase quantity and surface complexation. Unlike Zn, Cr concentrations were observed to decrease as the pH of the solution decreased, except at the lowest pH value (b4). The PHREEQC modeling showed that the Cr-bearing concrete phase, Cr-monosulfate, is not stable at pH values b12. Thus, concentrations modeled assuming only the equilibrium dissolution of this phase (assuming both 100% and 10% initial phase quantity) were unacceptably high relative to observed values. The surface complexation model with 10% initial phase quantity produced significantly lower concentrations of Cr especially at 5 b pH b 10 (solid line, Fig. 5e). One of the reasons for the discrepancies between the observed and the simulated concentrations is that our conceptualization uses a rather simplistic conceptualization of sorption. There could be different types of surfaces available for complexation, such as CSH, besides ferric hydrous oxides that we modeled (Moulin, 1999; Rose et al., 2000; Ziegler and Johnson, 2001). Furthermore, models show elevated trace metal concentrations at high pH values compared to the observed values. For example, although the modeled Zn concentration is about an order of magnitude higher than the observed values at high pH values, Zn bearing phases cannot reach equilibrium at high pH values or Zn cannot be adsorbed to charges surfaces. This is due to the speciation of the Zn at high pH values, where the formation of high concentrations of Zn(OH)−2 prevents Zn bearing 4 mineral phases from precipitating or being adsorbed to the surfaces. Nonetheless, surface adsorption help to explain the leaching behavior of trace metals better than the dissolution/ precipitation models alone. 3.2. 1-D column experiments and modeling Through the course of the 1-D column experiment, we monitored indicator variables, pH and specific conductance, selected major ions and selected trace metals. During the first

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190 h of the experiment, only acidic water, representing slightly acid rain, was passed through the column. During the last 125 h, synthetic urban runoff containing trace metals as passed through the column. Changes in pH and specific conductance through time are shown in Fig. 6a. The pH of the leachate decreased from pH 12.5 to pH 10.5 at the end of the experiment at 325 h. Small fluctuations associated with the pH measurements are likely related to the heterogeneity of the sample. Specific conductance decreased significantly in the first 60 h from 3000 μS/cm to 286 μS/cm which is approximately 28 times higher than the initial specific conductance of the influent solution. Subsequently, the rate of decline slowed with the specific conductance decreasing to 101 μS/cm at the end of the experiment. Modeling suggests that the observed trends in the pH and the specific conductance could be explained by mixing of high pH and high specific conductance pore waters in the immobile zone with low pH and low specific conductance pore waters in the mobile zone. The dual porosity formulation in the 1-D transport model generated the observed pH profile through time reasonably well (Fig. 6a). The concentration behavior of major ions is shown in Fig. 6b and c. In the case of Ca, and Al, there is an initial marked decrease in concentrations (Fig. 6b) followed by a slow but steady decline. Mg concentrations increase slightly and then remain steady through the remainder of the experiment. The rate of decline in concentration of K and Na becomes less in time with an overall reduction of two to three orders of magnitude (Fig. 6c). Si concentrations decreased slowly to the end of the experiment (Fig. 6d). The model fits these observed patterns of decline well (Fig. 6b and c). It points to a slow consumption of portlandite and CSH in the immobile zone, providing a continuous source for Ca and Si. In effect, the mobile zone concentrations of Ca and Si are not high enough to explain the observed values alone (Fig. 6b, d). The model replicates the tendency for Mg concentrations to increase slightly and decline back to a constant concentration towards the end of the experiment (Fig. 6b). The concentration of Mg is controlled by dolomite and hydrotalcite dissolution in the mobile zone. Specifically, the concentration of Mg decreases after the first 125 h in response to the decrease in dolomite. The slow hydrotalcite dissolution also contributes Mg. Al and Fe decline slowly throughout the experiment (Fig. 6b, d). This result is explained by the slow dissolution of Al and Fe bearing phases (e.g. ettringite, monocarboaluminate, hydrotalcite and monosulfate-Fe) in the mobile zones. These elements eventually become depleted in the immobile zones due to mixing. Thus, the immobile zone contribution of these elements is most evident for a short time at the beginning of the experiment. The model suggests that the temporal behavior of Na and K concentrations is caused by the depletion of these elements in the immobile zones. The process essentially involves the linear mixing of mass in the immobile zone with a smaller constant mass in the mobile zone. The behavior of the trace metals Cu, Pb and Zn is similar in the first 190 h. The measured concentrations are low and decrease slowly over the first 190 h (Fig. 6e). This is modeled reasonably well in terms of the slow dissolution of trace-metal bearing phases in the mobile zone and a decreasing contribution from the immobile zone. After a marked decrease in the first 50 h, Cr concentrations continued to decline somewhat linearly for an additional 140 h after which it remained nearly

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Fig. 6. pH, specific conductance and ICP-MS analysis of effluent through time and results of the PHREEQC simulation.

constant (Fig. 6d). The model suggests that the observed behavior in Cr concentrations can be explained by the slow loss of Cr from the immobile zone through time. After 190 h, only the slow dissolution of Monosulfate-Cr in the mobile zone continues to contribute Cr. The capacity of pervious concrete to immobilize certain trace metals was tested by spraying a synthetic solution representing contaminated urban runoff (10 ppb Cu, Pb and Zn) with a pH of

4.3 ± 0.1 over the concrete for 125 h. Spraying of the spiked influent solution began after 190 h which caused an increase in the concentrations of Cu, Pb and Zn relative to the concentrations observed earlier during the first 190 h. The concentrations of Cu and Pb are stable until the end of the experiment. Zn concentrations, however, decrease slowly. Fig. 7 shows the degree of the trace metal immobilization as a ratio of the concentration of leachate (C) to the initial concentration of Cu,

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Fig. 7. The degree of the trace metal immobilization as a ratio of concentration of leachate (C) to initial Cu, Pb and Zn spiked acidic solution (Co).

Pb and Zn in the spiked acidic solution (Co). The lowest and highest medians for the C/Co ratios are evident with Pb and Zn, respectively. Pb immobilization was greater than either Cu or Zn. Zn is the most mobile of the three and it also exhibits the largest variability in terms of this ratio. PHREEQC calculates positive saturation indices for CuO, Cu(OH)2 and Pb(OH)2 which can explain the immobilization of Cu and Pb. However, neither zincite nor Zn(OH)2 has positive saturation indices under the conditions of the experiment. This result is due to high concentrations of X(OH)−2 species which inhibits saturation of 4 Zn phases. Additionally, Zn immobilization could also be related to adsorption or surface precipitation, although the latter is associated with slow kinetics. Previously, Coleman et al. (2005) published an EDS spectrum of a fresh concrete surface, which indicated that calcium, aluminum, silicon, iron, magnesium, sulfur, sodium, potassium and trace quantities of titanium, manganese and chromium are the typical constituents of the Portland cement. SEM images of the pervious concrete block surfaces at the end of the 1-D column experiment after 132 h of spraying the spiked solution onto the column show flaky bright irregular precipitates in the pore spaces between the dolomite grains (Fig. 8a). The corresponding EDS analysis indicates the presence of trace

quantities of Zn, Cu and Pb in addition to other elements typically found in Portland cement (Fig. 8b). 4. Discussion The main goal of this study is to test whether pervious concrete could function as a passive reactive barrier to attenuate the spread of trace metals from parking lots in urban environments. First of all, the leaching behavior of concrete was studied by batch and column experiments and PHREEQC modeling to determine whether the concrete material itself could serve as a source of pollution. Modeling efforts to explain the leaching behavior of concrete were complicated by the extremely complex nature of this material geochemically, because of its varied mineralogy, reactive surfaces and different microenvironments created by the interaction of capillary and matrix porosities. Batch experiments show that there is an increasing trend towards higher concentrations of major elements (Ca, Si, Al, Mg) and trace metals (Cu, Pb and Zn) associated with lower pH values. This observed leaching behavior is similar to that determined from other studies (e.g. Karamalidis and Voudrias, 2007; Li et al., 2001; Schiopu et al., 2007, 2009; Van Der Sloot,

Fig. 8. SEM images of the pervious concrete block surfaces at the end of the 1-D column experiment following 132 h with the spiked solution spraying onto the column. (a) Zn, Cu and Pb bearing flaky bright irregular precipitates, (b) the EDS analysis of the precipitates.

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2002). The observed major element and trace element (Cr, Cu, Pb and Zn) leaching behavior is best explained by the dissolution/ precipitation of concrete phases and surface complexation, respectively. Generally higher pH values reduce loading but trace metals do tend to increase to some extent at pH N 9. However, surface complexation did not explain the experimental data at high pH values (pH N 9). The predicted model concentrations are especially high for Zn at high pH values. Karamalidis and Voudrias (2009) observed similar trends for Zn and suggested that a combination of surface complexation and surface precipitation was the dominant mechanism for lowering the model Zn values. Among the trace metals, only the leaching of Fe could be explained by the dissolution/precipitation alone with reactions of Fe-monosulfate and Fe(OH)3(a). The 1-D column experiment showed that water (pH ~ 4.3) passing through pervious concrete experiences an increase in pH because of reactions with portlandite, calcite etc. in the porous cement. However, the high pH values associated with pervious concrete are transient in nature and pH will decrease with time. High pH waters were also observed in long-term (≥100 days) dynamic leaching tests and field tests on concrete paving slabs (Schiopu et al., 2009). Schiopu et al. (2009) used impervious concrete slabs in their experiments and therefore, diffusion was important in their transport models. Nevertheless, their dynamic leaching experiments and stagnation field test showed high pH waters in the range from pH 10 to pH 12. For most of the elements, after an initial marked decline in concentrations, the decrease in the concentrations was generally slow. Ca and Mg had the highest and the lowest concentrations among the major elements, respectively. Pb displayed the least amount of leaching and Cu, Zn, Cr and Fe tending to be somewhat larger. The concentrations of Ca, Si and Al were comparable to the results of dynamic leaching tests of Schiopu et al. (2009). A dual porosity conceptualization for concrete, i.e., both capillary and matrix porosity, was invoked in other studies (e.g. Galindez et al., 2006) where capillary porosity has significantly greater influence on transport processes. Schiopu et al. (2009) also used the mixing between matrix pore waters and a leachate compartment to explain the dynamic leaching where they defined the leaching compartment as the immobile zone. Here, the capillary pores and matrix pores were defined as mobile and immobile zones, respectively. The observed leaching behaviors could be well explained by a conceptual model encompassing two main processes, dissolution of mineral phases in the pores and mixing of mobile and immobile waters in the medium. In order to show the effects of stagnant zone on the concentration behavior of the effluent solution, the relative contribution of the stagnant zone was both increased and decreased. Increasing stagnant zone contribution increased the pH and the Ca concentrations of the effluent solution (Fig. 6a, b). Decreasing the stagnant zone contribution lowered the pH and Ca concentrations of the effluent solutions (Fig. 6a, b). Our study envisions that pervious concrete could function as a passive reactive barrier to attenuate the spread of trace metals from parking lots in urban environments until the ANC is exhausted in future. Previous studies demonstrated that when porous pavements are used in the field, there is potential for improvements in the water quality of urban runoff such as reducing organic and metal contaminants,

suspended solids, and chemical oxygen demand (COD) loads compared to conventional pavements (e.g., Boving et al., 2008; Legret and Colandini, 1999; Legret et al., 1999; Newton, 2005; Ranchet et al., 1993). For example, Dierkes et al. (1999) charged porous pavements with synthetic rainwater containing elevated concentrations of Cu, Pb and Zn. They found that trace metal retention was very high (N95%) for gravel subbase and the most metals were precipitated in the upper 2 cm of the porous concrete. The 1-D column experiment, in this study, tested the retention potential of pervious concrete for dissolved trace metals and confirmed the previous studies in that the concentrations of trace metals were reduced as trace metal spiked synthetic rain water passed through the column (Figs. 6 and 7). The quantity of trace metal retention is high for Pb and Cu and relatively small for Zn. Johnson (2004) summarized basic types of binding mechanism for trace metals and metalloid ions in the cement matrix. A metal ion could be sorbed or precipitated onto surfaces of cement minerals, incorporated into hydrated cement minerals or precipitated in the alkaline cement matrix. Based on the secondary electron images and back-scattered electron images of the crushed concrete surfaces, Coleman et al. (2005) concluded that the uptake of Zn by crushed concrete occurred via the formation of three discretely precipitated layers on the surface of the matrix and Cu was immobilized through the formation of foliated precipitate on the surface of the cement matrix. Furthermore, the immobilization of Pb was controlled by diffusion where isomorphic substitution of Pb for Ca in CSH was the suggested mechanism (Coleman et al., 2005). In our model, we tested only the precipitation of metal hydroxides and oxides on the concrete surface which can explain the observed patterns of Cu, Pb and Zn immobilization which could be related to other mechanisms like surface precipitation. Li et al. (2001) reported that the hydroxy-complexes Zn(OH)−2 4 and Zn(OH)−3 5 can be present in a strong alkaline solution and their anionic properties preclude their adsorption onto the negative surface of the CSH, but they may form the calcium zinc complex hydrated compound CaZn2(OH)6.H2O. 5. Conclusions Batch experiments and PHREEQC modeling suggest that the leaching of major ions and trace metals from pervious concrete is mainly controlled by dissolution/precipitation and surface complexation reactions, respectively. The leachability of trace metals increases under acidic conditions except for Cr which has high leachability only when pH = 3. 1-D reactive column experiments show that the effluent has high pH (pH ~ 10) even after 325 h of spraying with low pH water (pH ~ 4.3). Specific conductance decreases rapidly in the first 50 h and then decreases more slowly. Trace metal leaching is higher in the earlier stages of the experiment but becomes lower after approximately 50 h and continues to decrease slowly with time. The water chemistry in the pervious concrete can be explained by the mixing of the interstitial alkaline pore waters with the capillary waters. The interstitial pore solutions are responsible for the relatively high concentrations of trace metals and major cations in the early stages of the experiment. Subsequently, the dissolution of concrete phases and trace metal bearing phases in the

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capillary pores controls the leachability of these elements as the interstitial pore solutions become diluted by capillary pore solutions. We conclude overall that prospects are good for pervious concrete to provide a passive barrier for the control of trace metals from urban surfaces. Acknowledgements Funding for this study was provided as part of the Global Research Laboratory (GRL), Korea project “Novel Technologies for Best Management of Non-point Source Pollution”. Authors thank Trace Element Research Laboratory (TERL) and Subsurface Characterization and Analysis Laboratory (SEMCAL) personnel at the School of Earth Sciences of the Ohio State University for their help. References Allison, J.D., Brown, D.S., Novo-Gradac, K.J., 1990. MINTEQA2/PRODEFA2—a geochemical assessment model for environmental systems—version 3.0 user's manual. Environmental Research Laboratory, Office of Research and Development, U.S. Environmental Protection Agency, Athens, Georgia (106 pp.). ASTM Standard C 33, 2013. Specification for Concrete Aggregates. ASTM International, West Conshohocken, PA. http://dx.doi.org/10.1520/C0033_ C0033M-13 (www.astm.org). Blanc, P., Bourbon, X., Lassin, A., Gaucher, E.C., 2010. Chemical model for cement-based materials: thermodynamic data assessment for phases other than C–S–H. Cement Concr. Res. 40, 1360–1374. Borkovec, M., Westall, J., 1983. Solution of the Poisson–Boltzmann equation for surface excesses of ions in the diffuse layer at the oxide-electrolyte interface. J. Electroanal. Chem. 150, 325–337. Boving, T.B., Stolt, M.H., Augenstern, J., Brosnan, B., 2008. Potential for localized groundwater contamination in a porous pavement parking lot setting in Rhode Island. Environ. Geol. 55, 571–582. Busenberg, E., Plummer, L.N., 1982. The kinetics of dissolution dolomite in CO2– H2O systems at 1.5 to 65 °C and 0 to 1 atm PCO2. Am. J. Sci. 282, 45–78. CEN/TS 14429, 2004. Leaching Behavior Test—Influence of pH on Leaching with Initial Acid/base Addition—Horizontal Standard. European Committee for Standardization, Brussels. Coleman, N., Lee, W.E., Slipper, I.J., 2005. Interactions of aqueous Cu+2, Zn+2 and Pb+2 ions with crushed concrete fines. J. Hazard. Mater. B121, 203–213. Davis, A.P., Shokouhian, M., Ni, S., 2001. Loading estimates of lead, copper, cadmium, and zinc in urban runoff from specific sources. Chemosphere 44, 997–1009. Dierkes, C., Holte, A., Geiger, W.F., 1999. Heavy metal retention within a porous pavement structure. 8th International Conference on Urban Drainage, Sidney. Dzombak, D.A., Morel, F.M.M., 1990. Surface Complexation Modeling: Hydrous Ferric Oxide. John Wiley & Sons Inc., New York (393 pp.). Galindez, J.M., Molinero, J., Samper, J., Yang, C.B., 2006. Simulating concrete degradation processes by reactive transport models. J. Phys. IV France 136, 177–188. Herngren, L., Goonetilleke, A., Ayoko, G.A., 2005. Understanding heavy metal and suspended solids relationships in urban stormwater using simulated rainfall. J. Environ. Manag. 76, 149–158. Johnson, C.A., 2004. Cement stabilization of heavy-metal-containing wastes. In: Giere, R., Stille, P. (Eds.), Energy, Waste and the Environment: a Geochemical Perspective. Geological Society, London, Special Publications, 236, pp. 595–606. Johnson, C.A., Glasser, F.P., 2003. Hydrotalcite-like minerals (M2Al(OH)6(CO3)0.5. XH2O, where M = Mg, Zn, Co, Ni) in the environment: synthesis, characterization and thermodynamic stability. Clay Clay Miner. 51 (1), 1–8. Karamalidis, A.K., Voudrias, E.A., 2007. Release of Zn, Ni, Cu, SO42− and CrO42− as a function of pH from cement-based stabilized/solidified refinery oily sludge and ash from incineration of oily sludge. J. Hazard. Mater. 141, 591–606. Karamalidis, A.K., Voudrias, E.A., 2009. Leaching and immobilization behavior of Zn and Cr from cement-based stabilization/solidification of ash produced from incineration of refinery oily sludge. Environ. Eng. Sci. 26 (1), 81–96.

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