**2.2. Pathophysiology of radiation-induced CVD**

anthracyclines [59]. Radiation-induced cardiovascular disorders are based rather on the damage to the blood vessels. Later, in the study of Darby et al., 2168 breast cancer patients were followed between 5 and more than 20 years after radiotherapy. It was found that women irradiated for left breast cancer (estimated mean heart dose 6.6 Gy) had higher rates of major coronary events than women irradiated for right breast cancer (estimated mean heart dose 2.9 Gy; P = 0.002). Excess relative risk (ERR), a measure that quantifies how much the level of risk among persons with a given level of exposure exceeds the risk of nonexposed persons [60] for major coronary events was 7.4% per Gy (95% CI, 2.9–14.5) when all follow-up times and all breast cancer patients were

Additional proofs of increased risk of CVDs after high-dose exposure were provided during the follow-up of Japanese atomic bomb survivors. During a 53-year follow-up of 86,611 members of the Life Span Study cohort, excess relative risk of death from heart disease per Gy was 0.14 (95% CI 0.06–0.23) (**Figure 2C**) [34]. Although there is a large number of epidemiological studies showing a clear excess of CVD risk above 0.5 Gy, they are of limited use for quantitative risk assessment, because individual dosimetry has yet to be performed [35]. In addition, even if an adverse effect can be evidenced at relatively high doses of ionizing radiation, mechanisms by which therapeutic doses affect the cardiovascular system are still not completely understood [28].

**Figure 2.** Epidemiological evidence for an increased risk of CVDs after exposure to ionizing radiation. (A) Cumulative risk curves for the occurrence of cardiac events in Hodgkin's lymphoma survivors [58]. (B) Rate of major coronary events according to mean radiation dose to the heart given during breast cancer radiotherapy, as compared with the estimated rate with no radiation exposure to the heart [54]. (C) Excess relative risk for death from heart disease in Japanese atomic

bomb survivors. Shaded area is the 95% confidence region for the fitted lines [34].

included (**Figure 2B**) [54].

370 Endothelial Dysfunction - Old Concepts and New Challenges

Following radiotherapy of the thoracic part of the human body for mediastinal lymphoma, breast, lung, and esophageal cancers, the heart incidentally receives a part of the therapeutic dose [46]. As indicated in the epidemiology section, high-dose radiation exposure of the heart and its vessels is associated with a risk of radiation-induced CVD [34, 54, 55]. In this context, coronary artery disease is considered to be the major cardiovascular complication [28, 30, 54]. Two studies provide molecular and cellular mechanisms accounting for increased morbidity and mortality of coronary artery disease following radiation exposure. First, radiation can influence the pathogenesis of age-related atherosclerosis, thereby accelerating the development of atherosclerosis in coronary arteries [28]. Growing atherosclerotic plaques narrow the blood vessel and hamper the blood stream (**Figure 3**). Second, damage to the heart microvasculature can reduce blood flow to the myocardium, causing myocardial ischemia, which promotes acute infarction [30]. Because endothelial activation and dysfunction are major causes of atherosclerosis, much of the current radiobiological research is exploring the molecular and phenotypic effects of ionizing radiation in endothelial cells in the context of radiation-induced CVD [66, 67]. It should be noted, however, that there are also other clinical manifestations of radiation-related CVD, such as pericarditis, congestive heart failure, and heart fibrosis [30, 68, 69]. Radiation-induced pericarditis is caused by damage to the cardiac microvascular network in combination with fibrosis of cardiac venous and lymphatic channels. This ultimately leads to accumulation of a fibrin-rich exudate in the pericardium, causing pericardial tamponade. Congestive heart failure is attributed to radiation-induced fibrosis of the myocardium, which ultimately leads to decreased elasticity and extensibility of cardiac walls, thereby reducing the ejection fraction [70]. To learn more about putative mechanisms, the interested reader is referred to some excellent recent reviews [69, 71].

potentially confounding risk factors. For example, occupational studies have to deal with the "healthy worker" effect, and the study of A-bomb survivors has to deal with the "healthy survivor" effect. Both selection effects occur when healthy individuals with lower mortality and morbidity rates are selectively retained at a specific site (work and living area, respectively) where they accumulate higher doses and therefore confound the dose-risk relationship [37]. Other potential confounders in epidemiological studies are lifestyle risk factors for CVD (e.g., smoking, alcohol consumption, obesity, diabetes, hypertension) [35, 55] prognosis of cancer treatment regimens [30], distribution of the dose range, accuracy of dosimetry, duration of follow-up after exposure, and correct assignment of the cause of mortality [62]. For these reasons, the number of people needed to quantify the excess risk of a dose <0.5 Gy is unfeasibly high. In the context of radiation-related cancer, for example, a cohort of 5 million people would be needed to quantify the excess risk of a 10 mGy dose, assuming that the excess risk is in proportion to the dose [7]. Moreover, CVD may occur a long time after exposure to doses below 30 Gy (approx. 10–30 year lag) [30, 72, 73]. As a result, a long follow-up period of time is needed to determine the nature

Selected Endothelial Responses after Ionizing Radiation Exposure

http://dx.doi.org/10.5772/intechopen.72386

373

Despite the fact that epidemiological studies have led to significant insights in radiationrelated CVD risk, there are still many uncertainties that need to be addressed. Does CVD risk occur only above a specific radiation dose? Is the latency of CVD development dependent on the dose? Which are the sensitive targets in the heart and vasculature (e.g., fibroblasts, vascular smooth muscle cells, and endothelial cells)? Does radiation exposure affect CVD incidence or progression or both? Is there a difference between single dose and fractionated and chronic exposure? How does the time interval between two consecutive dose fractionations play a role in the induction of damage? These questions need to be answered to provide a more accurate

Classical epidemiological studies are not adapted to provide answers to these questions. There is, therefore, a clear need for more detailed epidemiological studies that would be capable of addressing potential confounding factors and selection biases that could influence results. Furthermore, there is a particular need for a better understanding of the biological and molecular mechanisms responsible for the association between ionizing radiation and CVD [6]. Hence, a more directed approach is required, such as molecular epidemiology that integrates epidemiology and biology [55]. Radiobiological research is thus essential for understanding the radiation-related CVD risks, both at high and low doses. In other words, accurate risk estimation will be possible only based on comprehensive biological and molecular understanding of what ionizing radiation does to the cardiovascular system. To date, the induction of radiation-related CVD risks is believed to be the

dose risk assessment in order to improve the current radiation protection system.

result of endothelial dysfunction, which will be discussed in the next section [30].

**3. Endothelial cell responses after ionizing radiation exposure**

The endothelium could be a critical target in ionizing radiation-related CVD [74]. The endothelium is a single layer of cells that lines the interior of the vascular system and of the heart and has thus a strategic position between the blood and the surrounding tissues. It is a highly active organ system that is constantly sensing and responding to changes of the extracellular environment to maintain a normal function of the vascular system [75].

and magnitude of risks following individual exposure to lower doses.

#### **2.3. Gaps in the current knowledge of radiation-induced CVD**

Available epidemiological studies have limited statistical power to detect a possible excess risk of CVD following exposure to radiation doses lower than 0.5 Gy. Limited power is due both to the high background level of CVD in studied populations and to the existence of many

**Figure 3.** Longitudinal cut of a normal, healthy blood vessel (left) and of a blood vessel with an atherosclerotic plaque hampering the blood stream (right). Damage to the endothelium is an important trigger of atherosclerosis, itself a main cause of CVD.

potentially confounding risk factors. For example, occupational studies have to deal with the "healthy worker" effect, and the study of A-bomb survivors has to deal with the "healthy survivor" effect. Both selection effects occur when healthy individuals with lower mortality and morbidity rates are selectively retained at a specific site (work and living area, respectively) where they accumulate higher doses and therefore confound the dose-risk relationship [37]. Other potential confounders in epidemiological studies are lifestyle risk factors for CVD (e.g., smoking, alcohol consumption, obesity, diabetes, hypertension) [35, 55] prognosis of cancer treatment regimens [30], distribution of the dose range, accuracy of dosimetry, duration of follow-up after exposure, and correct assignment of the cause of mortality [62]. For these reasons, the number of people needed to quantify the excess risk of a dose <0.5 Gy is unfeasibly high. In the context of radiation-related cancer, for example, a cohort of 5 million people would be needed to quantify the excess risk of a 10 mGy dose, assuming that the excess risk is in proportion to the dose [7]. Moreover, CVD may occur a long time after exposure to doses below 30 Gy (approx. 10–30 year lag) [30, 72, 73]. As a result, a long follow-up period of time is needed to determine the nature and magnitude of risks following individual exposure to lower doses.

As indicated in the epidemiology section, high-dose radiation exposure of the heart and its vessels is associated with a risk of radiation-induced CVD [34, 54, 55]. In this context, coronary artery disease is considered to be the major cardiovascular complication [28, 30, 54]. Two studies provide molecular and cellular mechanisms accounting for increased morbidity and mortality of coronary artery disease following radiation exposure. First, radiation can influence the pathogenesis of age-related atherosclerosis, thereby accelerating the development of atherosclerosis in coronary arteries [28]. Growing atherosclerotic plaques narrow the blood vessel and hamper the blood stream (**Figure 3**). Second, damage to the heart microvasculature can reduce blood flow to the myocardium, causing myocardial ischemia, which promotes acute infarction [30]. Because endothelial activation and dysfunction are major causes of atherosclerosis, much of the current radiobiological research is exploring the molecular and phenotypic effects of ionizing radiation in endothelial cells in the context of radiation-induced CVD [66, 67]. It should be noted, however, that there are also other clinical manifestations of radiation-related CVD, such as pericarditis, congestive heart failure, and heart fibrosis [30, 68, 69]. Radiation-induced pericarditis is caused by damage to the cardiac microvascular network in combination with fibrosis of cardiac venous and lymphatic channels. This ultimately leads to accumulation of a fibrin-rich exudate in the pericardium, causing pericardial tamponade. Congestive heart failure is attributed to radiation-induced fibrosis of the myocardium, which ultimately leads to decreased elasticity and extensibility of cardiac walls, thereby reducing the ejection fraction [70]. To learn more about putative mechanisms, the interested reader is referred to some excellent recent reviews [69, 71].

Available epidemiological studies have limited statistical power to detect a possible excess risk of CVD following exposure to radiation doses lower than 0.5 Gy. Limited power is due both to the high background level of CVD in studied populations and to the existence of many

**Figure 3.** Longitudinal cut of a normal, healthy blood vessel (left) and of a blood vessel with an atherosclerotic plaque hampering the blood stream (right). Damage to the endothelium is an important trigger of atherosclerosis, itself a main

**2.3. Gaps in the current knowledge of radiation-induced CVD**

372 Endothelial Dysfunction - Old Concepts and New Challenges

cause of CVD.

Despite the fact that epidemiological studies have led to significant insights in radiationrelated CVD risk, there are still many uncertainties that need to be addressed. Does CVD risk occur only above a specific radiation dose? Is the latency of CVD development dependent on the dose? Which are the sensitive targets in the heart and vasculature (e.g., fibroblasts, vascular smooth muscle cells, and endothelial cells)? Does radiation exposure affect CVD incidence or progression or both? Is there a difference between single dose and fractionated and chronic exposure? How does the time interval between two consecutive dose fractionations play a role in the induction of damage? These questions need to be answered to provide a more accurate dose risk assessment in order to improve the current radiation protection system.

Classical epidemiological studies are not adapted to provide answers to these questions. There is, therefore, a clear need for more detailed epidemiological studies that would be capable of addressing potential confounding factors and selection biases that could influence results. Furthermore, there is a particular need for a better understanding of the biological and molecular mechanisms responsible for the association between ionizing radiation and CVD [6]. Hence, a more directed approach is required, such as molecular epidemiology that integrates epidemiology and biology [55]. Radiobiological research is thus essential for understanding the radiation-related CVD risks, both at high and low doses. In other words, accurate risk estimation will be possible only based on comprehensive biological and molecular understanding of what ionizing radiation does to the cardiovascular system. To date, the induction of radiation-related CVD risks is believed to be the result of endothelial dysfunction, which will be discussed in the next section [30].


**Author details**

Silvana Dakduk1 , David Van der Woude2 \* and Camilo Alarcon Nieto1

1 Universidad de los Andes School of Management, Bogotá, Colombia

2 CESA School of Business, Bogotá, Colombia

\*Address all correspondence to<EMAIL_ADDRESS>
© 2023 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


**Nucleic Acids — The Use of Nucleic Acid Testing in Molecular Diagnostics**

Gabrielle Heilek

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Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/61487

### **Abstract**

In 1989 Roche entered into an agreement with Cetus to develop diagnostic applications for the novel technique polymerase chain reaction (PCR). A new area of molecular diag‐ nostics began and genes and pathogen genomes have been used to diagnose disease since that point. Automated laboratory platforms were created to facilitate the workflow and allow for accurate and precise processing of patient blood samples in a highly stream‐ lined manner. In this chapter the use of nucleic acids in molecular diagnostics will be de‐ scribed and their application to important human diseases. Examples are discussed with respect to which nucleic acid marker has provided strong clinical utility and impact to healthcare.

**Keywords:** Real Time-PCR, molecular diagnostic, HIV-1, human genetic testing


**1. Introduction**

Steel slag is a byproduct of steel production, which accounts for about 15% of the mass of steel production [1–3]. In China, the generation of steel slag is huge, whereas the total utilization rate is low [4]. The accumulation of steel slag not only takes up a lot of land but also pollutes the surrounding environment [5]. Therefore, it is imperative to improve the utilization of steel slag [6–8]. The composition of steel slag is similar to that of cement, and as such, it has the potential of replacing cement as a cementitious material. If steel slag can be effectively used in the cement industry, it will benefit the solid waste utilization, energy

**Citation:** Guan, J.; Zhang, Y.; Yao, X.; Li, L.; Zhang, L.; Yi, J. Experimental Study on the Effect of Compound Activator on the Mechanical Properties of Steel Slag Cement Mortar. *Crystals* **2021**, *11*, 658. https://doi.org/10.3390/ cryst11060658

Academic Editors: Chuanqing Fu, Peng Zhang, Peter Taylor and Yifeng Ling

Received: 19 May 2021 Accepted: 6 June 2021 Published: 10 June 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 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 (https:// creativecommons.org/licenses/by/ 4.0/).

conservation, and environmental protection [9–13]. However, the inherent low activity of steel slag restricts its application in the cement industry [14–16].

In order to address this issue, researchers have been employing different methods to improve the activity of steel slag. Commonly used activation methods include physical activation, chemical activation, thermal activation, and steel slag restructuring. The physical activation method increases the specific surface area of the steel slag by grinding the steel slag into ultra-fine powder by using a ball mill, thereby increasing the hydration rate. Zhu et al. [17] used a grinding aid mixed with sulfonate, alcohol, and metaphosphoric to grind steel slag, which increased the early hydration rate of steel slag. Altun et al. [18] ground steel slag to 4000 cm2/g and 4700 cm2/g specific surface area and used 30% of it to replace Portland cement in mortar preparation which led to the 28-days compressive strength of mortar to be 38.5 MPa and 45.8 MPa, respectively. Chemical activation enhances the activity of steel slag by changing the mineral formation process, primarily including alkali activation and acid activation [19–21]. Peng et al. [22] used water glass as a steel slag activator, and the 28-days compressive strength of the mortar with 40% steel slag dosage reached 51.4 MPa. Sun et al. [23] used water glass to activate the steel slag activity, and the results showed that the pore structure of hardened cement paste was more compact than that of the steel slag paste activated by sodium silicate, while the compressive strength of alkali–activated steel slag hardened pastes was only 30–40% of the strength of ordinary cement pastes. Huo et al. [20,24] used phosphoric acid and formic acid to activate the activity of steel slag, and research result showed that the compressive strength of the activated steel slag pastes significantly improves at 3 days and 7 days ages. Zhang et al. [25] used water glass, industrial residues, and a mixture of sodium hydroxide, calcium oxide, and alum as the compound activator for steel slag; and the 28 days compressive strength of steel slag blended cement was reported as 47.7 MPa. Du et al. [26] used dihydrate gypsum and silica fume as steel slag compound activator in a ratio of 1:4, which greatly improved the strength of steel slag cementitious materials at the early and late ages. Thermal activation can depolymerize the vitreous phase in the steel slag, thereby increasing the activity. Lin et al. [27] investigated the effect of using thermally activated steel slag-fly ash-gypsum system (the autoclave temperature was 100 °C). It was shown that the 28 days compressive strength reached 46.8 MPa and 43.5 MPa for the pretreatment material dosages of 35% and 40%, respectively. Steel slag reconstruction is the addition of different materials to adjust the chemical composition of steel slag to cause a chemical reaction at high temperature for absorbing free calcium oxide to generate reactive substances such as dicalcium silicate, tricalcium silicate, tricalcium aluminate, which improved the activity of steel slag. Kang et al. [28] mixed basic oxygen furnace steel slag and electric arc furnace steel slag in an appropriate ratio and reheated these at a high temperature in the laboratory, which significantly improved the activity of steel slag. Yin et al. [29] reconstructed the steel slag by reducing FeOx to improve the hydraulic activity of the steel slag, and the result confirmed that the activity index was 92% when direct reduction slag replaced the cement with a mass ratio of 30%. Zhao et al. [30] reconstructed the steel slag by adding electric furnace slag and fly ash to the converter steel slag. The compressive strength of the paste with 30% reconstructed steel slag dosage could reach 99.9% of that for the pure cement paste. Many studies have shown that the physical activation takes a long time and has little effect on the activity of steel slag at a later age. Also, the cost of the chemical activator is much higher than the other methods. Whereas steel slag reconstruction can improve its activity to a certain degree, it still lags behind the cement clinker. Other methods can also improve the activity of steel slag, but these are still in the experimental stage [31,32].

There are many researches on chemical alkaline activation methods, but alkaline activation has high cost. Therefore, the compound activator composed of neutral materials (some salts such as sodium sulfate, sodium aluminate, and some mineral admixtures such as silica fume) was used to activate the activity of steel slag powder in this paper, and the optimal dosage of each component of compound activator in cementitious material was determined through experiments. A grey prediction model GM (0, *N*) was established to predict the strength of steel slag cement mortar with the optimal dosage of each component. The effectiveness of the proposed activation method was verified by the test results and the model prediction results. Furthermore, considering the different requirements of engineering for cementitious materials, economic benefit analysis is carried out for different mix proportions to check whether the proposed method will reduce the cost of cementitious materials and the extent of reduction to provide basis for engineering.

### **2. Experimental and Methods**

### *2.1. Raw Materials*

Seven different kinds of raw materials were used in the experimental program of this study: cement, sand, steel slag powder, activator, metakaolin, silica fume, and water. The cement was P·O·42.5 Portland cement with an apparent density of 3150 kg/m3 . The sand used in the study was ISO standard sand. The steel slag powder was produced by Taiyuan Iron & Steel Group Co., Ltd. in Taiyuan, China, with a specific surface area of 450 m2/kg. The activator was composed of various materials, and the corresponding components and mass percentages are shown in Table 1. Water quenched slag and stone powder in the activator can accelerate the hydration reaction. In addition, the particle size of water quenched slag is larger than that of steel slag powder, so the water quenched slag can be combined with steel slag powder to optimize the grading. Metakaolin was obtained by calcining kaolin at 700 °C for 24 h. Silica fume was purchased from the market with a density of 2.3 g/cm3 . Ordinary potable water was used as well. The chemical compositions of cement, steel slag powder, metakaolin, and silica fume are shown in Table 2, while the particle size distribution is given in Figure 1. The actual samples of cement, steel slag powder, activator, metakaolin, and silica fume are shown in Figure 2.

### **Table 1.** Components of activator (wt %).

**Table 2.** Main chemical compositions of materials (wt %).

**Figure 1.** Particle size distribution of materials. mortar, so the dosage of metakaolin is set at four levels: 5%, 10%, 15%, and 20%. The silica **Figure 1.** Particle size distribution of materials.

der; (**c**) activator; (**d**) metakaolin and (**e**) silica fume.

*2.2. Mix Proportions*

**Figure 2.** Samples of cementitious material components in this study (**a**) cement; (**b**) steel slag pow-

The experimental design method is often chosen according to the experimental purpose [33–35]. In order to study the influence of various factors on the mortar strength, the orthogonal method was adopted for designing the experimental mix proportions. Activator, steel slag powder, metakaolin and silica fume were taken as four factors. The dosage of each factor was set at several levels according to the mass percentage of cementitious material. Due to the low activity of steel slag powder itself, the activation method is difficult to have a good influence on the strength of steel slag cement mortar with excessive steel slag powder dosage, so the dosage of steel slag powder is set to 10%, 20%, 30%, and 40%, a total of four levels. Al2O3 in metakaolin can accelerate the hydration reaction of SiO2 in cementitious materials, so as to improve the strength of steel slag cement mortar. A small amount of metakaolin can have positive effect on the strength of steel slag cement

Cumulative passing (%)

Silica fume

Metakaolin

**Figure 1.** Particle size distribution of materials.

Cement

0.1 1 10 100 1000

Particle size (μm)

Steel slag powder

**Figure 2.** Samples of cementitious material components in this study (**a**) cement; (**b**) steel slag powder; (**c**) activator; (**d**) metakaolin and (**e**) silica fume. **Figure 2.** Samples of cementitious material components in this study (**a**) cement; (**b**) steel slag powder; (**c**) activator; (**d**) metakaolin and (**e**) silica fume.

### *2.2. Mix Proportions 2.2. Mix Proportions*

The experimental design method is often chosen according to the experimental purpose [33–35]. In order to study the influence of various factors on the mortar strength, the orthogonal method was adopted for designing the experimental mix proportions. Activator, steel slag powder, metakaolin and silica fume were taken as four factors. The dosage of each factor was set at several levels according to the mass percentage of cementitious material. Due to the low activity of steel slag powder itself, the activation method is difficult to have a good influence on the strength of steel slag cement mortar with excessive steel slag powder dosage, so the dosage of steel slag powder is set to 10%, 20%, 30%, and 40%, a total of four levels. Al2O3 in metakaolin can accelerate the hydration reaction of SiO2 in cementitious materials, so as to improve the strength of steel slag cement mortar. A small amount of metakaolin can have positive effect on the strength of steel slag cement mortar, so the dosage of metakaolin is set at four levels: 5%, 10%, 15%, and 20%. The silica The experimental design method is often chosen according to the experimental purpose [33–35]. In order to study the influence of various factors on the mortar strength, the orthogonal method was adopted for designing the experimental mix proportions. Activator, steel slag powder, metakaolin and silica fume were taken as four factors. The dosage of each factor was set at several levels according to the mass percentage of cementitious material. Due to the low activity of steel slag powder itself, the activation method is difficult to have a good influence on the strength of steel slag cement mortar with excessive steel slag powder dosage, so the dosage of steel slag powder is set to 10%, 20%, 30%, and 40%, a total of four levels. Al2O3 in metakaolin can accelerate the hydration reaction of SiO2 in cementitious materials, so as to improve the strength of steel slag cement mortar. A small amount of metakaolin can have positive effect on the strength of steel slag cement mortar, so the dosage of metakaolin is set at four levels: 5%, 10%, 15%, and 20%. The silica fume can improve the strength of steel slag cement mortar, but excessive dosage will lead to high cost, so the silica fume dosage is set at 4 levels: 2%, 4%, 6%, and 8%. Level details of factors are shown in Table 3. According to the orthogonal design method, sixteen groups of mix proportions were designed. Another group of pure cement mortar specimens was also designed as the control group for comparative study. The mix proportions are given in Table 4.

**Table 3.** Level details of factors (wt %).

**Table 4.** Mix proportions (g).


Water Quality Effects on Growth and Survival of *Oreochromis jipe* and *Oreochromis niloticus* Species in Aquaculture

*Job O. Omweno, Albert Getabu, Reuben Omondi and Paul S. Orina*

## **Abstract**

The study was conducted at Kegati Aquaculture Center (KMFRI) for 12 weeks to evaluate the effect of water quality parameters on the growth and survival rates of *Oreochromis jipe* and *Oreochromis niloticus*. The fingerlings (n = 270), were stocked in triplicates in 9m2 raised ponds arranged in double series and fed on a 30% CP ration twice a day at 10% total bodyweight. Water quality parameters were measured daily using the YSI multi-parameter meter. Fish total length and bodyweight was determined biweekly using a measuring board and a digital weighing balance, respectively, and survivals were determined from the initial and final counts of fingerlings. Data were analyzed with an unpaired sample t-test using R-software and statistical significance was considered at p < 0.05. Temperature, total dissolved solids (TDS), and salinity showed no significant differences, whereas the pH and dissolved oxygen (DO) levels were significantly different (p < 0.05) between the *O. jipe* and *O. niloticus*. Furthermore, *O. jipe* attained a lower final mean weight (12.16 ± 0.34 g) compared to *O. niloticus* (29.79 ± 0.91 g). The study recommends a further study be conducted in a controlled culture environment to establish optimal conditions for *O. jipe* culture.

**Keywords:** water quality parameters, fingerlings, growth in aquaculture, *Oreochromis jipe*, *Oreochromis niloticus*

## **1. Introduction**

Water quality encompasses all environmental and, to some extent, biological factors affecting fish growth, survival, and ecology. The most commonly monitored physicochemical parameters include temperature, pH, turbidity, dissolved oxygen concentration (DO), total dissolved solids (TDS), electrical conductivity, and total suspended solids [1]. Temperature controls dissolved oxygen (DO), pH levels, and chemical processes in water. Optimum temperature speeds up fish metabolic rates, which in turn increases feed consumption to meet the energy demands, while high

temperature and pH increase the ammonia toxicity to aquatic organisms [2, 3]. Temperature also increases the rate of decomposition and affects feed digestion and assimilation efficiencies in living organisms. The optimum temperatures range for warm water fish (20–30°C) results in maximum growth and survival of these organisms [4]. The lower and upper lethal limits for tilapia are 10–11°C and 37–38°C, respectively [5].

Water pH and DO concentrations regulate the general metabolism and many physiological processes in the cultured fish, such as nitrite and ammonia toxicity. Most aquatic organisms perform optimally in DO levels ranging between 5 and 9 mg L−1, while the DO concentrations below 3 mg L−1 and above 9 mg L−1 are detrimental to aquatic life [6]. Although tilapias are highly tolerant to low DO levels, *O. niloticus* can survive short-term exposure to low DO levels of 0.1 mg L −1 [7], the optimum performance of the species only occurs at DO concentrations ranging between 4.2 and 5.9 mg L−1.

Tilapia have been reported to tolerate a range of up to 3.7 and 11.0 pH but the recommended optimum pH range for most tilapia species is 6.5 to 9.0 [7, 8]. Deviations above or below this optimum range for the species are likely to bring about behavioral and physiological adjustments, which affect the growth performance and survival as fish try to adapt to stressful pH conditions. The highest and lowest lethal pH limits for most fish species are 3.7 and 11.0 respectively [8]. Low pH decreases the egg hatchability rates, while massive fish mortalities occur due to prolonged exposure to toxic un-ionized ammonia whose concentration increases to concentrations greater than 50% when the pH is greater than nine. Ammonia concentrations higher than 0.2 mg L−1 are detrimental to fish and are known to depress the appetite of tilapia [5].

## **2. Materials and methods**

#### **2.1 Study area**

The study experiment was conducted for 12 weeks from September to December 2019 at Kenya Marine and Fisheries Research Institute (KMFRI), Kegati Aquaculture Center.

Kegati Aquaculture Center is located in a high-altitude area (1974 m asl) between latitudes and longitudes 00420 50.44'S and 0344 470 59.4'E as shown in **Figure 1**. The center receives an averagely high rainfall amount of 1800 ± 100 mm per annum and has a mean temperature range of 20.3–23.9° C. It is drained by river Kuja with a total catchment area of 5180 km2 [9]. The sampling stations were selected based on accessibility, availability of a well-equipped hatchery facility, and technical support for the study.

Six raised wooden ponds measuring 9m2 in a double series and a regular arrangement were randomly assigned to the two treatments in three replicates. The ponds were filled with screened un-chlorinated spring water through an independent inlet to 0.6 m level and water exchange was provided through an outlet throughout the study to promote aeration and siphoning of wastewater containing feed remnants, bioflocs, and fecal waste at 4 days interval. The mixed-sex *O. niloticus* and *O. jipe* fingerlings with an initial mean weight of 2.93 ± 0.12 g and 2.69 ± 0.10 g, respectively, were stocked in the raised wooden ponds at a stocking density of 5 fish m-3.

The fingerlings were fed on 1 mm commercial floating pellets with 30% CP. The daily ration was divided into two equal portions, which were fed regularly to fish twice a day between 0930 h and 1500 h EAT time throughout the study period *Water Quality Effects on Growth and Survival of* Oreochromis jipe *and* Oreochromis niloticus*… DOI: http://dx.doi.org/10.5772/intechopen.106361*

**Figure 1.** *Map of the study area at KMFRI – Kegati aquaculture research Center in Kisii County, Kenya.*

as recommended by ref. [10]. Fish feed administration was done manually which enabled regular inspection of the individual fish and close monitoring of the feeding process, and identifying any dead fish in the pond, which was immediately removed, and the number of mortalities recorded. The feeding rate was at 10% per calculated bodyweight of all fish but the daily ration was divided into two halves for feeding the fish in the morning and afternoon.

#### **2.2 Data collection**

Fish growth performance was assessed using average and cumulative bodyweights, which were taken progressively on fingerlings sampled from each raised pond at the end of every 2 weeks. A digital electronic balance (TX 4202 L, SHIMADZU - Corporation, Philippines) with a precision level of 0.01 g was used to record bodyweights. The fish were starved for 24 hours before sampling. Sampling was done very early in the morning before 0900 h to reduce handling stress, which can cause mortality of fingerlings. A sample of 30 fish per tank was taken randomly from each raised pond using a 0.5-inch scoop net. The fingerlings were weighed and returned to their respective ponds. The fingerlings were blot-dried using filter paper before weighing as recommended by ref. [11] and the measurements were taken inside the hatchery to avoid the effects of wind on the digital weighing balance [12]. The fish total length was measured using a graduated board with a readability of 0.1 cm.

After taking both total length and bodyweight measurements, the fingerlings were immediately returned to their respective labeled raised wooden ponds and no feeding was administered until the fingerlings completely recovered from handling stress and were observed freely swimming in the pond. Any sick fish noticed was removed and given prophylactic treatment using 5% sodium chloride solution before returning it to the pond. This was when feeding was administered according to the experimental design. The progressive length-weight measurements were taken biweekly inside the

hatchery to avoid the effects of wind on the digital balance [12] and the adjustment of the feeding rates for the fingerlings was done accordingly. Fish survival was determined by counting all stocked fingerlings in each replicate at the start and the end of the growth experiment. After recording the total length and bodyweight, the fingerlings were restocked in their respective culture ponds and feeding resumed after they were observed to have recovered from stress.

The physicochemical parameters determined during the study were DO (mg L−1), temperature (°C), pH, conductivity (μS cm−1), TDS, and salinity. The measurements were regularly taken during the morning hours between 10 am and 11 am throughout the week before the fish were fed their normal daily ration. The water level was maintained at 0.6 m and all previous feed remains were removed before new feeding was administered. Water quality variables: temperature, pH, dissolved oxygen (DO), salinity, conductivity, and total dissolved solids were recorded using the YSI multi-parameter meter (H9829 model), by taking the readings thrice and finding the average. The samples for water physicochemical parameters were collected daily in triplicates and analyzed according to standard methods described by the American Public Health Association [13].

#### **2.3 Data analysis**

The collected data were tested for normality using the Shapiro–Wilk test [14], and homogeneity of variances using Bartlett's test. Data were normally distributed and variances homogenously spread, therefore, an unpaired sample t-test was used to compare the population means of *O. jipe* and *O. niloticus* treatments. The variations in observed datasets on growth performance, survival rates, and the water quality variables were compared between *O. jipe* and *O. niloticus* treatments using robust mean ± standard error and range and nonrobust mean ± standard deviation using the R-software programming procedures [15]. All data analyses were performed using the 64-bit R-software version 3.6.3 [15], and the observed differences were considered statistically significant at p < 0.05.

#### **2.4 Determination of growth and survival rates**

The fish growth and survival parameters were computed with the following equations according to [10]:

$$\text{Specific growth rate} \left( \text{SGR} \right) \% / \, day = \frac{\text{In } \text{Wt} - \text{In } \text{W0}}{\text{t}} \times \mathbf{100} \tag{1}$$

$$\text{Feed conversion ratio} \left(FCR\right) = \frac{\text{Consumed } feed \left(g\right)}{\mathbf{W1} - \mathbf{W0}} \tag{2}$$

where W0 and W1 are the initial fish weight and the final fish weight (g) of the fingerlings, in is the natural logarithm, and t is the number of culture days.

$$\text{Survival rate} \left( \% \right) = \frac{final\ fish \text{ count}}{initial\ fish \text{ count}} \times 100 \tag{3}$$

*Water Quality Effects on Growth and Survival of* Oreochromis jipe *and* Oreochromis niloticus*… DOI: http://dx.doi.org/10.5772/intechopen.106361*

( ) − = 3 *Net kg* yield m Harvest biomass – Stocking biomass (4)

## **3. Results**

#### **3.1 Fish growth, survival, and production parameters**

The general growth trend showed that there was a steady gain in bodyweight in both *O. jipe* and *O. niloticus* during the study period. The un-robust comparisons of combined boxplots and standard deviations show a wide variation in data recorded from the two treatments. The mean, standard deviations, and variance of fish bodyweights in *O. niloticus* and *O. jipe* exhibited a wide range of variation and increased exponentially during the culture period (**Figure 2**). The means and standard error variations for the growth, survival, and production indices of *O. jipe* and *O. niloticus* treatments are shown in **Table 1**.

#### *3.1.1 Survival rates*

Both species recorded high survival rates ranging between 74.6% and 95% in *O. jipe* treatment, while in *O. niloticus*, it ranged between 94.6% and 100%. Hence, the mean survival rate in *O. jipe* (92 ± 1.19%) was significantly lower (p < 0.05) than the mean survival rate (96.28 ± 0.65%) of *O. niloticus* treatment. Whereas, *O. niloticus* maintained high survival rates throughout the culture period, the lowest survival rates were recorded in *O. jipe* treatment due to mortalities that occurred during the culture period.

#### *3.1.2 Growth parameters*

The observed fish growth in terms of bodyweight indicated both species were initially growing slowly during the first few (1–3) weeks of the culture of the study

#### **Figure 2.**

*Data variation in* Oreochromis niloticus *and* Oreochromis jipe *using a comparison of boxplots and non-robust mean +/− SD.*

#### **Table 1.**

*Mean (± standard error) of growth, survival and production parameters for* Oreochromis jipe *and*  Oreochromis niloticus*.*

but increased exponentially after the second month. The highest and lowest fish total lengths observed during the final sampling were (16.7 cm and 6.5 cm) in *O. niloticus* and *O. jipe,* respectively. However, despite this wide range of variation observed in the total length measurements, the two-sample t-test indicated no significant growth difference (p > 0.05) between the mean final length (TL = 11.51 ± 0.13 cm) of *O. niloticus* and *O. jipe* (TL = 9.11 ± 0.12 cm).

The mean growth in final bodyweight in *O. jipe* (12.18 ± 35 g) was significantly lower (t = 2.42, p < 0.05) than the mean final weight (29.79 ± 0.91 g) for *O. niloticus* (**Figure 3**). Similarly, there was a significant difference (t = 2.57, p < 0.05) in the SGR between the two species; *O. jipe* had a lower mean SGR (1.45 ± 0.14 g/fish−1 day−1) in comparison with (2.28 ± 0.26 g fish−1 day−1) *O.* 3 *niloticus*. The DWG also showed the same trend in both species. The DWG for *O. niloticus* (0.32 ± 0.17 g fish−1 day−1) was significantly higher (t = 4.62, p < 0.05), than that of *O. jipe* (0.1128 ± 0.01 g fish−1 day−1). The %weight gain (WG) of *O. jipe* (1.58 ± 0.35 g fish-1) was significantly lower, (t = 3.57, P < 0.05) compared to the mean weight gain of *O. niloticus* (4.48 ± 1.6 g fish−1).

#### *3.1.3 Production parameters*

The mean fish yield of *O. jipe* species was significantly lower (t = 4.46, p < 0.05) in *O. jipe* compared to the *O. niloticus* species. The yield in *O. jipe* ranged between 0.79 and 0.99 kg m−3, while in *O. niloticus* it ranged between 2.79 and 3.15 kg m−3. Also, there was a significant difference (t = 2.5, p < 0.05) in FCR between *O. jipe* and *O. niloticus* species. The mean FCR varied between 2.35 and 2.57, whereas in *O. niloticus* it ranged between 1.21 and 1.68 during the culture period. Furthermore, the reciprocal of FCR (FCR−1) yielded food conversion efficiencies (FCEs) for *O. niloticus,* which were significantly higher than those of *O. jipe* species.

*Water Quality Effects on Growth and Survival of* Oreochromis jipe *and* Oreochromis niloticus*… DOI: http://dx.doi.org/10.5772/intechopen.106361*

#### **Figure 3.** *Weight gain in cultured Oreochromis jipe and Oreochromis niloticus.*

#### *3.1.4 Water quality parameters*

The mean values and variation of water quality parameters recorded during the study are shown in **Table 2**.

Water temperature showed no significant difference (t = 0.14, p > 0.05) between *O. jipe* treatment and the control during the culture period. The highest mean temperature recorded was 24.3 ± 0.58°C in *the O. jipe* culture system, while the temperature in *O. niloticus* treatment ranged between 18.5 and 24.8° C, with a mean of 22.54 ± 0.39° C. However, the DO level was significantly higher (t129 = 4.31, p < 0.05) in the control, whereas the pH was significantly (t129 = 2.91, p < 0.05) lower in *O. jipe* treatment than *O. niloticus* treatment. The water pH of both culture systems ranged between 7.34 ± 0.09 and 8.37 ± 0.14, whereas DO concentrations ranged between 4.9 ± 0.20 mg L−1 and 6.75 ± 0.14 mg L−1. Total dissolved solids (TDS), conductivity,

#### **Table 2.**

*Mean (± S.E) of water quality parameters recorded for* Oreochromis niloticus *and* Oreochromis jipe *treatments.*

and salinity showed no significant differences (p > 0.05) between the treatment and the control. Specifically, TDS and conductivity increased progressively after replenishing water in the experimental ponds until the water was exchanged again. However, lower values of conductivity, TDS, and salinity were recorded during the study. Low salinity could be a result of dilution during the rains and the frequent replenishment of water.

## **4. Discussion**

The most crucial interpretations of the study findings based on the overall objective are presented.

#### **4.1 Data variation in** *O. jipe* **and** *O. niloticus* **culture treatments**

The overall variation of the data set is greater for both the *O. jipe* and *O. niloticus* species was due to the outliers which ranged from 5 to 20 standard deviations above or below the mean of zero determined by the Partial Least Squares (PLS) regression model [16]. These were attributed to a small number of the faster-growing individuals (shooters), which gained weight faster than the rest of the mixed-sex fingerlings, resulting in un-uniform sizes of fish in each treatment group.

#### **4.2 Growth and survival rates**

The study used uniformly homogenous fingerlings, which were expected to grow uniformly throughout the culture period. Upon release into the pond, the surviving fingerlings of both species displayed good condition and consumed feed during the 12-hour monitoring period. However, both growth performance and survival rates of *O. jipe* were significantly lower than those of *O. niloticus.* This would have been due to the faster adaptability of *O. niloticus* to the aquaculture environment because of the previously reported good culture attributes [17]. Chenyambuga et al. [18], confirm that *O. niloticus* quickly adapts to aquaculture conditions in a comparative study in which the exotic Nile tilapia, *O. niloticus* attained a final mean weight of 67.6 ± 2.4 g and SGR of 2.2 ± 0.14 g fish−1 day−1, outperforming *O. jipe* with a final mean weight of 16.3 ± 2.0 and SGR of 1.5 g fish−1 day−1 attained within the same culture period of 90 days. This study however reported lower values of final mean weight and SGR in *O. jipe,* probably due to temperature difference between the two regions. Whereas, [18] recorded a mean temperature of 25.2 ± 2.0°C, the present study recorded a lower mean temperature of 22.93 ± 0.31°C for the *O. jipe* species. This temperature lies below the range of 20–30°C recommended for optimum growth and survival of most tilapia species [19, 20]. Nevertheless, the findings on survival rates of *O. jipe* and *O. niloticus* agree with the study by [18], who reported higher survival rates of 95.8% in *O. jipe* and 100% survival in *O. niloticus*, suggesting that both species can have exceptionally high survival rates if properly managed in the culture system. The findings on survival also concur with Hussain et al. [21], who reported 100% survival of *O. niloticus* in a polyculture experiment. These findings on survival however differ from the results of [22], who reported low survival rates of 25.8% for *O. jipe.* The low survival rates of *O. jipe* reported were attributed to decomposing feed remnants (bioflocs) which were not properly managed in the culture system during the culture trials.

*Water Quality Effects on Growth and Survival of* Oreochromis jipe *and* Oreochromis niloticus*… DOI: http://dx.doi.org/10.5772/intechopen.106361*

The present study used an open pond system that is semi-controlled by natural factors and the siphoning of bioflocs was done before they settled and decomposed at the pond bottom. This high level of management ensures that the initially slow-growing fish attained a steady and exponential increase in total length and bodyweight, which was recorded from the second month onwards. As a result of slow acclimatization to the captive environment, both species exhibited nonoptimal performance [23]. Low DO levels have been reported to cause fish mortalities and other challenges in the aquaculture production system. The amount of dissolved oxygen consumed by the fish depends on fish size, feeding rate, activity level, and water temperature. In this study, the *O. jipe* species displayed a remarkable reduction in feed intake immediately after the rains, which might have resulted in the observed low DO levels in this culture treatment. The same phenomenon has been reported in other cultured fish, such as *Labeo victorianus* by [24], in which the accumulation of unconsumed feed remnants resulted in prolonged low DO levels in the culture ponds, which stressed the fish, resulting in decreased growth and survival rates. Several studies have reported a decline in fish weight gain which has been attributed to decreased water temperatures during cold seasons characterized which directly affect feed intake [21, 25]. This might have occurred in the present study where water physicochemical parameters were not controlled although the pH and DO levels were within the recommended range for tilapia growth [26].

#### **4.3 Water quality parameters**

#### *4.3.1 Water temperature*

The narrow range of water temperature in both *O. niloticus* and *O. jipe* species could be due to the time that temperature readings were taken during the morning hours, which was between 9.00 am and 11.00 am daily. The study, however, did not record any lethal temperature values which range between 10–11°C and 37–38°C, respectively [4]. Although the mean temperatures for both treatments were within the optimum range of 20–30°C required for fish growth [4, 5, 27], these temperatures were below the specific optimum range of 25–30°C recommended for Tilapia growth. The low temperatures could be attributed to a high altitude location of the study area with low mean ambient temperatures ranging between 18°C and 26°C. Although water temperature did not show any significant differences between the *O. jipe* and *O. niloticus* treatments, it has been reported that water temperatures below the range of 20–22°C contribute to a nearly 30% decline in optimal growth [5]. This could be the reason for the nonoptimal growth performance of *O. jipe* and *O. niloticus* in aquaculture. Probably, the novel species *O. jipe* were more stressed due to slow adaptability to the changing physicochemical parameters than *O. niloticus*.

#### *4.3.2 Dissolved oxygen (DO) concentration*

The reduction of DO levels in *the O. jipe* culture system was probably due to low acceptance of commercial feeds, which were administered in their daily ration. *O. niloticus* species on the other hand consumed all the feed provided within a span of 2–3 hours. This necessitated frequent siphoning of feed remnants that settled at the bottom of *O. jipe* ponds and flushing of the culture systems with clean fresh water from the tanks was done uniformly to all ponds was done every 2 days. Although

frequent siphoning was done regularly, the lack of efficient removal of all bioflocs contributed to the deterioration of water quality resulting in significant differences in DO levels between the *O. jipe* and *O. niloticus* culture systems. This result on *O. jipe* DO levels corroborates with the findings of [22] that attributed low to feed remnants that settled at the bottom of the hapa-in-pond culture system. Lower DO levels have been reported to decrease feeding and respiration in most tilapia species and make it difficult for fish to assimilate the consumed feed [5, 28]. In addition, the amount of dissolved oxygen in the raised ponds was insufficient because the small-sized fingerlings tend to consume additional oxygen to meet the demands for their increased metabolic rates, which doubles for every 10 degrees increase in water temperature within the optimum range [29].

#### *4.3.3 pH*

The study found that the mean pH (8.01 ± 0.13) of *O. jipe* treatment was significantly higher than the mean pH (7.54 ± 0.09) of *O. niloticus.* Probably, unconsumed pellets particularly in *O. jipe* treatment could have been decomposed under low DO levels to liberate ammonia (NH3), which accounts for the range of pH values greater than nine. Although ammonia levels were not determined during the study, studies have shown that ammonia is derived from the breakdown of nontoxic ammonium ion (NH4+ ) contained in digestible crude protein and is a component of total nitrogen highly toxic to fish. However, good water management ensured that the pH did not affect the fish growth and survival because it prevented prolonged exposure of fish to toxic un-ionized ammonia concentrations of greater than 0.2 mg L−1, which can depress the appetite of Tilapia fish [5]. This partly explains why the pH was not consistently high during the study but showed fluctuations between high and low values.

### **5. Conclusions and recommendation**

The study findings showed that the investigated water quality parameters affected the growth performance and survival rates of *O. jipe* and *O. niloticus* cultured using raised wooden ponds. As a result, both species did not realize their optimal performance in the aquaculture environment because the temperature fell below the range of 25–30°C recommended for tilapia culture. This might have contributed to low food intake by the species, resulting in low DO and high pH levels. Fish growth was however influenced by the interplay of unmonitored physicochemical parameters in an open pond system. However, for the successful introduction of *O. jipe* species to aquaculture and optimal growth in the aquaculture environment, it is crucial to delineate optimal culture conditions for the species. This can be done by monitoring the effect of water quality parameters on the growth and survival of fish in a controlled fish culture system and monitoring growth for a longer period. The necessity for a repeat of this experiment in a different culture system, such as hatchery tanks or a greenhouse, to determine whether the results obtained can be replicated under different cultural conditions. In addition, water quality can be recorded using automatic sensor detectors, which take the readings over infinitesimal time intervals and the data collected can be modeled using advanced differential models to evaluate several latent variables in the underlying relationships between water quality and growth parameters.

*DOI: http://dx.doi.org/10.5772/intechopen.106361 Water Quality Effects on Growth and Survival of* Oreochromis jipe *and* Oreochromis niloticus*…*

## **Acknowledgements**

The authors wish to acknowledge the African Development Bank for funding this research through an MSc Scholarship awarded to Mr. J. O. Omweno. Thanks to the Kenya Marine and Fisheries Research Institute (KMFRI), and Kegati Aquaculture Center for technical support.

## **Conflict of interest**

The authors declare that they have no competing personal or professional interests. Further, the funding agency did not play any role in the conceptualization, design, and implementation of the study, as well as the decision to publish the results.

## **Ethics, approval, and compliance statement**

The study was conducted by following all the applicable guidelines stipulated by the National Commission for Science, Technology, and Innovation (NACOSTI) regulations (2014) under the research permit no NACOSTI/P/20/3982 and the Kenya Marine and Fisheries Research Institute (KMFRI) set by Cap 250 of the Science and Technology Act (1979), which has since been repealed by the Science, Technology and Innovation Act (Act no. 28, 2013) and Section 56 of the act that mandates KMFRI to research marine and freshwater resources.

## **Author details**

Job O. Omweno1 \*, Albert Getabu1 , Reuben Omondi1 and Paul S. Orina2

1 Department of Environment, Natural Resources and Aquatic Sciences, Kisii University, Kisii, Kenya

2 Kenya Marine and Fisheries Research Institute (KMFRI), Kegati Aquaculture Centre**,** Kisii, Kenya

\*Address all correspondence to<EMAIL_ADDRESS>
© 2022 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Section 3


**4. Conclusions**

Chemical additives play a significant role in several stages of the formation and transformation of CaCO3 crystals. In the present paper, the fast controlled precipitation method was used to study the influence of chemical additives on the CaCO3 crystallization process. The additives employed were three foreign salts (MgCl2, Na2SO4 and MgSO4) and two antiscalants (sodium polyacrylate and sodium-tripolyphosphate). The results showed that the nucleation time was retarded after the addition of each foreign salt, regardless of its ionic strength value. The MgSO4 ion greatly delayed the precipitation of CaCO3 compared to Mg2+ and SO4 2− ions. Moreover, the sulfate ion had more impact on the pH of prenucleation and precipitation. As for magnesium ion, it influenced the nucleation time. Thus, the presence of magnesium and sulfate ions affected nucleation and growth through different ways. At the macroscopic scale, it would seem that both magnesium and sulfate ions, although presenting opposite charges, had the same effect on the orientation of CaCO3 precipitation to the heterogeneous precipitation, regardless of their modes of action in solution. Furthermore, both antiscalants RPI and STPP had a great effect on the crystallization kinetic by greatly delaying the precipitation time. Indeed, the prenucleation stage duration (Δt=tprec − tprenuc) was longer when the antiscalant concentration was larger. Therefore, the distinction between the prenucleation and precipitation thresholds became easier. In addition, the presence of each antiscalant affected the precipitation threshold remarkably, leading to a large variation in the supersaturation coefficient. In addition, the chemical inhibition favorably promoted precipitation on the cell wall, detrimental to the bulk solution scaling. Finally, the X-ray diffraction patterns of deposit scale formed in the absence and presence of each chemical additive revealed the formation of different crystal forms of calcium carbonate, which were calcite, vaterite and aragonite.

**Author Contributions:** Conceptualization, R.H. and M.M.T.; methodology, R.H. and M.M.T.; software, R.H.; validation, R.H. and M.M.T.; formal analysis, R.H. and M.M.T.; investigation, R.H.; resources, R.H. and M.M.T.; data curation, R.H.; writing—original draft preparation, R.H.; writing review and editing, R.H. and M.M.T.; visualization, R.H.; supervision, R.H. and M.M.T.; project administration, R.H. and M.M.T.; funding acquisition, R.H. and M.M.T. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the Deputyship for Research and Innovation, Ministry of Education in Saudi Arabia, Project number (IF2/PSAU/2022/01/21605).

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Data are available on reasonable request.

**Acknowledgments:** The authors extend their appreciation to the Deputyship for Research and Innovation, Ministry of Education in Saudi Arabia for funding this research work through the Project number (IF2/PSAU/2022/01/21605).

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