Next Article in Journal
Influence of Crust Formation on Soil Porosity under Tillage Systems and Simulated Rainfall
Previous Article in Journal
Hydrodynamic Modeling of Nokoué Lake in Benin
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Provision of Desalinated Irrigation Water by the Desalination of Groundwater within a Saline Aquifer

by
David D. J. Antia
DCA Consultants Ltd., Haughend Farm, Bridge of Earn Road, Dunning, Perthshire PH2 9BX, UK
Hydrology 2017, 4(1), 1; https://doi.org/10.3390/hydrology4010001
Submission received: 30 June 2016 / Revised: 24 November 2016 / Accepted: 14 December 2016 / Published: 22 December 2016

Abstract

:
Irrigated land accounts for 70% of global water usage and 30% of global agricultural production. Forty percent of this water is derived from groundwater. Approximately 20%–30% of the groundwater sources are saline and 20%–50% of global irrigation water is salinized. Salinization reduces crop yields and the number of crop varieties which can be grown on an arable holding. Structured ZVI (zero valent iron, Fe0 pellets desalinate water by storing the removed ions as halite (NaCl) within their porosity. This allows an “Aquifer Treatment Zone” to be created within an aquifer, (penetrated by a number of wells (containing ZVI pellets)). This zone is used to supply partially desalinated water directly from a saline aquifer. A modeled reconfigured aquifer producing a continuous flow (e.g., 20 m3/day, 7300 m3/a) of partially desalinated irrigation water is used to illustrate the impact of porosity, permeability, aquifer heterogeneity, abstraction rate, Aquifer Treatment Zone size, aquifer thickness, optional reinjection, leakage and flow by-pass on the product water salinity. This desalination approach has no operating costs (other than abstraction costs (and ZVI regeneration)) and may potentially be able to deliver a continuous flow of partially desalinated water (30%–80% NaCl reduction) for $0.05–0.5/m3.

1. Introduction

Arable agriculture accounts for more than 70% of global water usage [1]. Total anthropogenic water usage is currently estimated at 10,688 km3·a−1 [2]. About 4000 km3·a−1 are abstracted from the riparian and groundwater environment [3]. Irrigation usage accounts for about 70% of this abstracted water [1,3]. Sixty-two percent of water withdrawals for irrigation [4] return to the groundwater and riparian system through a combination of evaporation (23%–30%), infiltration (7%–16%) and overland flow (26%–27%) [4,5]. Seventy-one percent of the global irrigated area is located in areas where the annual depletion of the watershed can exceed 75% [4].
About 24% of the total global harvested cropland is irrigated [6]. This irrigated cropland accounts for more than 40% of the global cereal yield [6] and more than 30% of global agricultural production [7]. Global cereal production would decrease by 20% without irrigation [3]. Global crop production will be required to more than double by 2050 in order to sustain the anticipated growth in global population [6]. The vast majority of this increase in agricultural production is expected to result from an increase in the amount of irrigated land [6] coupled with a more efficient use of irrigation [1,2,3,4,5,6,7,8].
The relative balance of irrigation water abstracted from the riparian system and irrigation water abstracted from groundwater varies regionally [8]. About 40% of global irrigation water is abstracted from shallow groundwater (aquifers) [8]. Locally, this can exceed 70% (e.g., India) [8]. Between 20% and 30% of global groundwater (320–480 km3·a−1) abstracted for irrigation is saline [9].
In some countries (e.g., Uzbekistan) irrigated land may account for less than 15% of arable production, but more than 90% of the arable crop [10]. The groundwater used for irrigation (average 3.7 km3·a−1) has salinities within the range 2.25–3.75 g·L−1 [10].
At least 20% of the global irrigated crop land is adversely affected by salinization [11,12,13,14,15,16,17]. Some estimates indicate that the combination of irrigation and leaching may result in more than 50% of the global irrigated land being affected by salinization [18]. Globally, salinization is associated with between 560 and 1400 km3·a−1 of irrigation water [11,12,13,14,15,16,17,18]. About 405 km2 of irrigated land is used for greenhouses [19]. The groundwater used to irrigate these greenhouses is saline in many coastal areas [19].
Salinized irrigation water reduces the associated potential crop yield by between 10% and 90% (relative to irrigation with freshwater) [16,18,19,20,21,22,23,24,25]. Salinization adversely affects the long term viability of the agricultural land. The associated saline irrigation runoff adversely affects: (i) the downstream riparian system; and (ii) the underlying aquifers receiving salinized infiltration water [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25].
It is estimated that the value of lost agricultural production due to salinization exceeds $27.3 billion/a [26]. The lost value associated with salinization of irrigation water also affects the broader local economy [27]. It is, therefore, reasonable to assume that any strategy, which can cost effectively help stabilize, or reverse, the adverse effects of salinization, will have a major economic impact on the local economy.
The amount of water required for irrigation, varies with crop and region [8]. It is also a function of the net amount of water received by precipitation and the associated evaporation [1,2,3,4,5]. Irrigated water requirements decrease as the irrigation type switches from flood, to drip, to sprinkler, and to more sophisticated irrigation technologies [8].
One possible solution, which can be used to combat salinization, is to use desalinated water for irrigation [28]. Desalinated irrigation water is currently used (on a small scale) in Spain, Israel, Kuwait, Saudi Arabia, UAE, Bahrain, Qatar, Chile, China, Australia [28,29]. The unsubsidized, full cycle cost of providing desalinated water from a modern large scale (>100,000 m3/day) desalination plant varies with installation date and region. It is currently more than $3.5/m3 for most (>100,000 m3/day) plants installed within the last 5 years [16,17]. This is addressed further in Section 9.
In many areas, which use desalinated irrigation water, the delivered water price is heavily subsidized (for strategic and political reasons) and may fall within the range $0.2 to $0.7/m3 [28,29].

1.1. Impact of Irrigation Water Salinity on Crop Yield

Desalination (or partial desalination) of irrigation water, prior to use, is expected to increase the yield of a specific crop [16,18,19,20,21,22,23,24,25]. With any desalination technology used for irrigation, there are two important questions:
(i)
Is the technology likely to produce an adequate amount of water for a reasonable cost over a reasonable timeframe?
(ii)
Is the application, by irrigation, of the desalinated (or partially desalinated) water, likely to make a significant impact on the revenue and profitability of the agricultural holding?
These two questions can be considered with reference to a specific crop. In a freshwater irrigation environment, the estimated crop yield can be approximated as [30]:
Yield (t/ha), Y = (a1b1c1/d1)(abcde)
a1 = plants per hectare (10,000 m2) (this is a function of the number of plants per meter length on a row and the row spacing, m); b1 = seed pods (or grain heads or fruit) per plant; c1 = fruit or seeds per pod/grain head; d1 = seeds or fruit per ton; a = fractional yield change due to fertilizer application; b = fractional yield change due to fungicide application; c = fractional yield change due to pesticide application; d = fractional yield change due to herbicide application; e = fractional yield change due to bactericide application. The fractional yield change is calculated as: (Yield with the application/Yield without the application).
Yield is strongly influenced by the planting strategy, variety, climatic variation, farming practice, fertilizers applied, local soil composition, irrigation water composition and irrigation strategy. The impact of salinity is to reduce the expected crop yield. An indicative relationship for crop yield decrement as a function of salinity is [16,20,21,22]:
Salinity Adjusted Yield (t/ha), YS = Y (1 − (c1s(Salinity, g/L) − c2s)), where 0 ≤ YSY
c1s and c2s are regression defined constants (see references [16,20,21,22] for the value of these constants associated with more than 60 different crops). By definition, the expression (c1a(Salinity, g/L) − c2a) has the limit values of 0.0 and 1.0. Each of the variables in Equations (1) and (2) can be described by a distribution. The amount of irrigation water required, is location and crop specific, and may fall within the range 0 to 10,000 m3/ha/a [31,32].

1.2. Maximum Sustainable Desalination Cost for Saline Irrigation Water

Most irrigated crops have a low sales value ($/ha, or $/t) and are unable to economically sustain a high desalination cost ($/m3) for irrigated water. The unsubsidized, delivered cost of potable quality desalinated water (from a conventional desalination plant) may fall within the range $3 to $100/m3 [16,17]. The incremental sales value (excluding operating, harvesting and storage costs) added by the use of desalinated water will be site specific. Historically, the high cost of providing desalinated water has prevented its widespread use for irrigation in areas, which currently use salinized irrigation water.
The maximum sustainable cost (Mc, $/m3) of desalination for irrigation water for a specific crop, or agricultural holding, can be estimated as:
Mc, $/m3 = (((YS2 − YS1)R1) − O1 − F1 − P1)/W1,
YS1 = Crop yield using salinized water, t; YS2 = Crop yield using partially desalinated water, t; R1 = Sale price of crop, $/t; O1 = Incremental increase in operating costs and infra-structure costs associated with the increased yield, $; F1= Incremental increase in financing costs associated with the increased yield, $; P1 = Profit required by the agricultural holding on the increased yield, $; W1 = amount of partially desalinated water which is required to achieve the increased yield, m3.
Irrigation using partially desalinated water is only economically viable if Mc is greater than the actual cost of supplying the partially desalinated irrigation water (Ac, $/m3), i.e., ((McAc) > 0).

1.3. Zero Valent Iron

It has recently (2010) been discovered that Zero Valent Iron (Fe0, ZVI) could be used to partially desalinate water [16,17,33,34,35,36,37,38]. The potential, unsubsidized, delivery cost of this partially desalinated water may be less than $0.1/m3 [16,17].
The two principal ZVI desalination approaches are:
(a)
placement of structured ZVI in pellets. The pellets are then placed in a water body and left for a period of time [16,17];
(b)
placement of structured ZVI in cartridges which are attached to a reactor incorporating fluid recycle [16,17,36].
This study investigates whether this trial technology could be transferred into the subsurface to produce an Aquifer Treatment Zone within a saline aquifer. The Aquifer Treatment Zone is created by the placement of removable ZVI desalination pellets (or cartridges), in infiltration devices (or wells) intersecting a saline aquifer. The abstracted water is used to provide partially desalinated irrigation water.

1.4. Definition and Construction of an Aquifer Treatment Zone

Trial data, from more than 300 ZVI desalination trials (0.0002 to 0.8 m3/batch) [16,17], are integrated with standard hydrological and chemical process engineering models. This integration is designed to identify the principal technical issues associated with a commercial scale-up (within an aquifer). The target proposed commercial scale Aquifer Treatment Zone will produce 1 to 100 m3/day of partially desalinated irrigation water.
The batch desalination trials used a diffusion process (with and without a water recycle) where structured ZVI catalysts (contained in pellets and cartridges) were placed in saline water. The associated methodology and desalination trial data have been placed in the Appendix A, Appendix B, Appendix C, Appendix D, Appendix E, Appendix F and Appendix G.
This study has the following Appendices:
A list of abbreviations and symbols used in this study is placed after the Appendices.
Conversion of an aquifer to an Aquifer Treatment Zone requires sequential consideration of three issues: (i) the nature of the desalination product and its potential recovery from the aquifer; (ii) the front-end engineering requirements of a specific site; and (iii) an assessment of the costs associated with the installed Aquifer Treatment Zone.

2. Desalination Product

The different desalination catalysts used in this study (ST, MT, C to K) are described further in Section 7.3, and their performance is detailed in Appendix B, Appendix E and Appendix F. ST, MT, D(i) are identifiers used for different Type A Catalysts. C, D(ii), E to K are identifiers used for Different Type B Catalysts. Notations such as ST3b refer to a trial using the ST catalyst, Trial Group Feed Water Salinity Grouping Identifier = 3 (e.g., 11.05 g NaCl/L), Statistical Trial identifier = b (e.g., Trial 2).
The desalination ZVI pellet trials [16] established that the NaCl was removed from the water (Figure B1, Figure B2 and Figure B3). The removed NaCl was held in ZVI pellets in some form. A number of pellets from the ST Trial Group [16] were allowed to dry in air (at a temperature of 0 to 20 °C) before being cut in half. The surfaces were then polished prior to examination using reflectance microscopy.
The petrography of the desalination pellets defines the desalination process. The petrography and its implications for the design of an Aquifer Treatment Zone are discussed in detail in Section 3, Section 4 and Section 5. Section 4 and Section 5 define the adsorption-desorption mechanisms. These mechanisms are used in Appendix A, Appendix B, Appendix C, Appendix D, Appendix E, Appendix F and Appendix G to demonstrate how desalination can be modeled and monitored within an aquifer.
The petrography is integrated with the front-end engineering associated with installing and operating an Aquifer Treatment Zone in Section 6 and Section 7.

3. Desalination Product Petrography

3.1. Hygroscopic Nature of Cut Pellets

Freshly cut surfaces of the ZVI (ST Trial Group) can develop hygroscopic brine droplets on their surface (see Figure 1). Ion analyses established the presence of Na+ and Cl ions in the hygroscopic and deliquescence brine droplets. NaCl is hygroscopic and has a well-defined deliquescence point (e.g., [39,40]).

3.2. Rough Surface of Cut Pellets Following Desalination

The freshly cut pellet surface (before polishing) is typically covered in white crystalline material (halite [41,42,43,44,45,46,47,48]), e.g., Figure 2.

3.3. Polished Surface of Cut Pellets Following Desalination

The polished ZVI surfaces established that the original porosity had been completely infilled with halite, in some pellets. e.g., Figure 3. Large open pores are infilled with halite, which has grown into the pore from the pore sides, e.g., Figure 4.

3.4. Material Redistribution within the Pellets during Desalination

The porosity of the ZVI is physically expanded by the growth of the NaCl crystals (<1 micron) to form sheets of NaCl within the ZVI, e.g., Figure 5. The borders of the NaCl sheets show that the ZVI matrix may be replaced locally by NaCl to create FeOx(OH)y regoliths (e.g., Figure 5 and Figure 6). The halite can contain regoliths of copper associated with the pellet shell, e.g., Figure 6. These regoliths indicate that the copper within the pellet can be affected by dissolution, during the desalination process.

3.5. Reprecipitation of Copper within the Pellets during Desalination

The copper contained within the pellet (Figure 7) can be dissolved and recrystallized as atacamite (Cu2(OH)3Cl) [41] and tenorite (CuO) [41] during desalination. The Eh-pH regime (Figure 7) (and Cl ion regime) associated with these pellets is consistent [50,51,52] with both tenorite and atacamite precipitation (Figure 8 and Figure 9).

3.6. Method of Halite Infill of Porosity within the Pellets during Desalination

The halite fills the pre-existing pores within the ZVI pellets using crystal masses which grow into the pore from the pore edge (Figure 10 and Figure 11).

3.7. Morphology of Halite Infill of Porosity within the Pellets during Desalination

The halite can be present as rounded nodules contained within purple ferrate (Na2FeOx, NazFeOx(OH)y) nodules (and within goethite) (Figure 12). These ferrate nodules can form regoliths (or authigenic nodules) within the halite, or can form as authigenic nodules within the FeOx(OH)y matrix (Figure 12).

3.8. Morphology of Halite Infill of Porosity

The NaCl which is concentrated in the pore waters within the ZVI pellets during desalination will go through a number of phases [54]:
L→ L + α → α
Where L = liquid phase, and α = solid (crystallized) phase. The pore water chemistry during desalination is complex and the relative ratio, or status, of these three potential phases may be influenced by temperature, pressure, ion concentrations, pH and Eh. A simple model of the form expressed by Equation (4) will result in halite formation from a mixture of L + α. Halite will form as either nodular halite (e.g., Figure 12) or by the crystalline infill of porosity by halite.
The iron (or copper) phase can be designated a β phase. In this instance, the following pore-water transitions are possible [54]:
L (t = 1) → L(t = 2) + α,
L (t = 2) → β + α, or
L (t = 2) → β,
L (t = 1) = pore water composition at time, t =1; L (t = 2) = pore water composition at time, t = 2. The final product structure will therefore comprise: α + (β + α). The α mineral will appear as grains of α mineral (e.g., authigenic halite). The β mineral will appear as grains of β mineral (e.g., authigenic FeOx(OH)y(H2O)z). This allows both phases to precipitate within the pellets as authigenic minerals during desalination.
Alternatively, when the original α mineral is a Fe species, the (β + α) phases can co-precipitate, or the β phase (halite) can precipitate after the α phase. This creates a characteristic “polygonal” mineral distribution of halite surrounding authigenic FeOx(OH)y(H2O)zNadCle species (Figure 13). In this instance the β phase forms a characteristic film around the α grains (Figure 13).
This brief analysis has established that the chemistry within the ZVI pellets during desalination is complex and involves dissolution and reprecipitation of Na+, Cl, and Fen+ species.

3.9. Ironstone Precipitation

The ZVI pellets can develop localized “oolitic” ironstone concretions (Figure 14). These concretions are characterized by a wustite rim [41,44,45,46,47] and contain cuboid authigenic Fe0 crystals [41,44,45,46,47].

4. Chemical Implications of the Petrography for Aquifer Desalination

4.1. Dubinin-Astakhov Catalytic Model for ZVI Desalination

The intra-particle porosity of the FeOx(OH)y(H2O)b+/− particles within the ZVI pellets act as the catalytic site [17]. The particles adsorb Na+ and Cl ions from the water, and then desorb the ions as a [Na+Cl] ion adduct (Figure 1, Figure 2, Figure 3, Figure 4, Figure 5, Figure 6, Figure 10, Figure 11, Figure 12 and Figure 13) within the inter-particle porosity (Figure 15).
This type of adsorption-desorption cycle is adopted by the Dubinin-Radushkevich model [55]. This model assumes that the adsorbed species is desorbed to fill the associated nano/meso/macro-dead-end porosity. Under this model [55]:
Ln(qe) = ln(qs) − kadε2,
qe = (qs) exp( − kadε2),
qe = ion removed at equilibrium/unit ZVI (g/g); qs = theoretical isotherm saturation capacity, g/g; kad = Dubinin-Radushkevich isotherm constant (M2·kJ−2); ε = Dubinin-Radushkevich isotherm constant, g. While qe is measured, the other variables are unknown.
Adsorption-desorption associated with goethite (FeOx(OH)y(H2O)z) [56,57], akaganeite (FeOx(OH)y(H2O)zCln) [58] and ferrate (FeOx(OH)y(H2O)zNanKm) [59] have been explained elsewhere using a Dubinin-Radushkevich adsorption model or a multilayer film model [55,56,57,58,59,60].

4.2. Significance of Porosity within the ZVI

The volumetric Dubinin-Radushkevich adsorption model is [60,61]:
Vx = Vo·exp[−(RT1Eo·ln(Ps/Pa))2],
Vx = volume (NaCl) adsorbed by the ZVI and desorbed into the dead-end porosity (Vo) at time t = n, and at a relative pressure of (Pa/Ps) and temperature, T; Ps = initial pressure; Pa = adsorption pressure; Vo = the initial inter-particle nano-meso-macro dead-end pore volume; Eo = characteristic energy of adsorption of a standard adsorbate [61]; β1 = the affinity coefficient of the adsorbate; E1 = β1Eo [61]; E1 = characteristic energy for a specific fluid-solid system; R = the gas constant.
This model is rewritten as the Dubinin-Astakhov equation where [60]:
Vx = Vo·exp[−(RT1Eo·ln(Ps/Pa))m],
m = a factor which is related to pore size distribution and morphology. It follows that [60,61]:
Vx/Vo = exp[−(RT·ln(Pa/Ps)/E)2(or m)],
Desalination ceases when Vx/Vo = 1. The porosity destruction rate constant associated with (Vx/Vo) is different to the desalination rate constant, kr (Appendix A, Equations (A1)–(A4)), associated with (Ct = n/Ct = 0), though both are related.
Equation (12) demonstrates that:
(a)
the desorbtion of Na+ and Cl ions into dead-end porosity will result in their sequestration (Figure 1, Figure 2, Figure 3, Figure 4, Figure 5 and Figure 6); Vo > 0% at t = 0;
(b)
Placement of desorbed Na+ and Cl ions into pores (Vop) which are in direct communication with the main water body will result in the desorbed Na+ and Cl ions returning to the main water body. Vop > 0% at t = 0;
(c)
The inter-particle porosity = ϕ = Vo + Vop, and Vo = a·ϕ; Vop = b·ϕ, where a + b = 1. Once Vx/Vo = 1, {a} = 0, the residual porosity is Vop, i.e., {b} = 1. An irreducible water salinity level, BS, occurs when {a} reduces to 0.

4.3. Significance of Fluid Pore Pressure within the ZVI Pellet

The fluid pore pressure within the pore, Pp, is different to the bulk (aquifer) pressure, Ps, and can be estimated as [61]:
Pp(dp) = Ps·(exp(−Ep(dp)/RT)),
Ep = average potential energy for the molecules being adsorbed (KJ/mole) [61] = 23.761dp−0.8782; dp = pore width, nm, i.e., the pressure drop between the bulk pressure and the pore pressure (within the ZVI pellet) controls the desalination rate.
It follows that [60,61]:
Vx/Vo = exp[−(RT·ln(Ps/Pp)/E)2],
These Equations (8)–(14) imply:
  • Na+ and Cl ion removal rates will increase with increased pressure differentials between the pressures in the pores and the wider aquifer;
  • Na+ and Cl removal can have different rate constants [17];
  • Smaller pores will be filled preferentially relative to larger pores.

5. Catalytic Implications of the Petrography

Desalination Model

The detailed catalytic desalination mechanism is unknown. It is believed [17] to involve a cycle of oxidation from Fe0−II to FeIII (and possibly FeIV to VIII) followed by reduction to Fe0−II (Figure 16).

6. Commercial Implications of the Petrography for Aquifer Desalination

The petrographic analyses demonstrate that the removed Na+ and Cl ions are extracted from the water and are stored as NaCl(s) within the ZVI pellets. These observations indicate that pellets with a large internal structural porosity are able to remove more Na+ and Cl ions. They will have a higher rate constant, kr, than similar pellets with a lower structural porosity. This is demonstrated in Figure B1.
Commercial scale up of the trial data, combined with a transfer of the reaction environment from a surface based reactor to a sub-surface aquifer, requires a conceptual and front end hydrological engineering and design analysis (FEHED) for each proposed location. This analysis is undertaken prior to the investment decision being made. The commercial investment decision will be controlled by the parameters defined in Equations (1)–(3). The purpose of the FEDEH is to establish that the partially desalinated water product can be delivered for a cost ($/m3) of less than Mc Equation (3).

7. Hydrological and Chemical Engineering Requirements for Commercialization

7.1. Conceptual and Front-End Hydrological Engineering and Design (FEHED)

The primary data requirements for the FEHED process are provided in Table 1. This process requires the kinetic parameters for desalination to be both known and modeled (Appendix A and Appendix B).
The first stage in the process is an assessment of the available catalyst data in order to establish the amount of catalyst required and the Aquifer Treatment Zone size required by each catalyst. This stage is addressed in Section 7.2.
The second stage in the FEHED process is the selection of the preferred catalyst. This stage is addressed in Section 7.3.
The third stage in the FEHED process is the creation of a control desalination data set (e.g., Figure B1, Figure B2 and Figure B3; Table E1 and Table F1), a kinetic model (e.g., Equations (A1)–(A4)) and a control hydrological model. Section 7.4 and Appendix C detail the equations and models that can be used to create the control hydrological model.
The fourth stage in the FEHED process is analysis of the different development strategies which could be used to create the Aquifer Treatment Zone. In this study two catalyst dependent strategies are considered (in Section 7.5 and Section 7.6): (i) a Static Strategy; (ii) a Recycle Strategy. Each strategy integrates the hydrological and chemical models (Appendix A, Appendix B and Appendix C) to define the design and operating parameters for the proposed Aquifer Treatment Zone. These models are summarized in Appendix E, Appendix F and Appendix G.
The base data set, which is used to illustrate the various hydrological and chemical models in Appendix A, Appendix B, Appendix C, Appendix D, Appendix E, Appendix F and Appendix G, is derived from the desalination trials using the ST catalyst (e.g., Figure 1, Figure 2, Figure 3, Figure 4, Figure 5, Figure 6, Figure 8, Figure 9, Figure 10, Figure 11, Figure 12, Figure 13 and Figure 14; Figure A1; Figure B1, Figure B2 and Figure B3).
These trials have demonstrated that if ZVI catalyst pellets are placed in a body of water, they will desalinate the water (Figure B1, Figure B2 and Figure B3). The two sided statistical 99% confidence limits for the mean desalination after 200 days are:
(a)
15 mm diameter Cu0 cased pellets (without operational optimization) = 59.1% to 65.8%, n = 50;
(b)
75 mm diameter MDPI cased pellets (without operational optimization) = 58.2% to 83.4%, n = 15. i.e., 99% confidence limits (BS2846) are: mean ± (Standard Deviation) (t0.995/(n)0.5), where n = number of samples; t0.995 is the value (for n) of the students t test distribution (at the 99% confidence limits).
Similar data sets can be obtained for alternative catalysts (e.g., Table 1, Table E1 and Table F1; Figure B1). This study assumes that the pellets (or cartridges) are placed in removable containers at a number of loci within the aquifer (e.g., in infiltration devices, wells, or boreholes [62,63,64,65]). Removal of the pellets from the aquifer, will allow the NaCl to be periodically removed from the water body.
Once the agricultural land owner has decided that the desalination project may be economically viable (e.g., Equations (1)–(3)), the FEHED process sequentially steps the project through three phases (Appendix D):
  • prospective resource;
  • contingent resource;
  • developed resource.

7.2. Scale Up of Trial Desalination Kinetic Data to Provide an Assessment of the Amount of ZVI Required and the Size of the Aquifer Treatment Zone

The trial data associated with a specific catalyst (Item 4, Table 1) forms the primary database for the conversion of a saline aquifer to a proposed Aquifer Treatment Zone. The initial stages in this process are [66,67,68,69,70,71,72,73,74]:
  • Conversion of the salinities (C) at t = 0 and t = n for batch trials to:
  • Up scaling the ZVI quantities used in the trials:
    • The petrography demonstrates (Figure 1, Figure 2, Figure 3, Figure 4, Figure 5, Figure 6, Figure 8, Figure 9, Figure 10, Figure 11, Figure 12 and Figure 13) that increasing the amount of ZVI will effectively increase the available ZVI voidage (porosity) available for the sequestered NaCl;
    • The trial data demonstrates that the amount of NaCl removed per unit time is independent of the concentration of ZVI, g/L, Pw (Figure 17a,b);
    • These trial observations indicate that:
      • Each 1 g of ZVI will be expected to remove at least 0.35 g NaCl before it needs to be removed from the reaction environment and is replaced (Figure 17a–c).
      • Scale up by the placement of 10 t ZVI in a saline aquifer would result in the removal of up to 3.5 t NaCl from the aquifer, before the replacement of the ZVI is required (Figure 17a–c). This experimental scale up allows:
        • the anticipated volume of water which can be desalinated by a single ZVI charge to be defined (Figure 17d)
        • the anticipated replacement frequency of the ZVI to be defined (Figure 17e).
  • Assessing from the trial data set, the size of the Aquifer Treatment Zone which is required to deliver the target volume of irrigation water:
    • In a commercial development the ZVI will be placed in a number of loci (wells) which intersect the aquifer. The number of loci required is defined by the ZVI concentration data used in the trials (e.g., Figure 17a,b).
    • The required residence time, td, to achieve the required irrigation water salinity is defined by the trials kinetic data (e.g., Equations (A1) to (A4); Figure B1, Figure B2 and Figure B3). The residence time is combined with the irrigation water volume required (Table 1) to define the minimum volume of water, and the minimum gross rock volume, contained within a proposed Aquifer Treatment Zone, Vaq (Equations (E1)–(E3) and (E5)).
    • This minimum water volume is then integrated with the known aquifer properties in order to identify an aerial extent for the proposed Aquifer Treatment Zone Equation (E4).

7.3. Selection of Catalyst for the Proposed Aquifer Treatment Zone

The trials [16,17] have established that Fe0 (Figure A1) can be used to create two types of structured ZVI desalination catalyst. They are:
  • A Type A Catalyst: This catalyst type (catalysts ST and MT) has a rigid porosity (Figure A2) and will, when placed in saline water, gradually remove NaCl from the water over a period of time (e.g., 100–1500 days). It has a low kr (Figure B1, Figure B2 and Figure B3) and a low BS. The irreducible (or equilibrium) salinity, BS, increases with decreasing catalyst porosity (Equations (8)–(14)). Fe0 powders, with no effective porosity, do not desalinate water (Figure B1) [36]. The statistical 1st and 3rd desalination quartiles for td = 200 days, for the three Type A catalysts considered in this study are: (i) ST catalyst (Figure A2; Figure B1, Figure B2 and Figure B3), 15 mm diameter pellet: 54.4% to 70.8%; n = 50; (ii) ST catalyst (Figure B3b), 75 mm diameter pellet: 63.2% to 76.8%; n = 13; (iii) MT catalyst (Figure B1), 20–25 mm diameter pellet: 6.1% to 13.7%; n = 24. The amount of NaCl that can be removed is controlled by the porosity (Equations (8)–(14)) and the amount of catalyst placed in the reaction environment (Figure 17). The Type A catalyst will require a residence time in the aquifer of 100–500 days before the maximum (design) equilibrium desalination is achieved (e.g., Figure F1, Figure F2 and Figure F3).
  • A Type B Catalyst: This catalyst type (catalysts C to K) has an expandable, or poro-elastic, porosity (e.g., Table E1 and Table F1). The catalyst will, when placed in saline water, in a reaction environment incorporating water recycle, result in a relatively rapid removal of NaCl from water (e.g., 3 to 50 h). It has a high kr (Table E1 and Table F1) and a relatively high BS (e.g., 20%–80% (e.g., Table 1, item 4e)). It can preferentially remove Na+ or Cl ions. Once an initial BS is achieved, or the water recycle ceases, desalination continues to progress at a substantially slower rate (e.g., Figure B1, Figure B2 and Figure B3). The Type B catalyst will require a residence time in the aquifer of 1–30 days before the maximum (design) equilibrium desalination (e.g., F4, F5).
Two subsurface ZVI desalination strategies have been identified. They are:
  • A Static Strategy: where the ZVI catalyst is placed in the aquifer and no water is recycled to the aquifer;
  • A Recycle Strategy: where some of the product water is recycled back to the aquifer.
A Type A catalyst is designed for use in the “Static Strategy”. A Type B catalyst is designed for use in the “Recycle Strategy”. The choice of catalyst controls the development strategy and costs associated with a proposed Aquifer Treatment Zone.
The desalination strategy used in a proposed Aquifer Treatment Zone can be switched from one approach to the other, during its operating life.

7.4. Control Hydrological Data and Models Required for the Proposed Aquifer Treatment Zone

Conversion of an aquifer to an Aquifer Treatment Zone requires the installation of one or more abstraction wells [75], and the installation of a number of loci (wells) containing the ZVI catalyst. Observation loci (wells) may be placed outside the proposed Aquifer Treatment Zone and a number of reinjection/infiltration loci (wells) may be placed within the proposed Aquifer Treatment Zone.
Each of these loci provides a spatial data point which provides information about: (i) the aquifer geology (thickness, porosity, permeability, mineralogy); (ii) aquifer hydrology and chemistry (piezometric level, water chemistry (pH, Eh, EC, ion concentrations, salinity)).
Three sets of hydrological control data are required prior to placement of the ZVI in the aquifer. They are: (i) aquifer prior to abstraction; (ii) aquifer during a prolonged abstraction test; (iii) aquifer following a prolonged abstraction test.
This information is used during the FEHED to build a hydrological, hydrochemical and geological model of the aquifer and its expected performance during operation.
Periodic monitoring of these loci following installation of a proposed Aquifer Treatment Zone will allow: (i) the performance of the aquifer to be monitored; (ii) any changes in performance to be identified. This will allow an appropriate remedial program to be instigated to mitigate against adverse changes in the aquifer.
The primary hydrological aquifer parameters, which are used to provide the control database, build the hydrological aquifer model, and monitor operation are documented in Table 2.

7.5. Construction of the Proposed Aquifer Treatment Zone Using the Static Strategy

A proposed Aquifer Treatment Zone constructed using the Static Strategy will have the following characteristics:
(a)
a radial pattern of ZVI loci;
(b)
a single abstraction well located within the “Aquifer Treatment Zone” ;
(c)
an excess of ZVI is placed in each ZVI loci to increase the life expectancy of each ZVI charge. The life expectancy of a ZVI charge could be designed to exceed 20 years (Figure 17);
(d)
a simple process flow diagram (Figure 18);
(e)
a simple construction (Figure 19).
This type of approach:
(a)
is suitable for most agricultural holdings;
(b)
can be installed using basic, simple, low cost, and widely available equipment and tools. Many of these tools may already be available on the agricultural holding, as the required augers are commonly used to install fence posts; and
(c)
requires no operating energy during its life (apart from power for the abstraction pump).
A side effect of this approach is that any water flowing through the Aquifer Treatment Zone (Figure 19) will be treated by the ZVI. ZVI treatment will remove a variety of cations and anions (e.g., nitrates and organic pollutants) from the water [16,66,68].

Costs Associated with the Static Strategy

The principal costs are the installation of the ZVI loci, ZVI, observation loci, abstraction loci, abstraction well/pump, distribution tank or impoundment, and associated pipe work.
This strategy will require the ZVI pellet charge to be installed with a view to removal and replacement every x years (e.g., Figure C3h).
The ZVI catalyst life expectancy and Aquifer Treatment Zone life expectancy has not been demonstrated in a commercial environment. The unsubsidized amortized cost (capex + opex) of the partially desalinated irrigation water, when this technology is eventually commercialized, may fall within the range $0.05/m3–$0.5/m3.

7.6. Construction of the Proposed Aquifer Treatment Zone Using the Recycle Strategy

The operation of a Type A desalination catalyst (e.g., ST catalyst) can be described and modeled using Equations (8)–(14), (A1)–(A4). The primary control on kr and BS (for a specific td, Pw, Pi and as) is Vo. Modeling demonstrates (Appendix F) that the introduction of a recycle loop in the process flow (Figure 20), can, depending on the location of the reinjection loci, result in the equilibrium BS increasing or decreasing (Figure F1, Figure F2 and Figure F3; Equations (F1)–(F3)).
The Type B desalination catalyst removes Vo as a constraint, by allowing Vo to expand with time.

7.6.1. Poro-Elastic vs. Rigid Vo

A specific catalyst formulation (catalyst D) was used to construct both a Type A catalyst (Catalyst D(i) with a rigid Vo and a Type B catalyst (Catalyst D(ii)) with a poro-elastic Vo.
The two catalysts were trialed in a 240 L recycle reactor (Figure 20) with a Pw of 0.5 g/L with a td of 10 to 24 h (at a temperature of between 5 and 20 °C). The 99% confidence limits on the mean desalination were:
(i)
catalyst D(i) = 8.4% to 13.7%, n = 31;
(ii)
catalyst D(ii) = 9.9% to 65.0%, n = 18.
Both catalysts continued to desalinate over the next 100–700 days using the rate constants demonstrated in Figure B1 (when (y + n) = 0 (Figure 20)). This trial established that a higher kr and lower BS is achieved (over a 24 h period) by constructing the catalyst with a poro-elastic Vo.

7.6.2. Recycle Strategy

In a recycle environment, in the presence of a Type B catalyst, a higher kr occurs when (y + n) ≥ 0 (Figure 20; Table E1 and Table F1), i.e., kr has a partial dependency with V (Figure F4). Further desalination ceases when a new equilibrium for (V − D) is achieved (Equations (C14)–(C21)). This equilibrium controls BS and the salinity of the product water. The trial results in Table E1 and Table F1 demonstrate that the equilibrium position for (V − D) can be associated with a rapid desalination of 40%–75%.
These trial observations demonstrate (Table E1 and Table F1) that it may be possible to construct an Aquifer Treatment Zone by incorporating a recycle loop in the process flow (Figure 20). The trial results (Table E1 and Table F1) indicate that this will allow, by comparison with the Static Strategy, the amount of partially desalinated irrigation water produced from a specific Aquifer Treatment Zone volume, to be substantially increased (Table F1).
The Recycle Strategy construction (Figure 20) requires that the volume of water abstracted from the Aquifer Treatment Zone is greater than the amount of water required for irrigation (Figure 20). The excess water is then reinjected into the Aquifer Treatment Zone.
The Recycle Strategy has infiltration loci (for water recycle) and a network of distribution pipes to the infiltration loci (Figure 21). The trial results (Table E1 and Table F1) indicate that Pi for a Recycle Strategy is lower than Pi for a Static Strategy (Figure 17). This reduces the number of ZVI loci required, and the amount of ZVI required, to achieve a specific irrigation water abstraction rate.
The principal additional operating cost of the Recycle Strategy (relative to the Static Strategy) is the increased operating cost of the abstraction well (Figure 20). For a specific target irrigation water abstraction rate, the Recycle Strategy, will have a smaller Aquifer Treatment Zone, and will require less ZVI (Table F1).
The amount of water abstracted for recycle is a function of the catalyst selected. The examples in Table E1 and Table F1 demonstrate that (y + n)/m (Figure 20) is between 0.45:1 and 7.45:1. i.e., the recycle water volume is between 9 and 149 m3/day for an Aquifer Treatment Zone producing 20 m3/day of partially desalinated irrigation water.
In most locations, the air-water contact in the distribution tank will be above the air-water contact in the aquifer (Figure 22). In these circumstances, a recycle pump is unlikely to be required. The recycle water distribution can be effected using buried pipe work, gravity drainage and infiltration (Figure 22). This strategy removes a requirement for a recycle pump and external power source.

8. Commercial Scale-Up Risk

Scaling-up the ZVI desalination trials, using a recycle strategy, to a 20 m3/day commercial Aquifer Treatment Zone involves a throughput scale-up of between 6 and 3000 (Figure C5, Table E1 and Table F1). A larger scale-up factor is required for the static strategy (Figure B1). The commercial risks associated with the scale-up of trial data relate to cost and performance. These commercial risks can increase as the magnitude of the scale-up increases. A commercial scale-up from trial data of between 6 and 3000 is at the lower end of conventional practice [76,77,78,79,80].
Standard chemical process engineering industrial practice is to use the results from small scale trials as a basis for an investment decision, e.g., [76,77,78,79,80]. It is not unusual for the direct commercial, throughput, scale-up (from bench/pilot trials to commercial plants) associated with petrochemical (and chemical) processes to be in the order of 500,000 to 100,000,000.
The scale-up associated with the commercialization of post-1980s ZVI water treatment processes includes:
  • Permeable Reactive Barriers (PRB’s) inserted in aquifers, e.g., [81,82]: Small scale (batch and continuous flow) trials containing 0.001 to 5 kg Fe0 were scaled-up to >300 PRB’s containing 2 to 1000 t Fe0 each;
  • Industrial Waste Water Treatment Plant, Shanghai: processing 60,000 m3/day [83,84]. A number of 2 h duration pre-development batch flow trials (containing 0.5 kg Fe0) were undertaken [83]. They were succeeded by a single continuous flow pre-development trial (containing 40 kg Fe0) at a rate of 120 L/d [83]. This pilot trial was operated for 6 months prior to commercialization [83]. The throughput scale-up factor was 500,000. The scale-up factor in the amount of Fe0 used was 22,950.

9. Comparative Desalination Cost Structure

Large (>100,000 m3·day−1) plants (Multi-stage flash distillation (MSFD), reverse osmosis (RO)), which are integrated with a thermal fuel power station have the lowest current desalination water costs ($/m3). The cost of the desalination process is subsidized by the electricity sales associated with the power station. The cost structures associated with large (>90,000 m3·day−1) integrated conventional desalination systems are summarized in Table 3.
An Aquifer Treatment Zone producing 90,000 m3/day will require access to a large aquifer. The normalized trial data (associated with different catalysts (Table F1)) indicates that the required target volume of water within the Aquifer Treatment Zone may be: (i) between 12,000 m3 and 170,000 m3 (RC1–RC3, RC5–RC9, Table F1; mean = 73,309 m3; 99% upper confidence limit = 169,108 m3), or (ii) <2,000,000 m3 (RC4, Table F1).
The standard chemical engineering cost scale up equation is [85,86]:
Ce = Ck·(Plc/Ppc)a,
Ce = Expected cost, $ (excluding site costs); Ck = Known cost of the constructed unit; Plc = Larger Plant throughput, m3 day−1; Ppc = Constructed unit throughput, m3 day−1; a = an exponent scale up factor (range = 0.3–1.2 [86]). It is commonly taken as 0.6, range 0.4–0.8 [85,86]. The constructed unit has a known cost and throughput capacity.
Table 3 includes an example target set of costs for a large 90,000 m3·day−1 ZVI in situ desalination aquifer complex. This provides an indication of how economies of scale Equation (15) may impact on the delivered cost of the partially desalinated irrigation water.
Conventional desalination delivers a water product with >97% of the Na+ ions removed. By way of contrast, ZVI desalination is only expected to remove between 20% and 70% of the Na+ ions (commercial target zone is 35%–60% Na+ removal).

10. Conclusions

This study has investigated whether ZVI desalination pellets and cartridges could potentially be used to desalinate a body of water within an aquifer. Two different aquifer reconstruction strategies have been identified and modeled, a static strategy and a recycle strategy.

10.1. The Static Strategy

This requires a relatively long residence time within the aquifer (e.g., 100–300 days). The expected pre-tax cost of this strategy is in the order of $0.05–$0.5/m3, where the bulk of the cost (excluding the costs associated with abstraction) is a capital cost. The principal operating cost is the abstraction pump.

10.2. The Recycle Strategy

This requires a relatively short residence time within the aquifer (e.g., 0.3–3 days). The delivered water cost may be less than $0.1/m3. This strategy will have a requirement for reinjection/infiltration. It has a higher pumped abstraction rate from the aquifer than the static strategy. The recycle strategy allows active management of the aquifer to be undertaken to control the salinity of the abstracted irrigation water.

10.3. Dual Use Strategy

An aquifer, which is initially developed using a Static Strategy, can be subsequently converted to allow operation using a Recycle Strategy, and vice-versa.
ZVI regeneration and replacement intervals are a function of catalyst and design. The replacement interval could potentially be once every 1 to 20 years.

10.4. Next Stage

The next stage will be controlled field testing in an aquifer stepping up through a range of volumes and throughput (e.g., 1 to 100 m3·day−1) sizes. This testing program will be designed to establish, and mitigate, the issues associated with scale-up, prior to commercialization.
A successful commercial scale test program will allow commercial saline aquifer reconstructions (targeting 1–1000 m3·day−1 of partially desalinated product water) to be undertaken.

Acknowledgments

This study was funded by DCA Consultants Ltd.

Author Contributions

The author undertook the research in this paper and wrote the paper.

Conflicts of Interest

The author declares no conflict of interest. The author undertook the design of the study; the data collection, analyses, and interpretation of data; and wrote the manuscript, and took the decision to publish the results.

Appendix A. Trial Materials and Kinetic Methodology

A1. ZVI

An earlier study [16] documented desalination associated copper sheathed structured ZVI pellets. This study uses the rate constant data from these trials [16] as a database.
The Fe powders have a natural loose packing density which results in the formation of tortuous macropores with narrow pore throats (Figure A1). This natural porosity is expanded during the pellet manufacturing process [16,17] into a rigid framework (Figure A2).
Figure A1. Typical clumped packing structure of Fe0 grains and associated porosity in a Fe0 column. Transmitted light. Macro pores occupy 22% of the visible porosity. Field of view = 0.93 mm. (a) Transmitted light. Fe0 = black, porosity = grey; (b) Pseudo-color analysis. Fe0 = Blue. Porosity = green.
Figure A1. Typical clumped packing structure of Fe0 grains and associated porosity in a Fe0 column. Transmitted light. Macro pores occupy 22% of the visible porosity. Field of view = 0.93 mm. (a) Transmitted light. Fe0 = black, porosity = grey; (b) Pseudo-color analysis. Fe0 = Blue. Porosity = green.
Hydrology 04 00001 g023
Figure A2. Pseudo-color analysis of a 15 mm diameter copper sheathed ZVI desalination pellet (Used in Trial ST1c [16]). Porosity = purple; Residual Fe0 = green; FexOyHz = blue; Cu0 sheath = green; Structural Porosity = 76%. ZVI: water ratio = 25.38 g/L; Feed water salinity = 8.19 g/L; Product water salinity after 230 days = 1.48 g/L. NaCl removed = 6.71 g/L (0.26 g/g ZVI). Feed Water pH = 6.21; Product water pH = 8.54. Pore classification: Macropores (>50 nm), the pore size distribution is multimodal with a mixture of largely interconnected, high surface area, sinusoidal, bulbous pores characterized by one or more narrow pore throats. The characteristics of fluid flow in this type of pore network are described in references [62,63,64,65,88,89].
Figure A2. Pseudo-color analysis of a 15 mm diameter copper sheathed ZVI desalination pellet (Used in Trial ST1c [16]). Porosity = purple; Residual Fe0 = green; FexOyHz = blue; Cu0 sheath = green; Structural Porosity = 76%. ZVI: water ratio = 25.38 g/L; Feed water salinity = 8.19 g/L; Product water salinity after 230 days = 1.48 g/L. NaCl removed = 6.71 g/L (0.26 g/g ZVI). Feed Water pH = 6.21; Product water pH = 8.54. Pore classification: Macropores (>50 nm), the pore size distribution is multimodal with a mixture of largely interconnected, high surface area, sinusoidal, bulbous pores characterized by one or more narrow pore throats. The characteristics of fluid flow in this type of pore network are described in references [62,63,64,65,88,89].
Hydrology 04 00001 g024

A2. Microscopy

A ME580TWB-PZ-2L-14MP dual light (reflected and transmitted light), trinocular, polarizing, metallurgical microscope ((×40 to ×2000) incorporating a 14 MP digital camera (14 MP Aptina color CMOS model MU1400-204)) was used in this study to examine the pellets, before and after usage. The microscope and camera were branded by Amscope Inc., Irvine, CA, USA and supplied by United Scope (Ning Bo) Co Ltd. Zhejiang, China. The microscope is a BH200M Series unit manufactured by Ningbo Sunny Instruments Co. Ltd., Zhejiang, China. The camera was linked to an Amscope x64, 3.7.6701 (version date: 31/12/2015) digital microscope software package (branded by Amscope, Irvine, CA, USA). This software package was used to analyze the microscope slides and resin blocks. Calibration slides were used to scale the digital microscope image:
(i)
divisions at 0.15 mm, 0.1 mm, 0.07 and 0.01 mm supplied by No. 1 Microscope Wholesale Store, Henan, China, and
(ii)
divisions at 0.01 mm supplied by United Scope (Ning Bo) Co Ltd. Zhejiang, China.
The microscope provided natural light, plane polarized light and circular polarized light. It had a plane polarizing analyzer. The microscope was able to operate in a polarized light mode under both transmitted light and reflected light to aid mineral identification.

A3. Saline Water Construction

The saline water was constructed by dissolving either NaCl (Trials using Catalysts ST, MT, C and E (Figure B1, Figure B2 and Figure B3, Table E1 and Table F1)) or Zechstein halite (Trials using Catalysts D, F-K (Table E1 and Table F1) in natural spring water. The natural spring water was extracted from a private water supply in Dunning, Ochil Hills, Perthshire, Scotland [16,17,36,37,38,67]. The water was reservoired in a fractured Devonian, Old Red Sandstone volcanic series aquifer (fractured andesites, fractured dacites, fractured pyroclastics) [16,17].
A typical ion composition of the natural spring water is [16,17]: Anions: Cl = 11.67 mg·L−1; N(NO3) = 11.28 mg·L−1; S(SO4) = 4.16 mg·L−1; P(PO4) ≤ 0.10 mg·L−1; F = 0.024 mg/L; N(NO2) = 0.04 mg·L−1; HCO3/CO32− ≤ 10 mg·L−1; Cations: K = 1.69 mg·L−1; Ca = 32.91 mg·L−1; Na = 6.32 mg·L−1; Al ≤ 150.0 µg·L−1; Fe ≤ 30.0 µg·L−1; Mn = 1.70 µg·L−1; P ≤ 0.005 mg·L−1; S = 4.31 mg·L−1; B = 29.40 µg·L−1; Ba = 135.60 µg·L−1; Cd = <0.2 µg·L−1; Co ≤ 0.2 µg·L−1; Cr ≤ 0.2 µg·L−1; Cu = 77.7 µg·L−1; Ni = <3 µg·L−1; Pb ≤ 10 µg·L−1; Si = 5.21 mg·L−1; Sr = 144.9 µg·L−1; Zn = 37.4 µg·L−1; As ≤ 5 µg·L−1; Mo = <20 µg·L−1; Se ≤ 20 µg·L−1; Sn ≤ 20 µg·L−1; Sb ≤ 10 µg·L−1. This water analysis is reproduced from References [16,17].

A4. Water Analyses

Four parameters were routinely monitored for each sample. They were:
(i)
ORP (oxidation reduction potential);
(ii)
pH;
(iii)
electrical conductivity (EC);
(iv)
temperature.

A4.1. pH

The pH meter used was manufactured, or branded, by Hanna Instruments Ltd., Leighton Buzzard, Bedfordshire, UK. It was calibrated at pH = 4, 7, 10 using calibration fluids manufactured or branded by Hanna Instruments Ltd., Leighton Buzzard, Bedfordshire, UK, and Milwaukee Instruments Inc., Rocky Mount, NC, USA.

A4.2. Eh

The ORP (oxidation reduction potential) meter used was manufactured, or branded, by Hanna Instruments Ltd., Leighton Buzzard, Bedfordshire, UK. The ORP measurements were converted to Eh values using a quinhydrone calibration [16,17,90]. The quinhydrone calibration (to the standard hydrogen electrode), was undertaken at pH = 4, and 7 (i.e., Eh, mV = −65.667 pH + 744.67 + ORP (mV) [17]).

A4.3. EC (Electrical Conductivity)

The EC meters used were manufactured, or branded, by Hanna Instruments Ltd., Leighton Buzzard, Bedfordshire, UK. The meters were calibrated at 0.7, 1.4, 2.0, 12.88 mS·cm−1. In the feed water the EC increases with increasing water salinity. The measured regression correlation between EC and water salinity (constructed from NaCl) is: Salinity (g/L) = 0.5401EC (mS·cm−1) − 0.1 (at 4–6 °C) [16]. The correlation between EC and water salinity (constructed from Zechsten halite) varies with the halite composition. An example correlation (R2 = 0.8581, n = 25) is: Salinity (g/L) = 0.7328EC (mS·cm−1) − 0.1287 (at 4–6 °C).

A4.4. Na+ and Cl Ion Concentrations

Bante 931 precision ion meters (manufactured by Bante Instrument Ltd., Shanghai, China), with Na+ and Cl ion selective electrodes, were used to analyze the Na+ and Cl ion concentrations. The ion meters were calibrated using 0.001, 0.01, 0.1 and 1.0 molar Na+ and Cl solutions.

A4.5. Temperature

Temperature was measured using meters manufactured, or branded, by Hanna Instruments Ltd., Leighton Buzzard, Bedfordshire, UK.

A5. Reactor

Two diffusion reactor types (0.3–840 L capacity) have been used (Static Diffusion and Diffusion reactors incorporating recycle) to obtain the desalination data [16,17].

A6. Pellets and Cartridges

This study defines a pellet as a cylinder with one or more impermeable side(s) and two or more permeable ends. A cartridge is defined as a cylinder with one or more impermeable sides, one or more impermeable ends and one permeable end. A cylinder is defined as an object (of any shape) which can be placed in the reaction environment. The examples (Appendix A, Appendix B, Appendix C, Appendix D, Appendix E and Appendix F) used pellets and cartridges with a circular cross section.

A7. Conversion of Batch Flow Kinetic Data to Continuous Flow Kinetic Data

The measured salinity concentrations (C) in the product water, as a function of time, t, for t = 0 to t = n, are routinely measured in trials for each catalyst (e.g., Figure B1, Table E1). This information is used to derive a statistical kinetic data set which can be used to model desalination. The reaction order associated with desalination can be modeled, as a first approximation, using pseudo-first order kinetics [69,70,71,72,73,74]. Desalination using Type A catalysts appears to approximate to a pseudo-first first order reaction where BS can be <0.1Ct = 0. Desalination using Type B catalysts appears to approximate to a pseudo-first first order reaction until equilibrium is achieved, BS can be >0.5Ct = 0.
Standard chemical engineering practice converts rate constants, kr, calculated from batch flow trial data (e.g., Figure B1, Figure B2 and Figure B3), into continuous flow reactor data [69,70,71,72,73,74]. In a batch flow reactor, for a specific residence time (reaction time) duration, td [69,70,71,72,73,74]:
kr = kactual Pw as,
kactual = normalized actual rate constant; Pw = amount of ZVI catalyst, e.g., g·L−1; t·m−3; as = normalized surface area of ZVI particle, m2·g−1; k10 = Log10(kr).
The continuous flow reactor can be operated to create a residence time for the water in the reaction environment of td, where td = Flow rate, or abstraction rate (m3/unit time)/Volume of the reaction environment, m3 [38,67,69,70,71,72,73,74]. In this instance [69,70,71,72,73,74]:
kr = kactual Pi as = kactual Pw as,
Pi = the amount of ZVI catalyst in the Aquifer Treatment Zone. kr is [69,70,71,72,73,74]:
kr = (ln(Ct = 0/Ct = n))/td,
If kr is known (e.g., Figure B1, Figure B2 and Figure B3, Table E1), and Ct = 0 (aquifer salinity) is known, then Ct = n (irrigation water salinity) is known for any value of td, i.e.,
Ct = n = (1/exp(kr td))Ct = 0,
td is a function of the size of the Aquifer Treatment Zone and the irrigation water abstraction rate. It follows that if the required, Ct = n, is known (or is defined by the irrigation water user), that the required td is also known.

Appendix B. Kinetic Data Associated with ST Series Desalination Pellets

Detailed desalination profiles for more than 70 ZVI desalination trials using ZVI desalination pellets have been published [16]. These trials fell into two groups:
  • trials using ZVI pellets with a high structural inter-particle porosity (e.g., ST Trial group [16]) and;
  • trials using ZVI pellets with a low structural inter-particle porosity (MT Trial group [16]).
Desalination, Dr, is assessed as:
Dr = 1 – (Ct = n/Ct = 0) = ((Ct = 0Ct = n)/Ct = 0),
where Ct = 0 = feed water salinity at time t = 0; Ct = n product water salinity at time t = n.

B1. Desalination Rate Constant

The rate of desalination is defined by kr, [16,17], Equation (A3). The relationship between Dr and trial duration, td, is provided in Figure B1. The rate of desalination (and amount of desalination achieved after a specific time period) is a function of the pellets inter-particle porosity (Figure B1), where:
(a)
kr is independent of the salinity of the feed water [16,17,35], or can increase with water salinity [33,34], Figure B2;
(b)
Dr increases, as Pw (Equation (A1)) increases, until a critical concentration of ZVI is present [16]. This critical concentration is within the range 10–30 g/L (Figure 17a,b);
(c)
Salinity (Ct = n) measured as a function of time t, follows the general decline pattern of a first order reaction (Figure B3).
The observations demonstrate that placement of the ZVI pellets in a body of water will result in desalination occurring.
Figure B1. Desalination associated with ZVI pellet desalination. Type A catalysts (ST and MT). ST Series trials = Green markers; MT Series Trials = Red Markers. Blue regression line is for control trials of Fe0 placed in saline water. n = 104; operating temperature, non-isothermal varying with atmospheric temperature over the range −15 °C to 20 °C; reactor size, variable, 0.2 to 10 L, ZVI concentration = 5 to >100 g/L [16]. Details of the ST and MT series trials are provided in Reference [16]. Ct = 0 = initial concentration; Ct = n = concentration at time t = n. Throughput scale up required to achieve 20 m3/day, for t = 200 days (4800 h) is between 400,000 and 20,000,000.
Figure B1. Desalination associated with ZVI pellet desalination. Type A catalysts (ST and MT). ST Series trials = Green markers; MT Series Trials = Red Markers. Blue regression line is for control trials of Fe0 placed in saline water. n = 104; operating temperature, non-isothermal varying with atmospheric temperature over the range −15 °C to 20 °C; reactor size, variable, 0.2 to 10 L, ZVI concentration = 5 to >100 g/L [16]. Details of the ST and MT series trials are provided in Reference [16]. Ct = 0 = initial concentration; Ct = n = concentration at time t = n. Throughput scale up required to achieve 20 m3/day, for t = 200 days (4800 h) is between 400,000 and 20,000,000.
Hydrology 04 00001 g025
Figure B2. Desalination associated with ZVI catalyst pellet desalination. Type A catalyst (ST) [16]. n = 59; (a) Feed Water Salinity vs. Product Water Salinity; (b) Feed water salinity vs. Log10 Rate Constant, k10. Red lines indicate the upper and lower bounds of the data set. The black line is the regression trend. Data: [16].
Figure B2. Desalination associated with ZVI catalyst pellet desalination. Type A catalyst (ST) [16]. n = 59; (a) Feed Water Salinity vs. Product Water Salinity; (b) Feed water salinity vs. Log10 Rate Constant, k10. Red lines indicate the upper and lower bounds of the data set. The black line is the regression trend. Data: [16].
Hydrology 04 00001 g026

B2. Desalination Pellet Dimensions

Trials have been undertaken using 15 mm diameter Cu0 cased pellets, and 20 mm, 25 mm, 40 mm and 75 mm diameter MDPE (medium density polyethylene) cased pellets and cartridges [16,17]. These trials established (Figure B3):
  • the composition of the impermeable pellet sheathing material does not affect the rate of desalination, or the amount of desalination that can occur. The ability to use MPDE (or an alternative) as a sheathing material (instead of copper) substantially reduces the pellet cost. Suitable alternatives include: (i) ABS: acrylonitrile-butadiene-styrene; (ii) Buna-N: Copolymer of butadiene and acrylonitrile; (iii) CPVC: chlorinated polyvinyl chloride; (iv) EPDM: Ethylene-propylene-diene monomer; (v) FPM: Fluoro-carbon elastomer; (vi) GRP: Glass reinforced plastics; (vii) HDPE: high density polyethylene; (viii) PA 11: polyamide 11; (ix) PB: polybutylene; (x) PE: polyethylene; (xi) PEX: cross-linked polyethylene; (xii) PIR: polyisocyanurate; (xiii) PK: polyketone; (xiv) PP: polypropylene; (xv) PTFE: polytetrafluoroethylene; (xvi) PVC: polyvinyl chloride; (xvii) PVDF: poly vinylidene fluoride; (xviii) PVP: polyvinylpyrrolidone; (xix) SBR: styrene-butadiene;
  • pellets with diameters of 15 mm, 20 mm, 25 mm, 40 mm and 75 mm are effective;
  • the pellets can have one, or two, open ends, which are in contact with the water body;
  • individual pellets can be structured with lengths of more than 0.5 m;
  • desalination continues to occur when air and water temperatures drop below 0 °C [16].
Figure B3. Desalination associated with ZVI catalyst pellet desalination. Type A catalyst (ST). (a) ST Trial ST3a [16]; (b) ST Trial PE3. Trial Details: (i) ST3a. Pellet Details: ZVI:water ratio = 31.94 g/L; Feed water salinity = 10.04 g/L; Product water salinity after 219 days = 2.60 g/L. NaCl removed = 7.44 g/L (0.23 g/g ZVI). Feed Water pH = 6.35; Product water pH = 8.4. Temperature Range = −15 to 14 °C. (ii) PE3. Pellet Details: Outer diameter = 75 mm (MDPE shell). ZVI:water ratio = 55 g/L; Feed water salinity = 10.64 g/L; Product water salinity after 185 days = 2.72 g/L. NaCl removed = 7.92 g/L (0.14 g/g ZVI). Feed water pH = 6.48; Product water pH = 8.62.
Figure B3. Desalination associated with ZVI catalyst pellet desalination. Type A catalyst (ST). (a) ST Trial ST3a [16]; (b) ST Trial PE3. Trial Details: (i) ST3a. Pellet Details: ZVI:water ratio = 31.94 g/L; Feed water salinity = 10.04 g/L; Product water salinity after 219 days = 2.60 g/L. NaCl removed = 7.44 g/L (0.23 g/g ZVI). Feed Water pH = 6.35; Product water pH = 8.4. Temperature Range = −15 to 14 °C. (ii) PE3. Pellet Details: Outer diameter = 75 mm (MDPE shell). ZVI:water ratio = 55 g/L; Feed water salinity = 10.64 g/L; Product water salinity after 185 days = 2.72 g/L. NaCl removed = 7.92 g/L (0.14 g/g ZVI). Feed water pH = 6.48; Product water pH = 8.62.
Hydrology 04 00001 g027

Appendix C. Equations and Models Which Are Required to Provide a Control Data Set for the Aquifer and to Provide Effective Monitoring of a Proposed Aquifer Treatment Zone Following Installation

This Appendix provides the principal equations and models which can be used to (i) provide a control (or reference) data set and (ii) monitor the hydrological performance of an Aquifer Treatment Zone following installation.

C1. Flow Rate and Iso-Potential Modelling

Prior to reconstruction as an Aquifer Treatment Zone, an aquifer has a permeability (kaquifer). The hydraulic head (h) and the underlying aquifer flow rate (Qaquifer) is controlled by the potential gradient (▽Φ). The vectorial direction of flow is spatially controlled by the iso-potential contours (iso-Φ) [91]. From Darcy’s Law (e.g., [75,92,93,94,95,96,97,98]):
u = K(h1 − h2)/l1 = K(h)/l1 = K·Φ/l1
Qaquifer = kaquifer·Φ,
Qaquifer = ▽kaquifer·▽Φ,
K = a permeability constant [75] = uI1/h; K = 1 Darcy when u = 1 cm·s−1; I1 = total length of flow path in the aquifer, m, [75,97]; u = flow velocity (cm·s−1) = measured Q (cm3·s−1)/A [75]; Φ = Fluid potential (Pa), e.g., Φ = PA/dw + gz [75]; z = (h1h2) [75]; h = head, e.g., m3 = h1 (upstream water source elevation) − h2 (downstream water discharge elevation) [75,92]; dw = density of water; g = gravitational constant. ▽ = gradient operator.
The iso-potential contours effectively solve [91,92] the Richards equation [99]:
Qaquifer = δ/δx(kaquifer·(δΦ /δx)) + δ/δy(kaquifer (δΦ/δy)) + δ/δz(kaquifer·(δΦ/δz)),

C1.1. Simple Reconstruction of an Aquifer

The simplest reconstruction of an aquifer (Figure C1) requires:
  • a single abstraction well to be used to provide water for irrigation;
  • The abstraction pump creates a point source with a negative head (sink) [75];
  • The pump reconfigures the potential, Φ, to create a radial series of iso-potential contours surrounding the abstraction well [75]. Their values range from ΦInitial to Φabstraction well, where ΦInitial is located a radial distance, re, from the well [75].
  • the abstraction well is surrounded by a number of point loci (e.g., infiltration devices, wells, boreholes) containing ZVI pellets or cartridges.
The measured elevation of the water table, at each loci allows the iso-potential contours to be defined [91,100].
Figure C1. Cross-Section through a schematic Aquifer which has been reconfigured to produce partially desalinated water. ZVI pellets and cartridges are shown in red.
Figure C1. Cross-Section through a schematic Aquifer which has been reconfigured to produce partially desalinated water. ZVI pellets and cartridges are shown in red.
Hydrology 04 00001 g028

C1.2. Identification of Heterogeneity in the Proposed “Aquifer Treatment Zone”

The initial assumption (for any aquifer) is that it is homogenous. Aquifers are locally heterogeneous (Appendix G). They can be associated with two, or more, no flow boundaries, or may be compartmentalized [75,91,92].
A review of the issues associated with transmissivity, storage coefficients, compartmentalization, channelized flow and boundaries is outwith the scope of this study.
Observation loci (Figure C2) can be used to confirm the boundaries of the installed Aquifer Treatment Zone.
Figure C2. Cross section through a schematic aquifer which has been reconfigured to produce partially desalinated water. ZVI pellets and cartridges are shown in red.
Figure C2. Cross section through a schematic aquifer which has been reconfigured to produce partially desalinated water. ZVI pellets and cartridges are shown in red.
Hydrology 04 00001 g029

C2. Assessment of the Size of a Proposed Aquifer Treatment Zone

C2.1. Hydrological Parameters Associated with a Proposed Aquifer Treatment Zone

For a constant abstraction rate, AR, the flow velocity, V, at a distance x1 from the well will be greater than the flow velocity at a distance x1+n from the well; the distance x1+n < re [75]. At the abstraction well, V = AR. If the abstraction well has perforated the entire aquifer height, and the aquifer, is homogenous with an even thickness, then as a first approximation, at a radial distance, r1:
V (m3·m−1·t−1) = AR/2πr1,
Drawdown [75] can result in part of an aquifer becoming under-saturated with time. The under-saturation results in the contents of the aquifer porosity changing:
  • at t = 0, the porosity contains mobile water + irreducible (bound) water;
  • at t = n, following drawdown, the porosity can contain mobile water + irreducible water + mobile air + irreducible air.
The two phases (air + water) can be accounted for by using a simple permeability modifier, e.g., the Brusaert [100] model:
k = k0·Swn1,
k = intrinsic permeability [92,96]; k = Q/Pr [92,96,101,102]; k0 = intrinsic permeability at satiation; Sw = mobile water saturation, 0 < Sw < (1 − Swi); Swi = irreducible water saturation, 0 < Swi < 1; n1 = a satiation constant [100]; Calculated as Swi = g + (j + d)/b where g + j + b + d are constants; and n1 is in the range 0.1 to 25; Pr = pressure difference, Pa, represented by h; Q = normalized flow rate, (volume) (unit time)−1 or flow rate, (volume) (unit time)−1(unit weight, area, or volume of ZVI (or aquifer))−1 = kPr = Φ.

C2.1.1. Radial Area of Influence of the Abstraction Well

A simple homogenous aquifer is bounded by two aquitards, separated by a thickness, Th. A single abstraction well is used to create a circular Aquifer Treatment Zone, with a radius, ra,. Under steady state conditions [75]:
AR (e.g., 20 m3·d−1) = ((2π·rakaquiferTh)/ɳ)(δPra),
AR = required abstraction rate; P =constant driving force, Pa (or head, m); kaquifer = permeability of the aquifer; ɳ= viscosity of water. In this study P = Pr = Φ.

C2.1.2. Volume Influenced by the Abstraction Well during a Control Pump Trial

At time t = n3 at a radial distance re [75]:
AV (P = PAquifer) = (AR)(n3)/(ϕ·SwiNG),
AV = Aquifer volume defined by the abstraction pump; Paquifer = P = Φ, at a radial distance, re, from the abstraction well bore. AV increases as AR increases.

C2.1.3. Flow Rate at Each Loci

At any instant in time, the flow rate (or velocity, V1) at each ZVI loci, is a function of its distance from the abstraction loci (AI).
V1, m/s = AR/(2π·Al),
Equations (C7)–(C9) allow: (i) the aquifer Treatment Zone Volume, created by a specific abstraction pump to be evaluated (Figure 3a); (ii) the relationship between AR, ϕ and td to be defined (Figure C3b). Integration of Equations (A1)–(A4) with Equations (C7)–(C9) and the data in Figure 17) allows the basic construction parameters associated with the installation of a proposed Aquifer Treatment Zone to be calculated (Figure C3c–f).
Figure C3. Example Modeled Aquifer Treatment Zone parameters. (a) Stored water volume in the aquifer within a radial distance of the abstraction well for porosities of 20%, 30% and 40%; (b) Residence time for the water within the a 100 m diameter) treatment zone as a function of aquifer porosity (20%, 30%, 40%) associated with continuous abstraction rates for irrigation in the range 10 to 50 m3/day. Aquifer thickness = 1 m; (c) Relationship between water residence time within the treatment zone, abstracted water salinity and aquifer thickness. The reference relationship is shown by the 1 m thickness case. The residence time required is independent of thickness but is dependent on the rate constant. The illustrated 2 m thickness case indicates that the effective aerial extent of the Aquifer Treatment Zone can be reduced by 50% to accommodate the increased aquifer volume. The actual residence time required by water in the 2 m case is provided in this graph by the 1 m example. Assumption V = 0 m/h, Rate Constant based on the regression relationship for the ST Trial Group ZVI Pellets (Figure B2b); Water salinity outside the treatment zone = 4 g/L (EC = 8 mS·cm−1); (d) Expected salinity as a function of effective V and residence time in the treatment zone; (e) Radius of treatment zone vs. number ZVI loci required at a 5 m radial spacing; (f) Radius of treatment zone vs. amount of ZVI required, based on 10 kg at each ZVI loci and a 5 m ZVI loci radial spacing; (g) The amount of water that can potentially be desalinated by a single ZVI charge as a function of the radius of the treatment zone (based on removal of 30 t NaCl/t Fe0 [17]); (h) The potential life expectance of the ZVI charge (days) vs. radius of the treatment zone and the abstraction rate for irrigation (based on removal of 30 t NaCl/t Fe0 [17]). The reference aquifer thickness = 1 m in Figure C3d–h.
Figure C3. Example Modeled Aquifer Treatment Zone parameters. (a) Stored water volume in the aquifer within a radial distance of the abstraction well for porosities of 20%, 30% and 40%; (b) Residence time for the water within the a 100 m diameter) treatment zone as a function of aquifer porosity (20%, 30%, 40%) associated with continuous abstraction rates for irrigation in the range 10 to 50 m3/day. Aquifer thickness = 1 m; (c) Relationship between water residence time within the treatment zone, abstracted water salinity and aquifer thickness. The reference relationship is shown by the 1 m thickness case. The residence time required is independent of thickness but is dependent on the rate constant. The illustrated 2 m thickness case indicates that the effective aerial extent of the Aquifer Treatment Zone can be reduced by 50% to accommodate the increased aquifer volume. The actual residence time required by water in the 2 m case is provided in this graph by the 1 m example. Assumption V = 0 m/h, Rate Constant based on the regression relationship for the ST Trial Group ZVI Pellets (Figure B2b); Water salinity outside the treatment zone = 4 g/L (EC = 8 mS·cm−1); (d) Expected salinity as a function of effective V and residence time in the treatment zone; (e) Radius of treatment zone vs. number ZVI loci required at a 5 m radial spacing; (f) Radius of treatment zone vs. amount of ZVI required, based on 10 kg at each ZVI loci and a 5 m ZVI loci radial spacing; (g) The amount of water that can potentially be desalinated by a single ZVI charge as a function of the radius of the treatment zone (based on removal of 30 t NaCl/t Fe0 [17]); (h) The potential life expectance of the ZVI charge (days) vs. radius of the treatment zone and the abstraction rate for irrigation (based on removal of 30 t NaCl/t Fe0 [17]). The reference aquifer thickness = 1 m in Figure C3d–h.
Hydrology 04 00001 g030

C2.2. Complex Treatment of Under-Saturated Flow

The simple permeability model (Equation (C6)) can be replaced by a more complex permeability model. The more complex model will treat each immiscible fluid (water and air) separately. This creates separate iso-potential relationships, permeability relationships, and flows for each fluid [75,92,103]. The two fluids can flow in different directions and at different rates (e.g., [92,104]).
When Sw < 1, Gas (air) slippage, Klinkenberg effects and Knudsen diffusion impact on kgas [104,105]. Their impact on permeability can be addressed by combining a Knudsen diffusion model with a forced viscous Hagen-Poiseuille flow model [104], when Sw < 1 i.e.,
kair = kt = 0(gas)·(1 + aKn)
kwater = kt = 0(water)·Swn1,
a = a constant; Kn = Knudsen Number [105,106]. As an initial simplification, k0 = kt = 0, and Sw = 1 (i.e., flow is saturated). The Hagen-Poiseuille model (e.g., [62,88,94,95,96,97,106]) defines the relationship between k, and ϕ as:
k = ϕ·r2/η, τσ,
ϕ = Inter-particle or inter-aggregate porosity; η = fluid viscosity, N·s−1·m−2; τ = pore tortuosity, e.g., (Lc/L)2 [92,94,107,108]; σ = complexity of pore geometry; r = pore throat radius, m; L = Apparent flow path length; Lc = Actual flow path length. Lc = L when the pores are represented by perfect cylinders. In most pores, Lc > L.
The principal versions and derivatives of Equation (C4) are provided elsewhere [109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124].

C3. Hydrological Issues Associated with the Placement of ZVI in an Aquifer

C3.1. ZVI Permeability

ZVI corrodes and re-crystallizes when placed in water (e.g., [125,126,127,128,129,130,131,132]). The corrosion products are hydrated (oxy) hydroxides (Fe(OH)x, FeOOH) [16,17]. The associated volume change results in the porosity, and permeability, kZVI, declining with time (e.g., [133,134,135,136,137,138,139,140,141]).
A constant head, continuous flow test through ZVI confirms (Figure C4) that the permeability of Fe0 reduces, (and the Eh and pH of the product water change), with time, t. The permeability is calculated as [36,62,75,92,94,104]:
k = Q/P
where k = permeability, m3·m−2·L−1·t−1·Pa−1; Q = flow rate m3·m−2·L−1·t−1; P = Measured potential or head, Pa; l = reference column length, m; t = unit time, seconds; m−2 = normalized cross sectional area of flow = 1 m2.
The re-crystallization of the ZVI increases the probability that open pores (within the ZVI) will be converted to dead-end pores over time.
The reduction in k with t (Figure C4) is associated with the bulk (>99%) of the through flow being confined to high permeability, low tortuosity, macropores [66]. This structural change results in the actual residence time of the through flow, td, (within the ZVI) reducing with time, t [66]. This observation may explain why NaCl removal is rarely reported in the product water from Fe0 permeable reactive barriers (PRB), e.g., [37,142]. Decreases in the inter-particle porosity are associated with the formation of intra-particle porosity [17].
Figure C4. ZVI permeability decline as a function of: (a) time; (b) pH of the water leaving the ZVI column; (c) Eh of the water leaving the ZVI column; (d) Eh vs. time. Further details of the ZVI, flow apparatus and testing procedures are provided in References [16,17,38]. Data Source: [17].
Figure C4. ZVI permeability decline as a function of: (a) time; (b) pH of the water leaving the ZVI column; (c) Eh of the water leaving the ZVI column; (d) Eh vs. time. Further details of the ZVI, flow apparatus and testing procedures are provided in References [16,17,38]. Data Source: [17].
Hydrology 04 00001 g031

C3.2. Haloclines: Interaction between the ZVI Loci and the Surrounding Aquifer

Haloclines are a natural feature of the marine environment (e.g., [143]) and develop within the ZVI desalination environment [16,17].
Three salinity zones may be associated with the ZVI pellets in a partially closed environment (Figure C4). They are:
(a)
zone of super saturation of NaCl within the ZVI pellets;
(b)
zone of elevated Na+ and Cl concentrations in the water immediately adjacent to the ZVI pellets. This can represent less than 2.5% of the water volume which is influenced by the ZVI [16,17];
(c)
zone of reduced Na+ and Cl concentrations in the bulk of the water surrounding the ZVI pellets [16,17];
Figure C5. Salinity distributions in a synthetic “Aquifer Treatment Zone” following 69.65 h operation. Type B Catalyst. Saline water enters the treatment zone at an average rate of 15.8% h−1 of the water volume in the treatment zone; Aquifer Treatment Zone Volume = 5.8 L; Water Volume Treated = 63.8 L; Temperature = 5–12 °C; Pw = 23 g Fe/L [16]; Pellet casing: MDPE (0.02 m × 0.5 m); Water Recycle Rate: 50% to 67% h−1 of the water volume contained in the Aquifer Treatment Zone; Average time spent in the treatment zone = 5.3 h; Average Na+ concentration in the feed water = 1.65 g·L−1; Average Cl concentration in the feed water = 2.56 g·L−1; Average Na+ concentration in the product water = 1.01 g·L−1; Average Cl concentration in the product water = 2.02 g·L−1; Average salinity reduction = 28.1%; Removed ions retained in the ZVI = 40.98 g Na+ ions (38.9%) + 34.46 g Cl ions (21.1%). Average pH of feed and product water = 5.99; Eh of feed water = 545 mV. Eh of product water = 587 mV. ZVI Type: E143 [16], see References [16,17] for batch operation details associated with this ZVI Type. Scale-up factor required to achieve a throughput to 20 m3/d = 833.3.
Figure C5. Salinity distributions in a synthetic “Aquifer Treatment Zone” following 69.65 h operation. Type B Catalyst. Saline water enters the treatment zone at an average rate of 15.8% h−1 of the water volume in the treatment zone; Aquifer Treatment Zone Volume = 5.8 L; Water Volume Treated = 63.8 L; Temperature = 5–12 °C; Pw = 23 g Fe/L [16]; Pellet casing: MDPE (0.02 m × 0.5 m); Water Recycle Rate: 50% to 67% h−1 of the water volume contained in the Aquifer Treatment Zone; Average time spent in the treatment zone = 5.3 h; Average Na+ concentration in the feed water = 1.65 g·L−1; Average Cl concentration in the feed water = 2.56 g·L−1; Average Na+ concentration in the product water = 1.01 g·L−1; Average Cl concentration in the product water = 2.02 g·L−1; Average salinity reduction = 28.1%; Removed ions retained in the ZVI = 40.98 g Na+ ions (38.9%) + 34.46 g Cl ions (21.1%). Average pH of feed and product water = 5.99; Eh of feed water = 545 mV. Eh of product water = 587 mV. ZVI Type: E143 [16], see References [16,17] for batch operation details associated with this ZVI Type. Scale-up factor required to achieve a throughput to 20 m3/d = 833.3.
Hydrology 04 00001 g032

C4. Modeling Diffusion Fluid Flows Which Affect the Water Salinity

C4.1. Diffusion Flow Analysis

In stagnant water, the change in aquifer salinity is a function of the mass flux (J) between the ZVI and the water, where [144]:
J = Jadv + Jdif + Jdis,
Jadv = advective mass flux; Jdif = mass flux due to molecular diffusion across the saturated pore space; Jdis = dispersive mass flux. From Ficks Second Law [144]:
δ(c2)/δt = −▽(Jadv + Jdif + Jdis),
δ(c2)/δt = −▽v(c2) + ▽[(DmI + Dd)▽(c)]
c2 = concentration of [A] where ([A]/[Ao]) increases with increasing distance from x; [A] = NaCl concentration at time t; t = unit time, e.g., seconds (s), hours (h), days (d), years (a); [Ao] = NaCl concentration at time t = 0; v = fluid velocity within the pore space; Dm= effective molecular diffusion coefficient; Dd = dispersion tensor; I = identity tensor.
A high kr is associated with a high value of {v} and J. A low kr and J occurs when {v} = 0, and J.

C4.2. Non-Fickian Diffusion Flow

The diffusion transport through heterogeneous porous material is generally non-Fickian [144]. Non-Fickian flow results from variations in porosity, thickness, net to gross, permeability, fluid flux, and potential. If we allow for a source, g1 = x/t, {where x is a point within a continuum and g1 is a ZVI Loci within a treatment (desalination) zone bounded by ra}, then the concentration (c2) at each point x,y,z within the desalination field is governed by the advection-dispersion equation [144], e.g.,:
δ(c2)/δt = −▽v(c2) + ▽Dd▽(c2) + g1,
A 1-Dimensional non-Fickian model (applies at each ZVI loci) where:
(i)
the fluid velocity (V) is constant, and
(ii)
the only direction of flow is towards the abstraction well, e.g., [144]:
δ(c2)/δt = −δ(c2x) + D·δα(c2)/δxα
D = constant dispersion coefficient. α = order of fractional differentiation; 1 < α ≤ 2. In a treatment field, (c), t, and V are known or can be predicted. Rearranging Equation (C18) results in:
δ(c2)/δt + V = (D·δα(c2)/δxα)/δ(c2x) = DB,
B = (δα(c2)/δxα)/δ(c2x),
If as a simplification, B is set as 1.0, then:
δ(c2)/δt = D − V,
i.e., increasing V reduces Ct = n and increases kr (Figure C6).
Figure C6. Rate constant vs. feed water salinity at fluid velocities of 0 m/h (Type A Catalyst. ST Trial Group Series) and 0.5 m/h (Type B Catalyst. E146/7 Trial series). The E146/7 Trial series (operated at 5–25 °C; Reactor Size = 240 L) established removal of 9.89 t Na+/t Fe + 23.65 t Cl/t Fe over 50 batch treatment cycles [16,17]. = ST Trial Series Data [16]; = E146/7 Trial Series Data [16,17].
Figure C6. Rate constant vs. feed water salinity at fluid velocities of 0 m/h (Type A Catalyst. ST Trial Group Series) and 0.5 m/h (Type B Catalyst. E146/7 Trial series). The E146/7 Trial series (operated at 5–25 °C; Reactor Size = 240 L) established removal of 9.89 t Na+/t Fe + 23.65 t Cl/t Fe over 50 batch treatment cycles [16,17]. = ST Trial Series Data [16]; = E146/7 Trial Series Data [16,17].
Hydrology 04 00001 g033

C5. Space Velocity

The salinity δ(c2)/δt at a point x,y is a function of SV [17,38,69,70,71,72,94]:
SV = Aquifer Flow Rate (Q)/ZVI Amount (t),
SV = Aquifer Treatment Zone water volume /ZVI Amount (t)/Ct,
Q (m3·t−1·t−1) = kΦ = SV,
Ln([Ao]/[A]) = krt = kr/SV= kr/kϕ,
Log([A]/[Ao]) = −krt/2.303 = −kr/SV/2.303 = −kr/kϕ/2.303,
kobserved = observed rate constant = kr; Ct = Irrigation water residence time in the Aquifer Treatment Zone; [Ao] = salinity entering the Aquifer Treatment Zone [Ao]; [A] = water salinity leaving the Aquifer Treatment Zone; [Ao] > [A].
The residence time associated with a specific space velocity is defined as [72].
WRT = 1/SV = 1/kΦ,

C6. Mineral Precipitation, Gas Occlusion, and Biofilms within the Aquifer

C6.1. Impact of Mineral Precipitation and Gas Discharge from the ZVI on Aquifer Permeability

The ZVI changes the pH and Eh of the aquifer water (Figure C7) [16,17,36,38,67,68]. This change results in the dissolution of cations present in the ZVI and the precipitation of hydroxides, oxyhydroxides and associated chlorides [68,145,146]. Their precipitation, either as colloids within the water body, or as precipitates adhering to pore walls, will alter the inter-particle porosity (ϕ), and permeability, of the aquifer (and ZVI) [16,145,146].
Figure C7. ST ZVI desalination pellets [16]; Type A Catalyst: Indicative Eh and pH of the irrigation water, maximum pH/minimum Eh in the aquifer as a function of Pw. (a) pH vs. Pw; (b) Eh vs. Pw; (c) Eh vs. pH. Data Source: Desalination Pellet Trials ST1a to ST5j [16]. Feed water pH = 5.8–6.6.
Figure C7. ST ZVI desalination pellets [16]; Type A Catalyst: Indicative Eh and pH of the irrigation water, maximum pH/minimum Eh in the aquifer as a function of Pw. (a) pH vs. Pw; (b) Eh vs. Pw; (c) Eh vs. pH. Data Source: Desalination Pellet Trials ST1a to ST5j [16]. Feed water pH = 5.8–6.6.
Hydrology 04 00001 g034

C6.2. Estimating Permeability Changes Due to Mineral Precipitation and Gas Occlusion

Extending Equation (C6), to address decreases in porosity due to mineral precipitation results in:
k(t = n) = k0·Spn2,
k(t = n) = k0·Swn1,
n1 = a satiation constant (Brusaert [92] model); Calculated as g + (j + d)/b where g + j + b + d are constants; and n1 is in the range 0.1 to 25; n2 = a reaction order constant, e.g., 1. Sp = water saturation after exclusion of porosity for precipitates, 0 < Sp < (1 − Swi); = estimated as ([ϕ(t = 0) − ϕ(t = n)]3/[(1− (ϕ(t = 0) + ϕ(t = n)))/(1 − (ϕ(t = 0)))]2), when n2 = 1 by [139]; Sw = mobile water saturation, 0 <Sw < (1 − Swi). Porosity changes due to precipitation and gas (or air) occlusion, k can be defined as:
k(t = n) = k0·Spn2·Swn1,
These changes interact on the iso-potentials through Equation (C4).

C6.3. ZVI Gas Generation

Gas production (H2, O2) associated with ZVI [66] can be abiotic [38,68,139,145,146,147,148,149,150,151] or biotic. These gases can occlude porosity and reduce permeability before they are either adsorbed by the matrix or dissolved in the water (e.g., [68]).

C6.4. Biofouling, Biofilms

All natural aquifer water contains bacteria and other micro-organisms. Permeability losses associated with biofouling occur at the upstream ZVI-water contact. ZVI creates a toxic habitat for some species, while for other species it is a preferred habitat [66,68,152,153]. Many of the iron bacteria species associated with mineralized ZVI biofilms, are aerobic species.
The standard abiotic, cathodic and anodic reactions in ZVI (during corrosion), which produce hydrogen include [32,66,68,139,145,146,147,148,149,150,151]:
4OH = 2H2O2 + 4e = H2(g) + 2O2H + 2e   (anodic)
2H2O + 2e = H2 (g) + 2OH   (cathodic)
The mineralized ZVI biofilms associated with iron bacteria form as a result of the following reactions, e.g., [66,154]:
Stage 1: m(Ar-OH)n + nFem+ = (mn)H+ + (mn)e + (mn)(Ar=O) + nFe0
Stage 2: Fe0 + nA = [Fen+(nA)] + ne
A = anions; [Fen+(nA)] = Mineralized Bacteria; Ar-OH = aromatic, or another chemical, with OH bonds; Ar=O = aromatic, or another chemical, containing an oxygen double bond. The micro-environment (inhabited by the iron bacteria) is converted from an anoxic environment (Equations (C31) and (C32)) to an aerobic environment by the following cyclic reactions, e.g., [154]:
Stage 3: Oxygen formation: Freshwater and Saline Environment
[Fe+A]ads + 2OH = [[Fe3+A] [(OH)2]]ads + 2e
[[Fe3+A] [(OH)2]]ads + 2e = [Fe+A]ads + O2 + 2H+ + 2e
[Fe3+ − 3A] ads + 2e = [Fe+A]ads + 2A   (cathodic)
[(OH)2]ads = O2 + 2H+ + 2e   (anodic)
Biofouling is a major problem, with some groundwater recharge schemes, where water is reinjected, or infiltrated, into an aquifer [155].

Appendix D: Classifying the Potential Aquifer Resource

The initial site screening will place the aquifer resource into one of three categories [156,157]:
  • Prospective resource;
  • Contingent resource;
  • Developed resource.
This categorization will then define what additional works are required in order to develop the resource. This study adopts the SPE 2001 resource classification scheme [156]. A review of the alternative resource schemes which have been approved by regulatory authorities globally is provided in Reference [158].

D1. Prospective Resource

A prospective resource falls into one of three categories [156]:
  • Play: Shallow aquifer which is believed to extend under the proposed Aquifer Treatment Zone ground surface but has not been demonstrated to be present by drilling or by the insertion of wells.
  • Lead: Shallow aquifer whose presence has been identified by one or more wells or infiltration devices, but requires more data acquisition before it can be deemed to be suitable for conversion to an Aquifer Treatment Zone.
  • Prospect: Shallow aquifer which has been identified and has been partially defined, but is insufficiently defined to represent a viable Aquifer Treatment Zone target.

D2. Contingent Resource

A contingent resource falls into one of three categories [156]:
  • Development Pending: Requires further data acquisition (aquifer, hydro/geochemical, regulatory, crop economics) and/or evaluation (financing, insurance, regulatory, crop analysis) in order to confirm commerciality;
  • Development on Hold: Awaiting changes in market conditions (e.g., crop prices) or removal of other constraints to development (e.g., political, regulatory, financing, insurance, customers, seed availability, etc.);
  • Development not Viable: No current plans exist to develop the defined resource or acquire additional data at this time due to aquifer constraints, legal, regulatory, chemical, commercial, insurance, crop, political, environmental issues, or one or more other constraints. A development site which is not viable at a specific time may become viable if one or more of the underlying constraints changes.

D3. Developed Resource

A developed resource falls into one of three categories [156]:
  • On Production: The Aquifer Treatment Zone has been installed and is in operation producing partially desalinated water;
  • Under Development: All necessary approvals and consents have been obtained, and construction of the Aquifer Treatment Zone is underway;
  • Planned for Development: The project satisfies all technical constraints, and there is a firm intent from the land owner to progress with the installation of an Aquifer Treatment Zone. Installation is being held up by one or more of detailed development planning, regulatory, financing and insurance approvals, and contracts finalization.

Appendix E. Calculating and modeling the Dimensions Associated with a Proposed Aquifer Treatment Zone

This appendix provides the primary equations and data which are required to: (i) define the size and aerial extent of a proposed Aquifer Treatment Zone; (ii) define the amount of ZVI required and the number of ZVI loci required; (iii) and the data required to define (for the Recycle Strategy (Figure 20)) the required abstraction pump capacity, and the required volume of water that requires to be reinjected or infiltrated.
The energy required for the abstraction pump is a function of pump type and manufacturer. A regression analysis of water pumps with a capacity of between 88 and 230,000 m3/day indicates that the power required, kWh = 0.0039 pump capacity, m3/d (R2 = 0.91; n = 24).
The recycle strategy requires that part of the abstracted water is recycled (Figure 20). The recycled water can either be pumped to an infiltration/injection loci, or it can flow (using gravity) to the infiltration loci. The latter strategy does not require a recycle pump.
The capacity of a gravity fed infiltration loci (for recycle water (Figure 20)) is controlled by its permeability, surface area, and the head of water in the infiltration loci (or between the loci and the distribution tank).
The data in Table E1 can be used to design the soakaway/infiltration device requirements for a proposed Aquifer Treatment Zone [62,63,64,65].

E1. Area of Influence of Each ZVI loci Placed within an Aquifer

The ZVI, placed within loci (located in the aquifer), will exert an influence on the chemistry of x m3 of the water (Ic) in the surrounding aquifer [16,66,67,68]:
Ic, m3 = 1000/PwZ1,
Pw = Unit weight of ZVI/Unit water volume treated, e.g., 20 g/L = 20 kg/m3; Z1 = weight of ZVI, e.g., kg.

E2. Sizing the Aquifer Treatment Zone

The required residence time (days) of the water in the proposed Aquifer Treatment Zone (prior to abstraction), will depend on the magnitude of the salinity reduction required (Figure B1, Figure B2 and Figure B3).
The volume of water required within the Aquifer Treatment Zone (Vaq, m3) is:
Vaq, m3 = IwR2 = Iwtd,
Iw = Irrigation water volume required, m3·day−1; R2 = Number of days the water is required to reside in the proposed Aquifer Treatment Zone (e.g., Figure B1, Figure B2 and Figure B3). The minimum volume of water (m3) located within the treatment zone (WTZ) is:
WTZ = AR WRT,
WRT = required water residence time (e.g., 200 days) in the reaction environment.
The required abstraction rate (AR) for partially desalinated water is n m3·day−1. For illustration purposes, Log10(kr) = −7.35 (determined from the regression equation in Figure B2); [Co] = 4 g·L−1; After 200 days, [A] = 1.84 g·L−1; After 400 days, [A] = 0.85 g·L−1; After 600 days, [A] = 0.395 g·L−1.

E3. Sizing Identifying the Ground Surface Area Which Overlies the Aquifer Treatment Zone

The minimum ground surface area (LT, m2) overlying the proposed Aquifer Treatment Zone can be calculated as:
LT, m2 = (Vaq/AT)/ϕ,
ϕ = Inter-particle porosity of the aquifer; AT = Net water column thickness in the aquifer, m.

E4. The Required Aquifer Gross Rock Volume

The required gross rock aquifer volume (AV), m3, enclosed within the Aquifer Treatment Zone becomes (e.g., [75,159]):
AV = WTZ /(ϕ SwiNG),
NG = Aquifer net to gross ratio (i.e., net aquifer volume/total aquifer volume). Swi = mobile water saturation within the aquifer which is recoverable by the abstraction well.

E5. Assessment of the Significance of Catalyst Selection on the Number of ZVI Loci Required

The area of influence of each ZVI loci is a function of V and Pw (Equations (C21), (E1) to (E5)). The range for Pw is:
  • Type A Catalyst Group: between 5 g/L and 300 g/L (Figure 17);
  • Type B Catalyst Group: between 0.072 and 28 g/L (Table E1).
Each ZVI loci is a perforated well (or infiltration device), which intersects the aquifer and contains ZVI pellets or cartridges. The ZVI can be removed, monitored and periodically replaced.
The ZVI is placed in a series of discrete loci to allow the water within the aquifer to flow between (and around) the loci (e.g., [160,161,162,163,164,165]). This avoids the flow by-pass situations which are associated with ZVI permeable reactive barriers (e.g., [166,167,168]).
Table E1. Trial results associated with the operation of semi-closed Aquifer Treatment Zone with Recycle (Figure 20). The recycle ratio, Rrc = y (m3/day)/m (m3/day). Rrt = average residence time (td) of the water in the reaction environment. Cartridge casing = MDPE. Each trial example used a different Type B catalysts (Table F1). A single catalyst charge was used in each trial group. Optimised operation assumes that td is matched to the onset of BS. This reduces Rrt (associated with each catalyst) into the range 3 to 12 h.
Table E1. Trial results associated with the operation of semi-closed Aquifer Treatment Zone with Recycle (Figure 20). The recycle ratio, Rrc = y (m3/day)/m (m3/day). Rrt = average residence time (td) of the water in the reaction environment. Cartridge casing = MDPE. Each trial example used a different Type B catalysts (Table F1). A single catalyst charge was used in each trial group. Optimised operation assumes that td is matched to the onset of BS. This reduces Rrt (associated with each catalyst) into the range 3 to 12 h.
Trial GroupReactor Size, LPw, g Fe0/LSub-Optimised RrcOptimised RrcA0, g/LAt = n, g/LRrt, hLog10 (kobserved)Log10 (kactual)Water Volume Treated, LScale-Up Required to Achieve 20 m3/dayScale-Up Required to Achieve 20 m3/day with Optimised Operation
RC15.823.283.602.484.213.035.80−4.80−6.1763.8833.3574.7
RC2240.00.5011.083.002.972.1422.15−5.39−5.0912000.076.920.8
RC38.028.0020.251.352.650.7945.00−5.13−6.5732.04687.5312.5
RC45.88.79302.287.454.252.81487.00−6.63−7.5734.869,971.31724.1
RC55.84.8011.753.1036.1725.7118.93−5.30−5.9892.82719.8718.4
RC65.80.8612.223.7215.668.6719.68−5.08−5.0175.42827.6862.1
RC75.84.669.623.108.847.0415.50−5.39−6.0629.02227.0718.4
RC85.80.862.841.866.465.304.58−4.92−4.8617.4658.0431.0
RC9800.00.073.210.452.101.2842.74−5.49−4.349600.044.56.3

Appendix F. Concepts and Models Associated with a Recycle Strategy

The trial results have established (Table E1) that the highest values of kr are associated with low values of td in a reaction environment incorporating recycle, where (y + n) >0 (Figure 20). In the trials (Table E1) (y/m) is between (i) 2.8 and 302 for sub-optimized operation; and (ii) 0.45 and 7.5 for optimized operation. This implies that optimized operation of a proposed Aquifer Treatment Zone producing 20 m3/day, will also abstract and recycle an additional 9 to 150 m3/day.
This Appendix highlights the principal issues which require to be addressed during a FEHED analysis for a proposed Aquifer Treatment Zone.

F1. Engineering Concepts Associated with Catalytic Recycle

The process flow diagram for the Recycle Strategy (Figure 20) defines the recycle rate ratio as (y + n)/(x + y) [169,170]. In the Static Strategy, (y + n) = 0. In the Recycle Strategy, (y + n) ≥ 0.

F1.1. Modelling the Recycle Water Flow Rate (V)

Increasing V (the recycle water flow rate within the aquifer) can increase kr (Figure C6). In the Static Strategy, V = 0. In the Recycle Strategy, V ≥ 0. Increases in V require the Aquifer Treatment Zone to be restructured to allow part of the abstracted water to be either:
(a)
reinjected into the aquifer within the proposed Aquifer Treatment Zone, or
(b)
recirculated within the proposed Aquifer Treatment Zone.
V is directly proportional to the recycle ratio [169,170], where:
(i)
The volume of water entering the Aquifer Treatment Zone is denoted as x + y, m3/d (Figure 20).
(ii)
The volume of abstracted water, which is radially reinjected into the Aquifer Treatment Zone at a distance rinj from the abstraction well is denoted as y m3/d (Figure 20).
(iii)
The volume of water, which is used for irrigation = m (Figure 20).
(iv)
The volume of water present in a confined (or closed) proposed Aquifer Treatment Zone during recycle is WTZ1.

F1.2. Closed Environment

In a closed environment, the amount of water entering the system exactly equals the amount of water leaving the system. The recycle ratio has the effect of increasing td within the reaction environment.
The iso-potentials will require any water (x + y) in the treatment zone to flow to the abstraction well [75,91,92,171].

F1.3. Recycle Loop

Introducing a recycle loop (Figure 20) with reinjection, (or infiltration), at the periphery of the Aquifer Treatment Zone, within a totally closed reactor system, increases the effective residence time (Ret) The effective residence time (Ret) of the water within the treatment zone is: (WTZ1/m)(Rrc + 1) [169,170,172,173]. The effective td in an environment incorporating recycle is Ret. It follows from (i) Equations (A1) to (A4), that increasing Rrc will increase kr; (ii) Equations (C14) to (C21), that the equilibrium establishment of (V − D) can result in BS increasing, as Rrc increases.

F2. Primary Engineering and Hydrological Concepts Associated with Catalytic Recycle

F2.1. Semi-Closed Aquifer Treatment Zone With Aggressive Recycle

A semi-closed Aquifer Treatment Zone is created when the water volume retained within the iso-potential contours remains constant with time. The hydrology of this type of system is complex, but relies on:
(i)
a strong negative potential being created by the abstraction well, and
(ii)
a dispersed, low positive potential being associated with reinjection, (or infiltration), of the recycle water on the periphery of the Aquifer Treatment Zone.
Volumetric balance is maintained within the bounding iso-potential contour:
(i)
by matching the net fluid outflow from the system for irrigation water, with
(ii)
a dispersed inflow around the treatment zone periphery by fresh saline water from the aquifer.
The permeability of the aquifer and the recycle ratio will control the number of reinjection sites and the shape of the iso-potential contours. Increasing the potential associated with a reinjection or infiltration loci beyond a critical level can, in some circumstances, result in leakage from the Aquifer Treatment Zone into the wider aquifer (Figure 20).
Trials (Figure C5, Table E1 and Table F1) of a “semi-closed Aquifer Treatment Zone with Recycle” established that increasing the recycle ratio increased the rate of desalination (where n = 0, and m ≥ 0, x = m).
The cost of installing and operating a proposed Aquifer Treatment Zone with recycle is reduced if:
(i)
the recycle ratio can be reduced;
(ii)
the number of ZVI loci and infiltration loci can be reduced;
(iii)
the volume of water contained within the aquifer treatment zone can be reduced.
Each of these parameters is controlled by the ZVI catalyst used. The ZVI catalyst controls (Table F1):
(i)
the ZVI requirement;
(ii)
the number of ZVI loci;
(iii)
the amount of desalination;
(iv)
the reinjection/reinfiltration requirement;
(v)
the size of the Aquifer Treatment Zone;
(vi)
the amount of irrigation water which can be abstracted from a specific Aquifer Treatment Zone Volume.
Reinjection is widely used in the petroleum industry to reconfigure an aquifer as an enhanced oil recovery zone (e.g., [174,175]). Reinjection creates additional aquifer issues relating to aerial, vertical and volumetric sweep efficiency, irregular water encroachment, water breakthrough, differential water mobility, integrity of the upper and lower bounding aquitards, etc. [174,175]. All of these problems may apply to an Aquifer Treatment Zone which incorporates recycle.
Table F1. Expected Performance of a semi-confined schematic Aquifer Treatment Zone containing 4000 m3 of water. The data values (and scaled volumes) are based on trial observations (Figure B2 and Figure C5, Table E1). The aquifer volumes and feed water salinities have been normalized to demonstrate the operational differences between the catalysts. The duration of all the sub-optimized trials was extended beyond the initial appearance of BS. Optimized irrigation water production rates assume that td approximates to the start of BS. Catalyst ST* is a Type A Catalyst. Catalysts C to K are Type B Catalysts. ZVI required is based on trial data and has not been optimized.
Table F1. Expected Performance of a semi-confined schematic Aquifer Treatment Zone containing 4000 m3 of water. The data values (and scaled volumes) are based on trial observations (Figure B2 and Figure C5, Table E1). The aquifer volumes and feed water salinities have been normalized to demonstrate the operational differences between the catalysts. The duration of all the sub-optimized trials was extended beyond the initial appearance of BS. Optimized irrigation water production rates assume that td approximates to the start of BS. Catalyst ST* is a Type A Catalyst. Catalysts C to K are Type B Catalysts. ZVI required is based on trial data and has not been optimized.
Trial GroupAquifer Volume, m3Irrigation Water (without Optimisation), m3·day−1Recycle Water, m3·day−1Optimised Irrigation Water, m3·day−1Feed Water Salinity, g·L−1Product Water Salinity, g·L−1Mean DesalinationMean Na+ RemovalZVI Required, tCatalyst
ST4000200204.001.8454.0%54.0%120ST
RC1400016,55259,58624,0004.002.8828.0% 93.12C
RC1a400018,11365,208 4.002.8828.1%38.9%93.12
RC24000433448,00016,0004.002.8827.9% 2.00D
RC34000213343,20032,0004.001.1970.2% 112.00E
RC4400019759,5868,0004.002.6433.9% 35.16F
RC54000507159,58619,2004.002.8428.9% 19.20G
RC64000487859,58616,0004.002.2144.6% 3.44H
RC74000619459,58619,2004.003.1920.4% 18.64I
RC8400020,96159,58632,0004.003.2818.0% 3.44J
RC9400022467,20016,0004.002.4339.3% 0.28K
RC9a400096007,200 4.002.4139.6%50.6%0.28

F3. Modeling of Catalytic Recycle

F3.1. Desalination Without Recycle: Type A Catalyst

The passage of saline (4 g/L) aquifer water through a 4000 m3 Aquifer Treatment Zone (using the ST* series catalyst, A2, B1–B3) which has:
(i)
a 60 m radius around the abstraction well,
(ii)
20 m3·day−1 abstraction (without recycle, i.e., y = 0 m3·day−1; m = x = 20 m3·day−1,
(iii)
td = 200 days,
is expected (Figure B2)to result in the salinity of the abstracted irrigation water, reducing from 4 g/L to about 1.84 g/L (Table F1). This is equivalent to a 54% reduction in salinity (R6), calculated as:
R6 = (100(1 − ((Input salinity(A0) − Output salinity(A))/Input salinity(A0)))),

F3.2. Desalination With Recycle: Type A Catalyst

In a closed system (n = 0 m3·day−1), the additional feed water, W4, (e.g., m= 20 m3·day−1) is mixed with the recycle water, W5, (e.g., y = 480 m3·day−1) prior to entering the Aquifer Treatment Zone (Figure F2 and Figure F3). The salinity of the water (C6) entering the Aquifer Treatment Zone can be calculated as:
C6 = (C0 (W4/(W4 + W5))) + (CR (W5/(W4 + W5))),
C0 = aquifer water salinity; CR = recycle water salinity. This model is expected to increase td from 200 days (using the ST catalyst) to Res = 5000 days. This strategy is expected (Figure B2) to reduce the salinity by >98%.

F4. Hydrological Issues Associated with the Reinjection Loci

F4.1. Gravity Fed Reinjection Sites

The low recycle water volumes received by each reinjection loci (Table E1 and Table F1) will allow gravity fed soakaways, or infiltration devices, to be used for reinjection. Infiltration devices can either be constructed to:
(a)
allow the infiltrating (reinjected) water to enter the groundwater (aquifer) by percolation from above (e.g., [62,63,64,65,176,177,178,179,180,181]). This situation occurs when the base of the infiltration device is always above the groundwater piezometric surface;
(b)
allow the infiltrating (reinjected) water to directly enter the groundwater (aquifer) by (e.g., [62,63,64,65]) when the base of the infiltration device is always below the groundwater piezometric surface [62,63,64,65]).

F5. Desalination Issues Associated with Recycle

F5.1. Impact of Increasing the Aquifer Abstraction Rate without Recycle

The effective reduction in salinity = R6. Increasing the abstraction rate from 20 m3·day−1 to 500 m3·day−1 (when y + n = 0) decreases the average length of time taken for water to travel from the periphery of the proposed Aquifer Treatment Zone to the abstraction well (t1) from 200 days to 8 days. R6 associated with 200 days = 54% (Table F1, Figure B2). R6 associated with 8 days = 3% (based on kr for Catalyst ST*, Figure B1, Figure B2 and Figure B3).
The resultant salinity of the product water at time intervals of t1 is:
CR = C6 (1 − R6),

F5.2. Impact of Different Recycle Strategies

The salinity of the irrigation water recovered from the proposed Aquifer Treatment Zone will gradually decline with increased residence time. Figure F1 illustrates four scenarios associated with the Type A Catalyst ST* (Table F1).
Recycle is to a peripheral location, where n = 0 (Figure 20). This approach decreases the expected amount of desalination by reducing the average effective residence time of the water in the treatment zone on each cycle.
A conventional surface based reactor overcomes this problem by effectively expanding the volume of the reactor by the recycle ratio [169,170,172,173], i.e., from 4000 m3 to 96,000 m3. This increase in effective volume increases the average residence time of the water in the reaction environment from 200 days to 4800 days. This change would allow the expected irrigation water salinity in Figure F1 to reduce to less than 0.1 g/L.
A proposed Aquifer Treatment Zone is unable to expand the size of the aquifer to accommodate re-injection. Therefore the impact of reinjection (at a peripheral location) combined with recycle is to increase the salinity of the irrigation water, relative to the position with no reinjection and recycle (Figure F1).
In this instance, the salinity of the irrigation water (y1) is less than the salinity of the aquifer (x1) and greater than the salinity of the irrigation water with no recycle (z1), i.e., x1 > y1 > z1 (Figure F1).
Figure F1. Change in salinity of the irrigation water abstracted from the modeled Aquifer Treatment Zone with time. Aquifer salinity = 4 g/L. Process Flow Diagram = Figure 20. Rrci = Reinjection ratio = y/(y + m). Water reinjected at the aquifers periphery. (i) = aquifer salinity = irrigation water salinity without the Aquifer Treatment Zone; (ii) irrigation water salinity if Rrci = 0; (iii) irrigation water salinity if Rrci = 0 for 200 days, followed by Rrci = 0.96 (96% of the abstracted water is reinjected); (iv) irrigation water salinity if Rrci = 0.96. Aquifer Thickness = 1 m; Rate Constant = Figure B2b. Recycle ratio = 480/20 = 24; Rrci = y/(y + m), m = 20 m3·day−1, x = 20 m3 day−1, y = 480 m3·day−1, n = 0 m3·day−1 (Figure 20).
Figure F1. Change in salinity of the irrigation water abstracted from the modeled Aquifer Treatment Zone with time. Aquifer salinity = 4 g/L. Process Flow Diagram = Figure 20. Rrci = Reinjection ratio = y/(y + m). Water reinjected at the aquifers periphery. (i) = aquifer salinity = irrigation water salinity without the Aquifer Treatment Zone; (ii) irrigation water salinity if Rrci = 0; (iii) irrigation water salinity if Rrci = 0 for 200 days, followed by Rrci = 0.96 (96% of the abstracted water is reinjected); (iv) irrigation water salinity if Rrci = 0.96. Aquifer Thickness = 1 m; Rate Constant = Figure B2b. Recycle ratio = 480/20 = 24; Rrci = y/(y + m), m = 20 m3·day−1, x = 20 m3 day−1, y = 480 m3·day−1, n = 0 m3·day−1 (Figure 20).
Hydrology 04 00001 g035

F5.3. Impact of Leakage of Reinjected Water to the Principal Aquifer

Injection of the recycle water at loci on the periphery of the treatment zone may result in leakage of recycle water to the wider aquifer (n ≥ 0 (Figure 20)). This will result in the inflow of fresh saline water from the aquifer into the Aquifer Treatment Zone (x, (Figure 20)) exceeding the amount of water abstracted for irrigation (m, (Figure 20)). In this instance:
(i)
the salinity of the irrigation water will increase (Figure F2) as the value of n increases;
(ii)
increasing n will result in the irrigation water salinity approaching the salinity of the saline aquifer (Figure F2).
The modeled examples in Figure F1 and Figure F2, indicate that there is no advantage in reinjecting abstracted water on the periphery of the Aquifer Treatment Zone, as this is likely to create the situation where x1 > y1 > z1, and in an extreme situation x1 = y1.
Figure F2. Change in salinity of the irrigation water abstracted from the modeled Aquifer Treatment Zone with time. Aquifer salinity = 4 g/L. Process Flow Diagram = Figure 20. Rrci = Reinjection ratio = 480/500. z8 (leakage) = (n/(y + n)). Assumptions: (i) n = 0 m3·day−1. Leakage = 0%; (ii) n = 120 m3·day−1; Leakage = 25%; (iii) n = 240 m3·day−1. Leakage = 50%; Aquifer Thickness = 1 m; Rate Constant = Figure B2b.
Figure F2. Change in salinity of the irrigation water abstracted from the modeled Aquifer Treatment Zone with time. Aquifer salinity = 4 g/L. Process Flow Diagram = Figure 20. Rrci = Reinjection ratio = 480/500. z8 (leakage) = (n/(y + n)). Assumptions: (i) n = 0 m3·day−1. Leakage = 0%; (ii) n = 120 m3·day−1; Leakage = 25%; (iii) n = 240 m3·day−1. Leakage = 50%; Aquifer Thickness = 1 m; Rate Constant = Figure B2b.
Hydrology 04 00001 g036

F6. Impact of Changing Reinjection Location on Desalination

F6.1. Placement of the Injection Loci within the Aquifer Treatment Zone

The position of the injection loci can be moved from the periphery, to a point within the proposed Aquifer Treatment Zone. This may result in the amount of leakage (n) reducing to 0 m3·day−1. The benefit of this approach can be demonstrated by considering an example where the proposed Aquifer Treatment Zone periphery is:
  • 50–60 m from the abstraction well (4524 m3 water volume), and
  • the injection loci are 30 m from the abstraction well (1131 m3 water volume),
The expected water salinity within the aquifer at the point 30 m from the abstraction well is 2.24 g·L−1. The impact of re-injection at this location is to gradually reduce the salinity of the irrigation water (Figure F3) into the range 1.3–1.4 g·L−1.
This reinjection strategy may create the situation where x1 > z1 > y1.
Figure F3. Change in salinity of the irrigation water abstracted from the modeled Aquifer Treatment Zone with time. Aquifer salinity = 4 g/L. Process Flow Diagram = Figure 20. Rrci = Reinjection ratio = 480/500. z8 = (n/(n + y)); n = 0 m3·day−1; Assumptions: (i) No Injection; n = 0 m3·day−1. Leakage = 0%; (ii) Injection at 50–60 m from the abstraction well; n = 0 m3 day−1; Leakage = 0%; (iii) Injection at 30 m from the abstraction well; n = 0 m3·day−1. Leakage = 0%; Aquifer Thickness = 1 m; Rate Constant = Figure B2b.
Figure F3. Change in salinity of the irrigation water abstracted from the modeled Aquifer Treatment Zone with time. Aquifer salinity = 4 g/L. Process Flow Diagram = Figure 20. Rrci = Reinjection ratio = 480/500. z8 = (n/(n + y)); n = 0 m3·day−1; Assumptions: (i) No Injection; n = 0 m3·day−1. Leakage = 0%; (ii) Injection at 50–60 m from the abstraction well; n = 0 m3 day−1; Leakage = 0%; (iii) Injection at 30 m from the abstraction well; n = 0 m3·day−1. Leakage = 0%; Aquifer Thickness = 1 m; Rate Constant = Figure B2b.
Hydrology 04 00001 g037

F7. Impact of Catalyst on Desalination

F7.1. Impact of Changing the Rate Constant

The modeled results in Figure F1, Figure F2 and Figure F3 assume that kr is constant. The salinity of the product irrigation water (c2) is a function of V:
Increasing aquifer fluid velocity, V (by increasing the recycle ratio) must
(1)
decrease c2 (and increase kr), unless
(2)
the increase in V is matched by a corresponding increase in the dispersion coefficient D (Equation (C21)).
In Figure F1, Figure F2 and Figure F3, the expression (DV) remains constant as V increases, or decreases. The trial data in Figure C6, Table E1 and Table F1 demonstrate that (DV) decreases (and kr increases) as V increases.
The implications of this observation are:
(i)
reinjection at the periphery of the Aquifer Treatment Zone without leakage, (with the desalination rate constants (for V = 0.5 m/h) will result in (DV) decreasing as V increases;
(ii)
suitable catalyst design (combined with reinjection) may allow the residence time required to reduce the water salinity to the required level to be substantially reduced, relative to a base case with no reinjection (Figure F4).
Figure F4. Change in salinity of the irrigation water abstracted from the modeled Aquifer Treatment Zone with time. Aquifer salinity = 4 g/L. Rrci = Reinjection ratio = 480/500. Assumptions: (i) No Injection; n = 0 m3·day−1. Leakage = 0%; (ii) Injection at 50–60 m from the abstraction well; n = 0 m3·day−1; Leakage = 0%; Aquifer thickness = 1 m.
Figure F4. Change in salinity of the irrigation water abstracted from the modeled Aquifer Treatment Zone with time. Aquifer salinity = 4 g/L. Rrci = Reinjection ratio = 480/500. Assumptions: (i) No Injection; n = 0 m3·day−1. Leakage = 0%; (ii) Injection at 50–60 m from the abstraction well; n = 0 m3·day−1; Leakage = 0%; Aquifer thickness = 1 m.
Hydrology 04 00001 g038

F7.2. Equilibrium or Base Salinity Level

If V is set at a constant value, then (for most Type B catalysts) the value for (DV) reaches an equilibrium value after 3 to 60 h [16,17]. The associated equilibrium salinity level, BS [17,36,37] is between 30% and 85% of the initial water salinity (A0, Ct = 0), i.e., between 0.3 Ct = 0 and 0.85 Ct = 0. Active management of the reinjection process may allow BS (Figure F5) to be controlled and actively manipulated.
Figure F5. Change in salinity of the irrigation water abstracted from the modeled Aquifer Treatment Zone with time. Aquifer salinity = 4 g/L. Rrci = Reinjection ratio = 480/500. Injection at 50–60 m from the abstraction well; n = 0 m3·day−1; Leakage = 0%; BS range taken from Table E1 and Table F1. Aquifer thickness = 1 m.
Figure F5. Change in salinity of the irrigation water abstracted from the modeled Aquifer Treatment Zone with time. Aquifer salinity = 4 g/L. Rrci = Reinjection ratio = 480/500. Injection at 50–60 m from the abstraction well; n = 0 m3·day−1; Leakage = 0%; BS range taken from Table E1 and Table F1. Aquifer thickness = 1 m.
Hydrology 04 00001 g039

Appendix G. Modeling Aquifer Heterogeneity, Static Strategy

G1. Impact of Geology on Desalination

Heterogeneous aquifers contain permeability variations, thickness variations, facies changes, and flow compartments. The net impact of increasing permeability variation within the aquifer is to:
(i)
increase differential flow from the aquifer to the abstraction well through the Aquifer Treatment Zone;
(ii)
decrease the effective residence time of the aquifer water as it passes through a fixed volume Aquifer Treatment Zone to the abstraction well. Figure B1 and Figure C3 demonstrate that any decrease in residence time, must for a specific kr, decrease the amount of desalination.

G2. Impact of Permeability Variation within the Aquifer

Permeability variations within the aquifer will result in differential flows within the Aquifer Treatment Zone. In a simple example, the abstraction well may intersect a higher permeability zone within the aquifer (e.g., Figure G1). In this instance, the abstraction well will receive water (J) from two sources: (i) a higher permeability zone; and (ii) a lower permeability zone.
J = [Qhigher permeability aquifer + Qlower permeability aquifer],
Figure G1. Schematic plan view of a schematic aquifer showing the 60 m radius of the treatment zone, abstraction well, and the distribution of the lower and higher permeability sand (aquifer).
Figure G1. Schematic plan view of a schematic aquifer showing the 60 m radius of the treatment zone, abstraction well, and the distribution of the lower and higher permeability sand (aquifer).
Hydrology 04 00001 g040
In the simple example illustrated in Figure G1, the higher permeability facies occupies 26% of the area (and rock volume). The permeability (or hydraulic conductivity) of the higher permeability facies, may be 10 times that of the lower permeability facies:
(i)
The initial design of the aquifer (based on the lower permeability aquifer) will assume that the average residence time is 200 days (based on a 20 m3·day−1 abstraction rate).
(ii)
The presence of the higher permeability sand will result in an average residence time within this higher permeability sand of 20 days. This will result in about 80% of the abstracted water being derived from this sand. The remaining 20% will be derived from the lower permeability sand.
This differential permeability will result in (Figure G2a):
(i)
water arriving at the abstraction well from both the lower permeability aquifer and the higher permeability sand;
(ii)
the water contribution to abstraction well from the higher permeability sand will be 16 m3·day−1; salinity (ch) = 3.7 g·L−1;
(iii)
the average equilibrium residence time of the water within the lower permeability sand increasing to 814 days (based on 3256 m3 water volume in the lower permeability sand and a 4 m3·day−1 abstraction rate); salinity (cl) = 0.17 g·L−1;

G3. Assessing the Relative Contribution of Different Water Sources

There are a number of different approaches which could be used to account for the relative contribution of the two different sandstone facies to the abstracted water (e.g., [182,183,184]). In this study, the assumption made (e.g., [182]) is:
J = (a/a + b)[khigher permeability aquifer Φ] + (b/a + b)[klower permeability aquifer Φ],
a = water volume in the higher permeability aquifer; b = water volume in the lower permeability aquifer. This simple assessment approach allows the expected salinity (c) of the abstracted irrigation water to be assessed, for illustration purpose, as:
c = (a/a + b)ch + (b/a + b)cl,
δ c/δt = (a/a + b)δcht + (b/a + b) δcl/δt,
The net impact of the higher permeability zone is to increase the salinity of the irrigation water, when compared with expected salinity associated with the homogenous design case (Figure G2). The net impact of increasing the ratio of [Qhigher permeability aquifer/Qlower permeability aquifer] is to increase the equilibrium salinity of the abstracted irrigation water (Figure G2a).
Figure G2. Expected (modeled) salinity of the irrigation water as a function of time. (a) The mixed permeability aquifer case illustrated in Figure G1; (b) Mixed permeability aquifer demonstrating the impact of changing the ratio of permeabilities between the higher permeability aquifer (kh) and the lower permeability aquifer (kl). Assumption: Average water residence time in the Aquifer Treatment Zone = 200 days when (kh) = (kl); 20 days when (kh) = 10(kl); 2 days when (kh) = 100(kl); Aquifer Thickness = 1 m; Rate Constant = Figure B2b. (y + n) = 0.
Figure G2. Expected (modeled) salinity of the irrigation water as a function of time. (a) The mixed permeability aquifer case illustrated in Figure G1; (b) Mixed permeability aquifer demonstrating the impact of changing the ratio of permeabilities between the higher permeability aquifer (kh) and the lower permeability aquifer (kl). Assumption: Average water residence time in the Aquifer Treatment Zone = 200 days when (kh) = (kl); 20 days when (kh) = 10(kl); 2 days when (kh) = 100(kl); Aquifer Thickness = 1 m; Rate Constant = Figure B2b. (y + n) = 0.
Hydrology 04 00001 g041

G4. Impact of Differential Permeability Variation (and Flow by-Pass) within the Aquifer

In most aquifers, permeability changes are associated with discrete facies within the aquifer [185]. These may form:
(i)
channels (e.g., Figure G1; Figure G2a), or;
(ii)
a series of higher permeability discrete lenses [185] within the treatment zone. This results in the effective by-pass of some of the surrounding lower permeability aquifer [185]. The net result of this change is to increase the equilibrium abstracted water salinity (when compared with a homogenous aquifer). The exact changes will be location specific, but the general trend expected is demonstrated in Figure G2b.

Abbreviations and Symbols

Across-sectional area of the flow pathway;
Aanion in chemical equations;
Acactual cost of supplying the partially desalinated irrigation water, $/m3;
Araromatic chemical with an OH group;
ARrequired abstraction rate, m3·day−1;
ATNet water column thickness in the aquifer, m;
AVrequired aquifer volume, m3;
Ainfarea of the base of the infiltration device, m2;
Asinfarea of the sides of the infiltration device, m2;
[A]NaCl, or Cl, or Na+ concentration at time t = n;
[Ao]NaCl concentration at time t = 0;
afractional yield change due to fertilizer application;
aa constant (Equation (26));
a1plants per hectare;
asnormalized surface area of ZVI particle, m2·g−1.
asiparticle size, e.g., nm, mm;
BSirreducible salinity;
bfractional yield change due to fungicide application;
b1seed pods, or grain heads, or fruit per plant;
Ct=0feed water salinity at time t = 0;
Ct=nproduct water salinity at time t = n;
C0aquifer water salinity;
CRrecycle water salinity.
CSwater salinity entering the Aquifer Treatment Zone;
cfractional yield change due to pesticide application;
c2concentration of [A] where ([A]/[Ao]) increases with increasing distance from x;
c1number of fruit or seeds per pod/head/plant;
c1s, c2slinear regression defined constants linking salinity to crop yield decrement;
Dconstant dispersion coefficient
Dddispersion tensor;
Dmeffective molecular diffusion coefficient;
Drfractional amount of desalination that has occurred;
dfractional yield change due to herbicide application;
d1seeds or fruit per unit weight, t;
dppore throat width (diameter), nm;
dwdensity of water;
efractional yield change due to bactericide application;
Eβ1Eo;
Eocharacteristic energy of adsorption of a standard adsorbate;
Epaverage potential energy for the molecules being adsorbed (KJ/mole);
E1characteristic energy for a specific fluid-solid system;
FFaraday Constant;
FdThe driving force for the discharge between the distribution tank and the invert point in the infiltration loci;
F1Incremental increase in financing costs associated with the increased yield, $;
ggravitational constant;
g1a ZVI Loci within a treatment (desalination) zone at a point x,y,z;
hhydraulic head, e.g., m3 = h1 (upstream water source elevation)−h2 (downstream water discharge elevation);
h1avertical distance between the invert point (intersect point) of the drainage pipe with the infiltration loci and the aquifer water table;
h1bvertical distance between the ground level and the aquifer water table;
h1cvertical distance between the water level in the distribution tank and the aquifer water table;
h1tdepth of water in the infiltration loci measured from the base of the loci, if the base is located aobve the aquifer water table, or the surface of the water table if the base of the loci is located below the base of the aquifer water table;
Iidentity tensor;
I1total length of flow path in the aquifer, m;
Icaquifer water volume (m3) where the chemistry of the aquifer water has been altered by the ZVI;
Ic1Installation cost, $;
IwIrrigation water volume required, m3·day−1;
Jmass flux;
Jthe infiltration rate or flux, m3·s−1, at time, t, at an infiltration loci;
Jadvadvective mass flux;
Jdifmass flux due to molecular diffusion across the saturated pore space;
Jdisdispersive mass flux;
Ka permeability constant;
kintrinsic permeability, m3·m−2·s−1·Pa−1; k = Q/P;
khinfintrinsic horizontal permeability of the sides of the infiltration device, m3·m−2·s−1·Pa−1;
kvinfintrinsic vertical permeability of the base of the infiltration device, m3 m−2·s−1·Pa−1;
kaquiferIntrinsic permeability of the aquifer;
k0intrinsic permeability at satiation;
kactualnormalized actual rate constant = kobserved/(as Pw)
kadDubinin-Radushkevich isotherm constant (M2·kJ−2);
kobservedobserved rate constant = kr;
kprate of particle growth;
krthe rate constant (unit (e.g., moles) per unit time (e.g., seconds) per unit ZVI);
k10Log10 kr;
Ka permeability constant = uI1/h; K = 1 Darcy when u = 1 cm·s−1;
KnKnudsen Number;
Lliquid phase;
LApparent flow path length;
LcActual flow path length. Lc = L when the pores are represented by perfect cylinders. In most pores, Lc > L;
LTrequired land take size, m2;
lreference column length, m;
Mcmaximum sustainable cost (Mc, $/m3) of desalination for irrigation water for a specific crop, or agricultural holding;
MnManning number for the recycle pipe;
ma factor which is related to pore size distribution;
NGAquifer net to gross ratio (i.e., net aquifer volume/total (aquifer + aquitard) volume within the aquifer unit);
ntnumber of electrons transferred;
n1a satiation constant; Calculated as g + (j + d)/b where g + j + b + d are constants; and n1 is in the range 0.1 to 25;
n2a reaction order constant, e.g., 1;
n3number of hours on line;
O.D.outer diameter;
O1Incremental increase in operating costs and infra-structure costs associated with the increased yield, $;
OcOperating Cost, $;
Pbulk pressure exerted by the water, constant driving force, Pa (or head, m); In this study P = Pr = Φ;
Paadsorption pressure;
Pithe amount of ZVI in the Aquifer Treatment Zone e.g., g·m−3;
Ppthe fluid pore pressure within the pore;
Prpressure difference, Pa, represented by h;
Psinitial pressure;
PAaquifer pressure;
P1Profit required by the agricultural holding on the increased yield, $;
Pwamount of ZVI in a batch flow reactor; e.g., g·L−1, or g·m−3;
Qnormalized flow rate, (volume) (unit time)−1 or flow rate, (volume) (unit time)−1 (unit weight, area, or volume of ZVI (or aquifer))−1 = kPr = k Φ.
Qaquiferaquifer flow rate prior to reconstruction as an Aquifer Treatment Zone;
qeion removed at equilibrium/unit ZVI (g/g);
qstheoretical isotherm saturation capacity, g/g;
qmass flux;
rpore throat radius, m;
raradius of circular desalination treatment field;
rerepresents the radial distance from the abstraction well bore where ΦInitial (aquifer) = Φabstraction well;
r1radial distance from the abstraction well;
rinjradial distance of an injection loci from the abstraction well;
Rthe gas constant;
R2coefficient of determination;
RefRemoval Efficiency (%);
RrcReinjection or Recycle Ratio = y/x;
RrciReinjection or Recycle Fraction of Abstracted Water = y/(x + y);
RrtAverage residence time of the water in the reaction environment;
R6Reduction in salinity (%);
Rwpwetted perimeter of the recycle pipe, m, ft;
R1Sale price of crop, $/t;
R2Number of days the water is required to reside in the Aquifer Treatment Zone in order for its salinity to reduce to the required level;
Sslope of recycle pipes;
Smminimum slope of recycle pipes required to meet local regulations;
SVspace velocity, m3 (water) t−1·W−1;
Spwater saturation after exclusion of porosity for precipitates, 0 < Sp < (1 − Swi); = [ϕ(t = 0) − ϕ(t = n)]3/[(1 − (ϕ(t = 0) + ϕ(t = n)))/(1 − (ϕ(t = 0)))]2 , when n2 = 1 [119];
Swmobile water saturation, 0 < Sw < (1 − Swi);
Swiirreducible water saturation, 0 < Swi < 1;
Ttemperature, K;
TFdispersed treatment field;
Thaquifer thickness, m;
tunit time, e.g., seconds (s), hours (h), days, years (a);
tdResidence time in the reaction environment = Flow rate, or abstraction rate (m3/unit time)/Volume of the reaction environment;
uflow velocity (cm·s−1);
Va constant fluid velocity at a point x, created by an abstraction well at time t;
Vothe initial inter-particle nano-meso-macro pore volume which is not in direct communication with the main water body (dead-end porosity);
Vopthe initial inter-particle nano-meso-macro pore volume, which is in direct communication with the main water body;
Vfb,minimum full bore velocity (or 75% full bore velocity) required in the recycle pipes to meet regulatory requirements, m·s−1, m3·m−2·s−1;
Vxvolume (NaCl) adsorbed at a relative pressure of (Pa/Ps) and temperature, T;
Vaqvolume of water required within the Aquifer Treatment Zone, m3;
vfluid velocity within the pore space;
WNormalized ZVI (or aquifer) unit, e.g., weight (t), volume (m3), cross sectional area (m2);
WRTrequired water residence time in the reaction environment;
WTZminimum amount of water (m3) located within the Aquifer Treatment Zone;
WTZ1amount of water (m3) located within the Aquifer Treatment Zone;
W1amount of partially desalinated water which is required to achieve the increased yield, m3;
W4additional feed water from the wider aquifer which is mixed with the recycle water in the Aquifer Treatment Zone. This water replaces water which is lost due to leakage of recycle water to the wider aquifer;
W5residual recycle water (after losses due to leakage into the wider aquifer);
x and x,y,za point within a continuum, e.g., a spatial location where x, y, z are referenced to specific locations and datum. For example, z refers to elevation; x refers to north-south position; y refers to east-west position;
xwater flow rate abstracted from the Aquifer Treatment Zone for irrigation; Saline water entering the Aquifer Treatment Zone from the aquifer;
x1salinity of the aquifer;
ywater flow rate abstracted from the Aquifer Treatment Zone for reinjection/recycle;
y1Salinity of the irrigation water abstracted from the Aquifer Treatment Zone with recycle—“Recycle Strategy”;
Ycrop yield, t/ha, when irrigated with non-saline (fresh) water;
YS1Crop yield using salinized water, t;
YS2Crop yield using partially desalinated water, t;
YSSalinity Adjusted Yield (t/ha);
Z1weight of ZVI.
z(h1 − h2); This application is used to define potential;
zwater flow rate associated with losses from the Aquifer Treatment Zone to the wider aquifer associated with reinjection/recycle; This results in the water volume entering the Aquifer Treatment Zone from the aquifer being calculated as (x + z);
zpthe negative pressure, Pa, created by the abstraction pump at the infiltration loci;
z1Salinity of the irrigation water abstracted from the Aquifer Treatment Zone without recycle—“Static Strategy”;
z8Reinjection loss ratio = z/y;
αsolid (crystallized) phase;
αorder of fractional differentiation; 1 < α ≤ 2;
βsolid (crystallized) phase;
β1the affinity coefficient of the adsorbate;
δdifferential;
εDubinin-Radushkevich isotherm constant, g.
Δ kaquiferpermeability gradient;
Δ Prpressure gradient;
Δ Qaquiferflow gradient;
Δgradient operator;
Δ ΦPotential gradient;
ϕInter-particle or inter-aggregate porosity = Vo + Vop;
ηfluid viscosity, N·s−1·m−2;
ΦFluid potential (Pa), e.g., Φ = PA/dw + gz;
ΦInitial (aquifer)Fluid potential (Pa) associated with the aquifer (at a location x m from the abstraction well) prior to the pump associated with the abstraction well being switched on;
Φabstraction wellFluid potential (Pa) associated with the aquifer (at a location x m from the abstraction well) after the pump associated with the abstraction well being switched on;
τpore tortuosity, e.g., (Lc/L)2;
σcomplexity of pore geometry.

Material and Other Abbreviations

ABSabacrylonitrile-butadiene-styrene;
Buna-NabCopolymer of butadiene and acrylonitrile;
BS2846abBritish Standard BS2846: Part 1, Routine analysis of quantitative data. Issued by the British Standards Institute.
CPVCabchlorinated polyvinyl chloride;
ECabelectrical conductivity, mS·cm−1; dS·m−1;
Ehabpotential calculated using the standard hydrogen electrode, mV; At pH 4 in quinhydrone solution Eh = ORP + 265.1 mV; at pH 7 in quinhydrone solution Eh = ORP + 87.4 mV;
EPDMabEthylene-propylene-diene monomer;
FEHEDabFront-end hydrological engineering and design;
FPMabFluoro-carbon elastomer;
GRPabGlass reinforced plastics;
HDPEabhigh density polyethylene;
MDPEabMedium density polyethylene;
MSFDabmultistage flash distillation;
ORPaboxidation reduction potential, mV;
Credabreductant concentration;
PA 11polyamide 11;
PBabpolybutylene;
PEabpolyethylene;
PEXabcross-linked polyethylene;
PIRabpolyisocyanurate;
PKabpolyketone;
PPabpolypropylene;
PRBabPermeable Reactive Barrier;
PTFEabpolytetrafluoroethylene;
PVCabpolyvinyl chloride;
PVDFabpoly vinylidene fluoride;
PVPabpolyvinylpyrrolidone;
ROabReverse osmosis;
SBRabstyrene-butadiene;
ZVIabZero valent iron, Fe0. The term is also used in this study to describe manufactured, structured, desalination catalysts, which have been manufactured using Fe0 as a starting ingredient, e.g., [16].

References

  1. Hoogeveen, J.; Faures, J.M.; Peiser, L.; Burke, J.; van de Giesen, N. GlobWat—A global water balance model to assess water usage in irrigated agriculture. Hydrol. Earth Syst. Sci. 2015, 19, 3829–3844. [Google Scholar] [CrossRef]
  2. Jaramillo, F.; Destouni, G. Local flow regulation and irrigation raise human water consumption and footprint. Science 2015, 350, 1248–1251. [Google Scholar] [CrossRef] [PubMed]
  3. Wada, Y.; Florke, M.; Hanasaki, N.; Eisner, S.; Fischer, G.; Tramberend, S.; Satoh, Y.; van Vlient, M.T.H.; Yillia, P.; Ringler, C.; et al. Modelling global water use for the 21st Century: The Water Futures and Solutions (WFaS) initiative and its approaches. Geosci. Model Dev. 2016, 9, 175–222. [Google Scholar] [CrossRef]
  4. Brauman, K.A.; Richter, B.D.; Postel, S.; Malsy, M.; Florke, M. Water depletion: An improved metric for incorporating seasonal and dry year water scarcity risk into water risk assessments. Elem. Sci. Anthropocene 2016, 83. [Google Scholar] [CrossRef]
  5. Jagermeyr, J.; Gerten, D.; Schapoff, S.; Heinke, J.; Lucht, W.; Rockstrom, J. Integrated crop management might sustainably halve the global food gap. Environ. Res. Lett. 2016, 11, 025002. [Google Scholar] [CrossRef]
  6. Jagermeyr, J.; Gerten, D.; Heinke, J.; Schaphoff, S.; Kummu, M.; Lucht, W. Water savings potentials of irrigation systems: Global simulation of process and linkages. Hydrol. Earth Syst. Sci. 2015, 19, 3073–3091. [Google Scholar] [CrossRef]
  7. Lekakis, E.H.; Antonopoulis, V.Z. Modeling the effects of different irrigation salinity on soil water movement, uptake and multicomponent solute transport. J. Hydrol. 2015, 530, 431–446. [Google Scholar] [CrossRef]
  8. Fishman, R.; Devineni, N.; Raman, S. Can improved agricultural water use efficiency save India’s groundwater? Environ. Res. Lett. 2015, 10, 084022. [Google Scholar] [CrossRef]
  9. OEDC. Tackling the Challenges of Agricultural Groundwater Use; Trade and Agriculture Directorate; OECD Publishing: Paris, France, 2016. [Google Scholar]
  10. Kulmatov, R.; Rasulov, A.; Kulmatova, D.; Rozilhodjaev, B.; Groll, M. The modern problems of sustainable use and management of irrigated lands on the example of the Bukhara region (Uzbekistan). J. Water Res. Protect. 2015, 7, 956–971. [Google Scholar] [CrossRef]
  11. Knapp, K.C.; Baerenklau, K.A. Ground water quantity and quality management: Agricultural production and aquifer salinization over long time scales. J. Agric. Resour. Econ. 2006, 31, 616–641. [Google Scholar]
  12. FAO. The State of the World’s Land and Water Resources for Food and Agriculture (SOLAW)—Managing Systems at Risk; Food and Agricultural Organization of the United Nations and Earth Scan: Abingdon, UK, 2011. [Google Scholar]
  13. Amarasinghe, U.A.; Smakhtin, V. Global Water Demand Projections: Past, Present and Future; Report 156; International Water Management Institute (IWMI): Colombo, Sri Lanka, 2014. [Google Scholar]
  14. Wada, Y.; Bierkens, M.F.P. Sustainability of global water use: Past reconstruction and future projections. Environ. Res. Lett. 2014, 9, 104003. [Google Scholar] [CrossRef]
  15. Panta, S.; Flowers, T.; Lane, P.; Doyle, R.; Haros, G.; Shaala, S. Halophyte agriculture: Success stories. Environ. Exp. Bot. 2014, 107, 71–83. [Google Scholar] [CrossRef]
  16. Antia, D.D.J. Desalination of water using ZVI, Fe0. Water 2015, 7, 3671–3831. [Google Scholar] [CrossRef]
  17. Antia, D.D.J. ZVI (Fe0) Desalination: Stability of product water. Resources 2016, 5, 15. [Google Scholar] [CrossRef]
  18. Potassium Nitrate Association. Effect of Salinity on Crop Yield Potential. PNA, Ternat, Belgium. Available online: http://www.kno3.org/en/product-features-a-benefits/potassium-nitrate-and-saline-conditions/effect-of-salinity-on-crop-yield-potential- (accessed on 15 June 2016).
  19. Kim, H.; Jeong, H.; Jeon, J.; Bae, S. Effects of irrigation with saline water on crop growth and yield in greenhouse cultivation. Water 2016, 8, 127. [Google Scholar] [CrossRef]
  20. Ayers, R.S.; Westcot, D.W. Water Quality for Agriculture; Irrigation and Drainage Paper No. 29, Rev. 1, Reprinted 1989, 1994; Food and Agriculture Organization of the United Nations: Rome, Italy, 1994. [Google Scholar]
  21. Hill, R.; Koenig, R.T. Water Salinity and Crop Yield; Utah Water Quality AG-425.3; Utah State University Co-Operative Extension: Logan, UT, USA, 1999. [Google Scholar]
  22. Grattan, S.R. Irrigation Water Salinity and Crop Production; Publication 8066. FWQP Reference Sheet 9.10; University of California, Agriculture and Natural Resources, Farm Water Quality Planning: Oakland, CA, USA, 2002. [Google Scholar]
  23. Munns, R.; Gilliham, M. Salinity tolerance of crops—What is the cost? New Phytol. 2015, 208, 668–673. [Google Scholar] [CrossRef] [PubMed]
  24. Yao, R.; Yang, J.; Wu, D.; Xie, W.; Gao, P.; Jin, W. Digital mapping of soil salinity and crop yield across a coastal agricultural landscape using repeated electromagnetic induction (EMI) surveys. PLoS ONE 2016, 11, e015377. [Google Scholar] [CrossRef] [PubMed]
  25. Satir, O.; Berberoglu, S. Crop yield prediction under soil salinity using satellite derived vegetation indices. Field Crops Res. 2016, 192, 134–143. [Google Scholar] [CrossRef]
  26. Qadir, M.; Quillerou, E.; Nangia, V.; Murtaza, G.; Singh, M.; Thomas, R.J.; Drechsel, P.; Noble, A.D. Economics of salt induced land degradation and restoration. Nat. Res. Forum 2014, 38, 282–295. [Google Scholar] [CrossRef]
  27. APA Citation: World Losing 2000 Hectares of Farm Soil Daily to Salt Damage. 2014. Available online: http://phys.org/news/2014-10-world-hectares-farm-soil-daily.html (accessed on 15 June 2016).
  28. Martinez-Alvarez, V.; Martin-Gorriz, B.; Soto-Garcia, M. Seawater desalination for crop irrigation—A review of current experiences and revealed key issues. Desalination 2016, 381, 58–70. [Google Scholar] [CrossRef]
  29. Barron, O.; Ali, R.; Hodgson, G.; Smith, D.; Qureshi, E.; McFarlane, D.; Campos, E.; Zarzo, D. Feasibility assessment of desalination application in Australian traditional agriculture. Desalination 2015, 364, 33–45. [Google Scholar] [CrossRef]
  30. Lee, C.; Herbek, J. Estimating Soybean Yield; AGR—188, Cooperative Extension Service; University of Kentucky—College of Agriculture: Frankfort, KY, USA, 2005. [Google Scholar]
  31. Heatherley, L.G. Soybean Irrigation Guide for Midsouthern US; Mississipi Soybean Promotion Board: Canton, MS, USA, 2014. [Google Scholar]
  32. Monsanto. 2010 Demonstration Report: Soybean Irrigation Recommendations; Monsanto Technology Development, The Learning Centre: Gothenburg, NE, USA, 2010. [Google Scholar]
  33. Fronczyk, J.; Pawluk, K.; Michniak, M. Application of permeable reactive barriers near roads for chloride ions removal. Ann. Wars. Univ. Life Sci. 2010, 42, 249–259. [Google Scholar] [CrossRef]
  34. Fronczyk, J.; Pawluk, K.; Garbulewski, K. Multilayer PRBs—Effective technology for protection of the groundwater environment in traffic infrastructures. Chem. Eng. Trans. 2012, 28, 67–72. [Google Scholar]
  35. Hwang, Y.; Kim, D.; Shin, H.-S. Inhibition of nitrate reduction by NaCl adsorption on a nano-zero valent iron surface during concentrate treatment for water reuse. Environ. Technol. 2015, 36, 1178–1187. [Google Scholar] [CrossRef] [PubMed]
  36. Antia, D.D.J. Desalination of groundwater and impoundments using nano-zero valent iron, Fe0. Meteor. Hydrol. Water Manag. 2015, 3, 21–38. [Google Scholar]
  37. Antia, D.D.J. Desalination of irrigation water, livestock water, and reject brine using n-ZVM (Fe0, Al0, Cu0). In Advanced Environmental Analysis: Applications of Nano-Materials; Advanced Detection Science Series No. 10; Hussain, C.M., Kharisov, B., Eds.; Royal Society Chemistry: Oxford, UK, 2017; pp. 237–272. [Google Scholar]
  38. Antia, D.D.J. Sustainable zero-valent metal (ZVM) water treatment associated with diffusion, infiltration, abstraction and recirculation. Sustainability 2010, 2, 2988–3073. [Google Scholar] [CrossRef]
  39. Gupta, D.; Kim, H.; Park, G.; Li, X.; Eom, H.-J.; Ro, C.-U. Hygroscopic properties of NaCl and NaNO3 mixture particles as reacted inorganic sea-salt aerosol surrogates. Atmos. Chem. Phys. 2015, 15, 3379–3393. [Google Scholar] [CrossRef]
  40. Wise, M.E.; Semenuik, T.A.; Bruintjes, R.; Martin, S.T.; Russell, L.M.; Busek, P.R. Hygroscopic behaviour of NaCl bearing natural aerosol particles using environmental transmission electron microscopy. J. Geophys. Res. Atmos. 2007, 112, D10224. [Google Scholar] [CrossRef]
  41. Bladh, K.W.; Bideaux, R.A.; Antony-Morton, E.; Nichols, B.G. Handbook of Mineralogy; Mineralogical Society of America: Chantilly, VA, USA, 2003. [Google Scholar]
  42. Gribble, C.D.; Hall, A.J. Optical Mineralogy, Principles and Practice; George Allen and Unwin: London, UK, 1985. [Google Scholar]
  43. Battey, M.H. Mineralogy for Students; Oliver & Boyd: Edinburgh, UK, 1972. [Google Scholar]
  44. Craig, J.R.; Vaughan, D.J. Ore Microscopy and Ore Petrography; John Wiley and Sons: New York, NY, USA, 1994. [Google Scholar]
  45. Short, M.N. Microscopic Determination of the Ore Minerals; US Geological Survey Bulletin 825; United States Department of the Interior: Washington, DC, USA, 1931.
  46. Short, M.N. Microscopic Determination of the Ore Minerals; US Geological Survey Bulletin 914; United States Department of the Interior: Washington, DC, USA, 1940.
  47. Cooke, S.R.B. Microscopic Structure and Concentratability of the Important Iron Ores of the United States; US Bureau of Mines Bull. 391; United States Department of the Interior: Washington, DC, USA, 1936.
  48. Stewart, F.H. Data of Geochemistry. Chapter Y. Marine Evaporites; Geological Survey Professional Paper 440-Y; US Government Printing Office: Washington, DC, USA, 1963.
  49. Kuster, Y.; Leiss, B.; Schramm, M. Structural characteristics of the halite fabric type ‘kristallbrocken’ from the Zechstein Basin with regard to its development. Int. J. Earth Sci. 2010, 99, 505–526. [Google Scholar] [CrossRef]
  50. Pourbaix, M. Atlas of Electrochemical Equilibria in Aqueous Solutions, 1st ed.; NACE International, Cebelcor: Houston, TX, USA, 1974. [Google Scholar]
  51. Hannington, M.D. The formation of atacamite during weathering of sulphides on the sea floor. Can. Min. 1993, 31, 945–956. [Google Scholar]
  52. Sharkey, J.B.; Lewin, S.Z. Conditions governing the formation of atacamite and paratacamite. Am. Min. 1971, 56, 179–192. [Google Scholar]
  53. Getahun, A.; Reed, M.H.; Symonds, R. Mount St Augustine volcano fumarole wall rock alteration: Mineralogy, zoning, composition and numerical models of its formation process. J. Volcanol. Geotherm. Res. 1996, 71, 73–107. [Google Scholar] [CrossRef]
  54. Scott, D.A. Metallography and Microstructure of ANCIENt and Historic Metals; The Getty Conservation Institute: Marina del Rey, CA, USA, 1991. [Google Scholar]
  55. Rodriguez, L.A.C.; Gonzalez, E.N.A. Nanoadsorbents: Nanoadsorbents for water protection. In CRC Concise Encyclopedia of Nano-Technology; Kharisov, B.I., Kharissova, O.V., Ortiz-Mendez, U., Eds.; CRC Press: Boca Raton, FL, USA, 2016; pp. 573–589. [Google Scholar]
  56. Yusan, S.D.; Erenturk, S.A. Sorption behaviours of uranium (VI) ions on α-FeOOH. Desalination 2011, 269, 58–66. [Google Scholar] [CrossRef]
  57. Wang, Z.-M.; Shindo, N.; Otake, Y.; Kaneko, K. Enhancement of NO adsorption on pitch-based activated carbon fibers by dispersion of Cu-doped α-FeOOH fine particles. Carbon 1994, 32, 515–521. [Google Scholar] [CrossRef]
  58. Yusan, S.D.; Erenturk, S.A. Adsorption equilibrium and kinetics of U(IV) on beta type of akaganeite. Desalination 2010, 263, 233–239. [Google Scholar] [CrossRef]
  59. Lan, B.; Wang, Y.; Wang, X.; Zhou, X.; Kang, Y.; Li, L. Aqueous arsenic (As) and antinomy (Sb) removal by potassium ferrate. Chem. Eng. J. 2016, 292, 389–397. [Google Scholar] [CrossRef]
  60. Kapoor, A.; Ritter, J.A.; Yang, R.T. On the Dubinin-Radushkevich equation for adsorption in microporous solids in the Henry’s law region. Langmuir 1989, 5, 1118–1121. [Google Scholar] [CrossRef]
  61. Nguyen, C.; Do, D.D. The Dubinin-Radushkevich equation and the underlying microscopic adsorption description. Carbon 2001, 39, 1327–1336. [Google Scholar] [CrossRef]
  62. Antia, D.D.J. Formation and control of self-sealing high permeability groundwater mounds in impermeable sediment: Implications for SUDS and sustainable pressure mound management. Sustainability 2009, 1, 855–923. [Google Scholar] [CrossRef]
  63. Antia, D.D.J. Interacting Infiltration Devices (Field Analysis, Experimental Observation and Numerical Modeling): Prediction of seepage (overland flow) locations, mechanisms and volumes—Implications for SUDS, groundwater raising projects and carbon sequestration projects. In Hydraulic Engineering: Structural Applications, Numerical Modeling and Environmental Impacts, 1st ed.; Hirsch, G., Kappel, B., Eds.; Nova Science Publishers: Hauppauge, NY, USA, 2010; pp. 85–156. [Google Scholar]
  64. Antia, D.D.J. Interpretation of overland flow associated with infiltration devices placed in boulder clay and construction fill. In Overland Flow and Surface Runoff; Wong, T.S.W., Ed.; Nova Science Publishers: Hauppauge, NY, USA, 2013; pp. 211–286. [Google Scholar]
  65. Antia, D.D.J. Prediction of overland flow and seepage zones associated with the interaction of multiple Infiltration Devices (Cascading Infiltration Devices). Hydrol. Process. 2008, 21, 2595–2614. [Google Scholar] [CrossRef]
  66. Antia, D.D.J. Water remediation—Water remediation using nano-zero-valent metals (n-ZVM). In CRC Concise Encyclopedia of Nanotechnology, 1st ed.; Kharisov, B.I., Kharissova, O.V., Ortiz-Mendez, U., Eds.; CRC Press, Taylor & Francis Group: Boca Raton, FL, USA, 2016; pp. 1103–1120. [Google Scholar]
  67. Antia, D.D.J. Modification of aquifer pore water by static diffusion using nano-zero-valent metals. Water 2011, 3, 79–112. [Google Scholar] [CrossRef]
  68. Antia, D.D.J. Groundwater water remediation by static diffusion using nano-zero valent metals [ZVM] (Fe0, Cu0, Al0), n-FeHn+, n-Fe(OH)x, n-FeOOH, n-Fe-[OxHy](n+/−). In Nanomaterials for Environmental Protection, 1st ed.; Kharisov, B.I., Kharissova, O.V., Dias, H.V.R., Eds.; Wiley Inc.: Hoboken, NJ, USA, 2014; pp. 3–25. [Google Scholar]
  69. Kodama, S.; Fukui, K.; Mazume, A. Relation of space velocity and space time yield. Ind. Eng. Chem. 1953, 45, 1644–1648. [Google Scholar] [CrossRef]
  70. Winterbottom, J.M.; King, M.B. Reactor Design for Chemical Engineers; Stanley Thornes: Cheltenham, UK, 1999. [Google Scholar]
  71. Smith, R. Chemical Process Design and Integration; John Wiley: Chichester, UK, 2005. [Google Scholar]
  72. Kent, J.A. Kent and Riegel’s Handbook of Industrial Chemistry and Biotechnology, 11th ed.; Springer Science: New York, NY, USA, 2007. [Google Scholar]
  73. Ebbing, D.D.; Gammon, S.D. General Chemistry, 6th ed.; Houghton Mifflin Co.: Boston, MA, USA, 1999. [Google Scholar]
  74. Wilkin, R.T.; McNeil, M.S. Laboratory evaluation of zero-valent iron to treat water impacted by acid mine drainage. Chemosphere 2003, 53, 715–725. [Google Scholar] [CrossRef]
  75. Dake, L.P. Fundamentals of Reservoir Engineering; Elsevier: Amsterdam, The Netherlands, 1998. [Google Scholar]
  76. Euzen, J.-P.; Trambouze, P.; Wauquier, J.-P. Scale up Methodology for Chemical Processes; Editions Technip: Paris, France, 1993. [Google Scholar]
  77. Zlokarnik, M. Scale-up in Chemical Engineering; Wiley: Weinheim, Germany, 2006. [Google Scholar]
  78. Li, J.; Kwauk, M. Exploring complex systems in chemical engineering-the multi-scale methodology. Chem. Eng. Sci. 2003, 59, 521–535. [Google Scholar] [CrossRef]
  79. Garurevich, I.A. Foundations of chemical kinetic modeling, reaction models and reactor scale up. J. Chem. Eng. Process. Technol. 2016, 7, 2. [Google Scholar]
  80. Piccinno, F.; Hischier, R.; Seeger, S.; Som, C. From laboratory to industrial scale: A scale-up framework for chemical processes in life cycle assessment studies. J. Clean. Prod. 2016, 135, 1085–1097. [Google Scholar] [CrossRef]
  81. Henderson, A.D.; Demond, A.H. Long-term performance of zero valent iron permeable reactive barriers: A critical review. Environ. Eng. Sci. 2007, 24, 401–423. [Google Scholar] [CrossRef]
  82. Araujo, R.; Castro, A.C.M.; Bapitista, J.S.; Fluza, A. Nanosized iron based permeable reactive barriers for nitrate removal—Systematic review. Phys. Chem. Earth 2016, 94, 29–34. [Google Scholar] [CrossRef]
  83. Ma, L.; Zhang, W.-X. Enhanced biological treatment of industrial wastewater with bimetallic zero-valent iron. Environ. Sci. Technol. 2008, 42, 5384–5389. [Google Scholar] [CrossRef] [PubMed]
  84. Fan, J.-H.; Ma, L.-M. The pre-treatment by the Fe-Cu process for enhancing biological degradability of the mixed waste water. J. Hazard. Mater. 2009, 164, 1392–1397. [Google Scholar] [CrossRef] [PubMed]
  85. Branan, C. Rules of Thumb for Chemical Engineers; Elsevier: Amsterdam, The Netherlands, 2005. [Google Scholar]
  86. Tsagkari, M.; Couturier, J.-L.; Kokossis, A.; Dubois, J.-L. Early-stage capital cost estimation of biorefinery processes: A comparative study of heuristic techniques. ChemSusChem 2016, 9, 2284–2297. [Google Scholar] [CrossRef] [PubMed]
  87. Wu, X.; Hu, Y.; Wu, L.; Li, H. Model and design of cogeneration system for different demands of desalination water, heat, and power production. Chin. J. Chem. Eng. 2014, 22, 330–338. [Google Scholar] [CrossRef]
  88. Antia, D.D.J. Oil polymerisation and fluid expulsion from low temperature, low maturity, over pressured sediments. J. Petrol. Geol. 2008, 31, 263–282. [Google Scholar] [CrossRef]
  89. Antia, D.D.J. Low temperature oil polymerisation and hydrocarbon expulsion from continental shelf and continental slope sediments. Indian J. Petrol. Geol. 2009, 16, 1–30. [Google Scholar]
  90. Clark, I. Groundwater Geochemistry and Isotopes; CRC Press: Boca Raton, FL, USA, 2015. [Google Scholar]
  91. Dahlberg, E.C. Applied Hydrodynamics in Petroleum Exploration; Springer: New York, NY, USA, 1982. [Google Scholar]
  92. Brusaert, W. Hydrology an Introduction; Cambridge University Press: Cambridge, UK, 2005. [Google Scholar]
  93. Neuman, S.P. Theoretical derivation of Darcy’s Law. Acta Mech. 1977, 25, 153–170. [Google Scholar] [CrossRef]
  94. Mulder, M. Basic Principles of Membrane Technology; Kluwer Academic Publishers: Dordrecht, The Netherlands, 1996. [Google Scholar]
  95. Ye, G.; van Breugel, P.L.K. Modeling of water permeability in cementatious materials. Mater. Struct. 2006, 39, 877–885. [Google Scholar] [CrossRef]
  96. Pereira, J.-M.; Arson, C. Retention and permeability properties of damaged porous rocks. Comp. Geotech. 2013, 48, 272–282. [Google Scholar] [CrossRef]
  97. Berg, C.F. Permeability description by characteristic length, tortuosity, constriction and porosity. Trans. Porous Media 2014, 103, 381–400. [Google Scholar] [CrossRef]
  98. Gudjonsdottir, M.; Palsson, H.; Eliasson, J.; Saevarsdottir, G. Calculation of relative permeabilities of water and steam from laboratory measurements. Geothermics 2015, 53, 396–405. [Google Scholar] [CrossRef]
  99. Richards, L.A. The usefulness of capillary potential to soil moisture and plant investigations. J. Agric. Res. 1928, 37, 719–742. [Google Scholar]
  100. Brusaert, W. A concise parameterization of the hydraulic conductivity of unsaturated soils. Adv. Water Resour. 2000, 23, 811–815. [Google Scholar] [CrossRef]
  101. Douglas, J.F.; Gasiorek, J.M.; Swaffield, J.A. Fluid Mechanics, 4th ed.; Pearson: Harlow, UK, 2001. [Google Scholar]
  102. Massey, B.; Ward-Smith, J. Mechanics of Fluids; Stanley Thornes: Cheltenham, UK, 1998. [Google Scholar]
  103. Amiri, H.A.A.; Hamouda, A.A. Pore-scale modeling of non-isothermal two phase flow in 2D porous media: Influences of viscosity, capillarity, wettability and heterogeneity. Int. J. Multiph. Flow 2014, 61, 14–27. [Google Scholar] [CrossRef]
  104. Guo, C.; Xu, J.; Wu, K.; Wei, M.; Liu, S. Study on gas flow through nano pores of shale gas reservoirs. Fuel 2015, 143, 107–117. [Google Scholar] [CrossRef]
  105. Brodkey, R.S.; Hershey, H.C. Transport Phenomena. Vol 1. Part 1- Basic Concepts in Transport Phenomena; Brodkey Publishing: Columbus, OH, USA, 1988. [Google Scholar]
  106. Childs, E.C.; Collis-George, N. The permeability of porous media. Proc. R. Soc. Lond. A 1950, 201, 392–405. [Google Scholar] [CrossRef]
  107. Carmen, P.C. Fluid flow through granular beds. Trans Inst. Chem. Eng. Lond. 1937, 15, 150–166. [Google Scholar] [CrossRef]
  108. Carmen, P.C. Flow of Gases through Porous Media; Academic Press: New York, NY, USA, 1956. [Google Scholar]
  109. Brooks, R.H.; Corey, A.T. Hydraulic Properties of Porous Media; Hydrology Paper 3; Colorado State University: Fort Collins, CO, USA, 1964. [Google Scholar]
  110. Mualem, Y. A new model for predicting the hydraulic conductivity of unsaturated porous media. Water Resour. Res. 1976, 12, 513–521. [Google Scholar] [CrossRef]
  111. Mualem, Y. Hydraulic conductivity of unsaturated porous media: Generalized macroscopic approach. Water Resour. Res. 1978, 14, 325–334. [Google Scholar] [CrossRef]
  112. Burdine, N.T. Relative permeability calculations from pore-size distribution data. Pet. Trans. Am. Inst. Min. Metal. Pet. Eng. 1953, 198, 71–78. [Google Scholar] [CrossRef]
  113. Alexander, L.R.; Skaggs, R.W. Predicting unsaturated hydraulic conductivity from the soil water retention curve. Trans. Am. Soc. Agric. Eng. 1986, 29, 176–184. [Google Scholar] [CrossRef]
  114. Van Genuchten, M.T.V. A closed form equation for predicting the hydraulic conductivity of unsaturated soils. Soil. Sci. Soc. Am. J. 1980, 44, 892–898. [Google Scholar] [CrossRef]
  115. Leong, E.C.; Rahardjo, H. Permeability function for unsaturated soils. J. Geotech. Geoenviron. Eng. 1997, 123, 1118–1126. [Google Scholar] [CrossRef]
  116. Fredlund, D.G.; Rahardjo, H.; Fredlund, M.D. Unsaturated Soil Mechanics in Engineering Practice; Wiley: New York, NY, USA, 2012. [Google Scholar]
  117. Yang, Z.; Mohanty, B.P. Effective parameterizations of three nonwetting phase relative permeability models. Water Resour. Res. 2015, 51, 6520–6531. [Google Scholar] [CrossRef]
  118. Iden, S.C.; Peters, A.; Dumer, W. Improving prediction of hydraulic conductivity by constraining capillary bundle models to a maximum pore size. Adv. Water Res. 2015, 85, 86–92. [Google Scholar] [CrossRef]
  119. Malama, B.; Kuhlman, K.L. Unsaturated hydraulic conductivity models based on truncated lognormal pore-size distributions. Groundwater 2015, 53, 498–502. [Google Scholar] [CrossRef] [PubMed]
  120. White, J.; Zardava, K.; Nayagum, D.; Powrie, W. Functional relationships for the estimation of van Genuchten parameter values in landfill processes models. Waste Manag. 2015, 38, 222–231. [Google Scholar] [CrossRef] [PubMed]
  121. Oh, S.; Kim, Y.K.; Kim, J.-W. A modified van Genuchten-Mualem Model of hydraulic conductivity in Korean Residual Soils. Water 2015, 7, 5487–5502. [Google Scholar] [CrossRef]
  122. Seki, K.; Ackerer, P.; Lehmann, F. Sequential estimation of hydraulic parameters in layered soil using limited parameters. Geoderma 2015, 247–248, 117–128. [Google Scholar] [CrossRef]
  123. Zhai, Q.; Rahardjo, H. Estimation of permeability function from the soil-water characteristic curve. Eng. Geol. 2015, 199, 148–155. [Google Scholar] [CrossRef]
  124. Bevington, J.; Piragnolo, D.; Teatini, P.; Vellidis, G.; Morari, F. On the spatial variability of soil hydraulic properties in a Holocene coastal farmland. Geoderma 2016, 262, 294–305. [Google Scholar] [CrossRef]
  125. Gilberg, M.R.; Seeley, N.J. The identity of compounds containing chloride ions in marine iron corrosion products: A critical review. Stud. Conserv. 1981, 26, 50–56. [Google Scholar] [CrossRef]
  126. Sarin, P.; Snoeyink, V.; Lytie, D.; Kriven, W. Iron corrosion scales: Model for scale growth, iron release and coloured water formation. J. Environ. Eng. 2004, 130, 364–373. [Google Scholar] [CrossRef]
  127. Foley, R.T. Role of chloride ion in iron corrosion. Corrosion 1970, 26, 58–70. [Google Scholar] [CrossRef]
  128. Larroumet, D.; Greenfield, D. Raman spectroscopic studies of the corrosion of model iron electrodes in sodium chloride solution. J. Raman Spectrosc. 2007, 38, 1577–1585. [Google Scholar] [CrossRef]
  129. Wang, Y.; Cheng, G.; Wu, W.; Qiao, Q.; Li, Y.; Li, X. Effect of pH and chloride on the micro-mechanism of pitting corrosion for high strength pipeline steel in aerated NaCl solutions. Appl. Surf. Sci. 2015, 349, 746–756. [Google Scholar] [CrossRef]
  130. Nesic, S. Key issues related to the internal corrosion of oil and gas pipelines—A review. Corros. Sci. 2007, 49, 4308–4338. [Google Scholar] [CrossRef]
  131. Perez, F.R.; Barrero, C.A.; Walker, A.R.H.; Garcia, K.E.; Nomura, K. Effects of chloride concentration, immersion time and steel composition on the spinel phase formation. Mater. Chem. Phys. 2009, 117, 214–223. [Google Scholar] [CrossRef]
  132. Ray, R.I.; Lee, J.S.; Little, B.J. Iron-Oxidizing Bacteria: A Review of Corrosion Mechanisms in Fresh Water and Marine Environment; Paper 10218, NACE Corrosion 2010 Conference and Expo; Naval Research Laboratory, Office of Naval Research: Arlington, VA, USA, 2010. [Google Scholar]
  133. Lide, D.R. CRC Handbook of Chemistry and Physics 89th Edition 2008–2009; CRC Press: Bocca Raton, FL, USA, 2008. [Google Scholar]
  134. Noubactep, C. Aqueous contaminant removal by metallic iron: Is the paradigm shifting? Water SA 2011, 37, 419–426. [Google Scholar] [CrossRef]
  135. Noubactep, C. Designing metallic iron packed-beds for water treatment: A critical review. Clean-Soil Air Water 2016, 44, 411–421. [Google Scholar] [CrossRef]
  136. Noubactep, C. Predicting the hydraulic conductivity of metallic iron filters: Modeling gone astray. Water 2016, 8, 162. [Google Scholar] [CrossRef]
  137. Luo, P.; Bailey, E.H.; Mooney, S.J. Quantification of changes in zero valent iron morphology using X-ray computed tomography. J. Environ. Sci. 2013, 25, 2344–2351. [Google Scholar] [CrossRef]
  138. Domga, R.; Togue-Kamga, F.; Noubactep, C.; Tchatchueng, J.-B. Discussing porosity loss of Fe0 packed water filters at ground level. Chem. Eng. J. 2015, 263, 127–134. [Google Scholar] [CrossRef]
  139. Li, L.; Benson, C.H.; Lawson, E.M. Modelling porosity reductions caused by mineral fouling in continuous wall permeable reactive barriers. J. Contam. Hydrol. 2006, 83, 89–121. [Google Scholar] [CrossRef] [PubMed]
  140. Btatkeu-K, B.D.; Olvera-Vargas, H.; Tchatchueng, J.B.; Noubactep, C.; Care, S. Determining the optimum Fe0 ratio for sustainable granular Fe0/sand water filters. Chem. Eng. J. 2014, 247, 265–274. [Google Scholar] [CrossRef]
  141. Guan, X.; Sun, Y.; Qin, H.; Li, J.; Lo, I.M.C.; He, D.; Dong, H. The limitations of applying zero valent iron technology in contaminants sequestration and the corresponding countermeasures: The development in zero-valent iron technology in the last two decades (1994–2014). Water Res. 2015, 75, 224–248. [Google Scholar] [CrossRef] [PubMed]
  142. Wilkin, R.T.; Puls, R.W.; Sewell, G.W. Long-term performance of permeable reactive barriers using zero valent iron: Geochemical and microbiological effects. Groundwater 2003, 41, 493–503. [Google Scholar] [CrossRef]
  143. Slip, W.P.; England, M.H. The control of polar haloclines by along-Isopycnal diffusion in climate models. J. Clim. 2009, 22, 486–498. [Google Scholar]
  144. Neuman, S.P.; Tartakovsky, D.M. Perspectives on theories of non-Fickian transport in heterogeneous media. Adv. Water Resour. 2009, 32, 670–680. [Google Scholar] [CrossRef]
  145. Mackenzie, P.D.; Horney, D.P.; Sivavec, T.M. Mineral precipitation and porosity losses in granular iron columns. J. Hazard. Mater. 1999, 68, 1–17. [Google Scholar] [CrossRef]
  146. Li, L.; Benson, C.H.; Lawson, E.M. Impact of mineral fouling on hydraulic behavior of permeable reactive barriers. Groundwater 2005, 43, 582–596. [Google Scholar] [CrossRef] [PubMed]
  147. Chen, K.-F.; Li, S.; Zhang, W.-X. Renewable hydrogen generation by bimetallic zero valent iron nanoparticles. Chem. Eng. J. 2011, 170, 562–567. [Google Scholar] [CrossRef]
  148. Bouniol, P. Influence of iron on water radiolysis in cement based materials. J. Nucl. Mat. 2010, 403, 167–183. [Google Scholar] [CrossRef]
  149. Ruhl, A.S.; Jekel, M. Degassing, gas retention and release in Fe(0) permeable reactive barriers. J. Cont. Hydrol. 2014, 159, 11–19. [Google Scholar] [CrossRef] [PubMed]
  150. Reardon, E.J. Capture and storage of hydrogen gas by zero valent iron. J. Cont. Hydrol. 2014, 157, 117–124. [Google Scholar] [CrossRef] [PubMed]
  151. Da Silva, M.L.B.; Johnson, R.L.; Alvarez, P.J.J. Microbial characterization of groundwater undergoing treatment with a permeable reactive barrier. Environ. Eng. Sci. 2007, 24, 1122–1127. [Google Scholar] [CrossRef]
  152. Xie, Y.; Dong, H.; Zeng, G.; Tang, L.; Jiang, Z.; Zhang, C.; Deng, J.; Zhang, L.; Zhang, Y. The interactions between nanoscale zero-valent iron and microbes in the subsurface environment: A review. J. Hazard. Mater. 2017, 321, 390–407. [Google Scholar] [CrossRef] [PubMed]
  153. Lefere, E.; Bossa, N.; Wiesener, M.R.; Gunsch, C.K. A review of the environmental implications of in situ remediation by nanscale zero valent iron (nZVI): Behavior, transport and impacts on microbial communities. Sci. Total Environ. 2016, 565, 889–901. [Google Scholar] [CrossRef] [PubMed]
  154. Avillera, A.; Cid, M.; Petit, M.C. Anodic reaction of iron in transpassive range. J. Electroanal. Chem. 1979, 105, 149–160. [Google Scholar] [CrossRef]
  155. Xia, L.; Zheng, X.; Shao, H.; Xin, J.; Sun, Z.; Wang, L. Effects of bacterial cells and two types of extracellular polymers on bioclogging of sand columns. J. Hydrol. 2016, 535, 293–300. [Google Scholar] [CrossRef]
  156. SPE. Guidelines for the Evaluation of Petroleum Reserves and Resources; Society Petroleum Engineers: Richardson, TX, USA, 2001. [Google Scholar]
  157. SPE. Guidelines for Application of the Petroleum Resources Management System; Society Petroleum Engineers: Richardson, TX, USA, 2011. [Google Scholar]
  158. Etherington, J.; Pollen, T.; Zuccolo, L. Oil and Gas Reserves Committee (OGRC) Mapping subcommittee final report—December 2005. In Comparison of Selected Reserves and Resource Classifications and Associated Definitions; Society Petroleum Engineers: Richardson, TX, USA, 2005. [Google Scholar]
  159. Lerche, I. Oil Exploration Basin Analysis and Economics; Academic Press: San Diego, CA, USA, 1992. [Google Scholar]
  160. Gavaskar, A.; Tatar, L.; Condit, W. Cost and Performance Report: Nanoscale Zero-Valent Iron Technologies for Source Remediation; Contract Report CR-05–007-ENV; NAVFAC Naval Facilities Engineering Command, US Navy Engineering Services Center: Port Hueneme, CA, USA, 2005. [Google Scholar]
  161. Gavaskar, A.; Bhargava, M.; Condit, W. Cost and Performance Report for a Zero Valent Iron (ZVI) Treatability Study at Naval Air Station North Island; Technical report TR-2307-ENV; NAVFAC Naval Facilities Engineering Command, US Navy Engineering Services Center: Port Hueneme, CA, USA, 2008. [Google Scholar]
  162. Tosco, T.; Papini, M.P.; Viggi, C.C.; Sethi, R. Nanoscale zero valent iron particles for groundwater remediation: A review. J. Clean. Prod. 2014, 77, 10–21. [Google Scholar] [CrossRef]
  163. Chowdhury, A.I.A.; Krol, M.M.; Kocur, C.M.; Boparal, H.K.; Weber, K.P.; Sleep, B.E.; O’Carroll, D.M. nZVI injection into variably saturated soils: Field and modeling study. J. Contam. Hydrol. 2015, 183, 16–28. [Google Scholar] [CrossRef] [PubMed]
  164. Nemecek, J.; Pokomy, P.; Lacinova, L.; Cernik, M.; Masopustova, Z.; Lhotsky, O.; Filipova, A.; Cajthami, T. Combined abiotic and biotic in situ reduction of hexavalent chromium in groundwater using n-ZVI and whey: A remedial pilot test. J. Hazard. Mater. 2015, 300, 670–679. [Google Scholar] [CrossRef] [PubMed]
  165. Kocur, C.M.D.; Lomheim, L.; Boparai, H.K.; Chowdhury, A.I.A.; Weber, K.P.; Austrins, L.M.; Edwards, E.A.; Sleep, B.E.; O’Carroll, D.M. Contributions of abiotic and biotic dechlorination following carboxymethyl cellulose stabilized nanoscale zero valent iron injection. Environ. Sci. Technol. 2015, 49, 8648–8656. [Google Scholar] [CrossRef] [PubMed]
  166. Hatfield, K.; Klammer, H. The problem of flow bypass at permeable reactive barriers. WIT Trans. Built Environ. 2008, 100, 15–24. [Google Scholar]
  167. Klammer, H.; Hatfield, K.; Kacimov, A. Analytical solutions for flow fields near drain and gate reactive barriers. Ground Water 2010, 48, 427–437. [Google Scholar]
  168. Wanner, C.; Zink, S.; Eggenberger, U.; Mader, U. Unraveling the partial failure of a permeable reactive barrier using a multi-tracer experiment and Cr isotope measurements. Appl. Geochem. 2013, 37, 125–133. [Google Scholar] [CrossRef]
  169. Field, R. Chemical Engineering; Macmillan: London, UK, 1988. [Google Scholar]
  170. Carberry, J.J. Chemical and Catalytic Reaction Engineering; Dover: New York, NY, USA, 2001. [Google Scholar]
  171. Misstear, B.; Banks, D.; Clark, L. Water Wells and Boreholes; John Wiley & Sons Ltd.: Chichester, UK, 2006. [Google Scholar]
  172. Hill, C.G. An Introduction to Chemical Engineering Kinetics & Reactor Design; John Wiley and Sons: New York, NY, USA, 1977. [Google Scholar]
  173. Hill, C.G.; Root, T.W. An Introduction to Chemical Engineering Kinetics & Reactor Design, 2nd ed.; John Wiley and Sons: New York, NY, USA, 2014. [Google Scholar]
  174. Thakur, G.C.; Satter, A. Integrated Waterflood Asset Management; Penwell Books: Tulsa, OK, USA, 1998. [Google Scholar]
  175. Baker, R. Reservoir management for waterfloods—Part II. J. Can. Petrol. Technol. 1998, 37, 12–17. [Google Scholar] [CrossRef]
  176. Roldin, M.; Locatelli, L.; Mark, O.; Mikkelsen, P.S.; Binning, P.J. A simplified model of soakaway infiltration interaction with a shallow groundwater table. J. Hydrol. 2013, 497, 165–175. [Google Scholar] [CrossRef]
  177. Roldin, M.; Fryd, O.; Jeppesen, J.; Mark, O.; Binning, P.J.; Mikkelsen, P.J.; Jensen, M.B. Modelling the impact of soakaway retrofits on combined sewage overflows in a 3 km2 urban catchment in Copenhagen, Denmark. J. Hydrol. 2012, 452–453, 64–74. [Google Scholar] [CrossRef]
  178. Roldin, M.; Mark, O.; Kuczera, G.; Mikkelsen, P.S.; Binning, P.J. Representing soakaways in a physically distributed urban drainage model—Upscaling individual allotments to an aggregated scale. J. Hydrol. 2012, 414–415, 530–538. [Google Scholar] [CrossRef]
  179. Locatelli, L.; Gabriel, S.; Mark, O.; Mikkelsen, P.S.; Arbjerg-Nielsen, K.; Taylor, H.; Brockhorn, B.; Larsen, H.; Kjelby, M.J.; Blicher, A.S.; et al. Modelling the impact of retention-detention units on sewer surcharge and peak and annual runoff reduction. Water Sci. Technol. 2015, 71, 898–903. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  180. Roldin, M. Distributed Models Coupling Soakaways, Urban Drainage and Groundwater. Ph.D. Thesis, Technical University Denmark, Lyngby, Denmark, 2012. [Google Scholar]
  181. Locatelli, L.; Mark, O.; Mikkelsen, P.S.; Arbjerg-Nielsen, K.; Wong, T.; Binning, P.J. Determining the extent of groundwater interference on the performance of infiltration trenches. J. Hydrol. 2015, 529, 1360–1372. [Google Scholar] [CrossRef] [Green Version]
  182. Stumpp, C.; Ekdal, A.; Gonenc, I.E.; Maloszewski, P. Hydrological dynamics of water sources in a Mediterranean lagoon. Hydrol. Earth Syst. Sci. 2014, 18, 4825–4837. [Google Scholar] [CrossRef]
  183. Strauch, G.; Oyarzun, J.; Fiebig-Wittmaack, M.; Gonzalez, E.; Weise, S.M. Contributions of the different water sources to the Elqui river runoff (northern Chile) evaluated by H/O isotopes. Isotopes Environ. Health Stud. 2006, 42, 303–322. [Google Scholar] [CrossRef] [PubMed]
  184. Dinar, A.; Zilberman, D. Economics of Water Resources: The Contributions of Dan Yaron; Springer Science + Business Media: New York, NY, USA, 2002. [Google Scholar]
  185. BGS and PHE. Arsenic Contamination of Groundwater in Bangladesh; Phase 2, Groundwater flow Modeling; Kinniburgh, D.G., Smedley, P.L., Eds.; British Geological Survey Technical Report WC/00/19; British Geological Survey: Keyworth, UK, 2001. [Google Scholar]
Figure 1. Trial ST3b pellet [16]. Zero valent iron, Fe0 (ZVI) = 24.9 g/L; Feed Water: 6.52 g Cl/L + 4.53 g Na+/L; Product water = 2.06 g Cl/L + 1.29 g Na+/L; Removed: 4.46 g Cl/L + 3.24 g Na+/L; Feed Water pH = 6.32; Product Water pH = 8.26. (a) Reflected light, no filter, showing a central saline water droplet forming due to the presence of hygroscopic, deliquescent, sequestered NaCl. Photograph taken 4 days after the surface was dry cut and polished. White patches are sequestered solid NaCl infilling porosity; (b) Reflected light, no filter, converted to a grey scale before false color is applied. The false color analysis clearly shows both the water droplet and the distribution of solid sequestered NaCl (light green). Field of view = 4.5 mm.
Figure 1. Trial ST3b pellet [16]. Zero valent iron, Fe0 (ZVI) = 24.9 g/L; Feed Water: 6.52 g Cl/L + 4.53 g Na+/L; Product water = 2.06 g Cl/L + 1.29 g Na+/L; Removed: 4.46 g Cl/L + 3.24 g Na+/L; Feed Water pH = 6.32; Product Water pH = 8.26. (a) Reflected light, no filter, showing a central saline water droplet forming due to the presence of hygroscopic, deliquescent, sequestered NaCl. Photograph taken 4 days after the surface was dry cut and polished. White patches are sequestered solid NaCl infilling porosity; (b) Reflected light, no filter, converted to a grey scale before false color is applied. The false color analysis clearly shows both the water droplet and the distribution of solid sequestered NaCl (light green). Field of view = 4.5 mm.
Hydrology 04 00001 g001
Figure 2. ST Trial Pellet (ST1a [16]) Following Desalination: Reflected Light; Halite on rough cut surface. Field of view = 0.378 mm; ZVI:water ratio = 18.31 g/L; Feed water salinity = 8.01 g/L; Product water salinity after 230 days = 2.52 g/L. NaCl removed = 5.49 g/L (0.30 g/g ZVI). Feed water pH = 6.27; Product water pH = 8.12.
Figure 2. ST Trial Pellet (ST1a [16]) Following Desalination: Reflected Light; Halite on rough cut surface. Field of view = 0.378 mm; ZVI:water ratio = 18.31 g/L; Feed water salinity = 8.01 g/L; Product water salinity after 230 days = 2.52 g/L. NaCl removed = 5.49 g/L (0.30 g/g ZVI). Feed water pH = 6.27; Product water pH = 8.12.
Hydrology 04 00001 g002
Figure 3. ST Trial Pellet (ST1b [16]) Following Desalination: Reflected Light; Halite infilling porosity on polished cut surface. Field of view = 0.49 mm; ZVI:water ratio = 23.10 g/L; Feed water salinity = 8.21 g/L; Product water salinity after 230 days = 1.99 g/L. NaCl removed = 6.22 g/L (0.27 g/g ZVI). Feed water pH = 6.25; Product water pH = 8.15.
Figure 3. ST Trial Pellet (ST1b [16]) Following Desalination: Reflected Light; Halite infilling porosity on polished cut surface. Field of view = 0.49 mm; ZVI:water ratio = 23.10 g/L; Feed water salinity = 8.21 g/L; Product water salinity after 230 days = 1.99 g/L. NaCl removed = 6.22 g/L (0.27 g/g ZVI). Feed water pH = 6.25; Product water pH = 8.15.
Hydrology 04 00001 g003
Figure 4. ST Trial Pellet (ST1c [16]) Following Desalination: Reflected Light; Halite infilling circular pore on polished cut surface. The halite infilling the circular carrier pore shows a characteristic “kristallbrocken” texture [49] indicating that the pore was infilled by crystal masses growing out from the pore boundaries. The bulk of the matrix in the photomicrograph is constructed from goethite [41,42,43,44,45,46,47,48]. All the pores are infilled with halite. The blue lath shaped crystals are atacamite. Field of view = 0.52 mm; ZVI:water ratio = 25.34 g/L; Feed water salinity = 8.19 g/L; Product water salinity after 230 days = 2.28 g/L. NaCl removed = 5.91 g/L (0.23 g/g ZVI). Feed water pH = 6.21; Product water pH = 8.54.
Figure 4. ST Trial Pellet (ST1c [16]) Following Desalination: Reflected Light; Halite infilling circular pore on polished cut surface. The halite infilling the circular carrier pore shows a characteristic “kristallbrocken” texture [49] indicating that the pore was infilled by crystal masses growing out from the pore boundaries. The bulk of the matrix in the photomicrograph is constructed from goethite [41,42,43,44,45,46,47,48]. All the pores are infilled with halite. The blue lath shaped crystals are atacamite. Field of view = 0.52 mm; ZVI:water ratio = 25.34 g/L; Feed water salinity = 8.19 g/L; Product water salinity after 230 days = 2.28 g/L. NaCl removed = 5.91 g/L (0.23 g/g ZVI). Feed water pH = 6.21; Product water pH = 8.54.
Hydrology 04 00001 g004
Figure 5. ST Trial Pellet (ST1b [16]) Following Desalination: Reflected Light; Halite mass infilling and expanding porosity. Field of View = 0.937 mm.
Figure 5. ST Trial Pellet (ST1b [16]) Following Desalination: Reflected Light; Halite mass infilling and expanding porosity. Field of View = 0.937 mm.
Hydrology 04 00001 g005
Figure 6. ST Trial Pellet (Trial ST2g [16]) Following Desalination showing halite accumulations on the side of a large pore: Reflected Light; (a) copper shell of pellet showing dissolution features; (b) authigenic ZVI product; (c) = porosity; (d) authigenic halite containing regoliths of copper and ZVI. Field of View = 0.945 mm; ZVI:water ratio = 26.72 g/L; Feed water salinity = 9.69 g/L; Product water salinity after 230 days = 2.47 g/L. NaCl removed = 7.22 g/L (0.27 g/g ZVI). Feed water pH = 6.31; Product water pH = 8.30.
Figure 6. ST Trial Pellet (Trial ST2g [16]) Following Desalination showing halite accumulations on the side of a large pore: Reflected Light; (a) copper shell of pellet showing dissolution features; (b) authigenic ZVI product; (c) = porosity; (d) authigenic halite containing regoliths of copper and ZVI. Field of View = 0.945 mm; ZVI:water ratio = 26.72 g/L; Feed water salinity = 9.69 g/L; Product water salinity after 230 days = 2.47 g/L. NaCl removed = 7.22 g/L (0.27 g/g ZVI). Feed water pH = 6.31; Product water pH = 8.30.
Hydrology 04 00001 g006
Figure 7. ST3f Trial Pellet [16]. (a) Standard Redox Stability fields for copper in the saline system Cu-O-H-Cl (at 25 °C) [51]. Data points are from Trial ST3f [16]; (b) Tenorite-Atacamite redox boundary adjusted for the inter-relationship between salinity and pH. Atacamite crystallizes when: [Log (H+)2 + Log(Cl)] is greater than −18 (units are in moles; i.e., Log([10−pH]2) + Log((0.6707(Salinity, g/L)/35.45)) [51]; = Atacamite precipitation favoured; = tenorite precipitation favoured; ZVI:water ratio = 57.50 g/L; Feed water salinity = 9.98 g/L; Product water salinity after 165 days = 2.85 g/L. NaCl removed = 7.13 g/L (0.12 g/g ZVI). Feed water pH = 6.41; Product water pH = 9.47; Atacamite and polymorphs = (Cu2Cl(OH)3); Tenorite = CuO.
Figure 7. ST3f Trial Pellet [16]. (a) Standard Redox Stability fields for copper in the saline system Cu-O-H-Cl (at 25 °C) [51]. Data points are from Trial ST3f [16]; (b) Tenorite-Atacamite redox boundary adjusted for the inter-relationship between salinity and pH. Atacamite crystallizes when: [Log (H+)2 + Log(Cl)] is greater than −18 (units are in moles; i.e., Log([10−pH]2) + Log((0.6707(Salinity, g/L)/35.45)) [51]; = Atacamite precipitation favoured; = tenorite precipitation favoured; ZVI:water ratio = 57.50 g/L; Feed water salinity = 9.98 g/L; Product water salinity after 165 days = 2.85 g/L. NaCl removed = 7.13 g/L (0.12 g/g ZVI). Feed water pH = 6.41; Product water pH = 9.47; Atacamite and polymorphs = (Cu2Cl(OH)3); Tenorite = CuO.
Hydrology 04 00001 g007
Figure 8. ST Trial Pellet (Trial ST3f [16]) Following Desalination showing atacamite and tenorite crystals: Reflected Light, with the characteristic blue-green internal reflections of atacamite (Cu2(OH)3Cl) species; The golden reflectance is associated with tenorite (CuO). The bright white reflectance is associated with halite. The field of view = 0.43 mm.
Figure 8. ST Trial Pellet (Trial ST3f [16]) Following Desalination showing atacamite and tenorite crystals: Reflected Light, with the characteristic blue-green internal reflections of atacamite (Cu2(OH)3Cl) species; The golden reflectance is associated with tenorite (CuO). The bright white reflectance is associated with halite. The field of view = 0.43 mm.
Hydrology 04 00001 g008
Figure 9. ST Trial Pellet (Trial ST3f [16]) Following Desalination showing a nest of atacamite crystals with the characteristic blue-green internal reflections of Cu2(OH)3Cl species. The strongly pleochroic golden grain is tenorite (CuO). Thickness = 2 microns; width = 14 microns; length = 25–110 microns. Reflected Light; Field of view = 0.133 mm.
Figure 9. ST Trial Pellet (Trial ST3f [16]) Following Desalination showing a nest of atacamite crystals with the characteristic blue-green internal reflections of Cu2(OH)3Cl species. The strongly pleochroic golden grain is tenorite (CuO). Thickness = 2 microns; width = 14 microns; length = 25–110 microns. Reflected Light; Field of view = 0.133 mm.
Hydrology 04 00001 g009
Figure 10. (ST3c Trial [16]) Pore within the ZVI product which is partially filled with NaCl (halite) crystals (blue-white). Reflected light. Field of view = 0.465 mm. a = ZVI (principally FeOOH species, i.e., goethite [43,44,45,46,47]). b = halite (NaCl) [41]; c = incompletely filled pore where the halite crystallites are growing into the pore from the pore edges. ZVI:water ratio = 30.12 g/L; Feed water salinity = 10.06 g/L; Product water salinity after 230 days = 2.82 g/L. NaCl removed = 7.24 g/L (0.234 g/g ZVI). Feed water pH = 6.31; Product water pH = 8.44.
Figure 10. (ST3c Trial [16]) Pore within the ZVI product which is partially filled with NaCl (halite) crystals (blue-white). Reflected light. Field of view = 0.465 mm. a = ZVI (principally FeOOH species, i.e., goethite [43,44,45,46,47]). b = halite (NaCl) [41]; c = incompletely filled pore where the halite crystallites are growing into the pore from the pore edges. ZVI:water ratio = 30.12 g/L; Feed water salinity = 10.06 g/L; Product water salinity after 230 days = 2.82 g/L. NaCl removed = 7.24 g/L (0.234 g/g ZVI). Feed water pH = 6.31; Product water pH = 8.44.
Hydrology 04 00001 g010
Figure 11. (ST2a Trial [16]) ZVI Product. Halite crystals partially infilling a pore and increasing pore tortuosity. The red-brown mineral is molysite (FeCl3). Molysite occurs naturally in halite assemblages, e.g., [53]. Reflected light. Field of view = 0.099 mm; ZVI:water ratio = 28.53 g/L; Feed water salinity = 9.61 g/L; Product water salinity after 230 days = 2.62 g/L. NaCl removed = 6.99 g/L (0.25 g/g ZVI). Feed water pH = 6.31; Product water pH = 8.40.
Figure 11. (ST2a Trial [16]) ZVI Product. Halite crystals partially infilling a pore and increasing pore tortuosity. The red-brown mineral is molysite (FeCl3). Molysite occurs naturally in halite assemblages, e.g., [53]. Reflected light. Field of view = 0.099 mm; ZVI:water ratio = 28.53 g/L; Feed water salinity = 9.61 g/L; Product water salinity after 230 days = 2.62 g/L. NaCl removed = 6.99 g/L (0.25 g/g ZVI). Feed water pH = 6.31; Product water pH = 8.40.
Hydrology 04 00001 g011
Figure 12. (ST2a Trial [16]) ZVI Product. (a) = ferrate (NazFeOx(OH)y) nodule containing rounded halite nodules. (c) = goethite; (d) = halite crystals (white) infilling porosity. Field of view = 0.133 mm. Reflected light.
Figure 12. (ST2a Trial [16]) ZVI Product. (a) = ferrate (NazFeOx(OH)y) nodule containing rounded halite nodules. (c) = goethite; (d) = halite crystals (white) infilling porosity. Field of view = 0.133 mm. Reflected light.
Hydrology 04 00001 g012
Figure 13. (ST3a Trial [16]) ZVI Product. Examples of halite crystals infilling a pore network around precipitated FeOOH grains. (a) Halite saturated (goethite + Fe(OH)3) surrounded by halite infilling a network of pores. The Fe(OH)3 may be derived from hydrolyzed molysite (FeCl3) [41]. Field of view = 0.142 mm; (b) goethite/ferrate grains surrounded by halite, infilling a network of pores. Field of view = 0.090 mm. Reflected light.
Figure 13. (ST3a Trial [16]) ZVI Product. Examples of halite crystals infilling a pore network around precipitated FeOOH grains. (a) Halite saturated (goethite + Fe(OH)3) surrounded by halite infilling a network of pores. The Fe(OH)3 may be derived from hydrolyzed molysite (FeCl3) [41]. Field of view = 0.142 mm; (b) goethite/ferrate grains surrounded by halite, infilling a network of pores. Field of view = 0.090 mm. Reflected light.
Hydrology 04 00001 g013
Figure 14. Trial ST1e [16] ZVI desalination pellet following desalination. (a) = authigenic oolitic goethite nodule; (b,c) = adsorbed oolitic goethite nodules containing authigenic cuboid Fe0 crystallites (1 to 5 microns in diameter) crystallizing within the goethite. The Fe0 crystallites vary in color from white to blue to yellow to pink (depending on orientation), are cuboid, isotropic, and have a reflectance of about 60%. Sub-nodule (c) includes a rim of (FeO (wustite) with exsolved Fe0 crystals) separating it from the main goethite (FeO(OH)) mass of the nodule; (d) = iron rich boundary of the nodule (FeO (wustite) with exsolved Fe0 crystals) and the associated active outer growth fringe (light brown) of the oolitic nodule. (e) = goethite matrix (dark). Reflected light. Field of view = 0.173 mm; ZVI:water ratio = 21.72 g/L; Feed water salinity = 8.22 g/L; Product water salinity after 230 days = 2.24 g/L. NaCl removed = 5.98 g/L (0.28 g/g ZVI). Feed water pH = 6.24; Product water pH = 8.42.
Figure 14. Trial ST1e [16] ZVI desalination pellet following desalination. (a) = authigenic oolitic goethite nodule; (b,c) = adsorbed oolitic goethite nodules containing authigenic cuboid Fe0 crystallites (1 to 5 microns in diameter) crystallizing within the goethite. The Fe0 crystallites vary in color from white to blue to yellow to pink (depending on orientation), are cuboid, isotropic, and have a reflectance of about 60%. Sub-nodule (c) includes a rim of (FeO (wustite) with exsolved Fe0 crystals) separating it from the main goethite (FeO(OH)) mass of the nodule; (d) = iron rich boundary of the nodule (FeO (wustite) with exsolved Fe0 crystals) and the associated active outer growth fringe (light brown) of the oolitic nodule. (e) = goethite matrix (dark). Reflected light. Field of view = 0.173 mm; ZVI:water ratio = 21.72 g/L; Feed water salinity = 8.22 g/L; Product water salinity after 230 days = 2.24 g/L. NaCl removed = 5.98 g/L (0.28 g/g ZVI). Feed water pH = 6.24; Product water pH = 8.42.
Hydrology 04 00001 g014
Figure 15. Catalytic NaCl sequestration model. Source: [17].
Figure 15. Catalytic NaCl sequestration model. Source: [17].
Hydrology 04 00001 g015
Figure 16. Catalytic ZVI desalination model [17]. The number of electrons transferred is a function of the oxidation number of the reduced state and the oxidized state. The potential range is from Fe0 to FeVIII. CO2 + H2O + Cl = HCO3 + HCl [16,17]; 0.5O2 + 2Na+ + H2O + 2e = 2NaOH [16,17]. NaOH = NaO + H+ [16,17]. FeIII can reduce to Fe0. FeIII can reduce to FeII. Fe can oxidize to FeIV to VIII. HCl can be replaced with HxClyOz [16]. Fe0 and FeI formation is demonstrated in Figure 14. HFeIICl and HFeIIICl formation is demonstrated in Figure 11 and Figure 13; FeIV to VIII formation is demonstrated in Figure 12 and Figure 13; HFeIIIOH formation (and associated species) is demonstrated in Figure 1, Figure 3, Figure 4, Figure 5, Figure 6, Figure 8, Figure 10, Figure 12 and Figure 14. NaCl formation is demonstrated in Figure 1, Figure 2, Figure 3, Figure 4, Figure 5, Figure 6, Figure 8, Figure 10, Figure 11, Figure 12 and Figure 13.
Figure 16. Catalytic ZVI desalination model [17]. The number of electrons transferred is a function of the oxidation number of the reduced state and the oxidized state. The potential range is from Fe0 to FeVIII. CO2 + H2O + Cl = HCO3 + HCl [16,17]; 0.5O2 + 2Na+ + H2O + 2e = 2NaOH [16,17]. NaOH = NaO + H+ [16,17]. FeIII can reduce to Fe0. FeIII can reduce to FeII. Fe can oxidize to FeIV to VIII. HCl can be replaced with HxClyOz [16]. Fe0 and FeI formation is demonstrated in Figure 14. HFeIICl and HFeIIICl formation is demonstrated in Figure 11 and Figure 13; FeIV to VIII formation is demonstrated in Figure 12 and Figure 13; HFeIIIOH formation (and associated species) is demonstrated in Figure 1, Figure 3, Figure 4, Figure 5, Figure 6, Figure 8, Figure 10, Figure 12 and Figure 14. NaCl formation is demonstrated in Figure 1, Figure 2, Figure 3, Figure 4, Figure 5, Figure 6, Figure 8, Figure 10, Figure 11, Figure 12 and Figure 13.
Hydrology 04 00001 g016
Figure 17. ST Desalination Pellets. Data Trial Duration: 230 days; n = 55; Data Source: [16]. (a) Relationship between NaCl removed (g·g−1 ZVI) and Pw, g NaCl·g−1 ZVI: Data [16]; (b) Relationship between the total amount of NaCl removed, g·L−1, and Pw, g NaCl·g−1 ZVI: Data [16]; (c) Implied relationship between probability and the amount of NaCl (kg) that can be sequestered in 20 kg of pellets. Mean = 3.45 kg; Standard Deviation = 1.44 kg; 1st Quartile = 2.30 kg; Median = 3.29 kg; 3rd Quartile = 4.77 kg; (d) Volume of water than can be treated by a single ZVI charge before the ZVI needs to be recharged as a function of the amount of ZVI placed in a 4000 m3 treatment zone where the aquifer salinity is 4 g/L and the irrigation water product salinity is 2 g/L; (e) Number of years between ZVI charges (or ZVI regeneration interval) vs. probability.
Figure 17. ST Desalination Pellets. Data Trial Duration: 230 days; n = 55; Data Source: [16]. (a) Relationship between NaCl removed (g·g−1 ZVI) and Pw, g NaCl·g−1 ZVI: Data [16]; (b) Relationship between the total amount of NaCl removed, g·L−1, and Pw, g NaCl·g−1 ZVI: Data [16]; (c) Implied relationship between probability and the amount of NaCl (kg) that can be sequestered in 20 kg of pellets. Mean = 3.45 kg; Standard Deviation = 1.44 kg; 1st Quartile = 2.30 kg; Median = 3.29 kg; 3rd Quartile = 4.77 kg; (d) Volume of water than can be treated by a single ZVI charge before the ZVI needs to be recharged as a function of the amount of ZVI placed in a 4000 m3 treatment zone where the aquifer salinity is 4 g/L and the irrigation water product salinity is 2 g/L; (e) Number of years between ZVI charges (or ZVI regeneration interval) vs. probability.
Hydrology 04 00001 g017aHydrology 04 00001 g017b
Figure 18. Process flow diagram for a schematic aquifer, which has been reconstructed as a Static Aquifer Treatment Zone.
Figure 18. Process flow diagram for a schematic aquifer, which has been reconstructed as a Static Aquifer Treatment Zone.
Hydrology 04 00001 g018
Figure 19. Cross section through a schematic Aquifer Treatment Zone. The zone containing the ZVI pellets/cartridges is colored red.
Figure 19. Cross section through a schematic Aquifer Treatment Zone. The zone containing the ZVI pellets/cartridges is colored red.
Hydrology 04 00001 g019
Figure 20. Process flow diagram for a schematic aquifer, which has been reconfigured as an Aquifer Treatment Zone with recycle (Recycle Strategy). S = salinity.
Figure 20. Process flow diagram for a schematic aquifer, which has been reconfigured as an Aquifer Treatment Zone with recycle (Recycle Strategy). S = salinity.
Hydrology 04 00001 g020
Figure 21. Cross section through a schematic Aquifer Treatment Zone incorporating recycle. The zone containing the ZVI pellets/cartridges is colored red.
Figure 21. Cross section through a schematic Aquifer Treatment Zone incorporating recycle. The zone containing the ZVI pellets/cartridges is colored red.
Hydrology 04 00001 g021
Figure 22. Cross section of a schematic Aquifer Treatment Zone showing gravity feed water recycle. The maximum head is controlled by the water level in the distribution tank, h1c. The subsurface pipe enters the infiltration device at a height, h1a, above the water table. The water level can rise in the infiltration device to the ground surface (height h1b). If the water level rises above height h1b, the infiltration device will act as a point source for overland flow. The infiltration rate is controlled by the surface area of the infiltration device within the aquifer, the permeability of the surface of the infiltration device, the head, h1t, created by the presence of recycle water in the infiltration device, and the negative pressure (or iso-potential gradient) created by the abstraction well.
Figure 22. Cross section of a schematic Aquifer Treatment Zone showing gravity feed water recycle. The maximum head is controlled by the water level in the distribution tank, h1c. The subsurface pipe enters the infiltration device at a height, h1a, above the water table. The water level can rise in the infiltration device to the ground surface (height h1b). If the water level rises above height h1b, the infiltration device will act as a point source for overland flow. The infiltration rate is controlled by the surface area of the infiltration device within the aquifer, the permeability of the surface of the infiltration device, the head, h1t, created by the presence of recycle water in the infiltration device, and the negative pressure (or iso-potential gradient) created by the abstraction well.
Hydrology 04 00001 g022
Table 1. Example set of primary data required to reconstruct an Aquifer Treatment Zone.
Table 1. Example set of primary data required to reconstruct an Aquifer Treatment Zone.
ItemData CategoryData SubcategoryExample
1Agricultural Holding Data
1aHolding Dimensions and Area25 ha
1bLegal rights to modify water composition in an aquifer
1cIrrigation Requirement20 m3·day−1
1dAquifer Thickness1 m
2Target Irrigation Water Salinity
2aAquifer Salinity4 g·L−1
2bRequired Irrigation Water Salinity2 g·L−1
2cCrop yield with irrigation using saline aquifer water1 t·ha−1
2dCrop yield with irrigation with partially desalinated water5 t·ha−1
3Agricultural Economics Data
3aCrop yield decrement as a function of salinity[16,18,19,20,21,22,23,24,25]
3bCrop market value$300/t
4Experimental Desalination Data
4aRate constantsEquations (A1)–(A4) and (B1); Figure B1, Figure B2 and Figure B3; Table E1
4bRelationship between residence time, td, in the reaction environment and desalinationEquations (A1)–(A4) and (B1); Figure B3
4cRate of Salinity Decline as a function of tdCatalyst ST: (Figure B3)
4dStatistical variation of desalination around the mean at the 99% confidence limitsCatalyst ST: 59.1% to 65.8% Catalyst K: 31.7% to 47.0%
4eStatistical desalination at the 1st and 3rd quartiles, e.g., Catalyst ST: 54.4% to 70.8%; Catalyst D: 24.8% to 65.8%; Catalyst E: 58.9% to 92.1%; Catalyst K: 33.2% to 45.5%
4fStatistical variation of Na+ Removal around the mean at the 99% confidence limitsCatalyst ST: 59.1% to 65.8% Catalyst K: 39.1% to 62.8%
Table 2. Hydrological measurements and modeling required in order to: (i) convert an aquifer into a proposed Aquifer Treatment Zone (ATZ); and (ii) monitor the performance of an installed Aquifer Treatment Zone. Further details are provided in Appendix A and Appendix G.
Table 2. Hydrological measurements and modeling required in order to: (i) convert an aquifer into a proposed Aquifer Treatment Zone (ATZ); and (ii) monitor the performance of an installed Aquifer Treatment Zone. Further details are provided in Appendix A and Appendix G.
ItemHydrological ParameterMeasured or Modelled ItemEquations and Figures
1Control Data Set (Aquifer)
1aPermeabilityEquations (G1)–(G4), Figure G1
1bPorosityEquations (G1)–(G4), Figure G1
1cThicknessEquations (G1)–(G4), Figure G1
1dPiezometric SurfaceEquations (C1) to (C4)
2Control Models
2aIsopache (thickness) ModelEquations (G1)–(G4), Figure G1
2bIso-permeability ModelEquations (G1)–(G4), Figure G1
2cIso-potential ModelEquations (C2) and (C3)
2dFlow Rate ModelEquations (C1)–(C4)
3Control Abstraction Test
3aPiezometric SurfaceEquations (C1)–(C4)
3bIso-potential ModelEquations (C2) and (C3)
3cFlow Rate ModelEquations (C1)–(C4)
3dDrawdown ModelEquation C5
3eDrawdown Permeability ModelEquations (C6) and (C10)–(C12)
3fRadius of Influence of the Abstraction WellEquation (C7)
3gATZ volume as a function of the abstraction rateEquation (C8)
3hFlow rate and flow direction at each lociEquation (C9)
3iDesign Parameters for the Aquifer Treatment ZoneEquations (C1)–(C9), Figure C3
4Operational Monitoring
4aIso-potentiaL, flow rate, and drawdown measurements and modellingEquations (C1)–(C12)
4bPermeability Reductions associated with ZVIEquation (C13), Figure C4
4cHalocline monitoringFigure C5
5Diffusion Modelling
5aImpact of fluid velocity changesEquations (C14)–(C21), Figure C6
6Space Velocity Modelling
6aImpact on the desalination rateEquations (C22)–(C26)
6bImpact of water residence time in the ATZEquation C27
7Modifications to Aquifer Properties during Prolonged Abstraction
7aRedox parametersFigure C7
7bPermeability reduction due to gas occlusion or mineralizationEquations (C28)–(C30)
7cPermeability reductions and redox modifications associated with biofilmsEquations (C31)–(C36)
Table 3. Comparison of integrated power station-desalination unit complexes. MSFD = Multi-stage flash distillation + power station; RO = Reverse Osmosis + power station; ZVID = ZVI desalination + power station; ZVI = ZVI desalination without the power station; Assumed Target Capital Investment for the schematic ZVI desalination Aquifer Treatment Zone is $300 MM, (calculated using Equation (15)), when {a} = 1.0. MM = million; $ = US dollars. MSFD, RO, and Power Station Costs and Structures are from Reference [87]. Power Station characteristics [87]: 24.2 MPa Steam Pressure; 6 KPa Condenser Pressure; 25.2 MJ·kg−1 Coal HLV; 92% boiler efficiency; 96.5%–98% turbine efficiency; 10,000 m3·h−1 water supply to the power station. Fuel Costs based on 2014 fossil fuel commodity prices. The power station costs used in the Target ZVID cases are also taken from Reference [87]. Different cost components may have different values of the scale-up constant {a}. The scaled target ZVI examples indicate the water delivery cost range which may be achievable. Power requirements associated with water abstraction and recycle are site and catalyst specific. They are therefore not shown for the ZVID and Scaled Target ZVI cases.
Table 3. Comparison of integrated power station-desalination unit complexes. MSFD = Multi-stage flash distillation + power station; RO = Reverse Osmosis + power station; ZVID = ZVI desalination + power station; ZVI = ZVI desalination without the power station; Assumed Target Capital Investment for the schematic ZVI desalination Aquifer Treatment Zone is $300 MM, (calculated using Equation (15)), when {a} = 1.0. MM = million; $ = US dollars. MSFD, RO, and Power Station Costs and Structures are from Reference [87]. Power Station characteristics [87]: 24.2 MPa Steam Pressure; 6 KPa Condenser Pressure; 25.2 MJ·kg−1 Coal HLV; 92% boiler efficiency; 96.5%–98% turbine efficiency; 10,000 m3·h−1 water supply to the power station. Fuel Costs based on 2014 fossil fuel commodity prices. The power station costs used in the Target ZVID cases are also taken from Reference [87]. Different cost components may have different values of the scale-up constant {a}. The scaled target ZVI examples indicate the water delivery cost range which may be achievable. Power requirements associated with water abstraction and recycle are site and catalyst specific. They are therefore not shown for the ZVID and Scaled Target ZVI cases.
Actual MSFDActual ROTarget ZVIDScaled Target ZVIScaled Target ZVIScaled Target ZVIScaled Target ZVIScaled Target ZVI
Power Plant Capacity, MWh291.0291.0291.0
Power Usage, MWh9.831.10.0
Net Power Available for Sale, MWh281.2259.9291.0
Net Power Sales Value, $ MM/a, at $70/MWh172.4159.4178.4
Pressure, MPa 7.00.0
ZVI Desalination scale up constant {a} 1.01.00.950.90.80.7
Sea Water Feed, m3/day390,921.6296,712.090,196.890,196.890,196.890,196.890,196.890,196.8
Product Water, m3/day149,803.290,196.890,196.890,196.890,196.890,196.890,196.890,196.8
Reject Brine, m3/day241,118.4206,515.20.00.00.00.00.00.0
Capital Investment, $ MM/a35.244.729.66.83.82.40.80.2
Pre-Treatment and Pressurisation Cost, $ MM/a 17.0
Labour Cost, $ MM/a8.86.72.02.01.10.70.20.1
Membrane Replacement Cost, $ MM/a 11.8
Chemical Treatment Cost, $ MM/a5.3
ZVI Replacement Cost, $ MM/a 10.010.05.73.51.10.4
Spares Cost, $ MM/a7.2
Power Station Fuel Cost, $ MM/a203.2143.3120.00.00.00.00.00.0
Operations and Maintenance Cost, $ MM/a18.824.921.02.01.10.70.20.1
Net Cost, $ MM/a106.189.04.120.811.77.32.30.7
Net Cost, $/m31.942.700.130.630.360.220.070.02

Share and Cite

MDPI and ACS Style

Antia, D.D.J. Provision of Desalinated Irrigation Water by the Desalination of Groundwater within a Saline Aquifer. Hydrology 2017, 4, 1. https://doi.org/10.3390/hydrology4010001

AMA Style

Antia DDJ. Provision of Desalinated Irrigation Water by the Desalination of Groundwater within a Saline Aquifer. Hydrology. 2017; 4(1):1. https://doi.org/10.3390/hydrology4010001

Chicago/Turabian Style

Antia, David D. J. 2017. "Provision of Desalinated Irrigation Water by the Desalination of Groundwater within a Saline Aquifer" Hydrology 4, no. 1: 1. https://doi.org/10.3390/hydrology4010001

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop