# Assessing the impact of biomass retention in membrane-assisted microalgae-bacteria sewage treatment

Jineth Arango, Aníbal Rojo, Francesca Casagli, Olivier Bernard, David Jeison

► To cite this version:

Jineth Arango, Aníbal Rojo, Francesca Casagli, Olivier Bernard, David Jeison. Assessing the impact of biomass retention in membrane-assisted microalgae-bacteria sewage treatment. Journal of Environmental Chemical Engineering, 2025, 13 (5), pp.118546. 10.1016/j.jece.2025.118546. hal-05458259

HAL Id: hal-05458259

https://inria.hal.science/hal-05458259v1

Submitted on 16 Jan 2026

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons CC BY 4.0 - Attribution - International License
Assessing the impact of biomass retention in membrane-assisted
microalgae-bacteria sewage treatment

Jineth Arango a, Aníbal Rojo a, Francesca Casagli b, Olivier Bernard b, David Jeison a*

david.jeison@pucv.cl, jineth.arango.o@mail.pucv.cl, anibal.rojo@pucv.cl

olivier.bernard@inria.fr, francesca.casagli@inria.fr

*Corresponding author:

david.jeison@pucv.cl

<footnote>ª Escuela de Ingeniería Bioquímica, Pontificia Universidad Católica de Valparaíso, Avenida Brasil 2085, Valparaíso, Chile.</footnote>

<footnote>b Centre de recherche INRIA Sophia-Antipolis Méditerranée, Francia.</footnote>
**Abstract**

Microalgae/bacteria (MBC) consortia are considered a promising platform for wastewater treatment. Biomass retention based on membrane filtration represents an effective alternative to enhance organic matter and nutrient removal. This study investigated the effects of uncoupling solid from hydraulic retention time (SRT and HRT, respectively) when treating synthetic municipal wastewater. For that purpose, 50 L high-rate algal pond was coupled with a membrane filtration step and operated at different conditions. The performance of the system was evaluated in terms of its load and concomitant removal of organic matter and nutrients. Organic matter removal exceeded 90%. Nitrogen removal efficiencies were in the range 46-68%, with the highest nitrogen removal rate exceeding 40 g N m⁻³ d⁻¹ at an HRT of 1 day. Phosphorus removal efficiencies varied between 39% and 70%. High loads resulting from low HRTs produced low oxygen levels in the reactor, that enabled the occurrence of denitrification. Experimental data supported the possibility of having in the same treatment unit different nitrogen removal mechanisms: assimilation, nitrification, and denitrification.

**Keywords:** Microalgae, bacteria, membrane, wastewater, solids retention time.
# 1. INTRODUCTION

Nowadays, about 80% of the produced wastewater (WW) worldwide is discharged into the environment without prior treatment. The development of novel, effective and affordable alternatives is thus needed to change this reality. Microalgae-bacteria consortia (MBC) are increasingly recognized as a powerful approach for wastewater treatment, with the potential to outperform solely bacterial-based systems [1,2]. Microalgae produce oxygen during photosynthesis, thus offering a promising alternative to reduce the energy demand associated with the mechanical aeration required in activated sludge systems [3]. In fact, the oxygen supplied by microalgae can partially support the needs of heterotrophic bacteria (HB) and nitrifying bacteria (NB) for the oxidation of organic matter and ammonia, respectively. On the other hand, CO₂ is produced by HB, which is used as a carbon source by microalgae and NB [4,5]. Therefore, the synergistic interactions within MBC support different microbial mechanisms involved in inorganic nitrogen treatment, including nitrification/denitrification, assimilation, and ammonia stripping. This results in a streamlined, single-stage treatment process [6], in contrast to traditional bio-process technologies that often require multiple stages (i.e., aerobic and anoxic).

However, this often requires long hydraulic retention times (HRT) to prevent biomass washout, which increases footprint and limits scalability. Additionally, the retention of slow-growing but functionally relevant microorganisms, such as NB and anoxic HB are also challenged under short HRT conditions, further complicating biomass management in these systems. Another critical bottleneck of MBC systems is the biomass separation, due to the low settleability of microalgae [7].
To address this challenge, technologies enabling the decoupling of the solid retention time (SRT) from HRT have recently gained attention [8–10]. Among these, the integration of membrane technology with MBC, in the form of membrane photobioreactors (MPBR) stand out as a promising solution, allowing complete biomass retention and a consistent effluent free of suspended solids [11,12]. This approach enhances system capacity and reduces footprint, allowing operation at shorter HRTs compared to non-membrane assisted MBC systems, which typically require 6-10 days of HRT [8,13].

The decoupling of SRT from HRT in microalgae-based systems presents several challenges. For example, increasing SRT will induce a higher biomass concentration, which may promote excessive self-shading, limiting microalgae growth, consequently reducing photo-oxygenation while promoting respiration [14]. Therefore, selecting the appropriate SRT is of crucial importance. Membrane technology has been extensively used in standard wastewater remediation technologies, but their integration and effectiveness within microalgae/bacteria-based systems has been relatively underexplored and quantified.

The performances of several MBC based systems for wastewater remediation as reported in literature are summarized in Table 4. It highlights how HRT and SRT significantly influence biomass productivity as well as the efficiency of phosphorus and nitrogen removal. Notably, most of the existing studies have concentrated on secondary and tertiary treatment, where C/N ratios are usually low, limiting the role of heterotrophic bacteria and associated removal mechanisms
[11,13]. A recent study from Aparicio et al. [15] explored the effects of HRT and wastewater concentration on a high-rate algal pond (HRAP) with biomass retention by membrane filtration, with an SRT of 6 days. On the other hand, Arango et al. [11] studied real sewage treatment on a similar system, at 7 and 4 days of HRT, and confirmed how deeply these parameters affect the process efficiency. It is worth remarking that most of the works in the literature focus on relatively low SRT conditions, (see Table 4), where HRT and SRT stay in the range of 4.7 to 10 days. Conditions involving high SRT have not been thoroughly evaluated. The effect of HRT and SRT has also been approached using modelling tools. For example, Crouchett-Catalán et al. [16] simulated different scenarios of a membrane-assisted raceway pond, showing how biomass retention can potentially increase treatment yield and reduce required area. However, more experimental validation is still required to confirm those results.

Despite recent advances, important knowledge gaps remain regarding the effect of retention times on the performance of membrane-assisted MBC systems, particularly when applied for the treatment of (raw) sewage. Previous studies have mainly focused on the use of MBC as a post-treatment, typically conducted at lab scale and within narrow operating ranges of HRT and SRT. Therefore, this study investigates the performance of membrane-assisted MBC systems under five distinct HRT-SRT combinations, including configurations with short HRT (1 day) and long SRT (up to 25 days), aiming to evaluate their impact on biomass productivity, organic matter and nutrient removal mechanisms, when treating domestic wastewater.
## 2. MATERIAL AND METHODS

### 2.1 Raceway photo-membrane bioreactors system

Two 50 L fiberglass raceway photoreactors equipped with paddlewheels at a speed of 10 rpm were used for this research. Both reactors had a surface area of 0.41 m² and were operated with a liquid depth of 0.125 m. Artificial LED illumination was used, providing a photosynthetically active radiation (PAR) of 324 ± 42 µmol m⁻² s⁻¹ on the liquid surface, with 12/12 hours light/dark cycles. The systems were operated in continuous mode, using synthetic media mimicking raw municipal wastewater. Synthetic wastewater was composed by powdered milk (350 mg L⁻¹), unflavored gelatin (250 mg L⁻¹), cornstarch (70 mg L⁻¹), MgSO₄·7H₂O (6 mg L⁻¹), KH₂PO₄ (35 mg L⁻¹); NH₄PO₄ (70 mg L⁻¹) and NaHCO₃ (800 mg L⁻¹). This composition was formulated to approximate the C/N/P ratio of municipal wastewater of Santiago, Chile). The resulting overall chemical composition of the synthetic wastewater is presented in Table 1, which presents resulting average and standard deviation of several parameters during photoreactors operation.

The reactors were inoculated with 10 L of algal-bacteria consortia from a pilot scale raceway located at Mapocho-Trebal wastewater treatment plant, in Santiago, Chile. The initial volatile suspended solids (VSS) concentration was 0.7 g VSS L⁻¹.
<table><caption>**Table 1.** Synthetic wastewater characteristics during the operation of photoreactors.</caption><thead><tr><th>Measurement</th><th>Unit</th><th>Value</th></tr></thead><tbody><tr><td>Total COD</td><td>mg COD L<sup>-1</sup></td><td>700 ± 16</td></tr><tr><td>Soluble COD</td><td>mg COD L<sup>-1</sup></td><td>513 ± 20</td></tr><tr><td>TAN</td><td>mg N L<sup>-1</sup></td><td>15 ± 0.7</td></tr><tr><td>N-NO<sub>3</sub></td><td>mg N L<sup>-1</sup></td><td>3.5 ± 0.4</td></tr><tr><td>Total Phosphorus</td><td>mg PO<sub>4</sub><sup>-3</sup>-P L<sup>-1</sup></td><td>9.3 ± 0.3</td></tr><tr><td>Total nitrogen</td><td>mg N L<sup>-1</sup></td><td>63 ± 0.5</td></tr><tr><td>Alkalinity</td><td>mg CaCO<sub>3</sub></td><td>657 ± 17</td></tr></tbody></table>

The four operating conditions evaluated in this study are presented in Table 2. The reference condition was HRT of 7 days, without biomass retention, since it could be considered as a standard condition for raceway operation [17,18]. Biomass retention was achieved coupling two different membrane filtration systems, depending on the needed permeate flow rate. When operating at HRT of 3.5 days and SRT of 15 days, a tubular polysulfone outside-in microfiltration membrane (Triqua, The Netherlands) was used. Membrane module consisted of 4 tubes of 36.7 and 0.9 cm of length and diameter, respectively. Membrane pore size was 0.2 µm and module nominal surface area was 0.042 m². During operation, membrane was embedded in an external membrane container of 1.5 L. When operating with longer SRT (25 days), a polyvinylidene outside-in hollow fiber ultrafiltration membrane was used (Zeeweed 500-1M). Membrane had a pore size of 0.04 µm and a surface area of 0.23 m². During operation, membrane was embedded
in an external membrane container of 7 L. Air sparging was applied in the membrane containers for fouling control. Mixed liquor was continuously circulated between the membrane containers and the reactors.

Permeate was collected using peristaltic pumps, that provided the required trans-membrane pressure (TMP). TMP was measured online by means of pressure transmitters (AE sensors ATM, The Netherlands). Both membrane modules were operated applying cycles consisting of 600 s filtration and 60 s backflush phases. During backflush flow was reversed while keeping the same flux. TMP was continuously monitored and pumps were operated using a LabView-based control system (National Instruments, USA), using CompactDAQ hardware (National Instruments, USA). A protective control loop was implemented to deactivate feed and permeate pumps when TMP exceeded 400 mbar to prevent membrane damage. Before each operation, membranes were subjected to an oxidative cleaning, using a 500 mg L⁻¹ NaOCl solution for 60 minutes, applying backflush flux.

<table><caption>**Table 2.** Operational condition and COD, nitrogen and phosphorus loading rate at each experimental period</caption><thead><tr><th rowspan="2">Membrane module</th><th rowspan="2">HRT<sup>*</sup> (d)</th><th rowspan="2">SRT<sup>*</sup> (d)</th><th rowspan="2">Average flux (L m<sup>-2</sup> h<sup>-1</sup>)</th><th>Organic</th><th>Nitrogen</th><th>Phosphorus</th></tr><tr><th>loading rate (g COD m<sup>-3</sup> d<sup>-1</sup>)</th><th>loading rate (g N m<sup>-3</sup> d<sup>-1</sup>)</th><th>loading rate (g P m<sup>-3</sup> d<sup>-1</sup>)</th></tr></thead><tbody><tr><td>✓</td><td>3.5</td><td>15</td><td>17.4</td><td>200</td><td>18</td><td>3.2</td></tr></tbody></table>
<table><tr><td>X</td><td>7</td><td>7</td><td>-</td><td>100</td><td>9</td><td>1.6</td></tr><tr><td>✓</td><td>3.5</td><td>25</td><td>3.2</td><td>200</td><td>18</td><td>3.2</td></tr><tr><td>✓</td><td>1</td><td>25</td><td>11.1</td><td>700</td><td>63</td><td>11.3</td></tr></table>

## 2.2 Analytical and biomass assessment procedures

Reactors were operated continuously following the operating conditions described in Table 2. When a stable operation was detected, the reactor content and the permeate were sampled and analysed daily. A reactor was considered under stable operation when consistent concentrations of VSS, chemical oxygen demand (COD), soluble nitrogen and phosphorus were detected in the reactor. Samples were pre-filtered using 0.2 µm membrane filters (Whatman, UK) and analysed for COD, total ammonia nitrogen (TAN), total nitrogen, nitrite, nitrate, and phosphate concentrations, using HACH kits. Kjendahl nitrogen, total phosphorus content, total suspended solids (TSS), VSS and alkalinity were measured from the bulk of the reactors, according to Standard Methods [19]. PAR was monitored using a handheld Apogee MQ-200X sensor at the reactor's surface and bottom. Light attenuation coefficient was determined using Lambert-Beer law, when the reactor operated at an HRT of 7 days, to determine mean light intensity over the reactor depth [20]. Dissolved oxygen (DO) profiles were recorded over three consecutive days using a GLOBAL WATER WQ-FDO sensor. Volumetric biomass production rate (BPR), nitrogen removal rate (NRR) and phosphorus removal rate (PRR) were calculated using the following equations.

<footnote>*HRT and SRT calculated using the volume of the reactor (not including membrane modules)</footnote>
$$BPR = \frac{Q_w}{V} \cdot X_R \qquad \text{Eq. 1}$$

$$NRR = \frac{Q_{in}}{V} \cdot (TN_{in} - TN_{Sout}) \qquad \text{Eq. 2}$$

$$PRR = \frac{Q_{in}}{V} \cdot (TP_{in} - TP_{Sout}) \qquad \text{Eq. 3}$$

where Q_in and Q_w are the influent flow rate and the biomass purge, respectively; V is the volume of the reactor; X_R is the VSS concentration in the reactor; TN_in, TP_in, TN_Sout and TN_Sout are the concentrations of total nitrogen and phosphorus in the influent, and of soluble nitrogen and phosphorus in the reactor effluent, respectively. Morphological characteristics of the consortia were observed by scanning electron microscopy (SEM) FEI Quanta 250. Before observation, biomass was fixed with 2.5% glutaraldehyde and dehydrated using ethanol, as reported by Boelee et al. [21].

Additionally, the volumetric mass transfer coefficient (K_La) was determined in both reactors, following the American Society of Civil Engineers (ASCE) method [22]. The K_La measurements were performed while the air sparging of membrane modules was working to account for the gas/liquid mass transfer occurring in both, the reactor and the membrane module.

## 2.3 Assessment of oxygen fluxes

Oxygen fluxes determining dissolved oxygen concentration in the reactors were quantified or estimated: oxygen produced by algae as a result of photosynthesis ($q_{O2_{algae photo}}$), respiration of
microalgae (qO2<sub>algae resp</sub>), oxidation of COD by HB (qO2<sub>HB</sub>), respiration of heterotrophic bacteria (qO2<sub>HB resp</sub>), nitrification by NB (qO2<sub>NB</sub>), oxygen transfer rate (OTR) from absorption/desorption from atmosphere (qO2<sub>OTR</sub>). The oxygen demand by nitrifiers was calculated using the stoichiometric equation from Liu and Wang [23], considering the observed rate of nitrification. Here, the denitrified NO3⁻ was estimated through the nitrogen mass balance. Respiration of HB was calculated based on the carbon used by HB for growth and the SRT, as proposed by Ekama and Wentzel [24]. The oxygen demand for the oxidation of organic matter (expressed as COD) was determined based on the total COD removal, excluding the fraction of COD utilized in denitrification (electron acceptor different than oxygen) [25].

## 2.4 Microbial identification using flow cytometry.

Flow cytometry was used to determine the proportion of photosynthetic microorganisms and bacteria [26]. Samples were sonicated for 3 minutes at 20 Hz to disaggregate the cells. After fixation in 2% paraformaldehyde and resuspension in PBS, samples were analysed in a cytometer (LSRFortessaX20 from Becton) for 4 minutes, then stained with SYTO9 for bacterial identification and analysed for an additional 3 minutes. Staining involved incubating bacteria labelled with SYTO9 at 2.5 µM for 15 minutes in darkness. Fluorescent size beads (1, 2, 3, and 6 µm, Fluoresbrite®) provided size reference for the analysis. Detector channels included FITC (525/50), PE (575/25), PerCP (695/40), and APC (670/30). The chlorophyll autofluorescence was detected by PerCP, and SYTO9-labeled bacteria were detected by the FITC channel. The data gates on the PerCP and FITC channels were set to exclude background noise and debris [27]. Flow rates for
cytometer acquisition were set to 94 µL min⁻¹ for photosynthetic counts and 18 µL min⁻¹ for total counts. Analysis was conducted using FlowJo™ v.7.12.0007 (BD Life Sciences), and proportions were calculated considering total counts and those for microalgae.

## 2.5 Statistical analysis

Single-factor ANOVA was used to determine the existence of significant differences between determinations of the observed parameters.
## 3. RESULTS AND DISCUSSION

### 3.1 Biomass characterization and productivity

Figure 1 presents VSS and TSS concentrations, volumetric biomass productivity and the proportion of microalgae and bacteria, under the different tested HRT and SRT. When operating at HRT and SRT of 7 days, the lowest biomass concentration (440 g VSS m⁻³) was associated to highest biomass productivity, 62.9 g VSS m⁻³ d⁻¹ (equivalent with 7.9 g VSS m⁻² d⁻¹). This productivity value is comparable with those reported by other authors [17,28,29], under equivalent HRTs and comparable reactor design and operational conditions.

The lowest biomass concentration observed at HRT 7 days resulted in the highest recorded mean light intensity (77 µmol m⁻² s⁻¹), which is a more favourable condition for enhancing photosynthetic growth [14,30,31]. At that condition, light intensity at the bottom of the raceway was about 9 µmol m⁻² s⁻¹, while under the other tested conditions were below the detection level.

Flow cytometer analysis revealed that at an HRT of 7 days, microalgae were about 10% of the total cell count. Bacteria dominated in all experiments, representing more than 85% of the total detected events (Figure 1C). This bacterial dominance aligns with the findings of Foladori et al. [27], who found only 2% of microalgae in a lab scale sequencing batch photobioreactor treating real wastewater. However, when accounting for the respective cell sizes, biomasses concentrations expressed in terms of mass are in favour of microalgae.
Increasing the SRT from 15 to 25 days, at an HRT of 3.5, days led to a significant reduction in productivity (P < 0.05), from 45 to 29 g VSS m⁻³ d⁻¹ (5.6 to 3.6 g VSS m⁻² d⁻¹, respectively). This decline in productivity was accompanied by a moderate increase in VSS and TSS concentrations of approximately 15 %. The higher biomass concentration at high SRTs, likely contributed to increase the self-shading effect and reduce light availability [30,32,33]. Indeed, the mean light intensity in the reactor at 15 and 25 days of SRT ranged from 44 to 53 µmol m⁻² s⁻¹, creating an unfavourable light condition for microalgae [34]. In addition, high SRT is expected to induce inert matter accumulation in the reactor, contributing to the reduction in light availability. Higher biomass concentrations induced by higher SRTs are also expected to increase the endogenous respiration of microalgae and bacteria [9,35]. These findings are consistent with state of the art in Table 4, showing that higher SRT reduces productivity while leading to higher biomass concentration. For instance, Luo et al. [36] observed a slight increase in biomass concentration from 910 to 1220 g TSS m⁻³ as the SRT increased from 18 to 30 days, in a laboratory-scale MPBR treating secondary wastewater. Similarly, Praveen et al. [37] reported minor impacts on biomass concentration with increasing SRT, recording concentrations of 1970, 1900 and 1860 g TSS m⁻³ at SRT values of 10, 20, and 50 days, respectively, during tertiary wastewater treatment.

Flow cytometry showed a slight decrease in the proportion of photosynthetic microorganisms
count as SRT increased to 25 days. Peniuk et al. [38] observed that light intensity significantly
affects species distribution, with certain microalgae thriving under higher light conditions (300 -
1800 µmol m⁻² s⁻¹), leading to increases in biomass.
As shown in Figure 1, reducing the HRT from 3.5 to 1 day, at 25 days of SRT, led to a significant increase in TSS, VSS and productivity (P < 0.05). Increase in biomass concentration led to an expected reduction of mean light intensity to 22 µmol m⁻² s⁻¹. At that condition the VSS/TSS ratio was the lowest (0.67), suggesting accumulation of inert matter in the reactor, representing a higher fraction of TSS. Despite limited light availability, increased nutrient load improved biomass productivity by 83%, up to 52.9 g m⁻³d⁻¹ (6.6 g m⁻²d⁻¹). The increased nutrient supply favoured both bacteria and microalgae, raising microalgae proportion count to around 12%. Similarly, Honda et al. [39] found that nutrient supply counteracts light limitation, supporting our observation that higher nutrients sustain productivity under reduced light.

SEM observation (Figure 2) revealed the heterogeneity of the culture for all the evaluated conditions. Among other species, microalgae resembling the morphological characteristics of *Achnanthes* sp., *Scenedesmus* sp. and *Chlorella* sp. were observed. These species are indeed the major ones commonly reported in wastewater treatment process [40–42].

Overall, SRT and HRT significantly influenced biomass productivity and community structure, by modulating the nutrient load and light availability in the reactor, as long as biomass retention is ensured, as is the case when membranes are used to keep a high SRT. Obviously, shorter HRT means higher loads, and a proportionally smaller required area. For instance, reducing the HRT from 7 to 1 day increases the wastewater processing rate from 0.017 to 0.119 m³ m⁻² d⁻¹. This
enhancement in processing capacity highlights the potential benefits of operating at shorter HRTs which may be possible when applying membrane-assisted biomass retention. Additionally, potential benefits or disadvantages of achieving high biomass productivity are highly dependent on the existing infrastructure and market conditions for biomass valorisation [7,43].

Throughout the experimental period, TMP remained below the safety threshold of 400 mbar. At HRT of 3.5 days and SRT of 15 days the system operated for 25 days, with TMP reaching values in the range 270-290 mbar, so no chemical cleaning was required. During the operations involving a SRT of 25 days, the system operated for 35 and 27 days, at HRT 3.5 and 1 days, respectively. TMP remained low, ranging from 13 to 18 mbar at HRT of 3.5 days and below 230 mbar at HRT of 1 day. Under both conditions, membrane performance remained stable, and no cleaning was needed. The higher membrane area of ZeeWeed 500-1M membrane (0.23 m²), resulted in lower permeate flux, likely contributing to lower TMP by reducing surface loading.
<figure><img src="image_2.png" /><figcaption>**Figure 1.** Reactors performance at different HRT and SRT: (A) Biomass concentration (values marked with "*" represent the ratio between VSS and TSS), (B) biomass productivity, and (C) proportion of microalgae and bacteria based on cell count (by flow cytometry).</figcaption></figure>
<figure><img src="image_3.png" /><figcaption>**Figure 2.** SEM images of microalgae-bacteria consortia. (A)-(B) HRT 7 SRT 7 days, (C)-(D) HRT 3.5 SRT 15 days, (E)-(F) HRT 3.5 SRT 25 days; (G)-H) HRT 1 SRT 25 days.</figcaption></figure>

**3.2 Oxygen dynamics in MBC systems**

Oxygen plays a crucial role in MBC systems, driving diverse phenomena such as organic matter oxidation and nitrification. Figure 3 presents typical DO daily evolution, over a 24-hour period, during systems operation. Higher concentrations of DO were observed at an HRT of 7 days: 6.7 ± 0.7 and 10.1 ± 0.8 g O₂ m⁻³, during dark and light phases, respectively. On the other hand, Table
4 presents the rates oxygen associated to the consumption, production and gas transfer. The oxygen rates were evaluated during the light phase.

<figure><img src="image_4.png" /><figcaption>**Figure 3.** Dissolved oxygen at different HRT and SRT conditions</figcaption></figure>

As expected, a higher HRT resulted in higher levels of DO, because of a lower load of nitrogen and organic matter, that reduces the oxygen requirements. Reducing the HRT from 3.5 to 1 day increases the organic and nitrogen loads from 200 to 700 g COD m⁻³d⁻¹ and from 18 to 63 g N m⁻³d⁻¹, respectively. Table 3 presents oxygen consumption rates estimated under the conditions prevailing during the light period. Reducing the HRT to 1 day resulted in an approximately 3-fold increase in HB respiration, due to the changes in biomass concentration. On the other hand, longer SRT are also expected to induce higher biomass concentrations, resulting also in higher rates of microbial respiration, increasing oxygen demand. This effect can be seen in Table 3 when
observing oxygen consumption rates at 3.5 days of HRT, that increased slightly from 3.6 to 3.9 g O₂ h⁻¹ when the SRT increased from 15 to 25 days. These results are consistent with the findings reported by Rada-Ariza et al. (2019).

Data also shows that oxygenation provided by microalgae plays a significant role in overall oxygen dynamics. Data presented in Table 3 shows that, during the light, period net algae oxygen production rate ($q_{O2_{algae net}}$) provided most of the oxygen required for COD oxidation, ammonia oxidation and HB respiration (between 65 and 90%, depending on applied conditions). On the other hand, during the dark period, atmospheric oxygen transfer is the only oxygen source [41,44,45]. This resulted in lower DO levels during the night, which improves oxygen absorption by increasing the driving force for oxygen transfer, i.e. the difference between DO concentration in the reactor and equilibrium conditions. At an HRT of 1 day and SRT of 25 days, DO was almost zero (0.05 ± 0.03 g O₂ m⁻³) during both light and dark phases. This was the result of a strong oxygen limitation, produced by the high COD and nutrient loads (700 mg COD L⁻¹ and 18 to 63 mg N L⁻¹).
<table><caption>**Table 3.** Oxygen transfer, consumption and production rates evaluated during the light phase, at different HRT and SRT.</caption><thead><tr><th>HRT</th><th>SRT</th><th>qO2<sub>algae net</sub><br>g m<sup>-3</sup> h<sup>-1</sup></th><th>qO2<sub>HB</sub><br>g m<sup>-3</sup> h<sup>-1</sup></th><th>qO2<sub>HB resp</sub><br>g m<sup>-3</sup> h<sup>-1</sup></th><th>qO2<sub>NB</sub><br>g m<sup>-3</sup> h<sup>-1</sup></th><th>qO2<sub>OTR</sub><br>g m<sup>-3</sup> h<sup>-1</sup></th></tr></thead><tbody><tr><td>7</td><td>7</td><td>3.2</td><td>1.3</td><td>1.5</td><td>0.8</td><td>-0.3</td></tr><tr><td>3.5</td><td>15</td><td>6.5</td><td>2.6</td><td>3.6</td><td>1.6</td><td>1.6</td></tr><tr><td>3.5</td><td>25</td><td>6.3</td><td>2.2</td><td>3.9</td><td>2.6</td><td>2.7</td></tr><tr><td>1</td><td>25</td><td>19.6</td><td>7.7</td><td>13.7</td><td>8.5</td><td>11.8</td></tr></tbody></table>

* $q_{O2_{algae net}}$ corresponds to net oxygen production by microalga, i.e. the difference between $q_{O2_{algae photo}}$ and $q_{O2_{algae resp}}$

* OTR was evaluated considering a temperature of 17 °C, oxygen solubility of 9.8 g/m³, a measured Kla values for each condition (27 h⁻¹ for first two conditions, 29 h⁻¹ for the next two)

### 3.3 COD, nitrogen, and phosphorus removals

Figure 4 presents the organic matter, nutrient loads and removals at the tested conditions. As shown in Figure 4A, COD removal efficiencies ranged between 92 and 98%, despite the great differences in load. For example, reducing the HRT from 7 to 1 day, significantly increased COD removal capacity, from 92.3 to 685.9 g COD m⁻³d⁻¹. The high removal efficiencies resulted in soluble COD concentrations below 50 g COD m⁻³, underscoring the capacity of HB to provide a
good treated wastewater quality, under different operational conditions, as it has been reported previously [11].

In MBC systems, the main nitrogen removal mechanisms are nitrification, denitrification, assimilation into biomass and potentially ammonia desorption. To evaluate the contribution of each mechanism, the concentration of different nitrogen species was analysed (Figure 4B). TAN concentration was consistently below 0.7 g TAN m⁻³, with removal efficiencies around 98% in all conditions. These results are comparable to those reported by Morillas-España et al. (2021), who observed effluent concentrations below 1 g TAN m⁻³, while they are significantly lower than those reported by Robles at al. (2020), who recorded concentrations higher than 20 g TAN m⁻³ in an outdoor raceway reactor with decoupled HRT and SRT. It is worth remarking that no ammonia volatilization was detectable for low TAN concentrations, which is beneficial since ammonia is an air pollutant [47].

On the other hand, the NO₂⁻ concentrations remained consistently below 5 g N-NO₂⁻ m⁻³ (Figure 4B), while ammonium stayed at a very low level, indicating a balance between ammonification, nitrification and effective conversion to NO₃⁻ throughout all experiments. Conversely, NO₃⁻ concentrations exhibited significant variations (P < 0.05). At HRT of 7 days and HRT/SRT of 3.5/15 days, nitrate concentrations were close to 30 g N-NO₃⁻ m⁻³, indicating substantial nitrification levels, faster than denitrification. In contrast, at SRT of 25 days, NO₃⁻ concentrations were lower,
below 18 g N-NO₃⁻ m⁻³. Similar behaviour was observed in our previous work [11], when we observed concentrations of nitrate of 10 g N-NO₃⁻ m⁻³, in an MBC system with biomass retention operated at an HRT of 4 days.

The fraction of removed nitrogen was in the range 50-70% for all the tested conditions (see Figure 4C). However, NRR experimented significant variations, and increased as HRT decreased and SRT increased, as seen in Figure 4C. Similar trends were found by Trebuch et al. (2020) and Robles et al. (2020) who reported an increase in the total NRR with reduced HRT in different wastewater treatment setups. Indeed, our results clearly show that the better performance in terms of nitrogen conversion and removal was achieved at the higher load, i.e. HRT of 1 day. At that condition, 68% of the incoming nitrogen was removed, providing a NRR surpassing 40 g m⁻³d⁻¹. Then, decoupling SRT from HRT enhanced the fraction of removed TN together with the removal efficiency.

To better understand system's behaviour, it is necessary to analyse the nitrogen mass balance, presented in Figure 4D, that identifies the different forms of nitrogen entering and leaving the system. It shows that most of incoming nitrogen was in the form of organic nitrogen, which reflects the organic composition of the influent. The nitrogen leaving the system in the form of biomass (NORGb), corresponds to the nitrogen removed by assimilation. At HRT of 7 days, assimilation was the mechanism responsible for 50% of nitrogen removal, contributing to NRR with a rate of 4.7 ± 0.9 g m⁻³ d⁻¹. This value is comparable with values reported in literature for
similar systems without biomass retention [28,49,50]. When increasing the SRT, a decrease in nitrogen assimilation was observed. In fact, for HRT of 3.5 days, when SRT increased from 15 to 25 days, nitrogen assimilation strongly decreased, reducing its contribution to NRR from 34 to 11%. This reduction is associated with the decrease in biomass productivity observed at longer SRT. In addition, a decrease of HRT from 3.5 to 1 day, at SRT of 25 days, induced an increase in the nitrogen assimilation rate, most likely because of the increase in biomass productivity and nutrients load (Figure 1B). However, the fraction of assimilation in overall NRR decreased to 9%. This occurs because the higher nutrient load at 1 day increases the total nitrogen input, so even though assimilation is higher in absolute terms, it represents a smaller proportion of the total nitrogen removed.

Figure 4D presents an unbalance between incoming and outgoing nitrogen in the range 40-50%, especially at SRT of 25 days (HRT of 3.5 and 1 day). The existence of a non-identified fraction of nitrogen leaving the system is most likely a sign of denitrification. Figure 3 shows that at HRT of 1 day DO concentration was virtually zero during both light and dark phases. The oxygen produced by microalgae during the light phase plus that transferred from the atmosphere was rapidly consumed by bacteria for organic matter oxidation and nitrification, creating oxygen-limited conditions. These created compatible conditions with the anoxic nature of denitrification [15]. Similar phenomenon was reported by Dang et al. (2023), who found that denitrification accounted for 21-32 % of the removed nitrogen in a lab scale MPBR, at HRT of 1 day and SRTs in the range of 7-10 days. At HRT of 3.5 days (and SRT of 25 days) oxygen concentration during the
dark period was in the range of 4 mg/L. For these low concentrations, it is likely that oxygen-
limited microenvironments taking place in biomass aggregates (or biofilm attached to the reactor
walls) may have enabled denitrification, as suggested by other authors [20,34,52–55].

Data indicates that denitrification was a key factor for enhancing the nitrogen removal observed
at low HRT, despite the high nitrogen load. In addition, results underscore the significant role
that HRT and SRT play in shaping nitrogen transformation pathways in MBC systems. The
combination of lower HRT and higher SRT can provide conditions of oxygen depletion that can
reduce the accumulation of nitrification products. This is a relevant factor considering that
nitrate content in the reactor effluent is an issue for MBC systems when applied to wastewater
treatment [11]. The challenge is to find a trade-off between conditions supporting
denitrification and ensuring sufficient oxygen availability for nitrification and organic matter
oxidation.
<table><caption>**Table 4.** Summary of performances of several reported MBC systems for wastewater treatment.</caption><thead><tr><th>Reactor type</th><th>WW type</th><th>Vol. (m³)</th><th>Config.</th><th><sup>A</sup>Light/ Temp. (°C)</th><th>HRT (days)</th><th>SRT (days)</th><th>COD removal (%)</th><th>TN removal (%)</th><th>P removal (%)</th><th>Biomass (g m<sup>-3</sup>)</th><th>Biomass prod. (g m<sup>-3</sup>d<sup>-1</sup>)</th><th>Ref.</th></tr></thead><tbody><tr><td rowspan="4">HRAP</td><td>Synthetic municipal</td><td>0.05</td><td>MBR-C</td><td>400/20</td><td>7</td><td>7</td><td>92</td><td>52</td><td>70.6<sup>B</sup></td><td>440<sup>2</sup></td><td>62.8<sup>f</sup>/7.7<sup>g</sup></td><td rowspan="4">This study</td></tr><tr><td rowspan="2">WW</td><td></td><td></td><td></td><td>3.5</td><td>15</td><td>98</td><td>46</td><td>42<sup>B</sup></td><td>671<sup>2</sup></td><td>44.7<sup>f</sup>/5.5<sup>g</sup></td></tr><tr><td></td><td></td><td></td><td>3.5</td><td>25</td><td>93</td><td>59</td><td>65.3<sup>B</sup></td><td>721<sup>2</sup></td><td>28.9<sup>f</sup>/3.6<sup>g</sup></td></tr><tr><td></td><td></td><td></td><td></td><td>1</td><td>25</td><td>93</td><td>68</td><td>38.5<sup>B</sup></td><td>1321<sup>2</sup></td><td>52.9<sup>f</sup>/6.6<sup>g</sup></td></tr><tr><td rowspan="2">HRAP</td><td>Urban</td><td></td><td></td><td></td><td>7</td><td>7</td><td>68</td><td>73.2</td><td>53<sup>B</sup></td><td>300<sup>1</sup></td><td>41.6<sup>f</sup>/12.5<sup>g</sup></td><td rowspan="2">[17]</td></tr><tr><td>WW</td><td>0.47</td><td>SC-C</td><td>457-1300 /21.7</td><td>4</td><td>4</td><td>67</td><td>57.9</td><td>64<sup>B</sup></td><td>250<sup>1</sup></td><td>48.5<sup>f</sup>/14.5<sup>g</sup></td></tr><tr><td rowspan="2">HRAP</td><td>Urban</td><td>8</td><td>SC-C</td><td>500-1500 /18-23</td><td>8</td><td>8</td><td>-</td><td>54.9</td><td>-</td><td>547<sup>2</sup></td><td>62.8<sup>2f</sup>/18.8<sup>2g</sup></td><td rowspan="2">[28]</td></tr><tr><td>WW</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td rowspan="2">HRAP</td><td>Synthetic municipal</td><td>16.8</td><td></td><td>433/22 /395/21.2</td><td>6</td><td>6</td><td>95</td><td>52.0</td><td>53<sup>B</sup></td><td>180<sup>2</sup></td><td>30.1<sup>f</sup>/9<sup>g</sup></td><td rowspan="2">[46]</td></tr><tr><td></td><td></td><td></td><td></td><td>4</td><td>6</td><td>98</td><td>69.3</td><td>85.2<sup>B</sup></td><td>397.2<sup>2</sup></td><td>66.2<sup>f</sup>/19.9<sup>g</sup></td></tr></tbody></table>
WW 420/24.5 2.5 6 89 53.7 63<sup>B</sup> 570.6<sup>2</sup> 95.1<sup>f</sup>/28.5<sup>g</sup>
MBR-C 253/14 2.5 6 - 39.11 45.8<sup>B</sup> 391.8 65.3<sup>f</sup>/19.6<sup>g</sup>

<table><tbody><tr><td>Lab-scale</td><td>Synthetic secondary</td><td>0.005</td><td>MBR-C</td><td>85/21</td><td>1</td><td>9</td><td>68<sup>d</sup></td><td>31</td><td>30<sup>B</sup></td><td>470<sup>1</sup></td><td>52<sup>f</sup></td><td>[36]</td></tr><tr><td rowspan="3">Flat panel</td><td rowspan="3">effluent</td><td></td><td></td><td></td><td>1</td><td>18</td><td>66<sup>d</sup></td><td>36</td><td>31<sup>B</sup></td><td>910<sup>1</sup></td><td>51<sup>f</sup></td><td></td></tr><tr><td></td><td></td><td></td><td>1</td><td>30</td><td>64<sup>d</sup></td><td>32</td><td>25<sup>B</sup></td><td>1220<sup>1</sup></td><td>41<sup>f</sup></td><td></td></tr><tr><td></td><td></td><td></td><td>4</td><td>30</td><td>34<sup>d</sup></td><td>84</td><td>80<sup>B</sup></td><td>850<sup>1</sup></td><td>28<sup>f</sup></td><td></td></tr><tr><td>Lab-scale</td><td>Synthetic secondary</td><td>0.005</td><td>MBR-C</td><td>235/room</td><td>1</td><td>3</td><td>-</td><td>30.3</td><td>4.5<sup>C</sup></td><td>798<sup>2</sup></td><td>118<sup>f</sup></td><td>[56]</td></tr><tr><td rowspan="3">Flat panel</td><td rowspan="3">effluent</td><td></td><td></td><td>T</td><td>1</td><td>6</td><td>-</td><td>21.96</td><td>3.3<sup>C</sup></td><td>823<sup>2</sup></td><td>61<sup>f</sup></td><td></td></tr><tr><td></td><td></td><td></td><td>1</td><td>12</td><td>-</td><td>27.6</td><td>14<sup>C</sup></td><td>801<sup>2</sup></td><td>51<sup>f</sup></td><td></td></tr><tr><td></td><td></td><td></td><td>1</td><td>24</td><td>-</td><td>26.67</td><td>18<sup>C</sup></td><td>860<sup>2</sup></td><td>43<sup>f</sup></td><td></td></tr><tr><td>Lab-scale</td><td>Real secondary</td><td>0.003</td><td>MBR-C</td><td>300/20</td><td>0.75</td><td>20</td><td>23</td><td>30</td><td>6.4<sup>C</sup></td><td>873<sup>1</sup></td><td>43<sup>f</sup></td><td>[57]</td></tr><tr><td rowspan="3">Flat panel</td><td rowspan="3">effluent</td><td></td><td></td><td></td><td>0.75</td><td>40</td><td>25</td><td>22.2</td><td>14.8<sup>C</sup></td><td>1285<sup>1</sup></td><td>32<sup>f</sup></td><td></td></tr><tr><td></td><td></td><td></td><td>0.75</td><td>80</td><td>27</td><td>5.6</td><td>5.2<sup>C</sup></td><td>1775<sup>1</sup></td><td>22<sup>f</sup></td><td></td></tr><tr><td></td><td></td><td></td><td>2</td><td>80</td><td>35</td><td>48.7</td><td>18.5<sup>C</sup></td><td>2115<sup>1</sup></td><td>27<sup>f</sup></td><td></td></tr><tr><td></td><td></td><td></td><td></td><td>5</td><td>80</td><td>45</td><td>40.6</td><td>34.7<sup>C</sup></td><td>2259<sup>1</sup></td><td>28<sup>f</sup></td><td></td></tr></tbody></table>
<table><tbody><tr><td rowspan="3">HRAP</td><td>Primary</td><td>4.4</td><td>MBR-C</td><td>-</td><td>4.7</td><td>4.7</td><td>84</td><td>-</td><td>66<sup>C</sup></td><td>512.3<sup>1</sup></td><td>109<sup>f</sup>/13<sup>g</sup></td><td rowspan="3">[13]</td></tr><tr><td>urban</td><td></td><td></td><td></td><td>3.5</td><td>4.7</td><td>87</td><td>-</td><td>58.6<sup>C</sup></td><td>545.2<sup>1</sup></td><td>116<sup>f</sup>/13.9<sup>g</sup></td></tr><tr><td>WW</td><td></td><td></td><td></td><td>2</td><td>4.7</td><td>99</td><td>-</td><td>57<sup>C</sup></td><td>752<sup>1</sup></td><td>160<sup>f</sup>/19.2<sup>g</sup></td></tr><tr><td rowspan="4">Lab-scale</td><td>Pre-settled</td><td>0.004</td><td>MBR-C</td><td>100/room</td><td>1</td><td>10</td><td>67</td><td>51</td><td>64<sup>B</sup></td><td>858<sup>2</sup></td><td>86<sup>f</sup></td><td rowspan="4">[51]</td></tr><tr><td>domestic</td><td></td><td>T</td><td></td><td>1</td><td>7</td><td>71</td><td>46.8</td><td>60.2<sup>B</sup></td><td>716<sup>2</sup></td><td>102<sup>f</sup></td></tr><tr><td>WW</td><td></td><td></td><td></td><td>1</td><td>5</td><td>38</td><td>31.2</td><td>59.1<sup>B</sup></td><td>451<sup>2</sup></td><td>85<sup>f</sup></td></tr><tr><td></td><td></td><td></td><td></td><td>1</td><td>3</td><td>29</td><td>22</td><td>67.1<sup>B</sup></td><td>250<sup>2</sup></td><td>83<sup>f</sup></td></tr><tr><td rowspan="3">Lab-scale</td><td>Real</td><td>0.005</td><td>SB-mpBR</td><td>185/-</td><td>2</td><td>-</td><td>-</td><td>82.79</td><td>35.71<sup>B</sup></td><td>630<sup>1</sup></td><td>10.5<sup>f</sup></td><td rowspan="3">[58]</td></tr><tr><td>secondary</td><td></td><td>MPBR</td><td></td><td>4</td><td>-</td><td>-</td><td>95.95</td><td>70<sup>B</sup></td><td>710<sup>1</sup></td><td>11.8<sup>f</sup></td></tr><tr><td>effluent</td><td></td><td></td><td></td><td>8</td><td>60</td><td>-</td><td>90.28</td><td>44.29<sup>B</sup></td><td>1000<sup>1</sup></td><td>16.6<sup>f</sup></td></tr><tr><td rowspan="4">Lab-scale</td><td>Synthetic</td><td>0.0096</td><td>MPBR</td><td>155/28</td><td>2.8</td><td>10</td><td>94</td><td>18.05</td><td>28.38</td><td>1340<sup>1</sup></td><td>134<sup>f</sup></td><td rowspan="4">[32]</td></tr><tr><td>Industrial</td><td>4</td><td></td><td></td><td>20</td><td>93</td><td>19.26</td><td>48.36</td><td>2250<sup>1</sup></td><td>112.5<sup>f</sup></td></tr><tr><td>anaerobic</td><td></td><td></td><td></td><td>30</td><td>90</td><td>14.6</td><td>54.95</td><td>3400<sup>1</sup></td><td>113.3<sup>f</sup></td></tr><tr><td>digestate</td><td></td><td></td><td></td><td>5.8</td><td>20</td><td>93</td><td>12.98</td><td>24.38</td><td>870<sup>1</sup></td><td>43.5<sup>f</sup></td></tr><tr><td rowspan="3">Lab-scale</td><td>Synthetic</td><td>0.0035</td><td>MPBR</td><td>130/-</td><td>2</td><td>350</td><td>-</td><td>96.5</td><td>71.4<sup>C</sup></td><td>2030<sup>1</sup></td><td>5.8<sup>f</sup></td><td rowspan="3">[37]</td></tr><tr><td>tertiary</td><td></td><td>DIFFERENT</td><td></td><td>50</td><td>-</td><td>-</td><td>95.7</td><td>74.2<sup>C</sup></td><td>1860<sup>1</sup></td><td>37.2<sup>f</sup></td></tr><tr><td></td><td>LIGHT</td><td></td><td></td><td>30</td><td>-</td><td>-</td><td>97.1</td><td>77<sup>C</sup></td><td>1900<sup>1</sup></td><td>63.33<sup>f</sup></td></tr></tbody></table>
<table><tbody><tr><td></td><td>wastewat</td><td></td><td>20</td><td>-</td><td>96.4</td><td>74.2<sup>C</sup></td><td>1980<sup>1</sup></td><td>99<sup>f</sup></td><td></td></tr><tr><td></td><td>er</td><td></td><td>10</td><td>-</td><td>96.5</td><td>73.1<sup>C</sup></td><td>1970<sup>1</sup></td><td>197<sup>f</sup></td><td></td></tr><tr><td></td><td></td><td></td><td>5</td><td>-</td><td>95.8</td><td>74.1<sup>C</sup></td><td>1070<sup>1</sup></td><td>214<sup>f</sup></td><td></td></tr><tr><td></td><td></td><td></td><td>2.5</td><td>-</td><td>95.5</td><td>78.2<sup>C</sup></td><td>780<sup>1</sup></td><td>312<sup>f</sup></td><td></td></tr><tr><td></td><td></td><td></td><td>2</td><td>-</td><td>94.9</td><td>71.8<sup>C</sup></td><td>540<sup>1</sup></td><td>270<sup>f</sup></td><td></td></tr><tr><td rowspan="3">Lab-scale</td><td>Synthetic</td><td>0.004-</td><td>batch-</td><td>101.5-</td><td>1</td><td>-</td><td>35.9*</td><td>76.9*</td><td>873<sup>1</sup></td><td>48.5<sup>f</sup> [8]</td></tr><tr><td>Secondar</td><td>0.005</td><td>MPBR</td><td>112.3/25-</td><td>2</td><td>-</td><td>76.9*</td><td>88*</td><td>878<sup>1</sup></td><td>48.78<sup>f</sup></td></tr><tr><td>y effluent</td><td></td><td></td><td>30</td><td>4</td><td>18</td><td>90.6*</td><td>94.9*</td><td>603<sup>1</sup></td><td>33.5<sup>f</sup></td></tr><tr><td></td><td></td><td></td><td></td><td>6</td><td>-</td><td>91.1*</td><td>94*</td><td>481<sup>1</sup></td><td>26.69<sup>f</sup></td></tr><tr><td rowspan="5">Lab-scale</td><td>Synthetic</td><td>0.0049</td><td>MPBR</td><td>150/23</td><td>0.04</td><td>∞</td><td>69.9</td><td>92.2<sup>B</sup></td><td>-</td><td>47.5<sup>f</sup> [59]</td></tr><tr><td>secondary</td><td></td><td>with</td><td></td><td>1</td><td>10</td><td>73.4</td><td>91.3<sup>B</sup></td><td>895<sup>1</sup></td><td>72.4<sup>f</sup></td></tr><tr><td>wastewat</td><td></td><td>thank in</td><td></td><td>0.5</td><td>10</td><td>58.3</td><td>90.1<sup>B</sup></td><td>1473<sup>1</sup></td><td>105.2<sup>f</sup></td></tr><tr><td>er</td><td></td><td>series</td><td></td><td>0.5</td><td>5</td><td>54</td><td>84.4<sup>B</sup></td><td>690<sup>1</sup></td><td>92.3<sup>f</sup></td></tr><tr><td></td><td></td><td></td><td></td><td>0.25</td><td>5</td><td>66.1</td><td>87.9<sup>B</sup></td><td>1023<sup>1</sup></td><td>131.7<sup>f</sup></td></tr><tr><td></td><td></td><td></td><td></td><td>1</td><td>5</td><td>95.3</td><td>94.9<sup>B</sup></td><td>1289<sup>1</sup></td><td>98.2<sup>f</sup></td></tr><tr><td></td><td></td><td>0.004</td><td>MPBR</td><td>100/19-24</td><td>1</td><td>-</td><td>70</td><td>96.7</td><td>314<sup>1</sup></td><td>26.2<sup>f</sup> [39]</td></tr></tbody></table>
Lab-scale flat
Synthetic secondary wastewat er

<table><tr><td></td><td>Primary</td><td>0.32</td><td>MPBR</td><td>Average</td><td>4</td><td>6</td><td>95</td><td>1.8</td><td>39.6</td><td>667<sup>2</sup></td><td>-</td><td></td></tr><tr><td></td><td>urban</td><td>S2</td><td></td><td>110-</td><td>2</td><td></td><td>95</td><td>90</td><td>71</td><td>995<sup>2</sup></td><td>-</td><td></td></tr><tr><td></td><td>wastewat</td><td></td><td></td><td>139/15-16</td><td>1</td><td></td><td>97</td><td>1.2</td><td>0.7</td><td>1629<sup>2</sup></td><td>-</td><td>[15]</td></tr><tr><td></td><td>er</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td rowspan="5">HRAP</td><td>Primary</td><td>0.32</td><td>MPBR</td><td>Average</td><td>6</td><td>6</td><td>92</td><td>26.3</td><td>31.5</td><td>145<sup>2</sup></td><td>-</td><td></td></tr><tr><td>urban</td><td>S1</td><td></td><td>221-255/29</td><td>4</td><td></td><td>89</td><td>5.4</td><td>1</td><td>307<sup>2</sup></td><td>-</td><td></td></tr><tr><td>wastewat</td><td></td><td></td><td></td><td>2</td><td></td><td>89</td><td>5.5</td><td>1.6</td><td>295<sup>2</sup></td><td>-</td><td></td></tr><tr><td>er after</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>primary</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr></table>

Lab-scale
0.002 MPBR 200/25 0.3 10 50-90 - 7-27 0.35-1.25<sup>1</sup> - [60]
<table><tr><td>Synthetic</td><td>20</td><td>60-90</td><td>-</td><td>6-29</td><td>1.21-</td><td>-</td></tr><tr><td>wastewater</td><td></td><td></td><td></td><td></td><td>1.97<sup>1</sup></td><td></td></tr><tr><td>er</td><td></td><td></td><td></td><td></td><td></td><td></td></tr></table>

¹TSS; ²VSS, Aµmol m⁻²s⁻¹, BTP, CPO₄³⁻P, SB-MBR sequential batch; *Dissolved inorganic nitrogen and phosphorus, dOrganic matter,

fVolumetric productivity, gAerial productivity, e Range reflects performance across aerated and non-aerated stages.
<figure><img src="image_5.png" /><figcaption>**Figure 4.** Nutrient removal in the MBC system at different HRT and SRT was calculated during light phase. (A) COD removal rate and efficiency. (B) Concentration of soluble nitrogen compounds; N<sub>ORGs</sub> is organic nitrogen. (C) Nitrogen load in the influent and effluent with N<sub>ORGb</sub> representing nitrogen in biomass. (D) Nitrogen removal rate (NRR) and efficiency. (E) Phosphorus load in the influent and effluent with P<sub>b</sub> representing nitrogen in biomass. (F) Phosphorus concentration, Phosphorus removal rate (PRR) and efficiency.</figcaption></figure>

<figure><img src="image_6.png" /></figure>

<figure><img src="image_7.png" /></figure>

<figure><img src="image_8.png" /></figure>
The expected mechanisms for phosphorus removal in MBC systems are biomass assimilation, and precipitation when pH reaches values higher than 8 [61]. Phosphorus removal efficiencies were in the range 40-70 %, as shown in Figure 4E. As for nitrogen, a decrease in HRT produced a concomitant increase in phosphorus load, resulting in significant differences in observed PRR (p < 0.05), that were in the range 1-3.5 g m⁻³ d⁻¹. PRR values were slightly higher than those reported by Robles et al. (2020) when operating a pilot reactor at SRT of 6 days and HRT in the range 2.5-6 days. Figure 4F presents phosphorus mass balances, identifying the different forms of phosphorus entering and leaving the system. The figure reveals that the main removal mechanism is biomass assimilation.

As with nitrogen, increasing SRT resulted in a decrease in the phosphorus removal rate via biomass assimilation, as a consequence of decreasing biomass productivity. When increasing the SRT from 15 to 25 days and at 3.5 days of HRT, the reduction in phosphorus assimilation was about 30%. A similar effect was reported by Dang et al. (2023), who observed a decrease in PRR from 3.8 to 2.3 g PO₄³⁻ m⁻³d⁻¹, as the SRT increased from 3 to 10 days. Application of an HRT of 1 day promoted the highest rate of phosphorus assimilation (1.6 g P m⁻³d⁻¹), but it was associated with the lowest removal efficiency (39 %). Interestingly, an imbalance between influent and effluent phosphorus appeared at a SRT of 25 days (Figure 4F). At that SRT the pH was between 8.2 and 8.6, conditions that may have induced phosphorus precipitation, as reported by other authors [62,63]. Several authors have recorded comparable phosphorus losses, attributing them to precipitation and deposition in certain areas of the reactor [64]. Intermittence of
aerobic/anaerobic condition produced by light and dark phases may also induce the development of polyphosphate accumulating organisms (PAOs) in MBC systems, contributing to phosphorus removal [51,65]. Promoting the development of these microorganisms may help to enhance phosphorus removal, to contribute to the achievement levels complying with existing discharge regulation limits, such as those established within the European Union.

Moreover, following a similar trend to nitrogen areal removal rates, reducing the HRT to 1 day while maintaining a high SRT increased phosphorus removal to 0.42 g P m⁻² d⁻¹, compared to 0.11 g P m⁻² d⁻¹ under non-retention biomass condition. This suggests that equivalent phosphorus removal could be achieved using significantly smaller surface areas. However, this increased compactness was accompanied by a decline in overall removal efficiency, reinforcing the need for complementary strategies such as enhancing phosphorus precipitation under alkaline pH or promoting PAOs to achieve both system compactness and compliance with effluent standards.

Results presented in this study experimentally confirmed that the capacity of the system can be improved by applying biomass retention through membrane filtration. The registered increment in load marginally affected COD and nitrogen removals, showing the capacity of the system to provide reasonable treatment at HRT as low as 1 day. Logically, comprehensive economic analyses would be needed to determine the actual convenience of membrane-assisted MBC. Recently Rossi et al. [66] and Nobre et al. [67] published analysis that included economic aspects of the use of microalgae cultures for wastewater treatment. However, economics will greatly depend on the
specific conditions of each application, scale, sewage characteristics and selected technology.

Anyhow, the decrease in membrane prices that during the last 2 decades has enabled the increasing implementation of aerobic and anaerobic MBR technology for wastewater treatment is expected to create conditions for the future development of membrane-assisted MBC.

## 4. CONCLUSIONS

The decoupling of HRT and SRT achieved by membrane assisted biomass retention significantly changed the behaviour of the wastewater treatment process by MBC, providing the following main outcomes:

* Biomass retention resulting in higher SRTs proved to be an effective way to enhance systems' capacity, without seriously affecting its performance, providing increasing levels of removal loads as HRT was increased. Indeed, operation of the system at 1 day of HRT was possible, providing an effluent quality comparable with that at 7 days, despite the great differences in organic matter, nitrogen and phosphorus loads.

* The increase of SRT results in higher biomass concentrations. It also induces a decrease in biomass productivity, because of an increased microbial respiration, affecting removal of nutrients by assimilation.

* Nitrate was the most common nitrogen specie leaving the system. At high SRT and low HRT, the dynamics of oxygen concentration established conditions conducive to simultaneous nitrification and denitrification, promoting high removal levels of total
nitrogen.

* Higher loads induced higher levels of oxygen requirement, resulting in lower oxygen levels. During the light phase, microalgae supplied most of the oxygen required for organic matter oxidation and to drive the nitrification process, even under high nutrient loads at an HRT of 1 day.

## ACKNOWLEDGEMENT

This work was financially supported by ANID through project FONDECYT 1230660. Jineth Arango acknowledges the support provided by ANID through the program DOCTORADO BECAS CHILE/2019-21191407. Authors also acknowledge the support provided by ANID through project ANID/ACT240022.

## REFERENCES

[1] B. Zhang, W. Li, Y. Guo, Z. Zhang, W. Shi, F. Cui, P.N.L. Lens, J.H. Tay, Microalgal-bacterial consortia: From interspecies interactions to biotechnological applications, Renewable and Sustainable Energy Reviews 118 (2020) 109563.
https://doi.org/10.1016/j.rser.2019.109563.

[2] T.P. Vo, S. Danaee, C. Chaiwong, B.T. Pham, N. Poddar, M. Kim, U. Kuzhiumparambil, C. Songsomboon, M. Pernice, H.H. Ngo, P.J. Ralph, P.H.N. Vo, Microalgae-bacteria consortia for organic pollutants remediation from wastewater: A critical review, J Environ Chem Eng 12 (2024). https://doi.org/10.1016/j.jece.2024.114213.

[3] N.G.A.I. Karya, N.P. van der Steen, P.N.L. Lens, Photo-oxygenation to support nitrification in an algal-bacterial consortium treating artificial wastewater, Bioresour Technol 134 (2013) 244-250. https://doi.org/10.1016/j.biortech.2013.02.005.
[4] G. Mujtaba, K. Lee, Advanced treatment of wastewater using symbiotic co-culture of microalgae and bacteria, Applied Chemistry for Engineering 27 (2016).
https://doi.org/10.14478/ace.2016.1002.

[5] R. Muñoz, B. Guieysse, Algal – bacterial processes for the treatment of hazardous contaminants: A review, Water Res 40 (2006) 2799–2815.
https://doi.org/10.1016/j.watres.2006.06.011.

[6] J.A. Oviedo, R. Muñoz, A. Donoso-Bravo, O. Bernard, F. Casagli, D. Jeison, A half-century of research on microalgae-bacteria for wastewater treatment, Algal Res 67 (2022).
https://doi.org/10.1016/j.algal.2022.102828.

[7] E.J. Olguín, Dual purpose microalgae-bacteria-based systems that treat wastewater and produce biodiesel and chemical products within a Biorefinery, Biotechnol Adv 30 (2012) 1031–1046.
https://doi.org/10.1016/j.biotechadv.2012.05.001.

[8] F. Gao, Y.Y. Peng, C. Li, W. Cui, Z.H. Yang, G.M. Zeng, Coupled nutrient removal from secondary effluent and algal biomass production in membrane photobioreactor (MPBR): Effect of HRT and long-term operation, Chemical Engineering Journal 335 (2018) 169–175.
https://doi.org/10.1016/j.cej.2017.10.151.

[9] A.M. Rada-Ariza, D. Fredy, C.M. Lopez-Vazquez, N.P. Van der Steen, P.N.L. Lens, Ammonium removal mechanisms in a microalgal-bacterial sequencing-batch photobioreactor at different solids retention times, Algal Res 39 (2019) 101468.
https://doi.org/10.1016/j.algal.2019.101468.

[10] V. Díaz, P. Maza-Márquez, L. Antiñolo, J.M. Poyatos, J. Martín-Pascual, M. del M. Muñio, Effect of urban wastewater ratio in the influent of a membrane photobioreactor for microalgae cultivation and nutrient removal, J Environ Chem Eng 12 (2024).
https://doi.org/10.1016/j.jece.2024.112527.

[11] J. Arango, C. Contreras, F. Crouchett-Catalán, J.P. Gallardo, A. Donoso-Bravo, R. Muñoz, Y. Lesty, D. Olivares, D. Jeison, The potential of membrane-assisted microalgae-bacteria consortia for the treatment of real municipal sewage, Journal of Water Process Engineering 56 (2023).
https://doi.org/10.1016/j.jwpe.2023.104527.

[12] J. Yang, Y. Gou, F. Fang, J. Guo, L. Lu, Y. Zhou, H. Ma, Potential of wastewater treatment using a concentrated and suspended algal-bacterial consortium in a photo membrane bioreactor, Chemical Engineering Journal 335 (2018) 154–160.
https://doi.org/10.1016/j.cej.2017.10.149.
[13] A. Morillas-España, A. Sánchez-Zurano, T. Lafarga, M. del Mar Morales-Amaral, C. Gómez-Serrano, F.G. Acién-Fernández, C.V. González-López, Improvement of wastewater treatment capacity using the microalga Scenedesmus sp. and membrane bioreactors, Algal Res 60 (2021). https://doi.org/10.1016/j.algal.2021.102516.

[14] P. Foladori, S. Petrini, G. Andreottola, How suspended solids concentration affects nitrification rate in microalgal-bacterial photobioreactors without external aeration, Heliyon 6 (2020) e03088. https://doi.org/10.1016/j.heliyon.2019.e03088.

[15] S. Aparicio, L. Borrás-Falomir, A. Jiménez-Benítez, A. Seco, Á. Robles, Urban wastewater treatment at ambient conditions using microalgae-bacteria consortia in a membrane high-rate algal pond (MHRAP): The effect of hydraulic retention time and influent strength, Environ Technol Innov 36 (2024). https://doi.org/10.1016/j.eti.2024.103846.

[16] F. Crouchett-Catalán, J. Arango, O. Bernard, C. Martínez, F. Casagli, D. Jeison, M-ALBA: A modelling framework to guide the optimization of membrane-assisted algae-bacteria systems, Science of the Total Environment 971 (2025). https://doi.org/10.1016/j.scitotenv.2025.179061.

[17] J. García, B.F. Green, T. Lundquist, R. Mujeriego, M. Hernández-Mariné, W.J. Oswald, Long term diurnal variations in contaminant removal in high rate ponds treating urban wastewater, Bioresour Technol 97 (2006) 1709–1715. https://doi.org/10.1016/j.biortech.2005.07.019.

[18] S. Petrini, P. Foladori, G. Andreottola, Laboratory-scale investigation on the role of microalgae towards a sustainable treatment of real municipal wastewater, Water Science and Technology 78 (2018) 1726–1732. https://doi.org/10.2166/wst.2018.453.

[19] APHA, AWWA, WEF, Standard Methods for the Examination of Water and Wastewater, centinial addition, 2005. https://doi.org/10.1111/j.1467-9922.2010.00586.x.

[20] L.T. Arashiro, A.M. Rada-Ariza, M. Wang, P. van der Steen, S.J. Ergas, Modelling shortcut nitrogen removal from wastewater using an algal bacterial consortium, (2017) 1–12. https://doi.org/10.2166/wst.2016.561.

[21] N.C. Boelee, H. Temmink, M. Janssen, C.J.N. Buisman, R.H. Wijffels, Nitrogen and phosphorus removal from municipal wastewater effluent using microalgal biofilms, Water Res 45 (2011) 5925–5933. https://doi.org/10.1016/j.watres.2011.08.044.

[22] A. Ewri, American Society of Civil Engineers, 1893. https://doi.org/10.1038/scientificamerican01281893-50.
[23] G. Liu, J. Wang, Probing the stoichiometry of the nitrification process using the respirometric approach, Water Res 46 (2012) 5954–5962.
https://doi.org/10.1016/j.watres.2012.08.006.

[24] G.A. Ekama, M.C. Wentzel, Organic Material Removal, 2008.

[25] G.A. Ekama, M.C. Wentzel, M.C.M. van Loosdrecht, Chapter 5: Nitrogen removal, 2020.

[26] J. González-Camejo, S. Aparicio, M. Pachés, L. Borrás, A. Seco, Comprehensive assessment of the microalgae-nitrifying bacteria competition in microalgae-based wastewater treatment systems: Relevant factors, evaluation methods and control strategies, Algal Res 61 (2022). https://doi.org/10.1016/j.algal.2021.102563.

[27] P. Foladori, S. Petrini, L. Bruni, G. Andreottola, Bacteria and photosynthetic cells in a photobioreactor treating real municipal wastewater: Analysis and quantification using flow cytometry, Algal Res 50 (2020) 101969. https://doi.org/10.1016/j.algal.2020.101969.

[28] J.B.K. Park, R.J. Craggs, Nutrient removal in wastewater treatment high rate algal ponds with carbon dioxide addition, Water Science and Technology, 2011.
https://doi.org/10.2166/wst.2011.114.

[29] J.B.K. Park, R.J. Craggs, Wastewater treatment and algal production in high rate algal ponds with carbon dioxide addition, Water Science and Technology 61 (2010) 633–639.
https://doi.org/10.2166/wst.2010.951.

[30] J.B.K. Park, R.J. Craggs, Algal production in wastewater treatment high rate algal ponds for potential biofuel use, Water Science and Technology 63 (2011) 2403–2410.
https://doi.org/10.2166/wst.2011.200.

[31] S. Pawar, Effectiveness mapping of open raceway pond and tubular photobioreactors for sustainable production of microalgae biofuel, Renewable and Sustainable Energy Reviews 62 (2016) 640–653. https://doi.org/10.1016/j.rser.2016.04.074.

[32] M. Zhang, K.T. Leung, H. Lin, B. Liao, Effects of solids retention time on the biological performance of a novel microalgal-bacterial membrane photobioreactor for industrial wastewater treatment, J Environ Chem Eng 9 (2021) 105500.
https://doi.org/10.1016/j.jece.2021.105500.

[33] D. Dionisi, Biological wastewater treatment processes: Mass and heat balances, 2017.
https://doi.org/10.1201/9781315163345.
[34] M. Wang, H. Yang, S.J. Ergas, P. van der Steen, A novel shortcut nitrogen removal process using an algal-bacterial consortium in a photo-sequencing batch reactor (PSBR), Water Res 87 (2015) 38–48. https://doi.org/10.1016/j.watres.2015.09.016.

[35] L. Sun, W. Zuo, Y. Tian, J. Zhang, J. Liu, N. Sun, J. Li, Performance and microbial community analysis of an algal-activated sludge symbiotic system: Effect of activated sludge concentration, J Environ Sci (China) 76 (2019) 121–132.
https://doi.org/10.1016/j.jes.2018.04.010.

[36] Y. Luo, P. Le-clech, R.K. Henderson, Assessment of membrane photobioreactor (MPBR) performance parameters and operating conditions, Water Res 138 (2018) 169–180.
https://doi.org/10.1016/j.watres.2018.03.050.

[37] P. Praveen, W. Xiao, B. Lamba, K. Loh, Low-retention operation to enhance biomass productivity in an algal membrane photobioreactor, Algal Res 40 (2019) 101487.
https://doi.org/10.1016/j.algal.2019.101487.

[38] G.T. Peniuk, P.J. Schnurr, D.G. Allen, Identification and quantification of suspended algae and bacteria populations using flow cytometry: applications for algae biofuel and biochemical growth systems, J Appl Phycol 28 (2016) 95–104.
https://doi.org/10.1007/s10811-015-0569-6.

[39] R. Honda, Y. Teraoka, M. Noguchi, S. Yang, Optimization of hydraulic retention time and biomass concentration in microalgae biomass production from treated sewage with a membrane photobioreactor, J Water Environ Technol 15 (2017) 1–11.
https://doi.org/10.2965/jwet.15-085.

[40] Y. Hu, X. Hao, M. van Loosdrecht, H. Chen, Enrichment of highly settleable microalgal consortia in mixed cultures for effluent polishing and low-cost biomass production, Water Res 125 (2017) 11–22. https://doi.org/10.1016/j.watres.2017.08.034.

[41] M. Mantovani, F. Marazzi, R. Fornaroli, M. Bellucci, E. Ficara, V. Mezzanotte, Outdoor pilot-scale raceway as a microalgae-bacteria sidestream treatment in a WWTP, Science of the Total Environment 710 (2020). https://doi.org/10.1016/j.scitotenv.2019.135583.

[42] K.A. Safi, J.B.K. Park, R.J. Craggs, Partitioning of wastewater treatment high rate algal pond biomass and particulate carbon, Algal Res 19 (2016) 77–85.
https://doi.org/10.1016/j.algal.2016.07.017.
[43] Y. Liu, J.-H. Tay, Strategy for minimization of excess sludge production from the activated sludge process, Biotechnol Adv 19 (2001) 97-107.
https://doi.org/https://doi.org/10.1016/S0734-9750(00)00066-5.

[44] M. Barceló-Villalobos, J.L. Guzmán Sánchez, I. Martín Cara, J.A. Sánchez Molina, F.G. Acién Fernández, Analysis of mass transfer capacity in raceway reactors, Algal Res 35 (2018) 91–97.
https://doi.org/10.1016/j.algal.2018.08.017.

[45] J.M. Valigore, P.A. Gostomski, D.G. Wareham, A.D. O'Sullivan, Effects of Hydraulic and Solids Retention Times on Productivity and Settleability of Microbial (Microalgal-Bacterial) Biomass Grown on Primary Treated Wastewater as a Biofuel Feedstock, Water Res. 46 (2012) 2957.

[46] Á. Robles, G. Capson-Tojo, A. Gales, A. Viruela, B. Sialve, A. Seco, J.P. Steyer, J. Ferrer, Performance of a membrane-coupled high-rate algal pond for urban wastewater treatment at demonstration scale, Bioresour Technol 301 (2020) 122672.
https://doi.org/10.1016/j.biortech.2019.122672.

[47] C. Zhang, X. Geng, H. Wang, L. Zhou, B. Wang, Emission factor for atmospheric ammonia from a typical municipal wastewater treatment plant in South China, Environmental Pollution (2017). https://doi.org/10.1016/j.envpol.2016.10.082.

[48] L.M. Trebuch, B.O. Oyserman, M. Janssen, R.H. Wijffels, L.E.M. Vet, T. V Fernandes, Impact of hydraulic retention time on community assembly and function of photogranules for wastewater treatment, Water Res 173 (2020) 115506.
https://doi.org/10.1016/j.watres.2020.115506.

[49] A. Anbalagan, S. Schwede, C.F. Lindberg, E. Nehrenheim, Influence of hydraulic retention time on indigenous microalgae and activated sludge process, Water Res 91 (2016) 277–284.
https://doi.org/10.1016/j.watres.2016.01.027.

[50] J. García, R. Mujeriego, M. Hernández-Mariné, High rate algal pond operating strategies for urban wastewater nitrogen removal, J Appl Phycol 12 (2000) 331–339.
https://doi.org/10.1023/A:1008146421368.

[51] B.T. Dang, X.T. Bui, T.T. Nguyen, H.H. Ngo, L.D. Nghiem, K.P.H. Huynh, T.K.Q. Vo, T.D.H. Vo, C. Lin, S.S. Chen, Effect of biomass retention time on performance and fouling of a stirred membrane photobioreactor, Science of the Total Environment 864 (2023) 161047.
https://doi.org/10.1016/j.scitotenv.2022.161047.
[52] K. Hibiya, A. Terada, S. Tsuneda, A. Hirata, Simultaneous nitrification and denitrification by controlling vertical and horizontal microenvironment in a membrane-aerated biofilm reactor, J Biotechnol (2003). https://doi.org/10.1016/S0168-1656(02)00227-4.

[53] P. van der Steen, K. Rahsilawati, A.M. Rada-Ariza, C.M. Lopez-Vazquez, P.N.L. Lens, A new photo-activated sludge system for nitrification by an algal-bacterial consortium in a photo-bioreactor with biomass recycle, (2015) 443-450. https://doi.org/10.2166/wst.2015.205.

[54] B. Zhang, P.N.L. Lens, W. Shi, R. Zhang, Z. Zhang, Y. Guo, X. Bao, F. Cui, Enhancement of aerobic granulation and nutrient removal by an algal-bacterial consortium in a lab-scale photobioreactor, Chemical Engineering Journal 334 (2018) 2373-2382.
https://doi.org/10.1016/j.cej.2017.11.151.

[55] C.C. Tang, W. Zuo, Y. Tian, N. Sun, Z.W. Wang, J. Zhang, Effect of aeration rate on performance and stability of algal-bacterial symbiosis system to treat domestic wastewater in sequencing batch reactors, Bioresour Technol 222 (2016) 156-164.
https://doi.org/10.1016/j.biortech.2016.09.123.

[56] A. Solmaz, I. Mustafa, Effect of Sludge Retention Time on Biomass Production and Nutrient Removal at an Algal Membrane Photobioreactor, Bioenergy Res (2019).

[57] E. Segredo-Morales, E. González, C. González-Martín, L. Vera, Secondary wastewater effluent treatment by microalgal-bacterial membrane photobioreactor at long solid retention times, Journal of Water Process Engineering 49 (2022) 0-11.
https://doi.org/10.1016/j.jwpe.2022.103200.

[58] A.L.K. Sheng, M.R. Bilad, N.B. Osman, N. Arahman, Sequencing batch membrane photobioreactor for real secondary effluent polishing using native microalgae: Process performance and full-scale projection, J Clean Prod 168 (2017) 708-715.
https://doi.org/10.1016/j.jclepro.2017.09.083.

[59] M. Xu, P. Li, T. Tang, Z. Hu, Roles of SRT and HRT of an Algal Membrane Bioreactor System with a Tanks-in-Series Configuration for Secondary Wastewater Effluent Polishing, Ecol. Eng. 85 (2015) 257.

[60] J. Yang, Y. Gou, F. Fang, J. Guo, H. Ma, X. Wei, B. Shahmoradi, Impacts of sludge retention time on the performance of an algal-bacterial bioreactor, Chemical Engineering Journal 343 (2018) 37-43. https://doi.org/10.1016/j.cej.2018.02.118.

[61] Y. Nurdogan, W.J. Oswald, Enhanced nutrient removal in high-rate ponds, Water Science and Technology 31 (1995) 33-43. https://doi.org/10.1016/0273-1223(95)00490-E.
[62] G.I. Romero Villegas, M. Fiamengo, F.G. Acién Fernández, E. Molina Grima, Outdoor production of microalgae biomass at pilot-scale in seawater using centrate as the nutrient source, Algal Res 25 (2017) 538–548. https://doi.org/10.1016/j.algal.2017.06.016.

[63] S. Zhu, L. Qin, P. Feng, C. Shang, Z. Wang, Z. Yuan, Treatment of low C/N ratio wastewater and biomass production using co-culture of Chlorella Vulgaris and activated sludge in a batch photobioreactor, Bioresour Technol 274 (2019) 313–320. https://doi.org/10.1016/j.biortech.2018.10.034.

[64] H.E.L. Halouani, B. Picot, J. Bontoux, E. Bataluon, Decrease of phosphate concentration in high rate pond by precipitation of calcium phosphate: theoretical and experimental results, 26 (1992) 1445–1450.

[65] A. Fallahi, F. Rezvani, H. Asgharnejad, E. Khorshidi, N. Hajinajaf, B. Higgins, Interactions of microalgae-bacteria consortia for nutrient removal from wastewater: A review, Chemosphere 272 (2021). https://doi.org/10.1016/j.chemosphere.2021.129878.

[66] S. Rossi, M. Mantovani, F. Marazzi, M. Bellucci, F. Casagli, V. Mezzanotte, E. Ficara, Microalgal cultivation on digestate: Process efficiency and economics, Chemical Engineering Journal 460 (2023). https://doi.org/10.1016/j.cej.2023.141753.

[67] M.L.F. Nobre, D. Tavares, C. Fraga, B. Oliveira, M. Dias, S. Mesquita, C.M. Oliveira, J.C.M. Pires, Techno-economic analysis of a circular microalgal approach for enhanced wastewater treatment and resource recovery in Northern Portugal, J Clean Prod 434 (2024). https://doi.org/10.1016/j.jclepro.2023.140389.