**2 CHAPTER TWO**

**2.6 Arsenic treatment techniques in drinking water**

**2.6.1 Adsorption**

Adsorption is the attachment of molecules or particles to a surface of a solid materials (adsorbents or sorptions) and is seen as a largely effective, robust, economical and environmentally friendly method in removing heavy metals in

Arsenic Treatment Methods

Oxidation

Chemical

Microbial

Coagulation

/flocculation Assorption

Activated Carbon

Red Mud

Activated Alumina

Biological Sorption Ion Exchange Treatment with Bio-Organism

Prokayotes

Eukaryotes

Aquatic Macrophytes

Genetically Engineered Microbes

Electrocoagulation Membrane Process

High Pressure

Low Pressure

34

water treatment. Sometimes the adsorbed solute is called the adsorbate and some researchers use the generic term ‘sorption’ to refer to a process where adsorption and/or absorption are involved or if adsorption and absorption cannot be distinguished (Henke, 2009). The adsorption process can be classified into physical adsorption or van der Waal’s adsorption and chemical adsorption or chemisorption (Fig. 2-11). Adsorption processes are flexible in design, operation and produce high quality treated effluent. Adsorbents can also be regenerated in a process called desorption (Fu and Wang, 2011). The adsorption process depends on different factors such as the pH of the solution, arsenic concentration, the temperature and the presence of other competing ions in the solution. Also, the surface area of the adsorbent plays a significant role in the adsorption process as most adsorbent sizes are in nanometres and have large internal pores (Ghosh (Nath) et al., 2019).

**2.6.1.1 Interferences **

Compared to other treatment processes such precipitation/coprecipitation,
adsorbents are more vulnerable to chemical interferences that hinder the removal
of arsenic from water. Some ions compete directly for adsorption sites with arsenic
species. Two prime examples are phosphate (PO43-) and silicate (SiO44-) which have
the same tetrahedral structure as arsenate (AsO43-) (Henke, 2009). Due to these
similarities, phosphate and silicate may desorb As (V) from clay, aluminium, iron
and other sorbents over a wide pH values or hinder the sorption of As (V) onto these
materials (Antonio Violante et al., 2006; D. Smith and Edwards, 2005). Carbonates
(H2CO30, HCO^{3-} and/or CO32-) present in water have little or no effect on As (V)
adsorption, although other evidence suggests that it may interfere with As (III)
sorption due to their similar trigonal molecular structure (Stollenwerk, 2003).

Dissolved organic materials may also compete with arsenic for a site. Fulvic acid is known to interfere with As (V) adsorption onto aluminium compounds whereas humic acids significantly inhibit As (III) and As (V) on goethite at pH conditions of 6 – 9 and 3 – 8, respectively (Grafe et al., 2001). In contrast, the presence of nitrogen rich humic acid on the surface of kaolinite (Al2Si2O5(OH)4) often improves As (V) sorption from water (Saada et al., 2003).

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**Figure 2-11: Physical and chemical adsorption (Sarkar and Paul, 2016). **

**2.6.1.2 Adsorption Kinetic Study **

The first step in understanding the adsorption process and the foundation for modelling is to depict equilibrium. Thus, the amount of material absorbed onto a media can be expressed in the mass balance shown in Eq. (2.1).

X

M = (C_{o}− C_{e})^{V}

M (2.1)

Where X/M (usually expressed as mg pollutant/g media) is the mass of pollutant per
mass of media, *C**o* is the initial pollutant concentration in solution, *C**e* is the
concentration of the pollutant in solution after equilibrium has been reached, *V is *
the volume of the solution to which the media mass is exposed, and M is the mass
of the media (Demirbas, 2008).

Several kinetic models have been applied to study the adsorptive kinetics of heavy metals. Panthi and Wareham (2014) use the Lagergren equation, which is a first-order kinetic rate equation for adsorption (Eq. 2.2).

**2.6.1.2.1 Pseudo-First-Order Model **

The pseudo-first-order equation is given as:

dqt

dt = k_{1} (q_{e}− q_{t}) (2.2)

Where *q**t* (mg/g) is the amount adsorbed at time t. *q**e* (mg/g) is the adsorption
capacity at equilibrium, *k**1* (1/min) is the pseudo first order rate constant, and t is
the contact time (min). The integration of (Eq. 2.2) with initial condition (q*t* = 0 at
*t = 0) leads to the equation below: *

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log(q_{e}− q_{t}) = log q_{e}− ^{k}^{1}

2.303 t (2.3)

**2.6.1.2.2 Pseudo-Second-Order Model **

The pseudo-second-order model is represented as:

dqt

dt = k_{2}(q_{e}− q_{t})^{2} (2.4)

Where k*2* is the pseudo-second order rate constant (g/mg min). Integrating (Eq. 2.4)
and noting that q*t* = 0 at t = 0, the following equation is obtained

t
qt= ^{1}

k2qe2+ ^{1}

qe t (2.5)

The equilibrium adsorption capacity, q*e *is obtained from the slope and k*2 *is obtained
from the intercept of the linear plot of t/q*t* vs t.

**2.6.1.2.3 Intra-particle Diffusion Model **

The intra-particle diffusion model is a theory proposed by Weber and Morris in identifying the diffusion mechanism by which adsorbate diffuses into the pores of adsorbent which can be the determining step (Weber and Morris, 1963). The intra-particle diffusion model is expressed as:

q_{t}= K_{id}√t + C (2.6)

Where *K**id* is the intra-particle diffusion rate constant (mg/g min ^{½}) with C as the
intercept along the q*t *axis. If a plot of q*t* vs t * ^{½}* gives a straight line passing through
the origin, the intra-particle diffusion is considered as the rate determining step. If
the straight line deviates from the origin, it indicates contributions from film
diffusion (Ahamad et al., 2018; Ho and Mckay, 1998). The liquid film diffusion
model is given by:

In (1 − F) = −k_{fd}t (2.7)

Where, F = *q**t**/q**e *is the fractional attainment of equilibrium, and *k**fd* is the film
diffusion rate constant. A linear plot of In (1 - F) vs. t with zero intercept and enables
*k**fd* to be calculated from the slope.

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**2.6.1.2.4 Elovich Model **

The Elovich Model is an interesting model that has extensively been accepted to describe a chemisorption process (Ahamad et al., 2018; Kaur et al., 2013). The Elovich model equation is expressed as:

dqt

dt = α exp(−βq_{t}) (2.8)

Simplifying the Elovich equation, Chien and Clayton (1980) assumed α β > > t and
by applying the boundary conditions *q**t* = 0 at *t = 0 and qt = 0 at t = 0 Eq. (2.8) *
becomes

q_{t}= ^{1}

β In (αβ) + ^{1}

β In (t) (2.9)

Where α (mg/g.min) and β (g/mg) are constants. The constant α is considered as the
initial adsorption rate, β is related to the extent of surface coverage and activation
energy for chemisorption and q*t* (mg/g) is the amount of absorbent absorbed at time
*t (min). The values of α and β are obtained from a linear plot of qt vs. In t. *

**2.6.1.2.5 Bangham’s Model **

Bangham’s equation has been used to illustrate pore diffusion during the adsorption process and it is expressed as (Aharoni et al., 1979).

Log log ( ^{C}^{0}

C0− q_{t}m) = log ( ^{k}^{0}^{m}

2.303 V) + α log t (2.10)

Where C*0* is the initial concentration of the adsorbate in solution (mg/L). V is the
volume of the solution (mL), *m is the weight of adsorbent (g/L), q**t* (mg/g) is the
amount of adsorbate retained at time t and α (< 1) and k*o* are the constants. A linear
plot of (Log log (C*o**/C**o* – q*t **m) vs. log t demonstrates the diffusion of adsorbate into *
pores of adsorbents.

**2.6.1.3 Adsorption Isotherm **

Several mathematical models have been developed to describe experimental data of adsorption isotherms. These models are Langmuir, Freundlich, Dubinin-Radushkevich (D-R), Langmuir – Freundlich (L-F) and Temkin isotherms.

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**2.6.1.3.1 Langmuir Isotherms **

The Langmuir model assumes uniform energies of adsorption onto the surface and is valid for monolayer adsorption containing a finite number of identical sites. It is represented as:

q_{e}= ^{q}^{m}^{K}^{L}^{C}^{e}

1+ K_{L}C_{e} (2.11)

Where q*e* is the amount of solute adsorbed per unit weight of adsorbent (mg/g), C*e*

is the equilibrium concentration of the solute in the solution (mg/L), *q**m* the
maximum adsorption capacity (mg/g), and *K**L* is the constant related to the free
energy of adsorption (L/mg). Table 2-6 below shows the different forms of
linearized Langmuir equations and the method to estimate the Langmuir constants
*q**m* and K*L*.

**Table 2-6: Isotherm models and their linear forms **

**Isotherms ** **Equation ** **Linear Form ** **Plot **

Langmuir 1

𝑞_{𝑒}= 𝑞𝑚𝐾𝐿𝐶𝑒

1 + 𝐾_{𝐿}𝐶_{𝑒}
1

𝑞_{𝑒}= 1

𝑞_{𝑚}𝐾_{𝐿}𝐶_{𝑒}+ 1
𝑞_{𝑚}

1
𝑞_{𝑒} 𝑣𝑠.1

𝐶_{𝑒}
Langmuir 2

𝑞𝑒= 𝑞_{𝑚}𝐾_{𝐿}𝐶_{𝑒}
1 + 𝐾_{𝐿}𝐶_{𝑒}

𝐶_{𝑒}
𝑞_{𝑒}= 1

𝑞_{𝑚} 𝐶𝑒+ 1
𝑞_{𝑚}𝐾_{𝐿}

𝐶_{𝑒}
𝑞_{𝑒} 𝑣𝑠. 𝐶𝑒

Langmuir 3

𝑞_{𝑒}= 𝑞𝑚𝐾𝐿𝐶𝑒

1 + 𝐾_{𝐿}𝐶_{𝑒} 𝑞_{𝑒}= − 1
𝐾_{𝐿}

𝑞𝑒

𝐶_{𝑒}+ 𝑞_{𝑚} 𝑞_{𝑒} 𝑣𝑠.𝑞𝑒

𝐶_{𝑒}
Langmuir 4

𝑞𝑒= 𝑞_{𝑚}𝐾_{𝐿}𝐶_{𝑒}
1 + 𝐾𝐿𝐶𝑒

𝑞_{𝑒}
𝐶𝑒

= −𝐾𝐿𝑞𝑒+ 𝑞𝑚𝐾𝐿

𝑞_{𝑒}
𝐶𝑒

𝑣𝑠. 𝑞𝑒

Langmuir 5

𝑞_{𝑒}= 𝑞𝑚𝐾𝐿𝐶𝑒

1 + 𝐾_{𝐿}𝐶_{𝑒}
1

𝐶_{𝑒}= 𝐾_{𝐿}𝑞_{𝑚} 1

𝑞_{𝑒}− 𝐾_{𝐿} 1
𝐶_{𝑒} 𝑣𝑠.1

𝑞_{𝑒}

**2.6.1.3.2 Freundlich Isotherms **

This isotherm is an empirical equation used to describe a heterogeneous system.

Freundlich equation can be expressed as:

q_{e}= K_{F}C_{e}

1

n (2.12)

The linear form of Freundlich equation can be expressed as:

log q_{e} = log K_{F}+ ^{1}

nlog C_{e} (2.13)

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Where *K**F *and *1/n are Freundlich constants. K**F* indicates the adsorption capacity
and 1/n is the heterogeneity factor. The values of n and K*F* are calculated from the
slopes and intercepts of the linear plots of log q*e* vs. log C*e*.

The Langmuir-Freundlich equation can be expressed as:

q_{e}= ^{K}^{L}^{q}^{m}^{C}^{e}

1 n

1+ KLC_{e}
1
n

(2.14)

**2.6.1.3.3 Dubinin and Radushkevich (D-R) Isotherm **

The D-R isotherm is generally used to describe the sorption of a single solute system.

The D-R model is analogous to Langmuir isotherm and it also rejects the homogenous surface or constant adsorption potential (Kaur et al., 2013). It is expressed as:

q_{e}= q_{s}exp(−K_{DR}ε^{2}) (2.15)

ε = RT In (1 + ^{1}

C_{e}) (2.16)

Where K*DR* is D-R isotherm constant (mol^{2}/KJ^{2}), ε is the Polanyi potential, q*s* is the
isotherm saturation capacity (mg/g), R is the universal gas constant (8.314 Jmol^{-1}K^{}

-1) and T is the temperature in Kelvin (K).

Eq. (2.15) can be linearized as shown in Eq. (2.17)

In q_{e} = In q_{s}− K_{DR}ε^{2} (2.17)

**2.6.1.3.4 Temkin Isotherm **

This model takes into account the interactions between adsorbent – adsorbate. The model suggests that the sorption energy (function of temperature) of all molecules in the layer will decrease linearly rather than logarithmically with coverage (Kaur et al., 2013). The model is given by the following equation:

q_{e}= ^{RT}

b In (A_{T}C_{e}) (2.18)

q_{e}= ^{RT}

b In A_{T}+ (^{RT}

b) In C_{e} (2.19)

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B = ^{RT}

b (2.20)

q_{e}= B In A_{T}+ B In C_{e} (2.21)

Where *A**T *is the Temkin isotherm equilibrium binding constant (L/g), *b is the *
Temkin isotherm constant, R is the universal gas constant (8.314 J/mol/K), T is the
Temperature at 298 K and B is the constant related to heat of sorption (J/mol). The
constants A*T* and B can be calculated from the linear plot of q*e* vs. In C*e*.

**2.6.1.4 Adsorption column models **

The performance of a column is evaluated through breakthrough curves. The
effluent adsorbate concentration (C*t*) from the column that reaches about 5 % of the
influent adsorbate concentration (C*0*) is the breakthrough point. The point where
the effluent concentration reaches 95 % is called the “point of column exhaustion”.

The breakthrough curve can be obtained by plotting the dimensionless
concentration *C**t**/C**0* vs *t or volume of effluent. The effluent volume, V**eff* (mL), is
calculated from the following equation (Xu et al., 2013):

Veff = Q x ttotal (2.22)

The total mass of adsorbate, q*total* (mg) adsorbed at specific column parameters can
be calculated from the following equation:

q_{total}= ^{Q}

1000 ∫_{0}^{total}C_{ad} dt= ^{Q}

1000 ∫_{0}^{total}(C_{0}− C_{t})dt (2.23)
Where Q is the volumetric flowrate (mL/min), t*total* is the total flow time (min), C*ad*

is adsorbed adsorbate concentration (mg/L). The integral in Eq. (2.23) is equal to the area in the breakthrough curve.

Maximum capacity of the column or equilibrium of adsorbate uptake per unit mass
of adsorbent, q*eq (exp) *(mg/g), is calculated as following:

q_{eq(exp)}= ^{q}^{total}

M (2.24)

Where M is the dry weight of resin packed in the column (g).

Total amount of adsorbate passing from the column (Wtotal) and total removal percentage of the adsorbate (Y %) are calculated from the following equation:

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W_{total} = ^{C}^{0}^{Qt}^{total}

1000 (2.25)

Y (%) = ^{q}^{total}

W_{total} X 100 (2.26)

The empty bed contact time (EBCT) in the column is described as:

EBCT (min) = bedvolume (mL)

flowrate (mL min⁄ ) (2.27)

A successful design of a column adsorption process requires prediction of the concentration-time profile or breakthrough curve for the effluent. Prior to the pilot-scale and industrial applications, lab-pilot-scale column studies should first be described and analysed. Over the years, several mathematical models have been developed for predicting the dynamic behaviour of a column namely; Thomas, Yoon-Nelson, Adams-Bohart and Clark models (Xu et al., 2013)

**2.6.1.4.1 Thomas Model **

The Thomas model is one of the most general and widely used theoretical methods to described column performance (Suksabye et al., 2008). This model behaviour matches the Langmuir kinetics of adsorption – desorption and obeys second-order reversible reaction kinetics. The expression by Thomas for an adsorption column is given below:

Ct

C0 = ^{1}

1+exp[kTHq0x ʋ⁄ − k_{TH}C0t] (2.28)

Where *k**TH *is the Thomas rate constant (mL/mg. min); *q**0* is the maximum solid
phase concentration (mg/g); x is the amount of adsorbent in the column (g); C*0* and
*C**t* are the inlet and outlet concentrations (mg/L) of the adsorbate at time *t *
respectively; ʋ is the flowrate (mL/min). The value of t is the flow time (min), (t =
*V**eff**/ʋ, V**eff* is effluent volume at time t).

The linearized form of the Thomas model is as follows:

In (^{C}^{0}

Ct− 1) = ^{k}^{TH}^{q}^{0}^{x}

ʋ − k_{TH}C_{0}t (2.29)

The values of k*TH* and q*0* can be obtained by the slope and intercept from plot of In
*(C**0**/C**t** – 1) vs. t *

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**2.6.1.4.2 Yoon – Nelson Model **

Yoon and Nelson developed a simple model that is based on the assumption that the rate of decrease in the probability of adsorption of an adsorbate molecule is proportional to the probability of the adsorbate breakthrough on the adsorbent (Ahmad and Hameed, 2010). The Yoon – Nelson equation for a single component system is expressed as (Han et al., 2009):

𝐶𝑡

𝐶0− 𝐶𝑡 = exp(𝑘_{𝑌𝑁} 𝑡 − 𝜏𝑘_{𝑌𝑁}) (2.30)

C_{t}

C0 = ^{1}

1+exp[K(τ−t)] (2.31)

The linearized model for Eq. (2.31) can be expressed as:

In ^{𝐶}^{𝑡}

𝐶0− 𝐶𝑡 = 𝑘_{𝑌𝑁} 𝑡 − 𝜏𝑘_{𝑌𝑁} (2.32)

Where 𝜏 is the time required for 50 % adsorbate breakthrough (min), k*YN* is the rate
constant (1/min) and t is the sampling time (min). The value of k*YN* and 𝜏 can be
found by plotting the graph of In (C*t**/(C**0** – C**t**)) versus t. *

**2.6.1.4.3 Adams – Bohart Model **

The Adams – Bohart model assumes that the adsorption rate is proportional to both the residual capacity of the adsorbent and the concentration of the adsorbing species.

The model is used for the description of the initial part of the breakthrough curve, expressed as (Ahmad and Hameed, 2010):

𝐶𝑡

𝐶_{0} = exp (𝑘_{𝐴𝐵}𝐶_{0}𝑡 − 𝑘_{𝐴𝐵}𝑁_{0}^{𝑍}

𝐹) (2.33)

Equation (33) can be linearized as:

In ^{C}^{t}

C0 = k_{AB}C_{0}t − k_{AB}N_{0}^{Z}

F (2.34)

Where C*0* and C*t* (mg/L) are the inlet and effluent concentration, k*AB* (L/mg min) is
the kinetic constant, *F (cm/min) is the linear velocity calculated by dividing the *
flowrate by the column sectional area, *Z (cm) is the bed depth of column and N**0*

(mg/L) is the saturation concentration. From this equation, values describing the
characteristics operational parameters of the column (k*AB* and N*0*) can be determine
from a plot of C*t**/C**0* against t.

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**2.6.1.4.4 Clark Model **

The Clark model is based on the use of a mass-transfer concept in combination with Freundlich isotherm (Nouri and Ouederni, 2013). Clark’s model can be expressed as:

Ct

C_{0} = ( ^{1}

1+Ae^{−rt})^{1 (n−1)}^{⁄} (2.35)

The values of A and r can be obtained from a nonlinear plot of C*t**/C**0* against t at a
given bed height and flow rate.

The linearized form of the model can be represented as:

In [(^{C}^{0}

Ct)^{n−1}− 1] = −rt + In A (2.36)

Where n is the Freundlich parameter, A and r (1/min) are the Clark constants. A and
*r are determined from the slope and the intercept of plot of In [(C**0**/C**t**)*^{n-1}* – 1] vs. t. *

In this review, adsorbents will be classified into three classes: (1) activated carbon adsorbents, (2) carbon nanotubes adsorbents and (3) low-cost adsorbents.

**2.6.1.5 Activated carbon adsorbents **

Activated carbon is an extremely effective adsorbent widely used to remove heavy metal contaminants in drinking water due to its high surface area resulting from large micropore and mesopore volumes (Fu and Wang, 2011). However, there has been an increase in production price of commercial coal based activated carbon due to depletion of the source. This has raised concern about searching for a cheaper alternative from renewable and cheaper precursors (Demiral and Güngör, 2016).

Burdinova et al (2006) also studied arsenic (III) removal from aquatic solutions at different concentrations and pH by using four different activated carbons from solvent extracted olive pulp and olive stone waste materials. Other alternative feedstocks proposed for the preparation of activated carbon are bones, blood, fish, coconut shell, rice hulls, refinery waste leather waste rubber waste etc. Adsorption capacity depends activated carbon properties, adsorbate chemical properties, temperature, pH and ionic strength (Mohan and Pittman Jr., 2007).

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**2.6.1.6 Carbon nanotubes adsorbents **

Carbon nanotubes (CNTs) have been increasingly studied for removing various contaminants from aqueous solutions due to their large surface area, high porosity, low density and hollow structure (Ihsanullah et al., 2016). As a new adsorbent, CNTs have been tested on removing chromium (VI) (Di et al., 2004), lead (II) (Wang et al., 2007) and nickel (Kandah and Meunier, 2007). CNTs are categorized as single-walled nanotubes (SWNTs) and multi-walled nanotubes (MWNTs). The mechanisms by which metal ions are adsorbed onto the CNTs is attributed to chemisorption, physisorption, electrostatic interaction, ion exchange and surface complexation (Fu and Wang, 2011; Ihsanullah et al., 2016). Ntim and Mitra (2012) conducted arsenic removal from water using a multiwall carbon nanotube-zirconia nanohybrid (MWCNT – ZrO2). The adsorption isotherms fitted both Langmuir and Freundlich isotherms and the maximum adsorption capacity for As (III) and As (V) were 2 mg/g and 5 mg/g respectively. Tawabini et al. (2011) conducted a study to remove As (III) from water using modified multi-walled carbon nanotubes (MCNTs). MCNTs modified with iron oxide (Fe-MCNTs) removed about 77.5 % of As (III) while MCNTs modified with carboxyl group (COOH-MCNTs) removed only 11 % at pH 5.

**2.6.1.7 Low-cost adsorbents **

Low-cost adsorbents have been studied as a substitute for activated carbon for removing heavy metal ions. Some of the low-cost adsorbents include (1) agricultural product and by-products, (2) industrial by-products/waste, (3) soils and constituents and (4) biosorbents. Agricultural by-products such as untreated rice husk (Agrafioti et al., 2014) and lignite and peat (Allen et al., 1997; Mohan and Chander, 2006) have also been studied. Manning and Goldberg (1996) studied the adsorption of arsenate on kaolinite, montmorillonite and illite. Lastly different forms of inexpensive biosorbents have been studied to remove heavy metals such as chitosan (Elson et al., 1980), fungal organisms (Pokhrel and Viraraghavan, 2006), eggshell (Park et al., 2007) and human hair (Wasiuddin et al., 2002). Biosorption is still in the experimental phase and widely favoured due to low cost and rapid adsorption but separation of the adsorbate is difficult after adsorption (Fu and Wang, 2011).

45
**2.6.1.8 Other commercial adsorbent **

* 2.6.1.8.1 New Zealand Ironsand (NZIS): - is a black, heavy, magnetic iron that *
originates as crystals within volcanic rocks before being transported to the coast by
rivers. In New Zealand, it occurs mainly in the North Island (Panthi and Wareham,
2014). The main iron-based mineral in NZIS is magnetite (Fe3O4) and/or
titanomagnetite (Fe3-xTixO4 (0 ≤ x ≥ 1), and other minerals include titanium oxide
(TiO2) and vanadium oxide. Panthi and Wareham (2014) and (2011) carried out
study on the kinetics and adsorption of arsenic onto New Zealand ironsand. The
maximum adsorption capacity for As (III) and As (V) using NZIS was 1.5 mg/g
and 0.5 mg/g respectively.

* 2.6.1.8.2 DMI-65: - is an extremely powerful silica sand based catalytic water *
filtration media designed for the removal of iron and manganese without the use of
potassium permanganate. DMI-65 acts as an oxidation catalyst with immediate
oxidation and filtration of the insoluble precipitate derived from this oxidation
reaction. It also known to remove arsenic, aluminium and other heavy metals under
certain conditions. Other advantages of DMI-65 include operating at a wide pH
range (5.8 – 8.6), operating at a temperature up to 45

^{o}C, long life and also operating at high flow rates. It found application in mining, protecting reverse osmosis membranes, drinking water applications, arsenic removal, cooling towers and boilers and in industrial applications (“Quantum Filtration Medium,” 2019).

* 2.6.1.8.3 Molecular imprinted polymers (MIPs): - is prepared with a reaction *
mixture composed of a template, a functional monomer (or two), a cross-linking
monomer (or two), and a polymerization initiator in a solvent. During
polymerization, there is a complex formation between the template and the
functional monomer, and the complex is surrounded by the surplus cross-linking
monomer, yielding a three-dimensional polymer network where the template
molecules are trapped after completion of polymerization (Cheong et al., 2013).

Reaction conditions such as formulation of MIP reaction mixture includes choice of cross-linking monomer, functional monomer, a porogenic solvent, reaction temperature, and time govern the properties, physical appearance, morphology, and performance of MIP. Since MIP was invented in 1972 (Song et al., 2009), it has

46

found applications in chromatography, sample pre-treatment, purification, catalysts, sensors and drug delivery.