Abstract
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.
HIGHLIGHTS
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.
INTRODUCTION
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.
MATERIALS AND METHODS
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.
Selected physicochemical characteristics of raw water for Legedadi water treatment plant
Parameters . | Raw water . | WHO 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 |
Parameters . | Raw water . | WHO 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 |
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).
Experimental design with three experimental factors and four responses
Run . | Block . | Factor 1 . | Factor 2 . | Factor 3 . | Response 1 . | Response 2 . | Response 3 . | Response 4 . |
---|---|---|---|---|---|---|---|---|
A: Coagulant types . | B: Coagulant dose (mg/l) . | C: pH . | Response pH . | Turbidity (NTU) . | Alkalinity (mg/l) . | E. coli (CFU/0.1 ml) . | ||
1 | R 1 | MS-SC | 50 | 6.5 | ** | ** | ** | ** |
2 | R 2 | MS-SC | 50 | 6.5 | ** | ** | ** | ** |
3 | R 1 | AV-SC | 50 | 6.5 | ** | ** | ** | ** |
4 | R 2 | AV-SC | 50 | 6.5 | ** | ** | ** | ** |
5 | R 1 | Ca-SC | 50 | 6.5 | ** | ** | ** | ** |
6 | R 2 | Ca-SC | 50 | 6.5 | ** | ** | ** | ** |
7 | R 1 | MS-SC | 100 | 7.5 | ** | ** | ** | ** |
8 | R 2 | MS-SC | 100 | 7.5 | ** | ** | ** | ** |
9 | 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 | ** | ** | ** | ** |
Run . | Block . | Factor 1 . | Factor 2 . | Factor 3 . | Response 1 . | Response 2 . | Response 3 . | Response 4 . |
---|---|---|---|---|---|---|---|---|
A: Coagulant types . | B: Coagulant dose (mg/l) . | C: pH . | Response pH . | Turbidity (NTU) . | Alkalinity (mg/l) . | E. coli (CFU/0.1 ml) . | ||
1 | R 1 | MS-SC | 50 | 6.5 | ** | ** | ** | ** |
2 | R 2 | MS-SC | 50 | 6.5 | ** | ** | ** | ** |
3 | R 1 | AV-SC | 50 | 6.5 | ** | ** | ** | ** |
4 | R 2 | AV-SC | 50 | 6.5 | ** | ** | ** | ** |
5 | R 1 | Ca-SC | 50 | 6.5 | ** | ** | ** | ** |
6 | R 2 | Ca-SC | 50 | 6.5 | ** | ** | ** | ** |
7 | R 1 | MS-SC | 100 | 7.5 | ** | ** | ** | ** |
8 | R 2 | MS-SC | 100 | 7.5 | ** | ** | ** | ** |
9 | 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
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.
RESULTS AND DISCUSSION
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.
Physicochemical properties of the powder and extracted biocoagulants
Types of plant species . | Parameters . | Raw powder . | Ethanol extraction . | 1 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 species . | Parameters . | Raw powder . | Ethanol extraction . | 1 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).
SEM microphotographs of the cactus extract at a magnification of (a) 200x and (b) 500x.
SEM microphotographs of the cactus extract at a magnification of (a) 200x and (b) 500x.
SEM microphotographs of the aloe vera extract at a magnification of (a) 200x and (b) 500x.
SEM microphotographs of the aloe vera extract at a magnification of (a) 200x and (b) 500x.
FTIR spectra of biocoagulants with the main absorption bands of (a) Moringa, (b) cactus, (c) aloe vera.
FTIR spectra of biocoagulants with the main absorption bands of (a) Moringa, (b) cactus, (c) aloe vera.
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.
Selected physicochemical characteristics of raw and treated water
Parameters . | Raw water . | Treated water . | WHO 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 | 0 | Absent |
Parameters . | Raw water . | Treated water . | WHO 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 | 0 | Absent |
Effect of biocoagulants, dose, and pH on turbidity
Dose of coagulant versus turbidity removal at pH of (a) 6.5, (b) 7.5, and (c) 8.5.
Dose of coagulant versus turbidity removal at pH of (a) 6.5, (b) 7.5, and (c) 8.5.
Effect of biocoagulants, dose, and pH on alkalinity
Effect of coagulant dose on alkalinity of the treated water at pH of (a) 6.5, (b) 7.5, and (c) 8.5.
Effect of coagulant dose on alkalinity of the treated water at pH of (a) 6.5, (b) 7.5, and (c) 8.5.
Effect of biocoagulants, dose, and pH on E. coli
Dose of coagulant versus E. coli percent reduction at pH of (a) 6.5, (b) 7.5, and (c) 8.5.
Dose of coagulant versus E. coli percent reduction at pH of (a) 6.5, (b) 7.5, and (c) 8.5.
Statistical optimization of the treatment process
Turbidity optimization with respect to the biocoagulants, dose, and pH
ANOVA for significance test between factors and turbidity removal (%)
Source . | Type III sum of squares . | df . | Mean square . | F . | Significance . |
---|---|---|---|---|---|
Corrected model | 27,094.897a | 17 | 1,593.817 | 28.141 | .000 |
Intercept | 21,246.153 | 1 | 21,246.153 | 375.134 | .000 |
Coa | 2,579.615 | 2 | 1,289.808 | 22.774 | .000 |
PH | 203.731 | 2 | 101.865 | 1.799 | .180 |
Dos | 4,564.354 | 1 | 4,564.354 | 80.591 | .000 |
Coa * PH | 697.875 | 4 | 174.469 | 3.081 | .028 |
PH * Dos | 278.704 | 2 | 139.352 | 2.460 | .100 |
Coa * Dos | 141.835 | 2 | 70.917 | 1.252 | .298 |
Coa * pH * Dos | 262.040 | 4 | 65.510 | 1.157 | .346 |
Error | 2,038.903 | 36 | 56.636 | ||
Total | 332,882.300 | 54 | |||
Corrected total | 29,133.800 | 53 |
Source . | Type III sum of squares . | df . | Mean square . | F . | Significance . |
---|---|---|---|---|---|
Corrected model | 27,094.897a | 17 | 1,593.817 | 28.141 | .000 |
Intercept | 21,246.153 | 1 | 21,246.153 | 375.134 | .000 |
Coa | 2,579.615 | 2 | 1,289.808 | 22.774 | .000 |
PH | 203.731 | 2 | 101.865 | 1.799 | .180 |
Dos | 4,564.354 | 1 | 4,564.354 | 80.591 | .000 |
Coa * PH | 697.875 | 4 | 174.469 | 3.081 | .028 |
PH * Dos | 278.704 | 2 | 139.352 | 2.460 | .100 |
Coa * Dos | 141.835 | 2 | 70.917 | 1.252 | .298 |
Coa * pH * Dos | 262.040 | 4 | 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).
Model summary for turbidity
R2 . | Adj R2 . | Pred. R2 . | Adeq precision . | CV % . |
---|---|---|---|---|
0.9879 | 0.9816 | 0.9695 | 35.392 | 8.49 |
R2 . | Adj R2 . | Pred. R2 . | Adeq precision . | CV % . |
---|---|---|---|---|
0.9879 | 0.9816 | 0.9695 | 35.392 | 8.49 |
The contour plot and surface response plot of turbidity versus two-factor interaction.
The contour plot and surface response plot of turbidity versus two-factor interaction.
Alkalinity optimization with respect to the biocoagulants, dose, and pH
Model summary for alkalinity
R2 . | Adj R2 . | Pred. R2 . | Adeq precision . | CV % . |
---|---|---|---|---|
0.906 | 0.861 | 0.6754 | 14.701 | 2.51 |
R2 . | Adj R2 . | Pred. R2 . | Adeq precision . | CV % . |
---|---|---|---|---|
0.906 | 0.861 | 0.6754 | 14.701 | 2.51 |
ANOVA for significance test between factors and alkalinity
Source . | Type III sum of squares . | df . | Mean square . | F . | Significance . |
---|---|---|---|---|---|
Corrected model | 34,417.780a | 17 | 2,024.575 | 20.365 | .000 |
Intercept | 2,425.920 | 1 | 2,425.920 | 24.402 | .000 |
Coa | 2,724.189 | 2 | 1,362.094 | 13.701 | .000 |
pH | 2,265.646 | 2 | 1,132.823 | 11.395 | .000 |
Dos | 9,986.671 | 1 | 9,986.671 | 100.455 | .000 |
Coa * Dos | 6,608.136 | 2 | 3,304.068 | 33.235 | .000 |
pH * Dos | 1,798.736 | 2 | 899.368 | 9.047 | .001 |
Coa * pH | 1,547.935 | 4 | 386.984 | 3.893 | .010 |
Coa * pH * Dos | 3,878.278 | 4 | 969.569 | 9.753 | .000 |
Error | 3,578.913 | 36 | 99.414 | ||
Total | 178,695.600 | 54 | |||
Corrected Total | 37,996.693 | 53 |
Source . | Type III sum of squares . | df . | Mean square . | F . | Significance . |
---|---|---|---|---|---|
Corrected model | 34,417.780a | 17 | 2,024.575 | 20.365 | .000 |
Intercept | 2,425.920 | 1 | 2,425.920 | 24.402 | .000 |
Coa | 2,724.189 | 2 | 1,362.094 | 13.701 | .000 |
pH | 2,265.646 | 2 | 1,132.823 | 11.395 | .000 |
Dos | 9,986.671 | 1 | 9,986.671 | 100.455 | .000 |
Coa * Dos | 6,608.136 | 2 | 3,304.068 | 33.235 | .000 |
pH * Dos | 1,798.736 | 2 | 899.368 | 9.047 | .001 |
Coa * pH | 1,547.935 | 4 | 386.984 | 3.893 | .010 |
Coa * pH * Dos | 3,878.278 | 4 | 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).
Model terms summary for E. coli removal
R-Squared . | AdjR-Squared . | Pred. R-Squared . | Adeq Precision . | C.V. % . |
---|---|---|---|---|
0.9565 | 0.9335 | 0.8902 | 17.53 | 2.33 |
R-Squared . | AdjR-Squared . | Pred. R-Squared . | Adeq Precision . | C.V. % . |
---|---|---|---|---|
0.9565 | 0.9335 | 0.8902 | 17.53 | 2.33 |
E. coli optimization with respect to biocoagulants, dose, and pH
ANOVA for significance test between factors and E. coli percent reduction (%)
Source . | Type III Sum of Squares . | df . | Mean Square . | F . | Sig. . |
---|---|---|---|---|---|
Corrected Model | 33,745.511a | 17 | 1,985.030 | 16.479 | .000 |
Intercept | 4,731.778 | 1 | 4,731.778 | 39.283 | .000 |
Coa | 1,370.458 | 2 | 685.229 | 5.689 | .007 |
pH | 518.504 | 2 | 259.252 | 2.152 | .131 |
Dos | 8,772.508 | 1 | 8,772.508 | 72.828 | .000 |
Coa * pH | 263.392 | 4 | 65.848 | .547 | .703 |
pH * Dos | 379.084 | 2 | 189.542 | 1.574 | .221 |
Coa * Dos | 7,825.362 | 2 | 3,912.681 | 32.483 | .000 |
Coa * pH * Dos | 515.877 | 4 | 128.969 | 1.071 | .385 |
Error | 4,336.364 | 36 | 120.455 | ||
Total | 207,347.484 | 54 | |||
Corrected Total | 38,081.875 | 53 |
Source . | Type III Sum of Squares . | df . | Mean Square . | F . | Sig. . |
---|---|---|---|---|---|
Corrected Model | 33,745.511a | 17 | 1,985.030 | 16.479 | .000 |
Intercept | 4,731.778 | 1 | 4,731.778 | 39.283 | .000 |
Coa | 1,370.458 | 2 | 685.229 | 5.689 | .007 |
pH | 518.504 | 2 | 259.252 | 2.152 | .131 |
Dos | 8,772.508 | 1 | 8,772.508 | 72.828 | .000 |
Coa * pH | 263.392 | 4 | 65.848 | .547 | .703 |
pH * Dos | 379.084 | 2 | 189.542 | 1.574 | .221 |
Coa * Dos | 7,825.362 | 2 | 3,912.681 | 32.483 | .000 |
Coa * pH * Dos | 515.877 | 4 | 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).
The contour plot and surface response plot for E. coli removal with two-factor interaction.
The contour plot and surface response plot for E. coli removal with two-factor interaction.
CONCLUSION
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.
ACKNOWLEDGEMENTS
The authors would like to give special thanks to Bahir Dar University for providing the necessary resources and support for this study.
DATA AVAILABILITY STATEMENT
All relevant data are included in the paper or its Supplementary Information.
CONFLICT OF INTEREST
The authors declare there is no conflict.