The effect of the produced biosurfactant on heavy metal removal from the soil was examined using metal-contaminated soil (Hazrati et al., 2020), for which the physical and chemical characteristics were measured by standard methods (Supplementary Material 1).

The Bacillus sp. SHA302 strain was obtained from the microbial collection of the University of Tehran, Iran, and prepared with the method of El-Sheshtawy and Doheim (2014). The strain was cultured at a 10⁸ CFU/ml density in the Bushnell-Haas medium with N-hexane (1% V/V) as a carbon source. The microbial culture medium consisted of ammonium nitrate (1 g/l), potassium dihydrogen phosphate (1 g/l), potassium monohydrogen phosphate (1 g/l), magnesium sulfate (0.2 g/l), iron chloride (0.05 g/l), and calcium chloride (0.02 g/l). The carbon source of the medium was sterilized by a filter (0.22-μm pore size), and the pH of the medium was set to 7.0 using 1 N NaOH before sterilization. The biosurfactant screening tests were done in a prior study (unpublished data).

The biosurfactant was extracted using the method of Sun et al. (2018). After 96 h of incubation, the bacterial suspension was centrifuged (8,000g) at 4°C for 20 min. Afterward, 6 N HCl was added to the supernatant to achieve a pH of 2, and the samples were stored at 4°C overnight. Afterward, samples were centrifuged at 8,000g for 20 min, and chloroform/methanol (2:1) was added to this solution and the resulting precipitate. Then, the entire process was repeated twice to enhance the extraction. In the end, the organic phase was separated, and the biosurfactant was weighed after evaporation of the solvent.

The critical micelle concentration (CMC) indicates the biosurfactant concentration at which surface tension (ST) reaches its lowest value and then remains constant. It was estimated by plotting the ST vs. biosurfactant concentration (Qiao and Shao, 2010). For this purpose, a stock solution of 1 g/l was prepared from the biosurfactant with sterile distilled water, and then different concentrations of biosurfactant were prepared from this stock solution. Afterward, ST in each concentration was measured in three replications.

The biosurfactant efficiency for soil heavy metal release rate was evaluated with a slightly modified method of da Rocha Junior et al. (2019). Different concentrations of 2 × CMC (230 mg/l), CMC (115 mg/l), and 1.2 × CMC (57.5 mg/l) (25°C, solution volume 50 ml, shaking speed 50 rpm) were made from the biosurfactant, and the pH values of biosurfactant solutions were adjusted to 5.5 ± 0.1. The heavy metal release rate in the treatments was assessed in 1 day. Distilled water was used as a control. To measure the release rate, 5 g of dry soil was added to the soil with 50 ml of the biosurfactant solution (1:10) at different concentrations, and the samples were centrifuged at 5,000g for 10 min. The supernatant was discarded, and the soil was dried at room temperature. The heavy metal release rate from the dried soil was measured with an atomic absorption device (Shimadzu, Tokyo, Japan) according to the method of Mulligan et al. (1999). The biosurfactant adsorption rate to the soil was measured according to Chu and Chan (2003).

In this study, a range of three salinity factors with different percentages of NaCl (0, 1, 2, 3, 4, and 5%; 30°C; and pH = 7), different temperatures (20, 25, 30, 35, 40, 45, 50, and 55°C and optimum NaCl concentration and pH), and pH values (5.5, 6, 6.5, 7, 7.5, 8, and 8.5 at 30°C) were applied in a nutrient broth medium. The bacterium was inoculated at a concentration of 4% (V/V) to the medium and incubated in a shaker incubator at 120 rpm for 24 h. Subsequently, optical densities (ODs) were read at 620 nm by a spectrophotometer (JENWAY 6705 UV/Vis), and the growth rate was measured in different conditions mentioned above.

It is difficult to predict the efficiency of removing heavy metals from the soil by surfactants, and this is due to the complex and different conditions of the soil system. These factors include soil type, soil contamination intensity, age of contaminants, soil structure, soil solution pH, cation exchange capacity and soil pore size, and surfactant characteristics and concentration, and all are involved in the removal efficiency of surfactant from the soil (Singh and Cameotra, 2004; Xue et al., 2018; Pourfadakari et al., 2019). A critical capability of surfactants is their ability to form micelles (Silva et al., 2014). An increase in biosurfactant concentration lowers ST in the solution, which continues to decrease until the biosurfactant reaches a concentration termed CMC in which micelles are formed (Marchant and Banat, 2012). The results of this study show that from a biosurfactant concentration of 115 mg/l, the ST value was stabilized (36.5 ± 0.6 mN/m) and continued until the end (Figure 1). Many researchers have suggested different CMC values for lipopeptide biosurfactants; for instance, the surface tension of 34 mN/m, CMC of 650 mg/l, and strain Agrobacterium fabrum SLAJ731 (Sharma et al., 2019); surface tension of 28–30 mN/m, CMC of about 100 mg/l, and strain B. megaterium pL 6 (Pueyo et al., 2009); and surface tension of 32 mN/m, CMC of 350 mg/l, and strain Bacillus subtilis ZNI5 (Mnif et al., 2022). The biosurfactant concentration effect on pollutant removal showed that zinc and lead release percentages from soil increased by increasing the biosurfactant concentration. The highest lead and zinc release rates (53.8% ± 1.4 and 39.3% ± 1.7, respectively) were measured in the CMC and acid treatment (Figure 2). With an increase in the biosurfactant concentration, heavy metals in adsorption sites form complexes with the biosurfactant and are further released into the soil solution. Anionic biosurfactants form non-ionic complexes with metallic cations in the soil with higher stability than metallic complexes with soil particles (Franzetti et al., 2014). da Rocha Junior et al. (2019) reported an increase in the removal rate of three metals (lead, zinc, and copper) with increasing the surfactant concentration. The results of Elouzi et al. (2012) in assessing the efficiency of rhamnolipid biosurfactant for the removal of lead and zinc showed an increase in metal removal with increasing the rhamnolipid concentration.

Additionally, the results of this study agree with those of Mulligan et al. (1999) in zinc and copper removal from sediments using a lipopeptide biosurfactant. Doong et al. (1998) presented evidence of an increase in metal removal by increasing the surfactant concentration up to CMC, beyond which a constant trend was seen in the removal of metals. The effective surfactant concentration for the removal of heavy metal pollutants depends on surfactant type and soil conditions (da Rocha Junior et al., 2019). The results of this study on metal removal efficiency by the addition of acid showed an increase in the release rates of both metals up to the CMC concentration and a lowered release rate at the 2 × CMC concentration. This shows that the added acid causes the H⁺ cation to attract to exchange sites and substitute the surface-adsorbed metals, followed by the entry of metals from the adsorption site into the soil solution and their placement within the biosurfactant micelle. At the 2 × CMC concentration, the increased biosurfactant concentrations lead to the competition of the biosurfactant molecules with soil particles for the adsorption of metals and H⁺ cations. Since the hydrated shell of metals is larger than H⁺, they are attached to the exchange sites with less energy than H⁺.

On the other hand, the biosurfactant tends to form a more stable complex, and an increase in its concentration in the soil solution forms a stable surfactant–H complex (McBride, 1994; Bohn et al., 2001). Heavy metals are prone to precipitation at high pH values, which results in lowered pollutant removal and increases metal solubility at a low pH (Yong and Phadungchewit, 1993; Sun et al., 2021). Wu et al. (2015) found that an increase in pH (increased pH-dependent negative charges) reduced the adsorption of a rhamnolipid biosurfactant to the soil, which causes more reactions of the biosurfactant with metals and makes them effective. The lowered metal release rate in the 2 × CMC concentration with acid treatment can be attributed to increased pH-dependent charges with reduced pH and the biosurfactant adsorption to soil with an increase in its concentration, thereby reducing its efficiency. Sarubbo et al. (2018) showed that the biosurfactant produced from Candida guilliermondii at a concentration of 0.42% could remove more than 98% of the initial amount of lead and zinc ions.

Grzywaczyk et al. (2021) displayed that soil washing with saponin solution removes 86% zinc and 70% copper. Their results in three different soil types displayed more removal of zinc against copper, and this biosurfactant was considered an effective and environmentally friendly remediation agent. In an investigation on lipopeptide biosurfactant, CMC plus acid treatment had the highest removal of lead (98.7%) and 2 × CMC plus acid treatment had the highest removal of zinc (59.1%) from sandy soil with artificial contamination (Jimoh and Lin, 2020). An important issue concerning surfactant efficiency is their adsorption to the soil. The results of this study show a 23.3% ± 1.2 adsorption of this anionic biosurfactant to the soil (Figure 3). Biosurfactant adsorption to the soil reduces its efficiency in eliminating organic and mineral pollutants (Lee et al., 2000). Biosurfactant adsorption to soil also depends on surfactant characteristics, ionic charge, and soil properties (Calvo et al., 2009).

The carbon and mineral nitrogen treatments were assessed at a pH of 7.0, 30°C, and an aeration speed of 150 rpm. The results demonstrated that the highest reduction in surface tension (32.2 ± 0.76 mN/m) was obtained in the presence of sugar beet molasses as a carbon source and ammonium chloride as a nitrogen source, which were significantly different from the other nitrogen and carbon sources. The smallest reduction (52.4 ± 0.7 mN/m) in ST was seen with orange peel as a carbon source and the ammonium ferric citrate salt. The results showed that ammonium chloride as a nitrogen source significantly lowered ST alongside three carbon sources of molasses, potato peel, and date syrup (Figure 4). The results of the previous study (data not shown) displayed that the lipopeptide biosurfactant produced in the Bushnell-Haas culture medium with n-hexane and glucose carbon sources had ST and produced biosurfactant values of 36.5 ± 0.23 mN/m and 0.92 ± 0.05 g/l, respectively. Presently, sugar beet molasses is used as a carbon source for biosurfactant production due to its low cost, mineral content, organic compounds, vitamins, and high total sugar content compared to other commonplace carbon sources, such as sucrose and glucose, and is very important for fermentation. Molasses has been reported as an economical carbon source in surfactant production by Bacillus spp. (Saimmai et al., 2011). Abdel-Mawgoud et al. (2008) reported a low-cost surfactin production by B. subtilis BS5 using an optimized culture medium containing 16% molasses and 5 g/l of NaNO₃, with a significant increase of 1.12 g/l in surfactin yield. Rane et al. (2017) assessed B. subtilis ANR-mediated surfactant production utilizing different carbon sources of glucose, molasses, orange peel, potato peel, banana peel, and bagasse extract. They concluded that 4% molasses carbon source led to the highest biosurfactant production of 0.19 g/l. Joshi et al. (2008) assessed Bacillus licheniformis K51, B. subtilis 20B, B. subtilis R1, and Bacillus sp. HS3 for surfactant production. They observed that sugar beet molasses as a carbon source could result in surface tensions of 29.67, 29.33, 30.33, and 30.67 mN/m, respectively. In another study on optimizing the production of lipopeptide biosurfactants produced by B. subtilis strain Al-Dhabi-130 with inexpensive sources, molasses was identified as the best source for the production of this biosurfactant (Al-Dhabi et al., 2020).

The screened factors were respectively molasses percentage (X₁), ammonium chloride percentage (X₂), inoculum percentage (X₃), salinity percentage (X₄), iron chloride concentration (X₅), magnesium sulfate (X₆), potassium monohydrogen phosphate concentration (X₇), potassium dihydrogen phosphate concentration (X₈), temperature (X₉), and pH (X₁₀), which are illustrated along with the three repetition average of ST and the produced biosurfactant in Table 1. Salinity range, temperature, and pH in the Plackett–Burman experiment design were determined using average bacterial growth in different ranges of salinity, temperature, and pH (Supplementary Material 2). The results showed that only four out of the 11 factors, i.e., ammonium chloride concentration, molasses percentage, pH, and magnesium sulfate concentrations, were chosen for further evaluation based on their significance levels (P-value < 5%) (Table 2). The significance of these factors means that changes in their values led to the highest effect on changes in ST and biosurfactant production. According to Table 2, the statistical analyses of the models show the significance of the two evaluated statistical models with the regression coefficient percentage (R²) and adjusted regression coefficient (R²_adj) > 80%. Rane et al. (2017) and Hippolyte et al. (2018) considered a regression coefficient of >80% as a threshold for assessing the statistical sufficiency of models. The model coefficients can be calculated using the multivariate regression analysis of responses, ST (y₁), and the extracted biosurfactant (y₂). Negative coefficients in the y₁ model show the positive effect of these factors in biosurfactant production and better efficiency in lowering ST. Additionally, the effect coefficients will be positive if changes in factor levels cause an increase in ST. The results of Eq. 2 demonstrated that factors with high effect coefficients showed bigger effects on ST changes in the culture medium, with the highest effects of ST changes caused by pH (P > 0.000, F = 646.84), ammonium chloride (P > 0.000, F = 139.28), magnesium sulfate (P > 0.001, F = 48.34), and molasses percentage (P > 0.008, F = 18.49) (except for pH, the other factors had negative effect coefficients). In Eq. 3, the effect coefficients for factors were similar to the y₁ model (except for pH, the other factors had positive effect coefficients). In both the estimated response variables, pH had the greatest effect coefficient. The four factors of pH, ammonium chloride, magnesium sulfate, and molasses percentage were chosen for the RSM to better evaluate and analyze their individual levels. Rane et al. (2017) studied optimum biosurfactant production by B. subtilis strain ANR with a Plackett–Burman experimental design. They showed that temperature and molasses were the most significant parameters among factors affecting the production. On the other hand, Almeida et al. (2017) reported that the inoculum percentage and molasses as a carbon source were the most effective factors in surfactant production. In another study on the optimization of lipopeptide biosurfactant produced by Paenibacillus D9 (optimal temperature of 30°C, 1.5 ml inoculation, 3% diesel fuel, 4 mM magnesium sulfate, 1% ammonium sulfate, and pH = 7.00), production was reported at 4.11 g/l with a surface tension of 32.2 mN/m (Jimoh and Lin, 2020).

At this stage, a CCD with six replications in the central point was used to determine the effect of each factor and their interactions on biosurfactant production. The ammonium chloride (X₁), magnesium sulfate (X₂), molasses (X₃), and pH (X₄) factors were assessed as orthogonal variables along with two response variables of ST (y₁) and the produced biosurfactant (y₂). Each parameter in this design was assessed at five levels (−α, +1, 0, −1, and +α), and zero was considered as the central coded value. Optimum amounts in CCD were acquired by solving regression equations and the analysis of results from two- and three-dimensional contour plots. The CCD experimental design matrix is shown in Table 3, and the regression equation analysis of each response variable, along with model coefficients and relevant equations, is demonstrated below.

The results of statistical competence evaluation of models Y₁ and Y₂ showed R² and R²-adj values >90%, suggesting that about 90% of independent variable responses can be explained by these models (Table 4). These results corroborate those of Sayyad et al. (2007), who reported that an R² of a model closer to 100% better explains the variation between predicted amounts by the model and empirical amounts. According to Table 3, the highest biosurfactant production and the lowest ST are seen in 10% molasses, 1.5 g/l of nitrogen, 0.3 g/l of magnesium sulfate, and a pH of 7.3. In both y₁ and y₂ models, the pH with the highest effect coefficient was most effective in both models (Eqs 4, 5). Additionally, Supplementary Material 3 shows the effect of predicted values vs. real model values. In this context, a proximal distribution of observed values to the line is indicative of the proximity of actual values to predicted ones so that R² values of 95.08% and 94.26% were calculated for Y₁ and Y₂ models, respectively.

The results clearly demonstrated that magnesium sulfate and ammonium chloride concentrations, molasses percentage, and pH significantly affected the reductions of ST and the produced level of the biosurfactant. The interactions of these variables with response values were assessed using three-dimensional plots against both response variables. The results showed that ST initially decreased and then increased at a low molasses concentration (2%) with an increase in ammonium chloride level. The same trend was also seen at higher molasses concentrations, but the intensity of ST changes was higher at low quantities of molasses than at higher levels (Figure 5A). The lowest ST amount (1.5 g/l) was seen in 10% molasses and the central point of ammonium chloride, and ST increased by increasing the distance from the central point of ammonium chloride. According to Figure 5B, an increase in molasses percentage increased the produced biosurfactant level, and ammonium chloride concentration in the central point had the highest effect on increasing biosurfactant production. Therefore, the medial quantity of ammonium chloride (1.5 g/l) and the highest molasses percentage (10%) had the most significant effect on biosurfactant production.

A nitrogen source is one of the critical factors in biosurfactant production (Burkovski, 2003). Nitrogen is a nutrient that plays an essential role in cellular metabolism. A decrease in nitrogen content in the culture medium interrupts protein production and shifts cellular metabolism to carbohydrate production, thereby increasing biosurfactant production. The presence of high amounts of nitrogen sources in the culture medium lowers biosurfactant production and directs metabolism toward cellular growth (Abalos et al., 2002). This study observed a reduction in biosurfactant production by increasing ammonium chloride concentrations above the central point concentration. High concentrations of salt and minerals in molasses lower biosurfactant production and activity due to an increase in osmotic pressure; therefore, it reduces cell viability and ceases biosurfactant production (Doelle and Doelle, 1990). Wei et al. (2005) showed that biosurfactant production increased initially with an increase in the molasses concentration until it reached a maximum, and the production became stable or decreased afterward. During the optimization of the Candida tropicalis strain, an increase in molasses percentage resulted in a significantly reduced surface tension (Almeida et al., 2017). Similarly, our results showed that an increase in molasses percentage increased the biosurfactant production level, which can be due to the halophilic property of the bacterium, for which salinity may not be a growth-limiting factor at a high molasses concentration (refer to Supplementary Material 2a).

At low ammonium chloride quantities, ST rose with increasing pH, and this increase was more pronounced at lower ammonium chloride concentrations (Figure 5C). In low pH values, an increase in ammonium chloride first led to an increase in biosurfactant production, but it decreased at concentrations higher than the central point of ammonium chloride. The highest biosurfactant production occurred at the central point concentration of ammonium chloride, and changing intensity was higher at low than at high pHs (Figure 5D). One of the important characteristics of most organisms is their strong dependency on pH conditions for growth and secondary metabolite production (Najafi et al., 2011). The SHA302 strain could grow in a pH range of 5.0–9.0, with the highest performance for biosurfactant production in pH 7.3. Since microorganisms grow well at a pH of 7, biosurfactant production is less affected at lower pHs than at higher pHs, which significantly reduces the production. The results of this study showed that the SHA302 strain had the highest growth at a pH of 7.5 (refer to Supplementary Material 2c), and biosurfactant production decreased with an increase in pH. As demonstrated in Figure 5E, ST initially decreased at low ammonium chloride quantities and then increased by increasing magnesium sulfate levels. ST showed a decrease at high ammonium chloride concentrations. As shown in Figure 5F, different ammonium chloride quantities and an increase in magnesium sulfate resulted in an initial increase in the biosurfactant production and then began to decrease with the highest production at the central point concentration (NHCl = 1.5 g/l, MgSO₄ = 0.3 g/l) for both factors.

The optimum conditions for both models (surface tension/biosurfactant production) are shown in Figure 6. Independent input variables can be adjusted to their maximum, minimum, or any target point, while the response variable is usually set on minimum or maximum values. The software adjusted these conditions by placing molasses at the highest concentration (10%) and the three factors of ammonium chloride, pH, and magnesium sulfate on predetermined levels of 1–2 g/l, 7–7.6, and 0.2–0.4 g/l, respectively. The results showed that by placing molasses at a maximum and the other factors in the determined range (Figure 3), the surface tension and the amount of extracted biosurfactant were 30.08 mN/m and 5.89 g/l, respectively. To compare the accuracy of the estimated response values by the software with the actual values, ST and the produced biosurfactant (BS) were measured at the amounts suggested by the software. The obtained amounts were 30.48 mN/m and 5.76 g/l, respectively, indicating the high precision of the production optimization model with the values obtained from the experiment.

The global market for biosurfactants is growing by 5.6% from 2017 to 2022 and may reach 5.52 billion dollars (Gaur and Manickam, 2021). Considering the increasing interest in the use of biosurfactants in recent decades, the market for the production of these compounds is fundamentally missing because of the lack of access to cheap raw materials and economic issues related to production (Helmy et al., 2011). The cost of producing biosurfactants commonly relies upon the volume of the bioreactor, the cost of the raw materials, and the cost of separating them. In the production process of biosurfactants, the rate of biosurfactant foaming, the volume of the bioreactor, and the biosurfactant’s viscosity can have harmful effects on its production (Dolman et al., 2019). Promising strategies for increasing the production of biosurfactants in the last decade have been used to overcome the economic challenges of its production, some of which are as follows: the use of growth stimulants (Roelants et al., 2016), application of nanoparticles (Kiran et al., 2014), production of biosurfactants simultaneously with other biological compounds (Kavuthodi et al., 2015), the use of immobilized microorganisms (Heyd et al., 2011) and, so on. These strategies can be cost-effective in reducing the cost of producing biosurfactants. Therefore, taking these strategies more seriously is an essential step towards increasing productivity, making the production process more economical, as well as resolving concerns about solid waste disposals by converting them efficiently into low-cost industrial solids. Such solutions make agricultural waste valuable and profitable products (Singh et al., 2019).