In this study, Moringa seeds, aloe vera leaves, and cactus leaves were used as biocoagulants for the treatment of drinking water. The effects of coagulant type, coagulant dosage, and pH were studied on the quality of the treated water. Response surface methodology was used to predict and optimize the parameters. The standard Six Jar test was used to measure the performance of coagulants. Three mixing modes were used in the jar test: quick mixing at 1 min at 120 rpm, slow mixing for 19 min at 40 rpm, and 15 min settling. The characterization results showed that extracts of Moringa seeds, aloe vera leaves, and cactus leaves contain 43.95 ± 0.49, 13.9 ± 0.42, and 10.94% ± 0.37 protein, respectively. It was revealed that coagulant type, coagulant dosage, and the interaction between (coagulant type (MS-SC and AV-SC) and pH) were significant (p < 0.05) for turbidity removal. Jar test results showed a removal efficiency of turbidity 98.83%, and 98.74% and 69.83% using MS-SC, and AV-SC and Ca-SC bio, respectively. These results imply that the three coagulants can be considered as effective, low-cost, and eco-friendly resources for the treatment of drinking water in rural communities of Ethiopia where access to clean water is scarce.

  • Biocoagulants extracted from Moringa stenopetala seed, aloe vera leaves, and cactus leaves.

  • Salt is used as low-cost extractive agent in mild conditions.

  • Turbidity seems the main contributor to the deterioration of water quality.

  • Complete removal of E. coli was achieved.

  • Biocoagulants are inexpensive solutions for water treatment in rural areas.

Water is the most plentiful resource on earth but only 3% of it is usable for human consumption; the rest is found in the oceans as salt water (Love & Luchsinger 2014). Although water is available in large quantities and in a variety of forms, its use for different purposes is subjected to quality. The absence of sufficient and clean water continues to be the most critical issue in developing countries (Markandya 2006). According to the WHO/UNICEF Joint Monitoring Program for Water Supply, Sanitation and Hygiene (JMP), about half of the world's population lacked access to securely managed sanitation, and around one in four people lacked access to managed drinking water in their homes (World Health Organization 2021). Only 81% of the world's population will have access to safe drinking water at home, leaving 1.6 billion without it in 2030.

Surface water has been experiencing increasing risks of contamination in recent years because of the release of immense amounts of waste created by different human activities. The surface water had high turbidity levels (Ramavandi 2014) which include impurities and infectious microbes which could impact human health. Particularly, in many rural areas, lack of wastewater treatment and surface water contamination results in millions of deaths annually due to waterborne diseases (Grant et al. 2012). In previous years, many researchers have tested different treatment processes, like the chemical and physical processes for water treatment, including coagulation, membrane filtration, and adsorption (Kaur 2021; Prajapati et al. 2021).

Thus, low-cost water treatment technologies are needed provide safe and clean drinking water for poor communities (Crittenden 2012; Sadhu et al. 2021). The raw water quality is affected by organic matter and inorganic salts and it contains carbonates, chlorides, sulfates, phosphates, and nitrates of calcium, magnesium, sodium, and potassium (Issa & Babiker 2014). And also, raw water is contaminated by different impurities and microorganisms that cause waterborne disease (Khan 2017).

Recently, chemical coagulants are predominantly used for the removal of impurities and microbial pathogens especially in developing countries with centralized water treatment systems (Gandiwa 2020). However, these chemical coagulants have serious problems of producing a lot of sludge which is difficult to manage and dispose of (Ng et al. 2022; Muniz et al. 2022). In addition, they can cause serious human health problems like Alzheimer's disease, Parkinson's, neurotoxic, and carcinogenic effects (Bellouk et al. 2022; Karnena & Saritha 2022). Several studies indicated that the remedial solution for such problems can be achieved by using natural coagulants. The idea of utilizing the natural coagulants for the water purification started in ancient times. India, China, and Africa are believed to have used plant byproducts as natural coagulants in their water supply in the last 2,000 years during ancient civilizations (Asrafuzzaman et al. 2011). Biocoagulants are consisting of both the plant- and animal-based origins. The animal origin Bacterial Exopolysaccharides (Al-Wasify et al. 2015), such as shredded fish bladders and chitosan from the shells of shellfishes (Bratby 2006), have been successfully tested. It was proven that plants-based biocoagulants from locally available plants such as Moringa oleifera (Franciele Pereira Camacho 2016; Gandiwa 2020), Moringa stenopetala (Megersa 2018), papaya seed (Syeda Azeem Unnisa 2018), cactus (Gandiwa 2020), aloe vera (Azni Idris 2011; Gulmire 2017), and Cassava starch (Jose Lugo-Arias 2020) have much higher extracted yield. Salt-based extraction is better than other methods (Muthu Raman 2013; Megersa 2018). In this study, M. stenopetala seeds, aloe vera leaves, and cactus leaves were used for the extraction of biocoagulants. They are easily available locally in the rural area of Ethiopia; usually used traditionally by the communities for food medicine and cosmetics; and used as a disinfectant and to inhibit microorganisms (Megersa 2018; Varkey 2020). Therefore, this research evaluates the performance of biocoagulant active extracts from M. stenopetala seeds, aloe vera leaves, and cactus leaves using 1 M NaCl solution for the effective removal of turbidity and Escherichia coli.

Raw water characterization

The experimental analysis was conducted in Addis Ababa Water and Sewerage Authority (AAWASA) and the Departments of Food Science and Nutrition and also Microbial, Cellular, and Molecular Biology at Addis Ababa University's College of Natural and Computational Sciences. The characteristic of the raw water collected from Legedadi dam is displayed in Table 1. As can be seen from Table 1, most raw water quality parameters exceed the WHO drinking water guidelines. In particular, the Legedadi dam has high turbidity and E. coli. This shows that the water was contaminated by photogenic organisms.

Table 1

Selected physicochemical characteristics of raw water for Legedadi water treatment plant

ParametersRaw waterWHO Guidelines for drinking water
pH ±7.53 6.5–8.5 
Turbidity 239.67 NTU Less than 5 NTU 
Alkalinity ±68.8 mg/l Less than 50 mg/l 
Conductivity 113.44 μS/cm ±  
TDS ±60.33 mg/l ± Less than 1,000 mg/l 
Total coliform ±593 CFU/0.1 ml Absent 
E. coli ±305 CFU/0.1 ml Absent 
ParametersRaw waterWHO Guidelines for drinking water
pH ±7.53 6.5–8.5 
Turbidity 239.67 NTU Less than 5 NTU 
Alkalinity ±68.8 mg/l Less than 50 mg/l 
Conductivity 113.44 μS/cm ±  
TDS ±60.33 mg/l ± Less than 1,000 mg/l 
Total coliform ±593 CFU/0.1 ml Absent 
E. coli ±305 CFU/0.1 ml Absent 

The Legedadi water treatment plant is shown in Figure 1, which is the largest freshwater supply dam. It is the major and the largest drinking water source in Addis Ababa. The water treatment plant is located between 9°20′ N and 38°45′ E at an altitude of 2,450 (m.a.s.l). The water treatment plant is constructed with a maximum depth of 34 m close to the dam and a minimum depth of 4 m at the periphery. The water samples for this study were collected from the Legedadi dam at a depth of 40 cm using polypropylene bottles, and the samples were taken in accordance with the procedures outlined in ASTM (1996). The experimental analysis was conducted in AAWASA, the Departments of Food Science and Nutrition, and also Microbial, Cellular, and Molecular Biology at Addis Ababa University's College of Natural and Computational Sciences. The characteristic of the raw water collected from Legedadi dam is displayed in Table 1. As can be seen from Table 1, most raw water quality parameters exceed the WHO drinking water guidelines. Particularly, the Legedadi dam has high turbidity and E. coli.
Figure 1

Map of the water sampling area (Legedadi water treatment plant).

Figure 1

Map of the water sampling area (Legedadi water treatment plant).

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Biocoagulants preparation

M. stenopetala seeds, Aloe vera leaves and cactus leaves were collected from the local area of Konso, Sululta, and Adam – Nazareth of Ethiopia respectively. The size of biocoagulants was reduced using a knife and dried in the open sun for 1 day in the case of Moringa and for 3 days in the sun and overnight in a 50 °C oven in the case of aloe vera and cactus leaves. Then, the dried samples were ground into a fine powder using a mortar and a homemade electrical machine (nima model BL-888A). By using two unique measuring strainers, the fine powder was meshed three times. Then, 350 mL of 95% ethanol was added to 60 g of the fine powder, and the mixture was manually agitated for 40 min. Centrifugation (model 800) was used to elute the suspension of the solution for 5 min at a speed of 4,000 rpm. In addition, the leftover solids were dried in the oven for 24 h at room temperature. One liter of distilled water was mixed with 58.44 g of analytical graded NaCl to prepare 1 M NaCl solution, and finally, 10 g of fine powder of the three coagulants was added to 1 liter of 1 M NaCl solution and extracted after 1 hour using a magnetic stirrer and settled for 30 min. The extract was kept at 4 °C until use for coagulation (Azni Idris 2011; Megersa 2018; Gandiwa 2020).

Experimental design

A total of 54 experimental runs were carried out for the three factors, namely, coagulants type, pH, and the dose of natural coagulants with three levels each and with two replications. About four responses were analyzed to characterize the water quality of the study area (Table 2).

Table 2

Experimental design with three experimental factors and four responses

RunBlockFactor 1Factor 2Factor 3Response 1Response 2Response 3Response 4
A: Coagulant typesB: Coagulant dose (mg/l)C: pHResponse pHTurbidity (NTU)Alkalinity (mg/l)E. coli (CFU/0.1 ml)
R 1 MS-SC 50 6.5 ** ** ** ** 
R 2 MS-SC 50 6.5 ** ** ** ** 
R 1 AV-SC 50 6.5 ** ** ** ** 
R 2 AV-SC 50 6.5 ** ** ** ** 
R 1 Ca-SC 50 6.5 ** ** ** ** 
R 2 Ca-SC 50 6.5 ** ** ** ** 
R 1 MS-SC 100 7.5 ** ** ** ** 
R 2 MS-SC 100 7.5 ** ** ** ** 
R 1 AV-SC 100 7.5 ** ** ** ** 
10 R 2 AV-SC 100 7.5 ** ** ** ** 
50 R 2 MS-SC 150 8.5 ** ** ** ** 
51 R 1 AV-SC 150 8.5 ** ** ** ** 
52 R 2 AV-SC 150 8.5 ** ** ** ** 
53 R 1 Ca-SC 150 8.5 ** ** ** ** 
54 R 2 Ca-SC 150 8.5 ** ** ** ** 
RunBlockFactor 1Factor 2Factor 3Response 1Response 2Response 3Response 4
A: Coagulant typesB: Coagulant dose (mg/l)C: pHResponse pHTurbidity (NTU)Alkalinity (mg/l)E. coli (CFU/0.1 ml)
R 1 MS-SC 50 6.5 ** ** ** ** 
R 2 MS-SC 50 6.5 ** ** ** ** 
R 1 AV-SC 50 6.5 ** ** ** ** 
R 2 AV-SC 50 6.5 ** ** ** ** 
R 1 Ca-SC 50 6.5 ** ** ** ** 
R 2 Ca-SC 50 6.5 ** ** ** ** 
R 1 MS-SC 100 7.5 ** ** ** ** 
R 2 MS-SC 100 7.5 ** ** ** ** 
R 1 AV-SC 100 7.5 ** ** ** ** 
10 R 2 AV-SC 100 7.5 ** ** ** ** 
50 R 2 MS-SC 150 8.5 ** ** ** ** 
51 R 1 AV-SC 150 8.5 ** ** ** ** 
52 R 2 AV-SC 150 8.5 ** ** ** ** 
53 R 1 Ca-SC 150 8.5 ** ** ** ** 
54 R 2 Ca-SC 150 8.5 ** ** ** ** 

Note: MS-SC, Moringa stenopetala with sodium chloride; AV-SC, aloe vera with sodium chloride; Ca-SC, cactus with sodium chloride.

Factorial design, total run: 54 = FK.n, F: level = 3, K: factors = 3, and n: replication = 2 (33 × 2 = 54).

Coagulation experiments and procedure

Using 1 l beakers and the conventional six Jar-Test apparatus (PHIPPS &BIRD), the coagulation test was performed. The treatment tests were carried out at room temperature with coagulant doses of 50, 100, and 150 mg/l, with a pH value of 6.5, 7.5, and 8.5 for the three coagulant types. To adjust the solution's pH, 1 M of NaOH and 1 M of HCl solution were employed (Vasanthi Sethu 2019).

Three alternative mixing modes were used in the jar test, with 1 min of rapid mixing (120 rpm), followed by 19 min of slow mixing (40 rpm) for flocculation and then 15 minutes of settling at room temperature (Jose Lugo-Arias 2020). For the purpose of the assessment of physicochemical and microbiological parameters, a sample was obtained from the middle of the supernatant, extracted using a syringe, and filtered through grade 0.7 Whatman paper 1.

Characterization of synthesized coagulants

Analytical methods

The pH of the raw and treated water at various pH levels, coagulant type, and coagulant dosages were measured. Turbidity (model: HI 98703) of raw and treated water was determined (American Public Health Association 1999). Alkalinity was determined by titrating 0.02 N sulfuric acid standard solutions into 50 ml water samples. The pH of the water samples was utilized to select the indicator solutions when a pH < 8.3 Bromocresol Green Methyl Red Solution (which turned pink), and pH > 8.3 Phenolphthalein. The values were determined using the following equation.
formula
(1)
formula
(2)
The bacteriological quality of raw and treated water (E. coli) from each sample was determined in triplicate. On 20 ml of Eosin–methylene blue (EMB) agar-prepared Petri dish media, E. coli was injected. The total grammage of agar powder required was calculated based on the agar powder manual's instructions for preparing the bacteria growing media using the powder and distilled water for each 20 ml Petri dishes. Assuming a gauge reading of 121 °C, the estimated gram of agar was heated with balanced distilled water, and this hot media, Falcon tube, and distilled water were sterilized in an autoclave (model YX-24M) for 15 min. To determine E. coli, the cooling media was filled on a plate and left in the hood overnight. Following collection and treatment, a sample of the raw water was taken, water was used to cultivate bacteria, and this was done right away. Spread plate techniques based on Standard Strategies (9215C of American Public Health Association 1998) were used for the study of E. coli. To reduce bacterial thickness, 1 ml of sample was serially diluted three times with 9 ml of distilled water. Then, onto the EMB agar plates for E. coli, 0.1 ml of the relevant dilutions was applied immediately. The Petri plates were placed in the incubator at 37 °C. After 24 h of incubation (APHA 1998), the number of colonies was counted using the colony counter.
formula
(3)

Statistical analysis

Turbidity, alkalinity, and E. coli were subjected to analysis of variance (ANOVA) using SPSS software version 20. ANOVA was also used to examine the relationships between various elements. The factorial model was created using the experimental design, which was constructed using Design Expert v7.0.0 software and General factorial designs (Table 2). Further, the response surface method was used to understand the interaction and the impact between the continuous factors (pH and coagulants dosage) and the responses of turbidity, alkalinity, and E. coli.

Characterization of synthesized coagulants

The extricated biocoagulants were characterized by their protein, moisture, and ash content. The characterization results of the protein, moisture, and ash content of the three biocoagulants are presented in Table 3.

Table 3

Physicochemical properties of the powder and extracted biocoagulants

Types of plant speciesParametersRaw powderEthanol extraction1 M NaCl extraction
Moringa stenopetala Protein (%) 39.73 ± 0.25 43.01 ± 0.09 43.95 ± 0.49 
Moisture (%) 5 ± 1.13 6.5 ± 0.71 9.85 ± 0.07 
Ash (%) 4.4 ± 0 4.6 ± 0.28 5.05 ± 0.21 
Aloe vera Protein (%) 13.56 ± 0.12 13.9 ± 0.42 
Moisture (%) 3.7 ± 0.71 11.05 ± 0.78 
Ash (%) 13.8 ± 0.28 31.6 ± 0.28 
Cactus Protein (%) 10.5 ± 0.74 10.94 ± 0.37 
Moisture (%) 2.4 ± 0 9.35 ± 0.21 
Ash (%) 26.2 ± 0.28 35.43 ± 0.81 
Types of plant speciesParametersRaw powderEthanol extraction1 M NaCl extraction
Moringa stenopetala Protein (%) 39.73 ± 0.25 43.01 ± 0.09 43.95 ± 0.49 
Moisture (%) 5 ± 1.13 6.5 ± 0.71 9.85 ± 0.07 
Ash (%) 4.4 ± 0 4.6 ± 0.28 5.05 ± 0.21 
Aloe vera Protein (%) 13.56 ± 0.12 13.9 ± 0.42 
Moisture (%) 3.7 ± 0.71 11.05 ± 0.78 
Ash (%) 13.8 ± 0.28 31.6 ± 0.28 
Cactus Protein (%) 10.5 ± 0.74 10.94 ± 0.37 
Moisture (%) 2.4 ± 0 9.35 ± 0.21 
Ash (%) 26.2 ± 0.28 35.43 ± 0.81 

It is shown in Table 3 that biocoagulants extracted from M. stenopetala, aloe vera, and cactus have a protein content of 43.95 ± 0.49, 13.9 ± 0.42, and 10.94 ± 0.37, respectively. Regarding the solvent effect, it could be noted that the protein content of the salt-based extracted biocoagulant is larger than that of the other types (Table 3). Protein–protein dissociations are increasing due to the salt-based extraction, and protein solubility increases with an increase in salt ionic strength (Muthu Raman 2013; Ramavandi 2014; Franciele Pereira Camacho 2016; Megersa 2018). Using an electrophoresis method, the zeta potentials of Moringa, aloe vera, and cactus were 18, 22, and 33 mV, respectively. Strong macromolecular repulsion in the cactus solution led to good stability; repulsion between macromolecules less than 30 indicates a weak repellent force (Lanan Fabm 2020).

The surface morphology of biocoagulants was examined using a scanning electron microscope (SEM). Figures 2 and 3 show the SEM image of the coagulants using two different magnifications of 200x and 500x as well as scale bar sizes of 100 and 50 μm. The SEM images of the biocoagulant of a cactus (Ca-SC) presented in Figure 2 revealed a large surface area at a magnification of 200x, which provides a large surface for coagulation efficiency (Lek et al. 2018) and needle-like shape at a magnification of 500x. Both 200x and 500x magnifications of images of the biocoagulant of aloe vera (AV-SC) in Figure 3 show a rougher surface morphology with irregularly shaped blocks and numerous pores of various sizes, which was why turbidity was effectively removed. Nonfodji et al. (2020) reported that the coagulant extract made from M. oleifera seeds had a heterogeneous morphology and was distinguished by a rougher surface with numerous pores of various sizes, and hence, they concluded that the existence of these pores made it easier for contaminants to be adsorbed from wastewater.
Figure 2

SEM microphotographs of the cactus extract at a magnification of (a) 200x and (b) 500x.

Figure 2

SEM microphotographs of the cactus extract at a magnification of (a) 200x and (b) 500x.

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Figure 3

SEM microphotographs of the aloe vera extract at a magnification of (a) 200x and (b) 500x.

Figure 3

SEM microphotographs of the aloe vera extract at a magnification of (a) 200x and (b) 500x.

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The active functional groups in the biocoagulants obtained from M. stenopetala, aloe vera, and cactus were examined using Fourier-transform infrared spectroscopy (FTIR). The image of M. stenopetala (MS-SC) in Figure 4(a) shows that the presence of C-H linkages at 2,913 cm−1 (Nandiyanto et al. 2019), while the band at 3,756 cm−1 was caused by O-H stretching vibrations. The carbonyl C = O stretching vibrations in primary and tertiary amides were indicated by peaks of 1,732 and 1,283 cm−1. The carboxyl groups were observed at 1,363 cm−1, and during the coagulation phase, the carboxyl groups served as solids' adsorption sites. Figure 4(b) shows that cactus (Ca-SC) had the band at the 3,757 cm−1 region due to O-H stretching vibrations, whereas the presence of C-H linkages was indicated at 2,918 cm−1. Peaks at 1,776 and 1,538 cm−1 showed carbonyl C = O stretching vibrations of primary and tertiary amides. Aloe vera (AV-SC), as observed in Figure 4(c), showed a band at 3,853 cm−1 due to O-H stretching vibrations, but the presence of C-H links was revealed at 2,918 cm−1 (Nandiyanto et al. 2019). The absorption band at 1,766 and 1,584 cm−1 showed the primary and tertiary amides' carbonyl C = O stretching vibrations and the N-H groups in those amide-generated intermolecular hydrogen bonds While carboxyl group peaks were seen at 1,312 cm−1, triple bond region C-C group peaks were seen at 2,371 cm−1. During the coagulation phase process, the N-H groups in amides created intermolecular hydrogen connections between the coagulants and suspended particulates, and also the carboxyl groups served as solids' adsorption sites (Fatombi et al. 2013).
Figure 4

FTIR spectra of biocoagulants with the main absorption bands of (a) Moringa, (b) cactus, (c) aloe vera.

Figure 4

FTIR spectra of biocoagulants with the main absorption bands of (a) Moringa, (b) cactus, (c) aloe vera.

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Characterization of raw and treated water

The results for selected water quality parameters after treatment by three biocoagulants (Moringa, aloe vera, and cactus) are displayed in Table 4. The most important water quality parameters are as follows: pH, turbidity, alkalinity, and E. coli. As can be seen in the table, all water quality parameters are within the standard limit of the WHO standard after the treatment.

Table 4

Selected physicochemical characteristics of raw and treated water

ParametersRaw waterTreated waterWHO Guidelines for drinking water
pH 7.53 6.64–8.03 6.5–8.5 
Turbidity 239.67 NTU 2.83 NTU less than 5 NTU 
Alkalinity 68.8 mg/l 18.2 mg/l less than 50 mg/l 
E. coli 305 CFU/0.1 ml Absent 
ParametersRaw waterTreated waterWHO Guidelines for drinking water
pH 7.53 6.64–8.03 6.5–8.5 
Turbidity 239.67 NTU 2.83 NTU less than 5 NTU 
Alkalinity 68.8 mg/l 18.2 mg/l less than 50 mg/l 
E. coli 305 CFU/0.1 ml Absent 

Effect of biocoagulants, dose, and pH on turbidity

Turbidity of Legedadi dam water treated with the three coagulants with a variation of dosage at different pH levels is presented in Figure 5. As shown in Figure 5, for all the three biocoagulants, turbidity removal increases with the increasing dose at pH 6.5. Turbidity removal was increased from 68.22 to 98.82%, 61.72 to 98.205%, and 45.55 to 69.395%, respectively, for biocoagulants from Moringa, aloe vera, and cactus when the dose increased from 50 to 150 mg/l. The most plausible mechanisms for coagulation activity could be sorption/adsorption, bridging of destabilized particles, neutralization of charges, and bridging of charges. Accordingly, charge neutralization is caused by the positive charges from the cationic protein of biocoagulants and between water colloids. The increasing protein active ingredient as the result of the increasing dose enhances the effective collision, neutralization, and bridging as reported by Lek et al. (2018). In this study, increasing the dose of those biocoagulants enhances the turbidity reduction due to the increment of protein active ingredient at a higher dose level. However, as shown in Figure 5(b) and 5(c), the turbidity removal was decreased with a further increase in dose from 50 to 100 mg/l for the pH of 7.5 and 8.5. According to Lanan Fabm (2020), the redispersion of aggregated particles started, which is responsible for the reduction in turbidity removal when the biocoagulant dose is above the optimal dose. Therefore, when biocoagulant from cactus at a concentration of 100 mg/l was used, the bridge mechanism was initiated, leading to the redispersion problem. Because of the tails and coils of a single adsorbed cactus polymer connected to a water pollutant can be overhanging and medium with the other colloidal particle (Shak 2014). When the pH increased from 6.5 to 7.5 and 7.5 to 8.5 at a dose of 150 mg/l, biocoagulants from cactus and Moringa decreased the turbidity removal. In addition, at a dose of 150 mg/l, the turbidity removal by aloe vera as a biocoagulant reduced when the pH increased from 6.5 to 7.5 but increased when the pH increased from 7.5 to 8.5. In accordance with the study by Muthu Raman (2013), pH has an influence on reducing turbidity in drinking water. In addition, according to Miller (2008), pH and turbidity in coagulation activities have a significant relationship.
Figure 5

Dose of coagulant versus turbidity removal at pH of (a) 6.5, (b) 7.5, and (c) 8.5.

Figure 5

Dose of coagulant versus turbidity removal at pH of (a) 6.5, (b) 7.5, and (c) 8.5.

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Effect of biocoagulants, dose, and pH on alkalinity

The alkalinity of the treated water increases with the increasing coagulant dose (50–150 mg/l) for Moringa and cactus (Figure 6). But, for aloe vera, alkalinity increment is marginal. It slightly increases when the dose increases from 50 to 100 mg/l and decreased beyond 100 mg/l of coagulant dose. The WHO guidelines stipulate that the permitted alkalinity for potable water is less than 50 mg/l, and the alkalinity obtained by employing the three biocoagulants was still within the desired limit (World Health Organization 2011). It is also worth noting that the alkalinity was much higher for the Moringa biocoagulant at a dose of 150 mg/l and pH values of 6.5 and 7.5 compared to that for other biocoagulants. The composition of Moringa increased the alkalinity of water because amine groups organized their positive charges differently at higher pH and doses (Saritha 2019).
Figure 6

Effect of coagulant dose on alkalinity of the treated water at pH of (a) 6.5, (b) 7.5, and (c) 8.5.

Figure 6

Effect of coagulant dose on alkalinity of the treated water at pH of (a) 6.5, (b) 7.5, and (c) 8.5.

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Effect of biocoagulants, dose, and pH on E. coli

In this study, the plate media was incubated for 24 h at 37 °C in the incubator with the immunized samples by utilizing the serial dilution. The colony on the plate was expressed by the colony-forming unit (CFU) and tallied by the colony counter. The results are presented in Figure 7. As shown in Figure 7(a)–7(c), when the coagulant increased from 50 to 150 mg/l, E. coli removal was increased from 20% to complete (100%) removal for Moringa for all pH tested. Increasing the dose of aloe vera biocoagulants increases E. coli reduction, which ranges from 40.655% to completely (100%). On the other hand, the E. coli removal was decreased when the coagulant dose of cactus biocoagulant is increased from 50 to 150 mg/l. A similar study (Ramavandi 2014; Varkey 2020) was reported for complete removal of germs using biocoagulants, while Charlotte Farrell (2017) shows that 86% of E. coli was eliminated by the attached Fe (ll). However, the usage of disinfectants is crucial following water treatment using biocoagulants due to the regrowth of microorganisms (Megersa 2018).
Figure 7

Dose of coagulant versus E. coli percent reduction at pH of (a) 6.5, (b) 7.5, and (c) 8.5.

Figure 7

Dose of coagulant versus E. coli percent reduction at pH of (a) 6.5, (b) 7.5, and (c) 8.5.

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Statistical optimization of the treatment process

Turbidity optimization with respect to the biocoagulants, dose, and pH

Table 5 shows that the interaction between coagulant type (MS-SC and AV-SC) and pH was significant (p < 0.05) for turbidity removal from drinking water. Turbidity removal was somewhat reduced for MS-SC and AV-SC through their dosage amplified from 50 to 150 mg/l. Turbidity removal remains marginal when cactus (Ca-SC) biocoagulant dose was increased from 50 to 100 mg/l, but it decreased after the dose was increased from 100 to 150 mg/l. Decreasing turbidity removal was induced by the increased suspended particles caused by the additional coagulant particles disrupting the neutralized particles (Marobhe 2007). MS-SC biocoagulant shows good turbidity removal at a dose of 150 mg/l and pH of 6.5. Since the lines are parallel (Figure 8), there was no relationship between pH and coagulant dosage as presented by ANOVA (Table 6). The fitted response surface model for turbidity was the two-factor interaction (2FI) model, and the interpretation of the interaction's effects between the responses and the two continuous factors are shown in the 2D contour and 3D surface plots (dose and pH) in Figure 8. The regression model for turbidity is given by the quadratic model presented in Equation (4). The model summary in Table 6 with a high coefficient of determination (R2) shows a strong relationship between the factors and response (Wan 2003). A value of more than 0.75 is regarded as meritorious (Shak 2014). Also, the turbidity coefficient of variation (CV) is less than 10%, demonstrating that the accuracy and dependability of the experimental findings are good (Lanan Fabm 2020) (Table 6).
Table 5

ANOVA for significance test between factors and turbidity removal (%)

SourceType III sum of squaresdfMean squareFSignificance
Corrected model 27,094.897a 17 1,593.817 28.141 .000 
Intercept 21,246.153 21,246.153 375.134 .000 
Coa 2,579.615 1,289.808 22.774 .000 
PH 203.731 101.865 1.799 .180 
Dos 4,564.354 4,564.354 80.591 .000 
Coa * PH 697.875 174.469 3.081 .028 
PH * Dos 278.704 139.352 2.460 .100 
Coa * Dos 141.835 70.917 1.252 .298 
Coa * pH * Dos 262.040 65.510 1.157 .346 
Error 2,038.903 36 56.636   
Total 332,882.300 54    
Corrected total 29,133.800 53    
SourceType III sum of squaresdfMean squareFSignificance
Corrected model 27,094.897a 17 1,593.817 28.141 .000 
Intercept 21,246.153 21,246.153 375.134 .000 
Coa 2,579.615 1,289.808 22.774 .000 
PH 203.731 101.865 1.799 .180 
Dos 4,564.354 4,564.354 80.591 .000 
Coa * PH 697.875 174.469 3.081 .028 
PH * Dos 278.704 139.352 2.460 .100 
Coa * Dos 141.835 70.917 1.252 .298 
Coa * pH * Dos 262.040 65.510 1.157 .346 
Error 2,038.903 36 56.636   
Total 332,882.300 54    
Corrected total 29,133.800 53    

aR Squared = .9879 (Adjusted R Squared = .9816).

Table 6

Model summary for turbidity

R2Adj R2Pred. R2Adeq precisionCV %
0.9879 0.9816 0.9695 35.392 8.49 
R2Adj R2Pred. R2Adeq precisionCV %
0.9879 0.9816 0.9695 35.392 8.49 
Figure 8

The contour plot and surface response plot of turbidity versus two-factor interaction.

Figure 8

The contour plot and surface response plot of turbidity versus two-factor interaction.

Close modal
Turbidity (NTU):
formula
(4)
where A is the coagulant dose, B is the pH, and C is the coagulant types.

Alkalinity optimization with respect to the biocoagulants, dose, and pH

The interaction among all the three factors has a synergetic effect on the alkalinity of treated water. The result is presented by ANOVA (Table 7). The quantity of alkalinity in the water solution was increased after doses of MS-SC and AV-SC biocoagulants were increased from 50 to 100 mg/l. Increases in dosage have an impact on the MS-SC biocoagulant performance. The MS-SC biocoagulant shows a considerably higher alkalinity of water solution at 150 mg/l and a significantly lower alkalinity at 50 mg/l. This is the consequence of the positive charge on the amine group arrangement brought on by pH and dosage change (Saritha 2019). When the pH of the water increased from 6.5 to 7.5, utilizing all biocoagulants significantly increased alkalinity. When the pH of water increased from 7.5 to 8.5, the alkalinity of a solution of water declined. The surface response results of the 2D contour and 3D surface plot presented in Figure 9 show that alkalinity was highly affected by the interaction of coagulant dosage and pH. The optimum alkalinity was found to be 21.76 by employing aloe vera (AV-SC) biocoagulant at the pH of 7.5 and the dose of 125 mg/l, as shown in Figure 9 of the 2D contour and 3D surface plot. The fitted response surface model of alkalinity for the 2FI model is represented by Equation (5). According to the summary result of R2 and adjusted R2 values, the model reasonably fitted the experimental data (Table 8).
formula
(5)
where A is the coagulant dose, B is the pH, and C is the coagulant type.
Table 7

Model summary for alkalinity

R2Adj R2Pred. R2Adeq precisionCV %
0.906 0.861 0.6754 14.701 2.51 
R2Adj R2Pred. R2Adeq precisionCV %
0.906 0.861 0.6754 14.701 2.51 
Table 8

ANOVA for significance test between factors and alkalinity

SourceType III sum of squaresdfMean squareFSignificance
Corrected model 34,417.780a 17 2,024.575 20.365 .000 
Intercept 2,425.920 2,425.920 24.402 .000 
Coa 2,724.189 1,362.094 13.701 .000 
pH 2,265.646 1,132.823 11.395 .000 
Dos 9,986.671 9,986.671 100.455 .000 
Coa * Dos 6,608.136 3,304.068 33.235 .000 
pH * Dos 1,798.736 899.368 9.047 .001 
Coa * pH 1,547.935 386.984 3.893 .010 
Coa * pH * Dos 3,878.278 969.569 9.753 .000 
Error 3,578.913 36 99.414   
Total 178,695.600 54    
Corrected Total 37,996.693 53    
SourceType III sum of squaresdfMean squareFSignificance
Corrected model 34,417.780a 17 2,024.575 20.365 .000 
Intercept 2,425.920 2,425.920 24.402 .000 
Coa 2,724.189 1,362.094 13.701 .000 
pH 2,265.646 1,132.823 11.395 .000 
Dos 9,986.671 9,986.671 100.455 .000 
Coa * Dos 6,608.136 3,304.068 33.235 .000 
pH * Dos 1,798.736 899.368 9.047 .001 
Coa * pH 1,547.935 386.984 3.893 .010 
Coa * pH * Dos 3,878.278 969.569 9.753 .000 
Error 3,578.913 36 99.414   
Total 178,695.600 54    
Corrected Total 37,996.693 53    

aR Squared = .9879 (Adjusted R Squared = .9816).

Table 9

Model terms summary for E. coli removal

R-SquaredAdjR-SquaredPred. R-SquaredAdeq PrecisionC.V. %
0.9565 0.9335 0.8902 17.53 2.33 
R-SquaredAdjR-SquaredPred. R-SquaredAdeq PrecisionC.V. %
0.9565 0.9335 0.8902 17.53 2.33 
Figure 9

The contour plot and surface response plot.

Figure 9

The contour plot and surface response plot.

Close modal

E. coli optimization with respect to biocoagulants, dose, and pH

The model summary in Table 9 shows a high coefficient of determination (R2) and reveals a significant relationship between the factors and response. The interaction between coagulant type (MS-SC and AV-SC) and their dosage had a significant (p < 0.05) effect on E. coli reduction as depicted in Table 10. The E. coli reduction of water was to some extent decreased by using MS-SC and AV-SC biocoagulants when the dose increased from 50 to 100 mg/l. MS-SC biocoagulant shows much higher E. coli reduction in water solution at a dose of 150 mg/l and a pH of 7.5 and 8.5, and also, AV-SC biocoagulant shows much higher E. coli reduction in treated water at a dose of 100 mg/l and at pH of 7.5. The 150 mg/l dose of the two biocoagulants (MS-SC and AV-SC) seems to perform good E. coli reduction in treated water. However, the E. coli reduction was decreased after the dose of cactus biocoagulant increased from 50 to 150 mg/l. The amount of E. coli reduction is related to the removal of turbidity, and it is believed that turbidity is a carrier of microorganisms (Charlotte Farrell 2017). The response surface method from design expert software exhibited the 2D contour and 3D surface plots for E. coli reduction, as shown in Figure 10. When utilizing M. stenopetala (MS-SC) biocoagulant at a pH of 7.5 and a dose of 125–150 mg/l, the optimum E. coli reduction was 46.9, as shown in the 2D contour and 3D surface plots in Figure 10. The fitted response surface model for E. coli was the quadratic model as determined by the central composite design from design expert software. The regression model for E. coli is given by Equation (6).
Table 10

ANOVA for significance test between factors and E. coli percent reduction (%)

SourceType III Sum of SquaresdfMean SquareFSig.
Corrected Model 33,745.511a 17 1,985.030 16.479 .000 
Intercept 4,731.778 4,731.778 39.283 .000 
Coa 1,370.458 685.229 5.689 .007 
pH 518.504 259.252 2.152 .131 
Dos 8,772.508 8,772.508 72.828 .000 
Coa * pH 263.392 65.848 .547 .703 
pH * Dos 379.084 189.542 1.574 .221 
Coa * Dos 7,825.362 3,912.681 32.483 .000 
Coa * pH * Dos 515.877 128.969 1.071 .385 
Error 4,336.364 36 120.455   
Total 207,347.484 54    
Corrected Total 38,081.875 53    
SourceType III Sum of SquaresdfMean SquareFSig.
Corrected Model 33,745.511a 17 1,985.030 16.479 .000 
Intercept 4,731.778 4,731.778 39.283 .000 
Coa 1,370.458 685.229 5.689 .007 
pH 518.504 259.252 2.152 .131 
Dos 8,772.508 8,772.508 72.828 .000 
Coa * pH 263.392 65.848 .547 .703 
pH * Dos 379.084 189.542 1.574 .221 
Coa * Dos 7,825.362 3,912.681 32.483 .000 
Coa * pH * Dos 515.877 128.969 1.071 .385 
Error 4,336.364 36 120.455   
Total 207,347.484 54    
Corrected Total 38,081.875 53    

aR Squared = .9879 (Adjusted R Squared = .9816).

Figure 10

The contour plot and surface response plot for E. coli removal with two-factor interaction.

Figure 10

The contour plot and surface response plot for E. coli removal with two-factor interaction.

Close modal
E. coli (CFU/0.1 ml):
formula
(6)
where A is the coagulant dose, B is pH, and C is coagulant types.

In this study, the removal of turbidity and microbiological contamination from Legedadi dam surface water (239.67 NTU turbidity) was evaluated using biocoagulants extracted from M. stenopetala seed, aloe vera leaves, and cactus leaves. The turbidity removal effectiveness and microbial reduction of biocoagulants made from M. stenopetala seeds (MS-SC) and aloe vera leaves (AV-SC) are better than those made from cactus leaves (Ca-SC). This study demonstrates that biocoagulants extracted from M. stenopetala, aloe vera, and cactus with low-cost 1 M NaCl lab grade salt are very effective for low to medium turbid raw water treatment. A turbidity removal efficiency of 98.83, 98.73, and 69.83% was achieved at an optimal dose of 150 mg/l for M. stenopetala, aloe vera, and cactus, respectively. However, biocoagulant extracted from aloe vera (AV-SC) is found to be effective for removal of total alkalinity when used at a dose of 50 mg/l and a pH of 7.5. However, at pH of 6.5 and 7.5, the dosage of 150 mg/l of Moringa (MS-SC) biocoagulant yielded the highest removal efficiency of turbidity and E. coli. At a pH of 7.5 and 6.5 and a dosage of 150 mg/l, respectively, MS-SC and AV-SC biocoagulants completely eliminated E. coli. In general, it was discovered that biocoagulants made from M. stenopetala seeds and aloe vera leaves performed better when used to treat water that exceeded the acceptable WHO drinking water turbidity guideline. Such inexpensive biocoagulants can be used in rural areas to purify surface water, the main source of water for rural communities in Ethiopia.

The authors would like to give special thanks to Bahir Dar University for providing the necessary resources and support for this study.

All relevant data are included in the paper or its Supplementary Information.

The authors declare there is no conflict.

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