Research Article(보문)
Environmental Microbiology (EM) | Biodegradation and Bioremediation
Microbiol. Biotechnol. Lett. 2024; 52(4): 397-415
https://doi.org/10.48022/mbl.2407.07011
Received: July 17, 2024; Revised: October 2, 2024; Accepted: October 14, 2024
Optimization and Depletion of Ammonia in the Liquid Phase Using Dried Bacillus subtilis Cells
Siti Baizura Mahat1,2, Syahanim Saidun1, Anis Amirah Ahmad Fuad1, Charles Ng Wai Chun1, Ramizah Kamaludin1, Azieyati Hani Hussain1, Muaz Mohd Zaini Makhtar1, and Husnul Azan Tajarudin1*
1Bioprocess Engineering Technology Division, School of Industrial Technology, Jalan Persiaran Sains, 11800, Universiti Sains Malaysia, Pulau Pinang, Malaysia
2Biomass Transportation Cluster, School of Industrial Technology, Jalan Persiaran Sains, 11800, Universiti Sains Malaysia, Pulau Pinang, Malaysia
Correspondence to :
Husnul Azan Tajarudin, azan@usm.my
Abstract
This study investigated the optimization and depletion of ammonia in the liquid phase using dried Bacillus subtilis cells. Parameters such as temperature, pH, agitation, inoculation concentration, and ammonia concentration were analyzed via OFAT analysis. Optimal conditions achieved significant reductions: 37℃ temperature, pH 7, 150 rpm agitation, 1.0 g/l inoculation concentration, and 4 ml/l ammonia concentration. One-way ANOVA was used to identify significant parameters influencing ammonia reduction and B. subtilis growth. The results showed that temperature, concentration of ammonia, and inoculation concentration significantly impacted ammonia reduction, while pH and agitation influenced B. subtilis growth. This study optimized the reduction of ammonia and biomass production of B. subtilis using response surface methodology (RSM) and central composite design (CCD), which achieved significant improvements. RSM and CCD enhanced the process, resulting in a 7.01 mg/l reduction in ammonia and a 1.07 g/l decrease in B. subtilis biomass. Model validation confirmed the efficacy of the optimized conditions. This underscores RSM and CCD's potential for environmental remediation and biomass production. The research highlights B. subtilis' role as a biocontrol agent for mitigating ammonia emissions, aiding sustainable environmental management and public health.
Keywords: Ammonia reduction, Bacillus subtilis, liquid phase, ANOVA, response surface methodology (RSM), central composite design (CCD)
Graphical Abstract
Introduction
Ammonia, a compound commonly released during agricultural and industrial activities, poses significant environmental challenges when it contaminates water bodies and soil. Elevated ammonia levels can harm aquatic ecosystems, degrade soil quality, and pose risks to human health. Specifically, ammonia contributes to water pollution, respiratory issues, and soil acidification [1, 2]. Addressing ammonia pollution requires a comprehensive understanding of its sources, transport mechanisms, and environmental impacts. Ammonia is primarily released from agricultural activities, such as the application of fertilizers and animal waste management. Industrial processes, including chemical manufacturing and wastewater treatment, also contribute to ammonia emissions. Once released into the environment, ammonia can undergo various transformations, including nitrification and volatilization, leading to the formation of other nitrogenous compounds that further impact the environment.
In aquatic ecosystems, elevated ammonia levels can be toxic to fish and other aquatic organisms [1]. Ammonia interferes with the oxygen-carrying capacity of fish blood, leading to respiratory distress and, in severe cases, death. Additionally, ammonia can promote the growth of harmful algal blooms, which deplete oxygen levels in water bodies and create dead zones, further stressing aquatic life [3]. In soil, ammonia can contribute to soil acidification, which affects soil fertility and plant growth. Acidic soils can lead to the leaching of essential nutrients, such as calcium and magnesium, and increase the availability of toxic metals like aluminum. These changes can negatively impact crop yields and soil health, posing challenges for sustainable agriculture.
Similarly, Kamaruddin
Optimization techniques like one-factor-at-a-time (OFAT) and response surface methodology (RSM) are crucial for enhancing process efficiency. OFAT involves varying one factor at a time while keeping other factors constant, allowing for the identification of the effect of individual variables on the process outcome. However, this method can be time-consuming and may not account for interactions between variables. RSM, in particular, is advantageous for evaluating multiple variables simultaneously and determining optimal conditions [8−10]. It uses statistical and mathematical methods to design, enhance, and optimize processes, making it more efficient than traditional methods. RSM involves the use of experimental designs, such as central composite design (CCD) and Box-Behnken design (BBD), to systematically explore the effects of multiple factors and their interactions on the response variable [11]. Several studies have utilized RSM for optimizing various processes. Zhou
This study aims to optimize ammonia reduction in liquid phases using dried
Materials and Methods
Preparation of culture media and bacteria (B. subtilis )
Nutrient agar and broth preparation. Nutrient agar was prepared by dissolving 28 g of nutrient agar powder in 1 L of distilled water, followed by autoclaving at 121℃ for 15 min. The sterilized liquid was poured into Petri dishes to solidify. Nutrient broth was prepared similarly by dissolving 13 g of nutrient broth powder in 1 L of distilled water, autoclaving, and then transferring to a conical flask for bacterial growth.
Bacterial preparation and cultivation.
For large-scale cultivation, a single colony was transferred to 1000 ml of autoclaved Penassay broth in a conical flask. The flask was incubated at 37℃ and 150 rpm for 24 h.
Harvesting and freeze-drying. Post-incubation, the culture was centrifuged at 5000 rpm for 6 min at 4℃. The bacterial pellet was collected, stored at -20℃, and then freeze-dried at -41℃ for 24 h. The dried bacteria were ground into a fine powder and stored in a desiccator as shown in Fig. 1.
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Figure 1.Powdered form of Bacillus subtilis .
Preparation of ammonia liquid solution
The ammonia liquid solution was prepared with different volumes of ammonia solution (2, 4, 6, 8 and 10 ml of ammonia) and mixed with 150 ml of deionized water and 0.15 g of
Optimization and validation process (OFAT → ANOVA → RSM → CCD)
The RSM optimization process consists of three steps: preliminary, experimental design selection, and model validation. The first preliminary stage will determine whether the independent variable has a statistically significant impact on the response variable. Then, experimental designs such as central composite design (CCD) or Box-Behnken design (BBD) with a series of tests will be selected. Moreover, CCD is the most prominent and often employed method for creating secondorder response surface models (Salehi
Results and Discussion
Investigation of one factor at one time (OFAT) parameters for the reduction of ammonia in the liquid phase
In this study, ammonia reduction was observed in the liquid phase with different parameters, including temperature, pH, agitation, inoculation concentration and ammonia concentration. Each sample for each parameter was measured at 8-h intervals. As shown in Supplementary Fig. S1 is the standard curve of
Correlation of temperature with the reduction of Ammonia and the Growth of B. subtilis (OFAT for temperature)
Supplementary Fig. S2 shows the correlation of 8 ml/l ammonia at different temperatures (30, 35, 37, and 40℃) over 8 h, with data on ammonia concentration, reduction, and
Fig. 2 shows the percentage reduction in ammonia at different temperatures over time, with the highest reduction (64.88%) at 37℃ and the lowest (53.25%) at 30℃. Table 1 summarizes the kinetic growth of
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Table 1 . Summary of kinetic growth and ammonia reduction by
B. subtilis under various conditions (OFAT analysis).Condition Parameter μ (h-1) Doubling Time, Td (h) Max Removal (mg/l/h) Yx/s (g/l/mg/l) Temperature (℃) 30 0.0242 28.64 0.020 1.139 35 0.0984 7.04 0.025 1.367 37 0.0864 8.02 0.031 1.847 40 0.0224 30.94 0.022 1.586 pH 4 0.0045 154.03 0.016 1.92 5 0.0141 49.16 0.019 2.243 6 0.0244 28.41 0.020 2.681 7 0.0098 70.73 0.022 2.762 8 0.0093 74.53 0.020 1.749 Agitation (rpm) 0 0.0062 111.80 0.002 0.881 50 0.0664 10.44 0.018 1.184 100 0.0069 100.46 0.023 1.451 150 0.0467 14.84 0.026 2.733 200 0.0116 59.75 0.024 2.529 Inoculation Concentration (g/l) 0.0 0.0024 288.81 0.000 1.178 0.3 0.0093 74.53 0.038 1.386 0.5 0.0480 14.44 0.046 1.487 1.0 0.2922 2.37 0.054 1.934 Ammonia Concentration (ml/l) 4 0.0714 9.71 0.029 1.846 6 0.0649 10.68 0.025 1.414 8 0.0506 13.70 0.023 0.931 10 0.0503 13.78 0.022 0.657
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Figure 2.Comparison of the percentage reduction in ammonia at different temperatures against time.
Correlation of pH with the reduction of ammonia and the growth of B. subtilis (OFAT for pH)
Supplementary Fig. S3 shows the correlation of 8 ml/l ammonia at different pH values (4, 5, 6, 7, and 8) over time, with data on ammonia concentration, reduction, and
Fig. 3 shows that ammonia reduction was greatest at pH 7 (73.42%) and lowest at pH 8 (65.75%). Different pH values affect
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Figure 3.Comparison of the percentage of ammonia reduction at different pH values against time.
Correlation of agitation with the reduction in ammonia and the growth of B. subtilis (OFAT for agitation)
Supplementary Fig. S4 shows the correlation of different agitation conditions (0, 50, 100, 150, and 200 rpm) with ammonia concentration, reduction, and
Fig. 4 shows that the highest ammonia reduction occurred at 150 rpm (73.21%), followed by 200 rpm (71.67%) and 0 rpm (46.83%). Different agitation rates affect
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Figure 4.Comparison of the percentage reduction in ammonia under different aeration conditions against time.
Correlation of inoculation concentration with the reduction in ammonia and growth of B. subtilis (OFAT for inoculation concentration)
Supplementary Fig. S5 illustrates the correlation between different concentrations of
Fig. 5 shows the percentages of ammonia reduction with different inoculation concentrations (0, 0.3, 0.5, and 1.0 g/l). The highest reduction in ammonia was observed at the 1.0 g/l dose of
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Figure 5.Reduction percentage of ammonia at different dosages of B. subtilis over time.
Correlation of the concentration of ammonia with the reduction in ammonia and the growth of B. subtilis (OFAT for concentration of ammonia)
Supplementary Fig. S6 illustrates the correlation between different concentrations of ammonia (4, 6, 8, and 10 ml/l) and time, showing data on ammonia concentration, ammonia reduction, and biomass production of
Fig. 6 presents the percentage reduction in ammonia at different ammonia concentrations over time. The greatest reduction in ammonia occurred at the lowest concentration (4 ml/l), while the lowest reduction was observed at the highest concentration (10 ml/l). This indicates that ammonia can be reduced more rapidly and efficiently at lower concentrations. Although 0.15 g of
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Figure 6.Comparison of the percentage reduction in ammonia at different ammonia concentrations over time.
Optimum conditions in the OFAT study
Table 2 summarizes the optimum conditions identified in the OFAT study for maximizing ammonia removal and biomass yield by
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Table 2 . Summary of the optimum conditions in the OFAT study.
No. Optimum Condition (Variable) Fixed value Maximum removal (mg/l/h) Yx/s (g/l/mg/l) 1 Temperature – 37℃ · 0.15 g/l – Inoculation concentration
· pH – 7
· Agitation – 150 rpm
· Concentration of ammonia - 8 ml/l0.031 1.847 2 pH – 7 · 0.15 g/l – Inoculation concentration
· Temperature 37℃
· Agitation – 150 rpm
· Concentration of ammonia - 8 ml/l0.022 2.762 3 Agitation – 150 rpm · 0.15 g/l - Inoculation concentration
· pH – 7
· Temperature 37℃
· Concentration of ammonia - 8 ml/l0.026 2.733 4 Inoculation concentration – 1.0 g/l · Temperature 37℃
· pH – 7
· Agitation – 150 rpm
· Concentration of ammonia - 8 ml/l0.054 1.934 5 Concentration of ammonia – 4 ml/l · 0.15 g/l - Inoculation concentration
· pH – 7
· Agitation – 150 rpm
· Temperature 37℃0.029 1.846
Investigation of significant parameters reflecting ammonia reduction and the growth of B. subtilis in the liquid phase
The present study examined five parameters: temperature, pH, agitation, ammonia concentration, and inoculation concentration, to determine which provided the best reduction of ammonia and biomass production of
Screening ANOVA at various temperatures for ammonia reduction and the growth of B. subtilis
Temperature significantly affects
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Table 3 . One-way ANOVA analysis of various parameters on ammonia reduction and
B. subtilis biomass production.Parameter Df Sum Sq Mean Sq F value Pr(>F) Temperature Effect of Temperature on Ammonia Reduction Temperature 3 1.719 0.5731 5.639 0.00322 ** Residuals 32 3.252 0.1016 Effect of Temperature on B. subtilis Biomass ProductionTemperature 3 0.0032 0.001072 0.046 0.987 Residuals 32 0.7527 0.023522 Inoculation Concentration Effect of Inoculation concentration on Ammonia Reduction Inoculation concentration 3 29.70 18.067 14.4 1.32e-12*** Residuals 32 3.125 0.09764 Effect of Inoculation concentration on B. subtilis Biomass ProductionInoculation concentration 3 25.908 8.636 2474 <2e-16*** Residuals 32 0.112 0.003 Ammonia Concentration Effect of Ammonia Concentration on Ammonia Reduction Concentration of Ammonia 3 67.50 22.499 18.98 2.95e-07*** Residuals 32 37.92 1.185 Effect of Ammonia Concentration on B. subtilis Biomass ProductionConcentration of Ammonia 3 0.0344 0.01145 0.628 0.602 Residuals 32 0.5836 0.01824 Agitation Effect of Agitation on Ammonia Reduction Agitation 4 0.757 0.1893 0.938 0.452 Residuals 40 8.067 0.2017 Effect of Agitation on B. subtilis Biomass ProductionAgitation 4 3.293 0.8234 40.24 1.62e-13*** Residuals 40 0.818 0.0205 pH Effect of pH on Ammonia Reduction pH 4 0.363 0.09065 1.072 0.383 Residuals 40 3.382 0.08454 Effect of pH on B. subtilis Biomass ProductionpH 4 0.10279 0.025698 19.82 4.63e-09*** Residuals 40 0.05187 0.001297 Significance codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Supplementary Fig. S8 shows that the highest biomass production of
Screening ANOVA at various inoculation concentration for ammonia reduction and the growth of B. subtilis
The effect of inoculation concentration (0, 0.3, 0.5, and 1.0 g/l) on ammonia reduction was studied under constant conditions: temperature 37℃, 4 ml/l ammonia, 150 rpm agitation, and pH 7. The highest reduction (65.92%) was at 1.0 g/l. Increasing the inoculation concentration from 0 to 1.0 g/l enhanced ammonia reduction, indicating that higher inoculation concentration improves the reduction process. ANOVA results depicted in Table 3 showed a significant effect of inoculation concentration on ammonia reduction with a
The effect of inoculation concentration (0, 0.3, 0.5, and 1.0 g/l) on the growth of
Screening ANOVA at various concentrations of ammonia for ammonia reduction and the growth of B. subtilis
The study examined ammonia concentrations of 4, 6, 8, and 10 ml/l under constant conditions: inoculation concentration of 1.0 g/l, pH 7, agitation at 150 rpm, and temperature 37℃. The highest reduction (64.87%) was at 4 ml/l. As the concentration increased, the reduction decreased, suggesting that higher ammonia concentrations hinder the reduction process. ANOVA results depicted in Table 3 confirmed the significant impact of ammonia concentration on reduction with a
The effect of ammonia concentration (4, 6, 8, and 10 ml/l) on the biomass production of
Screening ANOVA at various agitation conditions for ammonia reduction and the growth of B. subtilis
Agitation speeds (0, 50, 100, 150, and 200 rpm) were tested under constant conditions: an inoculation concentration of 1.0 g/l, ammonia concentration of 4 ml/l, pH 7, and temperature 37℃. The highest ammonia reduction (73.21%) was at 150 rpm. Increasing the speed to 200 rpm decreased the ammonia reduction due to excessive shear forces and heterogeneous mixing. ANOVA results indicated that agitation did not significantly affect ammonia reduction, with a
The effect of agitation (0, 50, 100, 150, and 200 rpm) on the biomass production of
Screening ANOVA at various pH values for ammonia reduction and the growth of B. subtilis
The effect of pH (4, 5, 6, 7, and 8) on ammonia reduction was investigated under constant conditions: inoculation concentration (1.0 g/l), ammonia concentration (4 ml/l), agitation (150 rpm), and temperature (37℃). The highest reduction (73.42%) was at pH 7. Increasing the pH to 8 decreased the reduction. ANOVA results showed that pH did not significantly affect ammonia reduction, with a
The effect of pH (4, 5, 6, 7, and 8) on the biomass production of
Based on the one-way ANOVA for all parameters shown in Table 3, it was found that temperature, ammonia concentration, and inoculation concentration had significant effects on the reduction of ammonia, as their
Optimization of Significant Parameters
Reduction of ammonia
These three parameters, ammonia concentration, temperature and inoculation concentration, had
According to the results obtained for the reduction of ammonia, the conditions for the optimization process were chosen based on their significant factors and suitable values for low and high ammonia concentrations: 3.0−5.0 ml/l (±1.0 ml/l), 0.8−1.2 g/l (±0.2 g/l), and 36.0− 38.0℃ (±1.0℃), which are summarized in Supplementary Fig. S17. Supplementary Fig. S18 summarizes the results of 18 experiments with randomized concentrations of ammonia, concentrations of
Moreover, the RSM is also capable of assessing the influence of several independent variables at a time and selecting the optimal operating parameters for the process [23]. Through multiple regression analysis, an enhanced model is produced, and the equation can be used to plot the response surface. Zhou
Similarly, Yu
Biomass production of B. subtilis
Five parameters were evaluated: temperature, pH, ammonia concentration, inoculation concentration and agitation. However, only two parameters, pH and agitation, had
According to the results obtained from the biomass production of
ANOVA of the second-order polynomial model and regression fitting
A test for the significance of the regression model and each model coefficient with a lack of fit test was carried out to fit a good model. Typically, the ranking of the significant factors is based on the
Reduction of ammonia
As shown in Table 4 below, ANOVA revealed that the model was able to represent the actual relationship between the reduction in ammonia and the three significant variables. The F value of the first order of the model (16.3969) with Pr(>F) = 0.0008879 and the pure quadratic 0.0036551 interactions indicated that the model was significant at the 95% confidence level, as the pvalue was lower than 0.05, while the two-way interaction was not significant (0.3523553). A lack of fit of a model is essential for assessing its fit. The lack of fit of the F value of 6.4118 reveals that the “lack of fit” is not as significant as the
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Table 4 . ANOVA of the second-order polynomial model for the reduction of ammonia.
Df Sum Sq Mean Sq F value Pr(>F) First Order 3 2.51502 0.83834 16.3969 0.0008879 Two-way interaction 3 0.19275 0.06425 1.2567 0.3523553 Pure Quadratic 3 0.59041 0.19680 3.8492 0.0036551 Residuals 8 0.40902 0.05113 Lack of fits 5 0.37402 0.07480 6.4118 0.0785444 Pure error 3 0.03500 0.01167
Table 4 below shows the results of the reduction in ammonia in response to the combination of input variables (concentration of ammonia, inoculation concentration and temperature) according to the experimental design. According to the negative polynomial coefficient values for the variables, the response variables increase as the independent variables decrease. Despite the negative coefficients, a
Y indicates the reduction in ammonia (mg/l), X1 is the concentration of ammonia (ml/l), X2 is the inoculation concentration (g/l) and X3 is the temperature (℃). A high correlation between the independent variables is shown by a high R2 value, which implies a good formula for determining the ideal conditions for maximizing the dependent variable.
The precision of the model can be checked by the coefficient of determination for both the predicted R2 and adjusted R2, as shown in Supplementary Fig. S21. The predicted R2 value was 0.9358, and the adjusted R2 value was 0.8635, which were closer to 1.00. Additionally, the model will represent more than 94% of the variance in the response, and no unaccounted for more than 6% of the total uncertainty. As a result, this quadratic model predicts a maximal value due to the significant correlation between the independent variables (concentration of ammonia, inoculation concentration and temperature), and the model has a high goodness of fit for predicting the reduction of ammonia. Moreover, when the value of Pr(>|t|) is smaller than 0.05, the model terms are significant. The individual terms (CAMM, CSUB and TEMP), interaction between different variable terms (CAMM: CSUB), and square terms (CAMM^2) were found to have significant effects on the reduction of ammonia.
Biomass production of B. subtilis
For Table 5 below, the first-order (0.49010) and twoway interaction (0.40251) interactions are not significant when the
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Table 5 . ANOVA of the second-order polynomial model for the biomass production of
B. subtilis .Df Sum Sq Mean Sq F value Pr(>F) First Order 2 0.010871 0.005435 0.8051 0.49010 Two-way interaction 1 0.005476 0.005476 0.8111 0.40251 Pure Quadratic 2 0.134210 0.067105 9.9391 0.01246 Residuals 6 0.040510 0.006752 Lack of fits 3 0.021982 0.007327 1.1864 0.44579 Pure error 3 0.018528 0.006176
Table 5 shows the results of the biomass production of
Y is the biomass production of
Validation
Response surface methodology (RSM) is also being used to construct three-dimensional (3D) response surface plots and contour plots by graphically presenting a polynomial equation, which is generated between independent and dependent variables.
Reduction of ammonia
The plots display the effects of the three variables on the ammonia concentration, inoculation concentration and temperature. Fig. 7(A) shows a plot of the predicted yield versus the actual yield of ammonia reduction. Based on the model, the actual ammonia reduction yield was generated from the experimental data, and the predicted ammonia reduction yield was evaluated from the models. Since the R2 value is 0.9358, there is a very good correlation between the experimental and expected values, as the value is near 1. This showed that the model was aligned and fit with the experimental data. Fig. 7(B) is a quantile-quantile plot (Q-Q plot), which is used to determine the data distribution. Fig. 7(C) and (D) clearly show the reciprocal influence of these three variables. The saddle shape of the 3D plot demonstrates the presence of correlations between the independent variables and ammonia reduction [24]. Based on the 3D plot in Fig. 7(C), an increase in both the inoculation concentration and temperature tended to lead to an increase in the concentration of ammonia, while the other variables remained constant. This suggests a positive correlation between these variables and the concentration of ammonia.
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Figure 7.(A) Plot of the predicted yield versus the actual yield of ammonia reduction, (B) quantile-quantile plot (Q-Q plot), and (C) and (D) 3D plot correlations between the independent variables and ammonia reduction.
Biomass production of B. subtilis
The plots below display the effects of the two variables of pH and agitation. Fig. 8(A) shows a plot of the predicted yield versus the actual yield of
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Figure 8.(A) Plot of the predicted yield versus the actual yield of Bacillus subtilis biomass; (B) quantile-quantile plot (Q-Q plot); (C) and (D) 3D plot of the correlations between the independent variables andBacillus subtilis biomass production.
Attaining Optimum Conditions and Model Validation
Reduction of ammonia
The optimum conditions for the reduction of ammonia were determined to be 0.7789% of the desirability value obtained by R 4.1.3 software. The experiment was carried out in triplicate according to the optimized conditions listed in Table 6 below. Since the R2 value is 0.9358, the R2 value shows the accuracy of the predicted value with respect to the experimental/actual value. The experimental value of the response (reduction of ammonia %) showed that at 37℃, 4.17 ml/l ammonia, and 1.03 g/l
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Table 6 . Predicted values for the reduction in ammonia concentration, independent variables and desirability values.
Optimum condition Reduction of ammonia (%) Test variables value Actual value Predicted value Desirability Differences Temperature (37℃) Concentration of ammonia (4.17 ml/l) 7.006333 7.114832 1 0.108499 Inoculation concentration (1.03 g/l)
Biomass production of B. subtilis
The experiment was carried out again in triplicate according to the optimized conditions listed in Table 7 below. The optimum conditions for the reduction of ammonia were determined by the 0.4607287 desirability value obtained by R 4.1.3 software. This indicated that the response was 46%, which is close to the optimal value. The experimental results of the response (biomass production of
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Table 7 . Predicted values for the biomass production of
B. subtilis , independent variables and desirability values.Optimum condition Biomass production of B. subtilis Test variables value Actual value Predicted value Desirability Differences pH (7.02) 1.071417 0.928917 0.4607287 0.1425 Agitation (150 rpm)
Conclusion
This study successfully determined the optimal conditions for mitigating ammonia emissions in the liquid phase through a thorough investigation. By analyzing the impact of various factors, such as temperature, pH, agitation, inoculation concentration, and ammonia concentration, we identified significant parameters influencing ammonia reduction in the liquid phase. The optimal conditions for maximal ammonia reduction were a temperature of 37℃, a pH of 7, agitation at 150 rpm, inoculation concentration of 1.0 g/l, and an ammonia concentration of 4 ml/l. The findings emphasized the crucial roles of temperature, ammonia concentration, and inoculation concentration in effectively reducing ammonia emissions, while pH and agitation were observed to affect the growth of
Supplemental Materials
Practitioner Points
· Optimized conditions, including a temperature of 37℃ and a pH of 7, lead to significant reductions in ammonia concentration.
· RSM and CCD enhanced the process, resulting in a 7.01 mg/l reduction in ammonia and a 1.07 g/l decrease in
· Findings support
Author Contributions
All authors contributed to the study conception and design. A.A.A.F., S.S., S.B.M. and C.N.W.C. prepared all the materials needed in the experiment, performed all the data collection and analysis, and was a major contributor to the writing of the manuscript. H.A.T. supervised the whole process of the experiment and provided all the resources needed for this research. A.A.A.F., S.S., C.N.W.C. and H.A.T. aided and guided the experiments, especially all the analyses. S.B.M., A.A.A.H., S.S., A.H.H. and R.K. performed and aided parts of the experiments. M.M.Z.M. and R.K. provided parts of the technical support needed for the experiments. All the authors have read and approved the final manuscript.
Acknowledgments
The authors would like to acknowledge Universiti Sains Malaysia for providing facilities to conduct this study.
Funding
This project was supported by the Ministry of Higher Education Malaysia (MOHE) under grant number 203.PTEKIND.6711701 (FRGS Grant).
Conflict of Interest
The authors have no financial conflicts of interest to declare.
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