Characterization of the treatment units and their microbial communities in a waste stabilization pond system treating wastewater from an industrial complex located in Northeastern Brazil

Characterization of the treatment units and their microbial communities in a waste stabilization pond system treating wastewater from an industrial complex located in Northeastern Brazil | 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 Characterization of the treatment units and their microbial communities in a waste stabilization pond system treating wastewater from an industrial complex located in Northeastern Brazil Luiz Pereira Silva Júnior, Bruna Kelly de Oliveira Silva, Nathália Bandeira Carvalho dos Santos, and 10 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6770575/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 6 You are reading this latest preprint version Abstract The Multifactory Wastewater Treatment Plant (MF-WWTP) in Recife, Brazil, receives effluents from 15 different industries as well as domestic waste and, unlike other facilities, has never undergone sludge removal over 20 years of operation. It allowed the establishment of a highly adapted microbial community and a high level of waste removal. This study investigated its physicochemical characteristics and how the microbial composition may contribute to its efficiency. The results indicated a COD and BOD removal of 84%, primarily occurring in Pond 1 (P1, anaerobic), while Pond 3 (P3, aerobic with high algal activity) aided in heavy metal removal. Despite this efficiency, toxicity persisted in the final effluent, evidenced by mitotic index values (9.20–11.44% vs. 6.79% control) and chromosomal alterations (1.49–2.20%), which could be a result of toxin-producing algae in P3. The microbial analysis identified Fervidobacterium as the dominant bacterial genus up to 38% of relative abundance, alongside methanogenic archaea ( Methanolinea and Methanosaeta ), suggesting their importance for the organic substrate conversion and degradation of industrial pollutants. Additionally, the cyanobacteria Cyclotella and the microalgae Planktothrix were highly abundant in P3 (10 mm³.L -1 and 9 mm³.L -1 , respectively), which might have contributed to the treatment system but also to potential toxin production in the final effluent. These findings suggest that the combination of anaerobic, microaerobic, and aerobic ponds, along with additional polishing units, can be a viable approach for industrial wastewater treatment. Furthermore, both the sludge and final effluent from MF-WWTP could serve as valuable inoculum sources for other treatment units dealing with complex industrial contaminants, aiding in establishing resilient microbial communities for optimized wastewater treatment. industrial wastewater waste stabilization ponds Fervidobacterium Cyclotella Planktothrix microbial community algal community Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Research highlights Serial anaerobic-microaerobic-facultative ponds provided good treatment performance. Fervidobacterium was the most abundant genus and might be a key for the treatment. Abundance and diversity of microalgae and cyanobacteria in ponds 2 and 3, with Cyclotella and the cyanobacteria Planktothrix with the greatest biovolume in P3 Introduction The increase of urban sprawl, deforestation, and agricultural land area as a result of the increase of anthropogenic land use is, nowadays, the main driver for the global decline of water quality (Mello et al. 2020; Giri Qiu 2016). According to the census performed in 2024, Brazil has over 210 million inhabitants, making the country the most populous in South America and the 7th in the world. Almost 80% of the Brazilian population lives in urban areas, while only 63% of Brazilian households have access to sanitary sewers or septic tanks (IBGE 2023 ). The Environmental Quality Report published in 2020 by the Brazilian Institute of Environment and Renewable Natural Resources ( Instituto Brasileiro de Meio Ambiente e dos Recursos Naturais Renováveis - IBAMA) indicated that the dumping of raw or partially treated domestic and industrial wastewater in the environment is the major source of contamination of water sources in Brazil. Regarding the domestic wastewater treatment, there were 2768 operational urban wastewater treatment plants in Brazil in 2017, responsible for treating 51% of the produced wastewater. The majority of those treatment plants are focusing on organic matter and pathogens removal, with an average of BOD removal ranging from 60 to 80% (ANA 2017 ; IBGE 2023 ; SNIS 2022). Thus, there is an urgent need for the expansion of treatment capacity, with the installation of new plants and also the expansion of the current working plants to implement nutrient removal units, and for the improvement of their performance. Studying the treatment plants that show good performance is also important, because it would give important information for the design of new plants and the improvement of poorly performing installed ones. Waste stabilization ponds (WSP) are the most used treatment processes in Brazil, representing 36% of the operational plants (anaerobic, aerobic, facultative, maturation ponds and their combination). While high in number, those plants only account for the removal of 21% of the equivalent population, indicating that they are not fully operating in highly dense urban areas (ANA 2017 ). Meanwhile, WSP became popular also in rural areas of Brazil, because of the favorable environmental conditions, land area availability, and the low maintenance required (Von Sperling 2017 ). Stabilization ponds are designed to receive domestic or industrial wastewater where it stays for days, allowing bacteria (aerobic in facultative and aerated ponds, or anaerobic bacteria in anaerobic ponds) to degrade the soluble organic compounds and fine particulate matter suspended in the liquid, while the large particulate organic matter settles and are stabilized specifically by anaerobic bacteria. The oxygen required for the aerobic bacteria in facultative ponds is supplied by algae through photosynthesis. In some cases, the ponds are designed to be shallow, allowing for enough light penetration to aid in the combat against pathogens. This type of treatment represents a viable economic alternative since environmental conditions, such as high temperature and long periods of light intensity, are favorable to the biological treatment of wastewater in tropical countries (Espinosa et al. 2017; Bressani-Ribeiro et al. 2019 ; Mahapatra et al. 2022 ). In those WSP ponds, a consortium of bacteria, archaea, and microalgae is established to work in synergy to biodegrade the domestic and industrial wastewater (Peil et al. 2016 ; Chai et al. 2021 ). Together, these organisms harbor an arsenal of molecular tools, such as biodegradation enzymes, that produce a gradient of metabolization in which the product of an enzyme from a microorganism might be the substrate for the next one. It creates a flux of nutrients that feeds the trophic chain and that favors their adaptation to such hazardous environments (Tatta et al. 2022 ). The Pernambuco State Sanitation Company ( Companhia Pernambucana de Saneamento - Compesa) operates a Multifactory Wastewater Treatment Plant (MF-WWTP), located in the municipality of Jaboatão dos Guararapes, Recife metropolitan area, Pernambuco (Brazil), that is responsible for the treatment of a complex effluent wastewater of the Multifactory Industrial Complex (MIC), from baking, dairy, metallurgy, laundry, and ink, among other industrial sectors. The MF-WWTP project was originally designed to work with two facultative ponds, followed by a maturation pond, and to solely receive domestic sewage. However, over time, following the industrialization of the area, industrial wastewater was also generated and mixed with the effluent. Nowadays, this wastewater is mainly composed of organic degradable compounds (carbonaceous organic matter) and recalcitrant industrial products such as dyes, hydrocarbons, and surfactants, as well as different types of metals. Despite this complexity, its ponds remained stable over the years and, according to Compesa, they are capable of removing more than 80% of the organic matter measured as carbon oxygen demand (COD), which stands as a highly efficient treatment unit regarding the pond system. In terms of the legislation, the output of the MF-WWTP has to comply with the parameters of the current resolution of the Environmental National Council (CONAMA 430/2011), with monthly measurement of the influent and the final effluent (COD, ammonium, solids, and pathogens). Considering the good overall performance of the MF-WWTP in terms of COD removal (80%), this work aimed to provide a deeper comprehensive evaluation of such a unique system in terms of (1) physicochemical evaluation of each treatment unit (2) microbial community structure throughout the treatment process, (3) and the ecotoxicity and cyto-genotoxicity of the system. It may reveal the complexity of its chemical and biological interactions in a way that could contribute to the technological development for replication or transference to other treatment units. Materials and methods Site description This research was conducted at the MF-WWTP, which started operating in the early 1990s and is currently co-managed under a public-private agreement involving the public company Compesa and the private company BRK Ambiental. This unit is located in the municipality of Jaboatão dos Guararapes (8°06'23.1"S 35°01'36.0" W), within the Recife metropolitan area, state of Pernambuco, Brazil. It is part of the Jaboatão industrial complex encompassing 15 segments, including food industry, metallurgy, machining, refrigeration, stationery, carpentry, graphic products, inks and solvents, galvanized products, polymers, among others (Fig. 1 ). In addition, the domestic sewage of the industries is also integrated into the collection network, making the effluent that arrives at the MF-WWTP a complex and diverse substrate. The unit comprises the stabilization Pond 1 (P1), Pond 2 ((P2) and Pond 3 (P3), each one with 2 m deep in average (Fig. 1 ). The largest pond P1 has 1,456 m 2 of surface area, characterized by a thick black surface layer composed predominantly of oils and greases. It receives the complex effluent in a highly varying flow rate, with averages of 3.4 L s − 1 and eventual peaks of 44 L s − 1 . The P2 pond has 910 m 2 of area and presents little oily coverage, while the P3 has 1,740 m 2 of surface area and already has the presence of some aquatic plants. The connection between P1 and P2 and between P2 and P3 is carried out by pipes of 200 mm in diameter, located from the surface downwards in the water layer. From the latter, treated water is discharged into the Jaboatão River in that municipality (Fig. 1 ). For this study, samples were collected at the inlet and the outlet of each pond and at internal points, as marked in Fig. S1 (Supplementary material). Sampling procedure Samples were collected during the spring (November) of 2021 and summer (February) of 2022 according to the scheme in Fig. S1 (Supplementary material), both during the dry season with monthly precipitation averages of 28.0 ± 21.9 mm and 90.6 ± 28.7 mm, respectively (Table S1 , Supplementary material). For physicochemical analysis, wastewater samples were collected in four points (Fig. S1 ): (1) the untreated wastewater sampling point 13 is located in the Parshall flume at the entrance of the treatment plant; (2) the P1-treated wastewater sampling point 14 located in the pipe that connects P1 and P2; (3) the P2-treated wastewater sampling point 15 located in the pipe that connects P2 and P3; (4) the P3-treated sampling point 16 located in the outfall pipe of P3 (Table S2 , Supplementary material). For each sampling point, four 1 L polyethylene (PE) bottles were filled, kept on ice, and transported to the laboratories, where some analyses were performed on the same day as recommended by the American Public Health Association - APHA ( 2017 ), and an aliquot of the samples was preserved for further analyses. For algal and bacterial community analysis, liquid and sludge samples were also collected from the three ponds. In P1, samples were taken from four points: two near the entrance, one in the middle near the edge, and one near the pipe connecting P1 and P2. A 3-meter-long extension arm was used since the thick top layer in this pond prevented the use of an inflatable boat. Liquid samples were collected from the surface, while sludge samples were taken from the bottom. In P2, samples were collected from two points: one in the middle of the pond and one near the effluent channel. In P3, samples were taken throughout the pond using an inflatable boat and a Van Dorn sampler (Fig. S1 , Supplementary material). Liquid samples for algal community, ecotoxicity, and cytogenotoxicity analysis were collected from the surface and at a depth of 60 cm, mixed, homogenized, and stored in 500 mL PE bottles on ice. Samples intended for algal community analysis were preserved in 4% formaldehyde before being sent to the laboratory. Sludge samples for bacterial and archaeal community analysis were also stored in PE bottles, kept on ice, and transported to the laboratory for DNA extraction within 24 to 36 hours after collection. Physicochemical analysis Conductivity, pH, Dissolved Oxygen (DO), and Redox Potential (ORP) were measured during the sampling. Biochemical Oxygen Demand (BOD), Chemical Oxygen Demand (COD), alkalinity (total and partial), and heavy metals and semi-metals were performed in the laboratories on the same day of sampling. For COD and BOD, samples were analyzed raw and filtered in a 1.2 µm glass fiber membrane (Satorius, Gottingen, Germany). After sample fractionation, 2 L of the filtered samples were acidified with H 2 SO 4 (40% v/v) to pH 2 and kept in amber glass flasks at 4°C for ammoniacal-nitrogen (N-NH 3 ) and Total Kjehdal Nitrogen (TKN), while another 2 L of filtered samples were stored for maximum of 14 days at 4°C for nitrate (N-NO 3 ), nitrite (N-NO 2 ), color, total and suspended solids analysis. For volatile fatty acids (VFA) analysis, aliquots of 10 mL were stored in 10 mL glass flasks without headspace and kept refrigerated at 4°C and then analyzed by gas chromatography (Agilent Technologies 7890A, Agilent Technologies Inc., Wilmington, DE, USA) coupled with a FID detector, and is described in the Supplementary Materials. Statistical analyses were performed using RStudio to assess normality and homogeneity of variance. Statistical analysis was performed to evaluate the data distribution, either parametric tests (t-test, ANOVA followed by Tukey’s post-hoc test) or non-parametric tests (Kruskal-Wallis and Mann-Whitney) were applied as appropriate. All statistical analyses were conducted using RStudio (Posit Team, 2025 ). Bacterial community analysis Sludge samples (Fig. S.1, Supplementary Materials) were subjected to total DNA extraction using the PowerSoil DNA extraction kit™ (Qiagen, Hilden, Germany), following the protocol recommended by the manufacturer. Total DNA was quantified using the NanoDrop 2000 equipment (Thermo Fisher Scientific, Waltham, USA) and then sent for its 16S rRNA amplicon sequencing at Neoprospecta company (Florianopolis, Brazil), which used a MiSeq Illumina device using metabarcoding applied to the V3/V4 domain of the 16S rRNA gene. Data were processed using Metagenomics Rapid Annotation Using Subsystems Technology (MG-RAST), and the SILVA SSU database was selected for analysis. Relative abundances of bacteria and archaea were calculated separately, and only taxa with a relative abundance greater than 1% were considered relevant to this study. Alpha (α) and beta (β) diversity analyses were performed using the phyloseq package in R, executed via RStudio (McMurdie and Holmes, 2013; McMurdie and Holmes, 2015). Statistical differences in the relative abundances of taxa between sample pairs were assessed using STAMP (Parks et al., 2014), employing a two-sided G-test with Yates’ correction and Fisher’s exact test, along with the Newcombe-Wilson method for calculating confidence intervals. Taxonomic classification data were used for functional inference using FAPROTAX (Louca et al., 2016 ). Identification and counting of phytoplankton Samples for phytoplankton identification were preserved in 4% formaldehyde and stored at 4°C for quantitative and qualitative analysis by optical microscopy (model DMLB, Leica Microsystems GmbH, Germany) following APHA ( 2017 ). The identification was performed at the genus level using the parameters in the specialized literature: Komárek ( 1983 ) and Prescott and Vinyard (1982) for Chlorophyta; Komárek and Anagnostidis ( 1989 ), Komárek and Komáková (2004), and Komárek and Cronberg ( 2001 ) for Cyanophyta; Popovský and Pfiester (1990) for Dinophyta; (Krammer, 1991a , 1991b ) for Heterokontophyta and (John et al., 2002 ) for other phytoflagellates, such as Euglenophyta and Cryptophyta. A minimum of 400 cells of each sample were counted in Sedgwick-Rafter chambers to determine phytoplankton density (cells mL − 1 ), following APHA 2017 ). The mean volume of each species was calculated considering the cellular measurements of 30 individuals, according to the geometric models suggested by the literature (Hillebrand et al. 1999 ), and biomass was expressed in mm 3 L − 1 . The taxonomic classifications were based on the specialized literature (Komárek and Komáková 2004; Oliveira et al. 2015 ). Ecotoxicity using Aliivibrio fischeri The acute toxicity of pond samples was assessed using lyophilized bioluminescent marine bacteria Aliivibrio fischer i ( A. fischeri NRRL B-11177, synonym Vibrio fischeri ), based on the inhibition of light emission when bacterial cells are exposed to toxic conditions. The method followed the Brazilian standardized procedures (ABNT NBR 15411-3) for untreated effluents. A. fischeri cells were exposed to untreated samples from three ponds and a series of dilutions (1:512; 1:256; 1:128; 1:64; 1:32; 1:16; 1:8; 1:4; 1:2; 1:1). Label K represents the reference where samples were replaced with 2% (w/v) NaCl. The positive control included the addition of 3,5-dichlorophenol (3,5-DF; Sigma Aldrich, No.. LRAC5200) at 4.5 mg L − 1 . After 15 minutes of exposure, the suspensions were analyzed for bacterial bioluminescence at the Hydrobiology and Toxicology Laboratory of the State Environment Agency (CPRH). The inhibitory effect of the aqueous samples was evaluated by the reduction in bioluminescence of the suspensions, which defined the Toxicity Factor (TF) according to the criteria established by the standardized procedure. Toxicity and cytogenotoxicity : Allium cepa test system The toxicity and cytogenotoxicity assays were performed using the Allium cepa test system according to Fiskesjö ( 1988 ), Fernandes et al. ( 2007 ), and Leme et al. ( 2008 ). Seeds from A. cepa cv. Vale Ouro IPA-11 variety were germinated on a layer of sterile cotton in Petri dishes covered with filter paper discs moistened with 15 mL of each MF-STS sample. Fifty seeds were placed per dish with three dishes per sample. For negative control, discs were moistened with sterile mineral water. For the genotoxicity analysis positive controls, the discs were moistened with the herbicide Trifluralin (0.84 ppm of active principle) or with the mutagenic agent Methyl Methanesulfonate (MMS, 400 µM). After 72 h of germination, root tips were collected, fixed in Carnoy (ethanol: acetic acid, 3:1) for 24 h at room temperature, and stored at -20 ºC. For the preparation, fixed roots were washed three times in distilled water, 5 min each, hydrolyzed in 1 mol L − 1 HCl at 60°C for 10 min, washed again, and stained with Schiff's Reagent (1.090033, Sigma-Aldrich) for 1 h in a dark room. After this period, the roots were washed in distilled water and the meristem was separated, placed in a slide with a drop of 2% acetic carmine, covered with a coverslip, and lightly crushed. After coverslip removal using liquid nitrogen, the slides were air dried and mounted with Entellan™ solution (Sigma-Aldrich, St Louis, USA). Cytological analyses were carried out using an optical light microscope at a magnification of 400x. Cell images were captured with a Leica DFC 340FX camera, using Leica's CW 4000 program. Images were optimized for brightness and contrast using Adobe Photoshop CS3 (Adobe Systems Incorporated). The toxic potential of the samples was evaluated based on the average Germination percentage (G), calculated by the ratio between the number of seeds germinated after 48 h, 72 h, and 20 days of incubation and the total number of incubated seeds multiplied by 100. On the other hand, the cytotoxic potential of the samples was evaluated by the Mitotic Index (MI) and genotoxicity by the Chromosomal Alteration Index (CAI) in meristematic cells of A. cepa . MI and CAI were obtained from the analysis of 500 meristematic cells per slide, with 10 slides per treatment, totaling 5000 cells per treatment. The MI was calculated as the ratio between the number of cells in division by the total number of surveyed cells. CAI was obtained by the ratio between the number of observed cellular alterations (C-metaphases, nuclear buds, micronuclei, multipolar anaphases, polyploid metaphases, and chromosomal adhesion, losses, delay, breaks, and bridges) and the total number of surveyed cells. Statistical analysis of A. cepa bioassays included data from the four samples from the MF-WWTP, one negative control, and two positive controls. For the toxicity test, three repetitions were used for each treatment, consisting of a Petri dish with 50 seeds. Cytotoxicity and genotoxicity tests consisted of one slide containing around 500 ), with 10 slides per treatment. Data normality and homogeneity were verified by Shapiro-Wilk and Kolmogorov-Smirnov tests, respectively. The data of the MI and the CAI tests were analyzed by the non-parametric Kruskal-Wallis test (p < 0.05), since they did not present a normal distribution nor were they homogeneous. On the other hand, the G data that showed normal distribution and homogeneity was submitted to Tukey's parametric test (p < 0.05). The three parameters were further tested using the statistical program GraphPad Prism (version 8.4.2). G and MI values were transformed using the formula arcsine(√frequency), while the CAI was transformed using the formula x + 1. The data were loaded on an Excel™ worksheet and plotted as a graphical representation. Results and Discussion Characteristics of the wastewater treatment unit Figure 2 shows the results of the measured physicochemical parameters from the treatment units. The ORP values indicate that P1 was operating as an anaerobic system with the redox potential values of -300 mV, while P2 can be considered as a microaerobic system with -70.5 mV, according to the classification reported in the literature (Nguyen and Khanal, 2018). The low dissolved oxygen concentration measured for P1 and P2 effluent corroborates this consideration (Fig. 2). The industrial effluent reached the treatment station slightly alkaline. It became neutral after the treatment from P1. This could be caused by the conversion of organic matter from the raw wastewater into organic acids, by the anaerobic processes of the microbial population in P1 (Fig. 3). Acetic and propionic acid, which were the major organic acids found in P1, are found as the most common organic acids produced in anaerobic digesters (Gujer and Zehnder 1983; Harirchi et al. 2022). These initial results showed that P1, which was first designed to act as a facultative pond, is now working anaerobically. On the other hand, the high dissolved oxygen concentration, positive redox potential and alkaline pH from the final effluent indicated that P3 was operating as a facultative stabilization pond (Fig. 2). The photosynthesis in stabilization P3 with high algal activity can increase the dissolved oxygen concentration and carbon dioxide consumption, resulting in an alkaline environment as it has been reported for other units (Moghazy et al. 2022). The high COD and BOD concentration from the raw untreated wastewater and the low BOD from the filtered untreated wastewater (Table 1) indicated the relevant presence of soluble suspended organic matter in the incoming effluent. Moreover, the high COD/BOD ratio from the filtered untreated wastewater suggests the presence of recalcitrant dissolved compounds, which is expected from a mix of industrial wastewater (Tchobanoglus et al., 2003; Vítězová et al., 2020). Alves et al. (2022) and Florencio et al. (2001) reported an average COD from 206 to 290 mg O 2 L -1 for the raw domestic wastewater from a location near the MF-WWTP, values far lower than those observed in this study. The wastewater treatment system demonstrated distinct removal patterns for particulate and dissolved organic matter across the sequential ponds. Statistical analysis revealed that while all three treatment ponds significantly reduced filtered COD compared to the untreated wastewater (p 0.38 between ponds). In contrast, raw COD showed a different behavior, where significant reductions only became apparent after P2 and were further enhanced in P3 (p < 0.05), with P1 showing no statistically significant removal. These findings suggest that dissolved organics are effectively treated in the initial stage, while particulate matter requires extended treatment through multiple ponds. The system's performance indicates that P1 plays a crucial role in dissolved organic removal but may benefit from optimization to improve particulate matter reduction, whereas P2 and P3 are essential for particulate removal. However, it showed diminishing returns for dissolved organics. Statistical analysis showed no significant reduction in either raw or filtered BOD across ponds, despite some numerical changes, indicating consistent treatment limitations likely influenced by variability and small sample size. The high raw COD and BOD concentrations after the final treatment from P3 might be a consequence of the presence of algae, which could be visually observed in the liquid samples collected from each pond and also in the high presence of volatile suspended solids (Fig. S2 and Table S3, Supplementary material). Overall, the MF-WWTP showed a high level of COD (84%) and BOD (83%) removal, results comparable to similar plants treating industrial wastewater elsewhere (Alves et al. 2020; da Silva et al. 2011; Mahapatra et al. 2022; Veeresh et al. 2010). In addition, the results indicated the necessity of a filtering unit after P3 to remove the algae from the final effluent and achieve good performance. The nitrogen series presented in Table 2 offered relevant complementary insights into the performance of the MF-WWTP. The untreated wastewater had lower ammonia (N-NH₃) and higher nitrate (N-NO₃⁻) concentrations compared to typical values reported for domestic wastewater in the region, which range from 32.5 to 42.0 mg N-NH₃ L -1 and 0.65 to 1.00 mg N-NO₃⁻ L -1 (Espinosa et al., 2016; Silva et al., 1995; Soares et al., 1996). No statistically significant differences in N-NH₃ and TKN concentrations were observed during treatment. It could be the result of the solubilization of organic matter via anaerobic metabolism in reactors P1 and P2. It may have contributed to the release of ammonia into the effluent (Haandel Lubbe, 2012; Chernicharo, 2007; Von Sperling, 2007b). Additionally, the presence of algae in P3 may have further influenced the elevated concentrations of N-NH₃ and TKN. The removal of nitrate and nitrite observed in reactor P1 (Table 2) is likely attributable to microbial denitrification processes, facilitated by the anaerobic conditions and availability of organic substrates. The treated wastewater from the MF-WWTP achieves N-NH₃ concentrations in compliance with the standards for the discharge conditions and standards of effluents established by the Brazilian legislation (CONAMA 430/2011). In addition to organic matter and minerals, heavy metals are commonly present in industrial effluent treatment systems in Brazil (Souza and Siqueira, 2023). Industries have the largest number of processes from which heavy metals can originate, as they are used from incorporation into the product to washing machinery, pipes, floors, for cooling and steam generators. In the present work, 18 metals and semimetals were quantified in the MF-WWTP, since the incoming effluent receives waste from the metallurgical and metal-mechanical industries (Table 3). Nine out 18 metals/metalloids analyzed have specific effluent disposal limits defined by the current legislation (CONAMA 430/2011): lead (0.5 mg L -1 ), cadmium (0.2 mg L -1 ), boron (5.0 mg L -1 ), barium (5.0 mg L -1 ), iron (15.0 mg L -1 ), manganese (1.0 mg L -1 ), nickel (2.0 mg L -1 ), silver (0.1 mg L -1 ), and zinc (5.0 mg L -1 ). All samples from the MF-WWTP system complied with the Brazilian legislation for these nine compounds (Table 3). Additionally, in P3 effluent, complete removal of aluminum and a reduction level of barium, calcium, lithium, copper, nickel, and zinc were observed, most probably by the microalgae population (see below). The overall data show that a combination of sequential anaerobic, microaerobic, and aerobic or facultative ponds resulted in adequate treatment performance in treating a mix of industrial and domestic wastewater, provided that final filtering units are installed for the removal of algae from the final effluent. Pond 1 played a crucial role in dissolved organic conversion and nitrate removal, while particulate matter requires extended treatment across multiple ponds. The presence of algae in P3 influenced final COD, BOD and TKN concentrations, suggesting that a post-treatment unit is necessary to improve effluent quality and ensure more stable removal efficiencies. Toxicity of the wastewater samples after treatment The ecotoxicity test with A. fischeri used in this work is widely used for the evaluation of wastewater and industrial effluents because it provides a quick and economical response for monitoring effluents (Abbas et al. 2018) and lies in its ability to provide comprehensive insights into the biological effects of the treatment process. These tests are crucial for understanding how the system works and determining the degree of toxicity reduction at various stages of treatment (Boehler et al. 2017). The results herein revealed the toxicity potential of all samples by this test, with the highest values for samples collected from the effluents of P1 and P3 (Table S4, Supplementary material). In a complementary analysis, the seed germination test with A. cepa corroborated the potential toxicity, cytotoxicity and genotoxicity of the samples (Table 5, Table S5, Supplementary material). After 48 hours of exposure, a significant reduction in germination values was observed in the presence of the untreated sample (16%) and samples from P1 (20%), P2 (11.33%), and P3 (31.33%), compared to the negative control (72%). At 72 hours, a significant reduction in the G72 value was observed only for the incoming effluent (66%) and P2 (59.33%) samples (Table 5) (p<0.05). At the end of 20 days, the germination values of the four points sampled from the MF-WWTP were similar to the negative control. Despite the toxicity observed in all three treatment ponds, the A. cepa data indicated that P2 presented higher toxicity, probably related to the presence of biodegradation byproducts formed in the first two ponds. Despite some reduction in toxicity compared to P2, the final effluent from P3 still exhibited persistent toxicity, likely influenced by the presence of algal toxins. This understanding is vital for replicating or transferring the system to other treatment units, ensuring that the same efficiency and effectiveness in reducing toxicity can be achieved elsewhere. Similar toxic potential was found previously for effluents from electroplating, paper and dye industries (Abbas et al. 2018). In the present work, the differences in toxic potential for A. fischeri and A. cepa seed germination can be explained by changes in the characteristics of the ponds and the bioavailability of products/substances present in the collected samples. Wastewater from paint manufacturing, for instance, contribute to the increment of the chemical oxygen demand and turbidity, besides the organic and toxic chemicals levels, such as surfactants, bactericides, oils, solvents, preservatives and heavy metals which can cause environmental damages (Nair K et al. 2021; Verma et al. 2012). It is worth noting that the multifactory complex under study includes two paint manufacturing companies. Additionally, the results suggested that some non-bioavailable products in P1 underwent chemical transformation, possibly generating toxic by-products in Pond 2. On the other hand, these products appear to have undergone additional transformations in P3. Consequently, it might result in a reduction of toxicity towards the A. cepa test system. Xylene, used in paints, rubber cleaning products, among others, is considered a toxic compound (Niaz et al. 2015). When not properly treated, it can be transformed into benzene, which is also a toxic environmental pollutant (Rana and Verma 2005). Moreover, a previous study with biotransformation of a tetra-azo dye showed a high acute ecotoxicity in anaerobic A. fischeri assay due to aromatic amines accumulation. The ecotoxicity may be only eliminated after aromatic amines removal in micro-aerated conditions (Menezes et al. 2019). Additionally, cytotoxicity and genotoxicity were evaluated by mitotic and chromosomal alteration indexes (Table 5). The samples at the exit of each pond showed a significant increase in the mitotic index between 9.20% and 11.44% over the negative control (6.79%), revealing a genotoxic potential in their composition (p<0.05). Additionally, a genotoxic effect was evidenced for all evaluated samples, attributed to the significant increase in the rate of chromosomal alterations, ranging from 1.49% in untreated to 2.20% in P3 samples, respectively (Table 5). The following alterations showed a significant increase when compared to the negative control: micronuclei and chromosomal bridges in all three ponds; chromosomal breaks in P2 and P3, and chromosomal losses and C-metaphases in P1. Additionally, nuclear buds, chromosomal delays, chromosomal adhesions and polyploid cells were also observed (Table S5; Fig. S5). The nuclear and chromosomal alteration types suggest both the clastogenic (chromosome break) and the aneugenic (mitotic fiber problem) potentials of the molecules present in the ponds. Thus, the cytogenotoxicity data show that there was the maintenance of the cytogenotoxic potential at the exit of P3, despite the improvement in the physicochemical aspects at the end of the biological treatment process. Products and byproducts present in the effluents generated by the several industries of the complex and released into the MF-WWTP also seem to have acted as inducers of cytogenotoxicity in A. cepa . According to Leme and Marin-Morales (2009), chemical compounds can influence the mitotic index, which can trigger uncontrolled cell growth that results in cellular and genetic alterations. The significant increase in the mitotic index observed in the present study in P1, P2, and P3 may be associated with the presence of cytotoxic substances that may have induced uncontrolled cell division in tissues (Grippa et al. 2010). Furthermore, different authors have associated chromosomal alterations with the presence of heavy metals (Sabeen et al. 2020), aromatic amines (Bomhard 2003), detergents (Pedrazzani et al. 2012), pesticides (Camilo-Cotrim et al. 2022), among others. The unique characteristic of the MF-WWTP, where a variety of products from textile cleaning, industrial washing, paint, varnish, enamel, lacquer, waterproofing, solvents, and related products, as well as domestic sewage, are collected, can also explain the significant genotoxic potential observed in the samples from all three ponds. The phenolic metabolites of benzene, for instance, can cause DNA strand breaks, chromosomal damage, sister chromatid exchange, inhibition of topoisomerase II and damage to the mitotic spindle (Rana and Verma 2005). Benzene itself showed cytotoxic and genotoxic effects in A. cepa , by stimulating cell division and increasing chromosomal alterations, respectively (Barbhuiya et al. 2018). Taking into account all the experimental data obtained for toxicity, we understand that, although toxicity has decreased at the P3 outlet, some methodologies can be implemented as an alternative to mitigate toxicity rates. For example, the installation of a primary coagulation treatment unit for some effluents could reduce toxicity and increase BOD. Bacterial community in the sludge High-performance sequencing of the V3/V4 regions of the 16S rRNA was applied to survey the bacterial communities in each of the three ponds in the MF-WWTP, revealing the overall presence of 582 bacterial genera and 15 archaeal genera. The α-diversity and b-diversity analyses of the microbial community composition showed that all ponds exhibited similar microbial diversity, with only minor variations in index values, indicating no substantial differences in community composition (Table 5). P1 showed the highest observed species richness (417 OTUs) and the greatest diversity according to both the Shannon (2.988) and Simpson (0.853) indices, indicating not only a larger number of genera but also a more even distribution compared to P2 and P3. In contrast, P2 had the lowest diversity values across all metrics (385 observed OTUs, Shannon = 2.340, Simpson = 0.768), suggesting a less diverse community potentially dominated by a few competitive or stress-tolerant genera. P3 pond showed the highest estimated total richness (Chao1 = 542.6, ACE = 523.6), while presenting slightly lower richness (412 OTUs) compared to P1. Therefore, a substantial pool of rare, low-abundance species seemed not to be fully captured in the sequencing. Previous studies reported the diversity of OTUs ranging from 530 to over 2000, and Chao1 estimates ranging from approximately 800 to over 1600, depending on the type of industrial wastewater and treatment systems. Shannon indices in those studies typically ranged between 2.6 and over 9.0, and Simpson values ranged from 0.10 to above 0.99 in some cases, indicating very high evenness and richness in those systems. In contrast, the Shannon diversity values for the ponds in this study ranged from 2.3 to 2.9, and Simpson indices ranged from 0.77 to 0.85. Nevertheless, the diversity observed in the three ponds falls toward the lower end of the spectrum when compared to values reported in the literature for sludge from treatment plants dealing with industrial and domestic wastewater, indicating that the microbial community from the MF-WTTP is less diverse and could be dominated by a few taxa (Wu et al., 2018; Qin et al., 2019; Wu et al., 2021). The β-diversity analysis (Fig. 3), which evaluated microbial community similarity among the ponds, revealed that while all three ponds shared highly similar compositions (Bray-Curtis distances ranging from 0.234–0.281), P2 and P3 exhibited the greatest similarity, whereas P1 and P3 showed the least similar communities. This must be connected to their respective physicochemical profiles: P1 was considered an anaerobic, fermentative system, while P3 was a facultative pond with high algal activity. P2, the intermediary facultative/microaerobic pond, was characterized by the absence of a top-layer sediment. It favors the algae activity and likely explains its stronger compositional resemblance to P3 than to P1. The functional composition of microbial communities across the three treatment ponds was inferred using FAPROTAX, based on taxonomic profiles obtained from 16S rRNA gene sequencing (Fig. 4). In P1, functions associated with chemoheterotrophy (30.4%) and fermentation (19.9%) predominated, consistent with the accumulation of VFAs, suggesting active anaerobic conversion of the organic matter entering the system. Nitrate-metabolizing bacteria were also present at 3.7% of the population, which correlates to the observed nitrate removal in this pond. In P2, the abundance of OTUs classified with fermentation functions decreased to 15.1%, while phototrophy (6.0%) and photoautotrophy (5.8%) increased, suggesting a transition toward oxidative and photoautotrophic activity. The largest P3 showed functional potentials for methanogenesis (5.4%) and hydrogenotrophic methanogenesis (4.5%) alongside sustained levels of chemoheterotrophy (27.1%) and aerobic chemoheterotrophy (13.3%). It reflects a mixed community structure capable of both aerobic and anaerobic metabolism. As stated below, this pond was also characterized by substantial algal activity. The physicochemical profile of P3, including the consumption of VFAs, and a slight increase in the nitrate concentration, supports the predominance of aerobic mineralization of organic matter and the presence of organisms linked to the nitrogen cycling processes. Overall, the predicted functional profiles correspond to the measured environmental gradients and highlight the shift from anaerobic to increasingly aerobic and phototrophic microbial processes along the treatment sequence. Figure 5 shows the microbial community composition of archaea (Fig. 5a) and bacteria (Fig. 5b) from the sludge of each pond, considering only the organisms with relative abundance over 1%. Regarding to the archaeal population, the three ponds were dominated by Methanolinea (P1 with 34%, P2 with 39% and P3 with 33%), Methanosaeta (P1 with 40%, P2 with 11% and P3 with 15%), and Methanoregula (P1 with 9%, P2 with 33% and P3 with 30%). All these genera have been reported as having either hydrogenotrophic or acetoclastic methanogenic metabolism, typical of anaerobic digestion reactors, and are extremely resilient to extreme survival conditions (Mori et al. 2012; Weerakoon et al. 2023). Other archaeal genera are also present with relative abundance higher than 1% in at least one of the ponds, such as Methanobacterium (hydrogenotrophic methanogen, 10% in P1 and 3% in P2), Methanofollis (hydrogenotrophic methanogen, 1.6% in P1), Methanosphaerula (hydrogenotrophic methanogen, 1.4% in P1) and Methanospirillum (hydrogenotrophic methanogen, 3.3% in P2 and 1.6% in P3). Their methanogenic activity was reported following their isolation and identification (Zellner et al., 1999; Cadillo-Quiroz et al., 2009; Ferry et al. 1974). The bacterial population of organisms with relative abundance higher than 1% (Fig. 5.b) shows that most of the genera detected corresponds to extremophile bacteria, characteristic of inhospitable environments with high organic load, high temperatures and acidic pH, which corroborates the results of the physicochemical parameters found in the ponds (Tables 1 and 2). From all identified genera, Fervidobacterium stood as the most prominent in the treatment station with relative abundance of 18%, 38% and 21% in the bacterial population from P1, 2 and 3 respectively (Fig. 5.b). This genus includes a set of Gram-negative bacterial species, in the form of motile rods, strictly anaerobic and thermophilic, isolated for the first time from an Icelandic hot spring (Kanoksilapatham et al. 2016). Some species are reported as keratinase producers (Dhanasingh et al. 2021; Dhanasingh and Lee 2019; Kang et al. 2020; La et al. 2020). In wider terms, Fervidobacterium belongs to the Thermotogaceae family, widely distributed in nature and frequently found in salt flats, oil and petroleum contaminated environment, and biodigesters treating refinery effluents (Schaechter 2010). Besides the use of carbohydrates, many representatives of the Thermotogaceae family are known for the use of petroleum-derived compounds as carbon source to lactic and acetic acids, ethanol, CO 2 and H 2 (DiPippo et al. 2009). The presence of this bacteria in a high abundance in the population of P1 (Fig. 5.b) could be associated with the presence of xenobiotic compounds and with the production of acetic acid in P1 (Fig. S3, Supplementary materials). Obviously, the production of acetic acid must not be due only to the activity of Fervidobacterium species, but mainly because of it. The biotechnological potential of the genus Fervidobacterium has been recently reported (De Oliveira Silva et al. 2025). The remarkable arsenal of degradation enzymes allows the members of this bacterial group to use a diversity of carbon sources, including xenobiotics, as nutrients for cell growth (De Oliveira Silva et al. 2025). The second most abundant organism (9% in P1 and 6% in P2) belongs to the Gammaproteobacteria class, which comprises aerobic and facultative organisms. This class includes some of the most significant and widely recognized hydrocarbon-degrading bacteria, frequently enriched in marine environments, that play a crucial role in the breakdown of petrochemical and other xenobiotic compounds. Additionally, organisms from this class can perform denitrification, a process observed in P1 (Gutierrez, 2019). The decrease in its relative abundance from P1 to P2 and P3 (Fig. S4, Supplementary material) suggests that the primary compounds degraded by this organism were largely broken down in the first two ponds. In P1, the genera Comamonas , Atopobium , Bacteroides and Porphyromonas compose a group of organisms with low but significant relative abundance (2.1 – 2.6%), suggesting functional importance. They are all associated with the fermentation of complex organic compounds and may also participate in the degradation of xenobiotic substances. Comamonas is particularly known for its ability to degrade aromatic hydrocarbons (e.g., phenols, benzoate) and alkanes under anaerobic conditions, and being able to perform nitrate reduction (Kwon, Kwon, and Kim 2019; Cummings and Branch 1986; Goyal and Zylstra 1996; Martinez-Burgos et al. 2020; Neal, Thiruppathy, and Zengler 2023; Willems and De Vos 2006; Wu, Zaiden, and Cao 2018; Xu et al. 2024). Fermentative genera such as Prevotella and Atopobium likely contribute to volatile fatty acid (VFA) production through polysaccharide or amino acid fermentation, while Porphyromonas, with its proteolytic capabilities, may aid in breaking down nitrogen-rich substrates like proteins and mucins. The significant decrease of their relative abundance in P2 and P3 (Fig. S4, Supplementary material) can also indicate their major role in the fermentation of organic compounds in P1. The genus Aminobacterium (Fig. 4) also showed high relative abundance (1.9% in P1, 2.5% in P2 and 4.1% in P3). This genus belongs to the Synergistaceae family and is composed of Gram-negative bacterial species that degrade amino acids, being isolated mainly in ponds that receive dairy waste (Baena et al. 2000), which is also the case of MF-WWTP. This indicates that a high potential for amino acid metabolizing activity can be found in that bacterial community. The genus Azoarcus was detected mainly in P1 (relative abundance of 1%), a pond with a surface layer of oil and grease residues. It is a genus of anaerobic, nitrogen-fixing bacteria, often with an endophytic lifestyle and with the metabolic capacity to degrade different aromatic compounds. Among these compounds, some are considered extremely toxic and carcinogenic, such as toluene and xylene that encompass the BTEX compounds (benzene, toluene, ethylbenzene and xylene) (Fernandes et al., 2014; Kato et al. 2019; Patil et al. 2021; Pournia et al. 2019; Wushke et al. 2018). A few genera with no significance in P1 and P2 increased their relative abundance in P3. Among them were members of the classes Betaproteobacteria , Gammaproteobacteria , and Alphaproteobacteria , suggesting an important role in the terminal stages of organic matter conversion. These classes include metabolically versatile genera capable of utilizing diverse carbon substrates, including residual volatile fatty acids (VFAs), algal metabolites, and more recalcitrant compounds derived from earlier fermentation and partial oxidation processes. For instance, Betaproteobacteria are known for their ability to thrive in nutrient-depleted, oxygen-rich environments and contribute to heterotrophic nitrification and biofilm formation (Zhang et al., 2019). Likewise, Gammaproteobacteria include organisms that specialize in degrading xenobiotics and algal-derived dissolved organic matter (Alves et al., 2022). The presence of members from the class Alphaproteobacteria, many of which form close associations with algal cells, further points to the ecological importance of algae-bacteria interactions in this pond. Some genera can metabolize aromatic compounds and polysaccharides, particularly those derived from algal exudates. They are also known for their capacity to produce extracellular polymeric substances (EPS), which enhances floc stability and nutrient capture (Landa et al., 2017). Interestingly, several taxa typically associated with anaerobic metabolism, such as Clostridium , Bacteroides , and Dethiosulfovibrio were also detected in this aerobic pond. Their persistence likely reflects the presence of anoxic microenvironments within flocs, sediments, or algal mats, where fermentative degradation of complex organics such as proteins, amino acids, and polysaccharides can happen. Clostridium and Bacteroides are well-documented anaerobes that play significant roles in polysaccharide and protein fermentation, while Dethiosulfovibrio is associated with sulfur metabolism and amino acid degradation in anaerobic niches (Thomas et al., 2011; Ravot et al., 1999; Wiegel et al., 2006). The detection of Alicyclobacillus in P3 also merits attention. Although typically associated with thermophilic and acidophilic environments, some species have demonstrated the ability to degrade lignin-derived compounds, such as those found in pulp and paper wastewater (Aston et al., 2016). Their presence in the third pond may indicate the persistence of recalcitrant aromatic compounds that resisted degradation in upstream units. The high relative abundance of Fervidobacterium across all ponds suggests its potential as a key player in treatment performance in the MF-WWTP. As a thermophilic anaerobe linked to hydrocarbon metabolism, including petroleum-derived compounds. The members of this genus might be significantly contributing to the breakdown of soluble organic substrates and xenobiotic contaminants in all ponds. Hence, the sludge from these ponds could be used as a viable inoculum source for other systems treating recalcitrant pollutants, facilitating the remediation of industrial effluents. The microbial communities in three sequential ponds exhibited slight functional shifts that matched the physicochemical analysis, resulting in a very well-established flux of metabolic events in the course of the waste water treatment process: (1) The Fervidobacterium genus was notably abundant across all ponds, highlighting its potential role in the biodegradation of industrial effluents. P1, dominated by anaerobic fermenters and xenobiotic degraders facilitated initial organic matter breakdown; (2) P2 represents a transition toward oxidative metabolism, with declining fermenters and rising phototrophic organisms; (3) P3 consolidates this transition to an aerobic processes due to the algal interactions and abundance of Betaproteobacteria and Alphaproteobacteria involved in nitrogen cycling, though the presence of a many facultative organisms indicated the presence of anoxic microenvironments. Despite lower α-diversity, each pond maintained specialized taxa adapted to the degradation of industrial wastewaters (Table 4). The sludge’s microbial richness, particularly Fervidobacterium , positions it as a promising inoculum for degrading recalcitrant pollutants, as recently assigned (De Oliveira Silva et al. 2025). This study underscores how structured microbial networks enable efficient wastewater treatment through phased anaerobic, phototrophic, and aerobic processes, offering insights for optimizing industrial bioremediation. Cyanobacteria and microalgae community In the analysis of 12 points of the MF-WWTP, a total of 27,183 organisms belonging to cyanobacteria and microalgae were observed under the optical microscope, which were classified into 17 genera: nine genera of cyanobacteria ( Oscillatoria , 14.22%; Geitlerinema , 13.58%; Phormidium , 0.10%; Pseudanabaena , 0.001%; Arthrospira , 11.55%; Planktorhrix , 9.21%; Planktolyngbya , 0.53%; Chroococcus , 14.91%, and Merismopedia , 2.55%) and eight genera of microalgae ( Fragillaria , 7.84%; Navicula , 0.001%; Aulacoseira , 0.02%; Cyclotella 10.32%; Chlorella , 13.92%; Trachelomonas , 0.28%; Oocystis , 15.15%, and Choricystis , 11.14%) (Fig. 6 and Fig. S6, Supplementary material). Most of these microorganisms were observed in P2 and P3 that present higher values of redox potential, conductivity, pH and DO. This indicates that most of the organic load of the industrial effluent is consumed by the microbial population in P1. Thus, the very low content of organic matter coming from P1 makes possible the establishment of cyanobacteria and microalgae populations in the further ponds (Fig. 6). In the ponds of the MF-WWTP, cyanobacteria such as Phormidium and Pseudanabaena were exclusively found in P1; Merismopedia, Planktolyngbya, and Planktothrix were exclusively found in P3, while the remaining algae species were observed in two or three ponds simultaneously. Regarding microalgae, Trachelomonas, Navicula and Aulacoseira were exclusively found in P1, while Fragillaria was exclusive of P3. The remaining ones were observed in P2 and P3 (Fig. 6). Oscillatoria was the most observed cyanobacteria/microalgae in P1 (85%) (Fig. S6, Supplementary material), indicating how inhospitable this environment is for this type of microorganism. This is the pond with the lowest amount of dissolved oxygen, as well as the highest concentration of ammonia and phosphorus, the highest temperature, the lowest redox potential and the highest concentration of pollutants. In addition, this pond is covered with a layer of oil that prevents the passage of sunlight to its interior. Oscillatoria comprises a group of mixotrophic cyanobacterial species capable of colonizing eutrophic environments characterized by an excess of nitrogen and phosphorus (Reynolds et al. 2002), and these species are also known for their ability to fix nitrogen and carry out chemosynthesis (O’Farrell et al. 2003). Their presence is associated with excessive pollution with an organic load and also indicates environments with little availability of dissolved oxygen (França et al. 2022), stratified environments with the presence of chemical and thermal gradients and even nutrient circulation (Li 2021), parameters that characterize P1. The green microalgae Chlorella, Oocystis and Choricystis, as well as the cyanobacteria Choococcus were frequently observed in P2. In this pond dissolved oxygen was higher than in P1, with lower temperature and absence of oil agglomerations on the surface. Consequently, this pond has a higher incidence of sunlight and allows the development of green microalgae. These organisms are associated with aerobic, illuminated and eutrophic environments (Reynolds et al. 2002), in addition to being resistant to several chemical compounds in rivers (Chen et al. 2022). However, the presence of Oscillatoria in P2 with the highest value in biovolume among the microorganisms observed (Fig. 6) is an indication that this pond still has a considerable organic load and mixotrophic activity. Given the presence of four unique microorganisms, P3 was the most diverse pond (9/17). The largest biovolume measured was that of the genus Cyclotella followed by Planktothri x. The first corresponds to a group of diatoms related to eutrophic environments with excess nitrogen and phosphorus (Arumugham et al. 2023; Wu 1999). These are two elements that reappear from P2 and are found in high concentration in P3 (Table 2). As highlighted above, P3 has the highest conductivity and pH values and the lowest temperature value among the lakes, and these are precisely variables that positively impact the abundance of Cyclotella (Heneash et al. 2022). Planktothrix is a quite diverse group of cyanobacteria that inhabit freshwater to turbid mixed lakes (Komárek and Komáková 2004). It means that this microorganism lives in environments with high levels of dissolved oxygen as is the case of P3 (Fig. 2). However, blooms of Planktothrix are associated with problems regarding human health and negative impact to agriculture due to the production of cyanotoxins by this group of cyanobacteria (Christiansen et al. 2003). The presence of this type of microorganism in large proportions in P3, from which the treated effluent should be discharged into the nearby hydrographic basin, may constitute an alert for the MF-WWTP treatment unit to monitor the release of cyanotoxins in the environment. Conclusions The MF-WTTP is one of the few units of the wastewater treatment systems in the state of Pernambuco that receives both domestic and industrial effluents. Unlike other units, it has never undergone the process of removing its sludge for at least 20 years of functioning. It allowed for the enrichment of a highly adapted mixed population of bacteria, archaea and algae adapted to extreme conditions. This context set a well-suited condition for breaking down industrial wastewater with notable efficiency in a sequential step. In the anaerobic P1 harboring species of Fervidobacterium and methanogenic archaea ( Methanolinea and Methanosaeta ) worked for the main removal of COD and BOD and the conversion of organic substrates and recalcitrant compounds from the industrial effluents to simpler molecules. The other metabolic step in the microaerobic P2 and a final aerobic P3 completed the process. However, the toxicity of the incoming effluent remained and was slightly increased in the course of the treatment, likely due to the production and bacterial metabolic activity and by the potential production of algae-associated toxins. Hence, the results highlighted the need for a post-treatment unit to reduce the presence of algae and the remaining toxicity from the effluent of P3. Finally, the combination of anaerobic, microaerobic, and aerobic ponds, along with additional polishing units, could serve as an effective strategy for a treatment facility in the region as long as the resilient microbial communities can be efficiently transplanted to other units. Declarations Data availability Part of the data is provided as supplementary material. Raw data on microbial community and chemical analysis will be available upon reasonable request. Acknowledgements The authors would like to thank the Companhia Pernambucana de Saneamento (Compesa) and the Agência Pernambucana de Meio Ambiente (CPRH) for their technical support. Author information Authors and Affiliations Universidade Federal de Pernambuco, Centro de Tecnologia e Geociências, Departamento de Engenharia Civil e Ambiental, Recife, Pernambuco, Brasil Luiz Pereira Silva Júnior, Fernanda Magalhães Amaral, Fabrício Motteran, Bruna Soares Fernandes Sávia Gavazza Universidade Federal de Pernambuco, Centro de Biociências, Departamento de Genética, Recife, Pernambuco, Brasil Bruna Kelly de Oliveira Silva, Nathália Bandeira Carvalho dos Santos, Natercia Correa de Araújo, Ana Christina Brasileiro Vidal, Marcos Antonio de Morais Junior Universidade Federal do Vale do São Francisco, Colegiado de Biologia, Petrolina, Pernambuco, Brasil Kyria Cilene de Andrade Bortoleti Companhia Pernambucana de Saneamento, Diretoria de Atenção ao Cliente, Recife, Pernambuco, 50110-006, Brasil Bartholomeu Siqueira Júnior, Fábio Henrique Portella Corrêa de Oliveira Contributions [SA, ARK, and MK]: study design, data preprocessing, methodology, visualization, writing draft. [SA and MK]: study design, supervision. [MK]: writing draft, reviewing, and editing. [ARK]: methodology, writing draft, reviewing, and editing [LPSJ, BKOS, NBCS, NCA, FMA]: conceptualization, methodology, validation, formal analysis, investigation and writing the original draft. [BSJ, and FHPCO]: conceptualization, review and project administration. [BSF, FM, KCAB, ACBV, MAMJ, and SGSP]: conceptualization, review and editing, project administration and supervision. Corresponding author Correspondence to Marcos A Morais Jr and to Sávia Gavazza Ethics declarations Ethical approval The authors would like to declare that no ethical approval was required for this study Clinical trial number not applicable Consent to participate The authors declare that they have the consent to participate in this study by their institutions signed in the moment of project submission for the funding call. Consent for publication Despite partial funding by a private institution, the call for research funding by the public agency CNPq, in agreement with the co-financier, requires the results to be publicized in all appropriate media. Competing interests BSJ and FHPCO are employees of the company COMPESA, which partially financed this work. However, they only participated in its administration and supervision, in the discussion of the results and the outcome of their application to other treatment units. Their employer did not intervene in carrying out the experiments or in the results obtained. The remaining co-authors declare no conflict of interest. Funding This work was supported with an expense budget and scholarships by the grants of the project “Biotechnological solutions in support of the universalization of water and sewage services by public sanitation companies”, approved by the National Council of Research (CNPq grant 403657/2020-2), co-financed by the company COMPESA. 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Chemical demand for oxygen (COD) and Biochemical Oxygen Demand (BOD) analysis and removal efficiencies of the untreated sample and pond 1, 2 and 3 samples from the Multifactory Wastewater Treatment Plant (MF-WWTP). Parameter (mg O 2 /L) Untreated Pond 1 Pond 2 Pond 3 Raw COD 1569.8 ± 439.5 1000.9 ± 529.7 728.7 ± 335.7 826.6 ± 563.8 Filtered COD 1301.8 ± 136.5 491.3 ± 544.2 347.0 ± 268.0 250.7 ± 159.3 Raw BOD 5,20 875.0 ± 530.3 475 ± 530.3 550.0 ± 565.6 375.0 ± 388.9 Filtered BOD 5,20 225.0 ± 247.4 500.0 ± 0.0 345.0 ± 275.7 150.0 ± 0.0 Raw COD/BOD 5,20 1.79 2.11 1.32 2.20 Filtered COD/BOD 5,20 5.79 0.98 1.01 1.67 Table 2 . Physicochemical parameters of the untreated and ponds 1, 2 and 3 samples from the Multifactory Wastewater Treatment Plant (MF-WWTP). Parameter Untreated Pond 1 Pond 2 Pond 3 Upper limit* Raw ammonia (mg N-NH 3 /L) 8.4 - 17.2 9.4 - 21.1 8.4 - 17.4 9.9 - 11.1 20.0 Filtered ammonia (mg N-NH 3 /L) 7.3 - 11.3 7.5 - 19.6 7.1 - 15.7 6.9 - 9.8 - Raw TKN (mg N/L) 23 - 50.2 17.5 - 67.4 23.3 - 26.3 28.2 - 51.5 - Nitrate (mg N-NO 3 /L) 4.65 ± 0.03 0.39 ± 0.02 0.35 ± 0 0.50 ± 0 - Nitrite (mg N-NO 2 /L) 0.12 ± 0.01 <0.01 <0.01 <0.01 - * Defined by the Brazilian legislation CONAMA 430/2011 and 357/2005 Table 3. Metal and semimetal data of the untreated sample and pond 1, 2 and 3 samples from the Multifactory Wastewater Treatment Plant (MF-WWTP). Metal (mg/L) Untreated Pond 1 Pond 2 Pond 3 Upper limit* Aluminum 7.5 <0.01 <0.01 <0.01 - Barium 0.097 0.061 0.065 0.056 5.0 Beryllium <0.04 <0.04 <0.04 <0.04 - Boron <0.1 <0.1 <0.1 <0.1 5.0 Cadmium <0.001 <0.001 <0.001 <0.001 0.2 Calcium 29.33 25.97 29.41 22.43 - Lead <0.01 0.013 0.017 0.015 0.5 Cobalt <0.005 <0.005 <0.005 <0.005 - Copper 0.048 0.027 0.028 0.023 - Iron <0.01 <0.01 <0.01 <0.01 15.0 Lithium 0.2 <0.1 <0.1 <0.1 - Magnesium <1 <1 <1 <1 - Manganese <0.02 <0.02 <0.02 <0.02 1.0 Nickel 0.018 <0.01 0.013 0.01 2.0 Silver <0.005 <0.02 <0.02 <0.02 0.1 Vanadium <0.02 <0.02 <0.02 <0.02 - Zinc 0.13 <0.05 <0.05 <0.05 5.0 * Defined by the Brazilian legislation CONAMA 430/2011 and 357/20 Table 4. Alpha diversity indices from the (Observed, Shannon, Simpson, Chao1, and ACE) calculated using the phyloseq package. These metrics describe the richness and evenness of microbial communities across the analyzed samples. Sample Observed Shannon Simpson Chao1 ACE Pond 1 417.000 2.988 0.853 516.000 515.191 Pond 2 385.000 2.340 0.768 513.775 498.507 Pond 3 412.000 2.865 0.831 542.634 523.645 Table 5. Evaluation of toxicity, cytotoxicity and genotoxicity of samples collected from the ponds of the Multifactory Wastewater Treatment Plant using the Allium cepa test system, through analysis of the Germination index (G), Mitotic Index (MI) and Chromosome Alteration Index (CAI) in meristematic cells. Sampling points: untreated sample (SP13), pond 1 (SP14), pond 2 (SP15) and pond 3 (SP16). Sample Toxicity Cytotoxicity Genotoxicity G 48 (%) G 72 (%) G (%) MI (%) CAI (%) NC 72.00 ± 2.86 81.33 ± 4.11 94.67 ± 1.89 6.79 ± 1.51 0.62 ± 0.30 MMS - - - - 1.36 ± 0.27* TRI - - - - 6.77 ± 0.76* Untreated 16.00 ± 2.83* 66.00 ± 6.53* 91.33 ± 7.36 7.75 ± 1.37 1.49 ± 0.65* Pond 1 20.00 ± 4.90* 74.00 ± 4.32 93.33 ± 9.43 9.83 ± 1.97* 1.60 ± 0.58* Pond 2 11.33 ± 4.11* 59.33 ± 9.29* 93.33 ± 6.80 9.20 ± 1.31* 1.88 ± 0.85* Pond 3 31.33 ± 5.73* 82.67 ± 7.54 98.67 ± 1.89 11.44 ± 1.47* 2.20 ± 0.92* NC = Negative Control; MMS = Methyl Methane Sulfonate; TRI = Trifluralin; G 48 = Partial germination at 48 h; G 72 = Partial germination at 72 h; G = Total germination; MI = Mitotic Index; CAI = Chromosomal Alteration Index. Statistically significant values different from NC (p < 0.05) are followed by an asterisk. Supplementary Files SupplementaryMaterial.pdf Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Major Revision 15 Oct, 2025 Reviewers agreed at journal 09 Sep, 2025 Reviewers invited by journal 25 Aug, 2025 Editor invited by journal 19 Aug, 2025 Editor assigned by journal 12 Jun, 2025 First submitted to journal 10 Jun, 2025 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-6770575","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":505093398,"identity":"2779f06d-a0ad-4bcc-b595-6211c465bdbd","order_by":0,"name":"Luiz Pereira Silva Júnior","email":"","orcid":"","institution":"UFPE: Universidade Federal de Pernambuco","correspondingAuthor":false,"prefix":"","firstName":"Luiz","middleName":"Pereira Silva","lastName":"Júnior","suffix":""},{"id":505093399,"identity":"ce7ebac8-b847-414a-9428-f5ac1005ba42","order_by":1,"name":"Bruna Kelly de Oliveira Silva","email":"","orcid":"","institution":"UFPE: Universidade Federal de Pernambuco","correspondingAuthor":false,"prefix":"","firstName":"Bruna","middleName":"Kelly de Oliveira","lastName":"Silva","suffix":""},{"id":505093400,"identity":"d00f96d7-8547-4966-ac66-fcd7f747b3d6","order_by":2,"name":"Nathália Bandeira Carvalho dos Santos","email":"","orcid":"","institution":"UFPE: Universidade Federal de Pernambuco","correspondingAuthor":false,"prefix":"","firstName":"Nathália","middleName":"Bandeira Carvalho dos","lastName":"Santos","suffix":""},{"id":505093401,"identity":"829ddc39-605b-4e2f-b68e-9321e6223378","order_by":3,"name":"Natercia Correa de Araújo","email":"","orcid":"","institution":"UFPE: Universidade Federal de Pernambuco","correspondingAuthor":false,"prefix":"","firstName":"Natercia","middleName":"Correa","lastName":"de Araújo","suffix":""},{"id":505093402,"identity":"eeba1d00-f9b3-4adc-823d-6a0811e38571","order_by":4,"name":"Fernanda Magalhães Amaral","email":"","orcid":"","institution":"UFPE: Universidade Federal de 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Pernambuco","correspondingAuthor":false,"prefix":"","firstName":"Ana","middleName":"Christina Brasileiro","lastName":"Vidal","suffix":""},{"id":505093409,"identity":"c0395422-7f53-42ef-bb76-fefd6e8fae42","order_by":11,"name":"Marcos Antonio de Morais Jr","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8klEQVRIie3QMQuCQBTA8XcItrxqNYr6BMGFYEvfpOUi0Okg6As41SI1+y2EFkdFcDJamyoXZ9sMGjopqEWjreH+w9304+49AJns3wsAoQ9KAEDsH4gOKvuBlM3sb2S43qd5fodpuxulYdE7WV7cDC/EP1USI7H0jrsC7m5MGiEuuRe35pQky2oSmKrStIF7CdAIkHHv7BgaWbFqcshUBe8laeRhgcyiMY6LWnIUr6BaEqQBImOCGFBPMkXMonHXwYWYhY1cMYs2S+o+ZhKxsQnfYmN3vTls0BIby69+NXmlPS/ilCcCfAXviheRyWQy2UcPQ9BVLA8VJzoAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-9727-0300","institution":"Federal University of Pernambuco: Universidade Federal de Pernambuco","correspondingAuthor":true,"prefix":"","firstName":"Marcos","middleName":"Antonio","lastName":"de Morais","suffix":"Jr"},{"id":505093410,"identity":"c3d70dea-2a08-404c-b561-a4caf91d2353","order_by":12,"name":"Sávia Gavazza","email":"","orcid":"","institution":"UFPE: Universidade Federal de Pernambuco","correspondingAuthor":false,"prefix":"","firstName":"Sávia","middleName":"","lastName":"Gavazza","suffix":""}],"badges":[],"createdAt":"2025-05-28 18:49:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6770575/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6770575/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":90416334,"identity":"5827ab2c-6a0f-400c-9513-4563c6c7052a","added_by":"auto","created_at":"2025-09-02 13:21:30","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":299157,"visible":true,"origin":"","legend":"\u003cp\u003eMultifactory Effluent Treatment Plant (MF-WWTP) in the municipality of Jaboatão dos Guararapes (8°06'23.1\"S 35°01'36.0\" W), Brazil, consisting of three stabilization ponds, which receive effluents from 15 industrial segments. In an enlarged view, it is possible to see the floor plan of the MF-WWTP, with its respective dimensions.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6770575/v1/3e4e9cd1c18bc3641999a9d8.png"},{"id":90415975,"identity":"c1cfd5c5-3505-4ddb-b976-3efb8e0a0d91","added_by":"auto","created_at":"2025-09-02 13:13:30","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":134481,"visible":true,"origin":"","legend":"\u003cp\u003eMultiparameter plot of the physicochemical variables of the influent (industrial effluent) and the effluent of the Multifactory Wastewater Treatment Plant (MF-WWTP).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6770575/v1/3112856a7194d0f759457ca3.png"},{"id":90415978,"identity":"55ff88ea-b2da-435a-a3c5-145bc95349eb","added_by":"auto","created_at":"2025-09-02 13:13:30","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":66331,"visible":true,"origin":"","legend":"\u003cp\u003ePrincipal Coordinates Analysis (PCoA) of microbial communities based on Bray-Curtis distances, showing compositional dissimilarity between samples (Pond1, Pond2, Pond3). Closer points indicate more similar communities, while farther points reflect higher dissimilarity. Values on connecting lines represent pairwise Bray-Curtis distances (range: 0 = identical, 1 = maximally distinct).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6770575/v1/84c44529db751051df7dc3ef.png"},{"id":90415981,"identity":"b7c0e6ee-3014-4416-ba61-6d8c9a34e494","added_by":"auto","created_at":"2025-09-02 13:13:30","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":229382,"visible":true,"origin":"","legend":"\u003cp\u003eRelative abundance of microbial functions predicted by FAPROTAX based on 16S rRNA gene sequencing data from three sequential wastewater treatment ponds (P1 to P3). Only functions with a relative abundance \u0026gt;1% in at least one pond are shown.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6770575/v1/46c012912ce53be068fffd0c.png"},{"id":90416337,"identity":"d7acf0c2-22f1-4eea-a078-03315b7d7712","added_by":"auto","created_at":"2025-09-02 13:21:30","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":141589,"visible":true,"origin":"","legend":"\u003cp\u003eDiversity of archaea (a) and bacteria (b) in the ponds of the MF-WWTB. The Y-axis indicates the relative abundance (RA) of organisms with an RA above 1% in at least one pond. The prefixes denote the most refined taxonomic classification level: p (phylum), c (class), o (order), f (family), and g (genus). Organisms classified solely at the kingdom level were excluded from the analysis.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6770575/v1/4ca525f560487e820954a626.png"},{"id":90415979,"identity":"2a5e40ce-4dc4-4df1-a89e-ad52e7eaf70c","added_by":"auto","created_at":"2025-09-02 13:13:30","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":95326,"visible":true,"origin":"","legend":"\u003cp\u003eDiversity of cyanobacteria (left) and microalgae (right) in ponds 1, 2, and 3 of the MF-WWTP.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-6770575/v1/767221e8d67bc38fc0c9002f.png"},{"id":90419216,"identity":"898f7950-a8e1-41bf-944c-e7ac09db7765","added_by":"auto","created_at":"2025-09-02 13:45:31","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2011657,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6770575/v1/5040ce7e-14ad-4d65-ac27-15527a5f8de2.pdf"},{"id":90416338,"identity":"8cd08581-1122-4a04-8fcc-46a6ea68cffe","added_by":"auto","created_at":"2025-09-02 13:21:30","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1062327,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6770575/v1/516f35870a713a72ad29eab8.pdf"}],"financialInterests":"","formattedTitle":"Characterization of the treatment units and their microbial communities in a waste stabilization pond system treating wastewater from an industrial complex located in Northeastern Brazil","fulltext":[{"header":"Research highlights","content":"\u003cul\u003e\n \u003cli\u003eSerial anaerobic-microaerobic-facultative ponds provided good treatment performance.\u003c/li\u003e\n \u003cli\u003e\u003cem\u003eFervidobacterium\u003c/em\u003e was the most abundant genus and might be a key for the treatment.\u003c/li\u003e\n \u003cli\u003eAbundance and diversity of microalgae and cyanobacteria in ponds 2 and 3, with Cyclotella and the cyanobacteria Planktothrix with the greatest biovolume in P3\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"Introduction","content":"\u003cp\u003eThe increase of urban sprawl, deforestation, and agricultural land area as a result of the increase of anthropogenic land use is, nowadays, the main driver for the global decline of water quality (Mello et al. 2020; Giri Qiu 2016). According to the census performed in 2024, Brazil has over 210\u0026nbsp;million inhabitants, making the country the most populous in South America and the 7th in the world. Almost 80% of the Brazilian population lives in urban areas, while only 63% of Brazilian households have access to sanitary sewers or septic tanks (IBGE \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The Environmental Quality Report published in 2020 by the Brazilian Institute of Environment and Renewable Natural Resources (\u003cem\u003eInstituto Brasileiro de Meio Ambiente e dos Recursos Naturais Renov\u0026aacute;veis\u003c/em\u003e - IBAMA) indicated that the dumping of raw or partially treated domestic and industrial wastewater in the environment is the major source of contamination of water sources in Brazil. Regarding the domestic wastewater treatment, there were 2768 operational urban wastewater treatment plants in Brazil in 2017, responsible for treating 51% of the produced wastewater. The majority of those treatment plants are focusing on organic matter and pathogens removal, with an average of BOD removal ranging from 60 to 80% (ANA \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; IBGE \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; SNIS 2022). Thus, there is an urgent need for the expansion of treatment capacity, with the installation of new plants and also the expansion of the current working plants to implement nutrient removal units, and for the improvement of their performance. Studying the treatment plants that show good performance is also important, because it would give important information for the design of new plants and the improvement of poorly performing installed ones.\u003c/p\u003e\u003cp\u003eWaste stabilization ponds (WSP) are the most used treatment processes in Brazil, representing 36% of the operational plants (anaerobic, aerobic, facultative, maturation ponds and their combination). While high in number, those plants only account for the removal of 21% of the equivalent population, indicating that they are not fully operating in highly dense urban areas (ANA \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Meanwhile, WSP became popular also in rural areas of Brazil, because of the favorable environmental conditions, land area availability, and the low maintenance required (Von Sperling \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eStabilization ponds are designed to receive domestic or industrial wastewater where it stays for days, allowing bacteria (aerobic in facultative and aerated ponds, or anaerobic bacteria in anaerobic ponds) to degrade the soluble organic compounds and fine particulate matter suspended in the liquid, while the large particulate organic matter settles and are stabilized specifically by anaerobic bacteria. The oxygen required for the aerobic bacteria in facultative ponds is supplied by algae through photosynthesis. In some cases, the ponds are designed to be shallow, allowing for enough light penetration to aid in the combat against pathogens. This type of treatment represents a viable economic alternative since environmental conditions, such as high temperature and long periods of light intensity, are favorable to the biological treatment of wastewater in tropical countries (Espinosa et al. 2017; Bressani-Ribeiro et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Mahapatra et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In those WSP ponds, a consortium of bacteria, archaea, and microalgae is established to work in synergy to biodegrade the domestic and industrial wastewater (Peil et al. \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Chai et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Together, these organisms harbor an arsenal of molecular tools, such as biodegradation enzymes, that produce a gradient of metabolization in which the product of an enzyme from a microorganism might be the substrate for the next one. It creates a flux of nutrients that feeds the trophic chain and that favors their adaptation to such hazardous environments (Tatta et al. \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe Pernambuco State Sanitation Company (\u003cem\u003eCompanhia Pernambucana de Saneamento\u003c/em\u003e - Compesa) operates a Multifactory Wastewater Treatment Plant (MF-WWTP), located in the municipality of Jaboat\u0026atilde;o dos Guararapes, Recife metropolitan area, Pernambuco (Brazil), that is responsible for the treatment of a complex effluent wastewater of the Multifactory Industrial Complex (MIC), from baking, dairy, metallurgy, laundry, and ink, among other industrial sectors. The MF-WWTP project was originally designed to work with two facultative ponds, followed by a maturation pond, and to solely receive domestic sewage. However, over time, following the industrialization of the area, industrial wastewater was also generated and mixed with the effluent. Nowadays, this wastewater is mainly composed of organic degradable compounds (carbonaceous organic matter) and recalcitrant industrial products such as dyes, hydrocarbons, and surfactants, as well as different types of metals. Despite this complexity, its ponds remained stable over the years and, according to Compesa, they are capable of removing more than 80% of the organic matter measured as carbon oxygen demand (COD), which stands as a highly efficient treatment unit regarding the pond system. In terms of the legislation, the output of the MF-WWTP has to comply with the parameters of the current resolution of the Environmental National Council (CONAMA 430/2011), with monthly measurement of the influent and the final effluent (COD, ammonium, solids, and pathogens). Considering the good overall performance of the MF-WWTP in terms of COD removal (80%), this work aimed to provide a deeper comprehensive evaluation of such a unique system in terms of (1) physicochemical evaluation of each treatment unit (2) microbial community structure throughout the treatment process, (3) and the ecotoxicity and cyto-genotoxicity of the system. It may reveal the complexity of its chemical and biological interactions in a way that could contribute to the technological development for replication or transference to other treatment units.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eSite description\u003c/h2\u003e\u003cp\u003eThis research was conducted at the MF-WWTP, which started operating in the early 1990s and is currently co-managed under a public-private agreement involving the public company Compesa and the private company BRK Ambiental. This unit is located in the municipality of Jaboat\u0026atilde;o dos Guararapes (8\u0026deg;06'23.1\"S 35\u0026deg;01'36.0\" W), within the Recife metropolitan area, state of Pernambuco, Brazil. It is part of the Jaboat\u0026atilde;o industrial complex encompassing 15 segments, including food industry, metallurgy, machining, refrigeration, stationery, carpentry, graphic products, inks and solvents, galvanized products, polymers, among others (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In addition, the domestic sewage of the industries is also integrated into the collection network, making the effluent that arrives at the MF-WWTP a complex and diverse substrate.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe unit comprises the stabilization Pond 1 (P1), Pond 2 ((P2) and Pond 3 (P3), each one with 2 m deep in average (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The largest pond P1 has 1,456 m\u003csup\u003e2\u003c/sup\u003e of surface area, characterized by a thick black surface layer composed predominantly of oils and greases. It receives the complex effluent in a highly varying flow rate, with averages of 3.4 L s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and eventual peaks of 44 L s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. The P2 pond has 910 m\u003csup\u003e2\u003c/sup\u003e of area and presents little oily coverage, while the P3 has 1,740 m\u003csup\u003e2\u003c/sup\u003e of surface area and already has the presence of some aquatic plants. The connection between P1 and P2 and between P2 and P3 is carried out by pipes of 200 mm in diameter, located from the surface downwards in the water layer. From the latter, treated water is discharged into the Jaboat\u0026atilde;o River in that municipality (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). For this study, samples were collected at the inlet and the outlet of each pond and at internal points, as marked in Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e (Supplementary material).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eSampling procedure\u003c/h3\u003e\n\u003cp\u003eSamples were collected during the spring (November) of 2021 and summer (February) of 2022 according to the scheme in Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e (Supplementary material), both during the dry season with monthly precipitation averages of 28.0\u0026thinsp;\u0026plusmn;\u0026thinsp;21.9 mm and 90.6\u0026thinsp;\u0026plusmn;\u0026thinsp;28.7 mm, respectively (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e, Supplementary material).\u003c/p\u003e\u003cp\u003eFor physicochemical analysis, wastewater samples were collected in four points (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e): (1) the untreated wastewater sampling point 13 is located in the Parshall flume at the entrance of the treatment plant; (2) the P1-treated wastewater sampling point 14 located in the pipe that connects P1 and P2; (3) the P2-treated wastewater sampling point 15 located in the pipe that connects P2 and P3; (4) the P3-treated sampling point 16 located in the outfall pipe of P3 (Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e, Supplementary material). For each sampling point, four 1 L polyethylene (PE) bottles were filled, kept on ice, and transported to the laboratories, where some analyses were performed on the same day as recommended by the American Public Health Association - APHA (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), and an aliquot of the samples was preserved for further analyses.\u003c/p\u003e\u003cp\u003eFor algal and bacterial community analysis, liquid and sludge samples were also collected from the three ponds. In P1, samples were taken from four points: two near the entrance, one in the middle near the edge, and one near the pipe connecting P1 and P2. A 3-meter-long extension arm was used since the thick top layer in this pond prevented the use of an inflatable boat. Liquid samples were collected from the surface, while sludge samples were taken from the bottom. In P2, samples were collected from two points: one in the middle of the pond and one near the effluent channel. In P3, samples were taken throughout the pond using an inflatable boat and a Van Dorn sampler (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e, Supplementary material). Liquid samples for algal community, ecotoxicity, and cytogenotoxicity analysis were collected from the surface and at a depth of 60 cm, mixed, homogenized, and stored in 500 mL PE bottles on ice. Samples intended for algal community analysis were preserved in 4% formaldehyde before being sent to the laboratory. Sludge samples for bacterial and archaeal community analysis were also stored in PE bottles, kept on ice, and transported to the laboratory for DNA extraction within 24 to 36 hours after collection.\u003c/p\u003e\n\u003ch3\u003ePhysicochemical analysis\u003c/h3\u003e\n\u003cp\u003eConductivity, pH, Dissolved Oxygen (DO), and Redox Potential (ORP) were measured during the sampling. Biochemical Oxygen Demand (BOD), Chemical Oxygen Demand (COD), alkalinity (total and partial), and heavy metals and semi-metals were performed in the laboratories on the same day of sampling. For COD and BOD, samples were analyzed raw and filtered in a 1.2 \u0026micro;m glass fiber membrane (Satorius, Gottingen, Germany). After sample fractionation, 2 L of the filtered samples were acidified with H\u003csub\u003e2\u003c/sub\u003eSO\u003csub\u003e4\u003c/sub\u003e (40% v/v) to pH 2 and kept in amber glass flasks at 4\u0026deg;C for ammoniacal-nitrogen (N-NH\u003csub\u003e3\u003c/sub\u003e) and Total Kjehdal Nitrogen (TKN), while another 2 L of filtered samples were stored for maximum of 14 days at 4\u0026deg;C for nitrate (N-NO\u003csub\u003e3\u003c/sub\u003e), nitrite (N-NO\u003csub\u003e2\u003c/sub\u003e), color, total and suspended solids analysis. For volatile fatty acids (VFA) analysis, aliquots of 10 mL were stored in 10 mL glass flasks without headspace and kept refrigerated at 4\u0026deg;C and then analyzed by gas chromatography (Agilent Technologies 7890A, Agilent Technologies Inc., Wilmington, DE, USA) coupled with a FID detector, and is described in the Supplementary Materials.\u003c/p\u003e\u003cp\u003eStatistical analyses were performed using RStudio to assess normality and homogeneity of variance. Statistical analysis was performed to evaluate the data distribution, either parametric tests (t-test, ANOVA followed by Tukey\u0026rsquo;s post-hoc test) or non-parametric tests (Kruskal-Wallis and Mann-Whitney) were applied as appropriate. All statistical analyses were conducted using RStudio (Posit Team, \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eBacterial community analysis\u003c/h3\u003e\n\u003cp\u003eSludge samples (Fig. S.1, Supplementary Materials) were subjected to total DNA extraction using the PowerSoil DNA extraction kit\u0026trade; (Qiagen, Hilden, Germany), following the protocol recommended by the manufacturer. Total DNA was quantified using the NanoDrop 2000 equipment (Thermo Fisher Scientific, Waltham, USA) and then sent for its 16S rRNA amplicon sequencing at Neoprospecta company (Florianopolis, Brazil), which used a MiSeq Illumina device using metabarcoding applied to the V3/V4 domain of the 16S rRNA gene. Data were processed using Metagenomics Rapid Annotation Using Subsystems Technology (MG-RAST), and the SILVA SSU database was selected for analysis. Relative abundances of bacteria and archaea were calculated separately, and only taxa with a relative abundance greater than 1% were considered relevant to this study. Alpha (α) and beta (β) diversity analyses were performed using the \u003cem\u003ephyloseq\u003c/em\u003e package in R, executed via RStudio (McMurdie and Holmes, 2013; McMurdie and Holmes, 2015). Statistical differences in the relative abundances of taxa between sample pairs were assessed using STAMP (Parks et al., 2014), employing a two-sided G-test with Yates\u0026rsquo; correction and Fisher\u0026rsquo;s exact test, along with the Newcombe-Wilson method for calculating confidence intervals. Taxonomic classification data were used for functional inference using FAPROTAX (Louca et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eIdentification and counting of phytoplankton\u003c/h3\u003e\n\u003cp\u003eSamples for phytoplankton identification were preserved in 4% formaldehyde and stored at 4\u0026deg;C for quantitative and qualitative analysis by optical microscopy (model DMLB, Leica Microsystems GmbH, Germany) following APHA (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The identification was performed at the genus level using the parameters in the specialized literature: Kom\u0026aacute;rek (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e1983\u003c/span\u003e) and Prescott and Vinyard (1982) for Chlorophyta; Kom\u0026aacute;rek and Anagnostidis (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e1989\u003c/span\u003e), Kom\u0026aacute;rek and Kom\u0026aacute;kov\u0026aacute; (2004), and Kom\u0026aacute;rek and Cronberg (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2001\u003c/span\u003e) for Cyanophyta; Popovsk\u0026yacute; and Pfiester (1990) for Dinophyta; (Krammer, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e1991a\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e1991b\u003c/span\u003e) for Heterokontophyta and (John et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2002\u003c/span\u003e) for other phytoflagellates, such as Euglenophyta and Cryptophyta. A minimum of 400 cells of each sample were counted in Sedgwick-Rafter chambers to determine phytoplankton density (cells mL\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), following APHA \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The mean volume of each species was calculated considering the cellular measurements of 30 individuals, according to the geometric models suggested by the literature (Hillebrand et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e1999\u003c/span\u003e), and biomass was expressed in mm\u003csup\u003e3\u003c/sup\u003e L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. The taxonomic classifications were based on the specialized literature (Kom\u0026aacute;rek and Kom\u0026aacute;kov\u0026aacute; 2004; Oliveira et al. \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003eEcotoxicity using\u003c/b\u003e \u003cb\u003eAliivibrio fischeri\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe acute toxicity of pond samples was assessed using lyophilized bioluminescent marine bacteria \u003cem\u003eAliivibrio fischer\u003c/em\u003ei (\u003cem\u003eA. fischeri\u003c/em\u003e NRRL B-11177, synonym \u003cem\u003eVibrio fischeri\u003c/em\u003e), based on the inhibition of light emission when bacterial cells are exposed to toxic conditions. The method followed the Brazilian standardized procedures (ABNT NBR 15411-3) for untreated effluents. \u003cem\u003eA. fischeri\u003c/em\u003e cells were exposed to untreated samples from three ponds and a series of dilutions (1:512; 1:256; 1:128; 1:64; 1:32; 1:16; 1:8; 1:4; 1:2; 1:1). Label K represents the reference where samples were replaced with 2% (w/v) NaCl. The positive control included the addition of 3,5-dichlorophenol (3,5-DF; Sigma Aldrich, No.. LRAC5200) at 4.5 mg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. After 15 minutes of exposure, the suspensions were analyzed for bacterial bioluminescence at the Hydrobiology and Toxicology Laboratory of the State Environment Agency (CPRH). The inhibitory effect of the aqueous samples was evaluated by the reduction in bioluminescence of the suspensions, which defined the Toxicity Factor (TF) according to the criteria established by the standardized procedure.\u003c/p\u003e\u003cp\u003e\u003cb\u003eToxicity and cytogenotoxicity\u003c/b\u003e: \u003cb\u003eAllium cepa\u003c/b\u003e \u003cb\u003etest system\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe toxicity and cytogenotoxicity assays were performed using the \u003cem\u003eAllium cepa\u003c/em\u003e test system according to Fiskesj\u0026ouml; (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e1988\u003c/span\u003e), Fernandes et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), and Leme et al. (\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Seeds from \u003cem\u003eA. cepa\u003c/em\u003e cv. Vale Ouro IPA-11 variety were germinated on a layer of sterile cotton in Petri dishes covered with filter paper discs moistened with 15 mL of each MF-STS sample. Fifty seeds were placed per dish with three dishes per sample. For negative control, discs were moistened with sterile mineral water. For the genotoxicity analysis positive controls, the discs were moistened with the herbicide Trifluralin (0.84 ppm of active principle) or with the mutagenic agent Methyl Methanesulfonate (MMS, 400 \u0026micro;M). After 72 h of germination, root tips were collected, fixed in Carnoy (ethanol: acetic acid, 3:1) for 24 h at room temperature, and stored at -20 \u0026ordm;C. For the preparation, fixed roots were washed three times in distilled water, 5 min each, hydrolyzed in 1 mol L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e HCl at 60\u0026deg;C for 10 min, washed again, and stained with Schiff's Reagent (1.090033, Sigma-Aldrich) for 1 h in a dark room. After this period, the roots were washed in distilled water and the meristem was separated, placed in a slide with a drop of 2% acetic carmine, covered with a coverslip, and lightly crushed. After coverslip removal using liquid nitrogen, the slides were air dried and mounted with Entellan\u0026trade; solution (Sigma-Aldrich, St Louis, USA). Cytological analyses were carried out using an optical light microscope at a magnification of 400x. Cell images were captured with a Leica DFC 340FX camera, using Leica's CW 4000 program. Images were optimized for brightness and contrast using Adobe Photoshop CS3 (Adobe Systems Incorporated).\u003c/p\u003e\u003cp\u003eThe toxic potential of the samples was evaluated based on the average Germination percentage (G), calculated by the ratio between the number of seeds germinated after 48 h, 72 h, and 20 days of incubation and the total number of incubated seeds multiplied by 100. On the other hand, the cytotoxic potential of the samples was evaluated by the Mitotic Index (MI) and genotoxicity by the Chromosomal Alteration Index (CAI) in meristematic cells of \u003cem\u003eA. cepa\u003c/em\u003e. MI and CAI were obtained from the analysis of 500 meristematic cells per slide, with 10 slides per treatment, totaling 5000 cells per treatment. The MI was calculated as the ratio between the number of cells in division by the total number of surveyed cells. CAI was obtained by the ratio between the number of observed cellular alterations (C-metaphases, nuclear buds, micronuclei, multipolar anaphases, polyploid metaphases, and chromosomal adhesion, losses, delay, breaks, and bridges) and the total number of surveyed cells.\u003c/p\u003e\u003cp\u003eStatistical analysis of \u003cem\u003eA. cepa\u003c/em\u003e bioassays included data from the four samples from the MF-WWTP, one negative control, and two positive controls. For the toxicity test, three repetitions were used for each treatment, consisting of a Petri dish with 50 seeds. Cytotoxicity and genotoxicity tests consisted of one slide containing around 500 ), with 10 slides per treatment. Data normality and homogeneity were verified by Shapiro-Wilk and Kolmogorov-Smirnov tests, respectively. The data of the MI and the CAI tests were analyzed by the non-parametric Kruskal-Wallis test (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), since they did not present a normal distribution nor were they homogeneous. On the other hand, the G data that showed normal distribution and homogeneity was submitted to Tukey's parametric test (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The three parameters were further tested using the statistical program GraphPad Prism (version 8.4.2). G and MI values were transformed using the formula arcsine(\u0026radic;frequency), while the CAI was transformed using the formula x\u0026thinsp;+\u0026thinsp;1. The data were loaded on an Excel\u0026trade; worksheet and plotted as a graphical representation.\u003c/p\u003e"},{"header":"Results and Discussion","content":"\u003cp\u003e\u003cstrong\u003eCharacteristics of the wastewater treatment unit\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFigure 2 shows the results of the measured physicochemical parameters from the treatment units. The ORP values indicate that P1 was operating as an anaerobic system with the redox potential values of -300 mV, while P2 can be considered as a microaerobic system with -70.5 mV, according to the classification reported in the literature (Nguyen and Khanal, 2018). The low dissolved oxygen concentration measured for P1 and P2 effluent corroborates this consideration (Fig. 2). The industrial effluent reached the treatment station slightly alkaline. It became neutral after the treatment from P1. This could be caused by the conversion of organic matter from the raw wastewater into organic acids, by the anaerobic processes of the microbial population in P1 (Fig. 3). Acetic and propionic acid, which were the major organic acids found in P1, are found as the most common organic acids produced in anaerobic digesters (Gujer and Zehnder 1983; Harirchi et al. 2022). These initial results showed that P1, which was first designed to act as a facultative pond, is now working anaerobically. On the other hand, the high dissolved oxygen concentration, positive redox potential and alkaline pH from the final effluent indicated that P3 was operating as a facultative stabilization pond (Fig. 2). The photosynthesis in stabilization P3 with high algal activity can increase the dissolved oxygen concentration and carbon dioxide consumption, resulting in an alkaline environment as it has been reported for other units (Moghazy et al. 2022).\u003c/p\u003e\n\u003cp\u003eThe high COD and BOD concentration from the raw untreated wastewater and the low BOD from the filtered untreated wastewater (Table 1) indicated the relevant presence of soluble suspended organic matter in the incoming effluent. Moreover, the high COD/BOD ratio from the filtered untreated wastewater suggests the presence of recalcitrant dissolved compounds, which is expected from a mix of industrial wastewater (Tchobanoglus et al., 2003; V\u0026iacute;tězov\u0026aacute; et al., 2020). Alves et al. (2022) and Florencio et al. (2001) reported an average COD from 206 to 290 mg O\u003csub\u003e2\u003c/sub\u003e L\u003csup\u003e-1\u003c/sup\u003e for the raw domestic wastewater from a location near the MF-WWTP, values far lower than those observed in this study. The wastewater treatment system demonstrated distinct removal patterns for particulate and dissolved organic matter across the sequential ponds. Statistical analysis revealed that while all three treatment ponds significantly reduced filtered COD compared to the untreated wastewater (p \u0026lt; 0.001), the majority of this removal occurred in P1, with subsequent ponds contributing little additional reduction (p \u0026gt; 0.38 between ponds). In contrast, raw COD showed a different behavior, where significant reductions only became apparent after P2 and were further enhanced in P3 (p \u0026lt; 0.05), with P1 showing no statistically significant removal. These findings suggest that dissolved organics are effectively treated in the initial stage, while particulate matter requires extended treatment through multiple ponds. The system\u0026apos;s performance indicates that P1 plays a crucial role in dissolved organic removal but may benefit from optimization to improve particulate matter reduction, whereas P2 and P3 are essential for particulate removal. However, it showed diminishing returns for dissolved organics. Statistical analysis showed no significant reduction in either raw or filtered BOD across ponds, despite some numerical changes, indicating consistent treatment limitations likely influenced by variability and small sample size. The high raw COD and BOD concentrations after the final treatment from P3 might be a consequence of the presence of algae, which could be visually observed in the liquid samples collected from each pond and also in the high presence of volatile suspended solids (Fig. S2 and Table S3, Supplementary material). Overall, the MF-WWTP showed a high level of COD (84%) and BOD (83%) removal, results comparable to similar plants treating industrial wastewater elsewhere (Alves et al. 2020; da Silva et al. 2011; Mahapatra et al. 2022; Veeresh et al. 2010). In addition, the results indicated the necessity of a filtering unit after P3 to remove the algae from the final effluent and achieve good performance.\u003c/p\u003e\n\u003cp\u003eThe nitrogen series presented in Table 2 offered relevant complementary insights into the performance of the MF-WWTP. The untreated wastewater had lower ammonia (N-NH₃) and higher nitrate (N-NO₃⁻) concentrations compared to typical values reported for domestic wastewater in the region, which range from 32.5 to 42.0 mg N-NH₃ L\u003csup\u003e-1\u003c/sup\u003e and 0.65 to 1.00 mg N-NO₃⁻ L\u003csup\u003e-1\u003c/sup\u003e (Espinosa et al., 2016; Silva et al., 1995; Soares et al., 1996). No statistically significant differences in N-NH₃ and TKN concentrations were observed during treatment. It could be the result of the solubilization of organic matter via anaerobic metabolism in reactors P1 and P2. It may have contributed to the release of ammonia into the effluent (Haandel Lubbe, 2012; Chernicharo, 2007; Von Sperling, 2007b). Additionally, the presence of algae in P3 may have further influenced the elevated concentrations of N-NH₃ and TKN. The removal of nitrate and nitrite observed in reactor P1 (Table 2) is likely attributable to microbial denitrification processes, facilitated by the anaerobic conditions and availability of organic substrates. The treated wastewater from the MF-WWTP achieves N-NH₃ concentrations in compliance with the standards for the discharge conditions and standards of effluents established by the Brazilian legislation (CONAMA 430/2011).\u003c/p\u003e\n\u003cp\u003eIn addition to organic matter and minerals, heavy metals are commonly present in industrial effluent treatment systems in Brazil (Souza and Siqueira, 2023). Industries have the largest number of processes from which heavy metals can originate, as they are used from incorporation into the product to washing machinery, pipes, floors, for cooling and steam generators. In the present work, 18 metals and semimetals were quantified in the MF-WWTP, since the incoming effluent receives waste from the metallurgical and metal-mechanical industries (Table 3). Nine out 18 metals/metalloids analyzed have specific effluent disposal limits defined by the current legislation (CONAMA 430/2011): lead (0.5 mg L\u003csup\u003e-1\u003c/sup\u003e), cadmium (0.2 mg L\u003csup\u003e-1\u003c/sup\u003e), boron (5.0 mg L\u003csup\u003e-1\u003c/sup\u003e), barium (5.0 mg L\u003csup\u003e-1\u003c/sup\u003e), iron (15.0 mg L\u003csup\u003e-1\u003c/sup\u003e), manganese (1.0 mg L\u003csup\u003e-1\u003c/sup\u003e), nickel (2.0 mg L\u003csup\u003e-1\u003c/sup\u003e), silver (0.1 mg L\u003csup\u003e-1\u003c/sup\u003e), and zinc (5.0 mg L\u003csup\u003e-1\u003c/sup\u003e). All samples from the MF-WWTP system complied with the Brazilian legislation for these nine compounds (Table 3). Additionally, in P3 effluent, complete removal of aluminum and a reduction level of barium, calcium, lithium, copper, nickel, and zinc were observed, most probably by the microalgae population (see below).\u003c/p\u003e\n\u003cp\u003eThe overall data show that a combination of sequential anaerobic, microaerobic, and aerobic or facultative ponds resulted in adequate treatment performance in treating a mix of industrial and domestic wastewater, provided that final filtering units are installed for the removal of algae from the final effluent. Pond 1 played a crucial role in dissolved organic conversion and nitrate removal, while particulate matter requires extended treatment across multiple ponds. The presence of algae in P3 influenced final COD, BOD and TKN concentrations, suggesting that a post-treatment unit is necessary to improve effluent quality and ensure more stable removal efficiencies.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eToxicity of the wastewater samples after treatment \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe ecotoxicity test with \u003cem\u003eA. fischeri\u003c/em\u003e used in this work is widely used for the evaluation of wastewater and industrial effluents because it provides a quick and economical response for monitoring effluents (Abbas et al. 2018) and lies in its ability to provide comprehensive insights into the biological effects of the treatment process. These tests are crucial for understanding how the system works and determining the degree of toxicity reduction at various stages of treatment (Boehler et al. 2017). The results herein revealed the toxicity potential of all samples by this test, with the highest values for samples collected from the effluents of P1 and P3 (Table S4, Supplementary material). In a complementary analysis, the seed germination test with \u003cem\u003eA. cepa\u003c/em\u003e corroborated the potential toxicity, cytotoxicity and genotoxicity of the samples (Table 5, Table S5, Supplementary material). After 48 hours of exposure, a significant reduction in germination values was observed in the presence of the untreated sample (16%) and samples from P1 (20%), P2 (11.33%), and P3 (31.33%), compared to the negative control (72%). At 72 hours, a significant reduction in the G72 value was observed only for the incoming effluent (66%) and P2 (59.33%) samples (Table 5) (p\u0026lt;0.05). At the end of 20 days, the germination values of the four points sampled from the MF-WWTP were similar to the negative control. Despite the toxicity observed in all three treatment ponds, the \u003cem\u003eA. cepa\u003c/em\u003e data indicated that P2 presented higher toxicity, probably related to the presence of biodegradation byproducts formed in the first two ponds. Despite some reduction in toxicity compared to P2, the final effluent from P3 still exhibited persistent toxicity, likely influenced by the presence of algal toxins. This understanding is vital for replicating or transferring the system to other treatment units, ensuring that the same efficiency and effectiveness in reducing toxicity can be achieved elsewhere.\u003c/p\u003e\n\u003cp\u003eSimilar toxic potential was found previously for effluents from electroplating, paper and dye industries (Abbas et al. 2018). In the present work, the differences in toxic potential for \u003cem\u003eA. fischeri \u003c/em\u003eand \u003cem\u003eA. cepa \u003c/em\u003eseed germination can be explained by changes in the characteristics of the ponds and the bioavailability of products/substances present in the collected samples. Wastewater from paint manufacturing, for instance, contribute to the increment of the chemical oxygen demand and turbidity, besides the organic and toxic chemicals levels, such as surfactants, bactericides, oils, solvents, preservatives and heavy metals which can cause environmental damages (Nair K et al. 2021; Verma et al. 2012). It is worth noting that the multifactory complex under study includes two paint manufacturing companies. Additionally, the results suggested that some non-bioavailable products in P1 underwent chemical transformation, possibly generating toxic by-products in Pond 2. On the other hand, these products appear to have undergone additional transformations in P3. Consequently, it might result in a reduction of toxicity towards the \u003cem\u003eA. cepa\u003c/em\u003e test system. Xylene, used in paints, rubber cleaning products, among others, is considered a toxic compound (Niaz et al. 2015). When not properly treated, it can be transformed into benzene, which is also a toxic environmental pollutant (Rana and Verma 2005). Moreover, a previous study with biotransformation of a tetra-azo dye showed a high acute ecotoxicity in anaerobic \u003cem\u003eA. fischeri\u003c/em\u003e assay due to aromatic amines accumulation. The ecotoxicity may be only eliminated after aromatic amines removal in micro-aerated conditions (Menezes et al. 2019).\u003c/p\u003e\n\u003cp\u003eAdditionally, cytotoxicity and genotoxicity were evaluated by mitotic and chromosomal alteration indexes (Table 5). The samples at the exit of each pond showed a significant increase in the mitotic index between 9.20% and 11.44% over the negative control (6.79%), revealing a genotoxic potential in their composition (p\u0026lt;0.05). Additionally, a genotoxic effect was evidenced for all evaluated samples, attributed to the significant increase in the rate of chromosomal alterations, ranging from 1.49% in untreated to 2.20% in P3 samples, respectively (Table 5). The following alterations showed a significant increase when compared to the negative control: micronuclei and chromosomal bridges in all three ponds; chromosomal breaks in P2 and P3, and chromosomal losses and C-metaphases in P1. Additionally, nuclear buds, chromosomal delays, chromosomal adhesions and polyploid cells were also observed (Table S5; Fig. S5). The nuclear and chromosomal alteration types suggest both the clastogenic (chromosome break) and the aneugenic (mitotic fiber problem) potentials of the molecules present in the ponds. Thus, the cytogenotoxicity data show that there was the maintenance of the cytogenotoxic potential at the exit of P3, despite the improvement in the physicochemical aspects at the end of the biological treatment process.\u003c/p\u003e\n\u003cp\u003eProducts and byproducts present in the effluents generated by the several industries of the complex and released into the MF-WWTP also seem to have acted as inducers of cytogenotoxicity in \u003cem\u003eA. cepa\u003c/em\u003e. According to Leme and Marin-Morales (2009), chemical compounds can influence the mitotic index, which can trigger uncontrolled cell growth that results in cellular and genetic alterations. The significant increase in the mitotic index observed in the present study in P1, P2, and P3 may be associated with the presence of cytotoxic substances that may have induced uncontrolled cell division in tissues (Grippa et al. 2010). Furthermore, different authors have associated chromosomal alterations with the presence of heavy metals (Sabeen et al. 2020), aromatic amines (Bomhard 2003), detergents (Pedrazzani et al. 2012), pesticides (Camilo-Cotrim et al. 2022), among others. The unique characteristic of the MF-WWTP, where a variety of products from textile cleaning, industrial washing, paint, varnish, enamel, lacquer, waterproofing, solvents, and related products, as well as domestic sewage, are collected, can also explain the significant genotoxic potential observed in the samples from all three ponds. The phenolic metabolites of benzene, for instance, can cause DNA strand breaks, chromosomal damage, sister chromatid exchange, inhibition of topoisomerase II and damage to the mitotic spindle (Rana and Verma 2005). Benzene itself showed cytotoxic and genotoxic effects in \u003cem\u003eA. cepa\u003c/em\u003e, by stimulating cell division and increasing chromosomal alterations, respectively (Barbhuiya et al. 2018).\u003c/p\u003e\n\u003cp\u003eTaking into account all the experimental data obtained for toxicity, we understand that, although toxicity has decreased at the P3 outlet, some methodologies can be implemented as an alternative to mitigate toxicity rates. For example, the installation of a primary coagulation treatment unit for some effluents could reduce toxicity and increase BOD.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e \u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBacterial \u003c/strong\u003e\u003cstrong\u003ecommunity in the sludge\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHigh-performance sequencing of the V3/V4 regions of the 16S rRNA was applied to survey the bacterial communities in each of the three ponds in the MF-WWTP, revealing the overall presence of 582 bacterial genera and 15 archaeal genera. The \u0026alpha;-diversity and b-diversity analyses of the microbial community composition showed that all ponds exhibited similar microbial diversity, with only minor variations in index values, indicating no substantial differences in community composition (Table 5). P1 showed the highest observed species richness (417 OTUs) and the greatest diversity according to both the Shannon (2.988) and Simpson (0.853) indices, indicating not only a larger number of genera but also a more even distribution compared to P2 and P3. In contrast, P2 had the lowest diversity values across all metrics (385 observed OTUs, Shannon = 2.340, Simpson = 0.768), suggesting a less diverse community potentially dominated by a few competitive or stress-tolerant genera. P3 pond showed the highest estimated total richness (Chao1 = 542.6, ACE = 523.6), while presenting slightly lower richness (412 OTUs) compared to P1. Therefore, a substantial pool of rare, low-abundance species seemed not to be fully captured in the sequencing.\u003c/p\u003e\n\u003cp\u003ePrevious studies reported the diversity of OTUs ranging from 530 to over 2000, and Chao1 estimates ranging from approximately 800 to over 1600, depending on the type of industrial wastewater and treatment systems. Shannon indices in those studies typically ranged between 2.6 and over 9.0, and Simpson values ranged from 0.10 to above 0.99 in some cases, indicating very high evenness and richness in those systems. In contrast, the Shannon diversity values for the ponds in this study ranged from 2.3 to 2.9, and Simpson indices ranged from 0.77 to 0.85. Nevertheless, the diversity observed in the three ponds falls toward the lower end of the spectrum when compared to values reported in the literature for sludge from treatment plants dealing with industrial and domestic wastewater, indicating that the microbial community from the MF-WTTP is less diverse and could be dominated by a few taxa (Wu et al., 2018; Qin et al., 2019; Wu et al., 2021). \u003c/p\u003e\n\u003cp\u003eThe \u0026beta;-diversity analysis (Fig. 3), which evaluated microbial community similarity among the ponds, revealed that while all three ponds shared highly similar compositions (Bray-Curtis distances ranging from 0.234\u0026ndash;0.281), P2 and P3 exhibited the greatest similarity, whereas P1 and P3 showed the least similar communities. This must be connected to their respective physicochemical profiles: P1 was considered an anaerobic, fermentative system, while P3 was a facultative pond with high algal activity. P2, the intermediary facultative/microaerobic pond, was characterized by the absence of a top-layer sediment. It favors the algae activity and likely explains its stronger compositional resemblance to P3 than to P1. \u003c/p\u003e\n\u003cp\u003eThe functional composition of microbial communities across the three treatment ponds was inferred using FAPROTAX, based on taxonomic profiles obtained from 16S rRNA gene sequencing (Fig. 4). In P1, functions associated with chemoheterotrophy (30.4%) and fermentation (19.9%) predominated, consistent with the accumulation of VFAs, suggesting active anaerobic conversion of the organic matter entering the system. Nitrate-metabolizing bacteria were also present at 3.7% of the population, which correlates to the observed nitrate removal in this pond. In P2, the abundance of OTUs classified with fermentation functions decreased to 15.1%, while phototrophy (6.0%) and photoautotrophy (5.8%) increased, suggesting a transition toward oxidative and photoautotrophic activity. The largest P3 showed functional potentials for methanogenesis (5.4%) and hydrogenotrophic methanogenesis (4.5%) alongside sustained levels of chemoheterotrophy (27.1%) and aerobic chemoheterotrophy (13.3%). It reflects a mixed community structure capable of both aerobic and anaerobic metabolism. As stated below, this pond was also characterized by substantial algal activity. The physicochemical profile of P3, including the consumption of VFAs, and a slight increase in the nitrate concentration, supports the predominance of aerobic mineralization of organic matter and the presence of organisms linked to the nitrogen cycling processes. Overall, the predicted functional profiles correspond to the measured environmental gradients and highlight the shift from anaerobic to increasingly aerobic and phototrophic microbial processes along the treatment sequence.\u003c/p\u003e\n\u003cp\u003eFigure 5 shows the microbial community composition of archaea (Fig. 5a) and bacteria (Fig. 5b) from the sludge of each pond, considering only the organisms with relative abundance over 1%. Regarding to the archaeal population, the three ponds were dominated by\u003cem\u003e Methanolinea\u003c/em\u003e (P1 with 34%, P2 with 39% and P3 with 33%), \u003cem\u003eMethanosaeta\u003c/em\u003e (P1 with 40%, P2 with 11% and P3 with 15%), and \u003cem\u003eMethanoregula\u003c/em\u003e (P1 with 9%, P2 with 33% and P3 with 30%). All these genera have been reported as having either hydrogenotrophic or acetoclastic methanogenic metabolism, typical of anaerobic digestion reactors, and are extremely resilient to extreme survival conditions (Mori et al. 2012; Weerakoon et al. 2023). Other archaeal genera are also present with relative abundance higher than 1% in at least one of the ponds, such as \u003cem\u003eMethanobacterium\u003c/em\u003e (hydrogenotrophic methanogen, 10% in P1 and 3% in P2), \u003cem\u003eMethanofollis\u003c/em\u003e (hydrogenotrophic methanogen, 1.6% in P1), \u003cem\u003eMethanosphaerula\u003c/em\u003e (hydrogenotrophic methanogen, 1.4% in P1) and \u003cem\u003eMethanospirillum\u003c/em\u003e (hydrogenotrophic methanogen, 3.3% in P2 and 1.6% in P3). Their methanogenic activity was reported following their isolation and identification (Zellner et al., 1999; Cadillo-Quiroz et al., 2009; Ferry et al. 1974).\u003c/p\u003e\n\u003cp\u003eThe bacterial population of organisms with relative abundance higher than 1% (Fig. 5.b) shows that most of the genera detected corresponds to extremophile bacteria, characteristic of inhospitable environments with high organic load, high temperatures and acidic pH, which corroborates the results of the physicochemical parameters found in the ponds (Tables 1 and 2). From all identified genera, \u003cem\u003eFervidobacterium\u003c/em\u003e stood as the most prominent in the treatment station with relative abundance of 18%, 38% and 21% in the bacterial population from P1, 2 and 3 respectively (Fig. 5.b). This genus includes a set of Gram-negative bacterial species, in the form of motile rods, strictly anaerobic and thermophilic, isolated for the first time from an Icelandic hot spring (Kanoksilapatham et al. 2016). Some species are reported as keratinase producers (Dhanasingh et al. 2021; Dhanasingh and Lee 2019; Kang et al. 2020; La et al. 2020). In wider terms, \u003cem\u003eFervidobacterium\u003c/em\u003e belongs to the Thermotogaceae family, widely distributed in nature and frequently found in salt flats, oil and petroleum contaminated environment, and biodigesters treating refinery effluents (Schaechter 2010). Besides the use of carbohydrates, many representatives of the Thermotogaceae family are known for the use of petroleum-derived compounds as carbon source to lactic and acetic acids, ethanol, CO\u003csub\u003e2 \u003c/sub\u003eand H\u003csub\u003e2 \u003c/sub\u003e(DiPippo et al. 2009). The presence of this bacteria in a high abundance in the population of P1 (Fig. 5.b) could be associated with the presence of xenobiotic compounds and with the production of acetic acid in P1 (Fig. S3, Supplementary materials). Obviously, the production of acetic acid must not be due only to the activity of \u003cem\u003eFervidobacterium\u003c/em\u003e species, but mainly because of it. The biotechnological potential of the genus \u003cem\u003eFervidobacterium\u003c/em\u003e has been recently reported (De Oliveira Silva et al. 2025). The remarkable arsenal of degradation enzymes allows the members of this bacterial group to use a diversity of carbon sources, including xenobiotics, as nutrients for cell growth (De Oliveira Silva et al. 2025).\u003c/p\u003e\n\u003cp\u003eThe second most abundant organism (9% in P1 and 6% in P2) belongs to the Gammaproteobacteria class, which comprises aerobic and facultative organisms. This class includes some of the most significant and widely recognized hydrocarbon-degrading bacteria, frequently enriched in marine environments, that play a crucial role in the breakdown of petrochemical and other xenobiotic compounds. Additionally, organisms from this class can perform denitrification, a process observed in P1 (Gutierrez, 2019). The decrease in its relative abundance from P1 to P2 and P3 (Fig. S4, Supplementary material) suggests that the primary compounds degraded by this organism were largely broken down in the first two ponds.\u003c/p\u003e\n\u003cp\u003eIn P1, the genera \u003cem\u003eComamonas\u003c/em\u003e, \u003cem\u003eAtopobium\u003c/em\u003e, \u003cem\u003eBacteroides\u003c/em\u003e and \u003cem\u003ePorphyromonas\u003c/em\u003e compose a group of organisms with low but significant relative abundance (2.1 \u0026ndash; 2.6%), suggesting functional importance. They are all associated with the fermentation of complex organic compounds and may also participate in the degradation of xenobiotic substances. \u003cem\u003eComamonas\u003c/em\u003e is particularly known for its ability to degrade aromatic hydrocarbons (e.g., phenols, benzoate) and alkanes under anaerobic conditions, and being able to perform nitrate reduction (Kwon, Kwon, and Kim 2019; Cummings and Branch 1986; Goyal and Zylstra 1996; Martinez-Burgos et al. 2020; Neal, Thiruppathy, and Zengler 2023; Willems and De Vos 2006; Wu, Zaiden, and Cao 2018; Xu et al. 2024). Fermentative genera such as \u003cem\u003ePrevotella\u003c/em\u003e and \u003cem\u003eAtopobium\u003c/em\u003e likely contribute to volatile fatty acid (VFA) production through polysaccharide or amino acid fermentation, while \u003cem\u003ePorphyromonas,\u003c/em\u003e with its proteolytic capabilities, may aid in breaking down nitrogen-rich substrates like proteins and mucins. The significant decrease of their relative abundance in P2 and P3 (Fig. S4, Supplementary material) can also indicate their major role in the fermentation of organic compounds in P1. \u003c/p\u003e\n\u003cp\u003eThe genus \u003cem\u003eAminobacterium\u003c/em\u003e (Fig. 4) also showed high relative abundance (1.9% in P1, 2.5% in P2 and 4.1% in P3). This genus belongs to the Synergistaceae family and is composed of Gram-negative bacterial species that degrade amino acids, being isolated mainly in ponds that receive dairy waste (Baena et al. 2000), which is also the case of MF-WWTP. This indicates that a high potential for amino acid metabolizing activity can be found in that bacterial community.\u003c/p\u003e\n\u003cp\u003eThe genus \u003cem\u003eAzoarcus\u003c/em\u003e was detected mainly in P1 (relative abundance of 1%), a pond with a surface layer of oil and grease residues. It is a genus of anaerobic, nitrogen-fixing bacteria, often with an endophytic lifestyle and with the metabolic capacity to degrade different aromatic compounds. Among these compounds, some are considered extremely toxic and carcinogenic, such as toluene and xylene that encompass the BTEX compounds (benzene, toluene, ethylbenzene and xylene) (Fernandes et al., 2014; Kato et al. 2019; Patil et al. 2021; Pournia et al. 2019; Wushke et al. 2018).\u003c/p\u003e\n\u003cp\u003eA few genera with no significance in P1 and P2 increased their relative abundance in P3. Among them were members of the classes \u003cem\u003eBetaproteobacteria\u003c/em\u003e, \u003cem\u003eGammaproteobacteria\u003c/em\u003e, and \u003cem\u003eAlphaproteobacteria\u003c/em\u003e, suggesting an important role in the terminal stages of organic matter conversion. These classes include metabolically versatile genera capable of utilizing diverse carbon substrates, including residual volatile fatty acids (VFAs), algal metabolites, and more recalcitrant compounds derived from earlier fermentation and partial oxidation processes. For instance, \u003cem\u003eBetaproteobacteria\u003c/em\u003e are known for their ability to thrive in nutrient-depleted, oxygen-rich environments and contribute to heterotrophic nitrification and biofilm formation (Zhang et al., 2019). Likewise, \u003cem\u003eGammaproteobacteria \u003c/em\u003einclude organisms that specialize in degrading xenobiotics and algal-derived dissolved organic matter (Alves et al., 2022).\u003c/p\u003e\n\u003cp\u003eThe presence of members from the class \u003cem\u003eAlphaproteobacteria,\u003c/em\u003e many of which form close associations with algal cells, further points to the ecological importance of algae-bacteria interactions in this pond. Some genera can metabolize aromatic compounds and polysaccharides, particularly those derived from algal exudates. They are also known for their capacity to produce extracellular polymeric substances (EPS), which enhances floc stability and nutrient capture (Landa et al., 2017).\u003c/p\u003e\n\u003cp\u003eInterestingly, several taxa typically associated with anaerobic metabolism, such as \u003cem\u003eClostridium\u003c/em\u003e, \u003cem\u003eBacteroides\u003c/em\u003e, and \u003cem\u003eDethiosulfovibrio\u003c/em\u003e were also detected in this aerobic pond. Their persistence likely reflects the presence of anoxic microenvironments within flocs, sediments, or algal mats, where fermentative degradation of complex organics such as proteins, amino acids, and polysaccharides can happen. \u003cem\u003eClostridium\u003c/em\u003e and \u003cem\u003eBacteroides\u003c/em\u003e are well-documented anaerobes that play significant roles in polysaccharide and protein fermentation, while \u003cem\u003eDethiosulfovibrio\u003c/em\u003e is associated with sulfur metabolism and amino acid degradation in anaerobic niches (Thomas et al., 2011; Ravot et al., 1999; Wiegel et al., 2006).\u003c/p\u003e\n\u003cp\u003eThe detection of \u003cem\u003eAlicyclobacillus\u003c/em\u003e in P3 also merits attention. Although typically associated with thermophilic and acidophilic environments, some species have demonstrated the ability to degrade lignin-derived compounds, such as those found in pulp and paper wastewater (Aston et al., 2016). Their presence in the third pond may indicate the persistence of recalcitrant aromatic compounds that resisted degradation in upstream units.\u003c/p\u003e\n\u003cp\u003eThe high relative abundance of \u003cem\u003eFervidobacterium\u003c/em\u003e across all ponds suggests its potential as a key player in treatment performance in the MF-WWTP. As a thermophilic anaerobe linked to hydrocarbon metabolism, including petroleum-derived compounds. The members of this genus might be significantly contributing to the breakdown of soluble organic substrates and xenobiotic contaminants in all ponds. Hence, the sludge from these ponds could be used as a viable inoculum source for other systems treating recalcitrant pollutants, facilitating the remediation of industrial effluents.\u003c/p\u003e\n\u003cp\u003eThe microbial communities in three sequential ponds exhibited slight functional shifts that matched the physicochemical analysis, resulting in a very well-established flux of metabolic events in the course of the waste water treatment process: (1) The \u003cem\u003eFervidobacterium\u003c/em\u003e genus was notably abundant across all ponds, highlighting its potential role in the biodegradation of industrial effluents. P1, dominated by anaerobic fermenters and xenobiotic degraders facilitated initial organic matter breakdown; (2) P2 represents a transition toward oxidative metabolism, with declining fermenters and rising phototrophic organisms; (3) P3 consolidates this transition to an aerobic processes due to the algal interactions and abundance of \u003cem\u003eBetaproteobacteria\u003c/em\u003e and \u003cem\u003eAlphaproteobacteria\u003c/em\u003e involved in nitrogen cycling, though the presence of a many facultative organisms indicated the presence of anoxic microenvironments. \u003c/p\u003e\n\u003cp\u003eDespite lower \u0026alpha;-diversity, each pond maintained specialized taxa adapted to the degradation of industrial wastewaters (Table 4). The sludge\u0026rsquo;s microbial richness, particularly \u003cem\u003eFervidobacterium\u003c/em\u003e, positions it as a promising inoculum for degrading recalcitrant pollutants, as recently assigned (De Oliveira Silva et al. 2025). This study underscores how structured microbial networks enable efficient wastewater treatment through phased anaerobic, phototrophic, and aerobic processes, offering insights for optimizing industrial bioremediation.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eCyanobacteria and microalgae community\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the analysis of 12 points of the MF-WWTP, a total of 27,183 organisms belonging to cyanobacteria and microalgae were observed under the optical microscope, which were classified into 17 genera: nine genera of cyanobacteria (\u003cem\u003eOscillatoria\u003c/em\u003e,\u003cem\u003e \u003c/em\u003e14.22%; \u003cem\u003eGeitlerinema\u003c/em\u003e, 13.58%;\u003cem\u003e Phormidium\u003c/em\u003e,\u003cem\u003e \u003c/em\u003e0.10%;\u003cem\u003e Pseudanabaena\u003c/em\u003e,\u003cem\u003e \u003c/em\u003e0.001%;\u003cem\u003e Arthrospira\u003c/em\u003e,\u003cem\u003e \u003c/em\u003e11.55%;\u003cem\u003e Planktorhrix\u003c/em\u003e,\u003cem\u003e \u003c/em\u003e9.21%;\u003cem\u003e Planktolyngbya\u003c/em\u003e,\u003cem\u003e \u003c/em\u003e0.53%;\u003cem\u003e Chroococcus\u003c/em\u003e,\u003cem\u003e \u003c/em\u003e14.91%, and\u003cem\u003e Merismopedia\u003c/em\u003e,\u003cem\u003e \u003c/em\u003e2.55%) and eight genera of microalgae (\u003cem\u003eFragillaria\u003c/em\u003e,\u003cem\u003e \u003c/em\u003e7.84%;\u003cem\u003e Navicula\u003c/em\u003e,\u003cem\u003e \u003c/em\u003e0.001%;\u003cem\u003e Aulacoseira\u003c/em\u003e,\u003cem\u003e \u003c/em\u003e0.02%;\u003cem\u003e Cyclotella \u003c/em\u003e10.32%;\u003cem\u003e Chlorella\u003c/em\u003e,\u003cem\u003e \u003c/em\u003e13.92%;\u003cem\u003e Trachelomonas\u003c/em\u003e,\u003cem\u003e \u003c/em\u003e0.28%;\u003cem\u003e Oocystis\u003c/em\u003e,\u003cem\u003e \u003c/em\u003e15.15%,\u003cem\u003e \u003c/em\u003eand \u003cem\u003eChoricystis\u003c/em\u003e,\u003cem\u003e \u003c/em\u003e11.14%) (Fig. 6 and Fig. S6, Supplementary material). Most of these microorganisms were observed in P2 and P3 that present higher values of redox potential, conductivity, pH and DO. This indicates that most of the organic load of the industrial effluent is consumed by the microbial population in P1. Thus, the very low content of organic matter coming from P1 makes possible the establishment of cyanobacteria and microalgae populations in the further ponds (Fig. 6). In the ponds of the MF-WWTP, cyanobacteria such as Phormidium and Pseudanabaena were exclusively found in P1; Merismopedia, Planktolyngbya, and Planktothrix were exclusively found in P3, while the remaining algae species were observed in two or three ponds simultaneously. Regarding microalgae, Trachelomonas, Navicula and Aulacoseira were exclusively found in P1, while Fragillaria was exclusive of P3. The remaining ones were observed in P2 and P3 (Fig. 6).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eOscillatoria\u003c/em\u003e was the most observed cyanobacteria/microalgae in P1 (85%) (Fig. S6, Supplementary material), indicating how inhospitable this environment is for this type of microorganism. This is the pond with the lowest amount of dissolved oxygen, as well as the highest concentration of ammonia and phosphorus, the highest temperature, the lowest redox potential and the highest concentration of pollutants. In addition, this pond is covered with a layer of oil that prevents the passage of sunlight to its interior. \u003cem\u003eOscillatoria\u003c/em\u003e comprises a group of mixotrophic cyanobacterial species capable of colonizing eutrophic environments characterized by an excess of nitrogen and phosphorus (Reynolds et al. 2002), and these species are also known for their ability to fix nitrogen and carry out chemosynthesis (O\u0026rsquo;Farrell et al. 2003). Their presence is associated with excessive pollution with an organic load and also indicates environments with little availability of dissolved oxygen (Fran\u0026ccedil;a et al. 2022), stratified environments with the presence of chemical and thermal gradients and even nutrient circulation (Li 2021), parameters that characterize P1.\u003c/p\u003e\n\u003cp\u003eThe green microalgae \u003cem\u003eChlorella, Oocystis\u003c/em\u003e and \u003cem\u003eChoricystis, \u003c/em\u003eas well as the cyanobacteria \u003cem\u003eChoococcus\u003c/em\u003e were frequently observed in P2. In this pond dissolved oxygen was higher than in P1, with lower temperature and absence of oil agglomerations on the surface. Consequently, this pond has a higher incidence of sunlight and allows the development of green microalgae. These organisms are associated with aerobic, illuminated and eutrophic environments (Reynolds et al. 2002), in addition to being resistant to several chemical compounds in rivers (Chen et al. 2022). However, the presence of \u003cem\u003eOscillatoria\u003c/em\u003e in P2 with the highest value in biovolume among the microorganisms observed (Fig. 6) is an indication that this pond still has a considerable organic load and mixotrophic activity.\u003c/p\u003e\n\u003cp\u003eGiven the presence of four unique microorganisms, P3 was the most diverse pond (9/17). The largest biovolume measured was that of the genus \u003cem\u003eCyclotella\u003c/em\u003e followed by \u003cem\u003ePlanktothri\u003c/em\u003ex. The first corresponds to a group of diatoms related to eutrophic environments with excess nitrogen and phosphorus (Arumugham et al. 2023; Wu 1999). These are two elements that reappear from P2 and are found in high concentration in P3 (Table 2). As highlighted above, P3 has the highest conductivity and pH values and the lowest temperature value among the lakes, and these are precisely variables that positively impact the abundance of \u003cem\u003eCyclotella\u003c/em\u003e (Heneash et al. 2022).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ePlanktothrix\u003c/em\u003e is a quite diverse group of cyanobacteria that inhabit freshwater to turbid mixed lakes (Kom\u0026aacute;rek and Kom\u0026aacute;kov\u0026aacute; 2004). It means that this microorganism lives in environments with high levels of dissolved oxygen as is the case of P3 (Fig. 2). However, blooms of \u003cem\u003ePlanktothrix\u003c/em\u003e are associated with problems regarding human health and negative impact to agriculture due to the production of cyanotoxins by this group of cyanobacteria (Christiansen et al. 2003). The presence of this type of microorganism in large proportions in P3, from which the treated effluent should be discharged into the nearby hydrographic basin, may constitute an alert for the MF-WWTP treatment unit to monitor the release of cyanotoxins in the environment.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThe MF-WTTP is one of the few units of the wastewater treatment systems in the state of Pernambuco that receives both domestic and industrial effluents. Unlike other units, it has never undergone the process of removing its sludge for at least 20 years of functioning. It allowed for the enrichment of a highly adapted mixed population of bacteria, archaea and algae adapted to extreme conditions. This context set a well-suited condition for breaking down industrial wastewater with notable efficiency in a sequential step. In the anaerobic P1 harboring species of \u003cem\u003eFervidobacterium\u0026nbsp;\u003c/em\u003eand methanogenic archaea (\u003cem\u003eMethanolinea\u003c/em\u003e and \u003cem\u003eMethanosaeta\u003c/em\u003e) worked for the main removal of COD and BOD and the conversion of organic substrates and recalcitrant compounds from the industrial effluents to simpler molecules. The other metabolic step in the microaerobic P2 and a final aerobic P3 completed the process. However, the toxicity of the incoming effluent remained and was slightly increased in the course of the treatment, likely due to the production and bacterial metabolic activity and by the potential production of algae-associated toxins. Hence, the results highlighted the need for a post-treatment unit to reduce the presence of algae and the remaining toxicity from the effluent of P3. Finally, the combination of anaerobic, microaerobic, and aerobic ponds, along with additional polishing units, could serve as an effective strategy for a treatment facility in the region as long as the resilient microbial communities can be efficiently transplanted to other units.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePart of the data is provided as supplementary material. Raw data on microbial community and chemical analysis will be available upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank the Companhia Pernambucana de Saneamento (Compesa) and the Agência Pernambucana de Meio Ambiente (CPRH) for their technical support.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors and Affiliations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUniversidade Federal de Pernambuco, Centro de Tecnologia e Geociências, Departamento de Engenharia Civil e Ambiental, Recife, Pernambuco, Brasil\u003c/p\u003e\n\u003cp\u003eLuiz Pereira Silva Júnior, Fernanda Magalhães Amaral, Fabrício Motteran, Bruna Soares Fernandes Sávia Gavazza\u003c/p\u003e\n\u003cp\u003eUniversidade Federal de Pernambuco, Centro de Biociências, Departamento de Genética, Recife, Pernambuco, Brasil\u003c/p\u003e\n\u003cp\u003eBruna Kelly de Oliveira Silva, Nathália Bandeira Carvalho dos Santos, Natercia Correa de Araújo, Ana Christina Brasileiro Vidal, Marcos Antonio de Morais Junior\u003c/p\u003e\n\u003cp\u003eUniversidade Federal do Vale do São Francisco, Colegiado de Biologia, Petrolina, Pernambuco, Brasil\u003c/p\u003e\n\u003cp\u003eKyria Cilene de Andrade Bortoleti\u003c/p\u003e\n\u003cp\u003eCompanhia Pernambucana de Saneamento, Diretoria de Atenção ao Cliente, Recife, Pernambuco, 50110-006, Brasil\u003c/p\u003e\n\u003cp\u003eBartholomeu Siqueira Júnior, Fábio Henrique Portella Corrêa de Oliveira\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e[SA, ARK, and MK]: study design, data preprocessing, methodology, visualization, writing draft.\u003c/p\u003e\n\u003cp\u003e[SA and MK]: study design, supervision.\u003c/p\u003e\n\u003cp\u003e[MK]: writing draft, reviewing, and editing.\u003c/p\u003e\n\u003cp\u003e[ARK]: methodology, writing draft, reviewing, and editing\u003c/p\u003e\n\u003cp\u003e[LPSJ, BKOS, NBCS, NCA, FMA]: conceptualization, methodology, validation, formal analysis, investigation and writing the original draft.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e[BSJ, and FHPCO]: conceptualization, review and project administration.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e[BSF, FM, KCAB, ACBV, MAMJ, and SGSP]: conceptualization, review and editing, project administration and supervision.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorresponding author\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCorrespondence to\u0026nbsp;\u003ca href=\"mailto:[email protected]\"\u003eMarcos A Morais Jr\u003c/a\u003e and to\u0026nbsp;\u003ca href=\"mailto:[email protected]\"\u003eSávia Gavazza\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to declare that no ethical approval was required for this study\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003enot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have the consent to participate in this study by their institutions signed in the moment of project submission for the funding call.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDespite partial funding by a private institution, the call for research funding by the public agency CNPq, in agreement with the co-financier, requires the results to be publicized in all appropriate media.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBSJ and FHPCO are employees of the company COMPESA, which partially financed this work. However, they only participated in its administration and supervision, in the discussion of the results and the outcome of their application to other treatment units. Their employer did not intervene in carrying out the experiments or in the results obtained. The remaining co-authors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported with an expense budget and scholarships by the grants of the project “Biotechnological solutions in support of the universalization of water and sewage services by public sanitation companies”, approved by the National Council of Research (CNPq grant 403657/2020-2), co-financed by the company COMPESA.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that the data supporting the findings of this study are available within the paper and its\u0026nbsp;Supplementary Materials files. Should any raw data files be needed in another format, they are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAbbas, M., Adil, M., Ehtisham-ul-Haque, S., Munir, B., Yameen, M., Ghaffar, A., Shar, G.A., Asif Tahir, M., Iqbal, M., 2018. Vibrio fischeri bioluminescence inhibition assay for ecotoxicity assessment: A review. Sci. Total Environ. 626, 1295\u0026ndash;1309. https://doi.org/10.1016/j.scitotenv.2018.01.066\u003c/li\u003e\n \u003cli\u003eAlves, M.S., da Silva, F.J.A., Ara\u0026uacute;jo, A.L.C., Pereira, E.L., 2020. First-Order Removal Rates for Organic Matter in Full-Scale Waste Stabilization Pond Systems in Northeastern Brazil. Water. Air. Soil Pollut. 231, 528. https://doi.org/10.1007/s11270-020-04855-w\u003c/li\u003e\n \u003cli\u003eAlves, O.I.M., Ara\u0026uacute;jo, J.M., Silva, P.M.J., Magnus, B.S., Gavazza, S., Florencio, L., Kato, M.T., 2022. 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Extremophiles 22, 965\u0026ndash;974. https://doi.org/10.1007/s00792-018-1053-4\u003c/li\u003e\n \u003cli\u003eXu, M., Liu, Y., Li, H., Yang, X., Yue, W., Zhang, Y., Liu, D., Wu, M., Wang, D., Xiong, G., Guo, L., Song, K., 2024. Anthracene degradation involved by antibiotic biosynthesis monooxygenase (ABM) in Comamonas testosteroni. Int. Biodeterior. Biodegrad. 190, 105790. https://doi.org/10.1016/j.ibiod.2024.105790\u003c/li\u003e\n \u003cli\u003eZellner, G., Boone, D.R., Keswani, J., Whitman, W.B., Woese, C.R., Hagelstein, A., Tindall, B.J., Stackebrandt, E., 1999. Reclassification of \u003cem\u003eMethanogenium tationis\u003c/em\u003e and \u003cem\u003eMethanogenium liminatans\u003c/em\u003e as \u003cem\u003eMethanofollis tationis\u003c/em\u003e gen. nov., comb. nov. and \u003cem\u003eMethanofollis liminatans\u003c/em\u003e comb. nov. and description of a new strain of \u003cem\u003eMethanofollis liminatans\u003c/em\u003e. Int. J. Syst. Bacteriol. 49, 247\u0026ndash;255. https://doi.org/10.1099/00207713-49-1-247\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e Chemical demand for oxygen (COD) and Biochemical Oxygen Demand (BOD) analysis and removal efficiencies of the untreated sample and pond 1, 2 and 3 samples from the Multifactory Wastewater Treatment Plant (MF-WWTP).\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"577\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003eParameter (mg O\u003csub\u003e2\u003c/sub\u003e/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003eUntreated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003ePond 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003ePond 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003ePond 3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003eRaw COD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e1569.8 \u0026plusmn; 439.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e1000.9 \u0026plusmn; 529.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e728.7 \u0026plusmn; 335.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e826.6 \u0026plusmn; 563.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003eFiltered COD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e1301.8 \u0026plusmn; 136.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e491.3 \u0026plusmn; 544.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e347.0 \u0026plusmn; 268.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e250.7 \u0026plusmn; 159.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003eRaw BOD\u003csub\u003e5,20\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e875.0 \u0026plusmn; 530.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e475 \u0026plusmn; 530.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e550.0 \u0026plusmn; 565.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e375.0 \u0026plusmn; 388.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003eFiltered BOD\u003csub\u003e5,20\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e225.0 \u0026plusmn; 247.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e500.0 \u0026plusmn; 0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e345.0 \u0026plusmn; 275.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e150.0 \u0026plusmn; 0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003eRaw COD/BOD\u003csub\u003e5,20\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e1.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e2.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e1.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e2.20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003eFiltered COD/BOD\u003csub\u003e5,20\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e5.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1.67\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cbr\u003e\u003c/strong\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e. Physicochemical parameters of the untreated and ponds 1, 2 and 3 samples from the Multifactory Wastewater Treatment Plant (MF-WWTP).\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"643\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003eParameter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eUntreated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003ePond 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003ePond 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003ePond 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eUpper limit*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003eRaw ammonia (mg N-NH\u003csub\u003e3\u003c/sub\u003e/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e8.4 - 17.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e9.4 - 21.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e8.4 - 17.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e9.9 - 11.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e20.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003eFiltered ammonia (mg N-NH\u003csub\u003e3\u003c/sub\u003e/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e7.3 - 11.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e7.5 - 19.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e7.1 - 15.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e6.9 - 9.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003eRaw TKN (mg N/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e23 - 50.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e17.5 - 67.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e23.3 - 26.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e28.2 - 51.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003eNitrate (mg N-NO\u003csub\u003e3\u003c/sub\u003e/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e4.65 \u0026plusmn; 0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.39 \u0026plusmn; 0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.35 \u0026plusmn; 0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.50 \u0026plusmn; 0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003eNitrite (mg N-NO\u003csub\u003e2\u003c/sub\u003e/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.12 \u0026plusmn; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e* Defined by the Brazilian legislation CONAMA 430/2011 and 357/2005\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3.\u003c/strong\u003e Metal and semimetal data of the untreated sample and pond 1, 2 and 3 samples from the Multifactory Wastewater Treatment Plant (MF-WWTP).\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"594\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3036%;\"\u003e\n \u003cp\u003eMetal (mg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.0592%;\"\u003e\n \u003cp\u003eUntreated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7502%;\"\u003e\n \u003cp\u003ePond 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7502%;\"\u003e\n \u003cp\u003ePond 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5957%;\"\u003e\n \u003cp\u003ePond 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 14.0592%;\"\u003e\n \u003cp\u003eUpper limit*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3036%;\"\u003e\n \u003cp\u003eAluminum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.0592%;\"\u003e\n \u003cp\u003e7.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7502%;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7502%;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5957%;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 14.0592%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3036%;\"\u003e\n \u003cp\u003eBarium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.0592%;\"\u003e\n \u003cp\u003e0.097\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7502%;\"\u003e\n \u003cp\u003e0.061\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7502%;\"\u003e\n \u003cp\u003e0.065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5957%;\"\u003e\n \u003cp\u003e0.056\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 14.0592%;\"\u003e\n \u003cp\u003e5.0\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3036%;\"\u003e\n \u003cp\u003eBeryllium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.0592%;\"\u003e\n \u003cp\u003e\u0026lt;0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7502%;\"\u003e\n \u003cp\u003e\u0026lt;0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7502%;\"\u003e\n \u003cp\u003e\u0026lt;0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5957%;\"\u003e\n \u003cp\u003e\u0026lt;0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 14.0592%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3036%;\"\u003e\n \u003cp\u003eBoron\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.0592%;\"\u003e\n \u003cp\u003e\u0026lt;0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7502%;\"\u003e\n \u003cp\u003e\u0026lt;0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7502%;\"\u003e\n \u003cp\u003e\u0026lt;0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5957%;\"\u003e\n \u003cp\u003e\u0026lt;0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 14.0592%;\"\u003e\n \u003cp\u003e5.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3036%;\"\u003e\n \u003cp\u003eCadmium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.0592%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7502%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7502%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5957%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 14.0592%;\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3036%;\"\u003e\n \u003cp\u003eCalcium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.0592%;\"\u003e\n \u003cp\u003e29.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7502%;\"\u003e\n \u003cp\u003e25.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7502%;\"\u003e\n \u003cp\u003e29.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5957%;\"\u003e\n \u003cp\u003e22.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 14.0592%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3036%;\"\u003e\n \u003cp\u003eLead\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.0592%;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7502%;\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7502%;\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5957%;\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 14.0592%;\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3036%;\"\u003e\n \u003cp\u003eCobalt\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.0592%;\"\u003e\n \u003cp\u003e\u0026lt;0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7502%;\"\u003e\n \u003cp\u003e\u0026lt;0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7502%;\"\u003e\n \u003cp\u003e\u0026lt;0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5957%;\"\u003e\n \u003cp\u003e\u0026lt;0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 14.0592%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3036%;\"\u003e\n \u003cp\u003eCopper\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.0592%;\"\u003e\n \u003cp\u003e0.048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7502%;\"\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7502%;\"\u003e\n \u003cp\u003e0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5957%;\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 14.0592%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3036%;\"\u003e\n \u003cp\u003eIron\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.0592%;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7502%;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7502%;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5957%;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 14.0592%;\"\u003e\n \u003cp\u003e15.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3036%;\"\u003e\n \u003cp\u003eLithium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.0592%;\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7502%;\"\u003e\n \u003cp\u003e\u0026lt;0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7502%;\"\u003e\n \u003cp\u003e\u0026lt;0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5957%;\"\u003e\n \u003cp\u003e\u0026lt;0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 14.0592%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3036%;\"\u003e\n \u003cp\u003eMagnesium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.0592%;\"\u003e\n \u003cp\u003e\u0026lt;1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7502%;\"\u003e\n \u003cp\u003e\u0026lt;1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7502%;\"\u003e\n \u003cp\u003e\u0026lt;1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5957%;\"\u003e\n \u003cp\u003e\u0026lt;1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 14.0592%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3036%;\"\u003e\n \u003cp\u003eManganese\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.0592%;\"\u003e\n \u003cp\u003e\u0026lt;0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7502%;\"\u003e\n \u003cp\u003e\u0026lt;0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7502%;\"\u003e\n \u003cp\u003e\u0026lt;0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5957%;\"\u003e\n \u003cp\u003e\u0026lt;0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 14.0592%;\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3036%;\"\u003e\n \u003cp\u003eNickel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.0592%;\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7502%;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7502%;\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 14.8317%;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.8232%;\"\u003e\n \u003cp\u003e2.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3036%;\"\u003e\n \u003cp\u003eSilver\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.0592%;\"\u003e\n \u003cp\u003e\u0026lt;0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7502%;\"\u003e\n \u003cp\u003e\u0026lt;0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7502%;\"\u003e\n \u003cp\u003e\u0026lt;0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 14.8317%;\"\u003e\n \u003cp\u003e\u0026lt;0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.8232%;\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3036%;\"\u003e\n \u003cp\u003eVanadium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.0592%;\"\u003e\n \u003cp\u003e\u0026lt;0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7502%;\"\u003e\n \u003cp\u003e\u0026lt;0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7502%;\"\u003e\n \u003cp\u003e\u0026lt;0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 14.8317%;\"\u003e\n \u003cp\u003e\u0026lt;0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.8232%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3036%;\"\u003e\n \u003cp\u003eZinc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.0592%;\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7502%;\"\u003e\n \u003cp\u003e\u0026lt;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7502%;\"\u003e\n \u003cp\u003e\u0026lt;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 14.8317%;\"\u003e\n \u003cp\u003e\u0026lt;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.8232%;\"\u003e\n \u003cp\u003e5.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e* Defined by the Brazilian legislation CONAMA 430/2011 and 357/20\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4.\u003c/strong\u003e Alpha diversity indices from the (Observed, Shannon, Simpson, Chao1, and ACE) calculated using the \u003cem\u003ephyloseq\u003c/em\u003e package. These metrics describe the richness and evenness of microbial communities across the analyzed samples.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"587\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003eSample\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eObserved\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eShannon\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003eSimpson\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eChao1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eACE\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003ePond 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e417.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e2.988\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e0.853\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e516.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e515.191\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003ePond 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e385.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e2.340\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e0.768\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e513.775\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e498.507\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003ePond 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e412.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e2.865\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e0.831\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e542.634\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e523.645\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\n\u003cp\u003e\u003cstrong\u003eTable 5.\u003c/strong\u003e Evaluation of toxicity, cytotoxicity and genotoxicity of samples collected from the ponds of the Multifactory Wastewater Treatment Plant using the \u003cem\u003eAllium cepa\u003c/em\u003e test system, through analysis of the Germination index (G), Mitotic Index (MI) and Chromosome Alteration Index (CAI) in meristematic cells. Sampling points: untreated sample (SP13), pond 1 (SP14), pond 2 (SP15) and pond 3 (SP16).\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"587\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eSample\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 282px;\"\u003e\n \u003cp\u003eToxicity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eCytotoxicity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eGenotoxicity\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eG\u003csub\u003e48\u003c/sub\u003e (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eG\u003csub\u003e72\u003c/sub\u003e (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003eG (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eMI (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eCAI (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003eNC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e72.00 \u0026plusmn; 2.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e81.33 \u0026plusmn; 4.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e94.67 \u0026plusmn; 1.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e6.79 \u0026plusmn; 1.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.62 \u0026plusmn; 0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003eMMS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e1.36 \u0026plusmn; 0.27*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003eTRI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e6.77 \u0026plusmn; 0.76*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003eUntreated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e16.00 \u0026plusmn; 2.83*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e66.00 \u0026plusmn; 6.53*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e91.33 \u0026plusmn; 7.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e7.75 \u0026plusmn; 1.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e1.49 \u0026plusmn; 0.65*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003ePond 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e20.00 \u0026plusmn; 4.90*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e74.00 \u0026plusmn; 4.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e93.33 \u0026plusmn; 9.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e9.83 \u0026plusmn; 1.97*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e1.60 \u0026plusmn; 0.58*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003ePond 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e11.33 \u0026plusmn; 4.11*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e59.33 \u0026plusmn; 9.29*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e93.33 \u0026plusmn; 6.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e9.20 \u0026plusmn; 1.31*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e1.88 \u0026plusmn; 0.85*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003ePond 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e31.33 \u0026plusmn; 5.73*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e82.67 \u0026plusmn; 7.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e98.67 \u0026plusmn; 1.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e11.44 \u0026plusmn; 1.47*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e2.20 \u0026plusmn; 0.92*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNC = Negative Control; MMS = Methyl Methane Sulfonate; TRI = Trifluralin; G\u003csub\u003e48\u003c/sub\u003e = Partial germination at 48 h; G\u003csub\u003e72\u003c/sub\u003e = Partial germination at 72 h; G = Total germination; MI = Mitotic Index; CAI = Chromosomal Alteration Index. Statistically significant values different from NC (p \u0026lt; 0.05) are followed by an asterisk.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"environmental-science-and-pollution-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"espr","sideBox":"Learn more about [Environmental Science and Pollution Research](https://www.springer.com/journal/11356)","snPcode":"11356","submissionUrl":"https://submission.nature.com/new-submission/11356/3","title":"Environmental Science and Pollution Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"industrial wastewater, waste stabilization ponds, Fervidobacterium, Cyclotella, Planktothrix, microbial community, algal community","lastPublishedDoi":"10.21203/rs.3.rs-6770575/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6770575/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe Multifactory Wastewater Treatment Plant (MF-WWTP) in Recife, Brazil, receives effluents from 15 different industries as well as domestic waste and, unlike other facilities, has never undergone sludge removal over 20 years of operation. It allowed the establishment of a highly adapted microbial community and a high level of waste removal. This study investigated its physicochemical characteristics and how the microbial composition may contribute to its efficiency. The results indicated a COD and BOD removal of 84%, primarily occurring in Pond 1 (P1, anaerobic), while Pond 3 (P3, aerobic with high algal activity) aided in heavy metal removal. Despite this efficiency, toxicity persisted in the final effluent, evidenced by mitotic index values (9.20–11.44% vs. 6.79% control) and chromosomal alterations (1.49–2.20%), which could be a result of toxin-producing algae in P3. The microbial analysis identified \u003cem\u003eFervidobacterium\u003c/em\u003e as the dominant bacterial genus up to 38% of relative abundance, alongside methanogenic archaea (\u003cem\u003eMethanolinea\u003c/em\u003e and \u003cem\u003eMethanosaeta\u003c/em\u003e), suggesting their importance for the organic substrate conversion and degradation of industrial pollutants. Additionally, the cyanobacteria \u003cem\u003eCyclotella\u003c/em\u003e and the microalgae \u003cem\u003ePlanktothrix \u003c/em\u003ewere highly abundant in P3 (10 mm³.L\u003csup\u003e-1\u003c/sup\u003e and 9 mm³.L\u003csup\u003e-1\u003c/sup\u003e, respectively), which might have contributed to the treatment system but also to potential toxin production in the final effluent. These findings suggest that the combination of anaerobic, microaerobic, and aerobic ponds, along with additional polishing units, can be a viable approach for industrial wastewater treatment. Furthermore, both the sludge and final effluent from MF-WWTP could serve as valuable inoculum sources for other treatment units dealing with complex industrial contaminants, aiding in establishing resilient microbial communities for optimized wastewater treatment.\u003c/p\u003e","manuscriptTitle":"Characterization of the treatment units and their microbial communities in a waste stabilization pond system treating wastewater from an industrial complex located in Northeastern Brazil","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-02 13:13:25","doi":"10.21203/rs.3.rs-6770575/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major Revision","date":"2025-10-15T10:34:18+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2025-09-09T08:39:04+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-25T07:32:20+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"Environmental Science and Pollution Research","date":"2025-08-19T13:23:07+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-12T04:25:14+00:00","index":"","fulltext":""},{"type":"submitted","content":"Environmental Science and Pollution Research","date":"2025-06-10T07:53:37+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"environmental-science-and-pollution-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"espr","sideBox":"Learn more about [Environmental Science and Pollution Research](https://www.springer.com/journal/11356)","snPcode":"11356","submissionUrl":"https://submission.nature.com/new-submission/11356/3","title":"Environmental Science and Pollution Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"3d3dcd45-38b5-449e-b1a0-9a99b39c5061","owner":[],"postedDate":"September 2nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-03-08T08:50:18+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-02 13:13:25","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6770575","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6770575","identity":"rs-6770575","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}