{"paper_id":"29b44c40-c5af-4100-8529-982cbe6e4585","body_text":"Nutrient removal from anaerobically treated pig farming wastewater by microalgae-bacteria consortia in outdoor raceway ponds | 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 Nutrient removal from anaerobically treated pig farming wastewater by microalgae-bacteria consortia in outdoor raceway ponds Manuel Sacristán-de-Alva, Ismael Oceguera‑Vargas, Elizabeth Lamas‑Cosío, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7889744/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The growth of the pig farming industry has led to an increase in wastewater generation in Yucatán, underscoring the need for efficient and cost-effective treatment alternatives. Microalgae–bacteria consortia represent a viable option; however, few studies have been conducted under outdoor conditions and with raw wastewater. This study aimed to cultivate an indigenous microalgae-bacteria consortium in 100 L outdoor raceway ponds using a batch mode to treat non-sterilized, non-diluted anaerobically digested pig wastewater, evaluating its performance in terms of nutrient removal, biomass productivity, and valorization potential under real environmental conditions, which could provide critical data for scaling this technology beyond the laboratory. In the final batch, removal efficiencies reached 93.6% for total nitrogen (removal rate: 231.6 mg L − 1 day − 1 ), 94.6% for orthophosphate, and 91.6% for chemical oxygen demand (COD). The consortium exhibited auto-flocculation, facilitating biomass harvesting with a yield of 1.17 mg L − 1 dry weight (dw). The biomass lipid content was 268.1 mg g − 1 dw. Fatty acid profile showed a predominance of saturated fatty acids (SFA > 50%) with an unsaturated fatty acids/SFA ratio from 0.6 to 1.0 and a SFA/monounsaturated fatty acid ratio of 2.0 to 3.2, indicating potential for biodiesel production. The protein content of the biomass was 13.4% dw. These results demonstrated that environmental conditions did not inhibit consortium growth nor nutrient removal, supporting their viability and sustainability as an alternative for pig farming wastewater treatment. phycoremediation nitrogen phosphorus biomass lipids fatty acids Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Introduction The expansion of the pig farming industry has been accompanied by an increasing volume of wastewater, often discharged with inadequate or no treatment in the Mexican state of Yucatán (Pedrozo-Acuña et al. 2025 ). These effluents are characterized by elevated concentrations of organic matter, phosphorus, and nitrogen, along with other pollutants that contribute to environmental degradation (He et al. 2025 ). The Yucatán aquifer (the only water source in the Yucatán Península) is particularly vulnerable to pollution due to its karstic geology. There is a significant concern about water pollution because of the release of pollutants to the environment, and the deficiency or absence of treatment systems in pig farms worsens the situation (Pedrozo-Acuña et al. 2025 ). There is a necessity to implement adequate water management techniques in places with the presence of pig farming practices (Pedrozo-Acuña et al. 2025 ). Anaerobic digestion is one of the most widely used processes at a large scale; however, it produces effluents with high nutrient loads that require further treatment. Other conventional treatment methods are costly and inefficient in terms of nutrient recovery. Therefore, there is a need for efficient and economically viable technologies (Thi Cam Van et al. 2025 ). Microalgae-based treatments have emerged as a promising alternative due to their capacity to absorb nutrients (Pham Thi and Bui 2025 ), reduced environmental impacts, and lower energy requirements compared to conventional treatment systems. Although various microalgae cultivation systems exist, only raceway ponds (RWPs) are considered a feasible option for large-scale wastewater treatment, primarily due to their lower operational costs compared to other cultivation systems (González-Camejo et al. 2021 ). Microalgae are promising for wastewater treatment and resource recovery because, in addition to treating wastewater, they fix CO 2 and produce value-added products (Li et al. 2023 ). A wastewater treatment system employing a symbiotic relationship between bacteria and microalgae could be an effective strategy to enhance treatment systems in terms of both efficiency and cost reduction. A problem that can arise when cultivating microalgae in residual waters is the high concentration of ammonium, which can be toxic, so nitrifying bacteria can reduce this toxicity by oxidizing ammonium to nitrate (Sánchez-Zurano et al. 2021 ). In general, there are no specific microalgae species universally recommended for wastewater treatment. The most viable approach is the isolation of local strains that can tolerate fluctuating environmental conditions (Masojídek et al. 2022 ). Microalgae-based treatment systems provide a viable option within the framework of the circular economy, facilitating the recovery of nutrients and the utilization of biomass. In this way, wastewater is no longer regarded as waste but as a source of energy, nutrients, and treated water (González-Camejo et al. 2021 ). However, microalgae harvesting remains a bottleneck in wastewater treatment due to high operational costs (Abate et al. 2024a ). Therefore, bio-flocculation, rather than centrifugation or filtration, is a more economically feasible technique for large-scale operations (Kong et al. 2024 ). Bioflocculation is a natural flocculation process in which microbial interactions tend to aggregate suspended particles, such as cells, through natural chemicals, offering economic and environmental advantages (Heredia-Martínez et al. 2025 ). Some benefits include the value improvement of the biomass obtained after treatment (Gonzalo Ibrahim et al. 2023a ), due to the preservation of biomass integrity for processing, as well as lowering costs since no chemical addition is needed, and no energy requirements are necessary (Heredia-Martínez et al. 2025 ). The advantages of microalgae-based treatment systems have been widely recognized; however, few systems have been implemented at a real-world scale (Gonzalo Ibrahim et al. 2023b ). Most research has been conducted at laboratory scale under controlled conditions, which are not comparable to those found in outdoor systems (Mu et al. 2021 ). Thus, in terms of scalability, these results are highly uncertain (González-Camejo et al. 2021 ). Consequently, wastewater treatment with microalgae is still viewed as a relatively unproven alternative with high environmental dependency (temperature, light, location, season) (Gonzalo Ibrahim et al. 2023b ). Furthermore, research on microalgae cultivation in swine wastewater is generally conducted using monoalgal cultures and pretreated wastewater through sterilization or dilution (Wang et al. 2024 ), which is not feasible at an industrial scale. There is limited research on the outdoor cultivation of microalgae in untreated swine wastewater (without dilution and/or sterilization). This continues to limit the implementation of these systems on an industrial scale. This study aimed to cultivate an indigenous microalgae-bacteria consortium in 100 L outdoor raceway ponds using batch mode to treat non-sterilized, non-diluted anaerobically digested pig wastewater, evaluating its nutrient removal, biomass productivity, and valorization potential under real environmental conditions. These could demonstrate that a highly adaptive microalgae-bacteria consortium could achieve efficient nutrient removal and valuable biomass production under non-ideal, real-world conditions (outdoor, fluctuating environment, high-strength raw wastewater), providing critical data for scaling this technology beyond the laboratory. Materials and methods Microalgae-bacteria consortium Microalgae consortium were isolated from a pig farm’s oxidation lagoon at Instituto Tecnológico de Conkal (Yucatán, México). The bacteria in the consortium corresponded to those already present in the pig farming wastewater. Wastewater The wastewater was obtained from a pig farm that produces approximately 48,000 pigs per production cycle, located in Yucatán, México. The wastewater was collected after undergoing a settling process and secondary treatment in an anaerobic lagoon. The effluent collected from this process was an odorless, dark brown water. Consortium culture conditions The consortium was cultured in 4 raceway ponds (RWPs) of 100 L capacity (1.10 m long, 0.70 m wide, and 0.15 m deep) (Fig. 1) in batch cultures. The mixing of the HRAP was performed using a submerged water pump with a capacity of 1800 L h − 1 , providing a water velocity of 34.3 L h − 1 . Figure 1 Schematic diagram of the raceway pond The RWPs were located at the Unidad de Química en Sisal, Facultad de Química, Universidad Nacional Autónoma de México (UNAM) in Sisal, Yucatán (21.163709, -90.046727). The RWP were grown under environmental conditions without control over factors such as temperature, photoperiod, pH, climatic conditions, and photon flux density. The study was conducted from April to July, during which the maximum daytime temperatures reached 39° C, while the minimum nighttime temperatures were 22° C, with average temperatures of 34° C during the day and 25° C at night. The maximum solar energy was approximately 7.0 kWh m − 2 , and the minimum was 5.7 kWh m − 2 . There was no precipitation in the period of study. Initially, 25 L of wastewater and 75 L of inoculum (containing 57.3 × 10^6 cells mL − 1 , previously grown in the same wastewater) were introduced into the RWP. Once total nitrogen (sum of nitrogen as nitrate, nitrogen as nitrite, and nitrogen as ammonia, N-NO 3 − + N-NO 2 − + N-NH 4 + ) removal achieved nearly 80%, half of the water of the HRAP was removed, and 50 L of wastewater was added. This was done to track the adaptation of the consortium to wastewater and environmental conditions across multiple batches. After three cycles of water exchange (where half of the volume of the RWP was replaced with wastewater), when total nitrogen removal achieved near 80%, 70 L of water was removed from the RWP and replaced with the same volume of wastewater. A total of 7 cycles were performed (87 days of operation of the RWPs), there were four cycles with removal of 50 L of culture at the end of the cycle and replaced with 50 L of wastewater at the beginning of the next cycle and the last three cycles with removal of 70 L of culture at the end of the cycle that were replaced with 70 L of wastewater where the next cycle begun. Samples were taken on Mondays, Wednesdays, and Fridays. Each time a sample was taken, tap water was added to prevent changes in nutrient concentration due to evaporation in the RWP. Microalgal growth was determined by direct cell enumeration using a Neubauer hemocytometer (Isolab) under an optical microscope (MS-560 Fisher Scientific). Wastewater characterization Wastewater was characterized by measuring nitrate (NO 3 − ), nitrite (NO 2 − ) (Miranda et al. 2001 ), ammonia (NH 4 + ) (Ruppersberg et al. 2017 ), phosphate (PO 4 3− ) (Ringuet et al. 2011 ), and chemical oxygen demand (COD) (Bridgewater LL et al. 2017 ), by spectrophotometric methods. Temperature (T), pH, oxidation-reduction potential (ORP), and dissolved oxygen (DO) were measured using a YSI ProQuatro probe, which had been previously calibrated according to the manufacturer’s guidelines. The photosynthetically active photon radiation at the surface of the lagoons was also measured using a fotoradiometer (HD 2302.0 LightMeter Delta OHM). These parameters were measured every time a sample was taken. Wastewater was characterized by taking 10 mL of the sample and filtering it through 0.45 µm nitrocellulose Millipore membranes. The removal rate and removal percentages were determined according to (González-Camejo et al. 2022 ). Protein content Protein content was determined by the Lowry method for protein quantification with the Folin-phenol reagent (Lowry et al. 1951 ). Lipid extraction and fatty acid profile Lipid extraction was performed according to Folch’s extraction procedure (Folch et al. 1957) with modifications. An ultrasound-assisted extraction was performed with dichloromethane:methanol solution (2:1 v/v). Total lipid percentage was determined by gravimetry (Magaña-Gallegos et al. 2018b ). Fatty acids were obtained from lipids via saponification, and trans-esterified to obtain fatty acid methyl esters (FAMEs). FAMEs were analyzed by capillary gas chromatography in a Perkin Elmer Clarus 500 gas chromatograph equipped with a Zebron ZB-WAX capillary column (Phenomenex, 7FD-G007-08; 20 m of length, 0.18 mm I.D. and 0.18 µm film thickness) and a flame ionization detector (FID) (Magaña-Gallegos et al. 2018a ; Cárdenas-Palomo et al. 2018 ). Statistics Statistical differences among treatment data sets were determined using one-way analysis of variance (ANOVA) comparing nutrient (nitrogen, phosphorus, or COD) removal per treatment batch. Multiple comparison was used to determine the difference between batch means. Statistics were performed with Matlab R2025a (Mathworks). Results The raceway ponds were operated during a period of 88 days, and a total of 7 batches. First batch lasted 18 days, second batch 14 days, and batches three to five 12 days, while the sixth batch lasted 11 days and the last one lasted 10 days. Consortium growth Figure 2 illustrates the growth of the microalgae-bacteria consortium, showing the ability to grow in this type of wastewater. Figure 2 Microalgae growth at the seven culture cycles During the initial cultivation batches, the consortium exhibited exponential growth. In the last two batches, the consortium seems to have reached the stationary growth phase; however, the microalgae continued to grow (Table 1 ). This is attributed to the presence of flocs, whose formation increased during these final batches, complicating the cell counting process (floc dispersion could not be achieved). As a result, only cells in suspension could be counted. The decrease observed in cell counts is a result of water replacement during different batch cultivations. Table 1 Dry biomass of the consortia harvested in each of the batches Batch 1 2 3 4 5 6 7 Dry biomass (g L − 1 ) 0.77 ± 0.12 0.82 ± 0.09 0.86 ± 0.18 0.91 ± 0.17 1.07 ± 0.23 1.13 ± 0.15 1.17 ± 0.13 *Mean ± standard eviation, n = 4 Environmental conditions and water physicochemical characteristics Initially, an increasing trend in the oxidation-reduction potential (ORP) was observed; subsequently, a decrease in ORP was noted in the different culture batches of the consortium (Fig. 3). Figure 3 Oxidation-reduction potential in the seven cycles of the cultures in the raceway ponds Initial pH in the cultures was close to 8.5. After adding wastewater to the raceway pond, a decrease in pH was generally observed during the first days. Subsequently, the pH increased to values above 9. In the first batch, the greatest pH value was achieved (rose above 9.75) (Fig. 4). In the later batches, pH variation was less pronounced. Figure 4 pH of the wastewater during the seven cycles of culture in the raceway ponds The temperature at the time of sampling generally ranged between 30°C and 36°C. The lowest recorded temperature was approximately 26° C on a day with rain and overcast conditions. The highest temperatures exceeded 37° C, with an average of 32.8° C (Fig. 5) for the entire operation period of the RWPs. Figure 5 Temperature during the seven cycles of culture in the raceway ponds Figure 6 shows the photosynthetically active photon flux density over the raceway ponds (RWPs) at the time of sampling. The average value was 2070 µmol m − ² s − 1 , with a maximum of 2404 µmol m − ² s − 1 and a minimum of 1649 µmol m − ² s − 1 , which coincided with the day when the lowest temperature was recorded (Fig. 5). Figure 6 Photosynthetically active photon flux density during the seven cycles of culture in the raceway ponds Dissolved oxygen concentrations did not vary considerably during the sampling period, ranging from 5.3 to 5.7 mg L − 1 . Nutrient removal from wastewater Nitrogen The varying nitrogen concentrations among the batches were due to differences in wastewater batches originating from the pig farm, primarily related to the pig’s growth stages. The differences between batches 1 to 4 and batches 5 to 7 were attributed to the smaller volume of inoculum added to the latter (Fig. 7). At the start of cultivation, the predominant nitrogen species was ammonium (constituting between 47 and 63% of total nitrogen), followed by nitrate, except in batch 1 (Table 2 ). Table 2 Nitrogen concentrations, percentage removal, and removal rate Nitrogen concentration (mg L − 1 ) Total N removal percentage (%) Total N removal rate (mg L − 1 day − 1 ) Batch N-NO 3 − N-NH 4 + N-NO 2 − Total N 1 Initial 580.8 461.4 493.5 1535.8 84.3 72.0 Final 136.4 16.7 87.4 240.5 2 Initial 617.3 1302.9 143.6 2063.8 79.5 117.1 Final 242.2 181.6 ND 423.8 3 Initial 886.4 1165.2 224.9 2276.5 79.0 149.9 Final 298.5 179.8 ND 478.3 4 Initial 524.5 903.6 471.3 1899.4 82.6 148.1 Final 197.9 132.7 ND 330.6 5 Initial 1048.2 1269.3 324.7 2642.2 87.8 193.4 Final 250.2 71.3 ND 321.5 6 Initial 953.6 1428.7 331.2 2713.5 89.7 221.3 Final 190.3 88.6 ND 278.9 7 Initial 896.4 1425.9 398.2 2720.5 93.6 231.6 Final 131.6 41.6 ND 173.2 x ND: non-detectable Figure 7 shows that nitrite was the nitrogen species removed most rapidly, since it was consumed within the first 4 to 5 days of culture. Ammonium was the second most rapidly consumed nitrogen species. Although it had the highest initial concentration, its final concentration was lower than that of nitrate. Figure 7 Nitrate, nitrite, ammonia, and total nitrogen concentration in the 7 cycles of the raceway ponds (n = 4) Total nitrogen removal in batch 1 reached approximately 85%. However, this efficiency declined in batches 2 to 4. A slight improvement in removal efficiency was observed in batch 4, with the mean value significantly different from those of the other batches, as indicated by the multiple comparison test, due to the increase in the volume of wastewater added to the RWPs. Batches 5 to 7 exhibited an increase in nitrogen removal efficiency and removal rate (Table 2 ). The increase in removal rate was associated with the reduction in nitrogen removal times, as observed for batch 1, where it lasted 18 days, in batch 2, it decreased to 14 days, then to 12 days in batches 3 and 5, and finally to 11 days in batches 4, 6, and 7. In the latest batches, nitrogen removal rates between 221 and 231 mg L − 1 day − 1 were achieved, which is three times that of the first batch, again showing an adaptation to culture conditions. Orthophosphate Figure 8 Orthophosphate concentration in the seven cycles of culture in the raceway ponds (n = 4) Orthophosphate removal ranged from 81.8% to 94.6% between days 5 and 9 of culture across different batches. The variability depended on the final detectable orthophosphate concentration, as the amount of orthophosphate in the culture at the end of each batch (averaging 12 days) was below the analytical method's detection limit (0.2 mg L − 1 P-PO43-), making it nondetectable (Fig. 8). Therefore, based on the detection limit, the removal rate exceeds 99% (Table 3 ) in all cases. Table 3 Orthophosphate concentrations and percentage removal from raceway ponds. Batch Concentration (mg L − 1 ) Percentage removal (%) 1 Initial 24.2 > 99 Final ND 2 Initial 32.1 > 99 Final ND 3 Initial 29.8 > 99 Final ND 4 Initial 20.3 > 99 Final ND 5 Initial 42.6 > 99 Final ND 6 Initial 45.1 > 99 Final ND 7 Initial 47.9 > 99 Final ND x ND: non detectable Due to the orthophosphate concentrations present in the water, very high N:P ratios were observed, ranging from 56:1 to 93:1 depending on the batch, which could limited growth considering the Redfield ratio of 16:1, which did not occur. Organic matter Organic matter was indirectly measured as chemical oxygen demand (COD), with removal efficiencies ranging from 86.6% to 91.6% across the different cultivation batches (Table 4 ). A reduction in COD can be observed in Fig. 9 throughout the different batches. In general, an increase in COD reduction was observed throughout the batches, except in batch 4, where the final concentration was below the method's detection limit (50 mg O₂ L − 1 ). Therefore, a removal efficiency greater than 86.6% is assumed for this batch. Table 4 Chemical oxygen demand concentration and reduction percentage in raceway ponds. Batch COD concentration (mg O 2 L − 1 ) COD reduction percentage (%) 1 Initial 368.3 84.4 Final 57.5 2 Initial 405.0 87.2 Final 52.0 3 Initial 453.0 90.9 Final 41.0 4 Initial 373.0 > 86.6 Final ND 5 Initial 652.3 91.1 Final 58.2 6 Initial 587.2 89.4 Final 62.4 7 Initial 623.1 91.6 Final 52.3 x ND: non-detctable Figure 9 Chemical oxygen demand reduction in the 7 cycles of culture of the raceway ponds Lipids and fatty acids Total lipids were measured from the biomass harvested by the seven different batches in the RWPs. An increase in lipid content was observed, from 173 mg g − 1 dw to 268 mg g − 1 dw (Table 5 ). This increase was more pronounced from batches 1 to 4, while the lipid content in batches 5 to 7 remained relatively constant. Table 5 Content of total lipids in the dry biomass of the RWPs. Batch Lipids (mg g − 1 ) 1 174.0 ± 26.9 2 189.1 ± 19.7 3 223.3 ± 34.3 4 230.6 ± 30.6 5 256.5 ± 34.0 6 266.0 ± 27.9 7 268.1 ± 14.5 *Mean ± standard eviation, n = 4 The average lipid content of the consortium during the last three cultivation batches was 26.4%. Fatty acid profiling was conducted only on the biomass collected from batch 7. The fatty acid concentrations among the four replicates of batch 7 showed significant variation in both levels and the presence of specific fatty acids (Table 6 ). Notably, in RWP 2, the levels of several fatty acids were lower compared to the other three RWPs, and stearic acid (which was present at notable levels in RWPs 1, 3, and 4) was not detected. Table 6 Concentration of fatty acids as µg of fatty acid per mg of lipid in biomass harvested from batch 7 of the 4 RWPs. Concentration (µg mg − 1 ) Fatty acid 1 2 3 4 Caprylic 155.5 ND ND 330.4 Lauric 105.7 ND ND 140.4 Myristic 1564.5 202.0 1049.7 3166.4 Myristoleic 436.5 ND 230.3 946.8 Palmitic 21176.6 1370.8 11863.6 52749.0 Palmitoleic 3880.3 316.6 1961.0 6276.8 Stearic 3063.3 0.0 1439.3 9236.9 Oleic 5453.8 169.4 4991.0 23035.4 Linoleic 5249.9 242.0 3960.4 22431.5 γ-linolenic 109.8 ND ND 596.6 α-linolenic 4122.9 213.6 3443.2 14219.4 Arachidic 166.3 ND 139.6 421.8 Eicosenoic 68.8 ND ND 288.5 Eicosadienoic ND ND ND 157.8 Eicospaentanoic 446.8 ND ND ND Docosanoic 297.3 ND ND ND x ND: non-detectable Palmitic acid was the most abundant fatty acid of all four replicates; however, its concentration varied greatly among them (p < 0.05). Other major fatty acids present in RWPs 1, 3, and 4 included palmitoleic, stearic, oleic, linoleic, and α-linolenic acids. Once again, concentrations varied significantly among RWPs (p < 0.05). In general, the fatty acid composition showed approximately 50% saturated fatty acids (SFAs), except for RWP 2, where several unsaturated fatty acids were not detected, resulting in a higher proportion of saturated fatty acids. The proportion of monounsaturated fatty acids (MUFAs) and polyunsaturated fatty acids (PUFAs) ranged between 18.1% and 27.9% (Fig. 10). Figure 10 Percentage of saturated fatty acids (SFAs), monounsaturated fatty acids (MUFAs), and polyunsaturated fatty acids (PUFAs) in biomass harvested from each replicate of the last batch Protein Protein content was determined only in the biomass harvested from batch 7 across the four RWPs, yielding an average value of 13.4 ± 2.2% dw. This is relatively low (since microalgae generally exhibit protein contents between 28 and 70% (Wang et al. 2021 ), considering that nitrogen was not limiting in the RWPs (Table 2 ), which is why the determination was not performed in the other batches. Discussion Consortium growth Table 1 shows an increase in biomass content across the cultivation batches, possibly due to acclimatization of the consortium to wastewater and environmental conditions. Biomass concentration rose from batch 1 to batch 7 by approximately 0.5 g L − 1 (Table 1 ), indicating that microalgae continued to grow even though this was not clear from the cell counts (Fig. 2), due to biofloc formation. The formation of bioflocs may be due to increased interactions between microalgae and bacteria within the consortium, which produce extracellular polymers (Fallahi et al. 2021 ), being one of the possible explanations of what happened in this study. Another explanation is the formation of structures by filamentous bacteria, where microalgae can adhere, forming granules (Sátiro et al. 2025b ). Biofloc formation was advantageous as it facilitated the harvesting of biomass after cultivation. The biomass obtained in the final batch was lower than that reported by (Qu et al. 2019 ), who achieved 6.2 g L − 1 of biomass; however, their study was conducted in a 500 mL photobioreactor. The result obtained here is higher than the 0.8 g L − 1 reported for Chlorella vulgaris (Wang et al. 2016 ), which may be attributed to the fact that their cultivation was carried out in a raceway pond (RWP) of nearly 43,000 L. Environmental conditions and water physicochemical characteristics The initial increase in ORP could indicate that the microalgae were producing oxygen, and the posterior decrease may be due to oxygen consumption by bacteria present in the culture (Fig. 3). This behavior was like that reported by (Lee et al. 2022 ), who observed an increase in ORP at the start of cultivation, followed by a decrease after 72 h in 250 L photobioreactors. The decrease in pH after adding wastewater may be attributed to bacterial activity. Subsequently, the pH increase may be due to the activity of the microalgae. The greatest pH value in the first batch may be related to the longer cultivation time (18 days) compared to subsequent batches, which had shorter cultivation periods (between 10 and 14 days) due to the consortium's acclimatization to the wastewater. Fluctuations of several degrees were recorded from day to day for the entire operation period of the RWPs; however, these variations did not significantly affect the growth (Table 1 ) of the consortium. The typical light intensities under which microalgae grow range from 26 to 400 µmol photons m − ² s − 1 (Maltsev et al. 2021 ). Although the intensity at the surface of the RWPs could be too high, it did not affect algal growth (Table 1 ). This is likely due to the dark brown color of the wastewater and/or shading of the biomass at the top of the RWPs, which limited light penetration, resulting in lower radiation reaching the deeper layers of the RWPs. The minor fluctuations in dissolved oxygen levels may result from the photosynthetic activity of microalgae. Although some conditions could be considered adverse, such as the temperature, which remained above 30° C (Fig. 5), a value that is regarded as high and could affect the growth rate by affecting proteins or increasing reactive oxygen species (Sátiro et al. 2025a ). As well as the high photon flux density (Fig. 6). Contrary to what might seem like adverse conditions, the experiment revealed that these conditions were actually favorable for this well-adapted consortium, as growth was not affected (Table 1 ). Nutrient removal from wastewater Nitrogen Ammonium is the predominant nitrogen species because the water originated from an anaerobic treatment process. Although ammonium is generally the preferred nitrogen source for microalgae, it can inhibit growth and photosynthesis at concentrations above 100 mg L − 1 (Salbitani and Carfagna 2021 ). However, in this study, despite the lowest initial ammonium concentration being approximately 460 mg L − 1 and others close to or exceeding 1,000 mg L − 1 (Table 2 ), no inhibition of microalgal growth was observed. This may have been due to the oxidation of ammonium to nitrate by nitrifying bacteria, as noted by (Abate et al. 2024b ), reducing its toxicity. This may also explain why nitrite was the nitrogen species that was removed most rapidly from the culture, its oxidation to nitrite was likely due to oxidizing conditions (Fig. 3), The faster removal of ammonium than nitrate could be attributed to the preferential uptake of ammonium by microalgae, as it does not require reduction before incorporation into biomass (Carletti et al. 2024 ). The oxidation of ammonium may also explain the quicker removal of this nitrogen species compared to nitrate due to oxidizing conditions (Fig. 3) (Abate et al. 2024b ). Another explanation for the removal of ammonium is that the alkaline pH (between 8 and 9.75) (Fig. 4) in the RWPs forms ammonia, which can be lost through volatilization. The consortium’s adaptation to the cultivation conditions is evident from the fact that an increase in nitrogen removal efficiency and removal rate was observed in batches 5 to 7, despite their higher initial nitrogen concentrations compared to earlier batches. The group means for batches 5 to 7 were significantly different from those of previous batches, according to the multicompare test, suggesting improved adaptation and performance of the consortium. Significant differences (p < 0.05) in nitrogen removal were observed among batches, indicating that reducing the amount of consortium inoculum may have affected removal efficiency. This supports the hypothesis of progressive adaptation of the microbial community to the cultivation environment. Specifically, no significant difference was found between batches 6 and 7 (similar to the results obtained with the removal rate), further confirming the stabilization and efficient functioning of the consortium. A similar trend was observed in nutrient removal rate (Table 2 ). The initial increase in removal rate from batch 1 to 2 was primarily due to higher nitrogen concentrations. Batches 3 and 4 exhibited similar removal rates, suggesting a temporary stabilization phase that justified the decision to increase the wastewater load in subsequent batches. The removal efficiencies (close to 90%) achieved in the final cultivation batches were higher than those reported by (Qu et al. 2019 ), who obtained 73 % nitrogen remoal in 500 mL photobioreactors with an initial concentration of 479 mg L − 1 . These results were comparable to those reported by (Wang et al. 2016 ), who achieved approximately 90 % total nitrogen removal in 12 das in an RWP under environmental conditions. However, the removal obtained in this study was lower than the 95 % reported by (You et al. 2021 ) in 500 mL culturesof a C. vulgaris and R. sphaeroides consortium, with initial total nitrogen concentrations of 1492 mg L − 1 . The removal rates obtained for batches 6 and 7 (221 and 231 mg L − 1 day − 1 , respectively) were higher than those reported by (Fuhrmann Dinnebier et al. 2021 ), who achieved removal rates of around 115 mg L − 1 day − 1 with initial ammonium concentrations ranging from 1000 to 1300 mg L − 1 . This difference may be attributed to the ammonium removal efficiencies reported in the referenced study, which ranged between 72% and 79%. Although (Rossi et al. 2022 ) reported lower nitrogen removal rates (19.7 mg L − 1 day − 1 ) than those obtained in the present study; however, the removal efficiency was similar (90 %), likely due to the lower initial nitrogen concetration of approximately 200 mg L − 1 . That study was conducted in RWPs with a volume of approximately 700 L. In another study in a high-rate algae pond (HRP) with a capacity of 2.4 m³, treating domestic wastewater (32.2 mg L − 1 total nitrogen), in semi-continuous operation, 84 % nitrogen removal was achieved (Sátiro et al. 2025a ), which is less thn the removal percentage reported in the present study. In the present study, ammonium removal in the last three batches was around 95%, higher than the 80% achieved in a 250 L reactor under environmental conditions with an initial concentration of 770 mg L − 1 (Lee et al. 2022 ). Orthophosphate A significant difference (p < 0.05) in orthophosphate removal was observed only for batches 3 and 4, which differed notably from the rest. This can be attributed to the high removal efficiencies achieved at low orthophosphate concentrations. The high percentage of orthophosphate removal was mainly due to microalgae absorption but could also have resulted from the presence of calcium in the regional water, which promoted orthophosphate precipitation. ate precipitation (Cerozi and Fitzsimmons 2016 ). The very high N:P ratios in the RWPs could suggest a potential inhibitory effect due to phosphorus limitation (Yaakob et al. 2021 ), possibly even influenced by the duration of the experiment (Magyar et al. 2024 ). Therefore, in this case, the resolubilization of phosphorus through the bacterial activity (Garba Jega et al. 2025 ) of the consortium, could be the reason why no growth limitation was observed (Table 1 ). The removal percentage of orthophosphate obtained in this study is higher than the 85% reported by (Wang et al. 2016 ). The removal efficiency is comparable to those reported by (Fuhrmann Dinnebier et al. 2021 ), and (You et al. 2021 ), who achieved 96 % removal with initial concentrations of 42.9 mg L − 1 and 154 mg L − , respectively. Similar results were also reported by (Luo et al. 2019 ), with a removal efficiency of approximately 92 % in 600 mL cultures. In an RWP of nearly 700 L, a 90 % removal was achieved with a initial concentration of 19.4 mg L − 1 (Rossi et al. 2022 ), which is comprable to the results obtained in this study. Organic matter Although the ANOVA test revealed a significant difference between batches (p < 0.05), the multiple comparisons test indicated no significant differences among them. The reduction in COD was likely due primarily by the bacteria present in the consortium. DO concentration (between 5.3 and 5.7) is an indicator of oxygen production during photosynthesis that bacteria can use to oxidize organic matter present in the wastewater. Microalgae also contribute to organic matter removal, as they can exhibit mixotrophic metabolism (Abate et al. 2024b ), and heterotrophic carbon uptake may occur more rapidly than photosynthetic assimilation (Lee et al. 2022 ). In a 500 mL photobioreactor, Parachlorella kessleri achieved a COD reduction of 58% (initial concentration: 637 mg L − 1 ) (Qu et al. 2019 ). In comparison, a 900 mL culture resulted in a 63.6 % reduction (intial concentration: 1502 mg L − 1 ), which is lower than the values obtained in the present study (approximately 90 % in the last tree batches). In another study, C. vulgaris and S. obliquus achieved a 73 % reduction wit an initial concentration of 760 mg L − 1 in autoclaved water (Wang et al. 2016 ). These lower reduction efficiencies may be due to the reduced presence of bacteria. In a separate study using a 250 L cultivation system, a COD reduction of 80.6% was reported (Lee et al. 2022 ), which is higher than the studies mentioned above but still lower than the one obtained in the present work. In the RWP of approximately 700 L used by (Rossi et al. 2022 ), with an initial COD of 678 mg L − 1 , only a 59 % reduction was achieved. In an HRP with a capacity of 2.4 m³, treatig domestic wastewater, a COD removal of 74.67 % was achieved (160.5 mg L − 1 COD in the affluent) [30], which is bothless than the removal achieved in this study. In an HRP with 2.4 m³ of capacity for treating domestic wastewater (5.4 mg L − 1 total phosphorus), in semi-continuous operation, 71.4 % of phosphorus removal was achieved (Sátiro et al. 2025a ), which is less than the removalpercentage reported in the present study. Lipids and fatty acids A significant difference (p < 0.05) was observed in the lipid content of the consortium across the different cultivation batches. Nonetheless, the multiple comparisons test revealed no significant differences among the last three batches, which may once again suggest that the consortium has reached a stabilized state. The more pronounced increase in lipid content from batches 1 to 4 may be attributed to the microalgae's adaptation to the cultivation conditions. The increase does not appear to be related to nitrogen deficiency stress, as the total nitrogen concentration at the end of the cultures ranged from 130 to 300 mg L − 1 (Table 2 ). It is likely associated with another type of stress, such as the presence of additional contaminants or the daytime temperature (Morales et al. 2021 ). However, this would need to be studied in future research. The average lipid content of 26.4% obtained in this study is higher than the 14.0% reported for C. vulgaris in a batch culture. (Wang et al. 2016 ), and the 8 % in a 500 mL culure (You et al. 2021 ). However, this value was lower than what was obtained in an HRAP of approximately 900 L (Oliveira et al. 2021 ). The lipid content obtained in this study was similar to the 21.89 % reported in a nearly 7 L photobioreactor (Mou et al. 2023 ). A similar lpid content to that obtained in this study was reported in an HRP of 2.4 m³, with a lipid content of 21.8 % (Sátiro et al. 2025a ). This variation in concentration and presence of fatty acids in the replicates of batch 7 could be due to the open systems, where, despite using the same wastewater and environmental conditions, differences in microalgal species composition and abundance in the consortium across RWPs can occur because of the introduction of airborne microorganisms that could modify the species in the consortium. The high SFA content and the presence of MUFAs and PUFAs make this oil suitable for biodiesel production (Morales et al. 2021 ). The UFA/SFA ratio tends to increase when cultures are under stress conditions (Li et al. 2011 ); in this case, the observed ratios ranged from 0.6 to 1.0. This suggests that the microalgae in the consortium were not under stress, which may be due to the absence of nitrogen limitation (Table 2 ). When cultivation temperature increases, the SFA/MUFA ratio also increases (Carletti et al. 2024 ). In this study, under high cultivation temperatures (Fig. 5), SFA/MUFA ratios ranged from 2.0 to 3.2 between different RWPs. Protein The protein content was lower than that reported by (Mou et al. 2023 ) (29.31 %), by (Fuhrmann Dinnebier et al. 2021 ) (59.5 %) by (You et al. 2021 ) (64 %) in a 500 mL culture of C. vulgari and R. sphaeroides , and by (Oliveira et al. 2021 ) who reported 45.9 % in a 900 L HRAP system. A study in an HRP of 2.4 m 3, a protein content of protein of .9 % was reported (Sátiro et al. 2025a ), which is less than that obtained in this study, may indicate low proten content in reactors such as those used in this study and the previously mentioned one. The findings of this study demonstrate that microalgae-bacteria consortia based systems are a viable alternative for treating wastewater from the swine industry. Environmental variability, particularly in terms of light intensity and temperature, as well as fluctuations in key physicochemical parameters (pH, oxidation-reduction potential, and photon flux), did not inhibit microalgal growth, highlighting the importance of using native consortia. Moreover, decreasing the inoculum concentration in raceway ponds (RWPs) had no significant impact on the development or performance of the microalgae-bacteria consortium. Nutrient removal efficiencies exceeded 90% for nitrogen, phosphorus, and chemical oxygen demand (COD), indicating that the microbial consortium was highly effective in removing these pollutants from the swine wastewater. Despite these high removal rates, considerable concentrations of residual nutrients (between 172 and 322 mg L − 1 of total nitrogen) were still detected in the treated effluent. This highlights the need for further research to optimize cultivation strategies, including adjustments to operational modes and scaling up the system to improve treatment efficiency and reduce hydraulic retention time. The harvested biomass contained approximately 25% lipids, with a fatty acid profile potentially suitable for biodiesel production, which could contribute to lowering the overall cost of treatment. However, considerable variability in the fatty acid profiles was observed, possibly due to differences in the microbial consortia that developed in each RWP, resulting in heterogeneous biomass characteristics. Furthermore, the protein content of the biomass remained low, despite high nitrogen availability during cultivation. Declarations Competing interests The authors declare no relevant financial or non-financial interests. Funding This work was supported by the Dirección General de Asuntos del Personal Académico (DGAPA), PAPIIT, UNAM, under Grant IA203123; and the Facultad de Química, Programa de Apoyo a la Investigación y el Posgrado (PAIP), UNAM, under Grants 5000–9204 and 5000–9146. Author Contribution MSA was involved in the conception and design of the study, as well as the collection, analysis, and interpretation of data, and drafting of the manuscript. IOV was involved in the conception and design of the study and methodology. ELC was involved in project administration, reviewing, and editing. FAC was involved in drafting the paper, examining it critically for its essential intellectual content and funding acquisition. All authors have read the manuscript and approve the final version to be published. All authors agree to be accountable for all aspects of the work. Acknowledgement Authors thank the UNAM PAPIIT Project IA203123 Recovery of nutrients and energy from pork-industry wastewater through a microalgae-bacteria consortium and UNAM-PAIP 5000-9146 and 5000-9204 for economic support to this research. The authors would like to thank reviewers for their insightful comments that significantly improved the quality of the manuscript. The authors thank Ms. Sofía Fargher for english grammar editing. Data Availability All data generated or analyzed during this study are included in this published article. 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4\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":107953,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003epH of the wastewater during the seven cycles of culture in the raceway ponds\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure4.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7889744/v1/c6f58cbeded0029be9199e77.jpg\"},{\"id\":95224441,\"identity\":\"9b868f66-dba1-4de1-9086-27244eddaca0\",\"added_by\":\"auto\",\"created_at\":\"2025-11-05 16:23:46\",\"extension\":\"jpg\",\"order_by\":5,\"title\":\"Figure 5\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":103174,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eTemperature during the seven cycles of culture in the raceway ponds\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure5.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7889744/v1/a34eeefdec3f8d31cd70731a.jpg\"},{\"id\":95111143,\"identity\":\"175cbd7d-60f5-4897-aa56-a9617a6b0eab\",\"added_by\":\"auto\",\"created_at\":\"2025-11-04 11:56:44\",\"extension\":\"jpg\",\"order_by\":6,\"title\":\"Figure 6\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":112754,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003ePhotosynthetically active photon flux density during the seven cycles of culture in the raceway ponds\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure6.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7889744/v1/acdcfd45a127a39261966888.jpg\"},{\"id\":95224553,\"identity\":\"b76ba7ee-f64d-4bd4-9a09-2e8294d117e5\",\"added_by\":\"auto\",\"created_at\":\"2025-11-05 16:23:54\",\"extension\":\"jpg\",\"order_by\":7,\"title\":\"Figure 7\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":158696,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eNitrate, nitrite, ammonia, and total nitrogen concentration in the 7 cycles of the raceway ponds (n=4)\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure7.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7889744/v1/1ce07e4a30e42122216c9cde.jpg\"},{\"id\":95111158,\"identity\":\"5556fed2-e17a-4797-adcc-160f383f1f0a\",\"added_by\":\"auto\",\"created_at\":\"2025-11-04 11:56:44\",\"extension\":\"jpg\",\"order_by\":8,\"title\":\"Figure 8\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":121833,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eOrthophosphate concentration in the seven cycles of culture in the raceway ponds (n=4)\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure8.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7889744/v1/13248768c677eb44e21e1001.jpg\"},{\"id\":95225124,\"identity\":\"6ee3e224-c6ab-46e7-957a-5ab6ec845479\",\"added_by\":\"auto\",\"created_at\":\"2025-11-05 16:24:36\",\"extension\":\"jpg\",\"order_by\":9,\"title\":\"Figure 9\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":123281,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eChemical oxygen demand reduction in the 7 cycles of culture of the raceway ponds\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure9.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7889744/v1/acf8670a8505c8a9dc3dd76b.jpg\"},{\"id\":95225808,\"identity\":\"fefded6a-39db-4e33-92fc-219b5dddbb1e\",\"added_by\":\"auto\",\"created_at\":\"2025-11-05 16:25:32\",\"extension\":\"jpg\",\"order_by\":10,\"title\":\"Figure 10\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":108100,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003ePercentage of saturated fatty acids (SFAs), monounsaturated fatty acids (MUFAs), and polyunsaturated fatty acids (PUFAs) in biomass harvested from each replicate of the last batch\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure10.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7889744/v1/fbb1b88c3b000106244a3c37.jpg\"},{\"id\":95230338,\"identity\":\"ef8a6f90-a330-483d-a529-6ac306d5f8bc\",\"added_by\":\"auto\",\"created_at\":\"2025-11-05 16:37:15\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":2492020,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7889744/v1/e75365aa-fd56-4640-b13a-fadf2fc23196.pdf\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Nutrient removal from anaerobically treated pig farming wastewater by microalgae-bacteria consortia in outdoor raceway ponds\",\"fulltext\":[{\"header\":\"Introduction\",\"content\":\"\\u003cp\\u003eThe expansion of the pig farming industry has been accompanied by an increasing volume of wastewater, often discharged with inadequate or no treatment in the Mexican state of Yucat\\u0026aacute;n (Pedrozo-Acu\\u0026ntilde;a et al. \\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e2025\\u003c/span\\u003e). These effluents are characterized by elevated concentrations of organic matter, phosphorus, and nitrogen, along with other pollutants that contribute to environmental degradation (He et al. \\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e2025\\u003c/span\\u003e). The Yucat\\u0026aacute;n aquifer (the only water source in the Yucat\\u0026aacute;n Pen\\u0026iacute;nsula) is particularly vulnerable to pollution due to its karstic geology. There is a significant concern about water pollution because of the release of pollutants to the environment, and the deficiency or absence of treatment systems in pig farms worsens the situation (Pedrozo-Acu\\u0026ntilde;a et al. \\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e2025\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003eThere is a necessity to implement adequate water management techniques in places with the presence of pig farming practices (Pedrozo-Acu\\u0026ntilde;a et al. \\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e2025\\u003c/span\\u003e). Anaerobic digestion is one of the most widely used processes at a large scale; however, it produces effluents with high nutrient loads that require further treatment. Other conventional treatment methods are costly and inefficient in terms of nutrient recovery. Therefore, there is a need for efficient and economically viable technologies (Thi Cam Van et al. \\u003cspan citationid=\\\"CR42\\\" class=\\\"CitationRef\\\"\\u003e2025\\u003c/span\\u003e). Microalgae-based treatments have emerged as a promising alternative due to their capacity to absorb nutrients (Pham Thi and Bui \\u003cspan citationid=\\\"CR33\\\" class=\\\"CitationRef\\\"\\u003e2025\\u003c/span\\u003e), reduced environmental impacts, and lower energy requirements compared to conventional treatment systems. Although various microalgae cultivation systems exist, only raceway ponds (RWPs) are considered a feasible option for large-scale wastewater treatment, primarily due to their lower operational costs compared to other cultivation systems (Gonz\\u0026aacute;lez-Camejo et al. \\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003eMicroalgae are promising for wastewater treatment and resource recovery because, in addition to treating wastewater, they fix CO\\u003csub\\u003e2\\u003c/sub\\u003e and produce value-added products (Li et al. \\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e). A wastewater treatment system employing a symbiotic relationship between bacteria and microalgae could be an effective strategy to enhance treatment systems in terms of both efficiency and cost reduction. A problem that can arise when cultivating microalgae in residual waters is the high concentration of ammonium, which can be toxic, so nitrifying bacteria can reduce this toxicity by oxidizing ammonium to nitrate (S\\u0026aacute;nchez-Zurano et al. \\u003cspan citationid=\\\"CR39\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e). In general, there are no specific microalgae species universally recommended for wastewater treatment. The most viable approach is the isolation of local strains that can tolerate fluctuating environmental conditions (Masoj\\u0026iacute;dek et al. \\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003eMicroalgae-based treatment systems provide a viable option within the framework of the circular economy, facilitating the recovery of nutrients and the utilization of biomass. In this way, wastewater is no longer regarded as waste but as a source of energy, nutrients, and treated water (Gonz\\u0026aacute;lez-Camejo et al. \\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e). However, microalgae harvesting remains a bottleneck in wastewater treatment due to high operational costs (Abate et al. \\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e2024a\\u003c/span\\u003e). Therefore, bio-flocculation, rather than centrifugation or filtration, is a more economically feasible technique for large-scale operations (Kong et al. \\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e). Bioflocculation is a natural flocculation process in which microbial interactions tend to aggregate suspended particles, such as cells, through natural chemicals, offering economic and environmental advantages (Heredia-Mart\\u0026iacute;nez et al. \\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e2025\\u003c/span\\u003e). Some benefits include the value improvement of the biomass obtained after treatment (Gonzalo Ibrahim et al. \\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e2023a\\u003c/span\\u003e), due to the preservation of biomass integrity for processing, as well as lowering costs since no chemical addition is needed, and no energy requirements are necessary (Heredia-Mart\\u0026iacute;nez et al. \\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e2025\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003eThe advantages of microalgae-based treatment systems have been widely recognized; however, few systems have been implemented at a real-world scale (Gonzalo Ibrahim et al. \\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e2023b\\u003c/span\\u003e). Most research has been conducted at laboratory scale under controlled conditions, which are not comparable to those found in outdoor systems (Mu et al. \\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e). Thus, in terms of scalability, these results are highly uncertain (Gonz\\u0026aacute;lez-Camejo et al. \\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e). Consequently, wastewater treatment with microalgae is still viewed as a relatively unproven alternative with high environmental dependency (temperature, light, location, season) (Gonzalo Ibrahim et al. \\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e2023b\\u003c/span\\u003e). Furthermore, research on microalgae cultivation in swine wastewater is generally conducted using monoalgal cultures and pretreated wastewater through sterilization or dilution (Wang et al. \\u003cspan citationid=\\\"CR45\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e), which is not feasible at an industrial scale.\\u003c/p\\u003e\\u003cp\\u003eThere is limited research on the outdoor cultivation of microalgae in untreated swine wastewater (without dilution and/or sterilization). This continues to limit the implementation of these systems on an industrial scale. This study aimed to cultivate an indigenous microalgae-bacteria consortium in 100 L outdoor raceway ponds using batch mode to treat non-sterilized, non-diluted anaerobically digested pig wastewater, evaluating its nutrient removal, biomass productivity, and valorization potential under real environmental conditions. These could demonstrate that a highly adaptive microalgae-bacteria consortium could achieve efficient nutrient removal and valuable biomass production under non-ideal, real-world conditions (outdoor, fluctuating environment, high-strength raw wastewater), providing critical data for scaling this technology beyond the laboratory.\\u003c/p\\u003e\"},{\"header\":\"Materials and methods\",\"content\":\"\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003eMicroalgae-bacteria consortium\\u003c/h2\\u003e\\u003cp\\u003eMicroalgae consortium were isolated from a pig farm\\u0026rsquo;s oxidation lagoon at Instituto Tecnol\\u0026oacute;gico de Conkal (Yucat\\u0026aacute;n, M\\u0026eacute;xico). The bacteria in the consortium corresponded to those already present in the pig farming wastewater.\\u003c/p\\u003e\\u003c/div\\u003e\\n\\u003ch3\\u003eWastewater\\u003c/h3\\u003e\\n\\u003cp\\u003eThe wastewater was obtained from a pig farm that produces approximately 48,000 pigs per production cycle, located in Yucat\\u0026aacute;n, M\\u0026eacute;xico. The wastewater was collected after undergoing a settling process and secondary treatment in an anaerobic lagoon. The effluent collected from this process was an odorless, dark brown water.\\u003c/p\\u003e\\n\\u003ch3\\u003eConsortium culture conditions\\u003c/h3\\u003e\\n\\u003cp\\u003eThe consortium was cultured in 4 raceway ponds (RWPs) of 100 L capacity (1.10 m long, 0.70 m wide, and 0.15 m deep) (Fig.\\u0026nbsp;1) in batch cultures. The mixing of the HRAP was performed using a submerged water pump with a capacity of 1800 L h\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e, providing a water velocity of 34.3 L h\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e.\\u003c/p\\u003e\\u003cp\\u003e\\u003cb\\u003eFigure\\u0026nbsp;1\\u003c/b\\u003e Schematic diagram of the raceway pond\\u003c/p\\u003e\\u003cp\\u003eThe RWPs were located at the Unidad de Qu\\u0026iacute;mica en Sisal, Facultad de Qu\\u0026iacute;mica, Universidad Nacional Aut\\u0026oacute;noma de M\\u0026eacute;xico (UNAM) in Sisal, Yucat\\u0026aacute;n (21.163709, -90.046727). The RWP were grown under environmental conditions without control over factors such as temperature, photoperiod, pH, climatic conditions, and photon flux density. The study was conducted from April to July, during which the maximum daytime temperatures reached 39\\u0026deg; C, while the minimum nighttime temperatures were 22\\u0026deg; C, with average temperatures of 34\\u0026deg; C during the day and 25\\u0026deg; C at night. The maximum solar energy was approximately 7.0 kWh m\\u003csup\\u003e\\u0026minus;\\u0026thinsp;2\\u003c/sup\\u003e, and the minimum was 5.7 kWh m\\u003csup\\u003e\\u0026minus;\\u0026thinsp;2\\u003c/sup\\u003e. There was no precipitation in the period of study.\\u003c/p\\u003e\\u003cp\\u003eInitially, 25 L of wastewater and 75 L of inoculum (containing 57.3 \\u0026times; 10^6 cells mL\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e, previously grown in the same wastewater) were introduced into the RWP. Once total nitrogen (sum of nitrogen as nitrate, nitrogen as nitrite, and nitrogen as ammonia, N-NO\\u003csub\\u003e3\\u003c/sub\\u003e\\u003csup\\u003e\\u0026minus;\\u003c/sup\\u003e + N-NO\\u003csub\\u003e2\\u003c/sub\\u003e\\u003csup\\u003e\\u0026minus;\\u003c/sup\\u003e + N-NH\\u003csub\\u003e4\\u003c/sub\\u003e\\u003csup\\u003e+\\u003c/sup\\u003e) removal achieved nearly 80%, half of the water of the HRAP was removed, and 50 L of wastewater was added. This was done to track the adaptation of the consortium to wastewater and environmental conditions across multiple batches. After three cycles of water exchange (where half of the volume of the RWP was replaced with wastewater), when total nitrogen removal achieved near 80%, 70 L of water was removed from the RWP and replaced with the same volume of wastewater. A total of 7 cycles were performed (87 days of operation of the RWPs), there were four cycles with removal of 50 L of culture at the end of the cycle and replaced with 50 L of wastewater at the beginning of the next cycle and the last three cycles with removal of 70 L of culture at the end of the cycle that were replaced with 70 L of wastewater where the next cycle begun. Samples were taken on Mondays, Wednesdays, and Fridays. Each time a sample was taken, tap water was added to prevent changes in nutrient concentration due to evaporation in the RWP.\\u003c/p\\u003e\\u003cp\\u003eMicroalgal growth was determined by direct cell enumeration using a Neubauer hemocytometer (Isolab) under an optical microscope (MS-560 Fisher Scientific).\\u003c/p\\u003e\\n\\u003ch3\\u003eWastewater characterization\\u003c/h3\\u003e\\n\\u003cp\\u003eWastewater was characterized by measuring nitrate (NO\\u003csub\\u003e3\\u003c/sub\\u003e\\u003csup\\u003e\\u0026minus;\\u003c/sup\\u003e), nitrite (NO\\u003csub\\u003e2\\u003c/sub\\u003e\\u003csup\\u003e\\u0026minus;\\u003c/sup\\u003e) (Miranda et al. \\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e2001\\u003c/span\\u003e), ammonia (NH\\u003csub\\u003e4\\u003c/sub\\u003e\\u003csup\\u003e+\\u003c/sup\\u003e) (Ruppersberg et al. \\u003cspan citationid=\\\"CR37\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e), phosphate (PO\\u003csub\\u003e4\\u003c/sub\\u003e\\u003csup\\u003e3\\u0026minus;\\u003c/sup\\u003e) (Ringuet et al. \\u003cspan citationid=\\\"CR35\\\" class=\\\"CitationRef\\\"\\u003e2011\\u003c/span\\u003e), and chemical oxygen demand (COD) (Bridgewater LL et al. \\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e), by spectrophotometric methods. Temperature (T), pH, oxidation-reduction potential (ORP), and dissolved oxygen (DO) were measured using a YSI ProQuatro probe, which had been previously calibrated according to the manufacturer\\u0026rsquo;s guidelines. The photosynthetically active photon radiation at the surface of the lagoons was also measured using a fotoradiometer (HD 2302.0 LightMeter Delta OHM). These parameters were measured every time a sample was taken.\\u003c/p\\u003e\\u003cp\\u003eWastewater was characterized by taking 10 mL of the sample and filtering it through 0.45 \\u0026micro;m nitrocellulose Millipore membranes. The removal rate and removal percentages were determined according to (Gonz\\u0026aacute;lez-Camejo et al. \\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e).\\u003c/p\\u003e\\n\\u003ch3\\u003eProtein content\\u003c/h3\\u003e\\n\\u003cp\\u003eProtein content was determined by the Lowry method for protein quantification with the Folin-phenol reagent (Lowry et al. \\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e1951\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cdiv id=\\\"Sec8\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003eLipid extraction and fatty acid profile\\u003c/h2\\u003e\\u003cp\\u003eLipid extraction was performed according to Folch\\u0026rsquo;s extraction procedure (Folch et al. 1957) with modifications. An ultrasound-assisted extraction was performed with dichloromethane:methanol solution (2:1 v/v). Total lipid percentage was determined by gravimetry (Maga\\u0026ntilde;a-Gallegos et al. \\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e2018b\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003eFatty acids were obtained from lipids via saponification, and trans-esterified to obtain fatty acid methyl esters (FAMEs). FAMEs were analyzed by capillary gas chromatography in a Perkin Elmer Clarus 500 gas chromatograph equipped with a Zebron ZB-WAX capillary column (Phenomenex, 7FD-G007-08; 20 m of length, 0.18 mm I.D. and 0.18 \\u0026micro;m film thickness) and a flame ionization detector (FID) (Maga\\u0026ntilde;a-Gallegos et al. \\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e2018a\\u003c/span\\u003e; C\\u0026aacute;rdenas-Palomo et al. \\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e).\\u003c/p\\u003e\\u003c/div\\u003e\\n\\u003ch3\\u003eStatistics\\u003c/h3\\u003e\\n\\u003cp\\u003eStatistical differences among treatment data sets were determined using one-way analysis of variance (ANOVA) comparing nutrient (nitrogen, phosphorus, or COD) removal per treatment batch. Multiple comparison was used to determine the difference between batch means. Statistics were performed with Matlab R2025a (Mathworks).\\u003c/p\\u003e\"},{\"header\":\"Results\",\"content\":\"\\u003cp\\u003eThe raceway ponds were operated during a period of 88 days, and a total of 7 batches. First batch lasted 18 days, second batch 14 days, and batches three to five 12 days, while the sixth batch lasted 11 days and the last one lasted 10 days.\\u003c/p\\u003e\\u003cdiv id=\\\"Sec11\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003eConsortium growth\\u003c/h2\\u003e\\u003cp\\u003eFigure\\u0026nbsp;2 illustrates the growth of the microalgae-bacteria consortium, showing the ability to grow in this type of wastewater.\\u003c/p\\u003e\\u003cp\\u003e\\u003cb\\u003eFigure\\u0026nbsp;2\\u003c/b\\u003e Microalgae growth at the seven culture cycles\\u003c/p\\u003e\\u003cp\\u003eDuring the initial cultivation batches, the consortium exhibited exponential growth. In the last two batches, the consortium seems to have reached the stationary growth phase; however, the microalgae continued to grow (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e). This is attributed to the presence of flocs, whose formation increased during these final batches, complicating the cell counting process (floc dispersion could not be achieved). As a result, only cells in suspension could be counted. The decrease observed in cell counts is a result of water replacement during different batch cultivations.\\u003c/p\\u003e\\u003cp\\u003e\\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab1\\\" border=\\\"1\\\"\\u003e\\u003ccaption language=\\\"En\\\"\\u003e\\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 1\\u003c/div\\u003e\\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\u003cp\\u003eDry biomass of the consortia harvested in each of the batches\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/caption\\u003e\\u003ccolgroup cols=\\\"8\\\"\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c7\\\" colnum=\\\"7\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c8\\\" colnum=\\\"8\\\"\\u003e\\u003c/div\\u003e\\u003cthead\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colspan=\\\"7\\\" nameend=\\\"c8\\\" namest=\\\"c2\\\"\\u003e\\u003cp\\u003eBatch\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003e1\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003e2\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003e3\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003e4\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003e5\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003e6\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003e7\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eDry biomass (g L\\u003c/b\\u003e\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e\\u003cb\\u003e)\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.77 \\u0026plusmn; 0.12\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.82\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.09\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.86\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.18\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.91\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.17\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e1.07\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.23\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e1.13\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.15\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e1.17\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.13\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003c/colgroup\\u003e\\u003ctfoot\\u003e\\u003ctr\\u003e\\u003ctd colspan=\\\"8\\\"\\u003e*Mean\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;standard eviation, n\\u0026thinsp;=\\u0026thinsp;4\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tfoot\\u003e\\u003c/table\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec12\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003eEnvironmental conditions and water physicochemical characteristics\\u003c/h2\\u003e\\u003cp\\u003eInitially, an increasing trend in the oxidation-reduction potential (ORP) was observed; subsequently, a decrease in ORP was noted in the different culture batches of the consortium (Fig.\\u0026nbsp;3).\\u003c/p\\u003e\\u003cp\\u003e\\u003cb\\u003eFigure\\u0026nbsp;3\\u003c/b\\u003e Oxidation-reduction potential in the seven cycles of the cultures in the raceway ponds\\u003c/p\\u003e\\u003cp\\u003eInitial pH in the cultures was close to 8.5. After adding wastewater to the raceway pond, a decrease in pH was generally observed during the first days. Subsequently, the pH increased to values above 9. In the first batch, the greatest pH value was achieved (rose above 9.75) (Fig.\\u0026nbsp;4). In the later batches, pH variation was less pronounced.\\u003c/p\\u003e\\u003cp\\u003e\\u003cb\\u003eFigure\\u0026nbsp;4\\u003c/b\\u003e pH of the wastewater during the seven cycles of culture in the raceway ponds\\u003c/p\\u003e\\u003cp\\u003eThe temperature at the time of sampling generally ranged between 30\\u0026deg;C and 36\\u0026deg;C. The lowest recorded temperature was approximately 26\\u0026deg; C on a day with rain and overcast conditions. The highest temperatures exceeded 37\\u0026deg; C, with an average of 32.8\\u0026deg; C (Fig.\\u0026nbsp;5) for the entire operation period of the RWPs.\\u003c/p\\u003e\\u003cp\\u003e\\u003cb\\u003eFigure\\u0026nbsp;5\\u003c/b\\u003e Temperature during the seven cycles of culture in the raceway ponds\\u003c/p\\u003e\\u003cp\\u003eFigure\\u0026nbsp;6 shows the photosynthetically active photon flux density over the raceway ponds (RWPs) at the time of sampling. The average value was 2070 \\u0026micro;mol m\\u003csup\\u003e\\u0026minus;\\u003c/sup\\u003e\\u0026sup2; s\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e, with a maximum of 2404 \\u0026micro;mol m\\u003csup\\u003e\\u0026minus;\\u003c/sup\\u003e\\u0026sup2; s\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e and a minimum of 1649 \\u0026micro;mol m\\u003csup\\u003e\\u0026minus;\\u003c/sup\\u003e\\u0026sup2; s\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e, which coincided with the day when the lowest temperature was recorded (Fig.\\u0026nbsp;5).\\u003c/p\\u003e\\u003cp\\u003e\\u003cb\\u003eFigure\\u0026nbsp;6\\u003c/b\\u003e Photosynthetically active photon flux density during the seven cycles of culture in the raceway ponds\\u003c/p\\u003e\\u003cp\\u003eDissolved oxygen concentrations did not vary considerably during the sampling period, ranging from 5.3 to 5.7 mg L\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e.\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec13\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003eNutrient removal from wastewater\\u003c/h2\\u003e\\u003cdiv id=\\\"Sec14\\\" class=\\\"Section3\\\"\\u003e\\u003ch2\\u003eNitrogen\\u003c/h2\\u003e\\u003cp\\u003eThe varying nitrogen concentrations among the batches were due to differences in wastewater batches originating from the pig farm, primarily related to the pig\\u0026rsquo;s growth stages. The differences between batches 1 to 4 and batches 5 to 7 were attributed to the smaller volume of inoculum added to the latter (Fig.\\u0026nbsp;7). At the start of cultivation, the predominant nitrogen species was ammonium (constituting between 47 and 63% of total nitrogen), followed by nitrate, except in batch 1 (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003e\\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab2\\\" border=\\\"1\\\"\\u003e\\u003ccaption language=\\\"En\\\"\\u003e\\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 2\\u003c/div\\u003e\\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\u003cp\\u003eNitrogen concentrations, percentage removal, and removal rate\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/caption\\u003e\\u003ccolgroup cols=\\\"8\\\"\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c7\\\" colnum=\\\"7\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c8\\\" colnum=\\\"8\\\"\\u003e\\u003c/div\\u003e\\u003cthead\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colspan=\\\"4\\\" nameend=\\\"c6\\\" namest=\\\"c3\\\"\\u003e\\u003cp\\u003eNitrogen concentration (mg L\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e)\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c7\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003eTotal N removal percentage (%)\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c8\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003eTotal N removal rate (mg L\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e day\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e)\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eBatch\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eN-NO\\u003csub\\u003e3\\u003c/sub\\u003e\\u003csup\\u003e\\u0026minus;\\u003c/sup\\u003e\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eN-NH\\u003csub\\u003e4\\u003c/sub\\u003e\\u003csup\\u003e+\\u003c/sup\\u003e\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003eN-NO\\u003csub\\u003e2\\u003c/sub\\u003e\\u003csup\\u003e\\u0026minus;\\u003c/sup\\u003e\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003eTotal N\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003e1\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eInitial\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e580.8\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e461.4\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e493.5\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e1535.8\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c7\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003e84.3\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c8\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003e72.0\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eFinal\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e136.4\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e16.7\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e87.4\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e240.5\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003e2\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eInitial\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e617.3\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1302.9\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e143.6\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e2063.8\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c7\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003e79.5\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c8\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003e117.1\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eFinal\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e242.2\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e181.6\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003eND\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e423.8\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003e3\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eInitial\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e886.4\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1165.2\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e224.9\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e2276.5\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c7\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003e79.0\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c8\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003e149.9\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eFinal\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e298.5\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e179.8\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003eND\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e478.3\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003e4\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eInitial\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e524.5\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e903.6\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e471.3\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e1899.4\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c7\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003e82.6\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c8\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003e148.1\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eFinal\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e197.9\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e132.7\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003eND\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e330.6\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003e5\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eInitial\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1048.2\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1269.3\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e324.7\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e2642.2\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c7\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003e87.8\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c8\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003e193.4\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eFinal\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e250.2\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e71.3\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003eND\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e321.5\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003e6\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eInitial\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e953.6\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1428.7\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e331.2\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e2713.5\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c7\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003e89.7\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c8\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003e221.3\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eFinal\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e190.3\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e88.6\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003eND\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e278.9\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003e7\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eInitial\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e896.4\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1425.9\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e398.2\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e2720.5\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c7\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003e93.6\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c8\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003e231.6\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eFinal\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e131.6\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e41.6\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003eND\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e173.2\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c4\\\" namest=\\\"c2\\\"\\u003e\\u003cp\\u003e\\u003csup\\u003ex\\u003c/sup\\u003eND: non-detectable\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003c/colgroup\\u003e\\u003c/table\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003cp\\u003eFigure\\u0026nbsp;7 shows that nitrite was the nitrogen species removed most rapidly, since it was consumed within the first 4 to 5 days of culture. Ammonium was the second most rapidly consumed nitrogen species. Although it had the highest initial concentration, its final concentration was lower than that of nitrate.\\u003c/p\\u003e\\u003cp\\u003e\\u003cb\\u003eFigure\\u0026nbsp;7\\u003c/b\\u003e Nitrate, nitrite, ammonia, and total nitrogen concentration in the 7 cycles of the raceway ponds (n\\u0026thinsp;=\\u0026thinsp;4)\\u003c/p\\u003e\\u003cp\\u003eTotal nitrogen removal in batch 1 reached approximately 85%. However, this efficiency declined in batches 2 to 4. A slight improvement in removal efficiency was observed in batch 4, with the mean value significantly different from those of the other batches, as indicated by the multiple comparison test, due to the increase in the volume of wastewater added to the RWPs. Batches 5 to 7 exhibited an increase in nitrogen removal efficiency and removal rate (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e). The increase in removal rate was associated with the reduction in nitrogen removal times, as observed for batch 1, where it lasted 18 days, in batch 2, it decreased to 14 days, then to 12 days in batches 3 and 5, and finally to 11 days in batches 4, 6, and 7.\\u003c/p\\u003e\\u003cp\\u003eIn the latest batches, nitrogen removal rates between 221 and 231 mg L\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e day\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e were achieved, which is three times that of the first batch, again showing an adaptation to culture conditions.\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec15\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003eOrthophosphate\\u003c/h2\\u003e\\u003cp\\u003e\\u003cb\\u003eFigure\\u0026nbsp;8\\u003c/b\\u003e Orthophosphate concentration in the seven cycles of culture in the raceway ponds (n\\u0026thinsp;=\\u0026thinsp;4)\\u003c/p\\u003e\\u003cp\\u003eOrthophosphate removal ranged from 81.8% to 94.6% between days 5 and 9 of culture across different batches. The variability depended on the final detectable orthophosphate concentration, as the amount of orthophosphate in the culture at the end of each batch (averaging 12 days) was below the analytical method's detection limit (0.2 mg L\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e P-PO43-), making it nondetectable (Fig.\\u0026nbsp;8). Therefore, based on the detection limit, the removal rate exceeds 99% (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e) in all cases.\\u003c/p\\u003e\\u003cp\\u003e\\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab3\\\" border=\\\"1\\\"\\u003e\\u003ccaption language=\\\"En\\\"\\u003e\\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 3\\u003c/div\\u003e\\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\u003cp\\u003eOrthophosphate concentrations and percentage removal from raceway ponds.\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/caption\\u003e\\u003ccolgroup cols=\\\"4\\\"\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e\\u003cthead\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eBatch\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eConcentration (mg L\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e)\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003ePercentage removal (%)\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003e1\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eInitial\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e24.2\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003e\\u0026gt;\\u0026thinsp;99\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eFinal\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eND\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003e2\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eInitial\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e32.1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003e\\u0026gt;\\u0026thinsp;99\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eFinal\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eND\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003e3\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eInitial\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e29.8\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003e\\u0026gt;\\u0026thinsp;99\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eFinal\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eND\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003e4\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eInitial\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e20.3\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003e\\u0026gt;\\u0026thinsp;99\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eFinal\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eND\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003e5\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eInitial\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e42.6\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003e\\u0026gt;\\u0026thinsp;99\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eFinal\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eND\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003e6\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eInitial\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e45.1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003e\\u0026gt;\\u0026thinsp;99\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eFinal\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eND\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003e7\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eInitial\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e47.9\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003e\\u0026gt;\\u0026thinsp;99\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eFinal\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eND\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c3\\\" namest=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003csup\\u003ex\\u003c/sup\\u003eND: non detectable\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003c/colgroup\\u003e\\u003c/table\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003cp\\u003eDue to the orthophosphate concentrations present in the water, very high N:P ratios were observed, ranging from 56:1 to 93:1 depending on the batch, which could limited growth considering the Redfield ratio of 16:1, which did not occur.\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec16\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003eOrganic matter\\u003c/h2\\u003e\\u003cp\\u003eOrganic matter was indirectly measured as chemical oxygen demand (COD), with removal efficiencies ranging from 86.6% to 91.6% across the different cultivation batches (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e). A reduction in COD can be observed in Fig.\\u0026nbsp;9 throughout the different batches. In general, an increase in COD reduction was observed throughout the batches, except in batch 4, where the final concentration was below the method's detection limit (50 mg O₂ L\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e). Therefore, a removal efficiency greater than 86.6% is assumed for this batch.\\u003c/p\\u003e\\u003cp\\u003e\\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab4\\\" border=\\\"1\\\"\\u003e\\u003ccaption language=\\\"En\\\"\\u003e\\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 4\\u003c/div\\u003e\\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\u003cp\\u003eChemical oxygen demand concentration and reduction percentage in raceway ponds.\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/caption\\u003e\\u003ccolgroup cols=\\\"4\\\"\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e\\u003cthead\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eBatch\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eCOD concentration (mg O\\u003csub\\u003e2\\u003c/sub\\u003e L\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e)\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eCOD reduction percentage (%)\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003e1\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eInitial\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e368.3\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003e84.4\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eFinal\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e57.5\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003e2\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eInitial\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e405.0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003e87.2\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eFinal\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e52.0\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003e3\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eInitial\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e453.0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003e90.9\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eFinal\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e41.0\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003e4\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eInitial\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e373.0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003e\\u0026gt;\\u0026thinsp;86.6\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eFinal\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eND\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003e5\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eInitial\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e652.3\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003e91.1\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eFinal\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e58.2\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003e6\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eInitial\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e587.2\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003e89.4\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eFinal\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e62.4\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003e7\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eInitial\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e623.1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003e91.6\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eFinal\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e52.3\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003c/colgroup\\u003e\\u003ctfoot\\u003e\\u003ctr\\u003e\\u003ctd colspan=\\\"4\\\"\\u003e\\u003csup\\u003ex\\u003c/sup\\u003eND: non-detctable\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tfoot\\u003e\\u003c/table\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003cp\\u003e\\u003cb\\u003eFigure\\u0026nbsp;9\\u003c/b\\u003e Chemical oxygen demand reduction in the 7 cycles of culture of the raceway ponds\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec17\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003eLipids and fatty acids\\u003c/h2\\u003e\\u003cp\\u003eTotal lipids were measured from the biomass harvested by the seven different batches in the RWPs. An increase in lipid content was observed, from 173 mg g\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e dw to 268 mg g\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e dw (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003e). This increase was more pronounced from batches 1 to 4, while the lipid content in batches 5 to 7 remained relatively constant.\\u003c/p\\u003e\\u003cp\\u003e\\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab5\\\" border=\\\"1\\\"\\u003e\\u003ccaption language=\\\"En\\\"\\u003e\\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 5\\u003c/div\\u003e\\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\u003cp\\u003eContent of total lipids in the dry biomass of the RWPs.\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/caption\\u003e\\u003ccolgroup cols=\\\"4\\\"\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e\\u003cthead\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eBatch\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c4\\\" namest=\\\"c2\\\"\\u003e\\u003cp\\u003eLipids (mg g\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e)\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003e1\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e174.0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e\\u0026plusmn;\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e26.9\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003e2\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e189.1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e\\u0026plusmn;\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e19.7\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003e3\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e223.3\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e\\u0026plusmn;\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e34.3\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003e4\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e230.6\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e\\u0026plusmn;\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e30.6\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003e5\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e256.5\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e\\u0026plusmn;\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e34.0\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003e6\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e266.0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e\\u0026plusmn;\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e27.9\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003e7\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e268.1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e\\u0026plusmn;\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e14.5\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003c/colgroup\\u003e\\u003ctfoot\\u003e\\u003ctr\\u003e\\u003ctd colspan=\\\"4\\\"\\u003e*Mean\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;standard eviation, n\\u0026thinsp;=\\u0026thinsp;4\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tfoot\\u003e\\u003c/table\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003cp\\u003eThe average lipid content of the consortium during the last three cultivation batches was 26.4%.\\u003c/p\\u003e\\u003cp\\u003eFatty acid profiling was conducted only on the biomass collected from batch 7. The fatty acid concentrations among the four replicates of batch 7 showed significant variation in both levels and the presence of specific fatty acids (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab6\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003e). Notably, in RWP 2, the levels of several fatty acids were lower compared to the other three RWPs, and stearic acid (which was present at notable levels in RWPs 1, 3, and 4) was not detected.\\u003c/p\\u003e\\u003cp\\u003e\\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab6\\\" border=\\\"1\\\"\\u003e\\u003ccaption language=\\\"En\\\"\\u003e\\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 6\\u003c/div\\u003e\\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\u003cp\\u003eConcentration of fatty acids as \\u0026micro;g of fatty acid per mg of lipid in biomass harvested from batch 7 of the 4 RWPs.\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/caption\\u003e\\u003ccolgroup cols=\\\"5\\\"\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e\\u003cthead\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colspan=\\\"4\\\" nameend=\\\"c5\\\" namest=\\\"c2\\\"\\u003e\\u003cp\\u003eConcentration (\\u0026micro;g mg\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e)\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eFatty acid\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003e1\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003e2\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003e3\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003e4\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eCaprylic\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e155.5\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eND\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eND\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e330.4\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eLauric\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e105.7\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eND\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eND\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e140.4\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eMyristic\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1564.5\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e202.0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1049.7\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e3166.4\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eMyristoleic\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e436.5\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eND\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e230.3\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e946.8\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003ePalmitic\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e21176.6\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1370.8\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e11863.6\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e52749.0\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003ePalmitoleic\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e3880.3\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e316.6\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1961.0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e6276.8\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eStearic\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e3063.3\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1439.3\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e9236.9\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eOleic\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e5453.8\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e169.4\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e4991.0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e23035.4\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eLinoleic\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e5249.9\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e242.0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e3960.4\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e22431.5\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eγ-linolenic\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e109.8\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eND\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eND\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e596.6\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eα-linolenic\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e4122.9\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e213.6\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e3443.2\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e14219.4\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eArachidic\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e166.3\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eND\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e139.6\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e421.8\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eEicosenoic\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e68.8\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eND\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eND\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e288.5\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eEicosadienoic\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eND\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eND\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eND\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e157.8\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eEicospaentanoic\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e446.8\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eND\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eND\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003eND\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eDocosanoic\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e297.3\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eND\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eND\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003eND\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003c/colgroup\\u003e\\u003ctfoot\\u003e\\u003ctr\\u003e\\u003ctd colspan=\\\"5\\\"\\u003e\\u003csup\\u003ex\\u003c/sup\\u003eND: non-detectable\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tfoot\\u003e\\u003c/table\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003cp\\u003ePalmitic acid was the most abundant fatty acid of all four replicates; however, its concentration varied greatly among them (p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05). Other major fatty acids present in RWPs 1, 3, and 4 included palmitoleic, stearic, oleic, linoleic, and α-linolenic acids. Once again, concentrations varied significantly among RWPs (p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05).\\u003c/p\\u003e\\u003cp\\u003eIn general, the fatty acid composition showed approximately 50% saturated fatty acids (SFAs), except for RWP 2, where several unsaturated fatty acids were not detected, resulting in a higher proportion of saturated fatty acids. The proportion of monounsaturated fatty acids (MUFAs) and polyunsaturated fatty acids (PUFAs) ranged between 18.1% and 27.9% (Fig.\\u0026nbsp;10).\\u003c/p\\u003e\\u003cp\\u003e\\u003cb\\u003eFigure\\u0026nbsp;10\\u003c/b\\u003e Percentage of saturated fatty acids (SFAs), monounsaturated fatty acids (MUFAs), and polyunsaturated fatty acids (PUFAs) in biomass harvested from each replicate of the last batch\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec18\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003eProtein\\u003c/h2\\u003e\\u003cp\\u003eProtein content was determined only in the biomass harvested from batch 7 across the four RWPs, yielding an average value of 13.4\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;2.2% dw. This is relatively low (since microalgae generally exhibit protein contents between 28 and 70% (Wang et al. \\u003cspan citationid=\\\"CR44\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e), considering that nitrogen was not limiting in the RWPs (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e), which is why the determination was not performed in the other batches.\\u003c/p\\u003e\\u003c/div\\u003e\"},{\"header\":\"Discussion\",\"content\":\"\\u003cdiv id=\\\"Sec20\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003eConsortium growth\\u003c/h2\\u003e\\u003cp\\u003eTable\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e shows an increase in biomass content across the cultivation batches, possibly due to acclimatization of the consortium to wastewater and environmental conditions. Biomass concentration rose from batch 1 to batch 7 by approximately 0.5 g L\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e), indicating that microalgae continued to grow even though this was not clear from the cell counts (Fig.\\u0026nbsp;2), due to biofloc formation. The formation of bioflocs may be due to increased interactions between microalgae and bacteria within the consortium, which produce extracellular polymers (Fallahi et al. \\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e), being one of the possible explanations of what happened in this study. Another explanation is the formation of structures by filamentous bacteria, where microalgae can adhere, forming granules (S\\u0026aacute;tiro et al. \\u003cspan citationid=\\\"CR41\\\" class=\\\"CitationRef\\\"\\u003e2025b\\u003c/span\\u003e). Biofloc formation was advantageous as it facilitated the harvesting of biomass after cultivation.\\u003c/p\\u003e\\u003cp\\u003eThe biomass obtained in the final batch was lower than that reported by (Qu et al. \\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e), who achieved 6.2 g L\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e of biomass; however, their study was conducted in a 500 mL photobioreactor. The result obtained here is higher than the 0.8 g L\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e reported for \\u003cem\\u003eChlorella vulgaris\\u003c/em\\u003e (Wang et al. \\u003cspan citationid=\\\"CR43\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e), which may be attributed to the fact that their cultivation was carried out in a raceway pond (RWP) of nearly 43,000 L.\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec21\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003eEnvironmental conditions and water physicochemical characteristics\\u003c/h2\\u003e\\u003cp\\u003eThe initial increase in ORP could indicate that the microalgae were producing oxygen, and the posterior decrease may be due to oxygen consumption by bacteria present in the culture (Fig.\\u0026nbsp;3). This behavior was like that reported by (Lee et al. \\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e), who observed an increase in ORP at the start of cultivation, followed by a decrease after 72 h in 250 L photobioreactors.\\u003c/p\\u003e\\u003cp\\u003eThe decrease in pH after adding wastewater may be attributed to bacterial activity. Subsequently, the pH increase may be due to the activity of the microalgae. The greatest pH value in the first batch may be related to the longer cultivation time (18 days) compared to subsequent batches, which had shorter cultivation periods (between 10 and 14 days) due to the consortium's acclimatization to the wastewater.\\u003c/p\\u003e\\u003cp\\u003eFluctuations of several degrees were recorded from day to day for the entire operation period of the RWPs; however, these variations did not significantly affect the growth (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e) of the consortium.\\u003c/p\\u003e\\u003cp\\u003eThe typical light intensities under which microalgae grow range from 26 to 400 \\u0026micro;mol photons m\\u003csup\\u003e\\u0026minus;\\u003c/sup\\u003e\\u0026sup2; s\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e (Maltsev et al. \\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e). Although the intensity at the surface of the RWPs could be too high, it did not affect algal growth (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e). This is likely due to the dark brown color of the wastewater and/or shading of the biomass at the top of the RWPs, which limited light penetration, resulting in lower radiation reaching the deeper layers of the RWPs.\\u003c/p\\u003e\\u003cp\\u003eThe minor fluctuations in dissolved oxygen levels may result from the photosynthetic activity of microalgae.\\u003c/p\\u003e\\u003cp\\u003eAlthough some conditions could be considered adverse, such as the temperature, which remained above 30\\u0026deg; C (Fig.\\u0026nbsp;5), a value that is regarded as high and could affect the growth rate by affecting proteins or increasing reactive oxygen species (S\\u0026aacute;tiro et al. \\u003cspan citationid=\\\"CR40\\\" class=\\\"CitationRef\\\"\\u003e2025a\\u003c/span\\u003e). As well as the high photon flux density (Fig.\\u0026nbsp;6). Contrary to what might seem like adverse conditions, the experiment revealed that these conditions were actually favorable for this well-adapted consortium, as growth was not affected (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e).\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec22\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003eNutrient removal from wastewater\\u003c/h2\\u003e\\u003cdiv id=\\\"Sec23\\\" class=\\\"Section3\\\"\\u003e\\u003ch2\\u003eNitrogen\\u003c/h2\\u003e\\u003cp\\u003eAmmonium is the predominant nitrogen species because the water originated from an anaerobic treatment process. Although ammonium is generally the preferred nitrogen source for microalgae, it can inhibit growth and photosynthesis at concentrations above 100 mg L\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e (Salbitani and Carfagna \\u003cspan citationid=\\\"CR38\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e). However, in this study, despite the lowest initial ammonium concentration being approximately 460 mg L\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e and others close to or exceeding 1,000 mg L\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e), no inhibition of microalgal growth was observed. This may have been due to the oxidation of ammonium to nitrate by nitrifying bacteria, as noted by (Abate et al. \\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2024b\\u003c/span\\u003e), reducing its toxicity. This may also explain why nitrite was the nitrogen species that was removed most rapidly from the culture, its oxidation to nitrite was likely due to oxidizing conditions (Fig.\\u0026nbsp;3),\\u003c/p\\u003e\\u003cp\\u003eThe faster removal of ammonium than nitrate could be attributed to the preferential uptake of ammonium by microalgae, as it does not require reduction before incorporation into biomass (Carletti et al. \\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e). The oxidation of ammonium may also explain the quicker removal of this nitrogen species compared to nitrate due to oxidizing conditions (Fig.\\u0026nbsp;3) (Abate et al. \\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2024b\\u003c/span\\u003e). Another explanation for the removal of ammonium is that the alkaline pH (between 8 and 9.75) (Fig.\\u0026nbsp;4) in the RWPs forms ammonia, which can be lost through volatilization. The consortium\\u0026rsquo;s adaptation to the cultivation conditions is evident from the fact that an increase in nitrogen removal efficiency and removal rate was observed in batches 5 to 7, despite their higher initial nitrogen concentrations compared to earlier batches. The group means for batches 5 to 7 were significantly different from those of previous batches, according to the multicompare test, suggesting improved adaptation and performance of the consortium. Significant differences (p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05) in nitrogen removal were observed among batches, indicating that reducing the amount of consortium inoculum may have affected removal efficiency. This supports the hypothesis of progressive adaptation of the microbial community to the cultivation environment. Specifically, no significant difference was found between batches 6 and 7 (similar to the results obtained with the removal rate), further confirming the stabilization and efficient functioning of the consortium. A similar trend was observed in nutrient removal rate (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e). The initial increase in removal rate from batch 1 to 2 was primarily due to higher nitrogen concentrations. Batches 3 and 4 exhibited similar removal rates, suggesting a temporary stabilization phase that justified the decision to increase the wastewater load in subsequent batches.\\u003c/p\\u003e\\u003cp\\u003eThe removal efficiencies (close to 90%) achieved in the final cultivation batches were higher than those reported by (Qu et al. \\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e), who obtained 73 % nitrogen remoal in 500 mL photobioreactors with an initial concentration of 479 mg L\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e. These results were comparable to those reported by (Wang et al. \\u003cspan citationid=\\\"CR43\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e), who achieved approximately 90 % total nitrogen removal in 12 das in an RWP under environmental conditions. However, the removal obtained in this study was lower than the 95 % reported by (You et al. \\u003cspan citationid=\\\"CR47\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e) in 500 mL culturesof a \\u003cem\\u003eC. vulgaris\\u003c/em\\u003e and \\u003cem\\u003eR. sphaeroides\\u003c/em\\u003e consortium, with initial total nitrogen concentrations of 1492 mg L\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e.\\u003c/p\\u003e\\u003cp\\u003eThe removal rates obtained for batches 6 and 7 (221 and 231 mg L\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e day\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e, respectively) were higher than those reported by (Fuhrmann Dinnebier et al. \\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e), who achieved removal rates of around 115 mg L\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e day\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e with initial ammonium concentrations ranging from 1000 to 1300 mg L\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e. This difference may be attributed to the ammonium removal efficiencies reported in the referenced study, which ranged between 72% and 79%. Although (Rossi et al. \\u003cspan citationid=\\\"CR36\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e) reported lower nitrogen removal rates (19.7 mg L\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e day\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e) than those obtained in the present study; however, the removal efficiency was similar (90 %), likely due to the lower initial nitrogen concetration of approximately 200 mg L\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e. That study was conducted in RWPs with a volume of approximately 700 L. In another study in a high-rate algae pond (HRP) with a capacity of 2.4 m\\u0026sup3;, treating domestic wastewater (32.2 mg L\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e total nitrogen), in semi-continuous operation, 84 % nitrogen removal was achieved (S\\u0026aacute;tiro et al. \\u003cspan citationid=\\\"CR40\\\" class=\\\"CitationRef\\\"\\u003e2025a\\u003c/span\\u003e), which is less thn the removal percentage reported in the present study.\\u003c/p\\u003e\\u003cp\\u003eIn the present study, ammonium removal in the last three batches was around 95%, higher than the 80% achieved in a 250 L reactor under environmental conditions with an initial concentration of 770 mg L\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e (Lee et al. \\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e).\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec24\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003eOrthophosphate\\u003c/h2\\u003e\\u003cp\\u003eA significant difference (p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05) in orthophosphate removal was observed only for batches 3 and 4, which differed notably from the rest. This can be attributed to the high removal efficiencies achieved at low orthophosphate concentrations. The high percentage of orthophosphate removal was mainly due to microalgae absorption but could also have resulted from the presence of calcium in the regional water, which promoted orthophosphate precipitation. ate precipitation (Cerozi and Fitzsimmons \\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003eThe very high N:P ratios in the RWPs could suggest a potential inhibitory effect due to phosphorus limitation (Yaakob et al. \\u003cspan citationid=\\\"CR46\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e), possibly even influenced by the duration of the experiment (Magyar et al. \\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e). Therefore, in this case, the resolubilization of phosphorus through the bacterial activity (Garba Jega et al. \\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e2025\\u003c/span\\u003e) of the consortium, could be the reason why no growth limitation was observed (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003eThe removal percentage of orthophosphate obtained in this study is higher than the 85% reported by (Wang et al. \\u003cspan citationid=\\\"CR43\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e). The removal efficiency is comparable to those reported by (Fuhrmann Dinnebier et al. \\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e), and (You et al. \\u003cspan citationid=\\\"CR47\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e), who achieved 96 % removal with initial concentrations of 42.9 mg L\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e and 154 mg L\\u003csup\\u003e\\u0026minus;\\u003c/sup\\u003e, respectively. Similar results were also reported by (Luo et al. \\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e), with a removal efficiency of approximately 92 % in 600 mL cultures. In an RWP of nearly 700 L, a 90 % removal was achieved with a initial concentration of 19.4 mg L\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e (Rossi et al. \\u003cspan citationid=\\\"CR36\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e), which is comprable to the results obtained in this study.\\u003c/p\\u003e\\u003cdiv id=\\\"Sec25\\\" class=\\\"Section3\\\"\\u003e\\u003ch2\\u003eOrganic matter\\u003c/h2\\u003e\\u003cp\\u003eAlthough the ANOVA test revealed a significant difference between batches (p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05), the multiple comparisons test indicated no significant differences among them. The reduction in COD was likely due primarily by the bacteria present in the consortium. DO concentration (between 5.3 and 5.7) is an indicator of oxygen production during photosynthesis that bacteria can use to oxidize organic matter present in the wastewater. Microalgae also contribute to organic matter removal, as they can exhibit mixotrophic metabolism (Abate et al. \\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2024b\\u003c/span\\u003e), and heterotrophic carbon uptake may occur more rapidly than photosynthetic assimilation (Lee et al. \\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003eIn a 500 mL photobioreactor, \\u003cem\\u003eParachlorella kessleri\\u003c/em\\u003e achieved a COD reduction of 58% (initial concentration: 637 mg L\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e) (Qu et al. \\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e). In comparison, a 900 mL culture resulted in a 63.6 % reduction (intial concentration: 1502 mg L\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e), which is lower than the values obtained in the present study (approximately 90 % in the last tree batches). In another study, \\u003cem\\u003eC. vulgaris\\u003c/em\\u003e and \\u003cem\\u003eS. obliquus\\u003c/em\\u003e achieved a 73 % reduction wit an initial concentration of 760 mg L\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e in autoclaved water (Wang et al. \\u003cspan citationid=\\\"CR43\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e). These lower reduction efficiencies may be due to the reduced presence of bacteria. In a separate study using a 250 L cultivation system, a COD reduction of 80.6% was reported (Lee et al. \\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e), which is higher than the studies mentioned above but still lower than the one obtained in the present work. In the RWP of approximately 700 L used by (Rossi et al. \\u003cspan citationid=\\\"CR36\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e), with an initial COD of 678 mg L\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e, only a 59 % reduction was achieved. In an HRP with a capacity of 2.4 m\\u0026sup3;, treatig domestic wastewater, a COD removal of 74.67 % was achieved (160.5 mg L\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e COD in the affluent) [30], which is bothless than the removal achieved in this study. In an HRP with 2.4 m\\u0026sup3; of capacity for treating domestic wastewater (5.4 mg L\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e total phosphorus), in semi-continuous operation, 71.4 % of phosphorus removal was achieved (S\\u0026aacute;tiro et al. \\u003cspan citationid=\\\"CR40\\\" class=\\\"CitationRef\\\"\\u003e2025a\\u003c/span\\u003e), which is less than the removalpercentage reported in the present study.\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec26\\\" class=\\\"Section3\\\"\\u003e\\u003ch2\\u003eLipids and fatty acids\\u003c/h2\\u003e\\u003cp\\u003eA significant difference (p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05) was observed in the lipid content of the consortium across the different cultivation batches. Nonetheless, the multiple comparisons test revealed no significant differences among the last three batches, which may once again suggest that the consortium has reached a stabilized state.\\u003c/p\\u003e\\u003cp\\u003eThe more pronounced increase in lipid content from batches 1 to 4 may be attributed to the microalgae's adaptation to the cultivation conditions. The increase does not appear to be related to nitrogen deficiency stress, as the total nitrogen concentration at the end of the cultures ranged from 130 to 300 mg L\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e). It is likely associated with another type of stress, such as the presence of additional contaminants or the daytime temperature (Morales et al. \\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e). However, this would need to be studied in future research.\\u003c/p\\u003e\\u003cp\\u003eThe average lipid content of 26.4% obtained in this study is higher than the 14.0% reported for \\u003cem\\u003eC. vulgaris\\u003c/em\\u003e in a batch culture. (Wang et al. \\u003cspan citationid=\\\"CR43\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e), and the 8 % in a 500 mL culure (You et al. \\u003cspan citationid=\\\"CR47\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e). However, this value was lower than what was obtained in an HRAP of approximately 900 L (Oliveira et al. \\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e). The lipid content obtained in this study was similar to the 21.89 % reported in a nearly 7 L photobioreactor (Mou et al. \\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e). A similar lpid content to that obtained in this study was reported in an HRP of 2.4 m\\u0026sup3;, with a lipid content of 21.8 % (S\\u0026aacute;tiro et al. \\u003cspan citationid=\\\"CR40\\\" class=\\\"CitationRef\\\"\\u003e2025a\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003eThis variation in concentration and presence of fatty acids in the replicates of batch 7 could be due to the open systems, where, despite using the same wastewater and environmental conditions, differences in microalgal species composition and abundance in the consortium across RWPs can occur because of the introduction of airborne microorganisms that could modify the species in the consortium.\\u003c/p\\u003e\\u003cp\\u003eThe high SFA content and the presence of MUFAs and PUFAs make this oil suitable for biodiesel production (Morales et al. \\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e). The UFA/SFA ratio tends to increase when cultures are under stress conditions (Li et al. \\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e2011\\u003c/span\\u003e); in this case, the observed ratios ranged from 0.6 to 1.0. This suggests that the microalgae in the consortium were not under stress, which may be due to the absence of nitrogen limitation (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e). When cultivation temperature increases, the SFA/MUFA ratio also increases (Carletti et al. \\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e). In this study, under high cultivation temperatures (Fig.\\u0026nbsp;5), SFA/MUFA ratios ranged from 2.0 to 3.2 between different RWPs.\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec27\\\" class=\\\"Section3\\\"\\u003e\\u003ch2\\u003eProtein\\u003c/h2\\u003e\\u003cp\\u003eThe protein content was lower than that reported by (Mou et al. \\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e) (29.31 %), by (Fuhrmann Dinnebier et al. \\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e) (59.5 %) by (You et al. \\u003cspan citationid=\\\"CR47\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e) (64 %) in a 500 mL culture of \\u003cem\\u003eC. vulgari\\u003c/em\\u003e and \\u003cem\\u003eR. sphaeroides\\u003c/em\\u003e, and by (Oliveira et al. \\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e) who reported 45.9 % in a 900 L HRAP system. A study in an HRP of 2.4 m\\u003csup\\u003e3,\\u003c/sup\\u003e a protein content of protein of .9 % was reported (S\\u0026aacute;tiro et al. \\u003cspan citationid=\\\"CR40\\\" class=\\\"CitationRef\\\"\\u003e2025a\\u003c/span\\u003e), which is less than that obtained in this study, may indicate low proten content in reactors such as those used in this study and the previously mentioned one.\\u003c/p\\u003e\\u003cp\\u003eThe findings of this study demonstrate that microalgae-bacteria consortia based systems are a viable alternative for treating wastewater from the swine industry. Environmental variability, particularly in terms of light intensity and temperature, as well as fluctuations in key physicochemical parameters (pH, oxidation-reduction potential, and photon flux), did not inhibit microalgal growth, highlighting the importance of using native consortia.\\u003c/p\\u003e\\u003cp\\u003eMoreover, decreasing the inoculum concentration in raceway ponds (RWPs) had no significant impact on the development or performance of the microalgae-bacteria consortium.\\u003c/p\\u003e\\u003cp\\u003eNutrient removal efficiencies exceeded 90% for nitrogen, phosphorus, and chemical oxygen demand (COD), indicating that the microbial consortium was highly effective in removing these pollutants from the swine wastewater.\\u003c/p\\u003e\\u003cp\\u003eDespite these high removal rates, considerable concentrations of residual nutrients (between 172 and 322 mg L\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e of total nitrogen) were still detected in the treated effluent. This highlights the need for further research to optimize cultivation strategies, including adjustments to operational modes and scaling up the system to improve treatment efficiency and reduce hydraulic retention time.\\u003c/p\\u003e\\u003cp\\u003eThe harvested biomass contained approximately 25% lipids, with a fatty acid profile potentially suitable for biodiesel production, which could contribute to lowering the overall cost of treatment. However, considerable variability in the fatty acid profiles was observed, possibly due to differences in the microbial consortia that developed in each RWP, resulting in heterogeneous biomass characteristics. Furthermore, the protein content of the biomass remained low, despite high nitrogen availability during cultivation.\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/div\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003ch2\\u003eCompeting interests\\u003c/h2\\u003e\\u003cp\\u003eThe authors declare no relevant financial or non-financial interests.\\u003c/p\\u003e\\u003c/p\\u003e\\u003ch2\\u003eFunding\\u003c/h2\\u003e\\u003cp\\u003eThis work was supported by the Direcci\\u0026oacute;n General de Asuntos del Personal Acad\\u0026eacute;mico (DGAPA), PAPIIT, UNAM, under Grant IA203123; and the Facultad de Qu\\u0026iacute;mica, Programa de Apoyo a la Investigaci\\u0026oacute;n y el Posgrado (PAIP), UNAM, under Grants 5000\\u0026ndash;9204 and 5000\\u0026ndash;9146.\\u003c/p\\u003e\\u003ch2\\u003eAuthor Contribution\\u003c/h2\\u003e\\u003cp\\u003eMSA was involved in the conception and design of the study, as well as the collection, analysis, and interpretation of data, and drafting of the manuscript. IOV was involved in the conception and design of the study and methodology. ELC was involved in project administration, reviewing, and editing. FAC was involved in drafting the paper, examining it critically for its essential intellectual content and funding acquisition. All authors have read the manuscript and approve the final version to be published. All authors agree to be accountable for all aspects of the work.\\u003c/p\\u003e\\u003ch2\\u003eAcknowledgement\\u003c/h2\\u003e\\u003cp\\u003eAuthors thank the UNAM PAPIIT Project IA203123 Recovery of nutrients and energy from pork-industry wastewater through a microalgae-bacteria consortium and UNAM-PAIP 5000-9146 and 5000-9204 for economic support to this research. The authors would like to thank reviewers for their insightful comments that significantly improved the quality of the manuscript. The authors thank Ms. Sof\\u0026iacute;a Fargher for english grammar editing.\\u003c/p\\u003e\\u003ch2\\u003eData Availability\\u003c/h2\\u003e\\u003cp\\u003eAll data generated or analyzed during this study are included in this published article.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\n\\u003cli\\u003eAbate R, Oon YL, Oon YS, Bi Y, Mi W, Song G, Gao Y (2024a) Diverse interactions between bacteria and microalgae: A review for enhancing harmful algal bloom mitigation and biomass processing efficiency. Heliyon 10:e36503. https://doi.org/10.1016/j.heliyon.2024.e36503\\u003c/li\\u003e\\n\\u003cli\\u003eAbate R, Oon YS, Oon YL, Bi Y (2024b) Microalgae-bacteria nexus for environmental remediation and renewable energy resources: Advances, mechanisms and biotechnological applications. 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Biology (Basel) 11:. https://doi.org/10.3390/biology11101380\\u003c/li\\u003e\\n\\u003cli\\u003eMiranda KM, Espey MG, Wink DA (2001) A rapid, simple spectrophotometric method for simultaneous detection of nitrate and nitrite. Nitric Oxide 5:62\\u0026ndash;71. https://doi.org/10.1006/niox.2000.0319\\u003c/li\\u003e\\n\\u003cli\\u003eMorales M, Aflalo C, Bernard O (2021) Microalgal lipids: A review of lipids potential and quantification for 95 phytoplankton species. Biomass Bioenergy 150:. https://doi.org/10.1016/j.biombioe.2021.106108\\u003c/li\\u003e\\n\\u003cli\\u003eMou Y, Liu N, Lu T, Jia C, Xu C, Song M (2023) The effects of carbon nitrogen ratio and salinity on the treatment of swine digestion effluent simultaneously producing bioenergy by microalgae biofilm. 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Sustainability (Switzerland) 17:. https://doi.org/10.3390/su17073069\\u003c/li\\u003e\\n\\u003cli\\u003ePham Thi M, Bui XD (2025) Selection Enhanced Nutrient Removal from Pig Farm Wastewater Using Co-Immobilized Chlorella vulgaris and Azospirillum brasilense Azo09 in Alginate Beads. European Journal of Biology and Biotechnology 6:1\\u0026ndash;5. https://doi.org/10.24018/ejbio.2025.6.2.541\\u003c/li\\u003e\\n\\u003cli\\u003eQu W, Zhang C, Zhang Y, Ho SH (2019) Optimizing real swine wastewater treatment with maximum carbohydrate production by a newly isolated indigenous microalga Parachlorella kessleri QWY28. Bioresour Technol 289:. https://doi.org/10.1016/j.biortech.2019.121702\\u003c/li\\u003e\\n\\u003cli\\u003eRinguet S, Sassano L, Johnson ZI (2011) A suite of microplate reader-based colorimetric methods to quantify ammonium, nitrate, orthophosphate and silicate concentrations for aquatic nutrient monitoring. Journal of Environmental Monitoring 13:370\\u0026ndash;376. https://doi.org/10.1039/c0em00290a\\u003c/li\\u003e\\n\\u003cli\\u003eRossi S, Pizzera A, Bellucci M, Marazzi F, Mezzanotte V, Parati K, Ficara E (2022) Piggery wastewater treatment with algae-bacteria consortia: Pilot-scale validation and techno-economic evaluation at farm level. Bioresour Technol 351:. https://doi.org/10.1016/j.biortech.2022.127051\\u003c/li\\u003e\\n\\u003cli\\u003eRuppersberg HS, Goebel MR, Kleinert SI, W\\u0026uuml;nsch D, Trautwein K, Rabus R (2017) Photometric Determination of Ammonium and Phosphate in Seawater Medium Using a Microplate Reader. J Mol Microbiol Biotechnol 27:73\\u0026ndash;80. https://doi.org/10.1159/000454814\\u003c/li\\u003e\\n\\u003cli\\u003eSalbitani G, Carfagna S (2021) Ammonium utilization in microalgae: A sustainable method for wastewater treatment. Sustainability (Switzerland) 13:1\\u0026ndash;17. https://doi.org/10.3390/su13020956\\u003c/li\\u003e\\n\\u003cli\\u003eS\\u0026aacute;nchez-Zurano A, Ciardi M, Lafarga T, Fern\\u0026aacute;ndez-Sevilla JM, Bermejo R, Molina-Grima E (2021) Role of microalgae in the recovery of nutrients from pig manure. Processes 9:1\\u0026ndash;11. https://doi.org/10.3390/pr9020203\\u003c/li\\u003e\\n\\u003cli\\u003eS\\u0026aacute;tiro J, dos Santos Neto A, Tavares J, Marinho I, Magnus B, Kato M, Albuquerque A, Florencio L (2025a) Impact of inoculum on domestic wastewater treatment in high-rate ponds in pilot-scale: Assessment of organic matter and nutrients removal, biomass growth, and content. 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Bioresour Technol 222:130\\u0026ndash;138. https://doi.org/10.1016/j.biortech.2016.09.128\\u003c/li\\u003e\\n\\u003cli\\u003eWang Y, Tibbetts SM, McGinn PJ (2021) Microalgae as sources of high-quality protein for human food and protein supplements. Foods 10\\u003c/li\\u003e\\n\\u003cli\\u003eWang Z, Hu G, Hong Y (2024) Strong Alliance of Microalgae and Bacteria: The State-of-the-Art Review and Future Prospects of Utilizing Microalgae-Bacteria Consortia for Comprehensive Treatment of Swine Wastewater. Curr Pollut Rep 10:744\\u0026ndash;764. https://doi.org/10.1007/s40726-024-00325-7\\u003c/li\\u003e\\n\\u003cli\\u003eYaakob MA, Mohamed RMSR, Al-Gheethi A, Ravishankar GA, Ambati RR (2021) Influence of nitrogen and phosphorus on microalgal growth, biomass, lipid, and fatty acid production: An overview. Cells 10:1\\u0026ndash;19\\u003c/li\\u003e\\n\\u003cli\\u003eYou K, Ge F, Wu X, Song K, Yang Z, Zhang Q, Liu Y, Ruan R, Zheng H (2021) Nutrients recovery from piggery wastewater and starch wastewater via microalgae-bacteria consortia. Algal Res 60\\u003c/li\\u003e\\n\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":true,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true},\"keywords\":\"phycoremediation, nitrogen, phosphorus, biomass, lipids, fatty acids\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-7889744/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-7889744/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eThe growth of the pig farming industry has led to an increase in wastewater generation in Yucat\\u0026aacute;n, underscoring the need for efficient and cost-effective treatment alternatives. Microalgae\\u0026ndash;bacteria consortia represent a viable option; however, few studies have been conducted under outdoor conditions and with raw wastewater. This study aimed to cultivate an indigenous microalgae-bacteria consortium in 100 L outdoor raceway ponds using a batch mode to treat non-sterilized, non-diluted anaerobically digested pig wastewater, evaluating its performance in terms of nutrient removal, biomass productivity, and valorization potential under real environmental conditions, which could provide critical data for scaling this technology beyond the laboratory. In the final batch, removal efficiencies reached 93.6% for total nitrogen (removal rate: 231.6 mg L\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e day\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e), 94.6% for orthophosphate, and 91.6% for chemical oxygen demand (COD). The consortium exhibited auto-flocculation, facilitating biomass harvesting with a yield of 1.17 mg L\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e dry weight (dw). The biomass lipid content was 268.1 mg g\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e dw. Fatty acid profile showed a predominance of saturated fatty acids (SFA\\u0026thinsp;\\u0026gt;\\u0026thinsp;50%) with an unsaturated fatty acids/SFA ratio from 0.6 to 1.0 and a SFA/monounsaturated fatty acid ratio of 2.0 to 3.2, indicating potential for biodiesel production. The protein content of the biomass was 13.4% dw. These results demonstrated that environmental conditions did not inhibit consortium growth nor nutrient removal, supporting their viability and sustainability as an alternative for pig farming wastewater treatment.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Nutrient removal from anaerobically treated pig farming wastewater by microalgae-bacteria consortia in outdoor raceway ponds\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2025-11-04 11:56:39\",\"doi\":\"10.21203/rs.3.rs-7889744/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"f72809c0-6757-4e7a-8016-f38d51e33d04\",\"owner\":[],\"postedDate\":\"November 4th, 2025\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2025-11-05T05:38:19+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2025-11-04 11:56:39\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-7889744\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-7889744\",\"identity\":\"rs-7889744\",\"version\":[\"v1\"]},\"buildId\":\"8U1c8b4HqxoKbykW_rLl7\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}