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Article

Microalgal-Bacterial Granular Sludge Process in Non-Aerated Municipal Wastewater Treatment under Natural Day-Night Conditions: Performance and Microbial Community

by 1, 2, 2,*, 1 and 3,*
1
School of Civil Engineering, Architecture and Environment, Hubei University of Technology, Wuhan 430068, China
2
Department of Water and Wastewater Engineering, Wuhan University of Science and Technology, Wuhan 430065, China
3
School of Chemical Engineering and Technology, Xi’an Jiaotong University, Xi’an 710049, China
*
Authors to whom correspondence should be addressed.
Academic Editor: Antonio Panico
Water 2021, 13(11), 1479; https://doi.org/10.3390/w13111479
Received: 19 April 2021 / Revised: 20 May 2021 / Accepted: 21 May 2021 / Published: 25 May 2021
(This article belongs to the Special Issue Innovative Technologies for Wastewater and Water Treatment)

Abstract

The microalgal-bacterial granular sludge (MBGS) process is expected to meet the future requirements of municipal wastewater treatment technology for decontamination, energy consumption, carbon emission and resource recovery. However, little research on the performance of the MBGS process in outdoor treatment was reported. This study investigated the performance of the MBGS system in treating municipal wastewater under natural alternate day and night conditions in late autumn. The results showed that the average removal efficiencies of Chemical oxygen demand (COD), NH4+-N and PO43−-P on daytime before cooling (stage I, day 1−4) could reach 59.9% ± 6.8%, 78.1% ± 7.9% and 61.5% ± 4.5%, respectively, while the corresponding average removal efficiencies at night were 47.6% ± 8.0%, 56.5% ± 17.9% and 74.2% ± 7.6%, respectively. Due to the dramatic changes in environmental temperature and light intensity, the microbial biomass and system stability was affected with fluctuation in COD and PO43−-P removal. In addition, the relative abundance of filamentous microorganisms (i.e., Clostridia and Anaerolineae) decreased, while Chlorella maintained a dominant position in the eukaryotic community (i.e., relative abundance > 99%). This study can provide a theoretical basis and technical support for the further engineering application of the MBGS process.
Keywords: microalgal-bacterial granular sludge; outdoor; wastewater treatment; microbial community succession microalgal-bacterial granular sludge; outdoor; wastewater treatment; microbial community succession

1. Introduction

Traditional activated sludge (CAS) process had been applied to treat municipal wastewater for more than ten decades. However, the CAS process typically consumes a large amount of energy for electrical aeration to oxidize organic matter and ammonia nitrogen, while it releases greenhouse gases (GHGs) and produces excessive sludge [1,2,3,4,5]. Thus, it is clear that the CAS process can hardly meet the increasingly demanding requirements in terms of energy consumption, carbon emissions and discharge water quality [6]. The microalgal-bacterial granular sludge (MBGS) process has been explored in various wastewater treatment processes. MBGS is a symbiotic microbial aggregate of microalgae (including simple cellular prokaryotes, eukaryotes and diatoms, etc.) and bacteria (including heterotrophic bacteria, phosphate-accumulating bacteria, etc.). In the MBGS process, microalgae can provide O2 for the oxidation of organic matter, as well as consume CO2 as a carbon source produced from bacterial respiratory [6,7]. Compared with CAS, the MBGS process owns the superb merits of low energy consumption, robust adaptability and resource recovery potential, which has been extensively reported [8,9,10,11].
Recently, it was found that the self-coupled MBGS process could remove respective 92.7%, 96.8%, 84.1% and 87.2% of organic matter, ammonia nitrogen, total nitrogen and phosphorus from simulated municipal wastewater under a continuous light condition in 6 h with respective effluent concentrations of 18.3, 0.8, 3.9 and 0.4 mg/L, indicating the excellent potential of the MBGS process [12]. In addition, the MBGS process was highly adaptable to antibiotics and heavy metals in actual wastewater [13,14] thus was extensively used for the treatment of actual municipal wastewater [15,16]. However, the study of municipal wastewater treatment by MBGS under outdoor day-night alternating conditions was rarely reported, which restricted the further engineering application of this process.
This study aims to treat simulated municipal wastewater by MBGS process under outdoor day and night alternating conditions. The nutrient removal in daytime and night during weather changes was investigated. Moreover, the microbial response mechanisms of the MBGS process under varying temperatures and light were further discussed. It is expected this study can provide new insights into the performance of the MBGS process under alternate day and night conditions and offer a theoretical basis for its further application.

2. Materials and Methods

2.1. MBGS and Simulated Wastewater

MBGS used in this study was pre-cultured using seeding sludge derived from a wastewater treatment plant in Wuhan City, China. Briefly, seeding sludge was cultured in an SBR reactor, followed by inoculation using simulated municipal wastewater [17]. After granular sludge formation, they were cultured in a 500 mL conical bottle with continuous aeration under 200 ± 10 µmol·m−2·s−1 of LED light irradiation for one month. The mature MBGS with a size of 710.2 ± 18.6 µm and a 5-min sludge volume index (SVI5) of 73.6 ± 10.1 mL/g were obtained. The volatile suspended solids (VSS) concentration was about 3 g/L.
The main components of simulated municipal wastewater were 527.0 mg/L NaAc, 113.1 mg/L NH4Cl, 30.3 mg/L K2HPO4, 50.0 mg/L MgSO4·7H2O, 20.0 mg/L CaCl2, 40.0 mg/L FeSO4·7H2O and 40.0 mg/L NaHCO3, which were equivalent to 400.0, 30.0 and 5.0 mg/L of initial Chemical oxygen demand (COD), NH4+-N and PO43−-P.

2.2. Experimental Setup

As shown in Figure 1a, 50 mL of glass anaerobic bottles were used to inoculate mature MBGS. The reaction devices were placed on the terrace of the Building of Engineering Training Center at Wuhan University of Science and Technology (longitude 116.31, latitude 39.98) from 10 September 2020 to 13 October 2020. The hydraulic residence time (HRT) was 12 h.

2.3. Analytical Methods

During the experimental processes, 10 mL of the samples was taken from the bottles at daily 8 a.m. and at 8 p.m. Chemical oxygen demand (COD), ammonia nitrogen (NH4+-N), phosphate (PO43−-P), nitrate nitrogen (NO3-N), nitrite nitrogen (NO2-N), VSS and SVI5 were determined according to standard methods [18]. The concentration of chlorophyll a and b in MBGS were determined by the acetone extraction method [19]. The granular size of MBGS was measured by a particle size analyzer (Malvern Mastersizer 2000, Westborough, MA, USA). Dissolved oxygen (DO) concentrations and pH were measured using a dissolved oxygen meter (Yellow Springs, OH, USA) and pH meter (STARTER3100, Ohaus, Parsippany, NJ, USA). The daily ambient temperature, water temperature and light intensity were recorded. SPSS software was used to analyze statistically significant differences of MBGS performance (p < 0.05), and the Spearman correlation coefficient (ANOVA, p < 0.05) was used to assess the correlation between temperature, light intensity and nutrient removals.
The Illumina Miseq sequencing method was used to analyze the differences in the microbial community of sludge samples [20] on the 1st, 17th and 33rd days of the experiment. DNA was extracted using DNA kits (OMEGA Biotek Inc., Norcross, GA, USA) and verified with 0.8% agarose gel electrophoresis. The bacterial primers 338F-806R and algal primers 528F-706R were used to amplify gene fragments [21]. The sequencing process, including the data analysis, was performed by the approach described elsewhere [22].

3. Results

3.1. Performance of MBGS Process

The average ambient temperature and water temperature during the experiment were shown in Figure 2a. As seen, the average daily ambient temperatures were 24.0 ± 1.5 °C (phase I, day 1–4), 19.7 ± 1.9 °C (phase II, day 5–14), 21.6 ± 0.9 °C (Phase III, day 15–23), 13.8 ± 1.6 °C (phase IV, day 24–29) and 19.3 ± 0.8 °C (phase V, day 30–33). The average water temperature during the daytime was 0–3 °C higher than the average ambient temperature except for sunny days (i.e., day 2, 3, 9, 15, 18, 21, 22, 29–32), while the average water temperature at night was 0–3 ℃ lower than the average ambient temperature (Figure 2a). It should be noted that experimental water temperatures at daytime and night were highly variable due to changing ambient temperature. On the other hand, the light intensities during the experiment were presented in Figure 2b. It was found that the maximum light intensity was 680 µmol·m−2·s−1 at day 2–3, while 955 µmol·m−2·s−1 at day 9, 15, 18 and 22. On day 21 and 29–32, the maximum light intensity reached 1200 µmol·m−2·s−1. The rest experimental days were cloudy or rainy with light intensity ranging from 20 to 300 µmol·m−2·s−1.
The removal efficiency of the MBGS process in terms of COD, NH4+-N and PO43−-P in municipal wastewater are presented in Table 1 and Figure 3. As can be seen, COD removal during the daytime was 44.1% ± 21.9%, while 31.9% ± 19.5% at night, suggesting that bacteria in MBGS could accelerate the degradation of organic matters using O2 produced from microalgae photosynthesis at daytime. COD removal in stage I was relatively stable due to the changeableness ambient temperature. While stage II experienced a drop in temperature, leading to decreased COD removal. In stage II at daytime, COD removal rate recovered from 32.9% ± 11.7% to 63.2% ± 0.85% (Figure 3a). Despite the increase of ambient temperature in Phase III, only sunny days (i.e., 4 days) provided sufficient light (Figure 2b), and thus, COD removal fluctuated sharply between −29% and 69%. In addition, the COD removal rate at night shifted at −43%–55% due to the changes in ambient temperature. A negative COD removal rate was possible due to organic matter releases from microalgal or bacterial cells impaired the stable operation of the MBGS process. It should be noted that COD removal was temperature-dependent. Our previous study indicated MBGS process could perform satisfactorily in the temperature ranging from 15 to 30 °C, and the maximum COD removal was achieved at 30 °C [10]. However, it was found that COD removal was insignificantly correlated with average water temperature and light intensity (ANOVA, p > 0.05) in this study.
On the other hand, the average removal rate of NH4+-N at daytime was 77.5% ± 13.1%, while 46.9% ± 16.4% at night with a significant difference (p < 0.05) (Figure 3b). NH4+-N removal at daytime was significantly related to the average water temperature (ANOVA, p < 0.05), but insignificant correlated with light intensity (ANOVA, p > 0.05), indicating that water temperature may have a greater impact on NH4+-N removal than the light intensity at daytime. However, it should be found that NH4+-N removal reached the highest on sunny days (i.e., day 2, 3, 9, 15, 18, 21, 22 and 29–32) with light intensity above 680 µmol·m−2·s−1. These results indicated that high light intensity was more favorable for NH4+-N removal, but a low light intensity had an insignificant effect on NH4+-N removal, which was consistent with reported research [23].
The average removal rates of PO43−-P at daytime and night were 45.1% ± 27.2% and 46.8% ± 22.2% with an insignificant difference (p > 0.05) (Figure 3c), showing no correlation with the average water temperature and light intensity (ANOVA, p > 0.05). In phase I, PO43−-P removal at night was much better than that at daytime (p < 0.05), while insignificantly different in phase II (p > 0.05). Due to the unstable operation, PO43−-P removal changed significantly (p > 0.05). In addition, little PO43−-P removal was achieved during the days with high light intensity.
As can be seen from the above results, COD, NH4+-N and PO43−-P could be effectively removed by MBGS at outdoor conditions. Due to lower temperature and absent light irradiation at night, removal rates of COD and PO43−-P were lower at night than that at daytime, but the difference was insignificant. Different from COD and PO43−-P removal, NH4+-N removal was significantly lower at night, indicating that NH4+-N was greatly affected by temperature and illumination. As for the actual wastewater treatment process in the presence of MBGS, enhanced NH4+-N removal was necessary on cold or cloudy days.

3.2. Basic Parameters of MBGS

The basic parameters of MBGS at the beginning and end of the experiment are presented in Table 2. After the operation, the granular size was reduced by 84.6 µm.This indicated that the stability of MBGS was likely destroyed due to blocked growth of microalgae and bacteria under changed outdoor ambient temperature and light intensity, leading to decreased removal rates of pollutants (Figure 3). On the other hand, SVI5 increased by 9.5 mL/g. As bacterial sludge had a much better settling capacity than microalgae [24,25], microalgae may be reduced in MBGS in this study, evidenced by reduced chlorophyll a (i.e., 19.3 mg/g) and chlorophyll b (i.e., 10.3 mg/g). In fact, chlorophyll a was commonly used as an algae biomass indicator [26,27]. As such, microalgae in MBGS were reduced during the experiment. It should be noted that the ratio of chlorophyll a to chlorophyll b increased, possibly due to the elevation of cyanobacteria.

3.3. The Community Structure of MBGS

The distribution of prokaryotes at class level and eukaryotes at genus level in MBGS were presented in Figure 4a,b. As seen, Gammaproteobacteria attributed to Aquimonas, Acinetobacter, Pseudomonas, Thauera, Clostridia attributed to Proteiniclasticum, Acetoanaerobium, Alphaproteobacteria attributed to Rhizobium, Phreatobacter, Psychroglaciecola and Rhodobacter were predominant prokaryotic microbial community, most of which were heterogenetic bacteria that oxidized organic pollutants and produced CO2 for microalgae [28]. In addition, nitrifying bacteria were not detected in MBGS, and nitrates and nitrites were not found in water, indicating that NH4+-N removal was mainly due to microbial assimilation [12]. As such, nutrient removal at daytime was higher than that at night due to O2 production from microalgal photosynthesis. On the other hand, common phosphate-accumulating organisms such as Actinobacteria (e.g., Microlunatus and Tetrasphaera), Gammaproteobacteria (e.g., Halomonas and Thiothrix), Betaproterbacteria (e.g., Accumulibacter, Dechloromonas and Comamonadaceae) and Pantanalinema were not the dominant bacteria in MBGS, indicating that PO43−-P removal was mainly attributed to microbial assimilation [29]. Thus, poor PO43−-P removal was obtained in this study (Figure 3c).
The relative abundance of Aquimonas on day 33 was 13.6% lower than that on day 17 (Figure 4b). The plausible reason was that Aquimonas was thermophilic and temperature in phase IV was the lowest. In addition, Aquimonas was likely to compete with denitrifying bacteria (i.e., Pseudomonas, Thauera, Rhizobium, Phreatobacter, Psychroglaciecola, Rhodobacter) [30,31,32,33]. The total abundance of these denitrifying bacteria increased from 13.0% to 18.0% after the whole experiment. The relative abundance of filamentous microorganisms (i.e., Clostridia and Anaerolineae) decreased from 35.7% on day 1 to 31.6% on day 17, which was helpful to improve the stability of granular structures [34,35], leading to smaller particle size (Table 1). On the other hand, Chlorella was the dominant genus of eukaryotes with an abundance of above 99% (Figure 4c), indicating that environmental changes insignificantly affected the dominance of Chlorella.

4. Conclusions and Perspectives

In this study, COD, NH4+-N and PO43−-P removal was investigated for the first time by microalgal-bacterial granular sludge (MBGS) process under actual 33 day-outdoor conditions experienced two cooling and lasted continuous rainy days. It was found that COD and NH4+-N removal was better at daytime than at night, while PO43−-P removal was insignificantly different at daytime and night. In addition, COD and PO43−-P fluctuated greatly with the operation of the MBGS system while they were of insignificant difference with water temperature and light intensity. However, NH4+-N removal was significantly related to water temperature while insignificantly correlated with light intensity. On the other hand, the prokaryotic community structure of MBGS changed significantly while Chlorella always maintained an absolutely dominant position in eukaryotes.
In view of the adaptability potential of the MBGS process to natural day-night alternation in wastewater treatment, it is expected to be the next generation of wastewater treatment technology. At present, the research of the MBGS process has made great progress, but there are still problems that need to be solved in the practical application. Future research should focus on the following aspects: (1) exploring a continuous-flow photo-bioreactor with good hydraulic conditions for real domestic wastewater treatment; (2) the reuse of the produced biomass of MBGS; (3) the potential of MBGS for the removal of emerging contaminants. These would accelerate the application of the MBGS process in the application of wastewater reclamation.

Author Contributions

Conceptualization, B.J.; methodology, B.J. and S.W.; validation, Y.M.; investigation, L.Z.; data curation, L.Z. and H.H.; writing—original draft preparation, S.W.; writing—review and editing, B.J. and Y.M. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Zhang, X.; Zhang, M.; Liu, H.; Gu, J.; Liu, Y. Environmental sustainability: A pressing challenge to biological sewage treatment processes. Curr. Opin. Environ. Sci. Health 2019, 12, 1–5. [Google Scholar] [CrossRef]
  2. Liu, Y.; Gu, J.; Zhang, M. AB Processes: Towards Energy Self-Sufficient Municipal Wastewater Treatment; IWA Publishing: London, UK, 2019. [Google Scholar]
  3. Ji, B.; Zhu, L.; Wang, S.; Qin, H.; Ma, Y.; Liu, Y. A novel micro-ferrous dosing strategy for enhancing biological phosphorus removal from municipal wastewater. Sci. Total Environ. 2020, 704, 135453. [Google Scholar] [CrossRef]
  4. Zhao, J.; Wang, X.; Li, X.; Jia, S.; Peng, Y. Advanced nutrient removal from ammonia and domestic wastewaters by a novel process based on simultaneous partial nitrification-anammox and modified denitrifying phosphorus removal. Chem. Eng. J. 2018, 354, 589–598. [Google Scholar] [CrossRef]
  5. Pang, H.; Li, L.; He, J.; Yan, Z.; Ma, Y.; Nan, J.; Liu, Y. New insight into enhanced production of short-chain fatty acids from waste activated sludge by cation exchange resin-induced hydrolysis. Chem. Eng. J. 2020, 388, 124235. [Google Scholar] [CrossRef]
  6. Zhang, M.; Ji, B.; Liu, Y. Microalgal-bacterial granular sludge process: A game changer of future municipal wastewater treatment? Sci. Total Environ. 2021, 752, 141957. [Google Scholar] [CrossRef]
  7. Wang, J.; Lei, Z.; Wei, Y.; Wang, Q.; Tian, C.; Shimizu, K.; Zhang, Z.; Adachi, Y.; Lee, D. Behavior of algal-bacterial granular sludge in a novel closed photo-sequencing batch reactor under no external O2 supply. Bioresour. Technol. 2020, 318, 124190. [Google Scholar] [CrossRef] [PubMed]
  8. Gonçalves, A.L.; Pires, J.C.M.; Simões, M. A review on the use of microalgal consortia for wastewater treatment. Algal Res. 2017, 24, 403–415. [Google Scholar] [CrossRef]
  9. Tiron, O.; Bumbac, C.; Patroescu, I.V.; Badescu, V.R.; Postolache, C. Granular activated algae for wastewater treatment. Water Sci. Technol. 2015, 71, 832–839. [Google Scholar] [CrossRef]
  10. Ji, B.; Zhu, L.; Wang, S.; Liu, Y. Temperature-effect on the performance of non-aerated microalgal-bacterial granular sludge process in municipal wastewater treatment. J. Environ. Manag. 2021, 282, 111955. [Google Scholar] [CrossRef]
  11. Liu, L.; Zeng, Z.; Bee, M.; Gibson, V.; Wei, L.; Huang, X.; Liu, C. Characteristics and performance of aerobic algae-bacteria granular consortia in a photo-sequencing batch reactor. J. Hazard. Mater. 2018, 349, 135–142. [Google Scholar] [CrossRef]
  12. Ji, B.; Zhang, M.; Gu, J.; Ma, Y.; Liu, Y. A self-sustaining synergetic microalgal-bacterial granular sludge process towards energy-efficient and environmentally sustainable municipal wastewater treatment. Water Res. 2020, 179, 115884. [Google Scholar] [CrossRef]
  13. Wang, S.; Ji, B.; Cui, B.; Ma, Y.; Guo, D.; Liu, Y. Cadmium-effect on performance and symbiotic relationship of microalgal-bacterial granules. J. Clean. Prod. 2021, 282, 125383. [Google Scholar] [CrossRef]
  14. Wang, S.; Ji, B.; Zhang, M.; Ma, Y.; Gu, J.; Liu, Y. Defensive responses of microalgal-bacterial granules to tetracycline in municipal wastewater treatment. Bioresour. Technol. 2020, 312, 123605. [Google Scholar] [CrossRef] [PubMed]
  15. Abouhend, A.S.; McNair, A.; Kuo-Dahab, W.C.; Watt, C.; Butler, C.S.; Milferstedt, K.; Hamelin, J.; Seo, J.; Gikonyo, G.J.; El-Moselhy, K.M.; et al. The oxygenic photogranule process for aeration-free wastewater treatment. Environ. Sci. Technol. 2018, 52, 3503–3511. [Google Scholar] [CrossRef]
  16. Abouhend, A.S.; Milferstedt, K.; Hamelin, J.; Ansari, A.A.; Butler, C.; Carbajal-González, B.I.; Park, C. Growth progression of oxygenic photogranules and its impact on bioactivity for aeration-free wastewater treatment. Environ. Sci. Technol. 2019, 54, 486–496. [Google Scholar] [CrossRef] [PubMed]
  17. Guo, D.; Zhang, X.; Shi, Y.; Cui, B.; Fan, J.; Ji, B.; Yuan, J. Microalgal-bacterial granular sludge process outperformed aerobic granular sludge process in municipal wastewater treatment with less carbon dioxide emissions. Environ. Sci. Pollut. Res. 2021, 28, 13616–13623. [Google Scholar] [CrossRef]
  18. APH Association. Standard Methods for the Examination of Water and Wastewater, 21st ed.; American Public Health Association: Washington, DC, USA, 2005. [Google Scholar]
  19. Lee, C.; Lee, S.; Ko, S.; Oh, H.; Ahn, C. Effects of photoperiod on nutrient removal, biomass production, and algal-bacterial population dynamics in lab-scale photobioreactors treating municipal wastewater. Water Res. 2015, 68, 680–691. [Google Scholar] [CrossRef] [PubMed]
  20. Cai, J.; Pan, A.; Li, Y.; Xiao, Y.; Zhou, Y.; Chen, C.; Sun, F.; Su, X. A novel strategy for enhancing anaerobic biodegradation of an anthraquinone dye reactive blue 19 with resuscitation-promoting factors. Chemosphere 2021, 263, 127922. [Google Scholar] [CrossRef]
  21. Zhang, B.; Lens, P.N.L.; Shi, W.; Zhang, R.; Zhang, Z.; Guo, Y.; Bao, X.; Cui, F. Enhancement of aerobic granulation and nutrient removal by an algal–bacterial consortium in a lab-scale photobioreactor. Chem. Eng. J. 2018, 334, 2373–2382. [Google Scholar] [CrossRef]
  22. Ji, B.; Wang, S.; Guo, D.; Pang, H. Comparative and comprehensive analysis on bacterial communities of two full-scale wastewater treatment plants by second and third-generation sequencing. Bioresour. Technol. Rep. 2020, 11, 100450. [Google Scholar] [CrossRef]
  23. Jia, H.; Yuan, Q. Nitrogen removal in photo sequence batch reactor using algae-bacteria consortium. J. Water Process Eng. 2018, 26, 108–115. [Google Scholar] [CrossRef]
  24. Choi, O.; Das, A.; Yu, C.; Hu, Z. Nitrifying bacterial growth inhibition in the presence of algae and cyanobacteria. Biotechnol. Bioeng. 2010, 107, 1004–1011. [Google Scholar] [CrossRef] [PubMed]
  25. Huang, W.; Li, B.; Zhang, C.; Zhang, Z.; Lei, Z.; Lu, B.; Zhou, B. Effect of algae growth on aerobic granulation and nutrients removal from synthetic wastewater by using sequencing batch reactors. Bioresour. Technol. 2015, 179, 187–192. [Google Scholar] [CrossRef] [PubMed]
  26. Liu, H.; Zhou, Y.; Xiao, W.; Ji, L.; Cao, X.; Song, C. Shifting nutrient-mediated interactions between algae and bacteria in a microcosm: Evidence from alkaline phosphatase assay. Microbiol. Res. 2012, 167, 292–298. [Google Scholar] [CrossRef] [PubMed]
  27. Wang, S.; Ji, B.; Zhang, M.; Gu, J.; Ma, Y.; Liu, Y. Tetracycline-induced decoupling of symbiosis in microalgal-bacterial granular sludge. Environ. Res. 2021, 197, 111095. [Google Scholar] [CrossRef]
  28. Qiao, S.; Hou, C.; Wang, X.; Zhou, J. Minimizing greenhouse gas emission from wastewater treatment process by integrating activated sludge and microalgae processes. Sci. Total Environ. 2020, 732, 139032. [Google Scholar] [CrossRef]
  29. Ji, B.; Zhang, M.; Wang, L.; Wang, S.; Liu, Y. Removal mechanisms of phosphorus in non-aerated microalgal-bacterial granular sludge process. Bioresour. Technol. 2020, 312, 123531. [Google Scholar] [CrossRef]
  30. Feng, T.; Wu, J.; Chai, K.; Liu, F. Effect of Pseudomonas sp. on the degradation of aluminum/epoxy coating in seawater. J. Mol. Liq. 2018, 263, 248–254. [Google Scholar] [CrossRef]
  31. Han, W.; Li, T.; Cheng, L.; Liu, L.; Yu, L.; Peng, Z. Effect of adding microorganism and carbon source to substrate on nitrogen removal treating the drainage of WWTP. Water Sci. Technol. 2019, 79, 1947–1955. [Google Scholar]
  32. Yang, C.; Wang, Q.; Simon, P.N.; Liu, J.; Liu, L.; Dai, X.; Zhang, X.; Kuang, X.; Igarashi, Y.; Pan, X.; et al. Distinct network interactions in particle-associated and free-living bacterial communities during a Microcystis aeruginosa bloom in a plateau lake. Front. Microbiol. 2017, 8, 1202. [Google Scholar] [CrossRef]
  33. Ji, B.; Yang, K.; Zhu, L.; Jiang, Y.; Wang, H.; Zhou, J.; Zhang, H. Aerobic denitrification: A review of important advances of the last 30 years. Biotechnol. Bioprocess Eng. 2015, 20, 643–651. [Google Scholar] [CrossRef]
  34. Zhao, Y.; Jiang, B.; Tang, X.; Liu, S. Metagenomic insights into functional traits variation and coupling effects on the anammox community during reactor start-up. Sci. Total Environ. 2019, 687, 50–60. [Google Scholar] [CrossRef] [PubMed]
  35. Ni, B.; Zeng, R.; Fang, F.; Xie, W.; Xu, J.; Sheng, G.; Sun, Y.; Yu, H. Evaluation on factors influencing the heterotrophic growth on the soluble microbial products of autotrophs. Biotechnol. Bioeng. 2011, 108, 804–812. [Google Scholar] [CrossRef]
Figure 1. (a) A diagram of the experimental set-up and (b) the appearance of microalgal-bacterial granular sludge (MBGS).
Figure 1. (a) A diagram of the experimental set-up and (b) the appearance of microalgal-bacterial granular sludge (MBGS).
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Figure 2. Experimental conditions in terms of average ambient temperature, water temperature (a) and light intensity (b).
Figure 2. Experimental conditions in terms of average ambient temperature, water temperature (a) and light intensity (b).
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Figure 3. Removal rates of (a) COD, (b) NH4+-N and (c) PO43−-P.
Figure 3. Removal rates of (a) COD, (b) NH4+-N and (c) PO43−-P.
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Figure 4. Distributions of prokaryotic diversity at class level (a) and genus level (b); eukaryotic diversity at genus level in microbial communities (c) on days 1, 17 and 33. Minority fractions of bacterial and algal communities indicated the sum of different classes and genus accounted for less than 1% of its total sequences in each sample.
Figure 4. Distributions of prokaryotic diversity at class level (a) and genus level (b); eukaryotic diversity at genus level in microbial communities (c) on days 1, 17 and 33. Minority fractions of bacterial and algal communities indicated the sum of different classes and genus accounted for less than 1% of its total sequences in each sample.
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Table 1. The influent and effluent values in terms of Chemical oxygen demand (COD), NH4+-N and PO43−-P.
Table 1. The influent and effluent values in terms of Chemical oxygen demand (COD), NH4+-N and PO43−-P.
Time (day)COD (mg/L)NH4+-N (mg/L)PO43−-P (mg/L)
DaytimeNightDaytimeNightDaytimeNight
InfluentEffluentInfluentEffluentInfluentEffluentInfluentEffluentInfluentEffluentInfluentEffluent
1415 ± 14178 ± 34477 ± 12268 ± 2940.1 ± 0.511.1 ± 0.735.1 ± 0.111.0 ± 0.76.83 ± 0.172.22 ± 0.026.15 ± 0.121.04 ± 0.17
2393 ± 6126 ± 11510 ± 70210 ± 2936.9 ± 0.13.8 ± 0.163.3 ± 6.222.3 ± 10.75.45 ± 0.342.11 ± 0.036.26 ± 0.271.41 ± 0.23
3352 ± 40168 ± 14525 ± 67276 ± 1236.3 ± 1.99.2 ± 0.537.2 ± 3.113.8 ± 0.16.13 ± 0.052.41 ± 0.478.37 ± 0.632.52 ± 0.27
4490 ± 57184 ± 9442 ± 9263 ± 1245.7 ± 2.511.1 ± 0.935.0 ± 1.724.5 ± 1.95.77 ± 0.642.51 ± 0.484.09 ± 0.191.38 ± 0.18
5417 ± 63190 ± 11303 ± 3202 ± 2926.6 ± 10.67.1 ± 0.225.8 ± 1.515.7 ± 1.63.56 ± 0.721.31 ± 0.442.23 ± 0.080.76 ± 0.10
6302 ± 37205 ± 60455 ± 26300 ± 124.0 ± 0.84.9 ± 0.329.4 ± 2.115.0 ± 0.62.26 ± 0.860.56 ± 0.313.86 ± 0.290.34 ± 0.23
7458 ± 29162 ± 52362 ± 6191 ± 329.7 ± 2.36.1 ± 1.123.4 ± 0.26.9 ± 0.92.72 ± 0.020.69 ± 0.172.01 ± 0.050.61 ± 0.04
8369 ± 74140 ± 37385 ± 84187 ± 4428.2 ± 1.91.5 ± 0.129.7 ± 0.810.4 ± 1.42.71 ± 0.630.90 ± 0.397.61 ± 0.122.37 ± 0.35
9373 ± 1138 ± 11305 ± 12436 ± 1230.3 ± 0.30.9 ± 1.329.5 ± 1.214.3 ± 2.86.83 ± 0.504.96 ± 0.286.72 ± 0.035.41 ± 0.25
10340 ± 34109 ± 23473 ± 31189 ± 8725.1 ± 0.66.2 ± 2.330.8 ± 2.011.3 ± 0.45.89 ± 0.232.03 ± 0.277.96 ± 0.182.80 ± 0.07
11450 ± 6188 ± 32342 ± 47150 ± 3827.5 ± 0.411.1 ± 2.320.8 ± 0.17.7 ± 0.46.74 ± 0.033.63 ± 0.185.72 ± 0.363.76 ± 0.09
12253 ± 9120 ± 9241 ± 15204 ± 3216.9 ± 0.92.8 ± 0.821.5 ± 0.913.0 ± 0.55.01 ± 0.233.02 ± 0.135.54 ± 0.294.08 ± 0.02
13334 ± 37184 ± 14237 ± 20177 ± 2317.2 ± 0.45.1 ± 0.630.7 ± 1.718.7 ± 0.46.03 ± 0.534.07 ± 0.105.22 ± 0.273.40 ± 0.02
14642 ± 117237 ± 49469 ± 35247 ± 1240.5 ± 1.46.8 ± 0.435.1 ± 1.519.9 ± 0.48.20 ± 0.644.22 ± 0.166.40 ± 0.283.40 ± 0.22
15298 ± 32324 ± 17354 ± 6288 ± 1229.3 ± 0.27.4 ± 0.327.3 ± 5.617.5 ± 2.94.18 ± 0.136.33 ± 0.044.92 ± 0.024.58 ± 0.05
16338 ± 20186 ± 63486 ± 41288 ± 628.9 ± 1.16.7 ± 0.727.6 ± 1.112.1 ± 1.24.44 ± 0.193.51 ± 0.168.02 ± 0.233.69 ± 0.14
17472 ± 37223 ± 23268 ± 64185 ± 2926.4 ± 0.24.1 ± 0.122.7 ± 3.311.7 ± 1.96.72 ± 0.035.91 ± 0.256.33 ± 0.584.36 ± 0.24
18324 ± 29249 ± 20278 ± 44121 ± 3221.6 ± 0.82.0 ± 0.920.6 ± 0.99.6 ± 0.46.04 ± 0.276.55 ± 0.175.61 ± 0.055.41 ± 0.15
19278 ± 37203 ± 29292 ± 29191 ± 2036.1 ± 0.84.2 ± 0.425.1 ± 2.212.5 ± 1.07.43 ± 0.375.14 ± 0.245.11 ± 0.453.15 ± 0.13
20393 ± 103227 ± 34259 ± 6171 ± 1526.7 ± 0.711.0 ± 0.827.8 ± 1.415.1 ± 0.64.64 ± 0.072.08 ± 0.012.42 ± 0.351.96 ± 0.08
21257 ± 32227 ± 1226 ± 6150 ± 338.4 ± 0.38.5 ± 0.234.4 ± 1.316.0 ± 3.05.51 ± 0.024.05 ± 0.044.68 ± 0.081.40 ± 0.13
22203 ± 29263 ± 31292 ± 41218 ± 2941.3 ± 12.68.4 ± 0.223.3 ± 0.419.4 ± 0.73.34 ± 0.192.34 ± 0.305.22 ± 0.972.25 ± 0.69
23340 ± 22221 ± 6286 ± 14239 ± 1440.7 ± 1.310.3 ± 1.533.2 ± 0.720.6 ± 0.45.98 ± 0.752.47 ± 0.315.64 ± 0.192.77 ± 0.04
24385 ± 3197 ± 11251 ± 64199 ± 4233.3 ± 3.214.8 ± 0.926.6 ± 0.424.9 ± 0.64.20 ± 0.191.25 ± 0.353.09 ± 0.252.75 ± 0.09
25349 ± 53148 ± 3197 ± 6150 ± 627.2 ± 1.216.1 ± 1.939.2 ± 0.429.9 ± 0.43.07 ± 0.231.24 ± 0.115.12 ± 0.212.89 ± 0.15
26310 ± 3178 ± 0207 ± 20118 ± 1743.3 ± 1.311.4 ± 0.432.9 ± 1.524.2 ± 0.25.52 ± 0.231.80 ± 0.204.50 ± 0.292.41 ± 0.23
27326 ± 25172 ± 42176 ± 8118 ± 636.6 ± 1.915.3 ± 0.228.1 ± 1.222.5 ± 0.64.42 ± 0.090.57 ± 0.103.01 ± 0.011.70 ± 0.17
28318 ± 31190 ± 6249 ± 1140 ± 326.1 ± 0.13.8 ± 1.020.8 ± 0.88.3 ± 0.25.85 ± 0.082.70 ± 0.055.96 ± 0.012.55 ± 0.07
29379 ± 56199 ± 3381 ± 20336 ± 1723.1 ± 0.32.1 ± 0.121.7 ± 0.19.6 ± 0.25.46 ± 0.192.21 ± 0.306.01 ± 0.123.61 ± 0.15
30152 ± 20124 ± 20397 ± 3363 ± 172.5 ± 0.11.0 ± 0.120.1 ± 0.512.3 ± 0.66.53 ± 0.504.42 ± 0.526.46 ± 0.184.64 ± 0.14
31490 ± 17296 ± 17395 ± 89318 ± 4218.2 ± 6.81.7 ± 0.722.2 ± 0.28.9 ± 0.47.08 ± 0.164.79 ± 0.316.80 ± 0.044.14 ± 0.06
32217 ± 1152 ± 1499 ± 20421 ± 1421.4 ± 0.11.3 ± 0.324.1 ± 0.312.6 ± 0.35.69 ± 0.014.02 ± 0.256.88 ± 0.034.44 ± 0.21
33154 ± 1764 ± 4458 ± 56308 ± 3928.1 ± 0.31.7 ± 0.423.7 ± 0.67.8 ± 0.77.41 ± 0.174.79 ± 0.146.21 ± 0.013.08 ± 0.11
Table 2. Basic parameters of MBGS before and after the experiment.
Table 2. Basic parameters of MBGS before and after the experiment.
Time (d)SVI5 (mL/g)Chlorophyll a (mg/g)Chlorophyll b (mg/g)Chlorophyll a/bGranular Size (µm)
173.6 ± 10.120.5 ± 0.710.7 ± 0.61.89 ± 0.04710.2 ± 18.6
3383.1 ± 0.11.2 ± 0.20.4 ± 0.12.99 ± 0.87625.6 ± 28.4
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