**1. Introduction**

High-salinity resources, such as seawater, are being used in the mining industry in countries such as Australia, Chile, and Indonesia. At first, saltwater was proceeded by desalination plants, primarily

through reverse osmosis (RO). Unfortunately, RO has created the new challenge of concentrated brine by-product, which is released into the environment; the effluent has a high temperature and a high demand for electricity, which involves fossil fuel consumption. Therefore, the direct use of seawater without ion removal is an attractive strategy that has recently been implemented in many mining industries. In this matter, engineers and researchers are constantly studying certain operational complexities. Some of these studies focus on the corrosion and incrustation of pipelines, the precipitate complexes, and the buffer effect of seawater. An example is the concentration of Cu/Mo ores, where a high level of pyrite is found, which drastically hinders the efficiency of the flotation process. The depression of pyrite in freshwater lime is used to bring the operation to an alkaline pH condition (>10.5), where the hydrophilic ferric hydroxide is formed. Nevertheless, this condition cannot function in seawater because the formation of solid Ca/Mg complexes strongly decreases the recovery of molybdenite [1,2] and chalcopyrite [3,4]. There is a debate as to the proper explanation of these findings in the sulphide flotation [5–8], but the recovery of water for the sustainability of the mining industry is more important. This is necessary due to the high cost of transporting seawater to mining sites; in Chile, the mining site can be as much as 4000 m above sea level. An essential process in the recovery of water is the thickening stage, where efficiency is linked to the flocculation of the suspended particles, which promotes solid–liquid separation. The flocculants are long water-soluble polyelectrolytes that can bridge multiple particles and form aggregates that are sedimentary because of the effects of gravity. The conventional flocculant used in copper mining is hydrolyzed polyacrylamide (HPAM) with a high molecular weight, which gives high settling rates, low densities, and proper rheological properties of concentrated underflows at relatively low doses and low cost [9,10]. Several efforts have been made in the study of HPAM behaviour in the solution [11,12] and high-saline conditions [13–15]. These studies provide enough proof that bridging flocculation may benefit from a saline medium because ions can act as a bridge between the anionic solid particles and anionic flocculants, commonly known as ion binding. However, the concentrated salt can reduce the size of the reagents by an excess of charge neutralization. These studies were performed on chloride salt solutions; seawater has a complex mix of ions, where Ca and Mg are present, and as we know, at high-alkaline conditions, they precipitate into a hydroxide complex. Recent studies show that kaolinite can adsorb several heavy metal ions [16,17] and also hydroxide complexes [18]; therefore, these precipitates have a strong interaction with solid particles and possibly, the flocculants. These results provide evidence that precipitates can affect the flocculation mechanism and negatively affect the thickener performance. However, this might be avoided by removing magnesium ions prior to incorporating the seawater in the operations.

A good strategy to analyze flocculation performance is by monitoring the aggregate size distribution over time by the focused beam reflectance measurement (FBRM), which has the advantage of being employed directly to the slurry without sampling or the need for dilution [19–22]. In flocculated systems, a maximum floc size is achieved after a short time following flocculant addition; it then follows a decay that emerges from the fragmentation of the aggregates and polymer depletion. Using the FBRM has provided an excellent method to analyze flocculation parameters such as the maximum aggregation size or fragmentation rate [23–26]. In this context, many researchers have described the kinetics of aggregate growth and fragmentation over time by population balance models (PBM) [27–30], which have practical applications in a wide variety of systems [31–35]. The PBM is based on the work by Smoluchowski [27,36], which describes how aggregates evolve over time. Such evolution depends on the mechanisms of the aggregation and the rupture of the equations. To update the PBM to flocculation, it is necessary to improve the physical description of the aggregates by (1) updating the collision efficiency to a time-dependent flocculant depletion [30,35], and (2) to use the fractal dimension as an indicator of the irregular structure of the aggregates [27,36–40]. Recently, we have proven that, for these flocculated systems, it is possible to use a constant fractal dimension when the shear rate is below 200 s−1.

In this work, we use PBM to describe the flocculation kinetics of tailing particles using treated seawater with the magnesium removed in order to improve the water recovery and the performance of the thickening stages. Then, we analyze the implications of precipitates being present for phenomena such as collision frequency, collision efficiency, fragmentation rate, and floc permeability. The outcomes of this work are of particular interest to mining industries that use seawater in concentration stages, and the implementation of this plan might allow for sustainable results without the need to totally remove seawater salts by reverse osmosis.

#### **2. Methodology**

### *2.1. Materials*

The seawater (SW) was obtained from the San Jorge Bay in Antofagasta (Chile); to eliminate bacterial activity, the SW was filtered at 1 μm using a UV filter system. This water had a conductivity of 50.4 mS/cm, while its ionic composition was the following: 10.9 g/L Na+, 1.38 g/L Mg2+, 0.4 g/L Ca2+, 0.39 K+, 19.6 Cl−, and 0.15 g/L HCO3 − [41].

Kaolin was acquired from Ward's Science, and a quantitative X-ray diffraction (XRD) analysis showed that it contained 84 wt% kaolinite (Al2Si2O5(OH)4) and 16 wt% halloysite (Al2Si2O5(OH)4·2H2O) (Figure 1). A D5000 X-ray diffractometer (Siemens S.A., Lac Condes, Chile) was used and the data were processed with Total Pattern Analysis Software (TOPAS) (Siemens S.A., Lac Condes, Chile). Quartz was acquired from a local Chilean store, where the SiO2 content detected by quantitative XRD was over 99 wt% (see Figure 2). Both quartz and kaolin had a density of 2.6 g/t. A Microtrac S3500 laser diffraction particle size analyzer (Verder Scientific, Newtown, PA, USA) was used. The analysis showed that 10% of the particles were smaller than d10 = 1.8 and 3.8 μm in the kaolin and quartz samples, respectively.

**Figure 1.** X-ray diffraction (XRD) for kaolin powder.

SNF 704, provided by SNF Chile S.A., was used as an anionic flocculant. The molecular weight was higher than 18 × 106. An initial stock solution was prepared at a concentration of 1 g/L. Then, it was mixed with a low RPM for 24 h before use, stored in a refrigerator, and discarded after two weeks in order to avoid the potential ageing effect. An aliquot of this stock flocculant solution was diluted at 0.1 g/L once a day for use in testing, with unused portions, then discarded. The flocculant dosages were determined in terms of grams of polymer per ton of dry solids (g/ton). The reagents used to modify the pH were lime and sodium hydroxide and were of analytical grade (greater than 98%).

**Figure 2.** X-ray diffraction (XRD) for quartz powder.

#### *2.2. Magnesium Removal*

The removal of magnesium was carried out by increasing the pH of the seawater with 0.06 M of lime in order to form hydroxide magnesium as a white precipitate. In parallel, bicarbonate was reacted with Mg to form magnesium carbonate [41]. After 30 min of intense mixing, the seawater was vacuum filtered, obtaining seawater with an ionic concentration of 10.9 g/L Na+, 0.01 g/L Mg2+, 2.35 g/L Ca2+, 0.39 K+, 19.6 Cl−, and 0.05 g/L HCO3.

#### *2.3. Flocculant-Suspension*

A suspension of 270 g was prepared at 8 wt%, with known masses of the solid phases to give mixtures containing 80 wt% quartz and 20 wt% kaolin. The suspension was vigorously mixed for 30 min using a 30 mm-diameter turbine type stirrer within a 100 mm-diameter vessel with a 1 L capacity. All the experiments were made by placing the stirrer 20 mm above the bottom of the vessel. Subsequently, the mixing rate was controlled at 250 rpm, and the volume of the solution (seawater and polymer) was added in a proportion fixed by the required polymer dosage.

#### *2.4. Batch Settling Tests*

These tests were conducted, after 30 s of flocculant-suspension mixing, by gently pouring the slurry into closed, 300 cm3 cylinders (35 mm internal diameter), and then slowly inverting the cylinder two times by hand (the whole cylinder rotation process took, in all cases, about 4 s). After 10 min of settling, the supernatant fluid was rescued and stirred in order to homogenize the suspended solids. Then, a 50 mL aliquot was used for turbidity measurements in a HANNA HI98713 turbidimeter (Hanna Instruments, Santiago, Chile), which performed ten readings in 20 s, delivering the average at the end of that period.

#### *2.5. Characterization of Aggregates*

The FBRM technique was used to record the chord length distribution of the aggregates. The probe was submerged vertically in the reaction vessel, 10 mm over the stirrer and 20 mm off-axis. The FBRM probe featured a laser that was focused through the sapphire window and scanned a circular path at a tangential velocity of 2 m/s. The backscattered light was then received when the laser beam intersected the path of the particle or aggregate. A chord length was determined from the duration of any unusual increase in the backscattered light intensity and laser velocity.

#### **3. Modeling**

The PBM equations used in this work are derived from several works [42–44] that discretize the aggregate size into the number *i* of the bins. Every bin is based on the classical geometric distribution for the aggregate volume (V*i*+1) = 2V*i*). The PBM equation is given by:

$$\begin{aligned} \frac{dN\_i}{dt} &= \sum\_{j=1}^{i-2} 2^{j-(i-1)} Q\_{i-1,j} N\_{i-1} N\_j + \frac{1}{2} Q\_{i-1,j-1} N\_{i-1} N\_{i-1} - \sum\_{j=1}^{i-1} 2^{j-i} Q\_{i,j} N\_i N\_j \\ &- \sum\_{j=1}^{i\_{\text{max1}}} Q\_{i,j} N\_i N\_j - S\_i N\_i + \sum\_{j=1}^{i\_{\text{max2}}} \Gamma\_{i,j} S\_j N\_j \end{aligned} \tag{1}$$

where *Ni* is the number concentration of the aggregates in bin *i*, whereas *N*1 is the number concentration of primary particles in bin 1. Every term on the right of the equation represents a physical process:

The superscript max1 is the maximum number of intervals used to represent the complete aggregate size spectrum; max2 corresponds to the largest interval from which the aggregates in the current range are produced.

#### *3.1. Aggregation Kernel*

The *Q* variable is the aggregation kernel, an expression that contains the collision frequency (β) and capture efficiency (α):

$$Q\_{i,j} = \beta\_{i,j} \alpha\_{i,j} \tag{2}$$

#### Collision Frequency β

It has been shown that fluid flow can penetrate through particle aggregates [45]. This means that the actual collision frequency is considerably lower than that predicted from rectilinear flow models. To add the permeability effects to the collision frequency, we use the parameter called "fluid collection efficiency" η. This measures the ratio between the flow passing through the aggregate and the flow approaching it. Veerapaeni and Wiesner [46] proposed a function to calculate the collision frequency, which includes the permeability and fractal dimension effects:

$$\beta\_{i,j} = \frac{1}{6} \left( \sqrt{\eta\_{\bar{l}}} d\_i + \sqrt{\eta\_{\bar{l}}} d\_j \right)^3 G \tag{3}$$

where, *di* and *dj* are the diameters of the aggregates sizes of *i* and *j*, respectively, *G* is the shear rate, and η is derived from the Brinkman' extension to Darcy's laws as a function of a dimensionless permeability (ξ) [47]:

$$\eta\_{i} = \frac{9(\xi\_{i} - \tanh \xi\_{i})}{(3\xi\_{i} + 2\xi\_{i}^{2} - 3\tanh \xi\_{i})} \tag{4}$$

where ξ*i* is defined as ξ*i* = *di*/2 √ *Ki* and *Ki* is the permeability; we use the expression from the work by Li and Logan [47]:

$$K\_i = \frac{d\_i^2}{72} \left( 3 + \frac{3}{1 - \phi\_i} - \sqrt[3]{\frac{8}{1 - \phi\_i} - 3} \right) \tag{5}$$

The porosity φ is related to the fractal dimension (*df*) using the expression by Vainshtein et al. [48]:

$$\phi\_{\bar{i}} = 1 - \mathcal{C} \left(\frac{d\_{\bar{i}}}{d\_0}\right)^{d\_f - 3} \tag{6}$$

where *C* is a packing coefficient (generally assumed to be 0.65) and *d*0 the primary particle diameter. *di* and *d*0 are related by the expression proposed by Mandelbrot [49]:

$$d\_i = d\_0 \left(\frac{2^{i-1}}{C}\right)^{\frac{1}{d\_f}} \tag{7}$$

Collision Efficiency α

There are several expressions for collision efficiency, which depend on the type of aggregate and the additives. In this case, we use an expression to represent the effect of high-weight polymers; this is the polymer depletion and it rearranges on the adsorbed surface of the particles. This has been implemented in a recent work by Vajihinejad and Soares [35], which showed an exponential decay between two fitted parameters:

$$
\alpha\_{i,j} = (\alpha\_{\text{max}} - \alpha\_{\text{min}})e^{-k\_ld} + \alpha\_{\text{min}} \tag{8}
$$

where α*max* is the maximum collision efficiency, α*min* is the minimum collision efficiency (at steady-state conditions), and *kd* is the collision efficiency decay constant (*s*−1).

#### *3.2. Breakage Kernel*

In regards to Equation (1), the *S* term is the breakage rate and Γ is the distribution breakage, which represents the breakage kernel. The S term is difficult to predict since there is no theory and is usually fitted to the size distribution data. In this case, we use a power-law function of the aggregate mass, as proposed by Pandya and Spielman [50]:

$$S\_{\bar{l}} = s\_1 G^{s\_2} d\_{\bar{l}} \tag{9}$$

where *s*1 and *s*2 are the fitted parameters.

To simplify the distribution breakage, we use a binary distribution, where an aggregate breaks into two pieces of equal mass. That is:

$$
\Gamma\_{i,j} = \begin{cases}
\frac{V\_j}{V\_i} & \text{for } j = i + 1 \\
0 & \text{for } j \neq i + 1
\end{cases}
\tag{10}
$$

#### *3.3. Shear Rate*

The shear rate required by the aggregation and breakage kernels is calculated from:

$$G = \left(\frac{\varepsilon \rho\_{\text{sus}}}{\mu\_{\text{sus}}}\right)^{\frac{1}{2}}\tag{11}$$

where ε is the average energy dissipation rate:

$$
\varepsilon = \frac{N\_p N^3 D^5}{V} \tag{12}
$$

where *Np* is the impeller power number (0.6 in our case for a plane disk with gentle agitation [51]), *N* is the rotation speed, *D* and *V* are, respectively, the diameter of the impeller and the working volume of the vessel. The density of the suspension ρ*sus* is calculated from:

$$
\rho\_{\rm sus} = \left(\frac{w}{\rho\_{\rm s}} + \frac{1 - w}{\rho\_{\rm w}}\right)^{-1} \tag{13}
$$

where *w* is the solid mass fraction of the solution, ρ*s* and ρ*w* are, respectively, the solid and water density. Finally, the viscosity of the solution μ*sus* is measured.

#### *3.4. Solution*

To resolve the PBM in Equation (1), we use a solver based on the numerical differentiation formula for stiff ODEs (ode23s) in MATLAB. The optimization was performed with the MATLAB function *fminsearch*, which uses the Nelder–Mead direct search to find the minimum in an unconstrained multivariable function. The objective function (*OF*) used to optimize the parameters is given by:

$$OF(a\_{\max}, a\_{\min}, k\_{d'}, s\_1, s\_2) = \sum\_{t\_i}^{t\_f} \left(d\_{\exp} - d\_{\text{mod}}\right)^2 \tag{14}$$

where *dexp* is the experimental diameter of the particles, and *dmod* is the model diameter obtained by:

$$d\_{mod} = \frac{\sum\_{i=1}^{\max} N\_i d\_i^4}{\sum\_{i=1}^{\max} N\_i d\_i^3} \tag{15}$$

Two criteria validate the model's fit and predictions: One is the coefficient of determination (*R*2), which measures the closeness of the model values to the experimental values. That is:

$$R^2 = 1 - \frac{\sum\_{i=1}^{\max} \left( d\_{a\xi\xi, c\exp, i} - d\_{a\xi\xi, m\alpha d, i} \right)^2}{\sum\_{i=1}^{\max} \left( d\_{a\xi\xi, c\exp, i} - d\_{a\xi\xi, c\exp} \right)^2} \tag{16}$$

and the other is the quality of the fit:

$$\text{Gof}(\%) = 100 \frac{d\_{\text{g.g.exp}} - \text{std}}{d\_{\text{g.g.exp}}} \tag{17}$$

std stands for the standard error calculated from:

$$\text{stdd} = \left(\frac{1}{n-f} \sum\_{t\_i} {t\_i} \left(d\_{\text{ag.g.}} - d\_{\text{ag.g.}} \text{rad}\right)^2\right)^{\frac{1}{2}} \tag{18}$$

where *n* is the number of data values and *f* is the number of parameters to be fitted. A *GoF* of 90% or higher means that the proposed model can predict the flocculation kinetics. Finally, conservation of the total volume of particles is verified after every integration in order to ensure that the simulations maintain the particle population.

#### **4. Results**

#### *4.1. Input Parameters and Distribution*

Three cases were studied: Seawater at pH 8 (SW pH 8), seawater at pH 11 (SW pH 11), and treated seawater at pH 11 (T-SW pH 11), with Mg removed. Solving the PBM equations requires a set of input parameters (Table 1). With these parameters, the shear rate can be calculated from Equations (11)–(13) and the rest are used for the PBM equation definitions. A particle number concentration is needed for each size class and for each unit of volume of suspension *N*0,*i*. For that, a number distribution is obtained from experimental results through *N*0,*i* = φ*v*(*d*0,*i*)/*V*0,*i*, where *v*(*d*0,*i*) is the volume fraction of particles with diameter *d*0,*i*, obtained from Equation (7), φ is the solid volume fraction and *V*0,*i* is the volume of the primary particles following the geometric progression *V*0,*i* = 2*i*−1*V*1. To obtain the *v*(*d*0,*i*), a volume distribution is needed. Figure 3A shows an experimental volume fraction distribution provided from the FRPM probe for three case studies before the flocculant is added; in this case, all distributions are similar. To calculate the fractal dimension from the settings test, we follow the

procedure by Heath et al. [20]. The fractal dimension results are shown in Figure 3B. The fractal dimension is higher when the flocculant dose increases. At pH 11, the fractal dimension decreases abruptly because the solid magnesium precipitates would produce open structures. Nevertheless, the treated seawater overcomes this phenomenon, giving similar structures to those of the pH 8 system.

**Table 1.** Input parameters and conditions.

**Figure 3.** (**A**) Normalized initial volume distribution of particles of synthetic tailings in seawater at a mixing rate of 150 rpm. (**B**) Fractal dimension from the settling experiments.

#### *4.2. Flocculation Kinetics and Modeling*

The results of the experimental data are plotted in Figure 4 for the three cases studied in this work. We see that the flocculant doses increase the size of the aggregates, which is similar to other work, as shown in Reference [35]. For SW pH 8, the aggregate increases to a maximum value of 300 μm for the flocculant dose of 28 g/ton. Smaller values are obtained when the flocculant dose decreases. If we increase the pH to 11 (SW pH 11), we see results similar to those in Figure 4B, where the aggregate falls to a smaller value of ~100 μm for all the flocculant doses in this work. This behaviour is primarily related to the formation of hydroxide complexes that hinder aggregate formation. Later, we see the results of Mg removal from the seawater before being added to the slurry at pH 11 (T-SW pH 11). In this case, we have a higher maximum aggregation than when Mg is present as a hydroxide. From this, we can clearly see that Mg hydroxide presence hinders the aggregation process drastically. Then, the PBM modeling in the figures are included in the results as continuous lines. As we see from Table 2,

there is a good agreement for the cases where the mean aggregate diameter is higher, but there are limitations associated with the model for the examples that are small in size.

**Figure 4.** Flocculation kinetics of synthetic tailings in seawater as a function of flocculant doses (mixing rate 150 rpm). Solid circles correspond to experimental data and solid lines to the best fit with the population balance model (PBM). (**A**) Seawater (SW) pH 8. (**B**) SW pH 11. (**C**) Treated (T)-SW pH 11.

**Table 2.** Quantitative results of *GoF* and *R*2 when the PBM model is used.

#### *4.3. Optimized Parameters*

The model fitted five parameters: The maximum and minimum collision efficiency (α*max*, α*min*), the collision efficiency decay constant (*kd*), and two breakage rate kernel parameters (*s*1 and *s*2). The first three parameters are related to the aggregation kernel and are shown in Figure 5. The α*max* show a steady increase when the flocculant dose increases. This reflects the activity of the flocculant to aggregate more significant quantities of solids at higher doses. Interestingly, the α*min* has small values compared to the α*max*, and all remain below 0.02, meaning that, at steady state (where α*min* becomes significant), the breakage kernel must also be small. *kd*, as we found in previous results, must follow the inverse tendency of the *amax*, *amin* because the higher the aggregates, the greater the probability of flocculant depletion. In this case, the fitted parameters also show that behaviour. Finally, if we compare the three trials, we see that the parameters of the SW at pH 8 are similar of the T-SW at pH 11, which implies that the removal of the Mg could keep the aggregation performance similar to that of pH 8. Finally, the two parameters for the breakage kernel are plotted in Figure 6. As discussed above, if the s1 is small, the *amin* is also small. This trend must be followed in order to satisfy steady-state conditions in the aggregation process. The *s*2 parameters show values between 1 and 2, but as the *s*1 parameter is small, its contribution is neglected from the global behavior. From this, we see that high dosage increments contribute to the aggregate kernel rather than the breakage kernel.

**Figure 5.** Optimum aggregation parameters vs. shear rate for constant and variable fractal dimension: (**A**) Maximum and (**B**) minimum collision efficiency and (**C**) collision efficiency decay constant *kd*.

**Figure 6.** Optimum breakage parameters vs. shear rate for constant and variable fractal dimension: (**A**) S1 and (**B**) collision efficiency decay constant *kd*.

#### *4.4. Aggregation, Breakage, and Permeability Modeling*

Once we have the optimized parameters, we can compute the physical parameters: Collision efficiency, collision frequency, breakage rate, and permeability with the mean aggregate size from the results in Figure 4. Collision frequencies are shown in Figure 7. We see that high doses of flocculant increase the collision frequency. This can be accounted for by the polymer concentration, which has an extended configuration that increases the likelihood of collisions. For SW pH 11, the small aggregates generated by the appearance of magnesium complexes lead to a low value in the collision frequency. This does not present significant changes with varied flocculant doses. Calculations for collision efficiency are presented in Figure 8 and follow the same trend as the collision frequency, where SW pH 11 has no significant values. In this case, only below 20 s is the parameter relevant to the kinetics, and drops to zero values after 20 s. Breakage rates are shown in Figure 9, which shows that, at SW pH 8, we see that low doses generate higher values because less flocculant cannot maintain the aggregates. We see similar behaviour in SW pH 11 and T-SW pH 11; this is accounted for because of the fractal dimensions. For SW at pH 11, the fractal dimension is low, and the diameter increase coincided with an increased breakage rate. For T-SW at pH 11, the fractal dimension is high, decreasing the aggregate diameter and decreasing the breakage rate, even if the aggregates are big. Finally, permeability results were plotted in Figure 10; we can see that very porous aggregates are found for SW pH 8 and a dose of 21 g/ton. This can be accounted for by the fractal dimension and the large mean size of the aggregates. For SW at pH 11, we see that permeability is lower even if the fractal dimension is small, because the size of the aggregate is small in these cases. Interestingly, the permeability value for T-SW pH 11 is low, as compared to other cases, even if the aggregate sizes are similar to those in SW pH 8. This is because the fractal dimension is high and creates dense aggregates that have little permeability.

**Figure 7.** Collision frequency for the experimental data for (**A**) SW pH 8, (**B**) SW pH 11, and (**C**) T-SW pH 11.

**Figure 8.** Collision efficiency for the experimental data for (**A**) SW pH 8, (**B**) SW pH 11, and (**C**) T-SW pH 11.

**Figure 9.** Breakage rate for the experimental data for (**A**) SW pH 8, (**B**) SW pH 11, and (**C**) T-SW pH 11.

**Figure 10.** Permeability for the experimental data for (**A**) SW pH 8, (**B**) SW pH 11, and (**C**) T-SW pH 11.

#### **5. Conclusions**

Experiments and modeling, through a population balance model (PBM), are used to characterize the particle aggregation of flocculated tailings on untreated and treated seawater (SW) at different flocculant doses. It was found that the Mg hydroxide complex presence could hinder the aggregate kinetics drastically over time; this generates small aggregates between 50–100 microns at pH 11. If the Mg is removed before the flocculation takes place, we can obtain larger aggregates, from 100–200 microns, at the same pH. The results of T-SW pH 11 are in good agreement with the SW pH 8 achievements, which may imply that pH is no longer a restrictive parameter for the aggregation process. The modeling of the aggregation was done with PBM, where fitted parameters can represent the flocculation aggregation kinetics. In this case, the effect of flocculant dose can neglect the impact of the breakage kernel, and the aggregation kernel dominates the flocculation. Additionally, it is found that, without Mg ions, the pH has little effect on both the flocculation kinetics and PBM fitted parameters. Only the fractal dimension shows us the main difference between SW pH 8 and T-SW pH 11, where it is reflected in its permeability. As we can see, experiments and modeling show us that the Mg removal is the main component that hinders particle aggregation, and without its presence, the process can operate similarly at neutral pH and high pH.

**Author Contributions:** All of the authors contributed to the analysis of the results and writing the paper. All authors have read and agreed to the published version of the manuscript

**Funding:** This research was funded by ANID Fondecyt 11171036, ANID ACM 170005, and Centro CRHIAM Project ANID/Fondap/15130015.

**Acknowledgments:** Gonzalo Quezada and Ricardo I. Jeldres thank the Centro CRHIAM Project ANID/Fondap/15130015. Ricardo I. Jeldres thanks ANID Fondecyt 11171036 and ANID ACM 170005. Pedro Robles thanks the Pontificia Universidad Católica de Valparaíso for the support provided.

**Conflicts of Interest:** The authors declare no conflict of interest.

## **References**

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).


*2.2. Monitoring of Coral Reef Fishes and Corals*

Corals and fishes were surveyed at ten different reef sites on Chale Reef, with some transects on the seaward side of the reef and some on the landward side to provide a range of different reef conditions. Within each site, three belt transects of 5 × 50 m were placed haphazardly, but mostly parallel to the shore. Transects were located at least 150 m apart so as to achieve reasonable independence of the samples. Sampling was conducted around low tide (<70 cm) to provide standardized assessment conditions, while time of day and weather conditions varied through the study. During each survey, one person snorkeled in front, counting fishes using established underwater visual survey methodology [46], while a second person swimming behind laid out a 50 m tape along a track following the first snorkeler (this sequence was followed to avoid the fish being disturbed before they could be counted). For this study, eight fish families, which are known indicators of coral reef biodiversity, were preselected for the census: *Scaridae*, *Acanthuridae*, *Siganidae*, *Ephippidae*, *Pomacanthidae*, *Chaetodontidae, Lutjanidae*, and *Serranidae*, all of which are moderately speciose on Kenya's southern coast [47]. Additionally, on the first half of the transect (25-m length) the hard corals were identified to genus level and the colonies counted.


**Geographic Information and Communication Technologies for Supporting Smallholder Agriculture and Climate Resilience**

#### **Billy Tusker Haworth 1,\*, Eloise Biggs 2, John Duncan 2, Nathan Wales 3, Bryan Boruff 2 and Eleanor Bruce 4**

Received: 9 November 2018; Accepted: 5 December 2018; Published: 10 December 2018

**Abstract:** Multiple factors constrain smallholder agriculture and farmers' adaptive capacities under changing climates, including access to information to support context appropriate farm decision-making. Current approaches to geographic information dissemination to smallholders, such as the rural extension model, are limited, yet advancements in internet and communication technologies (ICTs) could help augment these processes through the provision of agricultural geographic information (AGI) directly to farmers. We analysed recent ICT initiatives for communicating climate and agriculture-related information to smallholders for improved livelihoods and climate change adaptation. Through the critical analysis of initiatives, we identified opportunities for the success of future AGI developments. We systematically examined 27 AGI initiatives reported in academic and grey literature (e.g., organisational databases). Important factors identified for the success of initiatives include affordability, language(s), community partnerships, user collaboration, high quality and locally-relevant information through low-tech platforms, organisational trust, clear business models, and adaptability. We propose initiatives should be better-targeted to deliver AGI to regions in most need of climate adaptation assistance, including SE Asia, the Pacific, and the Caribbean. Further assessment of the most effective technological approaches is needed. Initiatives should be independently assessed for evaluation of their uptake and success, and local communities should be better-incorporated into the development of AGI initiatives.

**Keywords:** climate change adaptation; livelihoods; geographic information; agriculture; resilience


*4.1. Animals and Experimental Design*

Male Sprague-Dawley rats weighing 250–580 g each were purchased from Samtaco Animal Corp. All rats were housed individually in a room at 23 ◦C (room temperature) under an alternating 12 h light/dark cycle. The rats were fed a commercial diet and provided with tap water ad libitum throughout the study. Food and water were made accessible ad libitum. This experiment was conducted in accordance with the National Institutes of Health Guide for the Care and Use of Laboratory Animals,

revised in 1996, and was approved by the Institutional Animal Care and Use Committee of Kyung Hee University (KHUAP(SE)-18-073, 05/30/2018). In this study, the rats were randomly assigned to three groups: untreated, naïve (Normal, *n* = 9); TMT injected with vehicle (TMT, *n* = 5); and TMT injected along with 50 mg/kg−1 Bean-PS (Bean-PS, *n* = 5). The rats were injected intraperitoneally (i.p.) with TMT (8.0 mg/kg, body weight) dissolved in 0.9% saline and then returned to their home cages. The TMT+Bean-PS mixture (50 mg/kg, oral administration (p.o.)) was dissolved in 10% hexane and orally administered for two weeks after TMT-induced neurodegeneration.

## *4.2. Drug Treatment*

PS was produced from soy lecithin by enzymatic transphophatidylation, and comprised a mixture of 90% phosphatidylserine (PS), 2% phosphatidylcholine (PC), and 6% phosphatidic acid (PA). The Bean-PS contained palmitic (17.7%), palmitoleic (1.3%), stearic (1.3%), oleic (14.4%), limoleic (61.2%), linolenic (1.4%), eicosapentaenoic acid (EPA), and other fatty acids (9.4%). PS and 10% hexane, used as a solvent for PS, were provided by Doosan Co. Glonet BG (Youngin, Korea) and the PS was stored in a freezer (−60 ◦C).


**2. Study Area**

Ontario is one of Canada's most important agricultural centers, with a wide variety of agricultural products and orchards. Major agricultural products in Ontario include corn, wheat and soybeans, barley, forage, oat, canola, etc. A region in southern of Ontario was selected as the study area. This region is located at 82.5 degrees West and 42.5 degree North. The agricultural products of this area include the three main crops of corn, wheat and soybeans (Figure 1). The area of the study region is about 4324 km2 and the area under corn, wheat and soybean cultivation in this area is 1218, 284 and 1087 km2, respectively. The study area in terms of climatic division, has a warm summer humid continental based on the Köppen climate classification. The average annual temperature and rainfall are 17.0 ◦C and 424 mm, respectively. Therefore, due to the weather conditions and long winters in this area, most agricultural products are harvested by September. Criteria for selecting the study area were (1) high diversity of agricultural products, especially important crops in Canada; (2) proximity of the harvest time of the three types of agricultural products available in the region and the different farms available for each product; (3) access to the suitable cloud-free satellite image corresponding to the harvest date of all three crops for that area; and (4) easy access of the area in order to conduct field studies.

**Figure 1.** Maps of (**a**) the geographical location of the study area, (**b**) the land crop classes of the study area and (**c**) the geographical location and spatial distribution of RCF samples of different land crops.


**7. Strategies to reduce oxidative stress in male reproduction**

Antioxidant supplementation has proven to be effective against male reproductive dysfunction *in vivo*. Recent reports have acclaimed significant attention due to the quality of their study design and demonstrated compelling evidence regarding the efficacy of antioxidants towards improving semen parameters. On the other hand, numerous clinical trials studying the effects of dietary antioxidants on semen parameters are still uncontrolled, focus on rather on healthy individuals or have indirect end-points of success. The dose and duration of antioxidant administration also need to be thoroughly examined and standardized. **Table 4** presents the most effective doses for the treatment of male subfertility based on currently available studies that explored the impact of antioxidant supplementation on sperm parameters.

ROS-induced damage may have significant clinical implications in the context of ARTs. Numerous reports have indicated that significantly increased ROS levels may occur in response to repeated cycles of centrifugation involved in conventional sperm preparation techniques used for ARTs [179]. Spermatozoa selected for ART often face OS and a high risk for DNA damage. When intrauterine insemination or *in vitro* fertilization (IVF) is used, such damage does not represent a cause of concern as damage to the sperm membrane lipids ensures that fertilization will not occur. However, in case of intracytoplasmic sperm injection is used, this natural selection barrier may be overlooked and sperm with DNA damage may

patients resulted in a higher sperm count and membrane integrity. ZnSO4

experimental group and TNF-α and antisperm antibodies decreased [176]

Coenzyme Q10 • Supplementation of 60 mg/day of coenzyme Q10 for 103 days led to an increase in the

with low fertilization rates due to male factor infertility [177]

**Table 4.** Most pronounced studies on the effects of oral antioxidant supplementation on male infertility.

an immunological role as T-helper cytokines and interleukin-4 levels increased in the

fertilization rate but had no effect on motility, morphology or concentration in 17 patients

• 60 patients with idiopathic asthenozoospermia who received 200 mg/day of coenzyme Q10 demonstrated significant improvement in motility after 6 months of treatment [178]

twice daily for 3 months to 50 asthenozoospermic

Physiological and Pathological Roles of Free Radicals in Male Reproduction

also played

139

http://dx.doi.org/10.5772/intechopen.70793

Selection of an effective sperm preparation technique is important to minimize ROS overgeneration and eventual oxidative insults to the male gamete. The density gradient technique is able to separate leukocytes and immature or damaged spermatozoa from normal spermato-

Assisted reproduction techniques may benefit from *in vitro* supplementation of antioxidants [180]. Various antioxidants such as vitamin E, vitamin C, cysteine, taurine and hypotaurine present in the culture medium have been shown to improve the developmental ability of the

In cases of IVF, incubation times of more than 16–20 hours have been correlated with increased oxidative damage. Shortening the insemination timeframes (up to 1–2 hours or less) may reduce ROS overgeneration in culture media and possibly improve fertilization, embryogen-

Because high levels of ROS have been associated with a decreased male infertility, measuring ROS levels in semen is an important part of the initial evaluation as well as follow-up of men with reproductive dysfunction [10–12]. Chemiluminescence and flow cytometry are currently

Chemiluminescence measures light emitted following administration of specific reagents to a semen sample. Two major probes currently used to assess ROS generation by spermatozoa

the most common techniques in clinical andrology to assess and study seminal OS.

be directly injected into the ovum [77].

Zinc sulfate (ZnSO4

esis and pregnancy rates [77, 179].

zoa, which may be subsequently used in ARTs [77, 179, 180].

) • Administration of 250 mg of ZnSO4

**8. Methods for detecting reactive oxygen species**

embryos by counteracting the effects of ROS [93, 180].

**Table 4.** Most pronounced studies on the effects of oral antioxidant supplementation on male infertility.

Vitamin C • 13 infertile patients received 1000 mg of vitamin C twice daily for a maximum of 2 months.

Vitamin E • 110 asthenozoospermic patients received 300 mg of vitamin E daily over a period of

Glutathione • 600 mg of GSH per day given to 11 men suffering from dyspermia associated with

improvement in sperm kinetics and higher sperm concentration [165]. Carnitine • 3 g/day of L-carnitine was administered to 100 asthenozoospermic men. After 4 months of

[160]

Vitamin C and vitamin E

138 Spermatozoa - Facts and Perspectives

Vitamins A, C, E, N-acetyl-cysteine and zinc

Selenium and Vitamins

the studied population [161]

morphology [162]

mL post-treatment [164]

morphology [166].

supplemented for 6 months [168]

1 g L-acetyl-carnitine [169]

200 μg/day of selenium [170]

Vitamin C supplementation improved sperm count, motility and morphology [159] • 115 men with clinical varicocele and abnormal semen analyses were recruited. After surgery, the subjects received 250 mg vitamin C for 2 months. Vitamin C supplementation following surgery resulted in a better motility and morphology. Prior to surgery, vitamin C was not effective on the sperm count, but it improved sperm motility and morphology

26 weeks. At the end of the experiment, sperm motility increased, while LPO decreased in

• 1000 mg vitamin C and 800 mg vitamin E were administered to 31 subjects diagnosed with asthenozoospermia and normal or only moderately reduced sperm concentration for a period of 56 days. The treatment did not affect sperm concentration, motility and

• 64 men with unexplained infertility and an elevated percentage of DNA-fragmented spermatozoa received 1 g vitamin C and 1 g vitamin E daily for 2 months. No differences in basic sperm parameters were found following antioxidant treatment; however, the

• 20 post-varicocelectomy oligospermic patients were subjected to a daily administration of 0.06 IU/kg of vitamin A, 3 mg/kg of vitamin C, 0.2 mg/kg of vitamin E, 10 mg/kg of NAC and 0.01 mg/kg of zinc over a period of 13 weeks. Sperm count increased by 20-fold, and of the 20 subjects, 6 of the originally infertile men had sperm counts greater than 20million/

unilateral varicocele or germ-free genital tract inflammation over 2 months lead to an

treatment, a significant improvement was observed in sperm concentration, motility and

asthenozoospermia who underwent 6 months of daily treatment with 2 g L-carnitine and

• 9 oligoasthenoteratozoospermic men were supplemented for a period of 6 months with selenium and vitamin E, leading to improvements in sperm motility, morphology and

• 46 oligoasthenoteratozoospermic and 16 subfertile patients received selenium alone or in combination with vitamins A, C and E at daily doses of 100 μg, 1 mg, 10 mg and 15 mg, respectively. No improvement was observed in sperm concentration after 3 months,

• 28 infertile men were supplemented daily by vitamin E (400 mg) and selenium (225 μg) during 3 months. Following treatment, a significant decrease of LPO was observed

• Supplementation of 600 mg/day of NAC to 60 patients diagnosed with idiopathic infertility seemed to improve sperm motility, volume, viscosity and seminal oxidative status [175].

• 2 g/day of carnitine administered for 6 months led to a significant improvement in sperm concentration and motility in 100 patients with oligoasthenoteratozoospermia [167], while the following year an increased sperm count and motility were found in 56 infertile men after a combined daily treatment with 2 g carnitine and 1 g acetyl-L-carnitine

• No improvements in semen quality was detected in 26 men diagnosed with

Selenium • No positive effects were found in 33 subfertile men following 3 months of treatment with

although the motility was increased in the treated subjects [172]

together with an improvement of sperm motility [173] N-acetyl-cysteine • No improvements in sperm parameters were observed after 3 months of 600 mg/day

administration of NAC to 27 infertile men [174].

viability, although the concentration did not change significantly [171]

percentage of DNA-fragmented spermatozoa was markedly reduced [163].

ROS-induced damage may have significant clinical implications in the context of ARTs. Numerous reports have indicated that significantly increased ROS levels may occur in response to repeated cycles of centrifugation involved in conventional sperm preparation techniques used for ARTs [179]. Spermatozoa selected for ART often face OS and a high risk for DNA damage. When intrauterine insemination or *in vitro* fertilization (IVF) is used, such damage does not represent a cause of concern as damage to the sperm membrane lipids ensures that fertilization will not occur. However, in case of intracytoplasmic sperm injection is used, this natural selection barrier may be overlooked and sperm with DNA damage may be directly injected into the ovum [77].

Selection of an effective sperm preparation technique is important to minimize ROS overgeneration and eventual oxidative insults to the male gamete. The density gradient technique is able to separate leukocytes and immature or damaged spermatozoa from normal spermatozoa, which may be subsequently used in ARTs [77, 179, 180].

Assisted reproduction techniques may benefit from *in vitro* supplementation of antioxidants [180]. Various antioxidants such as vitamin E, vitamin C, cysteine, taurine and hypotaurine present in the culture medium have been shown to improve the developmental ability of the embryos by counteracting the effects of ROS [93, 180].

In cases of IVF, incubation times of more than 16–20 hours have been correlated with increased oxidative damage. Shortening the insemination timeframes (up to 1–2 hours or less) may reduce ROS overgeneration in culture media and possibly improve fertilization, embryogenesis and pregnancy rates [77, 179].