{"paper_id":"2f4b09fe-155d-4c7f-92b7-af7b5c457df8","body_text":"Application of fungal biomass of the genus Pleurotus in the bioremediation of Doce River waters after the crime disaster in Mariana/MG: an analysis between the years 2015 and 2018 | 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 Application of fungal biomass of the genus Pleurotus in the bioremediation of Doce River waters after the crime disaster in Mariana/MG: an analysis between the years 2015 and 2018 M. P. O. Santos, A. S. Van Der Maas, D. F. Jardim, J. L. Rodrigues, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4335579/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 The collapse of Samarco mining company's tailings dam in November 2015 is recorded as the most severe environmental calamity to ever occur in Brazil. The ensuing toxic sludge devastated towns along the Doce River, leaving an enduring legacy of socio-environmental and economic destruction. The Doce River basin continues to bear the scars of this disaster to this day. This study thus proposes to investigate the bioremediation potential of three Pleurotus spp. strains, assessing their physical-chemical parameters of pH and turbidity, as well as the ability of the tested isolates to eliminate toxic metals in solution, based on water samples collected in 2015 and 2018. The studied fungal strains tended to acidify the medium, lowering the pH of the samples. In raw water, these strains effectively removed turbidity, achieving a decline of 86.2-95.0% and 28.1-40.7% in the first and second collections, respectively. High concentrations of aluminum, arsenic, barium, lead, iron, and manganese were detected in the Doce River water samples through Inductively Coupled Plasma Mass Spectrometry (ICP-MS). Batch system experiments showed that Pleurotus spp. could efficiently remove toxic metals from the solution in seven days of incubation, with average elimination rates ranging from 94.0-99.1% and 70.3-99.0% for the six evaluated elements in the 2015 and 2018 samples, respectively. Therefore, the findings suggest that the genus Pleurotus holds significant biotechnological promise for the bioremediation of contaminated or deteriorated waters. Toxic metal. Fungus. Samarco. Fundão. Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Throughout history, society's progress has been driven by the exploitation of natural resources. Currently, the scarcity and degradation of natural resources have motivated efforts to preserve and recover them, especially in the case of water (Silva et al, 2023 , Ramadan et al, 2024 ). Since November 2015, with the collapse of the tailings dam in the municipality of Mariana/MG, the Doce River has been suffering the consequences of Brazil's biggest environmental disaster. The event led to the destruction of the town of Bento Rodrigues, causing 19 deaths and immeasurable environmental damage, with the negative impacts continuing to this day (Espíndola et al., 2019 ; Caldas, 2017 ; Sánchez et al., 2022 ). The wave of iron ore waste hit the Doce River more severely, compromising it from the region close to the upper course to its mouth, in the Atlantic Ocean. As it is the main river in its hydrographic basin, the social and economic impacts also spread throughout the region that depended directly or indirectly on this water resource (Lima et al., 2020 ; Prado; Pinto, 2020 ; Formigoni et al., 2022 ). At the time of the tragedy, public supply services, electricity generation, fishing, farming and industrial activities were suspended, creating a state of public calamity in several cities (Ana, 2016 ; Minas Gerais, 2016 ). Furthermore, examples of terrestrial flora and fauna were lost, with the destruction of areas within the Atlantic Forest biome. The aquatic biota was severely impacted, with the death of thousands of fish, as well as the extinction of endemic species (Miranda; Marques, 2016; Caldas, 2017 ; Girotto et al., 2020 ; Martins; Takahashi, 2022 ). As for physical-chemical parameters, the massive amount of suspended solids in the Doce River after the disaster raised turbidity levels to values never before recorded. Similarly, in terms of toxicity, the quality of the watercourse and adjacent soils was also affected, due to the identification of high concentrations of toxic metals (Guerra et al., 2017 ; Buch et al., 2020 ; Mulholland et al., 2022 ). The excessive availability of these elements in the aquatic environment can harm the development of living organisms in direct and/or indirect contact. Metabolic disorders, malformation, heart diseases and diseases related to the kidneys and liver, as well as problems with the skin, nervous and respiratory systems, as well as some types of cancer are reported as consequences associated with exposure to toxic metals (Vale, 2010 ; Ortiz- Monsalve, 2019; Moschem; Gonçalves, 2020 ). Due to bioaccumulator characteristics, toxic metals can be incorporated by aquatic biota from lower trophic levels and increase in concentration from the food chain, then going through the bioaugmentation process. Therefore, in addition to contamination through direct contact, humans can end up increasing their levels of metals in the body through the ingestion of foods, such as fish and crustaceans, that have bioaccumulated them in tissues and organs (Deforest et al., 2007 ; Palaniappan; Karthikeyan, 2009 ; Yousafzai et al., 2017 ). Thus, the contamination of ecosystems by toxic metals implies significant problems in terms of the toxicological and ecotoxicological risks to which exposed populations are subject, whether in small but prolonged and/or acute doses. This scenario leads to the discussion of the need to seek alternatives to mitigate the Doce River, given its political, environmental, social and economic importance. Biotechnology demonstrates growing potential for its use in processes aimed at recovering a contaminated environment. Bioremediation, an aspect of this macro area, has been presented as a promising, economically viable, easy to operate and non-invasive technique, as it uses living organisms, in general plants and microorganisms, to carry out the degradation of pollutants in the most varied means. The technique has already been applied at a commercial level to treat waste, solid or liquid, and degraded areas (DzioneK et al., 2016 ; Sharma et al., 2021 ; Tufail et al., 2022 ). Fungal metabolism has shown to be highly promising when applied in bioremediation processes, among which fungi of the genus Pleurotus have been commonly reported. These microorganisms have the ability to use a wide range of compounds, often contaminants, as a source of nutrition and energy. They are part of the basidiomycete group, known as mushrooms. They are edible and endemic to tropical and subtropical regions, however, they show remarkable adaptability to growth in different environmental conditions and on different substrates (Felinto, 1999 ; Santos, 2014 ). The literature indicates that Pleurotus is capable of acting on the degradation of various recalcitrant compounds, in addition to having properties that facilitate the adsorption of toxic metals, originating from contaminated soils and/or waters and industrial waste (Vimala; Das, 2011 ; Zhao et al., 2016;Yang et al., 2017 ; Wang et al., 2018 ; Zhuo et al., 2019 ; Alouache et al., 2022 ; Hadibarata et al., 2022 ). With a view to investigating alternatives to minimize the damage caused by the Mariana/MG disaster, the present study aimed to evaluate the effect of three fungal strains of the genus Pleurotus on the removal of toxic metals, pH variation and turbidity reduction, in water samples from the Doce River in 2015 and 2018, which can contribute to the management and recovery of this ecosystem. Material and Methods Study area and water sample collection Surface water samples were obtained in 2015 and 2018 from the Middle Doce River region, in the municipality of Governador Valadares/MG. Sampling occurred at two distinct sites: P1, near the water treatment plant at coordinates 18º52'55.31” S, 41º57'1.86” W, and P2, within the central area of the city at coordinates 18º51'22.54” S, 41º56'10.55” W (Fig. 1 ). The collection and storage of samples adhered to protocols outlined in the National Guide for Sample Collection and Preservation by CETESB (2011). Acquisition of Pleurotus sp. Strains Three strains of Pleurotus were provided by the Genetics Laboratory of Microorganisms from the State University of Londrina/Paraná. These strains underwent genetic analysis based on amplification of the ITS region (Internal Transcribed Space) of DNA ribosomal at Neoprospecta Microbiome Technologies, where they were identified as Pleurotus eryngii (ERY strain) and Pleurotus ostreatus (HI and SB strains). Their sequences were recorded in the GenBank database under accession numbers MT925998, MT925999, and MT926004, respectively. Culture maintenance of Pleurotus sp. Periodic replications were carried out in which a disc approximately 1 cm in diameter from a colonized plate was transferred to a fresh Potato Dextrose Agar (PDA) medium. These fresh plates were then incubated at 28 ºC until fully colonized and subsequently stored at 4 ºC. Treatment of water samples with Pleurotus sp. Each fungal strain (HI, SB, ERY) was introduced into 125-mL Erlenmeyer flasks containing 30 mL of water from either P1 or P2, using three 1 cm discs (approximately 0.2 g) of fresh biomass. The experiment included samples with 100% river water and those diluted to 50% and 25% with ultrapure water, all conducted in triplicate alongside a control (no fungal inoculation). The flasks were agitated on an orbital shaker at 110 rpm for seven days at an ambient temperature of around 28 ± 2 ºC. Post-incubation, the mycelium was filtered using Whatman filter paper (grade 1:11 µm) and the water samples stored in 50-mL polypropylene bottles (Falcon) free from metal contamination. Physicochemical analysis Physicochemical parameters, pH and turbidity, were assessed for all samples using a Digimed benchtop pH meter (Model DM-22) and a HACH Turbidimeter (Model 2100), respectively. The findings were evaluated against the benchmarks set by Brazilian Environmental Legislation for Class II waters, as per CONAMA Resolution No. 357/2005, applicable to the Doce River. Determination of toxic metals After fungal treatment, samples were digested using 2% HNO 3 in a 1:10 ratio to a final volume of 10 mL, completed with ultrapure water. The samples were then filtered using Whatman filter paper, grade 1:11 µm. Concentrations of Ag, Al, As, Ba, Be, Bi, Cd, Co, Cr, Cs, Cu, Fe, Hg, In, Li, Mg, Mn, Ni, Pb, Se, Sr, Tl, U, V, and Zn were analyzed using Inductively Coupled Plasma Mass Spectrometry (ICP-MS). The equipment utilized was the NexION 300D model from Perkin Elmer Inc. This instrument was equipped with a Meinhard nebulizer and a quartz cyclonic spray chamber for continuous nebulization. Operating conditions included: nebulizer gas flow at 0.95 L.min − 1 ; auxiliary gas flow at 1.2 L.min − 1 ; plasma gas flow at 15 L.min − 1 ; lens voltage at 7.25 V; ICP RF Power at 1100 W; CeO/Ce ratio of 0.031; and Ba ++ /Ba + ratio of 0.016. Blanks were prepared using diluted stock solutions (Perkin Elmer Inc.) containing 100 mg.mL − 1 of each element. Concentrations of toxic metals were expressed in µg.L − 1 . Data analysis The analyzed variables included: fungal strain, metal concentration, pH, and turbidity. Descriptive statistical techniques such as mean, standard deviation, and coefficient of variation were employed. To evaluate the effects of fungal strains on physicochemical parameters and metal concentrations, parametric ANOVA tests were conducted, followed by Tukey’s Test (p ≤ 0.05). Analyses were performed using GNU PSPP software version 1.6.2-g78a33a and Microsoft Excel 2016 MSO version 16.0.4266.1001. Results and discussion Potential of hydrogen In 2015, control samples (water from Doce River) exhibited pH values ranging from 8.9 to 10.1, while in 2018, these ranged from 7.4 to 8.4 under identical dilution conditions. The 2015 readings suggest an increase in watercourse alkalinity following the disaster, which contradicts the pH range of 6.0 to 9.0 recommended for Class II waters as per CONAMA Resolution No. 357/2005. Overall, the average pH variations between collection sites were negative, indicating that the fungal isolates decreased the pH of the medium, aiming to increase its acidity, as depicted in Figs. 2 and 3 below. Despite the discrepancies in pH reduction rates among treatments by different fungal strains, statistical analysis of variance (p ≤ 0.05) consistently showed that the strains tended to reduce the hydrogen potential of the medium, aiming to make it more acidic, as indicated by the percentages in Table 1 . Table 1 Average pH reductions induced by Pleurotus spp. in 2015 and 2018 pH reduction (%) Treatment (% of river water) 2015 2018 ERY HI SB ERY HI SB 25 16.6 24.0 34.0 14.1 4.9 1.7 50 17.3 17.4 16.8 3.1 1.9 -1.7 100 22.9 18.5 18.1 5.5 2.9 2.6 Moreover, when examining the influence of treatments alone, without comparing Pleurotus sp. isolates, ANOVA also did not identify significant differences in mean reductions (p-value = 0.522). Statistical analyses revealed significant differences in average pH variations between the years sampled. In 2015, the reductions in hydrogen potential of the treated samples were more pronounced, with an average of 20.64% (p < 0.001). This reduction was approximately five times greater than that observed in the water samples collected in 2018, which showed an average reduction of 3.91% across all treatments and strains evaluated. The wide range of average reductions observed in this study, which varied from 1.7–34.0%, supports the findings of Farias ( 2014 ), who reported pH changes ranging from 4.5–28.5% when assessing the capacity of the fungi Penicillium corylophilum to alter the pH of an aqueous solution in tests for removing the toxic metals Ni, Cu, Cr, and Zn. Farias ( 2014 ) noted that pH significantly influences the removal of toxic metals by microbial biomasses, as this parameter is able to interfere with the solubility of these elements in solution and their speciation. The work of Zhang et al. ( 2019 ) also corroborates the results obtained in this research. Their studies using the fungus Aspergillus niger for Pb immobilization demonstrated the ability of the isolate to significantly acidify the environment, reducing the initial pH of the medium from 3.5–6.5 to between 2.04 and 2.08. Our results indicate that while the majority of the strains evaluated tend to acidify the environment, a tendency to alkalize was also observed, as seen with the SB strain in the 50% treatment (2018 collection), which increased the sample pH by 1.7%. This alkalization capacity was similarly noted by Rani et al. ( 2014 ) during their tests with Aspergillus niger and Phanerochaete chrysosporium in the bioremediation of dyes. These results suggest that the ability of filamentous fungi to modify environmental pH is related to the initial pH conditions, temperature, and available nutrients. According to studies by Roessing ( 2023 ), Bellettini et al. ( 2019 ), and Sultana et al. ( 2018 ), fungi of the genus Pleurotus prefer to develop in more acidic environments, with pH ranging from 4.0 to 7.0 during the mycelial growth phase and from 3.5 to 5.0 during the basidiocarp growth stage. These studies further highlight that these pH ranges are directly impacted by the species of the genus and their metabolic processes, with fungi generally reducing the pH of their environments through the production of organic acids. Turbidity Table 2 presents the mean values of turbidity reduction induced by Pleurotus spp. at sites P1 and P2, from collections carried out in 2015 and 2018. Table 2 Average turbidity reduction induced by Pleurotus spp. strains in samples collected from sites P1 and P2, in 2015 and 2018 Turbidity removal (%) Treatment (% of river water) 2015 2018 ERY HI SB ERY HI SB 25 83.8 86.8 79.8 19.1 3.3 2.6 50 76.6 87.4 89.3 20.2 43.6 33.0 100 86.2 95.0 93.5 40.7 28.1 39.6 In the first collection in 2015, turbidity levels surpassed the standards set by CONAMA Resolution No. 357/2005 for Class II rivers, reaching up to 140,000 UNT following the Mariana/MG disaster, as reported by the National Water Agency (ANA) in 2016. During the tests, turbidity means varied between 789.5 and 2,275 UNT. It was reduced by over 76.6% by the fungal strains, with a maximum removal rate of 95%. Despite this reduction, the results did not meet Brazilian environmental standards. In the subsequent 2018 collections, mean turbidity levels in control groups were 14.9, 25.9, and 70.2 UNT, all within the limits established by CONAMA Resolution No. 357/2005. The highest removal, 72.3%, was achieved by the SB isolate at sampling site P2, without dilution. Visual examinations, as depicted in Fig. 4 , show that the suspended solid particles, previously dissolved in the mixture, predominantly adhered to the fungal mycelium. This interaction between the fungal biomass and suspended solids led to precipitate formation, effectively reducing turbidity levels. No significant differences were observed among Pleurotus spp. strains in reducing turbidity, regardless of the treatment applied. In 2015, varying proportions of Doce River water in the treatments did not result in significant turbidity reductions. However, in 2018, the treatment with 25% river water showed the least reduction, while the intermediate treatment (50%) and untreated river water exhibited similar reductions. The notable decrease in initial turbidity levels between collections indicates a reduction in the amount of suspended solids, which consequently provided fewer nutrients for the fungi, impacting their growth and effectiveness as biocoagulants. Thus, the results demonstrate that all three fungal strains are capable of significantly reducing turbidity, particularly in samples with high concentrations of suspended solids, eliminating the need for dilution. The collapse of the tailings dam in Mariana/MG drastically affected the turbidity of the Doce River, particularly in the region near Governador Valadares/MG. According to Matos et al. ( 2020 ), turbidity levels surged post-disaster, with a median of 137 UNT in 2015, a stark increase from the pre-disaster level of 14.6 UNT. Although there was a seasonal decrease by 2018, levels remained 4.5 times higher than pre-disaster figures. Higher turbidity increases operational costs at Water Treatment Plants, necessitating additional expenditures on chemical products like flocculants and coagulants, as observed in Governador Valadares/MG. Excessive use of these chemicals can leave residues in treated water, posing potential health risks to consumers, as noted by Nimesha et al. ( 2022 ) and Desta and Bote ( 2021 ). In related studies, Hassan and Obeid ( 2016 ) achieved an 84% reduction in turbidity using spores from three species of filamentous fungi in wastewater trials. Zainol et al. ( 2021 ) reported turbidity reduction efficiencies of 99.73% and 99.25% using residues from Pleurotus pulmonaryius culture and mushroom substrate, respectively, under optimal conditions (pH 4.0 and a dose of 5 mg.L − 1 of fungal biomass). Similarly, Pardede et al. ( 2018 ) managed to achieve an 84% redSultauction in turbidity in wastewater treated with Pleurotus ostreatus strains, using a dose of 600 mg.L − 1 of the biocoagulant and an agitation rate of 150 rpm. These findings highlight the importance of optimal initial doses for satisfactory results. Based on these insights, this study suggests that Pleurotus sp. strains have the potential to efficiently reduce environmental turbidity, acting as natural, environmentally friendly, sustainable, low-cost, and easily applied coagulants. Other researchers, such as Maas et al. ( 2018 ), Palmiei et al. (2005), Balan and Monteiro ( 2001 ), Hashmi and Saleem ( 2013 ), and Skariyachan et al. ( 2016 ), investigated the ability of Pleurotus species to decrease turbidity through discoloration and dye degradation tests. The application of species of this fungal genus in reducing vinasse turbidity has also been documented (Ferreira et al., 2011 ; Silva et al., 2015 ; Vila et al., 2018; Junior et al., 2020 ). The Pleurotus sp. strains proved effective in reducing water turbidity using straightforward methods. Their integration with other processes could ensure satisfactory outcomes, considering their economic viability. Toxic metals Among the metals analyzed, high concentrations of Al, As, Ba, Fe, Mn, and Pb were detected, which were the elements under study. However, ICP-MS was unable to detect concentrations of Ag, Be, Cd, Co, Cr, Cu, Li, Ni, U, V, or Zn. Additionally, the elements Bi, Cs, Hg, In, Se, Sr, and Tl were identified but remained within established thresholds, showing low concentrations. Table 3 provides details on the average concentrations of toxic metals found in raw water samples collected in 2015 and 2018. Table 3 Standards recommended by Conama Resolution No. 357/2005 for Class II waters and average concentration of toxic metals in water samples from Doce River collected in 2015 and 2018 Metal Threshold (µg.L − 1 ) Collection 1 (µg.L − 1 ) Collection 2 (µg.L − 1 ) As 10 93.4 4.4 Al 100 116,990.6 3,070.7 Mn 100 16,470.2 122.3 Fe 300 250,622.5 7,820.8 Pb 10 944.0 28.0 Ba 700 2,422.6 455.6 Variations in metal concentrations observed across different sampling events can be attributed to the seasonal dynamics of the Doce River, particularly influenced by rainfall patterns that mobilize deposited tailings sludge. Table 4 details the average percentage reductions of toxic metals at sites P1 and P2 for each dilution treatment, demonstrating significant reductions for the six metals analyzed. Table 4 Average percentage reduction of toxic metals in Doce River water samples treated with Pleurotus spp. strains at 25%, 50%, and 100% dilutions for the years 2015 and 2018 25% treatment Metal 2015 2018 ERY HI SB ERY HI SB As 93.2 95.6 93.7 43.4 40.6 79.9 Al 97.9 99.3 99.8 87.9 93.5 98.4 Mn 96.0 97.2 94.1 85.7 81.4 86.8 Fe 92.2 95.3 99.9 98.9 98.7 99.5 Pb 99.4 99.8 97.7 99.8 99.8 99.8 Ba 97.5 97.6 96.5 98.0 98.5 98.7 50% treatment Metal 2015 2018 ERY HI SB ERY HI SB As 91.5 94.2 92.4 69.5 76.6 77.7 Al 92.2 97.4 99.1 90.6 96.2 97.2 Mn 91.5 94.1 92.7 71.0 93.2 88.7 Fe 99.3 99.7 99.8 97.8 99.2 98.9 Pb 97.9 99.3 99.2 98.1 99.8 99.8 Ba 94.1 95.8 95.6 96.8 98.0 97.4 100% treatment Metal 2015 2018 ERY HI SB ERY HI SB As 94.8 95.9 94.4 81.3 80.1 83.2 Al 95.9 98.4 91.1 91.3 97.4 94.7 Mn 97.2 97.9 95.8 68.9 75.8 92.2 Fe 98.7 99.5 98.5 85.0 91.5 90.6 Pb 99.3 99.8 99.7 97.0 98.8 98.0 Ba 98.0 98.2 97.7 94.6 95.0 95.5 In the initial 2015 collection, although the ERY, HI, and SB strains achieved high average reductions in the 50% and 100% Doce River water treatments, the concentrations of Al, Mn, and Fe did not meet the limits set by environmental regulations, likely due to their exceptionally high initial concentrations. In the subsequent 2018 collection, raw water analyses of the control group showed that As and Ba levels complied with standards for Class II rivers. However, pre-treatment concentrations of Al, Mn, Fe, and Pb remained above the regulatory thresholds. Despite fungal treatment reducing concentrations significantly, with average reductions exceeding 85% across all three strains, the undiluted samples still had Al and Fe levels above acceptable limits. In contrast, the ERY, HI, and SB treatments to 25% and 50% river water managed to reduce concentrations to within the limits specified by CONAMA Resolution No. 357/2005. Table 5 summarizes the outcomes for toxic metal concentration reductions after treatment with Pleurotus sp. across both sampled years. Table 5 Average percentage reduction of toxic metals in waters from Doce River induced by Pleurotus spp. strains in samples collected in 2015 and 2018 Metal Treatment (% of river water) 25 50 100 ERY HI SB ERY HI SB ERY HI SB As 68.3 68.1 86.8 80.5 85.4 85.1 88.1 88.0 88.8 Al 92.9 96.4 99.1 91.4 96.8 98.1 93.6 97.9 92.9 Mn 90.9 89.3 90.5 81.3 93.6 90.7 83.0 86.8 94.0 Fe 95.6 97.0 99.7 98.6 99.5 99.4 91.9 95.5 94.6 Pb 99.6 99.8 98.7 98.0 99.6 99.5 98.1 99.3 98.8 Ba 97.8 98.1 97.6 95.5 96.9 96.5 96.3 96.6 96.6 Overall, the Pleurotus eryngii (ERY) and Pleurotus ostreatus (HI, SB) strains efficiently reduced the concentrations of the six evaluated metals. Analyzing the average reductions across 2015 and 2018 collections, the maximum reductions were noted for As (88.8%), Al (99.1%), Mn (94.0%), Fe (99.7%), Pb (99.8%), and Ba (98.3%). Analysis of variance revealed no significant differences in reduction rates between the ERY, HI, and SB strains (p-value = 0.299), nor between the treatment dilutions (p-value = 0.892), indicating that the percentage reductions are independent of the initial dilution of the samples. This consistency was evident from statistical analyses conducted for each collection separately. Tests demonstrate that fungal strains are effective even in high metal concentrations, highlighting the reliability of the reduction means across different collections, irrespective of initial metal levels, treatments, or Pleurotus sp. strains used. It was possible to compare mean reductions between the metals tested for all treatments, regardless of the fungal strain, in each collection (see Table 6 ). This comparison suggests potential natural preferences among the strains for assimilating specific elements. Table 6 Average percentage reduction in concentrations of elements induced by Pleurotus spp. strains, in the 2015 and 2018 collections Element As Al Mn Fe Pb Ba Collection 1 94.0 a 96.8 ab 95.2 a 98.1 b 99.1 b 96.9 ab Collection 2 70.3ª 94.1 c 82.6 b 95.6 c 99.0 c 97.0 c Common letters in the same row indicate means without significant statistical differences, for each collection, using Tukey’s test at a 5% significance level (p ≤ 0.05). In the 2015 collection, there was no significant difference in removal efficiency across different elements. However, the 2018 collection showed that As and Mn were removed less efficiently compared to other metals, with elements such as Al, Fe, Pb, and Ba consistently achieving removal rates above 94.0%. In mixtures of metals, the variation in metal removal efficiency is influenced by their affinity for fungal biomass and interactions among metal ions, including antagonism, synergism, or non-interaction. Hoque and Fritscher (2019) suggest that these phenomena can explain the observed discrepancies in the reduction of toxic metals. Numerous studies have explored the remediation potential of toxic metals using filamentous fungi, particularly Pleurotus sp. isolates (Adebayo, 2013 ; Stanley et al., 2017 ; Manna et al., 2018 ; Ferreira et al., 2019 ; Coelho et al., 2020 ; Mariconi et al., 2020 ; Bhatnagar et al., 2021 ; Vacar et al., 2021 ; Xu et al., 2021 ). However, most of these studies have been limited to evaluating the removal of up to three metals simultaneously, which does not fully capture the complexity of real-world contamination scenarios (Bhattacharya et al., 2020 ). This limitation often stems from the challenges posed by the toxicity of multi-metal mixtures, which can reduce the efficacy of fungal species in removing metals from solutions (Chahdi et al., 2019 ). In contrast, some researchers advocate for experiments involving metal mixtures to yield more representative results. Bhattacharya et al. ( 2020 ) studied the potential of Aspergillus fumigatus to reduce concentrations of toxic metals in a hexa-metal system, observing significant removals: Pb (80%), Cr (43%), Cu (98%), Zn (78%), Ni (99%), and Cd (99%). Their findings indicated that the fungus simultaneously utilized biosorption and bioaccumulation mechanisms, where metals were adsorbed onto the surface of the fungal biomass and within the cells, respectively. Similarly, Hoque and Fritscher (2019) reported removal efficiencies between 81–99% for metals such as Al, Cd, Co, Cr, Cu, Hg, Ni, Pb, U, and Zn when tested simultaneously with the fungus Mucor hiemalis in synthetic wastewater. In their study on the genus Pleurotus , Wu et al. ( 2016 ) reported that Pleurotus eryngii removed up to 92.17% of Mn from aqueous solutions after 15 days of incubation, supporting the findings of the present study. Aguilar et al. ( 2021 ) assessed the capability of Pleurotus ostreatus to remove metals from aqueous solutions, finding the highest removal efficiencies for Pb (75%), followed by Cr (42%) and Cd (2.25%). Their results for lead corroborate those of our research. Georgescu et al. ( 2019 ) also evaluated P. ostreatus as a Cd biosorbent, achieving a maximum biosorption efficiency of 78% for 0.5 mg.L Cd − 1 . Singh et al. ( 2020 ) studied the application of P. florida in multi-metal solutions, observing maximum reductions of 52.10% for Pb, 99.84% for Cd, 70.85% for Cr, 77.77% for Ni, 42.63% for Mn, and 76.23% for Zn. Contrary to our findings, where metal reduction rates in solution were unaffected by dilution treatments, Vaseem et al. ( 2017 ) noted significant differences in treatments with 25% and 50% dilution compared to raw effluent in their study of coal washing effluents in India. The authors found that P. ostreatus was more effective in 50% diluted effluent, achieving reduction rates of 57.2%, 82.6%, 98.0%, 99.9%, 99.3%, 99.1%, 89.2%, and 35.6% for Mn, Zn, Ni, Cu, Co, Cr, Fe, and Pb, respectively. Muhammad and Sukor ( 2016 ) examined the capacity of Pleurotus ostreatus to reduce heavy metal concentrations in synthetic solutions and chemical waste samples, with removal efficacy ranked as Fe > Pb > Cu > Zn. However, the removal rates were notably lower compared to those in real samples: Fe at 17.02%, Cu at 46.6%, Pb at 76%, and Zn at 17.88%. These differences may be attributed to organic materials and other toxic metals competing for binding sites on the fungal biomass surface (Kovadevic et al., 2000). Mota ( 2015 ) notes that fungal cell surfaces carry a negative charge, enhancing their interaction with metal ions. Earlier studies by Kapoor and Viraraghavan (1995) and Brady ( 1996 ) highlight that binding potential correlates with the ionic radii and free valence orbitals of each ion, with smaller ions such as Al³⁺ and Fe³⁺ often removed more efficiently in high-metal-concentration samples. Research indicates that various Pleurotus species possess different functional groups in their cell walls—hydroxyl, carboxylic, amino, and phosphate—, favoring metal biosorption (Joo et al., 2011 ; Javaid et al., 2011 ). However, it is important to recognize that, in addition to electrostatic affinities and intrinsic microbial characteristics, environmental conditions critically influence the mechanism and efficiency of sorption (Mota, 2015 ; Pande et al., 2022 ). Thus, the Pleurotus strains tested in this study (ERY, HI, SB) showed metal reduction capabilities consistent with previous literature findings. The results suggest these strains remarkably manage multi-metal mixtures in real samples, attributable to their intrinsic properties and the complex interplay of environmental conditions and sorption mechanisms. Conclusion This study examined the performance of three Pleurotus strains in treating water from the Doce River post the 2015 disaster and again in 2018. No significant differences were detected between the strains. Regarding pH, all strains tended to acidify the water, with greater reductions observed in 2015 than in 2018. In terms of turbidity, the strains were more effective in undiluted samples, particularly in 2015. When assessing toxic metals, the strains displayed consistent ability to reduce concentrations in both 2015 and 2018. These findings demonstrate the potential of Pleurotus strains for bioremediation, particularly for the waters of the Doce River following the 2015 disaster. Declarations Conflict of interest: The authors have no conflicts of interest to declare that are relevant to the content of this article. Acknowledgements The authors are grateful to FAPEMIG (Fundação de Amparo à Pesquisa do Estado de Minas Gerais) for financial support. Author contribution All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by M. P.O. Santos and A. S. Van der Maas. Formal analysis and investigation of metals concentrations were performed by J. L. Rodrigues. The statistical analyses were performed by D. F. Jardim. M. P. O. Santos and A. S. van der Maas wrote the main manuscript text. C. A. Bomfeti supervised the experiments, edited and reviewed the manuscript. All authors read and approved the final manuscript. Ethical approval This manuscript has not been published elsewhere in part or in entirety and is not under consideration by another journal. We have read and understood your journal’s policies, and we believe that neither the manuscript nor the study violates any of these . Conflict of interest The authors have no conflicts of interest to declare that are relevant to the content of this article. Funding There was no financing . References Adebayo, A. O (2013). Investigation on Pleurotus ferulae potential for the sorption of pb(ii) from aqueous solution. Bulletin of the Chemical Society of Ethiopia , v. 27, n. 1, p. 25-34, http://dx.doi.org/10.4314/bcse.v27i1.3 Ana (2016). Agência Nacional das Águas. Encarte especial sobre a bacia do rio doce. Conjuntura dos recursos hídricos no brasil: Informe 2015 . Ministério do Meio Ambiente. Brasília. Retrieved January 7,2024. 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Https://doi.org/10.1155/2017/5801432 Zainol, N. A.; Goh, H. T.; syed-zainal, S. F. F (2021). Effectiveness of mushroom ( Pleurotus pulmonarius) waste as natural coagulant for kaolin synthetic water via coagulation-flocculation process. Iop conference series: earth and environmental science, n.920,.Https://doi.org/10.1088/1755-1315/920/1/012 020 Zhang, L. et al. (2019). Lead immobilization assisted by fungal decomposition of organophosphate under various ph values. Scientific Reports , v. 9, n. 13353. Https://doi.org/10.1038/s41598-019-49976-3 Zhao, C. et al. (2016) . Characteristics of uranium biosorption from aqueous solutions on fungus Pleurotus ostreatus . Environmental Science and Pollution research, v. 23. Https://doi.org/10.1007/s11356-016-7722-x Zhuo, R. et al . (2019). The roles of Pleurotus ostreatus haucc 162 laccase isoenzymes in decolorization of synthetic dyes and the transformation pathways. Chemosphere , v. 234, p. 733-745. Https://doi.org/10.1016/j.chemosphere.2019.06.113 Additional Declarations No competing interests reported. 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-4335579\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":306558566,\"identity\":\"843d1059-ff7c-4351-b41a-c47fe976af78\",\"order_by\":0,\"name\":\"M. P. O. Santos\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Federal University of the Jequitinhonha and Mucuri Valleys\",\"correspondingAuthor\":false,\"submittingAuthor\":false,\"prefix\":\"\",\"firstName\":\"M.\",\"middleName\":\"P. O.\",\"lastName\":\"Santos\",\"suffix\":\"\"},{\"id\":306558567,\"identity\":\"fabe6bb4-befd-4943-b14a-30e13b3fa9a6\",\"order_by\":1,\"name\":\"A. S. Van Der Maas\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7ElEQVRIiWNgGAWjYFCCBIYDDAVgFuMDIMHDR5wWAzCLGUTxsBGjhQGqhU0CTBLSwN+e+/DABwObaP72488qv+bYybAxMD98dAOPFokzzw0OzjBIy51xJsfstuy2ZKDD2IyNc/BZcyON4TCPweHchgM5bLcltzEDtfCwSePTIg/S8sfgf+7888+fFUtuqyesxQCkhcHgQO6GGwlmjB+3HSasxfDMM4aDPQbJuRtvvDGWZtx2nIeNmYBf5I6nMX/4UWGXO+98+sOPP7dV2/OzNz98jNf7yICZB0wSqxwEGH+QonoUjIJRMApGDAAAvAtL1S2L1PcAAAAASUVORK5CYII=\",\"orcid\":\"\",\"institution\":\"Federal University of the Jequitinhonha and Mucuri Valleys\",\"correspondingAuthor\":true,\"submittingAuthor\":false,\"prefix\":\"\",\"firstName\":\"A.\",\"middleName\":\"S. Van Der\",\"lastName\":\"Maas\",\"suffix\":\"\"},{\"id\":306558568,\"identity\":\"906c31cd-695e-4426-bef4-ccc9059e8248\",\"order_by\":2,\"name\":\"D. F. Jardim\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Federal University of the Jequitinhonha and Mucuri Valleys\",\"correspondingAuthor\":false,\"submittingAuthor\":false,\"prefix\":\"\",\"firstName\":\"D.\",\"middleName\":\"F.\",\"lastName\":\"Jardim\",\"suffix\":\"\"},{\"id\":306558569,\"identity\":\"1bc78239-fded-4164-81e3-c4d9b4ead6e4\",\"order_by\":3,\"name\":\"J. L. Rodrigues\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Federal University of the Jequitinhonha and Mucuri Valleys\",\"correspondingAuthor\":false,\"submittingAuthor\":false,\"prefix\":\"\",\"firstName\":\"J.\",\"middleName\":\"L.\",\"lastName\":\"Rodrigues\",\"suffix\":\"\"},{\"id\":306558570,\"identity\":\"a098470b-4862-4a78-96cf-fe8b3f4c3bfc\",\"order_by\":4,\"name\":\"C. A. Bomfeti\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Federal University of the Jequitinhonha and Mucuri Valleys\",\"correspondingAuthor\":false,\"submittingAuthor\":false,\"prefix\":\"\",\"firstName\":\"C.\",\"middleName\":\"A.\",\"lastName\":\"Bomfeti\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2024-04-27 23:09:16\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-4335579/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-4335579/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":57768938,\"identity\":\"08be8fe5-95b8-44fd-ba97-86fdd59bdd82\",\"added_by\":\"auto\",\"created_at\":\"2024-06-05 11:40:51\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":104718,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eSpatial location of P1 and P2 on the Doce River.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4335579/v1/f934b94b2683d8ca176e5d12.png\"},{\"id\":57768939,\"identity\":\"ed20354d-c8ec-480d-888c-a4f99e0c791a\",\"added_by\":\"auto\",\"created_at\":\"2024-06-05 11:40:51\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":8533,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eAverage pH variations induced by \\u003cem\\u003ePleurotus\\u003c/em\\u003e spp. strains in water samples from sites P1 and P2, collected in 2015.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4335579/v1/a3f45dfd148d8b21f192c815.png\"},{\"id\":57768940,\"identity\":\"774afe50-8e9d-4111-8e8e-c6ce0ea8b651\",\"added_by\":\"auto\",\"created_at\":\"2024-06-05 11:40:51\",\"extension\":\"png\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":12194,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eAverage pH variations induced by \\u003cem\\u003ePleurotus\\u003c/em\\u003e spp. strains in water samples from sites P1 and P2, collected in 2018\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"3.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4335579/v1/e3cfdece0754a6f957990d96.png\"},{\"id\":57768941,\"identity\":\"398cc4a5-bdab-4dfb-85e2-24b48e33faa6\",\"added_by\":\"auto\",\"created_at\":\"2024-06-05 11:40:51\",\"extension\":\"png\",\"order_by\":4,\"title\":\"Figure 4\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":765413,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eSamples from the 2015 collection subjected to treatments of 100%, 50%, and 25% of river water, respectively. Waters treated with \\u003cstrong\\u003e(a)\\u003c/strong\\u003e \\u003cem\\u003ePleurotus eryngii\\u003c/em\\u003e., ERY.; \\u003cstrong\\u003e(b, c)\\u003c/strong\\u003e \\u003cem\\u003ePleurotus ostreatus\\u003c/em\\u003e, HI and SB.; \\u003cstrong\\u003e(d) \\u003c/strong\\u003eControl groups.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"4.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4335579/v1/190cd9b8bab25b47ca1ab7cb.png\"},{\"id\":62053431,\"identity\":\"68f95d0c-1909-46f3-84cc-6311f788b905\",\"added_by\":\"auto\",\"created_at\":\"2024-08-08 18:35:15\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":1834027,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4335579/v1/208440df-c57d-4b6b-88e2-1129a08b4414.pdf\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Application of fungal biomass of the genus Pleurotus in the bioremediation of Doce River waters after the crime disaster in Mariana/MG: an analysis between the years 2015 and 2018\",\"fulltext\":[{\"header\":\"Introduction\",\"content\":\"\\u003cp\\u003eThroughout history, society's progress has been driven by the exploitation of natural resources. Currently, the scarcity and degradation of natural resources have motivated efforts to preserve and recover them, especially in the case of water (Silva et al, \\u003cspan citationid=\\\"CR67\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e, Ramadan et al, \\u003cspan citationid=\\\"CR60\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eSince November 2015, with the collapse of the tailings dam in the municipality of Mariana/MG, the Doce River has been suffering the consequences of Brazil's biggest environmental disaster. The event led to the destruction of the town of Bento Rodrigues, causing 19 deaths and immeasurable environmental damage, with the negative impacts continuing to this day (Esp\\u0026iacute;ndola et al., \\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e; Caldas, \\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e; S\\u0026aacute;nchez et al., \\u003cspan citationid=\\\"CR63\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eThe wave of iron ore waste hit the Doce River more severely, compromising it from the region close to the upper course to its mouth, in the Atlantic Ocean. As it is the main river in its hydrographic basin, the social and economic impacts also spread throughout the region that depended directly or indirectly on this water resource (Lima et al., \\u003cspan citationid=\\\"CR40\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e; Prado; Pinto, \\u003cspan citationid=\\\"CR59\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e; Formigoni et al., \\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e ).\\u003c/p\\u003e \\u003cp\\u003eAt the time of the tragedy, public supply services, electricity generation, fishing, farming and industrial activities were suspended, creating a state of public calamity in several cities (Ana, \\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e; Minas Gerais, \\u003cspan citationid=\\\"CR46\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e). Furthermore, examples of terrestrial flora and fauna were lost, with the destruction of areas within the Atlantic Forest biome. The aquatic biota was severely impacted, with the death of thousands of fish, as well as the extinction of endemic species (Miranda; Marques, 2016; Caldas, \\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e; Girotto et al., \\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e; Martins; Takahashi, \\u003cspan citationid=\\\"CR43\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eAs for physical-chemical parameters, the massive amount of suspended solids in the Doce River after the disaster raised turbidity levels to values never before recorded. Similarly, in terms of toxicity, the quality of the watercourse and adjacent soils was also affected, due to the identification of high concentrations of toxic metals (Guerra et al., \\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e; Buch et al., \\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e; Mulholland et al., \\u003cspan citationid=\\\"CR51\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eThe excessive availability of these elements in the aquatic environment can harm the development of living organisms in direct and/or indirect contact. Metabolic disorders, malformation, heart diseases and diseases related to the kidneys and liver, as well as problems with the skin, nervous and respiratory systems, as well as some types of cancer are reported as consequences associated with exposure to toxic metals (Vale, \\u003cspan citationid=\\\"CR74\\\" class=\\\"CitationRef\\\"\\u003e2010\\u003c/span\\u003e; Ortiz- Monsalve, 2019; Moschem; Gon\\u0026ccedil;alves, \\u003cspan citationid=\\\"CR48\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eDue to bioaccumulator characteristics, toxic metals can be incorporated by aquatic biota from lower trophic levels and increase in concentration from the food chain, then going through the bioaugmentation process. Therefore, in addition to contamination through direct contact, humans can end up increasing their levels of metals in the body through the ingestion of foods, such as fish and crustaceans, that have bioaccumulated them in tissues and organs (Deforest et al., \\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e2007\\u003c/span\\u003e; Palaniappan; Karthikeyan, \\u003cspan citationid=\\\"CR54\\\" class=\\\"CitationRef\\\"\\u003e2009\\u003c/span\\u003e; Yousafzai et al., \\u003cspan citationid=\\\"CR82\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e). Thus, the contamination of ecosystems by toxic metals implies significant problems in terms of the toxicological and ecotoxicological risks to which exposed populations are subject, whether in small but prolonged and/or acute doses.\\u003c/p\\u003e \\u003cp\\u003eThis scenario leads to the discussion of the need to seek alternatives to mitigate the Doce River, given its political, environmental, social and economic importance. Biotechnology demonstrates growing potential for its use in processes aimed at recovering a contaminated environment. Bioremediation, an aspect of this macro area, has been presented as a promising, economically viable, easy to operate and non-invasive technique, as it uses living organisms, in general plants and microorganisms, to carry out the degradation of pollutants in the most varied means. The technique has already been applied at a commercial level to treat waste, solid or liquid, and degraded areas (DzioneK et al., \\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e; Sharma et al., \\u003cspan citationid=\\\"CR65\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e; Tufail et al., \\u003cspan citationid=\\\"CR72\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eFungal metabolism has shown to be highly promising when applied in bioremediation processes, among which fungi of the genus \\u003cem\\u003ePleurotus\\u003c/em\\u003e have been commonly reported. These microorganisms have the ability to use a wide range of compounds, often contaminants, as a source of nutrition and energy. They are part of the basidiomycete group, known as mushrooms. They are edible and endemic to tropical and subtropical regions, however, they show remarkable adaptability to growth in different environmental conditions and on different substrates (Felinto, \\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e1999\\u003c/span\\u003e; Santos, \\u003cspan citationid=\\\"CR64\\\" class=\\\"CitationRef\\\"\\u003e2014\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eThe literature indicates that \\u003cem\\u003ePleurotus\\u003c/em\\u003e is capable of acting on the degradation of various recalcitrant compounds, in addition to having properties that facilitate the adsorption of toxic metals, originating from contaminated soils and/or waters and industrial waste (Vimala; Das, \\u003cspan citationid=\\\"CR77\\\" class=\\\"CitationRef\\\"\\u003e2011\\u003c/span\\u003e; Zhao et al., 2016;Yang et al., \\u003cspan citationid=\\\"CR81\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e; Wang et al., \\u003cspan citationid=\\\"CR78\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e; Zhuo et al., \\u003cspan citationid=\\\"CR86\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e; Alouache et al., \\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e; Hadibarata et al., \\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eWith a view to investigating alternatives to minimize the damage caused by the Mariana/MG disaster, the present study aimed to evaluate the effect of three fungal strains of the genus \\u003cem\\u003ePleurotus\\u003c/em\\u003e on the removal of toxic metals, pH variation and turbidity reduction, in water samples from the Doce River in 2015 and 2018, which can contribute to the management and recovery of this ecosystem.\\u003c/p\\u003e\"},{\"header\":\"Material and Methods\",\"content\":\"\\u003cp\\u003eStudy area and water sample collection\\u003c/p\\u003e \\u003cp\\u003eSurface water samples were obtained in 2015 and 2018 from the Middle Doce River region, in the municipality of Governador Valadares/MG. Sampling occurred at two distinct sites: P1, near the water treatment plant at coordinates 18\\u0026ordm;52'55.31\\u0026rdquo; S, 41\\u0026ordm;57'1.86\\u0026rdquo; W, and P2, within the central area of the city at coordinates 18\\u0026ordm;51'22.54\\u0026rdquo; S, 41\\u0026ordm;56'10.55\\u0026rdquo; W (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e). The collection and storage of samples adhered to protocols outlined in the National Guide for Sample Collection and Preservation by CETESB (2011).\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eAcquisition of \\u003cem\\u003ePleurotus\\u003c/em\\u003e sp. Strains\\u003c/p\\u003e \\u003cp\\u003eThree strains of \\u003cem\\u003ePleurotus\\u003c/em\\u003e were provided by the Genetics Laboratory of Microorganisms from the State University of Londrina/Paran\\u0026aacute;. These strains underwent genetic analysis based on amplification of the ITS region (Internal Transcribed Space) of DNA ribosomal at Neoprospecta Microbiome Technologies, where they were identified as \\u003cem\\u003ePleurotus eryngii\\u003c/em\\u003e (ERY strain) and \\u003cem\\u003ePleurotus ostreatus\\u003c/em\\u003e (HI and SB strains). Their sequences were recorded in the GenBank database under accession numbers MT925998, MT925999, and MT926004, respectively.\\u003c/p\\u003e \\u003cp\\u003eCulture maintenance of \\u003cem\\u003ePleurotus\\u003c/em\\u003e sp.\\u003c/p\\u003e \\u003cp\\u003ePeriodic replications were carried out in which a disc approximately 1 cm in diameter from a colonized plate was transferred to a fresh Potato Dextrose Agar (PDA) medium. These fresh plates were then incubated at 28 \\u0026ordm;C until fully colonized and subsequently stored at 4 \\u0026ordm;C.\\u003c/p\\u003e \\u003cp\\u003eTreatment of water samples with \\u003cem\\u003ePleurotus\\u003c/em\\u003e sp.\\u003c/p\\u003e \\u003cp\\u003eEach fungal strain (HI, SB, ERY) was introduced into 125-mL Erlenmeyer flasks containing 30 mL of water from either P1 or P2, using three 1 cm discs (approximately 0.2 g) of fresh biomass. The experiment included samples with 100% river water and those diluted to 50% and 25% with ultrapure water, all conducted in triplicate alongside a control (no fungal inoculation).\\u003c/p\\u003e \\u003cp\\u003eThe flasks were agitated on an orbital shaker at 110 rpm for seven days at an ambient temperature of around 28\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;2 \\u0026ordm;C. Post-incubation, the mycelium was filtered using Whatman filter paper (grade 1:11 \\u0026micro;m) and the water samples stored in 50-mL polypropylene bottles (Falcon) free from metal contamination.\\u003c/p\\u003e \\u003cp\\u003ePhysicochemical analysis\\u003c/p\\u003e \\u003cp\\u003ePhysicochemical parameters, pH and turbidity, were assessed for all samples using a Digimed benchtop pH meter (Model DM-22) and a HACH Turbidimeter (Model 2100), respectively. The findings were evaluated against the benchmarks set by Brazilian Environmental Legislation for Class II waters, as per CONAMA Resolution No. 357/2005, applicable to the Doce River.\\u003c/p\\u003e \\u003cp\\u003eDetermination of toxic metals\\u003c/p\\u003e \\u003cp\\u003eAfter fungal treatment, samples were digested using 2% HNO\\u003csub\\u003e3\\u003c/sub\\u003e in a 1:10 ratio to a final volume of 10 mL, completed with ultrapure water. The samples were then filtered using Whatman filter paper, grade 1:11 \\u0026micro;m. Concentrations of Ag, Al, As, Ba, Be, Bi, Cd, Co, Cr, Cs, Cu, Fe, Hg, In, Li, Mg, Mn, Ni, Pb, Se, Sr, Tl, U, V, and Zn were analyzed using Inductively Coupled Plasma Mass Spectrometry (ICP-MS). The equipment utilized was the NexION 300D model from Perkin Elmer Inc. This instrument was equipped with a Meinhard nebulizer and a quartz cyclonic spray chamber for continuous nebulization. Operating conditions included: nebulizer gas flow at 0.95 L.min\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e; auxiliary gas flow at 1.2 L.min\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e; plasma gas flow at 15 L.min\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e; lens voltage at 7.25 V; ICP RF Power at 1100 W; CeO/Ce ratio of 0.031; and Ba\\u003csup\\u003e++\\u003c/sup\\u003e/Ba\\u003csup\\u003e+\\u003c/sup\\u003e ratio of 0.016. Blanks were prepared using diluted stock solutions (Perkin Elmer Inc.) containing 100 mg.mL\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e of each element. Concentrations of toxic metals were expressed in \\u0026micro;g.L\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e.\\u003c/p\\u003e \\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eData analysis\\u003c/h2\\u003e \\u003cp\\u003eThe analyzed variables included: fungal strain, metal concentration, pH, and turbidity. Descriptive statistical techniques such as mean, standard deviation, and coefficient of variation were employed. To evaluate the effects of fungal strains on physicochemical parameters and metal concentrations, parametric ANOVA tests were conducted, followed by Tukey\\u0026rsquo;s Test (p\\u0026thinsp;\\u0026le;\\u0026thinsp;0.05). Analyses were performed using GNU PSPP software version 1.6.2-g78a33a and Microsoft Excel 2016 MSO version 16.0.4266.1001.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"Results and discussion\",\"content\":\"\\u003cp\\u003ePotential of hydrogen\\u003c/p\\u003e \\u003cp\\u003eIn 2015, control samples (water from Doce River) exhibited pH values ranging from 8.9 to 10.1, while in 2018, these ranged from 7.4 to 8.4 under identical dilution conditions. The 2015 readings suggest an increase in watercourse alkalinity following the disaster, which contradicts the pH range of 6.0 to 9.0 recommended for Class II waters as per CONAMA Resolution No. 357/2005. Overall, the average pH variations between collection sites were negative, indicating that the fungal isolates decreased the pH of the medium, aiming to increase its acidity, as depicted in Figs.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e and \\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e below.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eDespite the discrepancies in pH reduction rates among treatments by different fungal strains, statistical analysis of variance (p\\u0026thinsp;\\u0026le;\\u0026thinsp;0.05) consistently showed that the strains tended to reduce the hydrogen potential of the medium, aiming to make it more acidic, as indicated by the percentages in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e.\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab1\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 1\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eAverage pH reductions induced by \\u003cem\\u003ePleurotus\\u003c/em\\u003e spp. in 2015 and 2018\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"7\\\"\\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=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c7\\\" colnum=\\\"7\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"7\\\" nameend=\\\"c7\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003epH reduction (%)\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eTreatment\\u003c/p\\u003e \\u003cp\\u003e(% of river water)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c4\\\" namest=\\\"c2\\\"\\u003e \\u003cp\\u003e2015\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c7\\\" namest=\\\"c5\\\"\\u003e \\u003cp\\u003e2018\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eERY\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eHI\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eSB\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eERY\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eHI\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eSB\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e25\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e16.6\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e24.0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e34.0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e14.1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e4.9\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e1.7\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e50\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e17.3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e17.4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e16.8\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e3.1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e1.9\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e-1.7\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e100\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e22.9\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e18.5\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e18.1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e5.5\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e2.9\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e2.6\\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\\u003eMoreover, when examining the influence of treatments alone, without comparing \\u003cem\\u003ePleurotus\\u003c/em\\u003e sp. isolates, ANOVA also did not identify significant differences in mean reductions (p-value\\u0026thinsp;=\\u0026thinsp;0.522).\\u003c/p\\u003e \\u003cp\\u003eStatistical analyses revealed significant differences in average pH variations between the years sampled. In 2015, the reductions in hydrogen potential of the treated samples were more pronounced, with an average of 20.64% (p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001). This reduction was approximately five times greater than that observed in the water samples collected in 2018, which showed an average reduction of 3.91% across all treatments and strains evaluated.\\u003c/p\\u003e \\u003cp\\u003eThe wide range of average reductions observed in this study, which varied from 1.7\\u0026ndash;34.0%, supports the findings of Farias (\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e2014\\u003c/span\\u003e), who reported pH changes ranging from 4.5\\u0026ndash;28.5% when assessing the capacity of the fungi \\u003cem\\u003ePenicillium corylophilum\\u003c/em\\u003e to alter the pH of an aqueous solution in tests for removing the toxic metals Ni, Cu, Cr, and Zn. Farias (\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e2014\\u003c/span\\u003e) noted that pH significantly influences the removal of toxic metals by microbial biomasses, as this parameter is able to interfere with the solubility of these elements in solution and their speciation.\\u003c/p\\u003e \\u003cp\\u003eThe work of Zhang et al. (\\u003cspan citationid=\\\"CR84\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e) also corroborates the results obtained in this research. Their studies using the fungus \\u003cem\\u003eAspergillus niger\\u003c/em\\u003e for Pb immobilization demonstrated the ability of the isolate to significantly acidify the environment, reducing the initial pH of the medium from 3.5\\u0026ndash;6.5 to between 2.04 and 2.08.\\u003c/p\\u003e \\u003cp\\u003eOur results indicate that while the majority of the strains evaluated tend to acidify the environment, a tendency to alkalize was also observed, as seen with the SB strain in the 50% treatment (2018 collection), which increased the sample pH by 1.7%. This alkalization capacity was similarly noted by Rani et al. (\\u003cspan citationid=\\\"CR61\\\" class=\\\"CitationRef\\\"\\u003e2014\\u003c/span\\u003e) during their tests with \\u003cem\\u003eAspergillus niger\\u003c/em\\u003e and \\u003cem\\u003ePhanerochaete chrysosporium\\u003c/em\\u003e in the bioremediation of dyes. These results suggest that the ability of filamentous fungi to modify environmental pH is related to the initial pH conditions, temperature, and available nutrients.\\u003c/p\\u003e \\u003cp\\u003eAccording to studies by Roessing (\\u003cspan citationid=\\\"CR62\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e), Bellettini et al. (\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e), and Sultana et al. (\\u003cspan citationid=\\\"CR71\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e), fungi of the genus \\u003cem\\u003ePleurotus\\u003c/em\\u003e prefer to develop in more acidic environments, with pH ranging from 4.0 to 7.0 during the mycelial growth phase and from 3.5 to 5.0 during the basidiocarp growth stage. These studies further highlight that these pH ranges are directly impacted by the species of the genus and their metabolic processes, with fungi generally reducing the pH of their environments through the production of organic acids.\\u003c/p\\u003e \\u003cp\\u003eTurbidity\\u003c/p\\u003e \\u003cp\\u003eTable\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e presents the mean values of turbidity reduction induced by \\u003cem\\u003ePleurotus\\u003c/em\\u003e spp. at sites P1 and P2, from collections carried out in 2015 and 2018.\\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\\u003eAverage turbidity reduction induced by \\u003cem\\u003ePleurotus\\u003c/em\\u003e spp. strains in samples collected from sites P1 and P2, in 2015 and 2018\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"7\\\"\\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=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c7\\\" colnum=\\\"7\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"7\\\" nameend=\\\"c7\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eTurbidity removal (%)\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eTreatment\\u003c/p\\u003e \\u003cp\\u003e(% of river water)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c4\\\" namest=\\\"c2\\\"\\u003e \\u003cp\\u003e2015\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c7\\\" namest=\\\"c5\\\"\\u003e \\u003cp\\u003e2018\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eERY\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eHI\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eSB\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eERY\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eHI\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eSB\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e25\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e83.8\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e86.8\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e79.8\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e19.1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e3.3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e2.6\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e50\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e76.6\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e87.4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e89.3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e20.2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e43.6\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e33.0\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e100\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e86.2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e95.0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e93.5\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e40.7\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e28.1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e39.6\\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\\u003eIn the first collection in 2015, turbidity levels surpassed the standards set by CONAMA Resolution No. 357/2005 for Class II rivers, reaching up to 140,000 UNT following the Mariana/MG disaster, as reported by the National Water Agency (ANA) in 2016. During the tests, turbidity means varied between 789.5 and 2,275 UNT. It was reduced by over 76.6% by the fungal strains, with a maximum removal rate of 95%. Despite this reduction, the results did not meet Brazilian environmental standards. In the subsequent 2018 collections, mean turbidity levels in control groups were 14.9, 25.9, and 70.2 UNT, all within the limits established by CONAMA Resolution No. 357/2005. The highest removal, 72.3%, was achieved by the SB isolate at sampling site P2, without dilution.\\u003c/p\\u003e \\u003cp\\u003eVisual examinations, as depicted in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e, show that the suspended solid particles, previously dissolved in the mixture, predominantly adhered to the fungal mycelium. This interaction between the fungal biomass and suspended solids led to precipitate formation, effectively reducing turbidity levels.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eNo significant differences were observed among \\u003cem\\u003ePleurotus\\u003c/em\\u003e spp. strains in reducing turbidity, regardless of the treatment applied. In 2015, varying proportions of Doce River water in the treatments did not result in significant turbidity reductions. However, in 2018, the treatment with 25% river water showed the least reduction, while the intermediate treatment (50%) and untreated river water exhibited similar reductions.\\u003c/p\\u003e \\u003cp\\u003eThe notable decrease in initial turbidity levels between collections indicates a reduction in the amount of suspended solids, which consequently provided fewer nutrients for the fungi, impacting their growth and effectiveness as biocoagulants. Thus, the results demonstrate that all three fungal strains are capable of significantly reducing turbidity, particularly in samples with high concentrations of suspended solids, eliminating the need for dilution.\\u003c/p\\u003e \\u003cp\\u003eThe collapse of the tailings dam in Mariana/MG drastically affected the turbidity of the Doce River, particularly in the region near Governador Valadares/MG. According to Matos et al. (\\u003cspan citationid=\\\"CR45\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e), turbidity levels surged post-disaster, with a median of 137 UNT in 2015, a stark increase from the pre-disaster level of 14.6 UNT. Although there was a seasonal decrease by 2018, levels remained 4.5 times higher than pre-disaster figures.\\u003c/p\\u003e \\u003cp\\u003eHigher turbidity increases operational costs at Water Treatment Plants, necessitating additional expenditures on chemical products like flocculants and coagulants, as observed in Governador Valadares/MG. Excessive use of these chemicals can leave residues in treated water, posing potential health risks to consumers, as noted by Nimesha et al. (\\u003cspan citationid=\\\"CR52\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e) and Desta and Bote (\\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eIn related studies, Hassan and Obeid (\\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e) achieved an 84% reduction in turbidity using spores from three species of filamentous fungi in wastewater trials. Zainol et al. (\\u003cspan citationid=\\\"CR83\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e) reported turbidity reduction efficiencies of 99.73% and 99.25% using residues from \\u003cem\\u003ePleurotus pulmonaryius\\u003c/em\\u003e culture and mushroom substrate, respectively, under optimal conditions (pH 4.0 and a dose of 5 mg.L\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e of fungal biomass).\\u003c/p\\u003e \\u003cp\\u003eSimilarly, Pardede et al. (\\u003cspan citationid=\\\"CR57\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e) managed to achieve an 84% redSultauction in turbidity in wastewater treated with \\u003cem\\u003ePleurotus ostreatus\\u003c/em\\u003e strains, using a dose of 600 mg.L\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e of the biocoagulant and an agitation rate of 150 rpm. These findings highlight the importance of optimal initial doses for satisfactory results. Based on these insights, this study suggests that \\u003cem\\u003ePleurotus\\u003c/em\\u003e sp. strains have the potential to efficiently reduce environmental turbidity, acting as natural, environmentally friendly, sustainable, low-cost, and easily applied coagulants.\\u003c/p\\u003e \\u003cp\\u003eOther researchers, such as Maas et al. (\\u003cspan citationid=\\\"CR44\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e), Palmiei et al. (2005), Balan and Monteiro (\\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e2001\\u003c/span\\u003e), Hashmi and Saleem (\\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e2013\\u003c/span\\u003e), and Skariyachan et al. (\\u003cspan citationid=\\\"CR69\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e), investigated the ability of \\u003cem\\u003ePleurotus\\u003c/em\\u003e species to decrease turbidity through discoloration and dye degradation tests. The application of species of this fungal genus in reducing vinasse turbidity has also been documented (Ferreira et al., \\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e2011\\u003c/span\\u003e; Silva et al., \\u003cspan citationid=\\\"CR66\\\" class=\\\"CitationRef\\\"\\u003e2015\\u003c/span\\u003e; Vila et al., 2018; Junior et al., \\u003cspan citationid=\\\"CR37\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eThe \\u003cem\\u003ePleurotus\\u003c/em\\u003e sp. strains proved effective in reducing water turbidity using straightforward methods. Their integration with other processes could ensure satisfactory outcomes, considering their economic viability.\\u003c/p\\u003e \\u003cp\\u003eToxic metals\\u003c/p\\u003e \\u003cp\\u003eAmong the metals analyzed, high concentrations of Al, As, Ba, Fe, Mn, and Pb were detected, which were the elements under study. However, ICP-MS was unable to detect concentrations of Ag, Be, Cd, Co, Cr, Cu, Li, Ni, U, V, or Zn. Additionally, the elements Bi, Cs, Hg, In, Se, Sr, and Tl were identified but remained within established thresholds, showing low concentrations. Table\\u0026nbsp;\\u003cspan refid=\\\"Tab3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e provides details on the average concentrations of toxic metals found in raw water samples collected in 2015 and 2018.\\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\\u003eStandards recommended by Conama Resolution No. 357/2005 for Class II waters and average concentration of toxic metals in water samples from Doce River collected in 2015 and 2018\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"4\\\"\\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 \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMetal\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eThreshold\\u003c/p\\u003e \\u003cp\\u003e(\\u0026micro;g.L\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eCollection 1\\u003c/p\\u003e \\u003cp\\u003e(\\u0026micro;g.L\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eCollection 2\\u003c/p\\u003e \\u003cp\\u003e(\\u0026micro;g.L\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e)\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAs\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e10\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e93.4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e4.4\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAl\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e100\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e116,990.6\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e3,070.7\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMn\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e100\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e16,470.2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e122.3\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eFe\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e300\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e250,622.5\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e7,820.8\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePb\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e10\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e944.0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e28.0\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBa\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e700\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2,422.6\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e455.6\\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\\u003eVariations in metal concentrations observed across different sampling events can be attributed to the seasonal dynamics of the Doce River, particularly influenced by rainfall patterns that mobilize deposited tailings sludge. Table\\u0026nbsp;\\u003cspan refid=\\\"Tab4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e details the average percentage reductions of toxic metals at sites P1 and P2 for each dilution treatment, demonstrating significant reductions for the six metals analyzed.\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab4\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 4\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eAverage percentage reduction of toxic metals in Doce River water samples treated with \\u003cem\\u003ePleurotus\\u003c/em\\u003e spp. strains at 25%, 50%, and 100% dilutions for the years 2015 and 2018\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"13\\\"\\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=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c7\\\" colnum=\\\"7\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c8\\\" colnum=\\\"8\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c9\\\" colnum=\\\"9\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c10\\\" colnum=\\\"10\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c11\\\" colnum=\\\"11\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c12\\\" colnum=\\\"12\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c13\\\" colnum=\\\"13\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"13\\\" nameend=\\\"c13\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003e25% treatment\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" morerows=\\\"1\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eMetal\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"6\\\" nameend=\\\"c8\\\" namest=\\\"c3\\\"\\u003e \\u003cp\\u003e2015\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"5\\\" nameend=\\\"c13\\\" namest=\\\"c9\\\"\\u003e \\u003cp\\u003e2018\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c4\\\" namest=\\\"c3\\\"\\u003e \\u003cp\\u003eERY\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c6\\\" namest=\\\"c5\\\"\\u003e \\u003cp\\u003eHI\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c8\\\" namest=\\\"c7\\\"\\u003e \\u003cp\\u003eSB\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c10\\\" namest=\\\"c9\\\"\\u003e \\u003cp\\u003eERY\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c12\\\" namest=\\\"c11\\\"\\u003e \\u003cp\\u003eHI\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c13\\\"\\u003e \\u003cp\\u003eSB\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eAs\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c4\\\" namest=\\\"c3\\\"\\u003e \\u003cp\\u003e93.2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c6\\\" namest=\\\"c5\\\"\\u003e \\u003cp\\u003e95.6\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c8\\\" namest=\\\"c7\\\"\\u003e \\u003cp\\u003e93.7\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c10\\\" namest=\\\"c9\\\"\\u003e \\u003cp\\u003e43.4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c12\\\" namest=\\\"c11\\\"\\u003e \\u003cp\\u003e40.6\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c13\\\"\\u003e \\u003cp\\u003e79.9\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eAl\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c4\\\" namest=\\\"c3\\\"\\u003e \\u003cp\\u003e97.9\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c6\\\" namest=\\\"c5\\\"\\u003e \\u003cp\\u003e99.3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c8\\\" namest=\\\"c7\\\"\\u003e \\u003cp\\u003e99.8\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c10\\\" namest=\\\"c9\\\"\\u003e \\u003cp\\u003e87.9\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c12\\\" namest=\\\"c11\\\"\\u003e \\u003cp\\u003e93.5\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c13\\\"\\u003e \\u003cp\\u003e98.4\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eMn\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c4\\\" namest=\\\"c3\\\"\\u003e \\u003cp\\u003e96.0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c6\\\" namest=\\\"c5\\\"\\u003e \\u003cp\\u003e97.2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c8\\\" namest=\\\"c7\\\"\\u003e \\u003cp\\u003e94.1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c10\\\" namest=\\\"c9\\\"\\u003e \\u003cp\\u003e85.7\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c12\\\" namest=\\\"c11\\\"\\u003e \\u003cp\\u003e81.4\\u003c/p\\u003e 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namest=\\\"c6\\\"\\u003e \\u003cp\\u003eSB\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c9\\\" namest=\\\"c8\\\"\\u003e \\u003cp\\u003eERY\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c11\\\" namest=\\\"c10\\\"\\u003e \\u003cp\\u003eHI\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c13\\\" namest=\\\"c12\\\"\\u003e \\u003cp\\u003eSB\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAs\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c3\\\" namest=\\\"c2\\\"\\u003e \\u003cp\\u003e94.8\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c5\\\" namest=\\\"c4\\\"\\u003e \\u003cp\\u003e95.9\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c7\\\" namest=\\\"c6\\\"\\u003e \\u003cp\\u003e94.4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c9\\\" namest=\\\"c8\\\"\\u003e \\u003cp\\u003e81.3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c11\\\" namest=\\\"c10\\\"\\u003e \\u003cp\\u003e80.1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c13\\\" namest=\\\"c12\\\"\\u003e \\u003cp\\u003e83.2\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAl\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c3\\\" namest=\\\"c2\\\"\\u003e \\u003cp\\u003e95.9\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c5\\\" namest=\\\"c4\\\"\\u003e \\u003cp\\u003e98.4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c7\\\" namest=\\\"c6\\\"\\u003e \\u003cp\\u003e91.1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c9\\\" namest=\\\"c8\\\"\\u003e \\u003cp\\u003e91.3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c11\\\" namest=\\\"c10\\\"\\u003e \\u003cp\\u003e97.4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c13\\\" namest=\\\"c12\\\"\\u003e \\u003cp\\u003e94.7\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMn\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c3\\\" namest=\\\"c2\\\"\\u003e \\u003cp\\u003e97.2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c5\\\" namest=\\\"c4\\\"\\u003e \\u003cp\\u003e97.9\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c7\\\" namest=\\\"c6\\\"\\u003e \\u003cp\\u003e95.8\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c9\\\" namest=\\\"c8\\\"\\u003e \\u003cp\\u003e68.9\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c11\\\" namest=\\\"c10\\\"\\u003e \\u003cp\\u003e75.8\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c13\\\" namest=\\\"c12\\\"\\u003e \\u003cp\\u003e92.2\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eFe\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c3\\\" namest=\\\"c2\\\"\\u003e \\u003cp\\u003e98.7\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c5\\\" namest=\\\"c4\\\"\\u003e \\u003cp\\u003e99.5\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c7\\\" namest=\\\"c6\\\"\\u003e \\u003cp\\u003e98.5\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c9\\\" namest=\\\"c8\\\"\\u003e \\u003cp\\u003e85.0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c11\\\" namest=\\\"c10\\\"\\u003e \\u003cp\\u003e91.5\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c13\\\" namest=\\\"c12\\\"\\u003e \\u003cp\\u003e90.6\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePb\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c3\\\" namest=\\\"c2\\\"\\u003e \\u003cp\\u003e99.3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c5\\\" namest=\\\"c4\\\"\\u003e \\u003cp\\u003e99.8\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c7\\\" namest=\\\"c6\\\"\\u003e \\u003cp\\u003e99.7\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c9\\\" namest=\\\"c8\\\"\\u003e \\u003cp\\u003e97.0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c11\\\" namest=\\\"c10\\\"\\u003e \\u003cp\\u003e98.8\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c13\\\" namest=\\\"c12\\\"\\u003e \\u003cp\\u003e98.0\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBa\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c3\\\" namest=\\\"c2\\\"\\u003e \\u003cp\\u003e98.0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c5\\\" namest=\\\"c4\\\"\\u003e \\u003cp\\u003e98.2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c7\\\" namest=\\\"c6\\\"\\u003e \\u003cp\\u003e97.7\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c9\\\" namest=\\\"c8\\\"\\u003e \\u003cp\\u003e94.6\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c11\\\" namest=\\\"c10\\\"\\u003e \\u003cp\\u003e95.0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c13\\\" namest=\\\"c12\\\"\\u003e \\u003cp\\u003e95.5\\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\\u003eIn the initial 2015 collection, although the ERY, HI, and SB strains achieved high average reductions in the 50% and 100% Doce River water treatments, the concentrations of Al, Mn, and Fe did not meet the limits set by environmental regulations, likely due to their exceptionally high initial concentrations. In the subsequent 2018 collection, raw water analyses of the control group showed that As and Ba levels complied with standards for Class II rivers. However, pre-treatment concentrations of Al, Mn, Fe, and Pb remained above the regulatory thresholds. Despite fungal treatment reducing concentrations significantly, with average reductions exceeding 85% across all three strains, the undiluted samples still had Al and Fe levels above acceptable limits. In contrast, the ERY, HI, and SB treatments to 25% and 50% river water managed to reduce concentrations to within the limits specified by CONAMA Resolution No. 357/2005. Table\\u0026nbsp;\\u003cspan refid=\\\"Tab5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003e summarizes the outcomes for toxic metal concentration reductions after treatment with \\u003cem\\u003ePleurotus\\u003c/em\\u003e sp. across both sampled years.\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab5\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 5\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eAverage percentage reduction of toxic metals in waters from Doce River induced by \\u003cem\\u003ePleurotus\\u003c/em\\u003e spp. strains in samples collected in 2015 and 2018\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"10\\\"\\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=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c7\\\" colnum=\\\"7\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c8\\\" colnum=\\\"8\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c9\\\" colnum=\\\"9\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c10\\\" colnum=\\\"10\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e \\u003cp\\u003eMetal\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"9\\\" nameend=\\\"c10\\\" namest=\\\"c2\\\"\\u003e \\u003cp\\u003eTreatment (% of river water)\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c4\\\" namest=\\\"c2\\\"\\u003e \\u003cp\\u003e25\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c7\\\" namest=\\\"c5\\\"\\u003e \\u003cp\\u003e50\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c10\\\" namest=\\\"c8\\\"\\u003e \\u003cp\\u003e100\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eERY\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eHI\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eSB\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eERY\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eHI\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eSB\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003eERY\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003eHI\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003eSB\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAs\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e68.3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e68.1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e86.8\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e80.5\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e85.4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e85.1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e88.1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e88.0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e88.8\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAl\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e92.9\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e96.4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e99.1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e91.4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e96.8\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e98.1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e93.6\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e97.9\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e92.9\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMn\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e90.9\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e89.3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e90.5\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e81.3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e93.6\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e90.7\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e83.0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e86.8\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e94.0\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eFe\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e95.6\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e97.0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e99.7\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e98.6\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e99.5\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e99.4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e91.9\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e95.5\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e94.6\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePb\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e99.6\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e99.8\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e98.7\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e98.0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e99.6\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e99.5\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e98.1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e99.3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e98.8\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBa\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e97.8\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e98.1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e97.6\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e95.5\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e96.9\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e96.5\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e96.3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e96.6\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e96.6\\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\\u003eOverall, the \\u003cem\\u003ePleurotus eryngii\\u003c/em\\u003e (ERY) and \\u003cem\\u003ePleurotus ostreatus\\u003c/em\\u003e (HI, SB) strains efficiently reduced the concentrations of the six evaluated metals. Analyzing the average reductions across 2015 and 2018 collections, the maximum reductions were noted for As (88.8%), Al (99.1%), Mn (94.0%), Fe (99.7%), Pb (99.8%), and Ba (98.3%). Analysis of variance revealed no significant differences in reduction rates between the ERY, HI, and SB strains (p-value\\u0026thinsp;=\\u0026thinsp;0.299), nor between the treatment dilutions (p-value\\u0026thinsp;=\\u0026thinsp;0.892), indicating that the percentage reductions are independent of the initial dilution of the samples. This consistency was evident from statistical analyses conducted for each collection separately.\\u003c/p\\u003e \\u003cp\\u003eTests demonstrate that fungal strains are effective even in high metal concentrations, highlighting the reliability of the reduction means across different collections, irrespective of initial metal levels, treatments, or \\u003cem\\u003ePleurotus\\u003c/em\\u003e sp. strains used.\\u003c/p\\u003e \\u003cp\\u003eIt was possible to compare mean reductions between the metals tested for all treatments, regardless of the fungal strain, in each collection (see Table\\u0026nbsp;\\u003cspan refid=\\\"Tab6\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003e). This comparison suggests potential natural preferences among the strains for assimilating specific elements.\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab6\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 6\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eAverage percentage reduction in concentrations of elements induced by \\u003cem\\u003ePleurotus\\u003c/em\\u003e spp. strains, in the 2015 and 2018 collections\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"7\\\"\\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=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c7\\\" colnum=\\\"7\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eElement\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eAs\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eAl\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eMn\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eFe\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003ePb\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eBa\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCollection 1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e94.0\\u003csup\\u003ea\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e96.8\\u003csup\\u003eab\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e95.2\\u003csup\\u003ea\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e98.1\\u003csup\\u003eb\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e99.1\\u003csup\\u003eb\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e96.9\\u003csup\\u003eab\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCollection 2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e70.3\\u0026ordf;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e94.1\\u003csup\\u003ec\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e82.6\\u003csup\\u003eb\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e95.6\\u003csup\\u003ec\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e99.0\\u003csup\\u003ec\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e97.0\\u003csup\\u003ec\\u003c/sup\\u003e\\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\\u003eCommon letters in the same row indicate means without significant statistical differences, for each collection, using Tukey\\u0026rsquo;s test at a 5% significance level (p\\u0026thinsp;\\u0026le;\\u0026thinsp;0.05).\\u003c/p\\u003e \\u003cp\\u003eIn the 2015 collection, there was no significant difference in removal efficiency across different elements. However, the 2018 collection showed that As and Mn were removed less efficiently compared to other metals, with elements such as Al, Fe, Pb, and Ba consistently achieving removal rates above 94.0%. In mixtures of metals, the variation in metal removal efficiency is influenced by their affinity for fungal biomass and interactions among metal ions, including antagonism, synergism, or non-interaction. Hoque and Fritscher (2019) suggest that these phenomena can explain the observed discrepancies in the reduction of toxic metals.\\u003c/p\\u003e \\u003cp\\u003eNumerous studies have explored the remediation potential of toxic metals using filamentous fungi, particularly \\u003cem\\u003ePleurotus\\u003c/em\\u003e sp. isolates (Adebayo, \\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e2013\\u003c/span\\u003e; Stanley et al., \\u003cspan citationid=\\\"CR70\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e; Manna et al., \\u003cspan citationid=\\\"CR41\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e; Ferreira et al., \\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e; Coelho et al., \\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e; Mariconi et al., \\u003cspan citationid=\\\"CR42\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e; Bhatnagar et al., \\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e; Vacar et al., \\u003cspan citationid=\\\"CR73\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e; Xu et al., \\u003cspan citationid=\\\"CR80\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e). However, most of these studies have been limited to evaluating the removal of up to three metals simultaneously, which does not fully capture the complexity of real-world contamination scenarios (Bhattacharya et al., \\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e). This limitation often stems from the challenges posed by the toxicity of multi-metal mixtures, which can reduce the efficacy of fungal species in removing metals from solutions (Chahdi et al., \\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eIn contrast, some researchers advocate for experiments involving metal mixtures to yield more representative results. Bhattacharya et al. (\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e) studied the potential of \\u003cem\\u003eAspergillus fumigatus\\u003c/em\\u003e to reduce concentrations of toxic metals in a hexa-metal system, observing significant removals: Pb (80%), Cr (43%), Cu (98%), Zn (78%), Ni (99%), and Cd (99%). Their findings indicated that the fungus simultaneously utilized biosorption and bioaccumulation mechanisms, where metals were adsorbed onto the surface of the fungal biomass and within the cells, respectively. Similarly, Hoque and Fritscher (2019) reported removal efficiencies between 81\\u0026ndash;99% for metals such as Al, Cd, Co, Cr, Cu, Hg, Ni, Pb, U, and Zn when tested simultaneously with the fungus \\u003cem\\u003eMucor hiemalis\\u003c/em\\u003e in synthetic wastewater.\\u003c/p\\u003e \\u003cp\\u003eIn their study on the genus \\u003cem\\u003ePleurotus\\u003c/em\\u003e, Wu et al. (\\u003cspan citationid=\\\"CR79\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e) reported that \\u003cem\\u003ePleurotus eryngii\\u003c/em\\u003e removed up to 92.17% of Mn from aqueous solutions after 15 days of incubation, supporting the findings of the present study. Aguilar et al. (\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e) assessed the capability of \\u003cem\\u003ePleurotus ostreatus\\u003c/em\\u003e to remove metals from aqueous solutions, finding the highest removal efficiencies for Pb (75%), followed by Cr (42%) and Cd (2.25%). Their results for lead corroborate those of our research.\\u003c/p\\u003e \\u003cp\\u003eGeorgescu et al. (\\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e) also evaluated \\u003cem\\u003eP. ostreatus\\u003c/em\\u003e as a Cd biosorbent, achieving a maximum biosorption efficiency of 78% for 0.5 mg.L Cd\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e. Singh et al. (\\u003cspan citationid=\\\"CR68\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e) studied the application of \\u003cem\\u003eP. florida\\u003c/em\\u003e in multi-metal solutions, observing maximum reductions of 52.10% for Pb, 99.84% for Cd, 70.85% for Cr, 77.77% for Ni, 42.63% for Mn, and 76.23% for Zn.\\u003c/p\\u003e \\u003cp\\u003eContrary to our findings, where metal reduction rates in solution were unaffected by dilution treatments, Vaseem et al. (\\u003cspan citationid=\\\"CR75\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e) noted significant differences in treatments with 25% and 50% dilution compared to raw effluent in their study of coal washing effluents in India. The authors found that \\u003cem\\u003eP. ostreatus\\u003c/em\\u003e was more effective in 50% diluted effluent, achieving reduction rates of 57.2%, 82.6%, 98.0%, 99.9%, 99.3%, 99.1%, 89.2%, and 35.6% for Mn, Zn, Ni, Cu, Co, Cr, Fe, and Pb, respectively.\\u003c/p\\u003e \\u003cp\\u003eMuhammad and Sukor (\\u003cspan citationid=\\\"CR50\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e) examined the capacity of \\u003cem\\u003ePleurotus ostreatus\\u003c/em\\u003e to reduce heavy metal concentrations in synthetic solutions and chemical waste samples, with removal efficacy ranked as Fe\\u0026thinsp;\\u0026gt;\\u0026thinsp;Pb\\u0026thinsp;\\u0026gt;\\u0026thinsp;Cu\\u0026thinsp;\\u0026gt;\\u0026thinsp;Zn. However, the removal rates were notably lower compared to those in real samples: Fe at 17.02%, Cu at 46.6%, Pb at 76%, and Zn at 17.88%. These differences may be attributed to organic materials and other toxic metals competing for binding sites on the fungal biomass surface (Kovadevic et al., 2000).\\u003c/p\\u003e \\u003cp\\u003eMota (\\u003cspan citationid=\\\"CR49\\\" class=\\\"CitationRef\\\"\\u003e2015\\u003c/span\\u003e) notes that fungal cell surfaces carry a negative charge, enhancing their interaction with metal ions. Earlier studies by Kapoor and Viraraghavan (1995) and Brady (\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e1996\\u003c/span\\u003e) highlight that binding potential correlates with the ionic radii and free valence orbitals of each ion, with smaller ions such as Al\\u0026sup3;⁺ and Fe\\u0026sup3;⁺ often removed more efficiently in high-metal-concentration samples.\\u003c/p\\u003e \\u003cp\\u003eResearch indicates that various \\u003cem\\u003ePleurotus\\u003c/em\\u003e species possess different functional groups in their cell walls\\u0026mdash;hydroxyl, carboxylic, amino, and phosphate\\u0026mdash;, favoring metal biosorption (Joo et al., \\u003cspan citationid=\\\"CR36\\\" class=\\\"CitationRef\\\"\\u003e2011\\u003c/span\\u003e; Javaid et al., \\u003cspan citationid=\\\"CR35\\\" class=\\\"CitationRef\\\"\\u003e2011\\u003c/span\\u003e). However, it is important to recognize that, in addition to electrostatic affinities and intrinsic microbial characteristics, environmental conditions critically influence the mechanism and efficiency of sorption (Mota, \\u003cspan citationid=\\\"CR49\\\" class=\\\"CitationRef\\\"\\u003e2015\\u003c/span\\u003e; Pande et al., \\u003cspan citationid=\\\"CR56\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eThus, the \\u003cem\\u003ePleurotus\\u003c/em\\u003e strains tested in this study (ERY, HI, SB) showed metal reduction capabilities consistent with previous literature findings. The results suggest these strains remarkably manage multi-metal mixtures in real samples, attributable to their intrinsic properties and the complex interplay of environmental conditions and sorption mechanisms.\\u003c/p\\u003e\"},{\"header\":\"Conclusion\",\"content\":\"\\u003cp\\u003eThis study examined the performance of three \\u003cem\\u003ePleurotus\\u003c/em\\u003e strains in treating water from the Doce River post the 2015 disaster and again in 2018. No significant differences were detected between the strains. Regarding pH, all strains tended to acidify the water, with greater reductions observed in 2015 than in 2018. In terms of turbidity, the strains were more effective in undiluted samples, particularly in 2015. When assessing toxic metals, the strains displayed consistent ability to reduce concentrations in both 2015 and 2018. These findings demonstrate the potential of \\u003cem\\u003ePleurotus\\u003c/em\\u003e strains for bioremediation, particularly for the waters of the Doce River following the 2015 disaster.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eConflict of interest:\\u003c/strong\\u003e The authors have no conflicts of interest to declare that are relevant to the content of this article.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAcknowledgements\\u0026nbsp;\\u003c/strong\\u003eThe authors are grateful to \\u0026nbsp;FAPEMIG (Funda\\u0026ccedil;\\u0026atilde;o de Amparo \\u0026agrave; Pesquisa do Estado de Minas Gerais) for financial support.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAuthor contribution\\u0026nbsp;\\u003c/strong\\u003eAll authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by M. P.O. Santos and A. S. Van der Maas. Formal analysis and investigation of metals concentrations were performed by J. L. Rodrigues. \\u0026nbsp; The statistical analyses were performed by D. F. Jardim. M. P. O. Santos and A. S. van der Maas wrote the main manuscript text. C. A. Bomfeti supervised the experiments, edited and reviewed the manuscript. All authors read and approved the final manuscript.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eEthical approval\\u0026nbsp;\\u003c/strong\\u003eThis manuscript has not been published elsewhere in part or in entirety and is not under consideration by another journal. We have read and understood your journal\\u0026rsquo;s policies, and we believe that neither the manuscript nor the study violates any of these\\u003cstrong\\u003e.\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConflict of interest\\u003c/strong\\u003e The authors have no conflicts of interest to declare that are relevant to the content of this article.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eFunding \\u0026nbsp;\\u003c/strong\\u003eThere was no financing\\u003cstrong\\u003e.\\u003c/strong\\u003e\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\n \\u003cli\\u003eAdebayo, A. O (2013). Investigation on \\u003cem\\u003ePleurotus ferulae\\u003c/em\\u003e potential for the sorption of pb(ii) from aqueous solution. \\u003cem\\u003eBulletin of the Chemical Society of Ethiopia\\u003c/em\\u003e, v. 27, n. 1, p. 25-34, http://dx.doi.org/10.4314/bcse.v27i1.3\\u003c/li\\u003e\\n \\u003cli\\u003eAna (2016). 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Https://doi.org/10.1016/j.chemosphere.2019.06.113\\u003c/li\\u003e\\n\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":true,\"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\":\"info@researchsquare.com\",\"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\":\"Toxic metal. Fungus. Samarco. Fundão.\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-4335579/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-4335579/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eThe collapse of Samarco mining company's tailings dam in November 2015 is recorded as the most severe environmental calamity to ever occur in Brazil. The ensuing toxic sludge devastated towns along the Doce River, leaving an enduring legacy of socio-environmental and economic destruction. The Doce River basin continues to bear the scars of this disaster to this day. This study thus proposes to investigate the bioremediation potential of three \\u003cem\\u003ePleurotus\\u003c/em\\u003espp. strains, assessing their physical-chemical parameters of pH and turbidity, as well as the ability of the tested isolates to eliminate toxic metals in solution, based on water samples collected in 2015 and 2018. The studied fungal strains tended to acidify the medium, lowering the pH of the samples. In raw water, these strains effectively removed turbidity, achieving a decline of 86.2-95.0% and 28.1-40.7% in the first and second collections, respectively. High concentrations of aluminum, arsenic, barium, lead, iron, and manganese were detected in the Doce River water samples through Inductively Coupled Plasma Mass Spectrometry (ICP-MS). Batch system experiments showed that \\u003cem\\u003ePleurotus\\u003c/em\\u003e spp. could efficiently remove toxic metals from the solution in seven days of incubation, with average elimination rates ranging from 94.0-99.1% and 70.3-99.0% for the six evaluated elements in the 2015 and 2018 samples, respectively. Therefore, the findings suggest that the genus \\u003cem\\u003ePleurotus\\u003c/em\\u003eholds significant biotechnological promise for the bioremediation of contaminated or deteriorated waters.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Application of fungal biomass of the genus Pleurotus in the bioremediation of Doce River waters after the crime disaster in Mariana/MG: an analysis between the years 2015 and 2018\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2024-06-05 11:40:47\",\"doi\":\"10.21203/rs.3.rs-4335579/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"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\":\"7cbb469c-017f-4d30-abe6-6639d2ef4091\",\"owner\":[],\"postedDate\":\"June 5th, 2024\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2024-08-08T18:27:08+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2024-06-05 11:40:47\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-4335579\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-4335579\",\"identity\":\"rs-4335579\",\"version\":[\"v1\"]},\"buildId\":\"cBFmMYwuxLRRLfASyISRj\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}