Wastewater treatment technology faces the challenge of increasing pollutant removal efficiency while maintaining sustainable resource recovery in biomass production [ 1]. Compared with conventional activated sludge, aerobic granular sludge (AGS) offers a more sustainable method of wastewater treatment [ 2]. Higher biomass concentrations and pollutant removal can be achieved in a single tank, with better settling properties of biomass separation and a lower energy requirement [ 3, 4]. However, the performance of AGS technology in treating wastewater with a low C/N ratio was considered poor and unstable [ 5]. In addition, the post-treatment of excess biomass has become a drawback of this technology [ 6]. The integration of microalgae and bacteria has gained considerable attention due to their advantageous synergistic metabolism. Microalgae are capable of direct nutrient assimilation without competing for organic substrates. Thus, they can improve nutrient removal without increasing the carbon-to-nutrient ratio in the wastewater [ 7]. Microalgae can also provide oxygen for bacteria to oxidize organics, and bacteria can provide CO 2 for microalgal photosynthesis [ 8]. Furthermore, excess microalgae biomass is suitable for biogas valorization through anaerobic digestion [ 9], and lipids and protein-rich microalgae biomass can be drawn out for biodiesel production [ 10]. Microalgal–bacterial interactions have been commonly studied under static batch conditions [ 11], with only a few studies involving granulation or aggregation [ 12]. At the same time, some studies also mention the role of filamentous microalgae in the granulation process [ 13] or filamentous microalgae developing a mat-like formation in static batch conditions [ 11]. Inoculation strategies for algae–aerobic granular sludge have also been reported in diverse ways. Previous studies not only used activated sludge as the seed [ 14] but also mature aerobic granular sludge to form photo-granulation [ 2, 5, 15, 16]. Other studies used exogenous algae supplemented at the beginning of the granulation process [ 7, 13]. Paucity of research has scrutinized the pollutant variation and removal performance, stability/adaptability for long-term application, and biomass valorization of microalgae–bacterial granules [ 17, 18]. Extensive studies have explored aerobic granular sludge for its capability to treat a wide range of wastewater [ 19] or have studied granule characterizations [ 20] and pollutant degradation mechanisms [ 21]. However, only a limited number of studies have thoroughly investigated similar aspects in the microalgae–bacterial granule system, especially when it comes to direct comparisons or distinctions between aerobic granular sludge and algae–aerobic granular sludge pollutant removal. Only some studies have investigated microalgae–bacterial interaction mechanisms, stability during long-term application, and biomass recovery [ 17, 18]. Furthermore, instead of growing endogenous microalgae through continuous illumination like most studies, this study specifically utilized exogenous algae (a mixture of green microalgae and cyanobacteria) as seeds, cultivated with activated sludge in batch conditions prior to cultivation in a sequence batch reactor (SBR), to speed up the granulation process. This study aimed to compare the pollutant removal efficiency, granule characterization, and biodiesel potential of aerobic granular sludge (AGS) and algae–aerobic granular sludge (AAGS). In this study, activated sludge was used as the seed in the AGS system, while activated sludge and a mix of various types of green algae and filamentous cyanobacteria were supplemented in the AAGS system. The granulation process was conducted in a sequencing batch reactor. The pollutant removal efficiency (COD, N, and P), biomass production, particle diameter alteration, EPS production, granule surface imaging, and fatty acid methyl ester production were investigated. 3. Results 3.1. Morphology Observation In this study, to evaluate granule development and capacity, particle size images of the biomass during the experiment in both AGS and AAGS were monitored. Subsequently, at the end of the experiment, the surface images of mature granules were probed. shows the changes in particle size/diameter and cumulative particle size distribution in AGS and AAGS during the experiment. The figure shows that more than 50% of the biomass aggregates had a particle size (median particle diameter) greater than 200 µm at the end of the experiment, indicating successful granulation [ 28]. As depicted in A, the median particle size (expressed as 50% cumulative distribution or D 50) in AAGS and the cumulative distribution patterns (D 10, D 50, and D 90 profiles) did not significantly change throughout the experiment. The average particle size in AAGS increased from 326 µm on day 0 to 390 µm on day 21, and then slightly decreased to 284 µm on day 49. Overall, the average particle size in AAGS throughout the experiment was ~359 µm. B illustrates the particle size and cumulative particle size distribution in the AGS system. The median particle diameter (D 50) in AGS on day 0 was 514 µm, which then increased drastically to 962 µm on day 23, and then to 2.66 mm on day 41, along with a substantial increase in the biomass concentration. However, on day 49, the average particle diameter of AGS decreased significantly to 163 µm, and the biomass concentration also dropped significantly (see biomass concentration profile in Figure 5). The median particle diameter of AGS was ~519 µm, which was larger than the median diameter of AAGS. However, despite a larger median diameter, abundant filamentous bacteria with poor settling properties (SVI 30) above 300 mL/g (see biomass concentration profile in Figure 5) were detected in the AGS system. In addition, the slow production of biomass/sludge in the AGS system was the reason for poor pollutant removal efficiency. illustrates microscopic images of the aggregates in AGS and AAGS on days 0, 35, and 56. It shows that biomass aggregates in AAGS were dominated by microalgae, with some filamentous green algae tangled in between (which could be closely related to Cladophora sp., depicted as size > ~100 µm, with multiple nuclei) [ 29]. also shows that the biomass aggregates in AAGS and AGS on day 0 were larger than those on day 35 and day 56 (as also in agreement with the particle diameter data in A,B). Scanning electron microscopy (SEM) images of the granule surfaces of AGS and AAGS are shown in . shows that the granule surface of AGS consisted primarily of a complex dense structure, a bacteria-rich matrix, with calcite or aragonite (CaCO 3) within the EPS matrix on the top layer, as also detected in a study by [ 30]. In this study, a high concentration of CaCO 3 might be due to the addition of 100 mg/L of NaHCO 3 and 5 mg/L of CaCl 2 in the inlet feed. Mineral precipitates, e.g., calcite or aragonite, due to biologically induced calcium carbonate precipitation (MICP), increase the structural integrity and settling speed of granules by filling voids in the microbial structure. Concurrently, the granule surface of AAGS was more porous, with filamentous algal structures acting as a skeleton embedded within the extracellular polymeric substance (EPS) matrix. 3.2. Biomass Concentration, Settling Properties, and EPS Production In this study, samples were taken and analyzed after the sedimentation time was reduced from 10 min to 5 min (regarded as day 0). depicts biomass concentrations (expressed as MLSS and MLVSS) and granule settling properties (expressed as SVI 5 and SVI 30) at settling times of 5 and 30 min throughout the experiment. The values of MLVSS and MLSS were nearly identical, indicating that almost all the solids in the reactors were biomass. The initial MLVSS in AAGS was 90 mg/L and steadily increased to 2600 mg/L by day 40 and then gradually decreased to 1800 mg/L by day 55. The same pattern also occurred in the AGS system, but the increase in MLVSS was much lower than that in the AAGS system. MLVSS in AGS, which was initially 14 mg/L, increased to 232 mg/L on day 35, peaked at 2300 mg/L on day 42, and then decreased to 1140 mg/L on day 55. The decrease in biomass growth on day 55 was due to aeration issues that also affected dissolved oxygen. However, a slight decrease in biomass growth on days 50 to 55 did not affect the pollutants removal efficiency in general (TN, TP and COD pollutant removal efficiency can be seen in Figure 8, Figure 9 and Figure 10). The initial SVI 5 and SVI 30 of AGS on days 0 to 13 were high (way above 1300 mL/g), which indicated poor settling ability. From day 21 to day 35, SVI 5 and SVI 30 were significantly reduced to below 272 mL/g and above 542 mL/g, respectively. On days 42 to 55, SVI 5 and SVI 30 in AGS reached 160 mL/g and 103 mL/g, respectively. Simultaneously, the initial SVI 5 and SVI 30 of AAGS from day 0 to 7 were very high, at 1363 and 681 mL/g, respectively. The values were gradually reduced to below 100 mL/g and 150 mL/g by the end of the experiment. In this study, as small-sized granules were generated, the SVI 5 and SVI 30 values were in the range of 100–150 mL/g, which generally suggests a good settleability. It is worth mentioning that, in both systems, at the end of the experiment, the ratio of SVI 5/SVI 30 was near 1, which signifies dense settling and high granulation. 3.3. Ammonia (NH 4+-N), Nitrate (NO 3-N), Nitrite (NO 2-N), and Total Nitrogen (TN) Removal Performance The average NH 4+-N concentration in the inlet was 20.51 ± 1.92 mg/L, while the inlet concentrations of NO 3-N and NO 2-N were below the limit of detection of the analysis of 0.05 mg/L (for NO 3-N) and 0.005 mg/L (for NO 2-N) (see Figure S1, Supplementary Material). The experiment ran for 56 days. The NH 4+-N and NO 2-N outlet concentration profiles in AGS and AAGS are shown in , while the concentrations of NO 3-N at the outlets of both AGS and AAGS are not reported because they were below the analysis’s detection limit (<0.05 mg/L). The results indicated that NH 4+-N concentrations at the AAGS outlet were always below 0.05 mg/L throughout the experiment. Conversely, NH 4+-N concentrations at the AGS outlet were initially high but gradually decreased. Furthermore, the concentrations at the AGS outlet declined to 0.05 mg/L on day 27; they were initially high, dropping gradually from 18 mg/L on day 0 to 7.6 mg/L on day 21, and then further declined to 0.05 mg/L on day 27, remaining at that level until the end of the experiment. The average NH 4+-N concentrations in the effluent for AAGS and AGS were 0.05 mg/L and 5.16 ± 1.1 mg/L, respectively. Simultaneously, the concentration of NO 2-N in the outlet of AAGS on day 0 was 4.56 mg/L, which then gradually decreased to below 0.005 mg/L by day 16 and remained below 0.005 mg/L until the end of the experiment. Conversely, the concentration of NO 2-N at the outlet of AGS was below 0.005 mg/L at the beginning of the experiment, increased steadily to 1.11 mg/L on day 11, reached a maximum of 3.84 mg/L on day 27, then dropped gradually to 0.05 mg/L on day 44, and remained at 0.05 mg/L until the end of the experiment. depicts a complete removal (at almost 99.9%) of total nitrogen (TN) in the AAGS system from day 16, while, while AGS needed 44 days to achieve the same amount of TN removal. Generally, AAGS had a higher TN removal efficiency (±25% higher) than AGS. The average total nitrogen (TN) removal efficiencies for AGS and AAGS were 70.95 ± 31.63% and 96.16 ± 6.8%, respectively. The p-value of <0.05 illustrates a statistically significant distinction between the TN values in AGS and AAGS ( Text S1; see Supplementary Material). 3.4. Total Phosphate (TP) Removal Performance The average of the total phosphate concentration in the inlet was 15.57 ± 1.9 mg/L. shows that AAGS had a higher TP removal efficiency than AGS. The TP outlet concentrations of AAGS were consistently stable (below 7.5 mg/L) from day 4 until the end of the operational time, while the TP outlet concentrations of AGS were mostly above 9 mg/L from day 0 until day 42, but decreased to approximately 7.5 mg/L on day 46 until the end of the experiment. The average TP outlet concentrations in AAGS and AGS were 6.45 ± 1.02 mg/L and 10.67 ± 1.67 mg/L, respectively. The total phosphate removal efficiencies of AAGS and AGS were 58.22 ± 5.44% and 29.53 ± 12.53%, respectively. The TP removal efficiency in AAGS in this study was slightly higher than in the previous study [ 33]. However, the TP removal efficiency in AGS in this study was much lower than that in several previous studies [ 34]. Low TP removal in AGS might be correlated with the slow production of biomass (MLSS and MLVSS) in the AGS system () [ 5]. The t-test showed a p-value of <0.001, indicating statistically significant differences in TP values between AGS and AAGS (see Text S1 in the Supplementary Material). 3.5. Chemical Oxygen Demand (COD) Removal Performance To measure the organic matter concentration in wastewater, COD was an indicator. The average COD concentration in the inlet was 570.63 ± 71.04 mg/L (see Supplementary Material). illustrates that the average COD removal efficiency of AAGS was slightly higher than that of AGS. The COD removal efficiencies of AAGS and AGS were 79.5 ± 5.48 and 74.8 ± 12.13, respectively. The average COD concentrations in the outlet of AAGS and AGS were 115.8 ± 30.5 and 140 ± 62, respectively, and the p-value of 0.1027 indicated statistically insignificant discrepancies between the COD values in AGS and AAGS ( Text S1 see Supplementary Material). Furthermore, the COD removal efficiency in AGS was somewhat correlated with biomass concentrations (expressed as MLSSs or MLVSSs in ). In the AGS system, the COD concentrations of the outlet from day 0 to day 9 were higher than 200 mg/L, with MLSS levels of lower than 73 mg/L (resulting in ~50% COD removal). Then, when the biomass concentration increased up to 250 mg/L from day 15 to day 37, the COD concentrations of the outlet decreased to below 130 mg/L (resulting in ~80% COD removal). The COD concentrations at the outlet of the AGS system decreased further to below 100 mg/L from day 42 to the end of the experiment, as MLSS levels increased to over 1000 mg/L (~90% COD removal). On the contrary, it seemed that in the AAGS system, the biomass concentration did not correlate with the COD concentrations of the outlet. The COD removal efficiency in the AAGS system slightly fluctuated between 72 to 90%, which did not align with the biomass concentration profile. It was assumed that not only algae–bacteria in the form of granules were responsible for the utilization of the organic matter, but microalgae attached to the surface of the reactor could also use organic matter. 3.6. Fatty Acid Methyl Ester (FAME) Proportion and Yield 4. Discussion The granule sizes in this study (in both the AGS and AAGs systems) were clustered as small-to-medium granules [ 37] due to an intensive aeration (1.25 L/min) and a high agitation speed (300 rpm), which created high shear force [ 38] and limited oxygen availability for heterotrophic growth [ 39, 40]. While, heterotopic bacteria were responsible for more optimum COD removal; smaller granules allow oxygen to penetrate more effectively to the core, enhancing the growth of nitrification bacteria, thus performing better in N removal [ 41]. Moreover, smaller-sized granules have less void space (lower porosity) and higher density [ 41], and, therefore, have greater physical strength and more stable performance [ 42], which is also confirmed by this study. Throughout the 56-day experiment, the removal performance of AAGS was stable, with low variation in the pollutant removal efficiency and good sedimentation properties. However, in the AGS system, biomass tended to grow more slowly than in AAGS. The pollutant removal efficiency and settling properties increased significantly with increasing biomass concentration, reaching nearly 100% ammonia removal. In this experiment, in the AAGS system, aerobic granular sludge was seeded with a mixture of activated sludge and some green microalgae and cyanobacteria. Typically, Chlorophyta and cyanobacteria were the dominant phyla of microalgae in AAGS [ 3]. With the addition of exogenous Chlorophyta and cyanobacteria as an inoculation strategy in the AAGS system, there was likely a predominance of Chlorophyta and cyanobacteria in the system. Unfortunately, there was no information on microbial and microalgal community distribution in this study to confirm the statement. However, the microscopic images show a big filament of filamentous green algae, with a size of around 21–28 µm in diameter and 83–272 µm in length, which was closely related to Chladophora sp. [ 29]. Chladophora sp. favored ammonia, absorbing and assimilating it, resulting in a nutrient consumption rate (NCR) for total nitrogen (TN) of 2.65 mg/(g∙d) [ 43]. The symbiosis between bacteria and Cladophora sp. contributed to pollutant removal and generated a stable and diverse community of microorganisms [ 43], providing a plausible explanation for the high nitrogen, phosphorus, and organic matter removal rates observed in the AAGS system in this study. The organic loading rate (OLR) in this study was around ±2.82 kg COD/(m 3·d), which is viewed as high-strength wastewater. The ideal OLR for AAGS is reported to be between 0.9 and 1.8 kg COD/(m 3·d), while AGS needs 2–4 kg COD/(m 3·d) to maintain a stable, compact, and fast-settling granular structure in many municipal wastewater applications [ 49]. However, previous study showed that an OLR between 0.6 and 2.6 kg COD/(m 3·d) in AAGS could also achieve 95% COD and a 90% ammonia nitrogen removal efficiency, with stable operation for 180 days [ 50]. In this study, although the average COD removal was lower, the total nitrogen removal was higher, and the system could run stably for 56 days of operation. The COD removal efficiencies in this study were lower than those reported in previous studies with the same COD inlet/organic loading rate [ 51, 52]. However, the average outlet concentrations of AAGS in this study already meet the European Union (EU) effluent standard regulation for wastewater [ 53]. The total nitrogen removal in this study was considered to be higher than in previous studies [ 54]. Compared with the AGS system, the AAGS system did not require high-organic-carbon substrates for denitrification. AAGS photosynthesis supplied organic carbon for denitrification in the anaerobic period, and organic carbon might be more important than a strict anaerobic environment. In the aerobic phase, the simultaneous nitrification–denitrification (SND) process in AAGS was mainly achieved by SND with direct ammonia assimilation by algae [ 55], while in AGS, the SND process was conventional simultaneous nitrification and denitrification (CSND). Hence, AGS needs a more balanced ratio of C/N and a higher OLR than AAGS [ 40]. In the AGS system, enhancement of phosphate-accumulating organisms (PAOs) in granules remains the principal and most effective way to achieve high P removal through the sequencing of anaerobic/aeration periods [ 55], as observed in this study [ 55]. In AAGS, the removal mechanisms of TP involved assimilation and precipitation. Some believed that significantly promoting P removal by increasing light density (up to 200 μmol/(m 2.s)) during the anaerobic phase in the AAGS system could increase P release (by PAOs) and P uptake simultaneously (by algae), with the pH controlled at 7.4–8.4 ( Figure S3; see Supplementary Material) [ 56]. elaborates on the overview of previous studies, which were similar in approach (utilizing activated sludge (AS) supplemented with a mix of microalgae as the seed) to this study. shows the variations in the results, depending on the type of inoculation system, the wastewater quality, and most importantly, the process parameter. The biodiesel potential of AAGS in this study was similar to previous studies [ 2, 6]. The major constituents of FAMEs in the biodiesel were saturated fatty acids (SFAs), monounsaturated fatty acids (MUFAs), and polyunsaturated fatty acids (PUFAs). It is worth mentioning that the ratio between saturated and unsaturated fatty acids is one of the most important components to determine the fuel quality. A balanced ratio (1:1) is required to meet industry standards such as ASTM D6751 or EN 14214 [ 60], as high saturation improves stability but hurts cold flow. In contrast, high unsaturation improves cold flow but reduces stability [ 6, 61, 62]. As opposed to the study conducted by Liu et al. (2018) [ 6], the composition of saturated fatty acids, for both AGS and AAGS in this study, was considered to be much higher than that of unsaturated fatty acids, which led to a better combustion property/high oxidation ability and improved stability. Meanwhile, AGS in this study had a lower unsaturated fatty acid level than AAGS, which created poorer cold flow properties, meaning it gels or crystallizes at higher temperatures. However, in this study, the FAME yield in AAGS was not significantly different from that in a previous study [ 6], where only the yield of AGS was slightly higher. The FAME yields obtained by Liu et al. (2018) [ 6] were 66.21 ± 1.08 mg/g SS (AAGS) and 35.44 ± 0.92 mg/g SS (AGS). As also emphasized by Liu et al. (2018) [ 6], a distinct proportion of saturated fatty acids due to the difference in bacterial composition in the granular sludge and microalgae integration in AAGS created a higher ratio of unsaturated fatty acids. Despite this, both studies agreed that integration between sludge and microalgae in the form AAGS has better potential for biodiesel than AGS or microalgae alone [ 60, 62]. This study compared granule development, pollutant removal performance, and biodiesel production between algae–aerobic granular sludge (AAGS) and aerobic granular sludge (AGS) for treating synthetic domestic wastewater. In general, AAGS had better pollutant removal performance than AGS. Moreover, AAGS needed a shorter time (16 days) than AGS (44 days) to reach complete TN removal. AAGS granules were more stable than AGS granules due to their greater production of bound EPSs. AGS had a dense surface morphology with mineral precipitate layers. In contrast, AAGS showed a porous surface with filamentous algae intertwined. AAGS produced higher levels of fatty acids, with a higher polyunsaturated fatty acid content than AGS, making it more promising for biodiesel application. Supplementary Materials The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/w18121395/s1: Figure S1: COD, total nitrogen (TN), and total phosphate (TP) inlet concentrations during experiment; Figure S2: Dissolved oxygen (DO) in Reactor AGS and AAGS during the experiment; Figure S3: pH in the inlet, inside the reactors and outlet of the reactors of AGS and AAGS during the experiment; Text S1: Statistical test results. Author Contributions Conceptualization, R.Y.; methodology, R.Y.; software, R.Y.; validation, R.Y.; formal analysis, R.Y.; investigation, R.Y.; resources, R.Y., Y.I. and K.M.; data curation, R.Y.; writing—original draft preparation, R.Y.; writing—review and editing, R.Y., Y.I. and F.K.; visualization, R.Y.; supervision, F.K., J.S. and T.W. All authors have read and agreed to the published version of the manuscript. Funding This research was funded by the German Academic Exchange Service (DAAD), Research Grants—Doctoral Programs in Germany, 2022/23 (Grant number: 57588370). Data Availability Statement The original contributions presented in this study are included in this article/ Supplementary Material. Further inquiries can be directed to the corresponding author. Acknowledgments This study was also technically supported by Andre Kupka, a staff member of the chair group for mechanical process engineering at TU Dresden. Conflicts of Interest Author FK is CEO of the company biotopa gGmbH. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Abbreviations The following abbreviations are used in this manuscript: AGS Aerobic Granular Sludge AAGS Algae–Aerobic Granular Sludge SBR Sequencing Batch Reactor PSBR Photo-Sequencing Batch Reactor FAME Fatty Acid Methyl Ester TN Total Nitrogen TP Total Phosphate COD Chemical Oxygen Demand SS Suspended Solid PBS Phosphate Buffer Solution PFA Paraformaldehyde MUFA Monounsaturated Fatty Acid SFA Saturated Fatty Acid PUFA Polyunsaturated Fatty Acid HRT Hydraulic Retention Time DO Dissolved Oxygen MLSS Mixed Liquor Suspended Solid MLVSS Mixed Liquor Volatile Suspended Solid VSS Volatile Suspended Solid PN Protein PS Polysaccharide MICP Biologically Induced Calcium Carbonate Precipitation SVI Sludge Volume Index EPS Extracellular Polymeric Substance ASTM American Standard for Testing and Materials EU European Union PAOs Phosphate-Accumulating Organisms References Hamza, R.; Rabii, A.; Ezzahraoui, F.; Morgan, G.; Iorhemen, O.T. 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Particle diameters and cumulative particle size distributions of AAGS ( A) and AGS ( B). Particle diameters and cumulative particle size distributions of AAGS ( A) and AGS ( B). Microscopic images of AGS and AAGS during the experiment. Microscopic images of AGS and AAGS during the experiment. Scanning electron microscopy (SEM) surface images of AGS and AAGS with 1000×, 5000×, and 10,000× magnifications. Scanning electron microscopy (SEM) surface images of AGS and AAGS with 1000×, 5000×, and 10,000× magnifications. Biomass concentrations (expressed as MLSS and MLVSS) and settling property (SVI 30 and SVI 5) profiles in AGS and AAGS. Biomass concentrations (expressed as MLSS and MLVSS) and settling property (SVI 30 and SVI 5) profiles in AGS and AAGS. EPS (as bound EPSs) profile in AGS and AAGS. EPS (as bound EPSs) profile in AGS and AAGS. Ammonia and nitrite outlet concentration profiles in AGS and AAGS. Ammonia and nitrite outlet concentration profiles in AGS and AAGS. Total nitrogen (TN) outlet concentration profiles and removal performances in AGS and AAGS. Total nitrogen (TN) outlet concentration profiles and removal performances in AGS and AAGS. Total phosphate concentrations in the outlet and removal performances in AGS and AAGS. Total phosphate concentrations in the outlet and removal performances in AGS and AAGS. COD outlet concentration and removal efficiency in AGS and AAGS. COD outlet concentration and removal efficiency in AGS and AAGS. Composition of fatty acid methyl esters (FAMEs) from biomass in RAGS and RAAGS (sampled on day 55). Composition of fatty acid methyl esters (FAMEs) from biomass in RAGS and RAAGS (sampled on day 55). FAME AGS AAGS Content (%) Yield (mg/g SSs) Content (%) Yield (mg/g SSs) Saturated Myristate (C14:0) ୭.୨୩ ବ୍ଦ ୦.୬୬ ୩.୧୮ ବ୍ଦ ୦.୨୯ ୬.୨୨ ବ୍ଦ ୦.୧ ୩.୯୮ ବ୍ଦ ୦.୦୪ Palmitate (C16:0) ୨୭.୫ ବ୍ଦ ୦.୧୩ ୧୨.୧୧ ବ୍ଦ ୦.୧୨ ୧୫.୦୩ ବ୍ଦ ୦.୭ ୯.୬୭ ବ୍ଦ ୦.୪୯ Stearate (C18:0) ୪.୦୬ ବ୍ଦ ୦.୦୮ ୧.୭୯ ବ୍ଦ ୦.୦୬ ୨.୮୯ ବ୍ଦ ୦.୩୩ ୧.୯୧ ବ୍ଦ ୦.୧୬ Others ୫୩.୦୮ ବ୍ଦ ୦.୭୭ ୨୩.୩୮ ବ୍ଦ ୦.୩୪ ୫୬.୭୬ ବ୍ଦ ୧.୯୪ ୩୬.୫୪ ବ୍ଦ ୧.୨୫ Unsaturated Oleate (C18:1) ୭.୧୭ ବ୍ଦ ୦.୦୬ ୩.୧୬ ବ୍ଦ ୦.୦୮ ୧୪.୨ ବ୍ଦ ୦.୬୨ ୯.୧୪ ବ୍ଦ ୦.୪୯ Linoleate (C18:2) ୦.୯୭ ବ୍ଦ ୦.୦୨ ୦.୪୩ ବ୍ଦ ୦.୦୧ ୪.୯ ବ୍ଦ ୦.୨୭ ୩.୧୬ ବ୍ଦ ୦.୧୮ Total (mg/g SSs) ୪୪.୦୪ ବ୍ଦ ୦.୯ ୬୪.୪ ବ୍ଦ ୨.୬୧ Algae–aerobic granular sludge overview in related studies. Algae–aerobic granular sludge overview in related studies. Seed Wastewater (mg/L) Type of Reactors Process Parameter Results Reference AS (85%); Scenedesmus sp. (17%) Real domestic wastewater, COD: 189, TN: 26, TP6.2 V: 1.5 L, PSBR LED: 54 μmol/(m 2·s), light: dark cycle: 24 h:0, aeration: 2.5 L/min, VER: 50%, HRT: 6 h Size: 6 mm, COD and N removal: 72% [ 57] AS, Leptolyngbya sp. Synthetic wastewater, DOC: 300, TN: 50, TP: 8 V: 1.3 L, SBR Solar light: 20 k–24 klx, light: dark cycle: 12 h:12 h, aeration: 0.5 cm/s, VER: 50%, HRT: 8 h Size: 0.61 mm, DOC: 95.7%, TP: 68.1%, ammonia: 95%, TN: 48.2% [ 58] AS (50%), Chlorella sorokiniana sp., Chlorococcum sp. (50%) Synthetic wastewater, COD: 200, NH 4+-N: 100 mg/L, TP: 10 V: 1.7 L, SBR Light: dark cycle: 12 h:12 h, aeration: 0.4 L/min, HRT: 0.33–2 d Size: 0.48–0.6 mm, pollutants removal not reported [ 59] AS (80%), mix of Chlorella vulgaris, Scenedesmus sp., and leptolyngbya sp. (20%) Synthetic wastewater, COD: 470; NH 4+-N: 20, TP: 15 V:1.08 L, PSBR Light: dark cycle: 12 h:12 h, aeration: 1.25 L/min, HRT: 8 h, VER: 50%, LED: 150 μmol/(m 2·s), agitation: 300 rpm Size: ± 0.36 mm, COD: 79%, TN: 96%, TP: 58% (Biodiesel production: 64.4 mg/g SS) This study Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. © 2026 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.
