Valorization of Human Urine with Mixed Microalgae Examined through Population Dynamics, Nutrient Removal, and Biogas Content

Abstract

The majority of nutrients in municipal wastewater originate from urine. However, when flush water is used, the urine is diluted and mixed with other organic household waste, losing its high-value stream content. This study investigated the effect of source-separated human urine on the population dynamics, nutrient removal, growth, and biogas content of mixed microalgae grown in 250 L raceway ponds. Overall, a maximum biomass concentration of 1847 mg/L was reached, with up to 90% nitrogen and 80% phosphorus removal efficiencies, along with 254.96 L/kg vs. biogas production. The microbial community analysis identified Chlorella sorokiniana (Chlorophyta, Trebouxiophyceae) as the species with the highest abundance, after confirmation with four different markers (16S rRNA, 18S rRNA, 23S rRNA, and tufA). Moreover, principal component analysis was applied to capture the effect of environmental factors on culture diversity. The abundance of Chlorella sorokiniana increased almost sevenfold when the culture was exposed to open systems compared to the small-scale study carried out in 1 L Erlenmeyer bottles in laboratory conditions, both grown in urine and synthetic media (BBM). In conclusion, the present study contributes to the potential to valorize urine with microalgae by showing its high biogas content, and reveals that microalgae can adapt to adverse environmental conditions by fostering their diversity.

1. Introduction

Due to the high dilution of human urine in wastewater, traditional municipal wastewater treatment is expensive and ineffective at recovering nutrients and energy. It has been suggested that new treatment concepts, which collect and treat separate wastewaters at their sources, are more effective [1]. In domestic wastewater, despite its low volume relative to the overall wastewater flow, urine is the primary source of more than half of the nutrient load. Urine contains 40% of the phosphorus load, 69% of the nitrogen load, and 60% of the potassium load in a volume that makes up only 1% of the total volume of wastewater [2]. This clearly shows that a substantial portion of the nutrients can be obtained directly from source-separated urine. However, when flush water is used, the urine is diluted and mixed with other organic household waste, losing its high-value stream content.

In the last few decades, microalgae cultivation has received a lot of interest as a means of achieving sustainability goals. As a result, research on microalgae grown in wastewater has attracted a lot of attention for practical use, since it supports biomass production for value-added products, including biodiesel, bioethanol, fertilizers, bioplastics, pharmaceuticals, and feed additives, in addition to nutrient recovery from wastewater. If microalgae can grow in urine, this may offer a practical method for extracting nutrients from urine [3]. Microalgal growth using human urine was initially investigated for use in space mission life-support systems. Some studies have shown that very diluted human urine can support the growth of microalgae. These studies employed either diluted synthetic urine or urine that had been diluted more than 100 times [2].

A practical strategy for lowering the cost of microalgae production is to combine their cultivation for biofuels with urine treatment. To generate biofuels, it is crucial to choose a microalgae species with high storage capacity or productivity, due to species-specific differences in the composition and metabolism of intracellular chemicals in microalgae. However, pure-culture cultivation raises the possibility of system contamination, which can lower production effectiveness. Additionally, it is both financially and operationally expensive to maintain a large-scale monoculture by sterilizing incoming and exiting fluxes. Given the high costs associated with pure-culture rearing, mixed microalgae with a high storage compound yield are considered acceptable. Controlling culture or environmental factors also appears to be a practical way to optimize or enhance the accumulation of target intracellular molecules [4]. According to Aditya et al. [5], in comparison to the pure microalgae system, the consortium system has a higher removal efficiency of up to 15% and shorter treatment time. Moreover, the combined synergistic metabolism of the consortium system is its primary advantage, where microalgae assimilate ammonia and produce oxygen through photosynthesis. Studies have shown that compared to single microalgal systems, the consortium system was able to remove 13.6% more ammonia and was 44% more efficient than activated sludge systems [6]. Additionally, the synergistic interaction between microalgae and bacteria led to a rapid nitrification process [7]. Furthermore, the consortium system only required 1 to 2 days of hydraulic retention time to remove 60–80% of the ammonia, while pure microalgae systems needed 2 to 5 days [8].

Both open ponds and closed photobioreactors are suitable for growing microalgae. The most beneficial aspect of raceway ponds is that they are sustainable, straightforward, well-known, and widely used. A paddlewheel is typically used to create mixing and circulation in a raceway pond’s closed-loop recirculation channel, which is typically about 0.3 m deep. The benefits of raceway ponds were investigated by Rawat et al. [9], who concluded that raceway ponds are the most affordable reactors for managing liquid waste and capturing solar energy. Although closed photobioreactors produce more microalgae biomass than raceways on average, they are much more expensive to construct and operate. Currently, raceway ponds are the most frequently used and cheapest cultivation systems for the commercial production of microalgae and wastewater treatment [10].

The integrated approach to wastewater treatment using algal strains and the biorefinery concept involves the conversion of the algal biomass generated after the wastewater treatment into biofuels and other valuable bioproducts, as outlined by Mishra and Mohanty [11]. This approach provides an opportunity to increase the economic viability of the wastewater treatment process through the cultivation of algae, which can otherwise be quite expensive. Given that CH₄ has a greater hydrogen to carbon ratio than other biofuels, biogas is unquestionably a form of biofuel that can contribute to affordable energy. However, in order to optimize energy content, CO₂ must be kept to a minimum in the composition. Microalgae fix CO₂ more quickly than terrestrial plants, allowing for the efficient use of biogas CO₂ content in the creation of high-density biomass [12]. In terms of producing high-value bioproducts from biomass during cogeneration, microalgae growing appears to be one of the most promising biological processes for biogas upgrading.

In light of this, the aim of this study was to observe the growth of mixed microalgae in source-separated urine in raceway ponds, investigate the biogas potential of mixed microalgae, and determine biogas production yields. Moreover, we compared small (laboratory)-scale and large-scale algal biomass grown in urine with synthetic media to investigate how urine and synthetic media affect the dominance of mixed culture, and how it is affected when exposed to open systems and grown in wastewater.

2. Materials and Methods

2.1. Urine Collection

Fresh human urine was collected from the Istanbul Technical University Environmental Engineering Department’s toilets using a special piping system connected to pissoirs for men and manually for women. The collected urine was stored in polyethylene tanks and then used as the algal growth culture medium in the experiments.

2.2. Preparation of Microalgal Inoculum

A mixture of several spontaneously reproducing indigenous species isolated from local ponds and tap water was acclimated to the urine by cultivating in low urine concentrations. Cultures were kept in an acclimation cabinet under approximately 150 µmol photon m⁻² s⁻¹ continuous illumination at 25 °C ± 2 °C during the acclimation period, as described by Ermis et al. [13]. The inoculum cell culture was checked using light microscopy, and mixed culture was morphologically characterized by using microalgae systematics books [14,15]. It was seen that the mixed culture mainly contained Chlorella sp. and Scenedesmus sp. species.

2.3. Experimental Set Up

The experiment was conducted by operating two duplicated raceway ponds with 200 L working volume outdoors in batch conditions. The ponds had a 0.3 m depth, 0.5 m width, and 2 m length, with 250 L working volume. The ponds were mixed at 35 rpm for aeration. The ponds were located on the roof of the Istanbul Technical University Environmental Engineering Department Building. Daily samples were taken from the ponds to observe microalgal growth and nutrient removal using different diluted urine concentrations by the end of the stationary growth phase, and kept for nutrient analysis. For both the lab-scale and large-scale experiments, 10% diluted urine was used as growth media. The characterization of 10% urine is given in

Table 1. Characterization of 10% urine.

10% Urine
pH	9.5
PO4-P (mg/L)	19.9
NH4-N (mg/L)	160
Mixed liquor suspended solids (mg/L)	400

.

Photosynthetically active radiation (PAR) data were collected continuously as photosynthetic photon flux density (PPFD) using an Apogee MQ-200 Quantum meter. The daily temperature and pH changes was monitored with a Hanna Instruments HI-83141 pH meter with electrode and temperature probe.

The microalgal biomass produced in the ponds was harvested after 35 days (hydraulic holding time) using a filter press, and microalgae cake layer was collected with scrapers from the surface of filter cassettes.

2.4. Biomethane Potential (BMP) Assay

To conduct BMP tests, serum bottles were used, with a total volume of 160 mL, a useful volume of 130 mL, and a gas headspace volume of 60 mL. These bottles were flushed with helium gas, sealed with butyl rubber stoppers, and then incubated at 35 °C until biogas production ceased. To determine the amount of methane produced by the inoculum, a blank treatment was included. All trials were conducted in triplicate to ensure the accuracy and consistency of the results.

Two different BMP bottles with different algal amounts were observed, along with a blank where no algal biomass was used (

Table 2. BMP experimental set up.t.

	BMP 1	BMP 2	BLANK
Microalgae Biomass (g)	4	6	-
Cow Manure (g)	12	12	12
Biogas Yield (L/kg VS)	254.96	196.38	35.71

). In the BMP 1 bottle, 12 gr cow manure was co-digested with 4 gr algal biomass, and in the BMP 2 bottle, the same was co-digested with 6 gr of algal biomass. The gas pressure inside the bottle was measured on a daily basis.

Considering that the volume of 1 mole of gas is equal to 22.4 L, the amount of biogas produced was determined. Biogas production was calculated using the ideal gas law given below (Equation (1)),

PV = nRT

(1)

For methane calculations, briefly, a specific volume of biogas (volume A) was injected into an airtight borosilicate glass bottle containing saturated KOH solution through a syringe inserted into the inlet tube of the bottle’s cap. The CO₂ contained in the biogas reacted with the potassium hydroxide to form soluble potassium carbonate (K₂CO₃), while the remaining gas consisted mainly of methane.

2.5. Analytical Methods

Total suspended solids (TSS) was used as a method to measure the microalgal biomass concentration X (mg-biomass/L) according to standard methods [16]. The absorbance of TSS at 680 nm (OD680) was used for the rapid measurement of the algal biomass concentration in suspensions. A Genesys 10S UV-VIS spectrometer was used for optical density measurements. The TSS for the calibration curve was determined gravimetrically according to standard methods. A correlation between X (mg/L), measured through TSS, and OD685 was determined as below (Equation (2)):

X = 0.501 × OD685nm – 0.078 (R2 = 0.998)

(2)

Nitrogen and phosphorus were analyzed as major nutrients for microalgal growth. Nitrogen was measured as total Kjeldahl nitrogen (TKN) and ammonium (NH4-N). Phosphorus was measured as total phosphorus (TP) and orthophosphate (PO₄). The SS, TKN, NH₄-N, TP, and PO₄ values were analyzed as mgL⁻¹ according to standard methods [16].

The mass balance was examined to affirm the correlation between nutrient and phosphorus removal and biomass yield. The stoichiometric equation shown below (Equation (3)) was used to calculate the theoretical microalgae production [17], equaling 6942 g biomass mol⁻¹. The theoretical nutrient removal was calculated and discussed according to the stoichiometric equation below, considering the observed experimental biomass.

16 NH₄ + + 92 CO₂ + 92 H₂O + 14 HCO₃ − + HPO₄ ²⁻ → C₁₀₆H₂₆₃O₁₁₀N₁₆P + 106 O₂

(3)

2.6. Microbial Community Analysis

The microbial composition in urine and synthetic media was evaluated using the multi-marker metabarcoding approach, using 16S rDNA, 18S rDNA, 23S rDNA, and tufA markers. PCR amplification and next-generation sequence analyses in Illumina MiSeq platform (2 × 300 paired-end reads) were performed as described in our previous study [13].

The data processing of sequencing reads was conducted with the QIIME 2 workflow (version 2022.8) as described previously in Ermis et al. [13]. Primers in raw demultiplexed reads were trimmed with the QIIME 2 plugin cutadapt (version 2022.8.0). The denoising and generation of amplicon sequence variants (ASVs) were performed using the QIIME 2 dada2 plugin. The taxonomic assignments of the resulting sequences were carried out by aligning individual reads against reference databases. SILVA databases were used for the taxonomic classifications of 16S rDNA, 23S rDNA, and 18S rDNA samples, whereas a specialized algal database was used for the taxonomic classification of tufA samples.

Custom SILVA databases were generated using RESCRIPt (QIIME 2 rescript plugin) in order to achieve a more accurate taxonomic assignment. SSU (small subunit rRNA) and LSU (large subunit rRNA) SILVA reference databases based on the curated non-redundant NR99 (version 138.1) database were prepared and used for the taxonomic classification of 16S rDNA, 18S rDNA, and 23S rDNA samples, respectively. Low-quality reference sequences were removed using default parameters (remove sequences containing 5 or more ambiguous bases and homopolymers that are 8 or more bases long). Length filtering was applied differentially based on the taxonomy of the reference sequence (Archaea (16S) ≥ 900 bp, Bacteria (16S) ≥ 1200 bp, Eukaryota (18S) ≥ 1400 bp) to remove sequences that may be too short or long. Identical reference sequences were dereplicated to remove redundant sequence data from the database.

QIIME 2 taxonomic classifiers trained in the SILVA and tufA databases were built using the q2feature-classifier plugin. The taxonomic assignment of ASVs was performed using these classifiers, and taxonomic bar plots were generated (Figure S1).

Phylogenetic analyses were performed with the FastTree algorithm as described previously by Ermis et al. [8]. Phylogenetic trees with the most abundant 50 taxa were visualized using the phyloseq R package (version 4.1.2) (Figure S2).

2.7. Principal Component Analysis (PCA)

The percent relative abundances of microorganisms were used to construct a PCA plot using R software (version 4.1.0) upon standardization to zero mean and unit variance. With this analysis, the microorganisms that contributed most to the environmental differences between the lab-scale and large-scale urine samples and BBM synthetic media were detected.

3. Results and Discussion

3.1. Monitoring of Physical and Chemical Conditions during the Operation

It was observed that the daily average PPFD values measured varied between about 600 and 1000 μmol photon m⁻² s⁻¹ during the operating periods (Figure 1). The average daily temperatures during the cultivation ranged from 23 to 27 °C. It was that the pH values of the ponds, measured at an initial pH of approximately 8.30, were elevated to 11.00 when the photosynthetic activity and CO₂ consumption approached the stationary growth phase, which was an expected result. The pH and PPFD changes throughout the operating period are shown in Figure 1.

3.2. Growth and Nutrient Removal Results in Raceway Ponds

According to the results, the maximum biomass amount reached was 1847 mg/L (Figure 2), and NH₄ and PO₄ removal were 90.6% and 80.4%, respectively. There are only a limited number of studies on human urine treatment with microalgae in raceway ponds. In Chatterjee et al.’s study (2019) [18], in a semi-continuously run outdoor raceway pond with a liquid volume of 2000 L and a hydraulic retention time of 15 d, a freshwater green microalga, Scenedesmus acuminatus, was grown in different dilutions (1:20 and 1:15) of source-separated human urine. Even at culture temperatures as low as 5 °C, the microalgae could remove 52% nitrogen and 38% phosphorus while achieving a biomass density of 0.34 g VSS/L (Figure 3).

Zou et al. [19] examined Chlorella vulgaris growth and nutrient removal in fresh pig urine cultivated in raceway ponds in both batch and continuous modes. The results indicated that C. vulgaris could stably accumulate biomass in a raceway pond when cultured in both BG-11 medium and the pretreated pig urine. About 1.72 g m⁻² day⁻¹ of microalgal biomass could be produced, and 98.20% of NH₄⁺-N and 68.48% of TP could be removed during batch treatment.

Another study [20] attempted to assess biomass production and nutrient recovery by growing the microalgae Scenedesmus acuminatus in a continuously fed outdoor 2000 L raceway pond with source-separated human urine that had been diluted15 times. After 32 days of culture, the greatest amount of biomass (OD 1.4 and TSS 1.1 g/L) was produced. When the temperature dropped below 25 °C (pH 8.5), the microalgal growth efficiency began to decline at day 50. In general, microalgal cells were able to hold 26% of the total nitrogen that was introduced, whereas 46% (between 0% and 100%) was volatilized. Microalgae or precipitation absorbed, on average, 36% (0–100%) of the phosphorus that was introduced. In conclusion, algal growth with high nutrient removal was successfully achieved in human urine in raceway ponds (

Table 3. Overall results of growth and nutrient removal.

	PO4 Removal (%)	NH4 Removal (%)	Biomass Production (g/L)
Urine treatment by mixed microalgae	80.4	90.6	1.84

).

Several studies have evaluated the use of microalgae for the biological removal of carbonaceous, nitrogenous, and phosphorus material in wastewater effluents. These studies have investigated various microalgal species in different types of wastewater, such as municipal, agricultural, brewery, refinery, and industrial effluents, with varying effectiveness in treatment performance and microalgal growth [21,22,23].

An example of microalgae utilization in domestic wastewater treatment can be seen in a study performed in Kuala Lumpur, Malaysia, where S. obliquus and activated sludge bacteria were used. However, this approach only achieved a 20% removal of total nitrogen over a 30-day period [24]. Previous studies have shown that C. sorokiniana microalgae is better suited for working with the Rhodobacteraceae and Rhizobiaceae families in activated sludge, as opposed to Nitrosomonas or Dechloromonas. When combined with activated sludge from the Rhodobacteraceae and Rhizobiaceae families, C. sorokiniana achieved a 98% nitrogen and 96% phosphorus removal within 7 h [25]. In contrast, when combined with activated sludge from the Nitrosomonas and Dechloromonas families, C. sorokiniana was only able to remove 71.4% of ammonia nitrogen over a 14-day period, as the microbial community required about 9 days to reach steady-state conditions [26]. These results indicate that selecting the appropriate species is a prerequisite for effective treatment, and each species has different survival rates. Additionally, species selection is important for obtaining the desired biomass quality for further use.

Agro-industrial wastewater, on the other hand, tends to have higher nitrogen and phosphorus concentrations than domestic wastewater [27,28]. Botanical food processing effluent differs depending on the fruit or vegetable used—for example, palm oil mill effluent (POME) wastewater was treated with three native microalgae (Coelastrella sp. UKM4, Chlamydomonas sp. UKM6, and Scenedesmus sp. UKM9) [29]. The raw POME contained Actinobacteria, Bacteroidetes, Planctomycetes, Firmicutes, and Proteobacteria. Over 80% of nitrogen was removed from both sterilized and raw POME. Sterilized POME could only remove 10% of the phosphorus, whereas raw POME could remove up to 70% [29]. Bacteria were eliminated in the sterilized POME, which explained the 60% difference in phosphorus removal efficiency. Botanical food processing wastewater is the most environmentally friendly, and it may be utilized as fertilizer, animal feed, and biofuel. The biomass of P. purpureum that flourished in POME was rich in essential minerals, including Fe, K, and N. These are crucial nutrients for plant development. Thus, this biomass was appropriate for conversion to biofertilizer. The biomass from coffee processing effluent boosts methane yield by up to 87%, and could be easily converted into biofuel [30].

Dairy wastewater tends to have higher nitrate content than most other wastewater [27,28]. Pure microalgae work slowly on this type of wastewater [31], possibly because the ratio between nutrients is insufficient for its metabolism. A pure microalgae culture could only remove about 65% of nitrate (from the initial concentration of 62.7 mg/L) after 6 days of treatment [32]. On the other hand, a microalgae–bacteria consortium removed more than 85% of 1730 mg/L [28]. The presence of nitrate-reducing bacteria helps higher nutrient removal. The lipid content of the microalgae–bacteria consortium biomass was more than 45%. This could be due to the nitrogen present, mostly in the form of amino acids. It also had a high lactose content. The overconsumption of lactose would be stored as fats. This biomass is a viable candidate for protein supplements for animal feed, biofertilizers, and defatted biomass as feedstock for bioethanol production [33,34].

In the analysis of nitrogen removal data, the amount of free ammonia due to high pH leaving the system has been taken into consideration in order to ensure correct results are obtained. It was observed that a pH above 9 was reached, where ammonia stripping can happen. For this reason, when microalgae growth is observed, free NH3-N due to pH changes should be included as a factor in calculations. To determine how much nitrogen contributing to the biomass synthesis was free ammonia nitrogen concentration, the mass balance was established (Equation (4)) to understand the N and P removal and to make sure it was not P precipitation or N stripping due to pH increase (

Table 4. Theoretical and experimental results for orthophosphate (PO₄-P) and ammonia nitrogen (NH4-N) removal in 10% urine.

Parameters	10% Urine
Experimental observed biomass (mg/L)	1840
Experimental PO4-P removal (mg/L)	16
Experimental NH4-N removal (mg/L)	145
Theoretical PO4-P removal according to experimentally observed biomass (mg PO4-P L-1)	26
Theoretical NH4-N removal according to experimentally observed biomass (mg NH4-N L-1)	136

). According to the mass balance results, the 9 mg/L more experimental NH₄-N removal can be explained as NH3 stripping, and the 10 mg/L more experimental PO₄-P removal can be explained as P- precipitation due to pH increase.

3.3. Biogas Results

The mono-digestion of microalgae has raised some questions about its practicality on the industrial scale. The presence of long-chain organic compounds, primarily in the cell wall, the low carbon to nitrogen (C/N) ratio, and the lengthy retention times required in the reactors mostly resulted in low methane yields, undigested organic matter in the digestate, and, more importantly, the inhibition of the AD process [35]. In light of this, co-digestion would be a good option for improving biogas production and productivity. Therefore, in this study, different amounts of microalgae were co-digested (4 g and 6 gr), and BMP 1, which co-digested with 6 gr microalgae, had a higher biogas amount than BMP 1 (9 g co-digestion) due to the reasons mentioned above: 254.96 L/kg vs. and 196.38 L/kg VS, respectively (Figure 4).

In Bojti et al.’s [36] study, in comparison to mono-digestion, methane generation increased by 24% when chicken manure and maize silage were co-digested. Beji et al. [37] also assessed the generation of methane during anaerobic digestion utilizing microalgae-bacterial flocs as the substrate and digestate or slurry as the inoculum at various substrate/inoculum ratios (0.2, 0.5, and 0.8). In contrast to digestion with flocs, anaerobic digestion without flocs performed best after 30 days (726.7 mL CH₄·g⁻¹ slurry, 245.6 mL CH₄ ·g⁻¹ digestate), whereas the optimum ratio for both inocula with floc digestion was 0.2 substrate/inoculum with 317.2 mL CH₄ g⁻¹ slurry and 165.7 mL CH₄ g⁻¹ digestate. Volatile solids are used to express all solid masses (VS).

The biochemical methane potential (BMP) of five different algae (Chlorella vulgaris)/manure (cattle) mixtures was examined by Mahdy et al. [38]. They found that the BMP of microalgae alone (100/0) was 415 mL CH₄ g VS⁻¹, while the BMP of the 80/20 mixture produced the highest BMP value (431 mL CH₄ g VS⁻¹). Then, the anaerobic digestion of the two substrates was evaluated in continuous stirred-tank reactors (CSTR). Using an ammonia-tolerant inoculum, the CSTR reactors produced methane yields that were quite high (i.e., 77.5% and 84% of the maximum predicted, respectively), despite the high ammonium levels (3.7–4.2 g NH₄-N L1). These findings showed that an effective strategy to successfully digest protein-rich microalgae and produce third-generation biogas might be ammonia tolerant-inocula.

In the study by Chatterjee et al. [18], the freshwater green microalga Scenedesmus acuminatus was grown in human urine, and the harvested biomass of the microalga could be used to generate methane with a yield of 285 L CH₄/kg volatile solids.

Arashiro et al. [39] evaluated how well microalgae grown in wastewater recovered natural pigments (phycobiliproteins) and bioenergy (biogas). After this, biogas was created using the leftover biomass from the extraction of phycobiliproteins, with final methane yields varying from 159 to 199 mL CH₄/g VS.

The work by Sole-Bundo et al. [40] sought to maximize the anaerobic digestion (AD) of biomass in systems that use microalgae for wastewater treatment. The co-digestion increased the AD kinetics initially, as demonstrated by a batch test comparing various microalgae (untreated and pretreated) and primary sludge proportions. The addition of 75% primary sludge to pretreatment microalgae (339 mL CH₄/g VS) produced the maximum methane output. The following step was to study this situation in mesophilic lab-scale reactors. In comparison to pretreatment microalgae mono-digestion, the average methane output was 0.46 L CH₄/g VS, a 2.9-fold increase. In contrast, despite the thermal pretreatment, the microalgae had a modest methane output (0.16 L CH₄/g VS). Microscopical examination did, in fact, reveal the existence of microalgae species with hardy cell walls (i.e., Stigioclonium sp. and diatoms). The HRT was extended from 20 to 30 days in order to enhance their anaerobic biodegradability, which resulted in a 50% increase in methane output. Co-digestion with primary sludge significantly increased microalgae AD overall, even without pretreatment, and raising the HRT increased the AD of the microalgae with resistant cell walls.

In conclusion, the biogas results reported in this study (254.96 L/kg VS) were in line with the literature, and proved the success of using microalgae for urine treatment along with biogas production to valorize the algal biomass grown in wastewater.

3.4. Small-Scale vs. Large-Scale Genetic Comparison Results along with Synthetic Media

Microbial community analysis with multi-marker metabarcoding identified taxonomic profiles in urine and synthetic media, as stated in

Table 5. Most abundant microorganisms with genus/species level identification (>2% abundance given in bold) in the cultures based on the taxonomic classification through QIIME 2 analysis.

	% Abundance
Marker Genes	Phylum	Family/Genus/Species	Large-Scale Urine Sample	Lab-Scale Synthetic Media (BBM) Sample	Lab-Scale Urine Sample
16S rRNA	Chlorophyta	Chlorella sorokiniana chloroplast	27.94	0.39	4.71
	Chlorophyta	Chlorella sorokiniana mitochondrion	16.44	0.41	4.55
	Cyanobacteria	Synechocystis PCC-6803	0.15	23.62	0.18
	Cyanobacteria	Cyanobium PCC-6307	0.04	12.31	0.04
	Gemmatimonadota	Gemmatimonas	0.14	11.55	0.25
	Bacteroidota	Aurantisolimonas	0.03	4.63	0.03
	Proteobacteria	Reyranella	0.03	3.67	0.04
	Cyanobacteria	Leptolyngbya PCC-6306	0.02	2.23	0.02
	Chlorophyta	Desmodesmus sp.	0.12	0.06	23.79
	Chlorophyta	Coelastrella sp.	0.21	0.09	15.20
	Proteobacteria	Porphyrobacter	0.81	1.64	8.33
	Chlorophyta	Chloroplast	0.42	1.08	6.87
	Proteobacteria	Sphingomonas piscinae	0.02	0.03	3.89
	Proteobacteria	Roseomonas stagni	0	0.02	2.70
	Proteobacteria	Ahniella	0	1.01	2.46
	Planctomycetota	Blastopirellula	0.02	0.54	2.16
18S rRNA	Chlorophyta	Trebouxiophyceae	47.92	18.38	2.87
	Blastocladiomycota	Paraphysoderma sedebokerense	17.87	12.62	0.26
	Amoebozoa	Dactylopodida	2.70	0.02	0.02
	Chlorophyta	Chlorophyceae	2.32	8.12	62.29
	Ochrophyta	Spumella-like flagellate	0.06	13.37	0.04
	Cryptomycota	Paramicrosporidium	0.40	10.16	0.53
	Collodictyonidae	Diphylleia rotans	0.47	5.01	0.40
	Ciliophora	Cyclidium	0	0.08	9.94
	Ciliophora	Telotrochidium	0.03	0.41	2.66
	Chlorophyta	Chlamydopodium starrii	0.10	0.20	2.39
	Ciliophora	Opisthonecta	0.09	0.15	2.07
23S rRNA	Chlorophyta	Chlorella sorokiniana chloroplast	75.45	0.62	9.72
	Chlorophyta	Chloroplast	2.88	1.47	53.21
	Proteobacteria	Acetobacteraceae	10.63	1.24	5.64
	Cyanobacteria	Synechocystis PCC-6803	0.81	49.94	0.46
	Cyanobacteria	Cyanobium gracile	0.19	37.30	0.17
	Cyanobacteria	Leptolyngbya boryana	0.12	3.93	0.10
	Proteobacteria	Roseomonas stagni	0.39	0.11	12.29
	Bacteroidota	Mariniradius saccharolyticus	0.18	0.07	10.83
	Proteobacteria	Brevundimonas	0.96	0.09	2.13
tufA	Chlorophyta	Chlorella sorokiniana	93.94	26.94	13.55

. Chlorella species (belonging to the Trebouxiophyceae class) were found to be the dominant microalgae species in raceway pond cultivation. Sequencing the tufA marker gene, which is a specialized marker used for the molecular identification of green microalgae [41], enabled the identification of these microalgae up to the species level as Chlorella sorokiniana. Partial mitochondrial and chloroplast genome sequences of C. sorokiniana species were also detected from 16S rRNA and 23S rRNA marker analyses. 16S rRNA marker analyses are mostly used to study the species and abundance of prokaryotic cells. However, 16S rRNA is also located in the mitochondria and chloroplasts of eukaryotes. It has also been indicated that the 16S rDNA operon exists in C. sorokiniana, and Chlorella species were identified using this operon [42]. There are also other studies that used 16S rRNA analysis in order to identify Chlorella species [43,44]. Nucleotide sequences of the nuclear/mitochondrial/chloroplast rRNAs (16S, 18S and 23S) of eukaryotic microalgal species were analyzed with 16S, 18S, and 23S for further confirmation of species identification. In 16S rRNA analysis, when the aim is to focus on bacterial species only, the general approach is to filter out chloroplast- and mitochondria-associated reads after taxonomic classification [45,46]. These reads were not filtered in our bioinformatic analysis; thus, the chloroplast and mitochondrial 16S rRNA regions of C. sorokiniana were detected separately. In 18S rRNA analysis, C. sorokiniana could not be identified at the species level. On the other hand, the use of the SILVA v138 database enabled the identification of the Trebouxiophyceae class in our analysis, which includes Chlorella species. Thus, for a confirmed species-specific resolution, the specialized tufA database was used and the Trebouxiophyceae class could be identified up to the species level, which was found to include C. sorokiniana.

Chlorella sp. are one of the most commonly used genera for the production of biomass and industrially valuable compounds, as well as for wastewater treatments. Maximum biomass production from the C. sorokiniana species was observed when cultured in open-system raceway ponds, and it was found to be very productive for biofuel and bioproduct production [4]. According to the results, there was an almost seven-fold higher C. sorokiniana abundance compared to the small-scale study, which contributes up to 94% of the total microbial abundance according to the tufA marker regions, highlighting the potential of urine-supplemented raceway ponds to be a viable option for the production of valuable bioproducts, including biogas, through high-density biomass creation. Chlorella sp. was reported to be the dominant species in another raceway pond experiment, and more biomass (0.73 g/L) was produced when compared to monocultures [47]. In this study, higher biomass (1.8 g/L) was obtained with the urine-treated microalgae cultivation in the raceway pond. Additionally, low bacterial co-existence was observed in the large-scale open-system reactor when compared to lab-scale cultivations. Bacterial contaminants can adversely affect microalgae composition, especially if microalgal predominance is not maintained. In the large-scale cultivation of urine-supplemented microalgal composition, where C. sorokiniana was found to be the dominant microalgae, very small amounts of bacteria were detected, along with other cyanobacteria species. Higher bacterial abundance with increased diversity was observed in lab-scale inoculations. Species diversity also changed between lab-scale urine and the BBM synthetic media, indicating that different species adapt to different media conditions. While cyanobacteria (Synechocystis sp. Cyanobium sp.) were more dominant in the BBM media, eukaryotic microalgae of Chlorophyta phylum were found to be more abundant in lab-scale urine media. The 16S rRNA marker results identified the green algae Desmodesmus sp. and Coelastrella sp. along with Chlorella sorokiniana in the lab-scale cultivation of urine-treated culture. Chlorella sorokiniana was also identified via 23S rRNA and tufA marker analyses, with lower abundance when compared to large-scale cultivation (almost seven-fold lower based on tufA results). Different Proteobacteria species were detected in lab-scale urine-supplemented media such as Porphyrobacter, Sphingomonas piscinae, and Roseomonas stagni. The Bacteroidota species Mariniradius saccharolyticus was also detected with almost 11% relative abundance based on the 23S rRNA results.

Fungal and amoeba parasites were detected in the BBM media. The intervention of such parasites has a negative impact on the proliferation of algae population [48,49]. In urine-supplemented inoculation, fungal abundance was very low, and some ciliates (Ciliophora phylum) were detected as contaminants along with some proteobacteria. This indicates that urine-supplemented medium is a better environment for the cultivation of microalgae since a decreased number of parasites exist. In the open-system cultivation, a small number of amoeba parasites were detected from 18S rRNA analysis. A relatively higher number (17%) of Blastocladiomycota parasites were also detected. Based on the 23S rRNA results, the Acetobacteraceae family of Proteobacteria was detected with almost 11% relative abundance. However, their presence will not be a threat in biomass production in large-scale open-system ponds since the higher dominance of C. sorokiniana is still maintained.

The PCA biplot in Figure 5 shows that Chlorella sorokiniana mostly explained the microbial community difference in the large-scale urine sample when compared to the lab-scale samples. In the lab-scale cultivation with urine supplementation, Coelastrella sp. and Desmodesmus sp. were found to be the most differentiating microalgal species. In conclusion, microbial composition analysis revealed that open-system microalgal cultivation with urine supplementation is a good option for industrial biofuel production. C. sorokiniana, belonging to the highest-yielding algae class Chlorophyceae, was found to be the dominant species.

4. Conclusions

In conclusion, using human urine as feedstock for microalgae and subsequent biogas production is a feasible option for both wastewater remediation and energy recovery. A maximum biomass concentration of 1847 mg/L was reached with the 10% diluted urine concentration, which suggests that it is possible for algal biomass production in human urine to be successful along with a high efficiency of nitrogen and phosphorus removal. Biogas production from algal biomass was consistent with values reported in the literature, and algae seem to be promising for biogas feedstock with 254.96 L/kg vs. However, using algal biomass for biogas feedstock needs to be further investigated, including pre-treatment options and larger scale improvements to prevent the disadvantages caused by the mono-digestion of microalgae such as a thick cell wall with the presence of long-chain organic compounds.