A bibliometric analysis of published literature on membrane photobioreactors for wastewater treatment from 2000 to 2022

In recent decades, the industrial sector has sought more sustainable processes with lower energy consumption, and the wastewater treatment sector is no exception. In addition, industrial and urban wastewater with high nutrient concentrations represent serious environmental risks such as eutrophication if not properly treated (Liu & Hong 2021). Traditional wastewater treatment systems such as activated sludge, or advanced treatments such as membrane bioreactors, which use micro and/or ultrafiltration membranes, require high amounts of energy for nutrient removal (Asano et al. 2007). Therefore, in recent years, less energy-intensive processes have been developed. Among them, the technology of membrane photobioreactors (MPBR) stands out, whose suspension is fully or partially illuminated, allowing the growth of microalgae in the suspension (Fallahi et al. 2021). Microalgae are a group of simple uni- or multicellular photosynthetic microorganisms of eukaryotic and prokaryotic cells (Wang et al. 2014) that possess qualities such as the ability to reproduce rapidly, adapt adequately to the environment and, above all, have the ability to convert nutrients present in a medium into biomass using solar energy due to their high photosynthetic efficiency (Liu & Hong 2021). It has been widely demonstrated that microalgae use the nutrients present in all types of wastewater to grow efficiently (Li et al. 2019).

A membrane photobioreactor (MPBR) is a system that operates continuously and that combines a photobioreactor with an integrated or separate system that uses a micro or ultrafiltration membrane for the separation of the produced biomass (Luo et al. 2017). In recent years, new MPBR have been designed using forward osmosis and ion exchange membranes (Zhao et al. 2023).

These MPBR arise as a solution to the low sedimentation capacity of biomass and its resulting loss, as well as the limitations of conventional photobioreactors (Luo et al. 2017). They are designed to maximise access to natural or artificial light and provide the necessary conditions for algae growth (Alcántara et al. 2015). Some authors consider this type of photobioreactors as hybrid photobioreactors (Vo et al. 2019; Sirohi et al. 2022), since they combine a photobioreactor with a flat panel system or an airlift type column inside which a microfiltration or ultrafiltration membrane can be inserted (Kumar et al. 2020). As reported in the literature, the application of membrane photobioreactor technology to water treatment is carried out in closed or semi-closed reactors where the membrane allows the complete retention of the biomass, obtaining a high-quality permeate. As in the case of conventional membrane bioreactors, the main advantage of the technology is the ability to decouple the hydraulic residence time from the solids residence time, which allows process optimisation (Luo et al. 2017). In addition, minimising the fouling of the membrane during the filtration process is key to achieving proper operation, as insufficient control can lead to a substantial increase in operating costs and energy consumption (Azizi et al. 2021b).

The use of MPBR for wastewater treatment is a recent technology with many avenues for study, both in terms of optimal operating parameters for the performance of the process and in terms of species suitable for obtaining products of a certain value.

The analysis of operational parameters of an emerging technology as well as possible future trends is complex since it is difficult to reach a macro perspective point of view. Bibliometric studies are a powerful tool to analyse scientific advances qualitatively and quantitatively in a particular area (Wallin 2005). According to Pritchard (1969), bibliometric studies are mathematical analyses through statistical analysis of the scientific production and who generates this literature. Bibliometric studies make it possible to evaluate the interest of the scientific community in a specific field by means of statistical and mathematical methods on the number of scientific contributions (articles, books, conference papers, or patents), references, and use of keywords (Mao et al. 2021). Bibliometric studies can be divided into two categories: the first focuses on scientific production and the second on the interaction relations of the different authors, understood as the interrelationships between different countries, institutions, and keyword usage. In the first category, the analysis of the global, institutional, or individual scientific production facilitates the increase of knowledge and information on a field of study, favouring the analysis of future trends. On the other hand, the second category allows for the identification of the degree of interest in each region, as well as the interrelationships between the different research groups. The analysis of keywords and their concurrence is crucial to establish the scientific community's perspective quickly and effectively on a particular field (Ramos-Rodríguez & Ruíz-Navarro 2004; de Battisti & Salini 2013). Many authors are including bibliometric analyses to analyse the current status of many water treatment technologies, e.g., industrial wastewater treatment (Mao et al. 2021), sulphate removal from wastewater (Ding & Zeng 2022), municipal wastewater treatment (Marcal et al. 2021), desalination treatments (Zapata-Sierra et al. 2022), and zero brine discharge (Díaz et al. 2022). However, there is no such document on an emerging technology such as MPBR for wastewater treatment. The bibliometric study allows the identification of technological trends and advances in membrane bioreactors. In addition, it allows to analyse and compare different operating parameters to determine the optimum operating conditions for a developing technology. A comprehensive view of the technology is provided by bibliometric studies such as the one presented in this manuscript, which allows us to analyse the blueprint in the field of membrane photobioreactor.

The aim of this paper is to highlight the interest that the membrane photobioreactor has aroused in authors in recent years by carrying out a bibliometric analysis which presents a detailed analysis of the current state of the art of the technology, focusing on the operating parameters studied for the optimisation of the process and presenting possible trends for future research.

The bibliometric review has been conducted using the Scopus database (www.scopus.com), which has more than 25,000 titles from over 5,000 international publishers and a total of 77.8 million publications (Elsevier 2020). It covers diverse fields such as medicine, arts, humanities, technology, and science and has digitised records since 1970.

The search was carried out at the end of April 2023 and publications have been found dating back to the year 2000. The choice of search terms is key for the data to be truly representative of the study being carried out. In this case, it was decided to take as representative those publications in which the terms ‘Photobioreactor’, ‘Membrane’ and ‘Wastewater’ appear in the title, abstract or keywords. Table 1 shows the search equation used.

The impacts of the publications analysed were performed using the Scimago Journal and Country Rank (SJR) and CiteScore from Scopus. Microsoft Excel was used to analyse the data obtained after the search. The world scientific production was represented by using the mapping tool provided by Microsoft Excel, which uses a coloured scale to identify the most productive countries. In this case, the grey colour suggests zero production and the increase in the intensity of the blue colour indicates a higher production. Vosviewer software (version 1.6.17) was used to obtain the figures relating to co-occurrence of the keywords. The software selected is a tool that allows the construction and visualisation of bibliometric networks in the field under study. In this study, a representation by nodes is used. The size of the node is proportional to the use of the keyword and the thickness of the lines of connection between nodes specifies the strength of concurrence of the keywords. In addition, the colour scale allows us to analyse the temporal evolution of the use of the keywords, since the programme indicates the temporality of the highest number of co-occurrences.

A total of 135 publications were found, of which 1 was discarded because, after analysis, it was not related to the subject, 8 because their year of publication was 2023 for not being representative (at the time of the search the year had not ended). Therefore, 126 publications were analysed relating to the application of MPBR to wastewater treatment.

Of the 126 publications found, the majority are scientific articles, nearly 87%, followed by communications to congresses, which account for 6% of the publications, with book chapters and conference reviews being a very small minority. Zapata-Sierra et al. (2022) indicate that the analysis of the type of publication can give an idea of the level of maturity of a topic among authors. Thus, a high degree of books and reviews among the publications can indicate a high degree of maturity, while a high percentage of conference presentations indicates a new and emerging technology. The results, therefore, justify that MPBR are growth and maturity phase (Zapata-Sierra et al. 2022), due to the high number of scientific articles. Another aspect to analyse the interest of the scientific community in MPBR is to analyse the percentage of results in this field compared to the number of papers published on photobioreactors. A search in SCOPUS using only the keywords ‘photobioreactor’ and ‘wastewater’ with the same temporal limitation (2000–2022) shows a total of 848 documents. Therefore, about 15% of the publications on photobioreactors relate to the use of MPBR. The percentage may seem low, but it must be considered that photobioreactors are a process without a consensus among the scientific community and therefore present a high heterogeneity of design and technological proposals (Sirohi et al. 2022). On the other hand, achieving the separation of algae from treated water necessitates a well-coordinated integration of a biological reactor and a separation system. In addition to MPBR, numerous alternative separation processes exist, including microalgae biofilm; microalgae-fungus co-culture, microalgae-activated sludge co-culture, and microalgae auto-flocculation (Huang et al. 2023). The main advantage of membrane photobioreactor processes is the total separation of the suspension from the treated wastewater, which represents a great benefit and an improvement in treatment efficiency.

Table 2 shows the time evolution of the documents displayed by the database and the ratio of authors per document. The data show a growing interest of the scientific community in the subject of MPBR. Between the years 2018 and 2021, the number of publications reached a peak. However, in the year 2022, a peak was observed reaching 26 publications, a number higher than the number found in the whole of the years 2021 and 2020. It is worth noting that there was no appreciable interest in the subject until 2011, when at least four publications per year can be found. The ratio of number of authors per paper allows an estimation of the interest of the scientific community, as it gives an idea of the number of researchers interested in the topic under analysis. However, no clear trend can be seen in the number of authors per document, remaining, over all the years at values of between 4 and 6 authors per document, with an average value of 5.15 ± 1.5, with a maximum value of 9.25 in 2014 and a minimum value of 2.67 in 2015.

Additionally, most of the documents have been published in English and only one document has been published in another language, namely French (Malériat et al. 2000).

Environmental Sciences stand out from the rest of the areas, with a steady upward trend over the years studied, accounting for 33% of the publications found. Next are the areas of Chemical Engineering and Energy, with 18 and 15%, respectively, followed by Engineering, with 10%, and Chemistry and Biochemistry, Genetics and Molecular Biology with 6% each, and Agricultural and Biological Sciences, with 5%. Publications in the remaining areas accounted for only 7% of the total number.

Within each of the areas, the most cited articles are shown in Table 3. It should be noted that for the areas of Energy and Chemical Engineering, the most cited article is the same and three of them focus on nutrient removal by means of a membrane photobioreactor (Ruiz-Martinez et al. 2012; Gao et al. 2016a; Praveen et al. 2018).

One of the most decisive aspects when carrying out a bibliometric analysis is the determination of the main publication sources of the authors. Their identification helps to analyse those sources, journals, books, publishers, etc., in which to carry out the search prior to initiating research or, alternatively, possible avenues of publication of future studies. In this case, we have analysed those sources with more than four publications on the subject, obtaining the results shown in Table 4. All sources analysed with more than four documents are journals.

In 2011, the first two publications appeared in the five journals with the most publications, the first journal being Bioresource Technology. From this year on, this journal has been a constant source of publications on the subject, and there has been only a single year in which no articles have been published on the subject. The remaining journals, on the other hand, have shown irregular behaviour, with years without publications on the subject.

The five most cited publications on MPBR applied to wastewater treatment are summarised in Table 5. All of them have more than 100 citations each, and only 2 coincide with the most cited publications by area of knowledge shown in Table 3.

Among the most cited articles, two of them are experimental research articles. Specifically, the most cited article deals with the treatment of water from aquaculture by means of a membrane photobioreactor using a pure culture of Chlorella vulgaris and Scenedesmus obliquus (Gao et al. 2016a). C. vulgaris showed better performance and was continuously cultivated in MPBR. The significant impact of this study lies in its pioneering demonstration of the remarkable nutrient removal capacity (86.1% for TN and 82.7% for TP) when applied to low-strength wastewater. Notably, the ammonium concentration in the effluent was effectively lowered to levels below 0.002 mg/L. Additionally, the research highlighted the feasibility of achieving substantial biomass productivity (42.6 mg/L day) at low hydraulic residence time (1 day). This capability to operate at low hydraulic retention times (HRTs) has served as a foundation for numerous subsequent studies that have adopted these values (Praveen et al. 2016; Novoa et al. 2020; Peng et al. 2020; Solmaz & Işık 2020). In the third most cited article, the authors evaluate the efficiency of a laboratory-scale membrane photobioreactor for the treatment of an effluent from a submerged anaerobic membrane bioreactor (SAnMBR). They found that this effluent is suitable for the growth of microalgae and where the removal of more than 67% of the ammonium present and about 98% of the phosphorus as phosphate is achieved by applying a solid retention time (SRT) of 2 days and operating semi-continuously for 42 days (Ruiz-Martinez et al. 2012). The principal contribution of this investigation resided in presenting a tangible proof of concept, wherein a mixed polyculture of indigenous species exhibited robust growth and proved to be efficacious in nutrient removal from real wastewater. Subsequent studies have emphasised this crucial finding (Marbelia et al. 2014; Xu et al. 2014; Praveen et al. 2019; Peng et al. 2020).

The other most cited publications are reviews, ranking in the top five in second, fourth, and fifth positions. The second most cited article focuses on the analysis of the conditions affecting the operability of a membrane photobioreactor, the characteristics of the wastewater, biological aspects such as the establishment of algae-bacteria consortia, etc., and how these aspects affect the nutrient removal capacity of the system. In addition, the economic aspects of the process are also studied in comparison with conventional wastewater treatment processes. This study also includes an introduction to the main methods for biomass harvesting and critically describes future trends in this field of study (Acién et al. 2016). The fourth most cited article analyses extensively the operational variables that affect the growth of biomass in MPBR, nutrient removal performance, etc. Among the multiple variables, they analyse the effect of pH, hydraulic retention time (HRT), SRT, light, aeration and temperature, as well as considering the interrelationships between these parameters (Luo et al. 2017).

Singapore and Canada finish the list of the five most productive countries with 11 publications each. The interrelationships between countries in publications can give an idea of the degree of collaboration that exists. Table 6 shows, by country, the top five countries with which they have collaborated, in order of highest to lowest interrelationships. The number of documents on which they have collaborated is indicated in brackets.

Table 6 shows that Spain, despite being the second largest producer of publications on the subject, has only collaborated with Italy. By contrast, countries with lower production have a fairly high level of collaboration, for example, Australia and India.

In third place is the National University of Singapore, which accounted for 100% of Singapore's publications. The 30 publications from China are divided between the Ministry of Education of China, Zhejiang Ocean University, Zhejiang Normal University, and Chongqing University, mostly. However, the country with the most institutions in the top 20 most productive institutions after China is Iran with four, namely Shahid Sadoughi University of Medical Science, Shiraz University of Medical Sciences, Tehran Univesity of Medical Sciences, Islamic Azad University, with three publications each. However, an analysis of these publications shows that they are the same ones for all the centres, i.e., all three publications are collaborations between these five research centres. This corroborates what is shown in Table 6, which shows that Iran has little collaboration with other countries.

As can be seen, the co-occurrence of keywords used by authors indicates that the use of MPBR focuses on nutrient removal in wastewater treatment. Forward osmosis is also beginning to appear as a keyword because recent studies have used this technology to concentrate secondary treatment effluents as a step prior to the implementation of a membrane photobioreactor, effectively removing N- and P- (Wang et al. 2020) or as a next step after the operation of a photobioreactor that allows for improved nutrient removal with minimal energy consumption (Praveen & Loh 2019). Membrane fouling also appears as a keyword that has been used by the authors in recent years, demonstrating the interest of the scientific community by appearing in 12 articles in the last 10 years. Authors’ efforts in this regard have focused on analysing the causes of membrane fouling, identifying the best operating conditions that reduce the deposition of materials on the membrane surface. For example, Lee et al. (2018) attribute the causes of fouling to the presence of soluble microbial products (SMP) and/or extracellular polymeric substances (EPS) and attempt to mitigate the presence of these substances by varying the HRT. Others have analysed the effect on fouling of other key parameters, such as exposure to light (Keramati et al. 2021) or nutrient loading of the influent (Zhang et al. 2021). The control of membrane fouling is a fundamental aspect as it affects the operability of the process because high fouling leads to a reduction in productivity, and an increase in the frequency of membrane cleaning and energy consumption (Liao et al. 2018).

This section analyses 50 articles, considered to be the most representative of the research trends developed in the last 10 years (Tables 7 and 8). Three primary research directions have been discerned in the field of investigation: (1) microalgae species and wastewater characteristics, (2) enhancement of operational parameters for optimisation, and (3) examination of process configuration.

Table 7

Main operating parameters analysed in membrane photobioreactors

ID .	Processa .	Feedwaterb .	Cc (mg/L) .	TN (mg/L) .	TP (mg/L) .	Microbial species .	Scaled .	Membranee .	Area (m2) .	Flux (L/m2 h) .	Light .	Aeration .	Temp (°C) .	pH .
Gao et al. (2016a)	MPBR	RIW	BOD5 (8.5)	6.8	0.4	Chlorella vulgaris	L	PVDF, HF, 0.1 μm	0.05	NA	9,000 lux	0.5 L/min, 99.9% CO2	25	6.8–7.2
Tan et al. (2014)	A-MPBR	RISEW	COD (702–1,026)	240.3–382.7	22.7–40.2	Chlorella vulgaris	P	DM	0.34	520–57	Outdoor	15–20 L/min, 5–9% CO2	summer (35–39); winter (7–12); spring (17–22); autumn (23–27)	> 9.25
Marbelia et al. (2014)	MPBR	SSEW	–	7.5–22.1	1.7–2.2	Chlorella vulgaris	L	PE, FS	4 × 0.016	2.6–13	72 W	5 L/day	15–22	8–9
Chitapornpan et al. (2013)	PAnMBR	RIW	COD (700–9,750)	1.0–35.0	NA	Native purple phototrophic bacteria	L	PE, HF, 0.4 μm	0.04	NA	120 W, IR-2,880 transmitting filter (270 W/m2)	–	28–39	6–8
Praveen et al. (2018)	MPBR	SSEW	COD (124–190)	N- (25–100); N- (5–51)	10–25	Chlorella vulgaris	L	PVDF, HF, 0.1 μm	0.0308	1.33	8,000 lux	1 L/min, 3% CO2	24–26	NA
Singh & Thomas (2012)	MPBR	RSEW	COD (30–50)	(50–80); (18–25)	(10–20)	Chlorella vulgaris	L	PES, 0.45 μm	0.1	12	12 h/12 h (4,000 lux)	4 L/min	24	7.2–7.5
Gao et al. (2016b)	MPBR	RSEW	COD (39.9)	13.3	0.8	Chlorella vulgaris	L	PVDF, HF, 0.1 μm	0.05	NA	120 μmol/m2 s	0.5 L/min, 4% CO2	25–30	6.5–7.8
Praveen & Loh (2016)	O-MPBR	SSEW, STEW	–	8.8–22.8	2.4–6.0	Chlorella vulgaris	L	FO (TFC)	0.036	1.28–1.59	1,000–1,500 lux	0.4 vvm, 5% CO2	NA	NA
Zhen-Feng et al. (2011)	MPBR	SISEW	–	8.0–22.0	0.2–1.2	Scenedesmus sp. LX1	P	HF, 0.2 μm	10	NA	6,000 lux	10 L/min	NA	7.7–8.5
Praveen et al. (2016)	O-MPBR, MPBR	SSEW	–	4.35	1.8	Chlorella vulgaris	L	FO (TFC), MF (PVDF)	FO (0.036), MF (0.036)	FO (3.18–6.37); MF (3.18–6.37)	1,500–2,000 lux	0.4 vvm, 5% CO2	NA	7.0–7.5
Sheng et al. (2017)	SB-MPBR	RSEW	–	24.7	3.5	Native microalgae (Euglena sp.)	L	PVDF, FS, 0.16 μm	0.012	NA	10,000 lux	1.8 L/min	NA	NA
Peng et al. (2020)	B-MPBR	RSEW	COD (7.8)	9.1	4.6	Chlorella vulgaris	L	PVDF, HF, 0.1 μm	0.05	0.4	102–112 μmol/m2 s	0.5 L/min, CO2	26	NA
Shi et al. (2018)	MPBR	SSALW	TOC (58.6)	NA	NA	Chlorella sp.	L	Ceramic, FS, 0.1 μm	0.045	3.3	2,000 Lux	0.5 L/min	25	NA
Boonchai & Seo (2015)	MPBR	RSEW	COD (10.5)	18.8	1.0	Chlorella sp. ADE4; Chlorella vulgaris	L	HDPE, HF, 0.4 μm	0.04	3.75	14 h/10 h (50 μmol/m2 s)	4 L/min	25	7.5–8.5
Luo et al. (2018)	MB-MPBR	SSEW	DOC (9.0)	14.1	2.5	Microalgal-bacterial consortia (dominated by Chlorella vulgaris)	L	PVDF, HF, 0.04 μm	NA	4	85 μmol/m2 s	2 L/min	NA	NA
González-Camejo et al. (2020b)	MB-MPBR	RSEW	COD (63)	48.8	4.4	Native microalgae-bacteria consortia (dominated by Chlorella sp.)	P	PVDF, HF, 0.03 μm	3.4	NA	Outdoor + artificial LED lamps (277–284 μmol/m2 s)	Air, CO2 (pH control)	Outdoor (16.9–18.8)	7.5
Viruela et al. (2018)	MB-MPBR	RSEW	COD (31)	51.3	6.8	Native microalgae-bacteria consortia (dominated by Scenedesmus sp.)	P	PVDF, HF, 0.03 μm	2 × 31	NA	Outdoor (169–378 μE/m2 s)	2 m3/h, 99.9% CO2 (pH control)	Outdoor (23.9–28.7)	7.5
Gao et al. (2019)	MPBR	RSEW	COD (25.6)	12.8	0.6	Chlorella vulgaris; Scenedesmus obliquus	L	PVDF, HF, 0.1 μm	NA	NA	102–112 μmol/m2 s	0.5 L/min, 99.9% CO2 (pH control)	25–28	6.8–7.5
Chitapornpan et al. (2012)	PAnMBR	RIW	COD (700–9,750)	NA	NA	Native purple phototrophic bacteria	L	PE, HF, 0.4 μm	0.04	3.75–7.5	180 W, IR-2880 transmitting filter	–	28–35	7
Lu et al. (2021)	MPBR	SSEW	–	40	5	Chlorella vulgaris	L	HF, 0.01 μm	NA	NA	140 μmol/m2 s	0.5–1.0 L/min, 4% of CO2	28	6.5–7.0
Derakhshan et al. (2018b)	MB-MPBR	RSEW, SSEW	COD (30–100)	NOx (8–9.9)	(12–9.3)	Native microalgae-bacteria consortia	L	PVDF, HF, 0.1 μm	0.043	20	12 h/12 h (8,000 lux)	Air, 0.04% CO2	26	7.0
González et al. (2017)	MB-MPBR	RSEW	COD (124)	46	6	Native microalgae-bacteria consortia	P	PVDF, HF, 0.04 μm	0.9	12	Outdoor (150–224 W/m2)	NA	Outdoor (18.9)	7.7
Derakhshan et al. (2019)	B-MB-MPBR, MB-MPBR	SSEW	COD (30–90)	5	1	Microalgae-bacteria consortia	L	PVDF, HF, 0.1 μm	0.043	20	12 h/12 h (8,500 lux)	Air, 0.04% CO2	33	7.0
Fortunato et al. (2020)	MPBR	SSEW	–	60	10	Chlorella vulgaris	L	PVDF, HF, 0.016 μm	3.7·10–3	20	130 μmol/m2 s	Air 30 L/h	25	7.0–8.0
Choi (2015)	MPBR	RPEW	COD (209.9)	40.0	9.2	Chlorella vulgaris	L	PES, 0.2 μm	0.2	8	16 h/8 h (270–310 μE/m2 s)	Air 0.5 L/min, CO2	25	7.2
Zhang et al. (2020)	MB-MPBR	SMW	COD (440.3)	36.9–46.5	38–9.5	Chlorella Vulgaris-activated sludge	L	PVDF, FS, 0.1 μm	0.03	6.8; 10.6	8,400 lux	Air 3.75 L/min, 0.05% CO2	26.9	7.3
González-Camejo et al. (2020a)	MB-MPBR	RSEW	COD (71)	45.0	4.7	Native microalgae-bacteria consortia (dominated by Chlorella sp.)	P	PVDF, HF, 0.03 μm	2 × 3.4	15–26	Outdoor + artificial LED lamps (281–344 μmol/m2 s)	Air, CO2 (pH control)	Outdoor + cooling system (23.9–25.5)	7.5
Parakh et al. (2020)	MPBR+ Settler	SSEW	-	14	0.65	Graesiella emersoni	L	PVDF, 0.1 μm	0.0588	NA	14 W	1.5 L/min, 5% CO2	NA	6.5–7.5
Honda et al. (2017)	MPBR	SSEW	TOC (5)	15	0.3	Chlorella vulgaris NIES-2170	L	PVDF, HF, 0.1 μm	0.085	NA	20 W/m2	Air, 1% CO2	24	NA
Praveen et al. (2019)	MPBR	SSEW	–	21	6	Scenedesmus obliquus FACHB-417	L	PVDF, FS	0.0308	2.4	130 μmol/m2 s	Air, 3% CO2	NA	6.5–7.5
Noguchi et al. (2017)	MPBR	SADS	TOC (0.66)	85.4	23.1	Chlorella vulgaris NIES-2170	L	HF, 0.1 μm	0.085	NA	24 h/9 h (6 W/m2)	–	21.7	7.0–8.0
Lee et al. (2018)	MPBR	RISEW	TOC (26.5)	41.6	0.7	Botryococcus braunii	L	PVDF, 0.5 μm	2.81·10−3	2.4; 3; 4	220 μE/m2 s	6 L/min	25	NA
González-Camejo et al. (2018)	MB-MPBR	RSEW	COD (72)	40–80	4–10	Native microalgae-bacteria consortia (dominated by Chlorella sp. and Scenedesmus sp.)	P	PVDF, HF, 0.03 μm	2 × 3.4	NA	Outdoor (119–357 μmol/m2 s) +artificial LED lamps (300 μmol/m2 s)	0.09 vvm, CO2 (pH control)	16–28	7.5
Azizi et al. (2021a)	MPBR	SSEW	–	(83)	(9)	Chlorella vulgaris	L	0.45 μm	NA	18	24 h/0 h; 16 h/8 h; 12 h/12 h (100; 300 μmol/m2 s)	Air, 0.04% CO2	25	NA
Derakhshan et al. (2018a)	B-MB-MPBR	RSEW	COD (59)	NOx (11)	(10)	Native microalgae-bacteria consortia	L	PVDF, HF, 0.1 μm	0.043	20	12 h/12 h (100 μmol/m2 s)	4–5 L/min, 0.04% CO2	26	NA
Gao et al. (2021)	MPBR	RSEW	TOC (107)	48.4	4.23	Chlorella pyrenoidosa FACHB-5	L	PVDF, HF, 0.1 μm	0.05	NA	82.4–90.6 μmol/m2 s	40 mL/min, CO2	30	7.3
Novoa et al. (2020)	MPBR	SSEW	–	65	10	Chlorella vulgaris UTEX 259	L	PVDF, HF, 0.018 μm	NA	10	130 μmol/m2 s	30 L/h	25	7.0–8.0
Solmaz & Işık (2019)a, 2019b)	MB-MPBR	RSEW	BOD (12)	5.2	0.6	Native microalgae-bacteria consortia	L	PVDF, HF, 0.45 μm	NA	20	12 h/12 h (6,000 lux)	5 L/min	23	7.5–9.0
Lau et al. (2019)	SB-MPBR	RSEW	NA	20–21	2.25	Staurastrum sp.	L	PVDF, FS, 0.16 μm	0.02	40	1,000 lux	3.5 L/min	20	NA
Wang et al. (2020)	O-MPBR	SSEW	Glucose (50)	NH4Cl (30)	K2HPO4 (21.4) + KH2PO4 (10.6)	Chlorella vulgaris UTEX 395, Scenedesmus sp. HTB1	L	HF	2.3	NA	16 h/8 h (100 μE/m2 s)	20% CO2/N2 (pH control)	20	NA
Solmaz & Işık (2020)	MB-MPBR	SSEW	COD (32.9)	18.0	8.8	Microalgae-bacteria consortia	L	PVDF, HF, 0.45 μm	NA	20	12 h/12 h (6,000 lux)	5 L/min	23	6.4–7.5
Dalaei et al. (2020)	PAnMBR	SPEW	COD (430) + Ethanol (200; 300)	43	6.5	Native purple phototrophic bacteria	L	FS, 0.45 μm	0.12	1.9	IR light (3; 1.4 W/m2)	–	22	7.3
Dalaei et al. (2019)	PAnMBR	SPEW	COD (370) + Ethanol (285; 290; 300; 310; 400)	48–56	6.6–8.2	Native purple phototrophic bacteria	L	FS, 0.45 μm	0.12	1.9	IR light (50 W/m2)	–	22; 11	7.2
Solmaz & Işik (2019)a, 2019b)	MB-MPBR	SSEW	COD (32.9)	18.4	8.8	Microalgae-bacteria consortia	L	PVDF, HF, 0.45 μm	NA	20	12 h/12 h (6,000 lux)	5 L/min	23	6.4–8.2
Keramati et al. (2021)	MPBR	RISEW	COD (67)	(49)	(36)	Chlorella vulgaris	L	FS, 0.4 μm	NA	20	LED flashing light (1 Hz; 1,000 Hz) (200 μmol/m2 s)	7 L/min	NA	7.0–8.0
Lee et al. (2020)	MPBR	SISEW	TOC (530)	183	15	Botryococcus braunii UTEX 2441	L	PVDF, HF, 0.5 um	2.81·10−4	2.4; 3; 4	24 h/0 h; 12/12 h (150 μmol E/m2 s)	NA	30	NA
Wang et al. (2019)	B-MB-MPBR	RIW+ RPEW	SCOD (1066)	156.3	9.7	Native microalgae-bacteria consortia	L	PVDF, HF, 0.04 μm	1	NA	200–250 μmol/m2 s	4 L/min	NA	NA
Zhang et al. (2021)	MB-MPBR	SMW	COD (440.3)	36.9–46.5	3.8–9.5	Chlorella vulgaris-activated sludge	L	PVDF, FS, 0.1 μm	0.03	6.8; 10.6	8,400 lux	3.75 L/min, 0.05% CO2	26.9	7.3
Spennati et al. (2021)	MPBR	RIW	COD (119.3)	NA	NA	Chlorella vulgaris CCAP 211, Arthrospira platensis UTEX 1926	L	NA	NA	NA	60 μmol/m2 s	NA	23–25	6.3–7.9
Cheng et al. (2021)	B-MPBR	SIW	COD (1025)	N-NH3 (98.4)	K2HPO4 (25)	Rhodopuesdomonas pulstris	L	GO/PSF composite	0.05	24.1	300 lux	1.5 m3/m2 h	25	7.5

ID .	Processa .	Feedwaterb .	Cc (mg/L) .	TN (mg/L) .	TP (mg/L) .	Microbial species .	Scaled .	Membranee .	Area (m2) .	Flux (L/m2 h) .	Light .	Aeration .	Temp (°C) .	pH .
Gao et al. (2016a)	MPBR	RIW	BOD5 (8.5)	6.8	0.4	Chlorella vulgaris	L	PVDF, HF, 0.1 μm	0.05	NA	9,000 lux	0.5 L/min, 99.9% CO2	25	6.8–7.2
Tan et al. (2014)	A-MPBR	RISEW	COD (702–1,026)	240.3–382.7	22.7–40.2	Chlorella vulgaris	P	DM	0.34	520–57	Outdoor	15–20 L/min, 5–9% CO2	summer (35–39); winter (7–12); spring (17–22); autumn (23–27)	> 9.25
Marbelia et al. (2014)	MPBR	SSEW	–	7.5–22.1	1.7–2.2	Chlorella vulgaris	L	PE, FS	4 × 0.016	2.6–13	72 W	5 L/day	15–22	8–9
Chitapornpan et al. (2013)	PAnMBR	RIW	COD (700–9,750)	1.0–35.0	NA	Native purple phototrophic bacteria	L	PE, HF, 0.4 μm	0.04	NA	120 W, IR-2,880 transmitting filter (270 W/m2)	–	28–39	6–8
Praveen et al. (2018)	MPBR	SSEW	COD (124–190)	N- (25–100); N- (5–51)	10–25	Chlorella vulgaris	L	PVDF, HF, 0.1 μm	0.0308	1.33	8,000 lux	1 L/min, 3% CO2	24–26	NA
Singh & Thomas (2012)	MPBR	RSEW	COD (30–50)	(50–80); (18–25)	(10–20)	Chlorella vulgaris	L	PES, 0.45 μm	0.1	12	12 h/12 h (4,000 lux)	4 L/min	24	7.2–7.5
Gao et al. (2016b)	MPBR	RSEW	COD (39.9)	13.3	0.8	Chlorella vulgaris	L	PVDF, HF, 0.1 μm	0.05	NA	120 μmol/m2 s	0.5 L/min, 4% CO2	25–30	6.5–7.8
Praveen & Loh (2016)	O-MPBR	SSEW, STEW	–	8.8–22.8	2.4–6.0	Chlorella vulgaris	L	FO (TFC)	0.036	1.28–1.59	1,000–1,500 lux	0.4 vvm, 5% CO2	NA	NA
Zhen-Feng et al. (2011)	MPBR	SISEW	–	8.0–22.0	0.2–1.2	Scenedesmus sp. LX1	P	HF, 0.2 μm	10	NA	6,000 lux	10 L/min	NA	7.7–8.5
Praveen et al. (2016)	O-MPBR, MPBR	SSEW	–	4.35	1.8	Chlorella vulgaris	L	FO (TFC), MF (PVDF)	FO (0.036), MF (0.036)	FO (3.18–6.37); MF (3.18–6.37)	1,500–2,000 lux	0.4 vvm, 5% CO2	NA	7.0–7.5
Sheng et al. (2017)	SB-MPBR	RSEW	–	24.7	3.5	Native microalgae (Euglena sp.)	L	PVDF, FS, 0.16 μm	0.012	NA	10,000 lux	1.8 L/min	NA	NA
Peng et al. (2020)	B-MPBR	RSEW	COD (7.8)	9.1	4.6	Chlorella vulgaris	L	PVDF, HF, 0.1 μm	0.05	0.4	102–112 μmol/m2 s	0.5 L/min, CO2	26	NA
Shi et al. (2018)	MPBR	SSALW	TOC (58.6)	NA	NA	Chlorella sp.	L	Ceramic, FS, 0.1 μm	0.045	3.3	2,000 Lux	0.5 L/min	25	NA
Boonchai & Seo (2015)	MPBR	RSEW	COD (10.5)	18.8	1.0	Chlorella sp. ADE4; Chlorella vulgaris	L	HDPE, HF, 0.4 μm	0.04	3.75	14 h/10 h (50 μmol/m2 s)	4 L/min	25	7.5–8.5
Luo et al. (2018)	MB-MPBR	SSEW	DOC (9.0)	14.1	2.5	Microalgal-bacterial consortia (dominated by Chlorella vulgaris)	L	PVDF, HF, 0.04 μm	NA	4	85 μmol/m2 s	2 L/min	NA	NA
González-Camejo et al. (2020b)	MB-MPBR	RSEW	COD (63)	48.8	4.4	Native microalgae-bacteria consortia (dominated by Chlorella sp.)	P	PVDF, HF, 0.03 μm	3.4	NA	Outdoor + artificial LED lamps (277–284 μmol/m2 s)	Air, CO2 (pH control)	Outdoor (16.9–18.8)	7.5
Viruela et al. (2018)	MB-MPBR	RSEW	COD (31)	51.3	6.8	Native microalgae-bacteria consortia (dominated by Scenedesmus sp.)	P	PVDF, HF, 0.03 μm	2 × 31	NA	Outdoor (169–378 μE/m2 s)	2 m3/h, 99.9% CO2 (pH control)	Outdoor (23.9–28.7)	7.5
Gao et al. (2019)	MPBR	RSEW	COD (25.6)	12.8	0.6	Chlorella vulgaris; Scenedesmus obliquus	L	PVDF, HF, 0.1 μm	NA	NA	102–112 μmol/m2 s	0.5 L/min, 99.9% CO2 (pH control)	25–28	6.8–7.5
Chitapornpan et al. (2012)	PAnMBR	RIW	COD (700–9,750)	NA	NA	Native purple phototrophic bacteria	L	PE, HF, 0.4 μm	0.04	3.75–7.5	180 W, IR-2880 transmitting filter	–	28–35	7
Lu et al. (2021)	MPBR	SSEW	–	40	5	Chlorella vulgaris	L	HF, 0.01 μm	NA	NA	140 μmol/m2 s	0.5–1.0 L/min, 4% of CO2	28	6.5–7.0
Derakhshan et al. (2018b)	MB-MPBR	RSEW, SSEW	COD (30–100)	NOx (8–9.9)	(12–9.3)	Native microalgae-bacteria consortia	L	PVDF, HF, 0.1 μm	0.043	20	12 h/12 h (8,000 lux)	Air, 0.04% CO2	26	7.0
González et al. (2017)	MB-MPBR	RSEW	COD (124)	46	6	Native microalgae-bacteria consortia	P	PVDF, HF, 0.04 μm	0.9	12	Outdoor (150–224 W/m2)	NA	Outdoor (18.9)	7.7
Derakhshan et al. (2019)	B-MB-MPBR, MB-MPBR	SSEW	COD (30–90)	5	1	Microalgae-bacteria consortia	L	PVDF, HF, 0.1 μm	0.043	20	12 h/12 h (8,500 lux)	Air, 0.04% CO2	33	7.0
Fortunato et al. (2020)	MPBR	SSEW	–	60	10	Chlorella vulgaris	L	PVDF, HF, 0.016 μm	3.7·10–3	20	130 μmol/m2 s	Air 30 L/h	25	7.0–8.0
Choi (2015)	MPBR	RPEW	COD (209.9)	40.0	9.2	Chlorella vulgaris	L	PES, 0.2 μm	0.2	8	16 h/8 h (270–310 μE/m2 s)	Air 0.5 L/min, CO2	25	7.2
Zhang et al. (2020)	MB-MPBR	SMW	COD (440.3)	36.9–46.5	38–9.5	Chlorella Vulgaris-activated sludge	L	PVDF, FS, 0.1 μm	0.03	6.8; 10.6	8,400 lux	Air 3.75 L/min, 0.05% CO2	26.9	7.3
González-Camejo et al. (2020a)	MB-MPBR	RSEW	COD (71)	45.0	4.7	Native microalgae-bacteria consortia (dominated by Chlorella sp.)	P	PVDF, HF, 0.03 μm	2 × 3.4	15–26	Outdoor + artificial LED lamps (281–344 μmol/m2 s)	Air, CO2 (pH control)	Outdoor + cooling system (23.9–25.5)	7.5
Parakh et al. (2020)	MPBR+ Settler	SSEW	-	14	0.65	Graesiella emersoni	L	PVDF, 0.1 μm	0.0588	NA	14 W	1.5 L/min, 5% CO2	NA	6.5–7.5
Honda et al. (2017)	MPBR	SSEW	TOC (5)	15	0.3	Chlorella vulgaris NIES-2170	L	PVDF, HF, 0.1 μm	0.085	NA	20 W/m2	Air, 1% CO2	24	NA
Praveen et al. (2019)	MPBR	SSEW	–	21	6	Scenedesmus obliquus FACHB-417	L	PVDF, FS	0.0308	2.4	130 μmol/m2 s	Air, 3% CO2	NA	6.5–7.5
Noguchi et al. (2017)	MPBR	SADS	TOC (0.66)	85.4	23.1	Chlorella vulgaris NIES-2170	L	HF, 0.1 μm	0.085	NA	24 h/9 h (6 W/m2)	–	21.7	7.0–8.0
Lee et al. (2018)	MPBR	RISEW	TOC (26.5)	41.6	0.7	Botryococcus braunii	L	PVDF, 0.5 μm	2.81·10−3	2.4; 3; 4	220 μE/m2 s	6 L/min	25	NA
González-Camejo et al. (2018)	MB-MPBR	RSEW	COD (72)	40–80	4–10	Native microalgae-bacteria consortia (dominated by Chlorella sp. and Scenedesmus sp.)	P	PVDF, HF, 0.03 μm	2 × 3.4	NA	Outdoor (119–357 μmol/m2 s) +artificial LED lamps (300 μmol/m2 s)	0.09 vvm, CO2 (pH control)	16–28	7.5
Azizi et al. (2021a)	MPBR	SSEW	–	(83)	(9)	Chlorella vulgaris	L	0.45 μm	NA	18	24 h/0 h; 16 h/8 h; 12 h/12 h (100; 300 μmol/m2 s)	Air, 0.04% CO2	25	NA
Derakhshan et al. (2018a)	B-MB-MPBR	RSEW	COD (59)	NOx (11)	(10)	Native microalgae-bacteria consortia	L	PVDF, HF, 0.1 μm	0.043	20	12 h/12 h (100 μmol/m2 s)	4–5 L/min, 0.04% CO2	26	NA
Gao et al. (2021)	MPBR	RSEW	TOC (107)	48.4	4.23	Chlorella pyrenoidosa FACHB-5	L	PVDF, HF, 0.1 μm	0.05	NA	82.4–90.6 μmol/m2 s	40 mL/min, CO2	30	7.3
Novoa et al. (2020)	MPBR	SSEW	–	65	10	Chlorella vulgaris UTEX 259	L	PVDF, HF, 0.018 μm	NA	10	130 μmol/m2 s	30 L/h	25	7.0–8.0
Solmaz & Işık (2019)a, 2019b)	MB-MPBR	RSEW	BOD (12)	5.2	0.6	Native microalgae-bacteria consortia	L	PVDF, HF, 0.45 μm	NA	20	12 h/12 h (6,000 lux)	5 L/min	23	7.5–9.0
Lau et al. (2019)	SB-MPBR	RSEW	NA	20–21	2.25	Staurastrum sp.	L	PVDF, FS, 0.16 μm	0.02	40	1,000 lux	3.5 L/min	20	NA
Wang et al. (2020)	O-MPBR	SSEW	Glucose (50)	NH4Cl (30)	K2HPO4 (21.4) + KH2PO4 (10.6)	Chlorella vulgaris UTEX 395, Scenedesmus sp. HTB1	L	HF	2.3	NA	16 h/8 h (100 μE/m2 s)	20% CO2/N2 (pH control)	20	NA
Solmaz & Işık (2020)	MB-MPBR	SSEW	COD (32.9)	18.0	8.8	Microalgae-bacteria consortia	L	PVDF, HF, 0.45 μm	NA	20	12 h/12 h (6,000 lux)	5 L/min	23	6.4–7.5
Dalaei et al. (2020)	PAnMBR	SPEW	COD (430) + Ethanol (200; 300)	43	6.5	Native purple phototrophic bacteria	L	FS, 0.45 μm	0.12	1.9	IR light (3; 1.4 W/m2)	–	22	7.3
Dalaei et al. (2019)	PAnMBR	SPEW	COD (370) + Ethanol (285; 290; 300; 310; 400)	48–56	6.6–8.2	Native purple phototrophic bacteria	L	FS, 0.45 μm	0.12	1.9	IR light (50 W/m2)	–	22; 11	7.2
Solmaz & Işik (2019)a, 2019b)	MB-MPBR	SSEW	COD (32.9)	18.4	8.8	Microalgae-bacteria consortia	L	PVDF, HF, 0.45 μm	NA	20	12 h/12 h (6,000 lux)	5 L/min	23	6.4–8.2
Keramati et al. (2021)	MPBR	RISEW	COD (67)	(49)	(36)	Chlorella vulgaris	L	FS, 0.4 μm	NA	20	LED flashing light (1 Hz; 1,000 Hz) (200 μmol/m2 s)	7 L/min	NA	7.0–8.0
Lee et al. (2020)	MPBR	SISEW	TOC (530)	183	15	Botryococcus braunii UTEX 2441	L	PVDF, HF, 0.5 um	2.81·10−4	2.4; 3; 4	24 h/0 h; 12/12 h (150 μmol E/m2 s)	NA	30	NA
Wang et al. (2019)	B-MB-MPBR	RIW+ RPEW	SCOD (1066)	156.3	9.7	Native microalgae-bacteria consortia	L	PVDF, HF, 0.04 μm	1	NA	200–250 μmol/m2 s	4 L/min	NA	NA
Zhang et al. (2021)	MB-MPBR	SMW	COD (440.3)	36.9–46.5	3.8–9.5	Chlorella vulgaris-activated sludge	L	PVDF, FS, 0.1 μm	0.03	6.8; 10.6	8,400 lux	3.75 L/min, 0.05% CO2	26.9	7.3
Spennati et al. (2021)	MPBR	RIW	COD (119.3)	NA	NA	Chlorella vulgaris CCAP 211, Arthrospira platensis UTEX 1926	L	NA	NA	NA	60 μmol/m2 s	NA	23–25	6.3–7.9
Cheng et al. (2021)	B-MPBR	SIW	COD (1025)	N-NH3 (98.4)	K2HPO4 (25)	Rhodopuesdomonas pulstris	L	GO/PSF composite	0.05	24.1	300 lux	1.5 m3/m2 h	25	7.5

^aA-MPBR, airlift membrane photobioreactor; B-MB-MPBR, biofilm microalgal-bacterial membrane photobioreactor; MB-MPBR, microalgal-bacterial membrane photobioreactor; MPBR, membrane photobioreactor; O-MPBR, osmotic membrane photobioreactor; PAnMBR, photo-anaerobic membrane bioreactor; SB-MPBR, sequencing batch membrane photobioreactor.

^bSSEW, synthetic secondary effluent wastewater; SSALW, synthetic saline wastewater; SPEW, synthetic primary effluent wastewater; SMW, synthetic municipal wastewater; SIW, synthetic industrial wastewater; SISEW, synthetic industrial secondary effluent wastewater; SADS, synthetic anaerobic digestion supernatant; RSEW, real secondary effluent wastewater; RPEW, real primary effluent wastewater; RIW, real industrial wastewater; RISEW, real industrial secondary effluent wastewater.

^cBOD, biological oxygen demand; COD, chemical oxygen demand; SCOD, soluble chemical oxygen demand; TOC, total organic carbon.

^dL, laboratory; P, pilot.

^eDM, dynamic membrane; MF, microfiltration; PSF, polysulfone; PVDF, polyvinylidene fluoride; TFC, thin film composite.

NA, not available.

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Table 8

Operating performance of membrane photobioreactors

ID .	HRT (day) .	SRT (day) .	PBR type .	Biomass concentration (g/L) .	Productivity (mg/L day) .	TN removal (%) .	TP removal (%) .
Gao et al. (2016a)	1	NA	Bubble column	0.44–1.1	42.6	86.1	82.7
Tan et al. (2014)	–	NA	Airlift	Summer (2.05); winter (0.86); spring (1.97); autumn (1.98)	Summer (0.63); winter (0.14); spring (0.33); autumn (0.51)	Summer (≈ 83); winter (≈ 40); spring (≈ 67); autumn (≈ 75)	Summer (≈ 94); winter (≈56); spring (≈ 96); autumn (≈ 94)
Marbelia et al. (2014)	1; 1.4; 2; 2.5; 3.3; 5	5xHRT	Bubble column	1 (≈ 0.2); 1.4 (≈ 0.3); 2 (≈ 0.6); 2.5 (≈ 0.6); 3.3 (≈ 0.4); 5 (≈ 0.4)a	1 (≈ 0.03); 1.4 (≈ 0.04);2 (≈ 0.06); 2.5 (≈ 0.04); 3.3 (≈ 0.02); 5 (≈ 0.02)a	2 (25); 2.5 (50); 3.3 (80); 5 (100)a	2 (50); 2.5 (50); 3.3 (80); 5 (100)a
Chitapornpan et al. (2013)	10	NA	Rectangular	≈ 1	8.6–12.9	NA	NA
Praveen et al. (2018)	3	> 200	Bubble column	0.11–5	49.6–167.0	NA	NA
Singh & Thomas (2012)	1.6	NA	Flat panel	1.93·106 cells/mL	NA	(30–40), (70–80)	(45–70)
Gao et al. (2016b)	2	NA	Bubble column	1.8	50.7	≈ 92	≈ 85
Praveen & Loh (2016)	2; 3; 4	Without purge	Rectangular	30–331	NA	N- (36–87), N- (45–81)	75–87
Zhen-Feng et al. (2011)	0.46–0.9	NA	Bubble column	0.14–0.22	NA	< 10–46	< 10–100
Praveen et al. (2016)	1; 1.5; 2	Without purge	Rectangular	FO (0.28 g/L); MF (0.23 g/L)	NA	MF (84–97); FO (92–99)	MF (28–47); FO (100)
Sheng et al. (2017)	2; 4; 8	60	Rectangular	2 (0.6 g/L); 4 (0.7 g/L); 8 (1.0 g/L)a	2 (9.7 mg/L day); 4 (11.2 mg/L day); 8(16.7 mg/L day)a	2 (82.8); 4 (96.0); 8 (90.3)a	2 (35.7); 4 (70.0); 8 (44.3)a
Peng et al. (2020)	1; 2	Without purge	Bubble column	1 (1.5 g/L); 2 (1.0 g/L)a	1 (22.0 mg/L day); 2 (14.0 mg/L day)a	1 (98.1); 2 (94.2)a	1 (95.3); 2 (95.6)a
Shi et al. (2018)	0.5	NA	Flat panel	4.5–5.5·106 cell/mL	NA	NA	NA
Boonchai & Seo (2015)	2	NA	Bubble column	0.2–1.2	55	66.5	94.5
Luo et al. (2018)	1; 4	9; 18; 30	Flat panel	1/9 (0.47);1/18 (0.91); 1/30 (1.22); 4/30 (0.85)b	1/9 (52);1/18 (51); 1/30 (41); 4/30 (28)b	1/9 (31);1/18 (36); 1/30 (32); 4/30 (84)b	1/9 (30); 1/18 (31); 1/30 (25); 4/30 (80)b
González-Camejo et al. (2020b)	1.25	2, 2.5; 4.5	Flat panel	2 (≈ 0.350 g VSS/L); 2.5 (0.347 g VSS/L); 4.5 (0.486 g VSS/L)c	2 (136 mg VSS/L day); 2.5 (139 mg VSS/L day); 4.5 (108 mg VSS/L day)c	2 (14.1 mg N/L day); 2.5 (19.7 mg N/L day); 4.5 (14.5 mg N/L day)c	NA
Viruela et al. (2018)	13.1–31.5 g N/day	4.5; 8; 9	Flat panel	NA	4.5 (66 mg VSS/L day)	4.5 (7.7 mg N/L day)	4.5 (2.2 mg P/L day)
Gao et al. (2019)	2	NA	Bubble column	Chlorella vulgaris (1.84); Scenedesmus obliquus (1.72)	Chlorella vulgaris (96.3); Scenedesmus obliquus (88.8)	NA	NA
Chitapornpan et al. (2012)	10	NA	Rectangular	1.0	12.9	NA	NA
Lu et al. (2021)	2; 3	10; 20; 40	Bubble column	2 (0.25–0.7); 3 (0.55–0.9)c	2/40 (10.9); 2/20 (28.3); 2/10 (NA); 3/40 (17.1); 3/20 (35.9)b	2/40 (26.2); 2/20 (25.6); 2/10 (34.6); 3/40 (44.6); 3/20 (42.3); 3/10 (40.8)b	2/40 (66,3); 2/20 (53,7); 2/10 (41,7); 3/40 (77.4); 3/20 (69.2); 3/10 (54.5)b
Derakhshan et al. (2018b)	0.25; 0.5; 1	NA	Bubble column	6	NA	> 93.7%	> 94.4%
González et al. (2017)	0.21	Without purge	Bubble column	1.1	37	39	21
Derakhshan et al. (2019)	0.17; 0.33; 0.5	NA	Bubble column	HMPBR (6); MPBR (4)	NA	HMPBR 0.5 (98); MPBR 0.5 (85)	HMPBR 0.5 (98); MPBR 0.5 (98)
Fortunato et al. (2020)	0.5	NA	Bubble column	0.5	NA	NA	NA
Choi (2015)	3.4	NA	Flat panel	1.12	0.00253	96.4	92.8
Zhang et al. (2020)	2; 3	20	Bubble column	2 (2.33–2.55); 3 (1.67–2.40)a	2 (116.5–127.5); 3 (83.5–120.0)a	2 (78.6–81.7); 3 (72.5–99.5)a	2 (45.4–99.5); 3 (61.8–99.2)a
González-Camejo et al. (2020a)	1.25; 1.5	3; 4.5	Flat panel	1.25/3 (531–731 mg VSS/L); 1.5/3 (823 mg VSS/L); 1.5/4.5 (801 mg VSS/L)b	1.5/3 (258 mg VSS/L day)b	80–85	90–99
Parakh et al. (2020)	1	5–25	Bubble column	0.6–3.4 (suspension); 16.1–31.1 (settled)	130–260	90–92	99–100
Honda et al. (2017)	0.33; 1	12	Flat panel	0.314–0.872	26.2–39.0	65.9–70.0	82.5–96.8
Praveen et al. (2019)	2	2; 2.5; 5; 10; 20; 30; 50; 350	Bubble column	2 (0.54); 2.5 (0.78); 5 (1.07); 10 (1.97); 20 (1.98); 30 (1.90); 50 (1.86); 350 (2.03)c	50–270	94.9–97.1	71.4–78.2
Noguchi et al. (2017)	1	12; 56	Rectangular	0.4	5.58	89	90
Lee et al. (2018)	3; 4; 5	14;15; 16	Cylindrical	3 (3.5); 4 (3); 5 (3.1)	201	96	85
González-Camejo et al. (2018)	2; 2,5; 3; 8; 14	4,5; 8; 14	Flat Panel	0.2–0.4 g VSS/L	2.5/4.5 (72 mg VSS/L day)	2.5/4.5 (12.5 m g N/L day)	2.5/4.5 (1.5 mg P/L day)
Azizi et al. (2021a)	1	Without purge	Flat Plate	NA	6–18	40–60	20–45
Derakhshan et al. (2018b)	0.125; 0.25; 0.5; 1	Without purge	Bubble column	5	0.17–0.73 g SS/g COD	NA	NA
Gao et al. (2021)	4	20	Cylindrical	1.7–1.9	91.1	8.4 mg N/L day	0.9 mg P/L day
Novoa et al. (2020)	0.5; 1; 1.5	Without purge	Bubble column	0.5 (1.7); 1 (1.4); 1.5 (0.8)a	0.5 (90); 1 (61.8)a	NA	NA
Solmaz & Işık (2019a, 2019b)	1	3	Bubble column	0.110	37	5.55 mg N/L day	0.4 mg P/L day
Lau et al. (2019)	4	150	NA	0.6	40–30	50	10
Wang et al. (2020)	1.7; 3.3; 6.7	9.4; 17.8; 25.3	NA	0.82	NA	≈ 100%	≈ 100%
Solmaz & Işık (2020)	1; 2; 2.5; 3	3	Bubble column	0.19–0.35	64–118	30.3–46.8	4.6–5.9
Dalaei et al. (2020)	0.375; 0.75; 1.5	5	Flat panel	0.29–1.25	NA	44–86	69–91
Dalaei et al. (2019)	0.375; 0.75; 1.5	3	Flat panel	0.4–1.6	NA	40–91	83–89
Solmaz & Işik (2019a, 2019b)	1	2; 3; 6; 12; 24	Bubble column	3 (0.35); 6 (0.35); 12 (0.61); 24 (1.03)c	3 (118); 6 (61); 12 (51); 24 (43)c	3 (5.55 mg/L day); 6 (4.02 mg/L day); 12 (5.05 mg/L day); 24 (4.88 mg/L day)c	3 (0.4 mg/L day); 6 (0.29 mg/L day); 12 (1.36 mg/L day); 24 (1.61 mg/L day)c
Keramati et al. (2021)	NA	NA	Flat panel	1.4–2–5	15–30	68–97	47–70
Lee et al. (2020)	3; 4; 5	15	Rectangular	0.66–2.75	NA	77–72	98–96
Wang et al. (2019)	5	15	NA	0.5–1.9	NA	69–99	NA
Zhang et al. (2021)	2; 3	20	Bubble column	1.6–2.55	NA	72.5–99.5	45.4–99.2
Spennati et al. (2021)	1.4; 2; 4.6	NA	Bubble column	0.82–6.10	39–240	NA	NA
Cheng et al. (2021)	16.7	30	Rectangular	NA	NA	84	NA

ID .	HRT (day) .	SRT (day) .	PBR type .	Biomass concentration (g/L) .	Productivity (mg/L day) .	TN removal (%) .	TP removal (%) .
Gao et al. (2016a)	1	NA	Bubble column	0.44–1.1	42.6	86.1	82.7
Tan et al. (2014)	–	NA	Airlift	Summer (2.05); winter (0.86); spring (1.97); autumn (1.98)	Summer (0.63); winter (0.14); spring (0.33); autumn (0.51)	Summer (≈ 83); winter (≈ 40); spring (≈ 67); autumn (≈ 75)	Summer (≈ 94); winter (≈56); spring (≈ 96); autumn (≈ 94)
Marbelia et al. (2014)	1; 1.4; 2; 2.5; 3.3; 5	5xHRT	Bubble column	1 (≈ 0.2); 1.4 (≈ 0.3); 2 (≈ 0.6); 2.5 (≈ 0.6); 3.3 (≈ 0.4); 5 (≈ 0.4)a	1 (≈ 0.03); 1.4 (≈ 0.04);2 (≈ 0.06); 2.5 (≈ 0.04); 3.3 (≈ 0.02); 5 (≈ 0.02)a	2 (25); 2.5 (50); 3.3 (80); 5 (100)a	2 (50); 2.5 (50); 3.3 (80); 5 (100)a
Chitapornpan et al. (2013)	10	NA	Rectangular	≈ 1	8.6–12.9	NA	NA
Praveen et al. (2018)	3	> 200	Bubble column	0.11–5	49.6–167.0	NA	NA
Singh & Thomas (2012)	1.6	NA	Flat panel	1.93·106 cells/mL	NA	(30–40), (70–80)	(45–70)
Gao et al. (2016b)	2	NA	Bubble column	1.8	50.7	≈ 92	≈ 85
Praveen & Loh (2016)	2; 3; 4	Without purge	Rectangular	30–331	NA	N- (36–87), N- (45–81)	75–87
Zhen-Feng et al. (2011)	0.46–0.9	NA	Bubble column	0.14–0.22	NA	< 10–46	< 10–100
Praveen et al. (2016)	1; 1.5; 2	Without purge	Rectangular	FO (0.28 g/L); MF (0.23 g/L)	NA	MF (84–97); FO (92–99)	MF (28–47); FO (100)
Sheng et al. (2017)	2; 4; 8	60	Rectangular	2 (0.6 g/L); 4 (0.7 g/L); 8 (1.0 g/L)a	2 (9.7 mg/L day); 4 (11.2 mg/L day); 8(16.7 mg/L day)a	2 (82.8); 4 (96.0); 8 (90.3)a	2 (35.7); 4 (70.0); 8 (44.3)a
Peng et al. (2020)	1; 2	Without purge	Bubble column	1 (1.5 g/L); 2 (1.0 g/L)a	1 (22.0 mg/L day); 2 (14.0 mg/L day)a	1 (98.1); 2 (94.2)a	1 (95.3); 2 (95.6)a
Shi et al. (2018)	0.5	NA	Flat panel	4.5–5.5·106 cell/mL	NA	NA	NA
Boonchai & Seo (2015)	2	NA	Bubble column	0.2–1.2	55	66.5	94.5
Luo et al. (2018)	1; 4	9; 18; 30	Flat panel	1/9 (0.47);1/18 (0.91); 1/30 (1.22); 4/30 (0.85)b	1/9 (52);1/18 (51); 1/30 (41); 4/30 (28)b	1/9 (31);1/18 (36); 1/30 (32); 4/30 (84)b	1/9 (30); 1/18 (31); 1/30 (25); 4/30 (80)b
González-Camejo et al. (2020b)	1.25	2, 2.5; 4.5	Flat panel	2 (≈ 0.350 g VSS/L); 2.5 (0.347 g VSS/L); 4.5 (0.486 g VSS/L)c	2 (136 mg VSS/L day); 2.5 (139 mg VSS/L day); 4.5 (108 mg VSS/L day)c	2 (14.1 mg N/L day); 2.5 (19.7 mg N/L day); 4.5 (14.5 mg N/L day)c	NA
Viruela et al. (2018)	13.1–31.5 g N/day	4.5; 8; 9	Flat panel	NA	4.5 (66 mg VSS/L day)	4.5 (7.7 mg N/L day)	4.5 (2.2 mg P/L day)
Gao et al. (2019)	2	NA	Bubble column	Chlorella vulgaris (1.84); Scenedesmus obliquus (1.72)	Chlorella vulgaris (96.3); Scenedesmus obliquus (88.8)	NA	NA
Chitapornpan et al. (2012)	10	NA	Rectangular	1.0	12.9	NA	NA
Lu et al. (2021)	2; 3	10; 20; 40	Bubble column	2 (0.25–0.7); 3 (0.55–0.9)c	2/40 (10.9); 2/20 (28.3); 2/10 (NA); 3/40 (17.1); 3/20 (35.9)b	2/40 (26.2); 2/20 (25.6); 2/10 (34.6); 3/40 (44.6); 3/20 (42.3); 3/10 (40.8)b	2/40 (66,3); 2/20 (53,7); 2/10 (41,7); 3/40 (77.4); 3/20 (69.2); 3/10 (54.5)b
Derakhshan et al. (2018b)	0.25; 0.5; 1	NA	Bubble column	6	NA	> 93.7%	> 94.4%
González et al. (2017)	0.21	Without purge	Bubble column	1.1	37	39	21
Derakhshan et al. (2019)	0.17; 0.33; 0.5	NA	Bubble column	HMPBR (6); MPBR (4)	NA	HMPBR 0.5 (98); MPBR 0.5 (85)	HMPBR 0.5 (98); MPBR 0.5 (98)
Fortunato et al. (2020)	0.5	NA	Bubble column	0.5	NA	NA	NA
Choi (2015)	3.4	NA	Flat panel	1.12	0.00253	96.4	92.8
Zhang et al. (2020)	2; 3	20	Bubble column	2 (2.33–2.55); 3 (1.67–2.40)a	2 (116.5–127.5); 3 (83.5–120.0)a	2 (78.6–81.7); 3 (72.5–99.5)a	2 (45.4–99.5); 3 (61.8–99.2)a
González-Camejo et al. (2020a)	1.25; 1.5	3; 4.5	Flat panel	1.25/3 (531–731 mg VSS/L); 1.5/3 (823 mg VSS/L); 1.5/4.5 (801 mg VSS/L)b	1.5/3 (258 mg VSS/L day)b	80–85	90–99
Parakh et al. (2020)	1	5–25	Bubble column	0.6–3.4 (suspension); 16.1–31.1 (settled)	130–260	90–92	99–100
Honda et al. (2017)	0.33; 1	12	Flat panel	0.314–0.872	26.2–39.0	65.9–70.0	82.5–96.8
Praveen et al. (2019)	2	2; 2.5; 5; 10; 20; 30; 50; 350	Bubble column	2 (0.54); 2.5 (0.78); 5 (1.07); 10 (1.97); 20 (1.98); 30 (1.90); 50 (1.86); 350 (2.03)c	50–270	94.9–97.1	71.4–78.2
Noguchi et al. (2017)	1	12; 56	Rectangular	0.4	5.58	89	90
Lee et al. (2018)	3; 4; 5	14;15; 16	Cylindrical	3 (3.5); 4 (3); 5 (3.1)	201	96	85
González-Camejo et al. (2018)	2; 2,5; 3; 8; 14	4,5; 8; 14	Flat Panel	0.2–0.4 g VSS/L	2.5/4.5 (72 mg VSS/L day)	2.5/4.5 (12.5 m g N/L day)	2.5/4.5 (1.5 mg P/L day)
Azizi et al. (2021a)	1	Without purge	Flat Plate	NA	6–18	40–60	20–45
Derakhshan et al. (2018b)	0.125; 0.25; 0.5; 1	Without purge	Bubble column	5	0.17–0.73 g SS/g COD	NA	NA
Gao et al. (2021)	4	20	Cylindrical	1.7–1.9	91.1	8.4 mg N/L day	0.9 mg P/L day
Novoa et al. (2020)	0.5; 1; 1.5	Without purge	Bubble column	0.5 (1.7); 1 (1.4); 1.5 (0.8)a	0.5 (90); 1 (61.8)a	NA	NA
Solmaz & Işık (2019a, 2019b)	1	3	Bubble column	0.110	37	5.55 mg N/L day	0.4 mg P/L day
Lau et al. (2019)	4	150	NA	0.6	40–30	50	10
Wang et al. (2020)	1.7; 3.3; 6.7	9.4; 17.8; 25.3	NA	0.82	NA	≈ 100%	≈ 100%
Solmaz & Işık (2020)	1; 2; 2.5; 3	3	Bubble column	0.19–0.35	64–118	30.3–46.8	4.6–5.9
Dalaei et al. (2020)	0.375; 0.75; 1.5	5	Flat panel	0.29–1.25	NA	44–86	69–91
Dalaei et al. (2019)	0.375; 0.75; 1.5	3	Flat panel	0.4–1.6	NA	40–91	83–89
Solmaz & Işik (2019a, 2019b)	1	2; 3; 6; 12; 24	Bubble column	3 (0.35); 6 (0.35); 12 (0.61); 24 (1.03)c	3 (118); 6 (61); 12 (51); 24 (43)c	3 (5.55 mg/L day); 6 (4.02 mg/L day); 12 (5.05 mg/L day); 24 (4.88 mg/L day)c	3 (0.4 mg/L day); 6 (0.29 mg/L day); 12 (1.36 mg/L day); 24 (1.61 mg/L day)c
Keramati et al. (2021)	NA	NA	Flat panel	1.4–2–5	15–30	68–97	47–70
Lee et al. (2020)	3; 4; 5	15	Rectangular	0.66–2.75	NA	77–72	98–96
Wang et al. (2019)	5	15	NA	0.5–1.9	NA	69–99	NA
Zhang et al. (2021)	2; 3	20	Bubble column	1.6–2.55	NA	72.5–99.5	45.4–99.2
Spennati et al. (2021)	1.4; 2; 4.6	NA	Bubble column	0.82–6.10	39–240	NA	NA
Cheng et al. (2021)	16.7	30	Rectangular	NA	NA	84	NA

^aData expressed as: HTR (value).

^bData expressed as: HTR/SRT (value).

^cData expressed as: SRT (value).

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In 70% of the studies, the microalgae C. vulgaris (e.g., Singh & Thomas 2012; Marbelia et al. 2014; Gao et al. 2016a, 2016b; Praveen et al. 2018) is used as a reference species, with some other studies using species such as Scenedesmus obliquus (Gao et al. 2019; Praveen et al. 2019), or Botryococcus braunii (Lee et al. 2018; Lee Jang et al. 2020). The feed is mainly municipal wastewater with a low C/N ratio, such as secondary treatment effluent (60%), synthetic (39%) or real (21%). Additionally, other types of industrial wastewater, usually subjected to secondary treatment (electronic device factory (Zhen-Feng et al. 2011), livestock industry, (Lee et al. 2018; Lee Jang et al. 2020), or whey wastewater (Keramati et al. 2021)), have been studied.

In relation to the operating parameters of the process, it is to be noted that microfiltration membranes (0.1–0.5 μm) of PVDF or PES, in hollow fibre or flat plate modules, are mostly used. Low filtration fluxes (1.3–12 L/h m²) are generally applied, a parameter that is not usually optimised. Studies usually apply continuous artificial (LED or fluorescent lamps) light or 12 h dark/12 h light photoperiods with PAR values between 60 and 300 μmol/m² s. Typically, microalgae growth increases with light intensity up to a saturation point. However, beyond this critical saturation threshold, the influence of light intensity on microalgae growth becomes negligible and does not further contribute to the observed growth rate (Wang et al. 2018). In addition, it is widely recognised that the provision of exogenous CO₂ through gasification (pure or air-enriched) is often necessary to improve the overall treatment performance (Table 7). Moreover, the introduction of CO₂ through gasification also aids in stabilising the pH at optimal levels, typically ranging between 7 and 8 (Table 7). The regulation of carbonation is generally accomplished by controlling the gasification rate or implementing on-demand injection to manage the pH effectively. Additionally, it should be noted that effective membrane fouling control necessitates air scouring, which can be integrated with the carbonation process. Operating temperature is usually 20–25 °C.

Given the essential role of HRT and SRT values on process performance, a significant number of the studies have focused on optimisation of both parameters (Table 8). Generally, HRT determines the degree of nutrient removal. The values studied are usually between 0.5 and 5 days, the optimisation of which depends on the species and feed characteristics (Marbelia et al. 2014; Novoa et al. 2020). It is suggested that at values above 1 day, nitrogen and phosphorus removal rates of more than 65 and 70%, respectively, can be achieved (Boonchai & Seo 2015; Gao et al. 2016b; Honda et al. 2017; Praveen et al. 2019). On the other hand, the SRT, which influences microalgae concentration, is usually studied in the range 2–40 days. Since biomass growth is favoured by higher irradiation in the photobioreactor, the highest productivity is obtained at low SRTs (2–3 days), where lower microalgae concentrations (<1 g/L) are obtained (Praveen et al. 2019; Parakh et al. 2020).

The most studied configuration is MPBR with pure microalgae cultures, which represent 46% of papers (Table 7). These studies focus mainly on the analysis of biomass productivity and nutrient removal capacity (Table 8). The application of MPBRs to real secondary effluents with an appreciable amount of carbonaceous matter (COD = 30–100 mg/L) or untreated wastewater (COD > 400 mg/L) has led to the development of microalgal-bacterial photobioreactors (MB-MPBRs), where synergies can be established between different microorganisms. In these studies, which represent 26% of the analysed works, native consortia of microalgae and bacteria are developed, which adapt to the imposed conditions. In laboratory-scale studies, a 12 h/12 h photoperiod is usually applied, studying the behaviour of this configuration for a range of HRT and SRT conditions (1–3 and 2–24 days, respectively) similar to that studied for MPBRs (Solmaz & Işik 2019a, 2019b; Solmaz & Işık 2020). Comparable results in terms of productivity and nutrient removal have been obtained with this process. In addition, studies at pilot scale and under ambient light and temperature conditions have laid the groundwork for larger-scale design (González et al. 2017; Viruela et al. 2018; González-Camejo et al. 2018, 2020a, 2020b). Sustainable operation with adequate nutrient removal efficiencies has been demonstrated for SRTs of 3–4.5 days and HRTs of 1.25–1.5 days. Also, for these conditions, low membrane fouling can be operated at filtration fluxes of 15 L/h m² and a specific membrane aeration flow rate of 16–20 Nm³ air/m³ permeate (González-Camejo et al. 2020a).

The rest of the works analysed (28%) studied other configurations of the technology, including biofilm microalgal-bacterial membrane photobioreactor (B-MP-MPBR), osmotic membrane photobioreactor (O-MPBR), and photo-anaerobic membrane bioreactor (PAnMBR). Derakhshan et al. (2018b) and Derakhshan et al. (2019) have shown an appreciable increase in COD and nutrient removal from a B-MP-MPBR over an MP-MPBR. In relation to O-MPBR, although removal efficiencies are higher than those obtained in MPBR, so are the total operating costs, which limits the widespread application of this configuration (Praveen et al. 2016). Finally, in the case of PAnMBR, its application is limited to industrial or domestic wastewater with high organics (Chitapornpan et al. 2012, 2013; González-Camejo et al. 2018; Dalaei et al. 2020). Although preliminary studies show the potential of this configuration, comparable to conventional treatment, more studies are needed on fundamental aspects of the process, such as the influence of the reactor type on light transmissivity, membrane fouling, or methane production (Dalaei et al. 2020).

Many authors have focused their studies on the application of MPBR in wastewater treatment; however, most have been based on laboratory-scale studies (Liu & Hong 2021). Regarding pilot-scale experiments, these have only been conducted in recent years (González-Camejo et al. 2020b; Viruela et al. 2021). It should be noted that outdoor cultivation requires an appropriate photobioreactor design for efficient light utilisation. Tested configurations are bubble columns and flat panels, but other photobioreactors should be explored. Another crucial factor requiring consideration is the biomass concentration, as its augmentation is required for reducing harvesting costs. Nevertheless, according to the literature, elevating the biomass concentration can be challenging due to the occurrence of self-shading effects. Consequently, achieving an optimal photobioreactor configuration that effectively addresses this limitation remains a significant and unresolved challenge.

One of the first research hot spots that can be identified is the great number of studies that evaluate the application of this technology with real wastewater in order to reach sound conclusions on the best cultivation conditions depending on the characteristics of the water entering the photobioreactor. Thus, the pilot phase is a key step for the industrial scaling-up of this technology. However, there are practically no studies dedicated to the analysis of the economic viability of the process under real conditions (Li et al. 2019).

The control of fouling, a parameter that has been found to be key in the operability of the process, is another possible future research area. Some authors consider that the substances excreted by microalgae are the main causes of fouling (Lee et al. 2018), since their composition may contain proteins with a hydrophobic character that can form gel-like structures that block the membrane pores (Novoa et al. 2021). However, despite the importance of this parameter in the operability of the process, there are almost no studies devoted to the continuous analysis of fouling and its characterisation. Even though, some authors have pointed out that understanding how fouling occurs in membranes represents one of the greatest challenges in this technology (Novoa et al. 2021).

Moreover, although some authors have used MPBRs for the treatment of raw wastewater, the feed to the system has been subjected to centrifugation (de Godos et al. 2009b, 2010) or screening and subsequent sedimentation (Godos et al. 2009a). This results in the suspended solids content being greatly reduced and only the soluble fraction of carbon, nitrogen and phosphorus being fed to the system. It is widely known that high organic matter loads are counterproductive for microalgae growth and that the turbidity generated by suspended organic matter causes photoinhibition (Wang et al. 2016). Consequently, some authors point out that this technology has the potential to be used as a secondary or tertiary wastewater treatment (Nagarajan et al. 2020).

Other authors consider that a potential difficulty of this technology is the need for long hydraulic retention times to achieve adequate nutrient removal, namely retention times of 7–10 days as opposed to the less than 1 day that is considered optimal for wastewater treatment (Nagarajan et al. 2020). A long HRT means that land availability for the implementation of MPBRs is an obstacle to overcome. Therefore, exploring alternative design and operational strategies to attain elevated microalgal or microalgal-bacterial concentrations within MPBRs emerges as a promising approach. Such strategies hold the potential to enhance the overall performance of these systems, particularly at low HRT.

On the other hand, the biodegradation and bioadsorption of persistent organic pollutants (POPs) are identified by some authors as a future application of the algae-bacteria consortium that can be developed in a membrane photobioreactor (Chan et al. 2022). Further studies on the degree of assimilation of these pollutants and the concentrations that are inhibitory to biomass growth are required to establish the best operating conditions.

The utilisation of the biomass generated in the operation of a membrane photobioreactor applied to wastewater treatment may also be a field to be developed in the coming years. A prominent constraint associated with MPBRs lies in the comparatively low biomass productivities reported, which significantly diverge from the yields achieved in conventional closed photobioreactors. Moreover, when considering the up-scaling of this process to accommodate large volumes of wastewater, it is suggested to employ open systems, such as raceways or thin-layer reactors. Through the implementation of such systems, the generated biomass can be effectively harvested for diverse applications, encompassing the utilisation of microalgae-based products as biofertilisers for agriculture and as a valuable resource for biofuels production (Silambarasan et al. 2021). Nevertheless, it is essential to acknowledge that the composition of the biomass, and hence its quality, can be influenced by the nature of the wastewater employed. Notably, the presence of heavy metals and a low phosphorus content in the wastewater has been identified as significant drawbacks in this regard (Morillas-España et al. 2022). In addition, biomass pyrolysis is presented as a method to produce biofuels from microalgae (Adeniyi et al. 2018). Some authors have made progress in this direction by establishing a model for the pyrolysis of biomass from the operation of a laboratory-scale membrane photobioreactor (González et al. 2020). In this context, it is crucial to address the issue of high ash content in microalgae to optimise the yields of pyrolysis products (Sotoudehniakarani et al. 2019).

Process modelling is another potential future research trend in this field. Indeed, the use of experimental data to make models that include the wide range of variables that influence the process (lighting, temperature, climate influence, microalgae species, type of feed water, etc.) (Li et al. 2019) can be a great tool to predict behaviour and design facilities in the future.

MPBR applied to wastewater treatment have proven to be a technology that has attracted growing interest in the scientific community in the last 5 years.

• 1.

  Most publications on the topic have been in the field of Environmental Sciences and the Journal Bioresource Technology is the outlet for most of the articles. China has been the country with the most publications; however, the author who has made the greatest contributions to the topic is Spanish and works at the University of Valencia.

• 2.

  Temporal analysis of keywords shows the scientific community's increasing interest in the use of this technology for nutrient removal, and the need to control a key parameter, membrane fouling, in order to save costs and improve the operability of the overall process.

• 3.

  The future of this technology seems to lie in improving operating conditions to increase the nutrient removal performance of the wastewater and in minimising membrane fouling. All these advances should be studied under real conditions, with pilot-scale experiments. In addition, options must be found for the subsequent use of the biomass obtained in the process to improve overall process performance.

• 4.

  This bibliometric review allows any researcher who is interested in this field, to know the most recent advances and the investigations carried out in the application of MPBR in wastewater treatment.

E. S.-M. wrote the original draft, and rendered support in data curation and editing. E. G. visualised, wrote the review and edited the article. A. F. rendered support in the investigation and data curation. O. D. conceptualised the whole article, developed the methodology, investigated the article, and wrote and reviewed the article.

All relevant data are included in the paper or its Supplementary Information.

The authors declare there is no conflict.