Multivariate optimization of multimetal biosorption by a microbial consortium: insights from bacterial community analysis

Multivariate optimization of multimetal biosorption by a microbial consortium: insights from bacterial community analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Multivariate optimization of multimetal biosorption by a microbial consortium: insights from bacterial community analysis Danusia Ferreira Lima, Gisele Moraes de Jesus, Sarah Adriana Rocha Soares, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9083659/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Metal contamination in aquatic environments requires efficient and environmentally sustainable remediation strategies. This study aimed to optimize the simultaneous biosorption of Cu, Pb, Zn, Cd, and Ni in aqueous solutions using a mixed microbial consortium through a multivariate factorial experimental design. The evaluated parameters included pH, contact time, initial multielement metal concentration, microbial cell density, and a multiple response index. The results demonstrated that pH and initial metal concentration were the most influential variables affecting biosorption efficiency, achieving removal rates above 80% for all evaluated metals. Although the consortium was composed of bacteria and fungi, community-level analyses were restricted to the bacterial fraction. Integrated microbiological data suggested complementary functional roles within the consortium, with the genus Bacillus contributing centrally to metal biosorption, while Pseudomonas played a supportive role, possibly associated with metal stress tolerance and system stability. Overall, the findings highlight the strong potential of this microbial consortium as a sustainable and low-cost biosorbent for the treatment of metal-contaminated effluents. Heavy metals Microbial biosorption Multimetal systems Fractional factorial design Multivariate optimization Wastewater treatment Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Highlights The mixed microbial consortium is efficient in removing the metals Cu, Pb, Zn, Ni, and Cd in aqueous solution; pH and incubation time directly influence the biosorption potential of the microorganisms; The microbial consortium is a potential bioproduct for wastewater remediation. 1. Introduction The intensification of anthropogenic activities and the advancement of industrialization have contributed to a significant increase in the concentrations of heavy metals such as Cu, Zn, Ni, Cd, and Pb in environmental compartments. These elements exhibit high persistence, bioaccumulation potential, and toxicity, posing substantial risks to aquatic biota and human health (Khan et al., 2009; Rahman, Z., & Singh, V. P., 2020; Balali-Mood et al., 2021 ; Ohiagu et al., 2022 ; Demarco et al., 2023 ). In aquatic ecosystems, metals can be transferred along the trophic chain, amplifying their ecological and sanitary impacts. Microorganisms play a fundamental role in the immobilization, transformation, and removal of metals, mainly through biosorption mechanisms, in which metal ions bind to functional groups present in the cell wall without direct metabolic energy demand (Ahemad, M., & Kibret, M., 2013 ; Cruz-Lopes et al., 2021 ; Phiri, J. T., & Oh, S., 2024). Structural components such as phospholipids, proteins, polysaccharides, and microbial exopolysaccharides (EPS) exhibit high affinity for metals, favoring processes such as ion exchange, complexation, and microprecipitation (Comte, S., Guibaud, G., & Baudu, M., 2006 ; Pagliaccia et al., 2022 ; Verma et al., 2025 ). Conventional physicochemical methods employed for metal removal—including chemical precipitation, ion exchange, ultrafiltration, and reverse osmosis—present relevant limitations, such as high operational cost, low selectivity, generation of secondary sludge, and reduced efficiency at low metal concentrations (Fomina & Gadd, 2014 ). In this context, alternative strategies have been investigated, involving low-cost biosorbents, agro-industrial residues, biomaterials, nanomaterials, and natural sorbents, aiming at greater environmental sustainability and economic feasibility (Esposito et al., 2001 ; Abbas et al., 2014 ; Chakraborty et al., 2022 ; Simón et al., 2022 ). Microbial biosorption stands out as a promising, efficient, and environmentally sustainable approach for metal removal, even at trace concentrations, due to the high density of active sites present on cell surfaces (Rizvi et al., 2020 ; Gupta, R., & Mohapatra, H., 2022; de Almeida Martins, B., & Takahashi, J. A., 2025). Several microbial genera, including Bacillus, Pseudomonas, Citrobacter, Enterobacter, Streptomyces, as well as filamentous fungi such as Aspergillus and Penicillium, have demonstrated high potential for metal biosorption in mono- and multielement systems (Malik, 2004 ; Patil, 2025; Alhammadi et al., 2025 ; Verma et al., 2025 ). However, biosorption performance strongly depends on operational factors such as pH, initial metal concentration, contact time, microbial load, and competitive interactions in multimetal systems (Aryal, M., & Liakopoulou-Kyriakides, M., 2015 ). Furthermore, the use of microbial consortia and synergistic technologies may enhance process robustness by combining complementary mechanisms of metal tolerance, binding, and transformation (Qader, M. Q., 2025; Khidr et al., 2025 ; Li et al., 2025 ). In this context, the present study aimed to evaluate and optimize the simultaneous biosorption of Cu, Zn, Ni, Cd, and Pb in aqueous solutions through a multivariate factorial design, using a mixed microbial consortium originally developed for the degradation of organic compounds. This approach seeks to explore the consortium’s potential as a sustainable alternative for the treatment of industrial effluents and the remediation of aquatic environments contaminated by heavy metals. 2. Materials and Methods 2.1 Mixed microbial consortium The microbial consortium used in this study consists of ten bacteria and twenty-four filamentous fungi and is protected by a patent filed at the Instituto Nacional da Propriedade Industrial (INPI) under number BR 10 2021 002341 4 (Lima et al., 2021). The genetic sequences are available in the National Center for Biotechnology Information (NCBI) database under accession numbers MW881196–MW881204, MW865711–MW865718, MW855898–MW855903, and MW865719–MW865729. The strains were isolated from mangrove sediments of Todos os Santos Bay and from petroleum samples of the Recôncavo Basin and previously demonstrated potential for hydrocarbon degradation (Lima et al., 2018 ). 2.2 Inoculum preparation Each strain of the consortium was inoculated into 100 mL of Brain Heart (BH; Difco™) medium using 1 cm diameter agar discs. Cultures were incubated in an orbital shaker (Tecnal™ TE-420) at 30 ± 0.2°C and 153 rpm for 6 days. From the concentrated suspension, dilutions were prepared in sterile 0.9% (w/v) saline solution to obtain final cell concentrations of 1 × 10⁶, 5 × 10⁶, and 1 × 10⁷ CFU mL⁻¹. Cell density was estimated by optical density using a microplate reader (LMR-96™, ELISA), at 500 nm for fungi and 600 nm for bacteria. The final stock solution was stored at 4°C until use. 2.3 Biosorption assays The biosorption assays evaluated the effects of pH, metal mixture concentration (Cu, Zn, Ni, Co, Cd, and Pb), contact time, and biomass concentration, aiming to maximize multimetal removal efficiency. A two-level fractional factorial design (2⁴⁻¹) was applied, with triplicate central points, totaling 11 experiments (Table 1 ). Table 1 Factors and coded levels for the 2⁴⁻¹ factorial design. Variables -1 PC 1 Metal mixture (mg) 10 20 30 Biomass (CFU/mL) 10 6 5 x 10 6 10 7 Time (h) 8 28 48 pH 2 4 6 A multiple response was employed to simultaneously evaluate the biosorption of all metals: Multiple Response = (%Cu/%CuMAX) + (%Pb/%PbMAX) + (%Zn/%ZnMAX) + (%Cd/%CdMAX) + (%Ni/%NiMAX) Each experiment was conducted in 250 mL Erlenmeyer flasks containing 1.5 g of magnesium sulfate and 10 g of sucrose. Stock metal solutions (1000 mg L⁻¹) were added to reach final concentrations of 10, 20, or 30 mg L⁻¹. The initial pH (1.75) was adjusted to 2, 4, or 6 using 0.1 mol L⁻¹ HCl or 0.1 mol L⁻¹ NaOH. The working volume (90 mL) was completed with sterile 0.9% (w/v) saline solution. The solutions were autoclaved at 121°C for 20 min, protected from light, and cooled prior to inoculation. Subsequently, 10 mL of the microbial consortium at the established concentration was added, completing the final volume to 100 mL. The flasks were incubated in an orbital shaker (Tecnal™ TE-420) at 150 rpm and 30°C for 8, 28, or 48 h. At the end of the experiment, samples were centrifuged at 4500 rpm for 20 min (Universal Centrifuge, model 320R). The supernatant was acidified with HNO₃ (1:1, v/v) to pH 2 and analyzed by Flame Atomic Absorption Spectrometry (FAAS; Varian 220 FS). 2.4 Biosorption calculation The removal percentage was calculated using the following equation: where C i is the initial metal concentration (mg L⁻¹) and Cₑ is the residual concentration after treatment (mg L⁻¹). 2.5 16S rRNA amplicon sequencing Bacterial identification was performed by sequencing the V3–V4 regions of the 16S rRNA gene. Libraries were prepared following the proprietary protocol of Neoprospecta Microbiome Technologies (Brazil). Amplification was carried out using the primers 341F (CCTACGGGRSGCAGCAG) and 806R (GGACTACHVGGGTWTCTAAT) (DOI: 10.1371/journal.pone.0007401 ; DOI: 10.1038/ismej.2012.8 ). Sequencing was performed on the Illumina MiSeq system using V3 kits (600 cycles) or V2 kits (500 cycles) for paired-end runs, and V2 kits (300 cycles) for single-end runs. Sequence processing and analysis were conducted using the Sentinel pipeline. 2.6 Statistical analysis The significance of the factors and the adequacy of the linear model were evaluated by Analysis of Variance (ANOVA), considering a significance level of 5% (p < 0.05). Statistical analyses were performed using Statistica 10.0, including the evaluation of interactions among factors and the estimation of experimental error from the triplicate central points. The multiple response was used as the dependent variable for the simultaneous optimization of biosorption of all metals. Graphs and visual representations were generated in Google Colab using scientific libraries for exploratory analysis and presentation of the results. 3. Results and Discussion 3.1 Optimization of biosorption conditions The main parameters influencing the simultaneous biosorption of Cu, Zn, Ni, Cd, and Pb by microorganisms were evaluated, including pH, initial metal mixture concentration, contact time, and biomass concentration (CFU mL⁻¹). For this purpose, a 2⁴⁻¹ fractional factorial experimental design was applied, with triplicate central points, totaling 11 experiments. The concept of multiple response was employed to integrate the removal performance of all metals and to identify the most favorable condition for simultaneous multimetal biosorption. The multiple response values were calculated according to the previously described equation. The maximum removals observed at the end of the assays were 82% for Cu, 90% for Pb, 87% for Zn, 93% for Cd, and 93% for Ni, demonstrating the high efficiency of the microbial consortium. The complete results of the experimental design are presented in Table 2 . Table 2 Fractional factorial design matrix (2⁴⁻¹), with triplicate central points, for the evaluation of Cu, Zn, Pb, Cd, and Ni biosorption by microorganisms. Coded values are shown in parentheses. Run Time (h) Metal concentration (mg L⁻¹) pH Cell concentration (CFU mL⁻¹) Removal (%) Multiple Response Index Cu Pb Zn Cd Ni 1 8/ (-) 10/ (-) 2/ (-) 1,00E + 06 / (-) 47 38 51 39 37 2,39 2 48/ (+) 10/ (-) 2/ (-) 1,00E + 07/ (+) 45 34 50 37 34 2,26 3 8/ (-) 30/ (+) 2/ (-) 1,00E + 07/ (+) 56 78 80 53 48 3,55 4 48/ (+) 30/ (+) 2/ (-) 1,00E + 06/ (-) 55 77 80 53 49 3,54 5 8/ (-) 10/ (-) 6/ (+) 1,00E + 07/ (+) 45 70 62 61 69 3,43 6 48/ (+) 10/ (-) 6/ (+) 1,00E + 06/ (-) 80 83 66 71 71 4,17 7 8/ (-) 30/ (+) 6/ (+) 1,00E + 06/ (-) 82 90 87 88 90 4,90 8 48/ (+) 30/ (+) 6/ (+) 1,00E + 07/ (+) 76 85 81 93 93 4,80 9 28/ (0) 20/ (0) 4/ (0) 5,00E + 06/ (0) 51 75 71 55 41 3,28 10 28/ (0) 20/ (0) 4/ (0) 5,00E + 06/ (0) 59 72 72 50 56 3,46 11 28/ (0) 20/ (0) 4/ (0) 5,00E + 06/ (0) 52 66 71 55 48 3,27 The analysis of the Pareto chart (Fig. 1 a) demonstrated, at a 95% confidence level, that pH and initial metal concentration exert statistically significant and positive effects on the multiple response. This indicates that higher values of these variables favor the simultaneous biosorption of the five metals by the microbial consortium. In contrast, contact time and cell concentration did not show significant influence within the evaluated experimental range, suggesting that the process is predominantly controlled by physicochemical factors, such as metal speciation, ionization state of cell wall functional groups, and availability of active sites on the biomass. Figure 1 b revealed the relative selectivity of the microbial consortium under a multimetal mixture scenario and the progressive contribution of each metal to the overall removal. Zn and Pb exhibited the highest average affinities and intrinsic efficiency toward biosorption by the microbial consortium, whether through ion exchange mechanisms, physical adsorption, or complexation. The dominant influence of pH is consistent with previous studies demonstrating that increasing pH promotes the deprotonation of functional groups—such as carboxyl, phosphate, and hydroxyl groups—present on the microbial cell wall, thereby enhancing electrostatic attraction and complexation of metal cations (Volesky, 2007 ; Wang & Chen, 2006 ). In addition, higher pH values reduce competition with protons for active sites, favoring the retention of metal ions. The graphical analysis of the multiple response under different experimental conditions (Fig. 2 ) confirmed this behavior, showing higher response values at pH 6 and an initial concentration of 30 mg L⁻¹, regardless of incubation time. Higher initial concentrations increase the mass transfer gradient between the aqueous phase and the biomass, enhancing the rate of occupation of biosorption sites—a phenomenon widely described in multimetal systems (Gadd, 2010 ; Gupta et al., 2015 ). The absence of a significant effect of contact time suggests that biosorption equilibrium is reached rapidly, indicating that the predominant mechanism involves passive surface adsorption rather than energy-dependent metabolic processes. This characteristic is particularly advantageous for industrial applications, as it allows reduced residence time, higher productivity, and lower operational costs. ANOVA demonstrated no significant lack of fit (p > 0.05), confirming the robustness and adequacy of the model within the evaluated experimental range (Table 3 ). Based on the statistical and operational results, the optimal condition for multimetal biosorption was defined as: pH 6, 30 mg L⁻¹ of metals, 5 × 10⁶ CFU mL⁻¹, 150 rpm, 30°C, and 4 h of contact time, prioritizing high efficiency, operational stability, and large-scale feasibility. Table 3 ANOVA for the evaluation of metal biosorption by the mixed microbial consortium. Factor ss df MS F p-value (1) Time 0,03 1 0,03 2,78 0,238 (2) Metal concentration (mg L -1 ) 2,57 1 2,57 222,8 0,004 (3) pH 3,88 1 3,88 335,9 0,003 (4) Cell concentration (CFU) 0,10 1 0,10 8,65 0,099 Lack of fit 0,36 4 0,09 7,79 0,117 Pure error 0,02 2 0,01 Total SS 6,96 10 ss = square sum / df= degrees freedom / MS = mean square. 3.2 Effect of pH and initial metal concentration Initial pH is one of the most critical parameters in metal biosorption, as it simultaneously influences chemical speciation, metal ion solubility, and the degree of dissociation of functional groups on the microbial surface, thereby controlling the availability of active binding sites (Fertu et al., 2022 ). The biosorption of Cu, Zn, Ni, Cd, and Pb was evaluated at pH values of 2, 4, and 6 and initial concentrations of 10, 20, and 30 mg L⁻¹, using biomass concentrations ranging from 1 × 10⁶ to 1 × 10⁷ CFU mL⁻¹, at 30°C, 150 rpm, and contact times of 8, 28, and 48 h. The selected pH range allowed the exploration of relevant variations in the protonation state of carboxyl, phosphate, hydroxyl, and amine groups, which are recognized as key determinants in biosorption mechanisms. The results presented in Figs. 3 , 4 , and 5 demonstrated a strong dependence of the process on pH, with a marked increase in removal efficiency as pH increased from 2 to 6, reaching values between 34% and 93.3%, depending on the metal and its initial concentration. At acidic pH, protonation of anionic active sites on the cell surface occurs, increasing competition between protons and metal ions, which significantly reduces biosorption. As pH approaches neutrality, progressive deprotonation takes place, favoring electrostatic interactions, complexation, and ion exchange, resulting in greater metal retention (Chintalpudi et al., 2021 ; Chintalpudi et al., 2022 ). Although pH values close to or above neutrality may promote chemical precipitation of metals (Jin et al., 2018 ; Wang et al., 2021), the results indicated that pH 6 provided the most favorable biosorption conditions without evidence of precipitation-related limitations. At an initial concentration of 10 mg L⁻¹, biosorption was maximized mainly for Cu and Pb at pH 6, although all metals exhibited better performance under less acidic conditions (Fig. 3 ). At 30 mg L⁻¹, Cu, Pb, Cd, and Ni also showed higher removal percentages at pH 6, whereas Zn exhibited lower sensitivity to pH variations (Fig. 4 ). The evaluation of the triplicate central point (20 mg L⁻¹, pH 4) revealed high experimental reproducibility, demonstrating methodological robustness. Even under intermediate conditions and reduced contact time (28 h), the microbial consortium removed up to 71% of the metals, highlighting its strong potential for practical application (Fig. 5 ). 3.3 Effect of incubation time and initial metal concentration Overall, the results indicate that maximum efficiency tends to occur during the initial periods (8–28 h), particularly at near-neutral pH, reinforcing the importance of simultaneously optimizing contact time, initial concentration, and pH (Fig. 8 ). 3.4 Role of Bacillus and Pseudomonas in biosorption Multivariate analysis using hierarchical clustering (Fig. 9 ) revealed distinct response patterns between the genera Bacillus and Pseudomonas under different environmental conditions. Higher response intensities were associated with Bacillus, particularly under acidic pH, high bacterial loads, and short contact times, indicating a strong structural or functional contribution to metal retention. This behavior is consistent with studies reporting Bacillus as highly metal-resistant and efficient in biosorption (Anusha et al., 2024 ). In contrast, Pseudomonas exhibited lower relative intensities, especially at pH 6, higher metal concentrations, and longer exposure times, possibly reflecting ionic competition or differences in metal stress adaptation mechanisms. Nevertheless, Pseudomonas remains widely recognized for its high environmental adaptability, biotransformation capacity, and bioremediation potential, as demonstrated by Chen et al. ( 2024 ) and Naz et al. ( 2015 ; 2016 ). These findings indicate that Bacillus acts as the main biosorption agent within the consortium, whereas Pseudomonas may play a complementary role related to system resilience and tolerance to metal stress. 3.5 Biosorption experiments under optimal conditions Experiments under optimal conditions were conducted in triplicate using 30 mg L⁻¹ of metals, pH 6, 30°C, 150 rpm, and a biomass concentration of 1 × 10⁸ CFU mL⁻¹. Kinetics were monitored at 8, 28, and 48 h. The highest efficiency was observed up to 28 h for Cu, Ni, Pb, and Cd, indicating stabilization of the process at intermediate times (Fig. 10 ). Cd showed lower removal (~ 30%), suggesting multimetal competition or lower relative affinity. Optical density remained practically constant (~ 1.56–1.66 × 10⁷ CFU mL⁻¹), indicating the absence of significant growth and reinforcing that biosorption was predominantly passive. These results suggest that excessively long contact times may reduce efficiency and that the optimal interval lies between 8 and 28 h. Strategies such as biostimulation or bioaugmentation may be explored to maximize performance in practical applications. 4. Conclusions A mixed microbial consortium originally developed for the degradation of organic compounds demonstrated high performance in the simultaneous biosorption of Cu, Zn, Ni, Cd, and Pb in aqueous solutions, highlighting its potential as a multimetal biosorbent in complex environmental systems. Experimental optimization indicated that the highest removal efficiency was achieved under an initial metal mixture concentration of 30 mg L⁻¹, pH 6, temperature of 30°C, agitation at 150 rpm, and contact time of up to 28 h, confirming the strong dependence of the process on the physicochemical conditions of the system. Kinetic evaluation revealed that biosorption occurs predominantly within the first hours of contact, with maximum values observed between 8 and 28 h, followed by a gradual decrease in efficiency at longer times (48 h). This behavior suggests the establishment of equilibrium between metal ions and active sites on the biomass, possibly associated with functional group saturation, ionic competition in multimetal systems, and a reduction in ion exchange capacity over time. Thus, the interval between 8 and 28 h represents a strategic period for operational interventions, such as biostimulation or bioaugmentation, aiming to maintain or enhance process efficiency in practical applications. The biosorptive potential of the consortium was reinforced by its application in water samples, highlighting its feasibility as an environmentally sustainable, low-cost, and efficient technology for the removal of metal ions from industrial effluents, wastewater, and contaminated areas. The best performance was observed under conditions characterized by near-neutral pH (~ 6), high initial biomass concentrations (~ 10⁶–10⁷ CFU mL⁻¹), higher metal loads (30 mg L⁻¹), and intermediate incubation times, emphasizing the interdependence between biotic and abiotic factors in optimizing multimetal biosorption. On the other hand, extreme conditions such as highly acidic pH (~ 2), low initial bacterial concentration (~ 10⁶ CFU mL⁻¹), and short contact times resulted in significantly lower performance, indicating limitations in the availability of active binding sites and in the adaptive capacity of the microbial consortium under severe environmental stress. Although the consortium demonstrated consistent and robust biosorption capacity, further studies are needed to deepen the understanding of the system’s operational limits, including evaluation across broader metal concentration ranges, different metal/biomass ratios, dynamic hydrodynamic conditions, and continuous-flow operations. In addition, future investigations into the metabolic, functional, and genomic potential of the consortium may contribute to improving biosorption performance and expanding its application in the sustainable management of industrial effluents and environmental remediation. Declarations Acknowledgment The authors gratefully acknowledge financial and institutional support provided through a national research and development program in the fields of geomicrobiology and petroleum biotechnology. The authors also recognize the strategic importance of support from a national energy regulatory agency under research and development funding mechanisms. Additional support from a graduate education funding agency is acknowledged. Funding Shell Brasil Ltda. Author Contributions Danusia Ferreira Lima: Conceptualization, Methodology, Investigation, Formal analysis, Writing – Original Draft, Supervision, Funding acquisition. Gisele Moraes de Jesus: Investigation, Data curation, Visualization, Writing – Review & Editing. Sarah Adriana Rocha Soares: Methodology, Resources, Writing – Review & Editing. Antonio Fernando de Souza Queiroz: Formal analysis, Data curation, Validation, Writing – Review & Editing. Olívia Maria Cordeiro de Oliveira: Methodology, Investigation, Supervision, Writing – Review & Editing. Ethical Approval Not applicable Consent to Participate Not applicable. This study does not involve human participants, human data, or identifiable personal information. Competing Interests ☐ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. ☒ The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Danusia Ferreira Lima reports financial support was provided by Shell Brazil Oil. DANUSIA FERREIRA LIMA has patent #BR 10 2021 002341 4 pending to Universidad Federal da Bahia. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Data Availability Statement All data generated or analyzed during this study are included in this published article and its supplementary information files. Mandatory if your study involves humans and/or animals Not applicable Declaration of AI Use The authors declare that artificial intelligence tools (ChatGPT, OpenAI) were used solely to assist with language editing, grammar correction, and formatting of the manuscript. All scientific content, data, results, interpretations, and conclusions presented in this work are the original work of the authors and have not been generated or influenced by AI. The use of AI did not affect the integrity, analysis, or originality of the research Data Availability Statement The datasets generated and analyzed during the current study are included in this published article and its supplementary information files. Additional data are available from the corresponding author on reasonable request. Clinical trial number: Not applicable References Abbas SH, Ismail IM, Mostafa TM, Sulaymon AH (2014) Biosorption of heavy metals: a review. J Chem Sci Technol 3(4):74–102 Ahemad M, Kibret M (2013) Recent trends in microbial biosorption of heavy metals: a review. Biochem Mol Biol 1(1):19–26. Alhammadi E, Halimoon N, Zulkeflee Z, Johari WLW (2025) The co-biosorption possibility of single and quaternary heavy metals by multi-metal-tolerant bacteria isolated from electroplating effluents. Fine Chem Eng 211–249. https://doi.org/10.37256/fce.6220256530 Anusha P, Natarajan D, Rengarajan S, Alfarraj S, Kandasamy S (2024) An assessment of metal absorption competence of indigenous metal tolerant bacterial species—an in-vitro study. Environ Res 251:118700. https://doi.org/10.1016/j.envres.2024.118700 Aryal M, Liakopoulou-Kyriakides M (2015) Bioremoval of heavy metals by bacterial biomass. Environ Monit Assess 187(1):4173. https://doi.org/10.1007/s10661-014-4173-z Balali-Mood M, Naseri K, Tahergorabi Z, Khazdair MR, Sadeghi M (2021) Toxic mechanisms of five heavy metals: mercury, lead, chromium, cadmium, and arsenic. Front Pharmacol 12:643972. https://doi.org/10.3389/fphar.2021.643972 Chakraborty R, Asthana A, Singh AK, Jain B, Susan ABH (2022) Adsorption of heavy metal ions by various low-cost adsorbents: a review. Int J Environ Anal Chem 102(2):342–379. https://doi.org/10.1080/03067319.2020.1722811 Chen Z, Liu Y, Jiang L, Zhang C, Qian X, Gu J, Song Z (2024) Bacterial outer membrane vesicles increase polymyxin resistance in Pseudomonas aeruginosa while inhibiting its quorum sensing. J Hazard Mater 478:135588. https://doi.org/10.1016/j.jhazmat.2024.135588 Chintalpudi VK, Kanamarlapudi RKSL, Mallu UR, Muddada S (2022) Isolation, identification, biosorption optimization, characterization, isotherm, kinetic and application of novel bacterium Chelatococcus sp. biomass for removal of Pb (II) ions from aqueous solutions. Int J Environ Sci Technol 19(3):1531–1544. https://doi.org/10.1007/s13762-021-03169-6 Chintalpudi VK, Kanamarlapudi RKSL, Mallu UR, Muddada S (2021) Isotherm, kinetic and application of novel bacterium Chelatococcus sp. biomass for removal of Pb (II) ions from aqueous solutions. Int J Environ Sci Technol 19:1–14 Comte S, Guibaud G, Baudu M (2006) Biosorption properties of extracellular polymeric substances (EPS) resulting from activated sludge according to their type: soluble or bound. Process Biochem 41(4):815–823. https://doi.org/10.1016/j.procbio.2005.10.014 Cruz-Lopes LP, Macena M, Esteves B, Guiné RP (2021) Ideal pH for the adsorption of metal ions Cr6+, Ni2+, Pb2+ in aqueous solution with different adsorbent materials. Open Agric 6(1):115–123. https://doi.org/10.1515/opag-2021-0225 de Almeida Martins B, Takahashi JA (2025) Overview of bioremediation as a method for metal-contaminated wastewater treatment. Environ Sci Pollut Res. https://doi.org/10.1007/s11356-025-36195-9 Demarco CF, Quadro MS, Selau Carlos F, Pieniz S, Morselli LBGA, Andreazza R (2023) Bioremediation of aquatic environments contaminated with heavy metals: a review of mechanisms, solutions and perspectives. Sustainability 15(2):1411. https://doi.org/10.3390/su15021411 Esposito A, Pagnanelli F, Lodi A, Solisio C, Veglio F (2001) Biosorption of heavy metals by Sphaerotilus natans: an equilibrium study at different pH and biomass concentrations. Hydrometallurgy 60(2):129–141. https://doi.org/10.1016/S0304-386X(00)00195-X Fertu DI, Bulgariu L, Gavrilescu M (2022) Modeling and optimization of heavy metals biosorption by low-cost sorbents using response surface methodology. Processes 10(3):523. https://doi.org/10.3390/pr10030523 Fomina M, Gadd GM (2014) Biosorption: current perspectives on concept, definition and application. Bioresour Technol 160:3–14. https://doi.org/10.1016/j.biortech.2013.12.102 Gadd GM (2010) Metals, minerals and microbes: geomicrobiology and bioremediation. Microbiology 156(3):609–643. https://doi.org/10.1099/mic.0.037143-0 Gupta R, Mohapatra H (2003) Microbial biomass: an economical alternative for removal of heavy metals from waste water. Indian J Exp Biol 41(9):945–966 Gupta VK, Nayak A, Agarwal S (2015) Bioadsorbents for remediation of heavy metals: current status and their future prospects. Environ Eng Res 20(1):1–18. https://doi.org/10.4491/eer.2015.018 Jin Y, Teng C, Yu S, Song T, Dong L, Liang J, Qu J (2018) Batch and fixed-bed biosorption of Cd (II) from aqueous solution using immobilized Pleurotus ostreatus spent substrate. Chemosphere 191:799–808. https://doi.org/10.1016/j.chemosphere.2017.08.154 Khan S, Hesham AEL, Qiao M, Rehman S, He JZ (2010) Effects of Cd and Pb on soil microbial community structure and activities. Environ Sci Pollut Res 17(2):288–296. https://doi.org/10.1007/s11356-009-0134-4 Khidr R, Qurbani K, Muhammed V, Salim S, Abdulla S, Wsw H (2025) Synergistic effects of indigenous bacterial consortia on heavy metal tolerance and reduction. Environ Geochem Health 47(3):79. https://doi.org/10.1007/s10653-025-02392-1 Li H, Cui X, Sun Y, Zheng P, Wang L, Shi X (2025) Advances in microbial remediation of heavy metal-contaminated soils: mechanisms, synergistic technologies, field applications and future perspectives. Toxics 13(12):1069. https://doi.org/10.3390/toxics13121069 Lima DF, Queiroz AFS, Oliveira OMC (2018) Biodegradation of hydrocarbons by microbial consortia isolated from mangrove sediments and petroleum samples. Mar Pollut Bull 136:406–414. https://doi.org/10.1016/j.marpolbul.2018.09.014 Malik A (2004) Metal bioremediation through growing cells. Environ Int 30:261–278. https://doi.org/10.1016/j.envint.2003.08.001 Naz SA, Jabeen N, Sohail M, Rasool SA (2015) Biophysicochemical characterization of pyocin SA189 produced by Pseudomonas aeruginosa SA189. Braz J Microbiol 46:1147–1154. https://doi.org/10.1590/S1517-838246420140737 Naz T, Khan MD, Ahmed I, Rehman SU, Rha ES, Malook I, Jamil M (2016) Biosorption of heavy metals by Pseudomonas species isolated from sugar industry. Toxicol Ind Health 32(9):1619–1627. https://doi.org/10.1177/0748233715569900 Ohiagu FO, Chikezie PC, Ahaneku CC, Chikezie CM (2022) Human exposure to heavy metals: toxicity mechanisms and health implications. Mater Sci Eng Int J 6(2):78–87 Pagliaccia B, Carretti E, Severi M, Berti D, Lubello C, Lotti T (2022) Heavy metal biosorption by extracellular polymeric substances (EPS) recovered from anammox granular sludge. J Hazard Mater 424:126661. https://doi.org/10.1016/j.jhazmat.2021.126661 Patil A, Chakraborty S, Yadav Y, Sharma B, Singh S, Arya M (2025) Bioremediation strategies and mechanisms of bacteria for resistance against heavy metals: a review. Bioremediation J 29(4):448–480. https://doi.org/10.1080/10889868.2024.2375204 Phiri JT, Oh S (2024) Biosorption of Cd (II), Co (II), and Cu (II) onto microalgae under acidic and neutral conditions. Sustainability 16(15):6342. https://doi.org/10.3390/su16156342 Qader MQ (2025) Multi-microbial consortia incorporating microalgae, bacteria, and fungi for effective heavy metal removal. Bioremediation J. https://doi.org/10.1080/10889868.2025.2552770 Rahman Z, Singh VP (2020) Bioremediation of toxic heavy metals (THMs) contaminated sites: concepts, applications and challenges. Environ Sci Pollut Res 27(22):27563–27581. https://doi.org/10.1007/s11356-020-08903-0 Rizvi A, Ahmed B, Zaidi A, Khan MS (2020) Biosorption of heavy metals by dry biomass of metal tolerant bacterial biosorbents: an efficient metal clean-up strategy. Environ Monit Assess 192(12):801. https://doi.org/10.1007/s10661-020-08758-5 Simón D, Palet C, Costas A, Cristóbal A (2022) Agro-industrial waste as potential heavy metal adsorbents and subsequent safe disposal of spent adsorbents. Water 14(20):3298. https://doi.org/10.3390/w14203298 Verma L, Ekka A, Banjara RA, Ambade B, Kumar A, Gautam S (2025) A review on bacterial exopolysaccharides for heavy metal remediation: mechanisms, challenges, and sustainable applications. Water Environ Res 97(10):e70184. https://doi.org/10.1002/wer.70184 Verma T, Aggarwal A, Sharma S, Dhyani P (2025) Heavy metal resistant bacteria for bioremediation of toxic heavy metals—arsenic and lead. In: Heavy metal contamination in wastewater and its bioremediation by microbial-based approaches. Springer, Singapore, pp 123–149 Volesky B (2007) Biosorption and me. Water Res 41(18):4017–4029. https://doi.org/10.1016/j.watres.2007.05.062 Wang F, Wang H, Dong W, Yu X, Zuo Z, Lu X, Zhang X (2024) Enhanced multi-metals stabilization: synergistic insights from hydroxyapatite and peroxide dosing strategies. Sci Total Environ 927:172159. https://doi.org/10.1016/j.scitotenv.2024.172159 Wang J, Chen C (2006) Biosorption of heavy metals by Saccharomyces cerevisiae: a review. Biotechnol Adv 24(5):427–451. https://doi.org/10.1016/j.biotechadv.2006.03.001 Supplementary Files GraphicalAbstract.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9083659","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":611126117,"identity":"50ac8c1c-8692-4856-8975-df690aab0452","order_by":0,"name":"Danusia Ferreira Lima","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA40lEQVRIiWNgGAWjYDCCAyDCgIGBjxlIf2CQS2Bg4CFSCxtQC+MMBmNitQABGxAz8xCjhe/42WOSPwrsGNjYuRM/27YZ5PE38B77gE+L5Jm8NGkeg2Sgw3g3S+e2GRRLHOBLnoFPi8GBHDNpBgNmkJYNQC1/EhsO8BjjdZjB+Tdmkj8M6sG2/LZsM0icT1DLjRwzCR6DwyAt26QZgVo2ENIieeONsTWPwXEekBbLnnMGxYaH+ZLxauE7n2N488efajl+/rObb/woM8iTO957GK8WGECKC2aiNIyCUTAKRsEowAcAp8I/Lpf+KAQAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-8412-9148","institution":"Universidade Federal da Bahia Instituto de Geociencias","correspondingAuthor":true,"prefix":"","firstName":"Danusia","middleName":"Ferreira","lastName":"Lima","suffix":""},{"id":611126118,"identity":"d19bd5fd-2ccc-4507-9159-35c98f411d32","order_by":1,"name":"Gisele Moraes de Jesus","email":"","orcid":"","institution":"Universidade Federal da Bahia Instituto de Geociencias","correspondingAuthor":false,"prefix":"","firstName":"Gisele","middleName":"Moraes","lastName":"de Jesus","suffix":""},{"id":611126119,"identity":"691d812d-c4e0-454e-8e24-75d9b22120f3","order_by":2,"name":"Sarah Adriana Rocha Soares","email":"","orcid":"","institution":"Universidade Federal da Bahia Instituto de Geociencias","correspondingAuthor":false,"prefix":"","firstName":"Sarah","middleName":"Adriana Rocha","lastName":"Soares","suffix":""},{"id":611126120,"identity":"b51b518f-3f42-4811-afbf-4da8ed26a278","order_by":3,"name":"Antonio Fernando de Souza Queiroz","email":"","orcid":"","institution":"Universidade Federal da Bahia Instituto de Geociencias","correspondingAuthor":false,"prefix":"","firstName":"Antonio","middleName":"Fernando de Souza","lastName":"Queiroz","suffix":""},{"id":611126121,"identity":"cb0588dd-856c-4e3d-8cca-f6a5260d5722","order_by":4,"name":"Olívia Maria Cordeiro de Oliveira","email":"","orcid":"","institution":"Universidade Federal da Bahia Instituto de Geociencias","correspondingAuthor":false,"prefix":"","firstName":"Olívia","middleName":"Maria Cordeiro","lastName":"de Oliveira","suffix":""}],"badges":[],"createdAt":"2026-03-10 12:05:02","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9083659/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9083659/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105485687,"identity":"3185ee95-a5b6-450c-baf5-ca4a2e0e46e7","added_by":"auto","created_at":"2026-03-26 14:27:58","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":55750,"visible":true,"origin":"","legend":"\u003cp\u003ePareto charts of the standardized effects of the experimental variables on the multiple response of the simultaneous biosorption of Cu, Zn, Ni, Cd, and Pb by the microbial consortium.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9083659/v1/751b09f08734709c3f1ddd52.png"},{"id":105485595,"identity":"b37d8790-78d4-41eb-8c35-2a258b123346","added_by":"auto","created_at":"2026-03-26 14:27:43","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":65285,"visible":true,"origin":"","legend":"\u003cp\u003eMultiple response of Cu, Zn, Ni, Cd, and Pb biosorption as a function of pH, initial metal concentration, and contact time.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9083659/v1/8e5fc97a0d18360491cddb27.png"},{"id":105566496,"identity":"cf56dc35-bb9e-4a5c-8082-ed7ce234b4d2","added_by":"auto","created_at":"2026-03-27 12:56:32","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":82284,"visible":true,"origin":"","legend":"\u003cp\u003eInfluence of pH (2 and 6) on the biosorption of Cu, Zn, Ni, Cd, and Pb under initial metal mixture concentrations of 10 mg L⁻¹.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9083659/v1/a434e0be71bd6a97ace3a42d.png"},{"id":105485655,"identity":"d26af0cf-51f7-46aa-9322-428344c88135","added_by":"auto","created_at":"2026-03-26 14:27:47","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":59549,"visible":true,"origin":"","legend":"\u003cp\u003eInfluence of pH (2 and 6) on the biosorption of Cu, Zn, Ni, Cd, and Pb under initial metal mixture concentrations of 30 mg L⁻¹.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-9083659/v1/f74271d780d8af267f815ae3.png"},{"id":105485695,"identity":"07a1c257-d528-46f9-aeef-64c3acdcf065","added_by":"auto","created_at":"2026-03-26 14:28:01","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":62842,"visible":true,"origin":"","legend":"\u003cp\u003eBiosorption at pH 4 under initial metal mixture conditions of 20 mg L⁻¹ (experimental central point), demonstrating process reproducibility.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-9083659/v1/09c49548c156914f0c6aeb71.png"},{"id":105485657,"identity":"6bd74cb1-0b49-47b9-9477-1c0fac821a74","added_by":"auto","created_at":"2026-03-26 14:27:48","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":41462,"visible":true,"origin":"","legend":"\u003cp\u003ePercentage of biosorption after 8 and 48 h, with an initial concentration of 10 mg L⁻¹, at (a) pH 2 and (b) pH 6.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-9083659/v1/14e19cd249f0db8852bea188.png"},{"id":105485656,"identity":"8129df36-d0be-4dae-b2c6-ce348394a414","added_by":"auto","created_at":"2026-03-26 14:27:47","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":41492,"visible":true,"origin":"","legend":"\u003cp\u003ePercentage of biosorption after 8 and 48 h, with an initial concentration of 30 mg L⁻¹, at (a) pH 2 and (b) pH 6.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-9083659/v1/f593d376da8493d6f129cd01.png"},{"id":105485690,"identity":"3b7b03ae-1eaf-4b51-b392-268505fcbb11","added_by":"auto","created_at":"2026-03-26 14:27:59","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":66886,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic representation of multimetal biosorption kinetics as a function of time.\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-9083659/v1/4986a989b7d289d5235cce3c.png"},{"id":105485689,"identity":"71a61d8b-ada4-421e-a6a8-1949e1f4f1c5","added_by":"auto","created_at":"2026-03-26 14:27:59","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":201923,"visible":true,"origin":"","legend":"\u003cp\u003eHeatmap with hierarchical clustering analysis of responses associated with the genera \u003cem\u003eBacillus\u003c/em\u003eand \u003cem\u003ePseudomonas\u003c/em\u003e under different experimental conditions of pH, bacterial load, metal mixture concentration, and exposure time.\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-9083659/v1/5c0edd1e74f4ebbb0400aea2.png"},{"id":105485633,"identity":"9a5251c1-bc94-4f52-965f-b9e89e0505a7","added_by":"auto","created_at":"2026-03-26 14:27:47","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":68522,"visible":true,"origin":"","legend":"\u003cp\u003ePercentage of biosorption of Cu, Ni, Pb, and Cd by the mixed microbial consortium as a function of time (8, 28, and 48 h), under an initial metal mixture concentration of 30 mg L⁻¹, pH 6, 30 °C, and 150 rpm.\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-9083659/v1/cac5096ee7f96449bf8f1928.png"},{"id":108977576,"identity":"cce709f6-a46c-4de9-83b6-d33da50f040f","added_by":"auto","created_at":"2026-05-11 11:32:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1100351,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9083659/v1/e8ad4b66-cb17-49eb-8929-9018136ca9b4.pdf"},{"id":105485592,"identity":"6df40303-0f9c-49eb-b070-45f5f91377ba","added_by":"auto","created_at":"2026-03-26 14:27:41","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":361372,"visible":true,"origin":"","legend":"","description":"","filename":"GraphicalAbstract.docx","url":"https://assets-eu.researchsquare.com/files/rs-9083659/v1/d9763aadfb7f9197da045bb0.docx"}],"financialInterests":"","formattedTitle":"Multivariate optimization of multimetal biosorption by a microbial consortium: insights from bacterial community analysis","fulltext":[{"header":"Highlights","content":"\u003cul\u003e\n \u003cli\u003eThe mixed microbial consortium is efficient in removing the metals Cu, Pb, Zn, Ni, and Cd in aqueous solution;\u003c/li\u003e\n \u003cli\u003epH and incubation time directly influence the biosorption potential of the microorganisms;\u003c/li\u003e\n \u003cli\u003eThe microbial consortium is a potential bioproduct for wastewater remediation.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"1. Introduction","content":"\u003cp\u003eThe intensification of anthropogenic activities and the advancement of industrialization have contributed to a significant increase in the concentrations of heavy metals such as Cu, Zn, Ni, Cd, and Pb in environmental compartments. These elements exhibit high persistence, bioaccumulation potential, and toxicity, posing substantial risks to aquatic biota and human health (Khan et al., 2009; Rahman, Z., \u0026amp; Singh, V. P., 2020; Balali-Mood et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Ohiagu et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Demarco et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In aquatic ecosystems, metals can be transferred along the trophic chain, amplifying their ecological and sanitary impacts.\u003c/p\u003e \u003cp\u003eMicroorganisms play a fundamental role in the immobilization, transformation, and removal of metals, mainly through biosorption mechanisms, in which metal ions bind to functional groups present in the cell wall without direct metabolic energy demand (Ahemad, M., \u0026amp; Kibret, M., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Cruz-Lopes et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Phiri, J. T., \u0026amp; Oh, S., 2024). Structural components such as phospholipids, proteins, polysaccharides, and microbial exopolysaccharides (EPS) exhibit high affinity for metals, favoring processes such as ion exchange, complexation, and microprecipitation (Comte, S., Guibaud, G., \u0026amp; Baudu, M., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Pagliaccia et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Verma et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eConventional physicochemical methods employed for metal removal\u0026mdash;including chemical precipitation, ion exchange, ultrafiltration, and reverse osmosis\u0026mdash;present relevant limitations, such as high operational cost, low selectivity, generation of secondary sludge, and reduced efficiency at low metal concentrations (Fomina \u0026amp; Gadd, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). In this context, alternative strategies have been investigated, involving low-cost biosorbents, agro-industrial residues, biomaterials, nanomaterials, and natural sorbents, aiming at greater environmental sustainability and economic feasibility (Esposito et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Abbas et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Chakraborty et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Sim\u0026oacute;n et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMicrobial biosorption stands out as a promising, efficient, and environmentally sustainable approach for metal removal, even at trace concentrations, due to the high density of active sites present on cell surfaces (Rizvi et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Gupta, R., \u0026amp; Mohapatra, H., 2022; de Almeida Martins, B., \u0026amp; Takahashi, J. A., 2025). Several microbial genera, including Bacillus, Pseudomonas, Citrobacter, Enterobacter, Streptomyces, as well as filamentous fungi such as Aspergillus and Penicillium, have demonstrated high potential for metal biosorption in mono- and multielement systems (Malik, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Patil, 2025; Alhammadi et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Verma et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHowever, biosorption performance strongly depends on operational factors such as pH, initial metal concentration, contact time, microbial load, and competitive interactions in multimetal systems (Aryal, M., \u0026amp; Liakopoulou-Kyriakides, M., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Furthermore, the use of microbial consortia and synergistic technologies may enhance process robustness by combining complementary mechanisms of metal tolerance, binding, and transformation (Qader, M. Q., 2025; Khidr et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this context, the present study aimed to evaluate and optimize the simultaneous biosorption of Cu, Zn, Ni, Cd, and Pb in aqueous solutions through a multivariate factorial design, using a mixed microbial consortium originally developed for the degradation of organic compounds. This approach seeks to explore the consortium\u0026rsquo;s potential as a sustainable alternative for the treatment of industrial effluents and the remediation of aquatic environments contaminated by heavy metals.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e2.1 Mixed microbial consortium\u003c/h2\u003e\n \u003cp\u003eThe microbial consortium used in this study consists of ten bacteria and twenty-four filamentous fungi and is protected by a patent filed at the Instituto Nacional da Propriedade Industrial (INPI) under number BR 10 2021 002341 4 (Lima et al., 2021). The genetic sequences are available in the National Center for Biotechnology Information (NCBI) database under accession numbers MW881196\u0026ndash;MW881204, MW865711\u0026ndash;MW865718, MW855898\u0026ndash;MW855903, and MW865719\u0026ndash;MW865729.\u003c/p\u003e\n \u003cp\u003eThe strains were isolated from mangrove sediments of Todos os Santos Bay and from petroleum samples of the Rec\u0026ocirc;ncavo Basin and previously demonstrated potential for hydrocarbon degradation (Lima et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e2.2 Inoculum preparation\u003c/h2\u003e\n \u003cp\u003eEach strain of the consortium was inoculated into 100 mL of Brain Heart (BH; Difco\u0026trade;) medium using 1 cm diameter agar discs. Cultures were incubated in an orbital shaker (Tecnal\u0026trade; TE-420) at 30\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2\u0026deg;C and 153 rpm for 6 days.\u003c/p\u003e\n \u003cp\u003eFrom the concentrated suspension, dilutions were prepared in sterile 0.9% (w/v) saline solution to obtain final cell concentrations of 1 \u0026times; 10⁶, 5 \u0026times; 10⁶, and 1 \u0026times; 10⁷ CFU mL⁻\u0026sup1;. Cell density was estimated by optical density using a microplate reader (LMR-96\u0026trade;, ELISA), at 500 nm for fungi and 600 nm for bacteria. The final stock solution was stored at 4\u0026deg;C until use.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003e2.3 Biosorption assays\u003c/h2\u003e\n \u003cp\u003eThe biosorption assays evaluated the effects of pH, metal mixture concentration (Cu, Zn, Ni, Co, Cd, and Pb), contact time, and biomass concentration, aiming to maximize multimetal removal efficiency. A two-level fractional factorial design (2⁴⁻\u0026sup1;) was applied, with triplicate central points, totaling 11 experiments (Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003e\u003c/p\u003e\u0026nbsp;\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eFactors and coded levels for the 2⁴⁻\u0026sup1; factorial design.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003ePC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eMetal mixture (mg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e\u003cstrong\u003e10\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u003cstrong\u003e20\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e\u003cstrong\u003e30\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eBiomass (CFU/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e\u003cstrong\u003e10\u003c/strong\u003e \u003csup\u003e\u003cstrong\u003e6\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u003cstrong\u003e5 \u0026nbsp;x \u0026nbsp;10\u003c/strong\u003e \u003csup\u003e\u003cstrong\u003e6\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e\u003cstrong\u003e10\u003c/strong\u003e \u003csup\u003e\u003cstrong\u003e7\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eTime (h)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e\u003cstrong\u003e8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u003cstrong\u003e28\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e\u003cstrong\u003e48\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003epH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e\u003cstrong\u003e6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003eA multiple response was employed to simultaneously evaluate the biosorption of all metals:\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eMultiple Response = (%Cu/%CuMAX) + (%Pb/%PbMAX) + (%Zn/%ZnMAX) + (%Cd/%CdMAX) + (%Ni/%NiMAX)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eEach experiment was conducted in 250 mL Erlenmeyer flasks containing 1.5 g of magnesium sulfate and 10 g of sucrose. Stock metal solutions (1000 mg L⁻\u0026sup1;) were added to reach final concentrations of 10, 20, or 30 mg L⁻\u0026sup1;. The initial pH (1.75) was adjusted to 2, 4, or 6 using 0.1 mol L⁻\u0026sup1; HCl or 0.1 mol L⁻\u0026sup1; NaOH. The working volume (90 mL) was completed with sterile 0.9% (w/v) saline solution.\u003c/p\u003e\n \u003cp\u003eThe solutions were autoclaved at 121\u0026deg;C for 20 min, protected from light, and cooled prior to inoculation. Subsequently, 10 mL of the microbial consortium at the established concentration was added, completing the final volume to 100 mL. The flasks were incubated in an orbital shaker (Tecnal\u0026trade; TE-420) at 150 rpm and 30\u0026deg;C for 8, 28, or 48 h.\u003c/p\u003e\n \u003cp\u003eAt the end of the experiment, samples were centrifuged at 4500 rpm for 20 min (Universal Centrifuge, model 320R). The supernatant was acidified with HNO₃ (1:1, v/v) to pH 2 and analyzed by Flame Atomic Absorption Spectrometry (FAAS; Varian 220 FS).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003e2.4 Biosorption calculation\u003c/h2\u003e\n \u003cp\u003eThe removal percentage was calculated using the following equation:\u003c/p\u003e\n \u003cp\u003e\u003cimg src=\"https://myfiles.space/user_files/58895_8739fc6c57c1c19a/58895_custom_files/img1774534414.png\" width=\"391\" height=\"67\"\u003e\u003c/p\u003e\n \u003cp\u003ewhere C\u003csub\u003ei\u003c/sub\u003e is the initial metal concentration (mg L⁻\u0026sup1;) and Cₑ is the residual concentration after treatment (mg L⁻\u0026sup1;).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003e2.5 16S rRNA amplicon sequencing\u003c/h2\u003e\n \u003cp\u003eBacterial identification was performed by sequencing the V3\u0026ndash;V4 regions of the 16S rRNA gene. Libraries were prepared following the proprietary protocol of Neoprospecta Microbiome Technologies (Brazil). Amplification was carried out using the primers 341F (CCTACGGGRSGCAGCAG) and 806R (GGACTACHVGGGTWTCTAAT) (DOI: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1371/journal.pone.0007401\u003c/span\u003e\u003c/span\u003e; DOI: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/ismej.2012.8\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eSequencing was performed on the Illumina MiSeq system using V3 kits (600 cycles) or V2 kits (500 cycles) for paired-end runs, and V2 kits (300 cycles) for single-end runs. Sequence processing and analysis were conducted using the Sentinel pipeline.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003e2.6 Statistical analysis\u003c/h2\u003e\n \u003cp\u003eThe significance of the factors and the adequacy of the linear model were evaluated by Analysis of Variance (ANOVA), considering a significance level of 5% (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Statistical analyses were performed using Statistica 10.0, including the evaluation of interactions among factors and the estimation of experimental error from the triplicate central points.\u003c/p\u003e\n \u003cp\u003eThe multiple response was used as the dependent variable for the simultaneous optimization of biosorption of all metals. Graphs and visual representations were generated in Google Colab using scientific libraries for exploratory analysis and presentation of the results.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3. Results and Discussion","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Optimization of biosorption conditions\u003c/h2\u003e \u003cp\u003eThe main parameters influencing the simultaneous biosorption of Cu, Zn, Ni, Cd, and Pb by microorganisms were evaluated, including pH, initial metal mixture concentration, contact time, and biomass concentration (CFU mL⁻\u0026sup1;). For this purpose, a 2⁴⁻\u0026sup1; fractional factorial experimental design was applied, with triplicate central points, totaling 11 experiments. The concept of multiple response was employed to integrate the removal performance of all metals and to identify the most favorable condition for simultaneous multimetal biosorption.\u003c/p\u003e \u003cp\u003eThe multiple response values were calculated according to the previously described equation. The maximum removals observed at the end of the assays were 82% for Cu, 90% for Pb, 87% for Zn, 93% for Cd, and 93% for Ni, demonstrating the high efficiency of the microbial consortium. The complete results of the experimental design are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFractional factorial design matrix (2⁴⁻\u0026sup1;), with triplicate central points, for the evaluation of Cu, Zn, Pb, Cd, and Ni biosorption by microorganisms. Coded values are shown in parentheses.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eRun\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTime (h)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMetal concentration (mg L⁻\u0026sup1;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003epH\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCell concentration (CFU mL⁻\u0026sup1;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c10\" namest=\"c6\"\u003e \u003cp\u003eRemoval (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMultiple Response Index\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCu\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePb\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eZn\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eCd\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNi\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8/ (-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10/ (-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2/ (-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,00E\u0026thinsp;+\u0026thinsp;06 / (-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e2,39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48/ (+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10/ (-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2/ (-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,00E\u0026thinsp;+\u0026thinsp;07/ (+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e2,26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8/ (-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30/ (+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2/ (-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,00E\u0026thinsp;+\u0026thinsp;07/ (+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e3,55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48/ (+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30/ (+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2/ (-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,00E\u0026thinsp;+\u0026thinsp;06/ (-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e3,54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8/ (-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10/ (-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6/ (+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,00E\u0026thinsp;+\u0026thinsp;07/ (+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e3,43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48/ (+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10/ (-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6/ (+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,00E\u0026thinsp;+\u0026thinsp;06/ (-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e4,17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8/ (-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30/ (+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6/ (+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,00E\u0026thinsp;+\u0026thinsp;06/ (-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e4,90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48/ (+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30/ (+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6/ (+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,00E\u0026thinsp;+\u0026thinsp;07/ (+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e4,80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28/ (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20/ (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4/ (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5,00E\u0026thinsp;+\u0026thinsp;06/ (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e3,28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28/ (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20/ (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4/ (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5,00E\u0026thinsp;+\u0026thinsp;06/ (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e3,46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28/ (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20/ (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4/ (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5,00E\u0026thinsp;+\u0026thinsp;06/ (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e3,27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe analysis of the Pareto chart (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea) demonstrated, at a 95% confidence level, that pH and initial metal concentration exert statistically significant and positive effects on the multiple response. This indicates that higher values of these variables favor the simultaneous biosorption of the five metals by the microbial consortium.\u003c/p\u003e \u003cp\u003eIn contrast, contact time and cell concentration did not show significant influence within the evaluated experimental range, suggesting that the process is predominantly controlled by physicochemical factors, such as metal speciation, ionization state of cell wall functional groups, and availability of active sites on the biomass.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb revealed the relative selectivity of the microbial consortium under a multimetal mixture scenario and the progressive contribution of each metal to the overall removal. Zn and Pb exhibited the highest average affinities and intrinsic efficiency toward biosorption by the microbial consortium, whether through ion exchange mechanisms, physical adsorption, or complexation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe dominant influence of pH is consistent with previous studies demonstrating that increasing pH promotes the deprotonation of functional groups\u0026mdash;such as carboxyl, phosphate, and hydroxyl groups\u0026mdash;present on the microbial cell wall, thereby enhancing electrostatic attraction and complexation of metal cations (Volesky, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Wang \u0026amp; Chen, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). In addition, higher pH values reduce competition with protons for active sites, favoring the retention of metal ions.\u003c/p\u003e \u003cp\u003eThe graphical analysis of the multiple response under different experimental conditions (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) confirmed this behavior, showing higher response values at pH 6 and an initial concentration of 30 mg L⁻\u0026sup1;, regardless of incubation time. Higher initial concentrations increase the mass transfer gradient between the aqueous phase and the biomass, enhancing the rate of occupation of biosorption sites\u0026mdash;a phenomenon widely described in multimetal systems (Gadd, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Gupta et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe absence of a significant effect of contact time suggests that biosorption equilibrium is reached rapidly, indicating that the predominant mechanism involves passive surface adsorption rather than energy-dependent metabolic processes. This characteristic is particularly advantageous for industrial applications, as it allows reduced residence time, higher productivity, and lower operational costs.\u003c/p\u003e \u003cp\u003eANOVA demonstrated no significant lack of fit (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05), confirming the robustness and adequacy of the model within the evaluated experimental range (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Based on the statistical and operational results, the optimal condition for multimetal biosorption was defined as: pH 6, 30 mg L⁻\u0026sup1; of metals, 5 \u0026times; 10⁶ CFU mL⁻\u0026sup1;, 150 rpm, 30\u0026deg;C, and 4 h of contact time, prioritizing high efficiency, operational stability, and large-scale feasibility.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eANOVA for the evaluation of metal biosorption by the mixed microbial consortium.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFactor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ess\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(1) Time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0,03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0,03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2,78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0,238\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(2) Metal concentration (mg L \u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2,57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e222,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0,004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(3) pH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3,88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3,88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e335,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0,003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(4) Cell concentration (CFU)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0,10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0,10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8,65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0,099\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLack of fit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0,36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0,09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7,79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0,117\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePure error\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0,02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0,01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal SS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6,96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003ess\u0026thinsp;=\u0026thinsp;square sum / df= degrees freedom / MS\u0026thinsp;=\u0026thinsp;mean square.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Effect of pH and initial metal concentration\u003c/h2\u003e \u003cp\u003eInitial pH is one of the most critical parameters in metal biosorption, as it simultaneously influences chemical speciation, metal ion solubility, and the degree of dissociation of functional groups on the microbial surface, thereby controlling the availability of active binding sites (Fertu et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe biosorption of Cu, Zn, Ni, Cd, and Pb was evaluated at pH values of 2, 4, and 6 and initial concentrations of 10, 20, and 30 mg L⁻\u0026sup1;, using biomass concentrations ranging from 1 \u0026times; 10⁶ to 1 \u0026times; 10⁷ CFU mL⁻\u0026sup1;, at 30\u0026deg;C, 150 rpm, and contact times of 8, 28, and 48 h. The selected pH range allowed the exploration of relevant variations in the protonation state of carboxyl, phosphate, hydroxyl, and amine groups, which are recognized as key determinants in biosorption mechanisms.\u003c/p\u003e \u003cp\u003eThe results presented in Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, and \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e demonstrated a strong dependence of the process on pH, with a marked increase in removal efficiency as pH increased from 2 to 6, reaching values between 34% and 93.3%, depending on the metal and its initial concentration.\u003c/p\u003e \u003cp\u003eAt acidic pH, protonation of anionic active sites on the cell surface occurs, increasing competition between protons and metal ions, which significantly reduces biosorption. As pH approaches neutrality, progressive deprotonation takes place, favoring electrostatic interactions, complexation, and ion exchange, resulting in greater metal retention (Chintalpudi et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Chintalpudi et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAlthough pH values close to or above neutrality may promote chemical precipitation of metals (Jin et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Wang et al., 2021), the results indicated that pH 6 provided the most favorable biosorption conditions without evidence of precipitation-related limitations.\u003c/p\u003e \u003cp\u003eAt an initial concentration of 10 mg L⁻\u0026sup1;, biosorption was maximized mainly for Cu and Pb at pH 6, although all metals exhibited better performance under less acidic conditions (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). At 30 mg L⁻\u0026sup1;, Cu, Pb, Cd, and Ni also showed higher removal percentages at pH 6, whereas Zn exhibited lower sensitivity to pH variations (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe evaluation of the triplicate central point (20 mg L⁻\u0026sup1;, pH 4) revealed high experimental reproducibility, demonstrating methodological robustness. Even under intermediate conditions and reduced contact time (28 h), the microbial consortium removed up to 71% of the metals, highlighting its strong potential for practical application (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Effect of incubation time and initial metal concentration\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eOverall, the results indicate that maximum efficiency tends to occur during the initial periods (8\u0026ndash;28 h), particularly at near-neutral pH, reinforcing the importance of simultaneously optimizing contact time, initial concentration, and pH (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Role of Bacillus and Pseudomonas in biosorption\u003c/h2\u003e \u003cp\u003eMultivariate analysis using hierarchical clustering (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e) revealed distinct response patterns between the genera Bacillus and Pseudomonas under different environmental conditions.\u003c/p\u003e \u003cp\u003eHigher response intensities were associated with Bacillus, particularly under acidic pH, high bacterial loads, and short contact times, indicating a strong structural or functional contribution to metal retention. This behavior is consistent with studies reporting Bacillus as highly metal-resistant and efficient in biosorption (Anusha et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn contrast, Pseudomonas exhibited lower relative intensities, especially at pH 6, higher metal concentrations, and longer exposure times, possibly reflecting ionic competition or differences in metal stress adaptation mechanisms.\u003c/p\u003e \u003cp\u003eNevertheless, Pseudomonas remains widely recognized for its high environmental adaptability, biotransformation capacity, and bioremediation potential, as demonstrated by Chen et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and Naz et al. (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThese findings indicate that Bacillus acts as the main biosorption agent within the consortium, whereas Pseudomonas may play a complementary role related to system resilience and tolerance to metal stress.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Biosorption experiments under optimal conditions\u003c/h2\u003e \u003cp\u003eExperiments under optimal conditions were conducted in triplicate using 30 mg L⁻\u0026sup1; of metals, pH 6, 30\u0026deg;C, 150 rpm, and a biomass concentration of 1 \u0026times; 10⁸ CFU mL⁻\u0026sup1;. Kinetics were monitored at 8, 28, and 48 h.\u003c/p\u003e \u003cp\u003eThe highest efficiency was observed up to 28 h for Cu, Ni, Pb, and Cd, indicating stabilization of the process at intermediate times (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e). Cd showed lower removal (~\u0026thinsp;30%), suggesting multimetal competition or lower relative affinity.\u003c/p\u003e \u003cp\u003eOptical density remained practically constant (~\u0026thinsp;1.56\u0026ndash;1.66 \u0026times; 10⁷ CFU mL⁻\u0026sup1;), indicating the absence of significant growth and reinforcing that biosorption was predominantly passive.\u003c/p\u003e \u003cp\u003eThese results suggest that excessively long contact times may reduce efficiency and that the optimal interval lies between 8 and 28 h. Strategies such as biostimulation or bioaugmentation may be explored to maximize performance in practical applications.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Conclusions","content":"\u003cp\u003eA mixed microbial consortium originally developed for the degradation of organic compounds demonstrated high performance in the simultaneous biosorption of Cu, Zn, Ni, Cd, and Pb in aqueous solutions, highlighting its potential as a multimetal biosorbent in complex environmental systems. Experimental optimization indicated that the highest removal efficiency was achieved under an initial metal mixture concentration of 30 mg L⁻\u0026sup1;, pH 6, temperature of 30\u0026deg;C, agitation at 150 rpm, and contact time of up to 28 h, confirming the strong dependence of the process on the physicochemical conditions of the system.\u003c/p\u003e \u003cp\u003eKinetic evaluation revealed that biosorption occurs predominantly within the first hours of contact, with maximum values observed between 8 and 28 h, followed by a gradual decrease in efficiency at longer times (48 h). This behavior suggests the establishment of equilibrium between metal ions and active sites on the biomass, possibly associated with functional group saturation, ionic competition in multimetal systems, and a reduction in ion exchange capacity over time. Thus, the interval between 8 and 28 h represents a strategic period for operational interventions, such as biostimulation or bioaugmentation, aiming to maintain or enhance process efficiency in practical applications.\u003c/p\u003e \u003cp\u003eThe biosorptive potential of the consortium was reinforced by its application in water samples, highlighting its feasibility as an environmentally sustainable, low-cost, and efficient technology for the removal of metal ions from industrial effluents, wastewater, and contaminated areas. The best performance was observed under conditions characterized by near-neutral pH (~\u0026thinsp;6), high initial biomass concentrations (~\u0026thinsp;10⁶\u0026ndash;10⁷ CFU mL⁻\u0026sup1;), higher metal loads (30 mg L⁻\u0026sup1;), and intermediate incubation times, emphasizing the interdependence between biotic and abiotic factors in optimizing multimetal biosorption.\u003c/p\u003e \u003cp\u003eOn the other hand, extreme conditions such as highly acidic pH (~\u0026thinsp;2), low initial bacterial concentration (~\u0026thinsp;10⁶ CFU mL⁻\u0026sup1;), and short contact times resulted in significantly lower performance, indicating limitations in the availability of active binding sites and in the adaptive capacity of the microbial consortium under severe environmental stress.\u003c/p\u003e \u003cp\u003eAlthough the consortium demonstrated consistent and robust biosorption capacity, further studies are needed to deepen the understanding of the system\u0026rsquo;s operational limits, including evaluation across broader metal concentration ranges, different metal/biomass ratios, dynamic hydrodynamic conditions, and continuous-flow operations. In addition, future investigations into the metabolic, functional, and genomic potential of the consortium may contribute to improving biosorption performance and expanding its application in the sustainable management of industrial effluents and environmental remediation.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors gratefully acknowledge financial and institutional support provided through a national research and development program in the fields of geomicrobiology and petroleum biotechnology. The authors also recognize the strategic importance of support from a national energy regulatory agency under research and development funding mechanisms. Additional support from a graduate education funding agency is acknowledged.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eShell Brasil Ltda.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eDanusia Ferreira Lima: Conceptualization, Methodology, Investigation, Formal analysis,\u003c/li\u003e\n \u003cli\u003eWriting \u0026ndash; Original Draft, Supervision, Funding acquisition.\u003c/li\u003e\n \u003cli\u003eGisele Moraes de Jesus: Investigation, Data curation, Visualization, Writing \u0026ndash; Review \u0026amp;\u003c/li\u003e\n \u003cli\u003eEditing.\u003c/li\u003e\n \u003cli\u003eSarah Adriana Rocha Soares: Methodology, Resources, Writing \u0026ndash; Review \u0026amp; Editing.\u003c/li\u003e\n \u003cli\u003eAntonio Fernando de Souza Queiroz: Formal analysis, Data curation, Validation, Writing \u0026ndash;\u003c/li\u003e\n \u003cli\u003eReview \u0026amp; Editing.\u003c/li\u003e\n \u003cli\u003eOl\u0026iacute;via Maria Cordeiro de Oliveira: Methodology, Investigation, Supervision, Writing \u0026ndash; Review\u003c/li\u003e\n \u003cli\u003e\u0026amp; Editing.\u0026nbsp;\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable. This study does not involve human participants, human data, or identifiable personal information.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e☐\u0026nbsp;The authors declare that they have no known competing financial interests or personal relationships\u003c/p\u003e\n\u003cp\u003ethat could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e☒\u0026nbsp;The authors declare the following financial interests/personal relationships which may be considered as\u003c/p\u003e\n\u003cp\u003epotential competing interests:\u003c/p\u003e\n\u003cp\u003eDanusia Ferreira Lima reports financial support was provided by Shell Brazil Oil. DANUSIA FERREIRA LIMA has patent #BR 10 2021 002341 4 pending to Universidad Federal da Bahia. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003cbr clear=\"all\"\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analyzed during this study are included in this published article and its\u003c/p\u003e\n\u003cp\u003esupplementary information files.\u003cbr clear=\"all\"\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMandatory if your study involves humans and/or animals\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003cbr clear=\"all\"\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of AI Use\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that artificial intelligence tools (ChatGPT, OpenAI) were used solely to assist with language editing, grammar correction, and formatting of the manuscript. All scientific content, data, results, interpretations, and conclusions presented in this work are the original work of the authors and have not been generated or influenced by AI. The use of AI did not affect the integrity, analysis, or originality of the research\u003cbr clear=\"all\"\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and analyzed during the current study are included in this published article and its supplementary information files. Additional data are available from the corresponding author on reasonable request.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eClinical trial number:\u0026nbsp;\u003c/strong\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAbbas SH, Ismail IM, Mostafa TM, Sulaymon AH (2014) Biosorption of heavy metals: a review. J Chem Sci Technol 3(4):74\u0026ndash;102\u003c/li\u003e\n \u003cli\u003eAhemad M, Kibret M (2013) Recent trends in microbial biosorption of heavy metals: a review. Biochem Mol Biol 1(1):19\u0026ndash;26.\u003c/li\u003e\n \u003cli\u003eAlhammadi E, Halimoon N, Zulkeflee Z, Johari WLW (2025) The co-biosorption possibility of single and quaternary heavy metals by multi-metal-tolerant bacteria isolated from electroplating effluents. Fine Chem Eng 211\u0026ndash;249. https://doi.org/10.37256/fce.6220256530\u003c/li\u003e\n \u003cli\u003eAnusha P, Natarajan D, Rengarajan S, Alfarraj S, Kandasamy S (2024) An assessment of metal absorption competence of indigenous metal tolerant bacterial species\u0026mdash;an in-vitro study. Environ Res 251:118700. https://doi.org/10.1016/j.envres.2024.118700\u003c/li\u003e\n \u003cli\u003eAryal M, Liakopoulou-Kyriakides M (2015) Bioremoval of heavy metals by bacterial biomass. Environ Monit Assess 187(1):4173. https://doi.org/10.1007/s10661-014-4173-z\u003c/li\u003e\n \u003cli\u003eBalali-Mood M, Naseri K, Tahergorabi Z, Khazdair MR, Sadeghi M (2021) Toxic mechanisms of five heavy metals: mercury, lead, chromium, cadmium, and arsenic. Front Pharmacol 12:643972. https://doi.org/10.3389/fphar.2021.643972\u003c/li\u003e\n \u003cli\u003eChakraborty R, Asthana A, Singh AK, Jain B, Susan ABH (2022) Adsorption of heavy metal ions by various low-cost adsorbents: a review. Int J Environ Anal Chem 102(2):342\u0026ndash;379. https://doi.org/10.1080/03067319.2020.1722811\u003c/li\u003e\n \u003cli\u003eChen Z, Liu Y, Jiang L, Zhang C, Qian X, Gu J, Song Z (2024) Bacterial outer membrane vesicles increase polymyxin resistance in Pseudomonas aeruginosa while inhibiting its quorum sensing. J Hazard Mater 478:135588. https://doi.org/10.1016/j.jhazmat.2024.135588\u003c/li\u003e\n \u003cli\u003eChintalpudi VK, Kanamarlapudi RKSL, Mallu UR, Muddada S (2022) Isolation, identification, biosorption optimization, characterization, isotherm, kinetic and application of novel bacterium Chelatococcus sp. biomass for removal of Pb (II) ions from aqueous solutions. Int J Environ Sci Technol 19(3):1531\u0026ndash;1544. https://doi.org/10.1007/s13762-021-03169-6\u003c/li\u003e\n \u003cli\u003eChintalpudi VK, Kanamarlapudi RKSL, Mallu UR, Muddada S (2021) Isotherm, kinetic and application of novel bacterium Chelatococcus sp. biomass for removal of Pb (II) ions from aqueous solutions. Int J Environ Sci Technol 19:1\u0026ndash;14\u003c/li\u003e\n \u003cli\u003eComte S, Guibaud G, Baudu M (2006) Biosorption properties of extracellular polymeric substances (EPS) resulting from activated sludge according to their type: soluble or bound. Process Biochem 41(4):815\u0026ndash;823. https://doi.org/10.1016/j.procbio.2005.10.014\u003c/li\u003e\n \u003cli\u003eCruz-Lopes LP, Macena M, Esteves B, Guin\u0026eacute; RP (2021) Ideal pH for the adsorption of metal ions Cr6+, Ni2+, Pb2+ in aqueous solution with different adsorbent materials. Open Agric 6(1):115\u0026ndash;123. https://doi.org/10.1515/opag-2021-0225\u003c/li\u003e\n \u003cli\u003ede Almeida Martins B, Takahashi JA (2025) Overview of bioremediation as a method for metal-contaminated wastewater treatment. Environ Sci Pollut Res. https://doi.org/10.1007/s11356-025-36195-9\u003c/li\u003e\n \u003cli\u003eDemarco CF, Quadro MS, Selau Carlos F, Pieniz S, Morselli LBGA, Andreazza R (2023) Bioremediation of aquatic environments contaminated with heavy metals: a review of mechanisms, solutions and perspectives. Sustainability 15(2):1411. https://doi.org/10.3390/su15021411\u003c/li\u003e\n \u003cli\u003eEsposito A, Pagnanelli F, Lodi A, Solisio C, Veglio F (2001) Biosorption of heavy metals by Sphaerotilus natans: an equilibrium study at different pH and biomass concentrations. Hydrometallurgy 60(2):129\u0026ndash;141. https://doi.org/10.1016/S0304-386X(00)00195-X\u003c/li\u003e\n \u003cli\u003eFertu DI, Bulgariu L, Gavrilescu M (2022) Modeling and optimization of heavy metals biosorption by low-cost sorbents using response surface methodology. Processes 10(3):523. https://doi.org/10.3390/pr10030523\u003c/li\u003e\n \u003cli\u003eFomina M, Gadd GM (2014) Biosorption: current perspectives on concept, definition and application. Bioresour Technol 160:3\u0026ndash;14. https://doi.org/10.1016/j.biortech.2013.12.102\u003c/li\u003e\n \u003cli\u003eGadd GM (2010) Metals, minerals and microbes: geomicrobiology and bioremediation. Microbiology 156(3):609\u0026ndash;643. https://doi.org/10.1099/mic.0.037143-0\u003c/li\u003e\n \u003cli\u003eGupta R, Mohapatra H (2003) Microbial biomass: an economical alternative for removal of heavy metals from waste water. Indian J Exp Biol 41(9):945\u0026ndash;966\u003c/li\u003e\n \u003cli\u003eGupta VK, Nayak A, Agarwal S (2015) Bioadsorbents for remediation of heavy metals: current status and their future prospects. Environ Eng Res 20(1):1\u0026ndash;18. https://doi.org/10.4491/eer.2015.018\u003c/li\u003e\n \u003cli\u003eJin Y, Teng C, Yu S, Song T, Dong L, Liang J, Qu J (2018) Batch and fixed-bed biosorption of Cd (II) from aqueous solution using immobilized Pleurotus ostreatus spent substrate. Chemosphere 191:799\u0026ndash;808. https://doi.org/10.1016/j.chemosphere.2017.08.154\u003c/li\u003e\n \u003cli\u003eKhan S, Hesham AEL, Qiao M, Rehman S, He JZ (2010) Effects of Cd and Pb on soil microbial community structure and activities. Environ Sci Pollut Res 17(2):288\u0026ndash;296. https://doi.org/10.1007/s11356-009-0134-4\u003c/li\u003e\n \u003cli\u003eKhidr R, Qurbani K, Muhammed V, Salim S, Abdulla S, Wsw H (2025) Synergistic effects of indigenous bacterial consortia on heavy metal tolerance and reduction. Environ Geochem Health 47(3):79. https://doi.org/10.1007/s10653-025-02392-1\u003c/li\u003e\n \u003cli\u003eLi H, Cui X, Sun Y, Zheng P, Wang L, Shi X (2025) Advances in microbial remediation of heavy metal-contaminated soils: mechanisms, synergistic technologies, field applications and future perspectives. Toxics 13(12):1069. https://doi.org/10.3390/toxics13121069\u003c/li\u003e\n \u003cli\u003eLima DF, Queiroz AFS, Oliveira OMC (2018) Biodegradation of hydrocarbons by microbial consortia isolated from mangrove sediments and petroleum samples. Mar Pollut Bull 136:406\u0026ndash;414. https://doi.org/10.1016/j.marpolbul.2018.09.014\u003c/li\u003e\n \u003cli\u003eMalik A (2004) Metal bioremediation through growing cells. Environ Int 30:261\u0026ndash;278. https://doi.org/10.1016/j.envint.2003.08.001\u003c/li\u003e\n \u003cli\u003eNaz SA, Jabeen N, Sohail M, Rasool SA (2015) Biophysicochemical characterization of pyocin SA189 produced by Pseudomonas aeruginosa SA189. Braz J Microbiol 46:1147\u0026ndash;1154. https://doi.org/10.1590/S1517-838246420140737\u003c/li\u003e\n \u003cli\u003eNaz T, Khan MD, Ahmed I, Rehman SU, Rha ES, Malook I, Jamil M (2016) Biosorption of heavy metals by Pseudomonas species isolated from sugar industry. Toxicol Ind Health 32(9):1619\u0026ndash;1627. https://doi.org/10.1177/0748233715569900\u003c/li\u003e\n \u003cli\u003eOhiagu FO, Chikezie PC, Ahaneku CC, Chikezie CM (2022) Human exposure to heavy metals: toxicity mechanisms and health implications. Mater Sci Eng Int J 6(2):78\u0026ndash;87\u003c/li\u003e\n \u003cli\u003ePagliaccia B, Carretti E, Severi M, Berti D, Lubello C, Lotti T (2022) Heavy metal biosorption by extracellular polymeric substances (EPS) recovered from anammox granular sludge. J Hazard Mater 424:126661. https://doi.org/10.1016/j.jhazmat.2021.126661\u003c/li\u003e\n \u003cli\u003ePatil A, Chakraborty S, Yadav Y, Sharma B, Singh S, Arya M (2025) Bioremediation strategies and mechanisms of bacteria for resistance against heavy metals: a review. Bioremediation J 29(4):448\u0026ndash;480. https://doi.org/10.1080/10889868.2024.2375204\u003c/li\u003e\n \u003cli\u003ePhiri JT, Oh S (2024) Biosorption of Cd (II), Co (II), and Cu (II) onto microalgae under acidic and neutral conditions. Sustainability 16(15):6342. https://doi.org/10.3390/su16156342\u003c/li\u003e\n \u003cli\u003eQader MQ (2025) Multi-microbial consortia incorporating microalgae, bacteria, and fungi for effective heavy metal removal. Bioremediation J. https://doi.org/10.1080/10889868.2025.2552770\u003c/li\u003e\n \u003cli\u003eRahman Z, Singh VP (2020) Bioremediation of toxic heavy metals (THMs) contaminated sites: concepts, applications and challenges. Environ Sci Pollut Res 27(22):27563\u0026ndash;27581. https://doi.org/10.1007/s11356-020-08903-0\u003c/li\u003e\n \u003cli\u003eRizvi A, Ahmed B, Zaidi A, Khan MS (2020) Biosorption of heavy metals by dry biomass of metal tolerant bacterial biosorbents: an efficient metal clean-up strategy. Environ Monit Assess 192(12):801. https://doi.org/10.1007/s10661-020-08758-5\u003c/li\u003e\n \u003cli\u003eSim\u0026oacute;n D, Palet C, Costas A, Crist\u0026oacute;bal A (2022) Agro-industrial waste as potential heavy metal adsorbents and subsequent safe disposal of spent adsorbents. Water 14(20):3298. https://doi.org/10.3390/w14203298\u003c/li\u003e\n \u003cli\u003eVerma L, Ekka A, Banjara RA, Ambade B, Kumar A, Gautam S (2025) A review on bacterial exopolysaccharides for heavy metal remediation: mechanisms, challenges, and sustainable applications. Water Environ Res 97(10):e70184. https://doi.org/10.1002/wer.70184\u003c/li\u003e\n \u003cli\u003eVerma T, Aggarwal A, Sharma S, Dhyani P (2025) Heavy metal resistant bacteria for bioremediation of toxic heavy metals\u0026mdash;arsenic and lead. In: Heavy metal contamination in wastewater and its bioremediation by microbial-based approaches. Springer, Singapore, pp 123\u0026ndash;149\u003c/li\u003e\n \u003cli\u003eVolesky B (2007) Biosorption and me. Water Res 41(18):4017\u0026ndash;4029. https://doi.org/10.1016/j.watres.2007.05.062\u003c/li\u003e\n \u003cli\u003eWang F, Wang H, Dong W, Yu X, Zuo Z, Lu X, Zhang X (2024) Enhanced multi-metals stabilization: synergistic insights from hydroxyapatite and peroxide dosing strategies. Sci Total Environ 927:172159.\u003c/li\u003e\n \u003cli\u003ehttps://doi.org/10.1016/j.scitotenv.2024.172159\u003c/li\u003e\n \u003cli\u003eWang J, Chen C (2006) Biosorption of heavy metals by Saccharomyces cerevisiae: a review. Biotechnol Adv 24(5):427\u0026ndash;451. https://doi.org/10.1016/j.biotechadv.2006.03.001\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Heavy metals, Microbial biosorption, Multimetal systems, Fractional factorial design, Multivariate optimization, Wastewater treatment","lastPublishedDoi":"10.21203/rs.3.rs-9083659/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9083659/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMetal contamination in aquatic environments requires efficient and environmentally sustainable remediation strategies. This study aimed to optimize the simultaneous biosorption of Cu, Pb, Zn, Cd, and Ni in aqueous solutions using a mixed microbial consortium through a multivariate factorial experimental design. The evaluated parameters included pH, contact time, initial multielement metal concentration, microbial cell density, and a multiple response index. The results demonstrated that pH and initial metal concentration were the most influential variables affecting biosorption efficiency, achieving removal rates above 80% for all evaluated metals. Although the consortium was composed of bacteria and fungi, community-level analyses were restricted to the bacterial fraction. Integrated microbiological data suggested complementary functional roles within the consortium, with the genus Bacillus contributing centrally to metal biosorption, while Pseudomonas played a supportive role, possibly associated with metal stress tolerance and system stability. Overall, the findings highlight the strong potential of this microbial consortium as a sustainable and low-cost biosorbent for the treatment of metal-contaminated effluents.\u003c/p\u003e","manuscriptTitle":"Multivariate optimization of multimetal biosorption by a microbial consortium: insights from bacterial community analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-26 14:26:06","doi":"10.21203/rs.3.rs-9083659/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"15b09221-b802-4700-97eb-f2c2a77dadbc","owner":[],"postedDate":"March 26th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Reject","date":"2026-05-09T02:42:51+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-09T22:07:51+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-26 14:26:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9083659","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9083659","identity":"rs-9083659","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}