Curve for a 1d Continuous Injection Where Plug Flow is Observed in a Soil Column

Column Experiment

In a separate soil column experiment conducted at the same conditions, a tracer experiment is performed to hydraulically characterize the column.

From: Frontiers of Nanoscience , 2014

Numerical Modeling of Clogging in a Permeable Reactive Barrier and Rejuvenation by Alkaline Fluid Injection in the Shoalhaven Floodplain, Australia

Udeshini Pathirage , ... Glenys Lugg , in Ground Improvement Case Histories, 2015

7.2 Materials and methods

7.2.1 Laboratory column experiments

Laboratory column experiments were carried out to validate the developed model. Twin columns were run in order to measure the pressure readings from one and to take samples from the other ( Fig. 7.2). The reason for running twin columns was to minimize the disturbance from sampling to the pressure readings (Johnson et al., 2005). A constant flow of 1.2   ml/min was supplied using a Masterflex peristaltic pump. Luer adapters were used to take samples every 100   mm along the sampling column (SC), and pressure transducers were used to measure the pressure in the pressure measuring column (PTC) at similar heights as those of SC. All other environmental conditions and input parameters were maintained similar for both columns. Recycled concrete collected from a demolished site was crushed, and uniform particle size (passed through a 4.75-mm sieve and retained on a 3.35-mm sieve) was chosen to fill the columns. The mineralogical analysis carried out by x-ray diffraction revealed the weight percentages as follows: Ca (57.3%), Fe (21.4%), Al (9.85%), Mg, (5.27%), Si (3.06%), and others (3.04%) (Regmi et al., 2011b; Pathirage et al., 2012). Acrylate transparent columns 650   mm in length with a 50-mm internal diameter were used, for which the first 100   mm and last 50   mm were placed with pure silica sand wrapped in geotextile material and the middle 500   mm was placed with crushed concrete particles. Synthetic acidic groundwater was prepared similar to the chemistry of acidic groundwater at the ASS terrains in southeast New South Wales (Regmi et al., 2009b; Pathirage et al., 2012) and fed into columns from bottom to top. Collected samples from the effluent and at sampling points were analyzed for alkalinity and Al, Fe, and Ca concentrations. Inductively coupled plasma–mass spectrometry (ICP-MS) was used to analyze for Al and Ca, and atomic absorption spectroscopy was used to analyze for Fe following the standard guidelines provided by the American Public Health Association (1998).

Figure 7.2. Schematic of the laboratory column experiments: A, Sampling column; B, pressure measuring column (Indraratna et al. (2014)).

7.2.2 Geochemical algorithm

Transition state theory was used to develop the geochemical algorithm capturing all the dominant chemical reactions. Equation (7.1) gives the transition state theory that is well adopted in contaminant transport modeling (Yabusaki, 2001; Li et al., 2005; Mayer et al., 2006; Jeen et al., 2012). The alkalinity to neutralize the acidity in groundwater comes from the Ca-bearing minerals present within recycled concrete, which are CaAl₂Si₂O₈ (16.8% by weight), CaCO₃ (4.4% by weight), and Ca(OH)₂ (0.3% by weight) according to Regmi et al. (2011a). Twelve dominant mineral dissolution/precipitation reactions (Regmi et al., 2009b) are used in the geochemical algorithm, which is the first such algorithm developed for the remediation of acidic groundwater generated from ASS as listed in the appendix. Other mineral components given in Table 7.1 did not take part in the remediation process, as can be seen in Fig. 7.3, in which the concentrations of those ions did not change significantly with the pore volume (PV).

Table 7.1. Water chemistry of the influent solution prepared for column experiment simulating the water chemistry of the acidic groundwater in ASS terrain presented in Regmi et al. (2009a) ^b

pH	ORP (mV)	Acidity (mmol eq/l) a	mg/l
			Na+	K+	Ca2   +	Mg2   +	Al3   +	Fe3   +	Cl–	${{\mathbf{SO}}_{\mathbf{4}}}^{\mathbf{2}\mathbf{–}}$
2.67	610	6.45	504.2	50.1	152.2	118.0	54.0	49	849.0	1450.0

ORP, oxygen reduction potential.

a

Acidity was measured equivalent with respect to CaCO₃.

b

Indraratna et al. (2014).

Figure 7.3. Other ions in the effluent that do not change significantly with time (Indraratna et al. (2014)).

(7.1) $r=-k_{\mathrm{eff}}\left(1-\frac{\mathrm{I}\mathrm{A}\mathrm{P}}{K_{\mathrm{eq}}}\right)$

The value for IAP/K _eq was taken by calculating the saturation indices (SI) by PHREEQC software using Eq. (7.2) (Regmi et al., 2011a; Walter et al., 1994):

(7.2) $\mathrm{S}\mathrm{I}=log\left(\mathrm{I}\mathrm{A}\mathrm{P}\right)-log\left(K_{\mathrm{eq}}\right)$

PHREEQC software calculates the saturation indices by the influent chemistry of groundwater, such as the concentration of all ions, pH, alkalinity, and temperature. Saturation indices (SI) can be used to identify the dissolving and precipitating minerals because the SI values are less than 0 for dissolving minerals and more than 0 for precipitating minerals. The effective rate coefficient (k _eff) was taken by calibrating against a different data set and was assumed to be a spatially homogeneous and time-independent parameter during the simulation (Li et al., 2006).

7.2.3 Change of mineral quantity over time

The change in volume due to secondary mineral precipitation and the dissolution of Ca-bearing minerals in recycled concrete were calculated from their reaction kinetics and molar volume as suggested by Steefel and Lasaga (1994) in Eq. (7.3). Dissolved and precipitated minerals were assumed to be immobile within the system. The pore space occupied by each mineral was calculated from the relevant molar volume. Therefore, the associated porosity reductions with time can be calculated from Eq. (7.4):

(7.3) $\frac{\partial {\mathrm{\Phi }}_k}{\partial t}=M_k{R}_k$

(7.4) $n_t=n_0-{\displaystyle \sum _{k=1}^{N_{\mathrm{m}}}{M}_k{R}_{k}t}$

M_kR_k is a constant value for one time step. For the corresponding time step, a new value of R_k is substituted to Eq. 7.4 as calculated from Eqs. (7.1) and (7.3). The normalized Kozeny–Carmen equation (Eq. (7.5)) can be utilized to estimate the change in hydraulic conductivity at different time steps due to the dissolution and precipitation of minerals (Li et al., 2006; Pathirage et al., 2012):

(7.5) $K=K_0{\left[\frac{n_0-\mathrm{\Delta }{n}_t}{n_0}\right]}^3/{\left[\frac{1-n_0+\mathrm{\Delta }{n}_t}{1-n_0}\right]}^2$

7.2.4 Groundwater flow model and reactive transport flow model

MODFLOW and RT3D are finite different numerical codes that were used to simulate the contaminant transport of Ca, Al, and Fe in the column experiment and field PRB. In MODFLOW, groundwater flow is solved using the block-centered finite difference method ( Harbaugh, 2005). Both column experiments and the field PRB were treated as confined aquifers with transient groundwater flow conditions. The recycled concrete-filled media was considered homogeneous and isotropic because a relatively uniform particle gradation was chosen and the particle angularity is assumed similar. Table 7.2 summarizes the experimental parameters and model inputs. The sides of the column were no-flow boundaries.

Table 7.2. Experimental and model parameters ^a

Property	Experiment	Model (lab)	Model (field)
Flow	1.2   ml/min	1.2   ml/min	1.1   ×   106  l/year
Initial porosity (n 0)	0.69	0.69	0.5
Initial hydraulic conductivity (K 0)	0.9565   m/day	0.9565   m/day	0.1   m/s
pH of influent	2.8	2.8	3.6

a

Indraratna et al. (2014).

The governing equation for transient groundwater flow in one dimension is given by

(7.6) $\frac{{\partial }^{2}h}{\partial x^2}={\nabla }^{2}h=\frac{S}{T}\left(\frac{\partial h}{\partial t}\right)$

(7.7) $T=Kb$

The Kozeny–Carmen equation (Eq. (7.5)) calculates the change in hydraulic conductivity due to mineral dissolution and precipitation. The head solution for governing Eq. (7.6) with the variations in hydraulic conductivity is given by

(7.8) $\begin{array}{c}h=\left(exp\left[-\frac{{\mu }^{2}b{K}_0}{S{\displaystyle \sum _{k=1}^{N_m}{M}_k{R}_k}}\frac{{\left(1-n_0\right)}^2}{n_0^3}\left\{{\alpha }^2\left(1.5+\raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$\beta $}\right.\right)-3\left(\alpha +ln\beta \right)\right\}\right]\right)\cdot \\ \left(Csin\mu x+Dcos\mu x\right)\end{array}$

where

$\alpha =n_0+{\displaystyle \sum _{k=1}^{N_{\mathrm{m}}}{M}_k{R}_{k}t}$

$\beta =1-n_0-{\displaystyle \sum _{k=1}^{N_{\mathrm{m}}}{M}_k{R}_{k}t}$

μ, C, and D can be calculated by the following initial conditions:

$\begin{array}{l}h=h_1\mathrm{at}x=\mathit{0}\mathrm{and}t=\mathit{0},\\ h=h_2\mathrm{at}x=l\mathrm{and}t=\mathit{0},\\ \frac{\partial h}{\partial t}=0\mathrm{at}x=\mathit{0}\mathrm{and}t=\mathit{0}\end{array}$

MODFLOW uses the solution from Eq. (7.8) as the starting head for every time step. Therefore, the head solution carries the change in porosity and hydraulic conductivity because MODFLOW cannot change these values automatically unless they are entered manually. For example, after the simulation was run for the first time step, the resulting values for porosity and hydraulic conductivity were updated in the second time step. Corresponding head for the next time step is now calculated using Eq. (7.8) as input for MODFLOW. Whereas MODFLOW simulated the groundwater flow for every time step, advection, diffusion, and dispersion (Eq. (7.9)) for contaminant transport were simulated using RT3D for every time step. RT3D (three-dimension multicomponent transport model) solves coupled partial differential equations using the implicit finite different method. In the current study, "user-defined module" in RT3D was used because the available seven predefined reaction modules did not cater for acidic water remediation due to ASS:

(7.9) $R_e\frac{\partial C}{\partial t}=D\frac{{\partial }^2\left[C\right]}{\partial x^2}-u_b\frac{\partial \left[C\right]}{\partial x}-R_k{M}_{k}C$

Once RT3D got the head solution from MODFLOW as the input, groundwater flow velocity (u_b ) was then calculated using Eq. (7.10) for the subsequent time step; thus,

(7.10) $u_b=-\frac{K}{n}\frac{\partial h}{\partial x}$

Therefore, MODFLOW and RT3D were run in tandem to get the concentrations of reactants at every time step. The kinetic reaction rate coefficients (k _eff) for Ca^{2   +}, Al^{3   +}, and total Fe (Fe^{2   +} and Fe^{3   +}) (Table 7.3) were obtained through calibration as stated in Li et al. (2006). Trial-and-error calibration was carried out using the data provided for the laboratory column experiments by Regmi et al. (2011b). The calibrated values for kinetic reaction rate coefficients are listed in Table 7.3. The developed model was validated using the data obtained from the laboratory column experiments carried out in this study. Both calibration and validation were carried out for to 40- to 190-PV range because this was the most important experiment zone in which a neutral pH was maintained and Al and Fe were completely removed from the influent. The concentration of each substance at each cell in the finite difference grid was calculated for each time step.

Table 7.3. Kinetic reaction rate coefficients (k _eff) for the mineral dissolution/precipitation that are calibrated values from the data provided by Regmi et al. (2011b) ^b

Mineral phase	Kinetic reaction rate coefficient (k eff) (mol/l.s)	Kinetic reaction rate coefficient (k eff) in literature (mol/l.s) a
Ca2   +	2.27   ×   10−   7	(1   ×   10−   6)
Al3   +	6.86   ×   10−   8	(9.0   ×   10−   7 – 1.0   ×   10−   8)
Total Fe (Fe2   + and Fe3   +)	5.87   ×   10−   8	(1.0   ×   10−   7 – 1.2   ×   10−   8)

a

Source: Ouangrawa et al. (2009) and Jurjovec et al. (2004).

b

Indraratna et al. (2014).

7.2.5 Model application to the field PRB

One-dimensional reactive transport modeling was carried out through the centerline of the PRB. The total width of 1.2   m was discretized into 12 elements of 0.1 m. The governing equations used to predict the vertical flow of column experiments were adopted and assumed similar to the horizontal flow through centerline of the PRB. The same chemical reactions were assumed to occur in the PRB; thus, the geochemical algorithm used for column experiments was utilized for this study. The PRB was considered as a saturated flow domain with specified head boundaries where top, bottom, and lateral faces of the domain were no-flow boundaries. An average hydraulic gradient of 0.006 was used, which was similar to the field conditions observed from October 2006 to January 2012.

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Studying radionuclide migration on different scales: the complementary roles of laboratory and in situ experiments

L. Van Loon , ... C. Latrille , in Radionuclide Behaviour in the Natural Environment, 2012

12.5.1 Design of column experiments on non-consolidated porous media

The principle of column experiments is based on a controlled solution flow through a column filled with a given porous medium. Up-and downwards experiments with steady-state flow are usually practised on various sizes of columns. Radionuclide detection is monitored using α, β, γ and X-ray detectors on-line. In some cases, samples are collected from the outlet solution and emission measurements are performed using a γ-counter or liquid scintillation counter. Analyses of colloids and radionuclide breakthrough curves permit one to study enhancement or retardation effects on transport processes. A non-reactive tracer is frequently applied to the experimental approach as a reference to describe a free fate and to determine hydrodynamic properties of the porous media. Anoxic conditions are imposed in a glove box and monitored in order to control the physico-chemical conditions (Fig. 12.12). An electrical conductivity cell and pH electrode are frequently placed on line at the inlet and outlet of the column. Physico-chemical conditions are selected to highlight the processes to evidence.

12.12. Example of an experimental column setup under anoxic condition (Ar/CO2) (Artinger et al., 1998).

Reprinted from Journal of Contaminant Hydrology 35: 261–275, R. Artinger et al., Effects of humic substances on the ²⁴¹Am migration in a sandy aquifer: column experiments with Gorleben groundwater/sediment systems, © 1998, with permission from Elsevier.

In order to reproduce precisely the natural conditions of migration in the laboratory, researchers have been forced to further develop and to improve the devices. To reproduce and control the hydration condition of soils, time domain reflectometry (TDR) probes have been introduced at different levels inside the column to monitor water content. In the same focus, pressure transducers and electrical conductivity probes have been introduced in the column perpendicularly to the flow (Mortensen et al., 2006; Nützman et al., 2002; Padilla et al., 1999).

X-ray radiography is a widely applied method used in various diffusion process investigations on geological materials. One of the main advantages of X-ray methods is that they are non-destructive and non-intrusive. X-ray images (usually in two dimensions) provide temporal restitution of the absorbent X-ray tracers disseminated through porous media (Altman et al., 2004; Cavé et al., 2009; Klise et al., 2008; Tidwell et al., 2000). They also provide information on the medium porosity, thus allowing calculation of the diffusion coefficient in two spatial dimensions. Tracer concentration is evaluated by X-ray attenuation contrast measured on the image by using an internal reference; usually, this technique adopts iodide as a tracer, due to its high atomic number, i.e. its good absorption power with respect to the incident radiation beam. Solute distribution is quantified at each time step by using a digital image of the tracers. X-ray tomography scanners (Schembre and Kovscek, 2003) have also been extensively used to provide insight in two or three phase flow and are analogously based on relative X-ray absorption images applied to small-size samples. An alternative to X-rays for nondestructive measures in the interior of porous media is provided, e.g., by γ-ray attenuation techniques, which have been developed to follow migration of two or three phases in a large column filled with porous materials (Caubit et al., 2004; Mazet, 2008; Szenknect, 2003). Usually, in this case one or two γ-ray sources are displaced along the sample. These devices are limited by the superposed sources which analyse two slightly different locations in the column and the decrease in intensity of the sources.

An experimental device has been specifically conceived by CEA to assess the spatial and temporal dynamics of tracers along a column of porous media. This device consists of a vertical column equipped with a dichromatic X-ray spectrometer composed of a dichromatic X-ray generator and a Nal detector moving along the column by a controlled rack-rail. The X-ray counting received by the detector depends on the thickness and the nature of the crossed phases (fluid, porous material or tracer) and the counting time. It is possible to discriminate three different components within each column layer of 5 mm, by resorting to the Beer–Lambert law, which is used to convert the transmitted beam intensity to physical quantities (such as bulk density, porosity, or tracer concentration). The method is non-intrusive and non-destructive. Physico-chemical and hydraulic conditions are controlled using online conductivity and pH cells, and flow-rate measurements during the time of experiment. Porosity and distribution of X-ray attenuating or γ-emitting tracers are measured in saturated or unsaturated porous media (Latrille and Cartalade, 2010; Latrille and Zoia, 2011; Zoia et al., 2009).

Experimental studies dedicated to understanding radionuclide transport behaviour in the previous environment have emphasised the necessity to describe and predict the results with numerical models reflecting the processes implied in each experiment. Consequently, generations of models, more and more complex, are being developed (e.g. Sims et al., 1996; Nitzsche and Merkel, 1999; Mathias et al., 2008).

Column experiments performed on natural and more or less preserved materials have made a large contribution to the understanding of radionuclide transport processes under various environmental conditions. The following two sections give some illustrations of their application. Column experiments have also been used to investigate the colloidal transport of radionuclides. This topic is described in Chapter 10 and not discussed hereafter.

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Pollution Control and Resource Recovery for Landfill Gas

Zhao Youcai , Lou Ziyang , in Pollution Control and Resource Recovery, 2017

5.11.3 Methane Oxidation Profiles of Biocovers

At the end of the column experiments, aged refuse and Xanthozem were collected from different depths for further investigation. Fig. 5.25 presented the methane oxidation capacity profiles of the two materials. For the biocover with leachate L4, the highest methane oxidation rate was 504   μLCH₄/(g   dw   h) at 5   cm under the surface, which was obviously higher than that at other depths (below 190   μLCH₄/(g   dw   h)). The methane oxidation rate in the top layer for the biocover with leachate L1A was 537   μLCH₄/(g   dw   h), and the value was below 173   μLCH₄/(g   dw   h) at the bottom. Therefore, it can be stated that an active area of methane oxidation existed in the new material at approximately 5   cm from the surface.

Figure 5.25. Methane oxidation ability profiles of materials in columns. Distances of 5, 15, 25, and 35   cm were used between the sampling port and the material surface.

Physiochemical and microbial properties of materials also revealed the active area of methane oxidation in the biocover (in Fig. 5.26 and Table 5.11). The highest values of organic matter content in the biocover ranged from 15.0% to 18.0% near the surface and below 16.5% at other positions, which suggested that the accumulation of organic matter from methane oxidation was more obvious near the material surface. Water contents of the material at the top layer were substantially higher (31.4–32.1%) than those at other layers (below 30%), indicating the microbial activities including methane oxidation were much more active near the surface. Methanotrophic population densities in aged refuse were 985.9–1515.8   ×   10⁴/g at the depth of 5   cm under the surface, which were distinctly higher than that at other depths. Profiles of all parameters of aged refuse suggested the presence of an active zone of methane oxidation near the material surface. Thus, the optimum organic matter and water content in leachate-modified aged refuse landfill cover should be 15.0–18.0% and 31.4–32.1%, respectively.

Figure 5.26. Profiles of physiochemical properties of materials in columns. Distances of 5, 15, 25, and 35   cm were used between the sampling port and the material surface.

Table 5.11. Methanotrophic Population Densities in Column Materials (×10⁴/g)

Column Depth (cm)	Xanthozem	Aged Refuse With L4	Aged Refuse With L1A
5	7.2	1515.8	985.9
15	45.9	45.9	15.7
25	11.5	72.8	40.2
35	24.3	12.2	19.7

For Xanthozem, the organic matter content near the surface was 5.54% after the column experiment and hardly varied at all levels (5.09–5.82%). Methane oxidation rates of Xanthozem were close at different depths and no active zone of methane oxidation was found. Methanotrophic population densities in Xanthozem with a peak value of 45.9   ×   10⁴/g were significantly lower than in aged refuse with a peak value of 1515.8   ×   10⁴/g) after the column experiment. Compared to Xanthozem, aged refuse modified with leachate was more suitable for constructing a thin landfill cover with a high methane oxidation capacity.

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From Zeolites to Porous MOF Materials - The 40th Anniversary of International Zeolite Conference

S. Brandani , ... R. Staudt , in Studies in Surface Science and Catalysis, 2007

3.4 ZLC with partial loading

The crystallite assemblages typically considered in ZLC experiments are so small that any transport resistance outside of the individual crystals may essentially be neglected. The option to easily initiate or stop adsorption from the carrier gas instantaneously by a corresponding change in the partial pressure of the guest molecules in the gas flow permits an elegant way to distinguish whether overall uptake or release is controlled by internal resistances or by a transport resistance on the outer crystal surface (a "surface barrier") [8, 9]. This distinction is based on the fact that at "partial loading", i.e. as long as the sorbate distribution within the crystallites has not yet attained equilibrium, this distribution will notably depend on the uptake mechanism. In particular, for dominating surface barriers the distribution of guest molecules within the crystallite will be uniform already under partial loading conditions, while under diffusion control the sorbate concentration in an outer shell will be higher than in the centre. This latter effect will lead to a faster decay of the guest concentration in the carrier gas stream if desorption is initiated before equilibrium has been established. As an example, Fig. 4 shows the results of such a partial-loading ZLC experiment with n-decane in zeolite NaCaA. The experimentally observed partial-loading behaviour is in excellent agreement with the pattern expected for uptake limitation by internal resistances. Therefore, the diverging PFG NMR and ZLC data shown in Fig. 2 for chain lengths larger than 10 carbon atoms, should be explained by an increase of internal transport resistances (with increasing chain lengths) rather than by the impact of a surface barrier.

Fig. 4. Experimental ZLC desorption curves of n-decane in NaCaA (at 398 K, p=0.006 torr), fully-equilibrated sample (♦) and partially-saturated sample (O), showing a good agreement of quantitative theoretical predicition for diffusion-limited desorption with the experimental results.

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Development of Synthetic Hydroxyapatite-Based Household Defluoridation Unit

Ayushi Khare , ... Sanjeev Chaudhari , in Advances in Water Purification Techniques, 2019

11.4 Preliminary Lab Testing of Household Defluoridation Unit

In order to evaluate the effect of flow rate variation for field conditions, preliminary studies were performed in lab. Firstly, 6.5   L bucket having dimensions as 18   cm diameter and height 25 cm was used. From the preliminary studies and earlier studies (packed-bed continuous flow column experiment) it was concluded that HAP particles in the size range 0.355–2  mm can be used, so that head loss is not very high in HAP filter bed. Subsequently, a 16   L bucket household defluoridation unit was fabricated. 8–10   L of residual volume for filling water was ensured after placing aggregates and HAP particles in the bucket. HAP particles ranged in size from 1.18 to 2.0   mm and were spread over aggregates followed by top layer of HAP particles having size 0.5–1.18   mm. Total depth of HAP layer was ~   2.5   cm and dry volume of particles was 1   L. Fig. 11.6 is showing schematic representation of household defluoridation unit.

Fig. 11.6. Schematic representation of household defluoridation unit.

By amending 50   mg/L Ca^{2   +} in feed water spiked with 10   mg/L F⁻, phosphate concentration in treated water was ~   0.5   mg/L. This indicates that higher calcium concentration is required to reduce phosphate concentration to below detectable limits. So, after amending 100   mg/L Ca^{2   +}, phosphate concentration below detectable limits was achieved in treated water. From trials in laboratory, it was concluded that a 16   L bucket with outlet arrangement shown in Fig. 11.6 will be suitable for field studies. The filter can treat 8   L of water in 5–6   h when initial flow rate was adjusted to 40   mL/min. Thus, maximum 24   L of fluoridated water can be treated in a day by using such bucket, which seemed to be sufficient in a family of —five to six persons for drinking and cooking purpose

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Environmental aspects

Johannes Fink , in Hydraulic Fracturing Chemicals and Fluids Technology (Second Edition), 2020

Simulation of a waste water surface spill on agricultural soil

Hydraulic fracturing waste waters contain synthetic organic components and metal ions derived from the formation waters. The risk of spills of hydraulic fracturing waste water that could impact soil quality and water resources is of great concern.

The ability of synthetic components, such as surfactants, in hydraulic fracturing waste water to be transported through soil and to mobilize metals in soil was examined using column experiments [125].

A spill of hydraulic fracturing waste water was simulated in bench-scale soil column experiments that used an agricultural soil and simulated seven 10 y rain events representing a total of one year's worth of precipitation for Weld County, Colorado.

Although no surfactants or their transformation products were found in leachate samples, copper, lead, and iron were mobilized at environmentally relevant concentrations. In general, after the initial spill event, the metal concentrations increased until the fourth rain event before decreasing.

The results from this study suggest that the transport of metals can be caused by the high concentrations of salts present in hydraulic fracturing waste water. A significant decrease in the water infiltration rate of the soil was observed, leading to the point where water was unable to percolate through due to increasing salinity, potentially having a severe impact on crop production [125].

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Locally Derived Activated Carbon From Domestic, Agricultural and Industrial Wastes for the Treatment of Palm Oil Mill Effluent

O. Abdulrahman Adeleke , ... Mahmood Hijab , in Nanotechnology in Water and Wastewater Treatment, 2019

2.3.5 Porosity Volume

Saturation method is mostly used to determine the porosity volume of the adsorbent. The porosity volume is much applicable for the determination of composite adsorbent in a continuous fixed column. In the case of fixed bed adsorption, water-saturated composite samples are introduced into the column until equilibrium concentration was achieved (Alonso et al., 2005 ). Saturation in a fixed column experiment is performed according to the packed column. An example of a fixed bed column adsorption is illustrated in Fig. 2-3.

Figure 2-3. Column for fixed bed adsorption.

In the study of Halim et al. (2010b), porosity volume experiment was conducted by filling the composite sample to maximum height of the column. The volume of the sample was measured and recorded. A separate beaker was filled with deionized water containing more than the water needed to saturate the sample. The volume of the deionized water used at the saturation point was recorded. The volume of water used is the difference between the total volumes of water before saturation. This gives the pore volume of the sample. Therefore;

(2.4) $\mathrm{Porosity}\left(Pt\right)=\frac{\text{Pore}\text{volume}(Vp)}{\text{Total}\text{volume}(Vt)}$

Where total volume=solid volume (Vs)+pore volume (Vp)

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Subsurface pollutant transport

Abdel-Mohsen O. Mohamed , ... Munjed A. Maraqa , in Pollution Assessment for Sustainable Practices in Applied Sciences and Engineering, 2021

7.7.2 Circulation-through-column method

The circulation-through-column method consists of a closed system in which a solution containing the target solute can be circulated through a soil column until equilibrium is reached (Fig. 7.8). The system is thus like the column method in terms of soil-to-water ratio , but it is operated in a batch mode. As in the batch experiments, circulation-through-column experiments should include rate and isotherm studies ( Maraqa, 2001b). In the rate experiment, aqueous samples with known volumes are withdrawn from the attached bottle over the course of the experiment and analyzed for the target solute. In the isotherm experiment, a sample is withdrawn from the attached bottle after the equilibration time (Maraqa, 2001b).

Figure 7.8. Schematic of circulation-through-column experiment (Maraqa, 2001b).

The circulation-through-column method was found to be compatible with the batch method, but it is more difficult to conduct because it requires a separate column run to generate each data point on the sorption isotherm curve (Maraqa, 2016). To expedite the experimental time, several parallel columns need to be operated simultaneously, each injected with different initial solute concentration within the range of interest. A control experiment should be conducted to ensure there is no sorption to the reactor material. In such an experiment, the solution containing the target solute is circulated through a column either originally empty or filled with an inert material. Meanwhile, biodegradation should be eliminated by using an appropriate biocide.

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Inorganic Nanoadsorbent: Akaganéite in Wastewater Treatment

Eleni A. Deliyanni , ... Kostas A. Matis , in Composite Nanoadsorbents, 2019

4 Conclusions

The impact of nanostructures on the properties of high surface area materials is a field of increasing importance to understand, create, and improve inorganic materials for diverse technological applications. Inorganic sorbents have received far less attention for the treatment of wastewaters. Heavy metals cations and also oxyanions (such as arsenic) removal from dilute aqueous streams, being often of great significance, was the main focus in the present review for the adsorbents application. The adsorptive capacity of akaganéite (also modified, even as composite) was found, among other sorbents, to be effective; batch and column experiments were conducted. Advantage of the sorbent, which was found to be nanostructured, was its high surface area and narrow pore size distribution. Various instruments and equipment are generally used in the studies of this area, such as X-ray diffraction (XRD), scanning electron microscopy (SEM), usually equipped with an energy dispersive X-ray (EDX) micro-analytical system, TEM, XPS, thermogravimetric analyzer (TGA), FTIR spectroscopy, nitrogen adsorption-desorption isotherm measurement, potentiometric and Boehm titration, surface pH measurement, atomic absorption or UV–vis spectrophotometer, elemental analyzer, electrokinetic measurement apparatus, etc. Selected figures, originating from experimental data, were presented. As is has been shown, being apparent even from the introduction, sorption is not an "easy" process to deal with. The application of fundamental chemistry (including sorption isotherms, thermodynamic, and kinetic studies) usually assists to investigate and explain the corresponding process mechanism. Over the last years, considerable research on the use of inorganic nanoadsorbents for adsorption of pollutants (i.e., heavy metals) has shown great progress.

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Introduction to Zeolite Science and Practice

Douglas M. Ruthven , in Studies in Surface Science and Catalysis, 2007

8 Notation

A	intensity of NMR spin-echo signal (Eqn. 18)
B	molecular mobility in adsorbed phase
c	sorbate concentration in fluid phase
C p	heat capacity of adsorbent sample
D	transport diffusivity
D ij	Masewell–Stefan mutual diffusivity
D e	effective diffusivity in macropore = ɛp D p[1 + (1 – ɛp)K]
D o	corrected transport diffusivity (Eqn. 5)
D p	pore diffusivity (for macroporous pellet)
D	self-diffusivity or tracer diffusivity
F	purge flow rate in ZLC experiment
G	gradient magnetic field
h	heat transfer coefficient (from particle or adsorbent sample)
H	height equivalent to a theoretical plate (HETP)
J	diffusive flux (relative to fixed coordinates)
k	intrinsic (first-order) reaction rate constant
k e	apparent first-order reaction rate constant
k s	surface mass transfer coefficient
K	dimensionless Henry's Law constant based on particle volume
K c	dimensionless Henry's Law constant based on crystal volume
L	dimensionless parameter (Eqn. 31)
M t /M ∞	fractional approach to equilibrium in an uptake experiment
q	adsorbed phase concentration
q s	saturation limit
r	radial coordinate
r c	crystal radius
R	particle radius
R	gas constant
t	time
u	diffusive velocity (Eqn. 8)
w	volume fraction of zeolite in composite adsorbent particle
z	distance coordinate

Greek symbols

α, β	functions in Eqn. 7; dimensionless parameter in Eqn. 24
γ	dimensionless parameter (Eqn. 31)
δ, Δ	duration of gradient pulse and time interval between gradient pulses in PFGNMR method (see Figure 3)
ɛ	voidage of column or adsorbent bed
ɛp	porosity of composite adsorbent particle
θ	fractional saturation of adsorbent (q/q s)
Φ	Thiele modulus (defined by $\Phi =R\sqrt{k/K_{\mathrm{c}}{D}_{\mathrm{c}}}$ or $R\sqrt{k/D_{\mathrm{c}}}$ )
μ	chemical potential; mean retention time
η	effectiveness factor (k app /k)

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