Effects of biomass pyrolysis derived wood vinegar on microbial activity and communities of activated sludge

Bioresource Technology 279 (2019) 252–261

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Effects of biomass pyrolysis derived wood vinegar on microbial activity and communities of activated sludge

T

⁎

Fang Zhanga, Jingai Shaoa,b, , Haiping Yanga, Dabin Guoc, Zhihua Chend, Shihong Zhanga, Hanping Chena,b a

State Key Laboratory of Coal Combustion, School of Energy and Power Engineering, Huazhong University of Science and Technology, Wuhan 430074, China Department of New Energy Science and Technology, School of Energy and Power Engineering, Huazhong University of Science and Technology, Wuhan 430074, China c School of Environmental Science & Engineering, Huazhong University of Science and Technology, Wuhan 430074, China d School of Environment, Henan Normal University, No. 46, Jianshe Road, Xinxiang 453007, Henan, China b

A R T I C LE I N FO

A B S T R A C T

Keywords: Wood vinegar Microbial activity Microbial community Sequencing batch reactor Activated sludge

The effects of wood vinegar (WVG) on microbial activity and communities of activated sludge were investigated in a sequencing batch reactor (SBR) process. Results showed that the optimal WVG concentration was 4 μL/L when the pollutants removal efficiency and microbial activity were promoted by a WVG dilution factor of 1000. WVG could reduce the increase in microbial species richness, which led to a more notable variety of microbial species diversity. The enhanced microbial activity and communities were addressed to the promotion of 7 main classes of microbes in Proteobacteria, Bacteroidetes, Acidobacteria, and Nitrospirae phyla. The growth of ammoniaoxidizing bacteria (AOB), nitrite-oxidizing bacteria (NOB), and main genera of denitrifying bacteria (DNB), phosphorus-accumulating organisms (PAOs), and glycogen-accumulating organisms (GAOs) could be promoted by WVG, which improved the sewage treatment effectiveness in a SBR.

1. Introduction Biomass is a worldwide and respected renewable energy source for its carbon neutrality and low pollution of its use in energy generation. Pyrolysis is a widely recognized technology for bioenergy development, which could effectively avoid severe environmental issues caused by traditional burning of biomass, and convert almost any biomass into biochar, bio-oils and syngas (Zhang et al., 2018a) through using different reactors (Li et al., 2017). Wood vinegar (WVG) is a liquid byproduct derived from biomass pyrolysis for biofuels-biochar poly-generation (Yang et al., 2016a), and it is usually obtained from the downstream condensation and separation of pyrolytic vapors and gases. WVG usually contains more than ∼80% of water and dozens of organic compounds such as acids, phenols and alcohols (Wu et al., 2015). It has been found to be used for antibacterial application, pest control and antioxidant because its organic components could affect the activities of microbes (Yang et al., 2016b). Some organic acids and phenols with relatively low concentration could promote activities of microbes in activated sludge (He et al., 2017; Jiang et al., 2002). Activated sludge process has been widely used in domestic sewage treatment worldwide. Therefore, the application of WVG in activated sludge process has a potentially significance to improve the sewage treatment efficiency and ⁎

maximize the utilization of biomass pyrolysis products. An activated sludge sequencing batch reactor (SBR) process is widely used for sewage treatment, in which pollutants’ concentration of effluent can be effectively controlled (Li et al., 2018a). Biodegradation and transformation of pollutants in a sewage treatment system are largely influenced by the biochemical activity of microorganisms in activated sludge (Wang et al., 2015). Dehydrogenase activity (DHA) (Huang et al., 2015), specific oxygen uptake rate (SOUR) (Nguyen et al., 2010), electron transfer system (ETS) activity (Cao et al., 2017), specific adenosine triphosphate(Feng et al., 2014) and nitrification rate (Gutiérrez et al., 2002) are commonly used to characterize the activity of activated sludge. The efficiency of a sewage treatment is also affected by the microbial community structure, attributes to the relative abundance of microorganisms is in favor of organic matter degradation and nitrogen/phosphorus removal (Huang et al., 2017). It has been demonstrated that the organic acids and phenols at a certain concentration level could promote microorganism metabolisms and microbial enzymes, affect the microbial community structure and distribution, and improve the sewage treatment capacities in activated sludge processes (Fan et al., 2014; Jiang et al., 2002). To benefit from these advantages, WVG was prepared from different biomass species and further utilized in sewage treatment (Yang et al., 2016a). However, studies on

Corresponding author. E-mail address: [email protected] (J. Shao).

https://doi.org/10.1016/j.biortech.2019.01.133 Received 10 January 2019; Received in revised form 25 January 2019; Accepted 28 January 2019 Available online 29 January 2019 0960-8524/ © 2019 Elsevier Ltd. All rights reserved.

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metering pumps. The experiment was carried out with WVG of different dilution factors (raw liquor, 10, 100 and 1000) and different volumes (6 mL/L, 3 mL/L) injected into the system. Samples with WVG concentration of 6000, 3000, 600, 300, 60, 30, 6 and 3 μL/L were labeled as A-H, respectively. The sample without WVG injection was used as comparative SBR run and labeled as I. The temperature of the system was kept at 20 ± 2 °C by automatic heating and temperature control device. The mixture was stirred for 5 min with 10 r/min. Afterwards, aeration was realized by an air pump to supply a ∼3 mg/L dissolved oxygen (DO) concentration. After 2 h of aeration, the mixture was sampled and filtered by polypropylene membrane (0.45 μm), and the obtained filtrate was effluent of the system. The concentration of COD, TN, NH3-N and TP being in the effluent were determined. According to the pollutants’ concentration in the effluent of sample A-H, the removal efficiency was calculated and the dilution factor with the highest pollutants’ removal efficiency was determined as the optimal dilution factor, and thus the range of WVG concentration was further optimized. The above experiment was repeated with the optimized range of WVG concentration, and specific oxygen uptake rate (SOUR), specific oxygen uptake rate of endogenous (SOURe), specific oxidation rate of NH3-N (SOR(NH3-N)) and specific oxidation rate of HNO2-N (SOR(HNO2-N)) were also determined. After 2 h of aeration: (1) the mixture was sampled and filtered with polypropylene membrane (0.45 μm), and concentration of COD, TN, NH3-N and TP of the effluent were determined; (2) total dehydrogenase activity (Dt) and endogenous dehydrogenase activity (De) of the mixture was determined. The WVG concentration related to the highest pollutants’ removal efficiency was recognized to the optimal concentration.

the effects of WVG on microbial activity and community structure of activated sludge in a SBR are still scarce. In this study, the effects of WVG on microorganisms in activated sludge were studied in SBR process. The impacts of WVG on pollutants removal efficiency, microbial activity and communities were investigated. The results of this study are expected to provide a new way to improve sewage treatment efficiency by means of WVG, which also is a new way for effective utilization of WVG derived from biomass pyrolysis poly-generation. 2. Materials and methods 2.1. Materials The WVG was obtained from a Biomass Pyrolysis Poly-generation Demonstration Plant (Ezhou city, Hubei province, China), in which volatiles (downstream cooled at 40–60 °C) was derived from poplar branch pyrolysis in a moving bed reactor (Zhang et al., 2018b). Basic properties and main components of the studied WVG were listed in previous studies (Zhang et al., 2019). The activated sludge was taken from an aeration tank of a municipal wastewater treatment plant (Wuhan, China), which was further cultivated (20 ± 2 °C) in an aeration tank of 50 L with synthetic domestic sewage (SynDS). Before experiment, the activated sludge was cultured for one month to adapt to the SynDS. The as-prepared SynDS included components of: glucose, 278 mg/L; starch, 278 mg/L; peptone, 14 mg/L; KH2PO4, 26.4 mg/L; MgSO4·7H2O, 39 mg/L; MnSO4·H2O, 13.9 mg/L; CaCl2, 8 mg/L; NaHCO3, 111 mg/L; NH4Cl, 87.5 mg/L; CoCl2·6H2O, 1.2 mg/L; FeCl3, 5 mg/L and CuSO4·5H2O, 5 mg/L. The chemical oxygen demand (COD), total nitrogen (TN), ammonia nitrogen (NH3-N), and total phosphorus (TP) of the as-prepared SynDS was 312.4 ± 2.16 mg/L, 22.7 ± 0.88 mg/l, 18.0 ± 1.41 mg/L and 5.9 ± 0.56 mg/L, respectively. The mixture used in the following tests was prepared by mixing SynDS with the bottom sludge, which was obtained from the mixture of the cultivation aeration tank through static settling for 30 min. The mixed liquid suspended solids (MLSS) and mixed liquor volatile suspended solids (MLVSS) of mixture was consistently maintained at ∼2800 mg/L and ∼1720 mg/L, respectively.

2.3. Long-term operation The long-term operation (lasted for 60d) was carried out in the reactor as shown in Fig. 1. The initial activated sludge was sampled and labeled S0. About 5.0 L mixture with initial MLSS of ∼2800 mg/l was injected into the rector. The concentration of DO and the temperature of the system was kept at ∼3 mg/L and 20 ± 2 °C, respectively. Each SBR reaction cycle was lasted for 12 h, included 0.5 h of influent, 10 h of aeration, 1 h of precipitation, and 0.5 h of drainage. WVG with optimal concentration was injected into the reactor with the influent at the beginning of each cycle. On the 30th and 60th day, the sludge was sampled at the end of the first cycle. The comparative experiment was carried out at the same condition without WVG injection and marked as 1#, compared with the WVG injection one of 2#. The sludge samples obtained from 1# on the 30th and 60th day were labeled S30 (1#) and S60 (1#), and samples obtained from 2# on the 30th and 60th day were labeled S30 (2#) and S60 (2#), respectively. Then the DNA extraction and high throughput sequencing analysis of the samples were completed.

2.2. Experiments The SBR system in this study was showed in Fig. 1. The mixture (∼5.0 L) was added into the SBR reactor with a volume of ∼6.0 L by

2.4. Methods 2.4.1. Determination of DHA The Dt could be calculated as the sum of De and substrate metabolic dehydrogenase activity (Ds). The Dt and De were determined according to literature (Wang et al., 2016a) and expressed as:

D=

c tX

(1)

where D was the dehydrogenase activity, mgTF/(g MLVSS·h); c was the concentration of TF, μgTF/mL; t was the culture time, h; X was the MLVSS, g/L. 2.4.2. Determination of SOUR and SOURe SOUR was the sum of SOURe and specific oxygen uptake rate of substrate transformation (SOURs), which could be expressed as (Wang et al., 2016b):

Fig. 1. Schematic diagram of the SBR system. 253

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SOUR =

60OUR X

concentration decreased. The negative pollutants removal efficiency of A and B might be mainly attributed to organics of high concentration coupled with lower microbial activity caused by the WVG injection. The removal efficiency of pollutants in C, D, E and F increased gradually, but remained at a lower level than the comparative results (I). This should be the result of the WVG affecting the microbial metabolism since the organic acids and phenols in the WVG with high concentration could contribute to antimicrobial activity, which led to microbial cell death or metabolic activity decreases. The bacteria with decreased activity would present poor removal capacity of pollutants, while the dead cells even released intracellular substances after they were destroyed. These results indicated the WVG inhibited pollutant removal by microorganisms at relatively high concentrations (with dilution factors of 10 and 100), and even destroyed the function of microbial cells. Interestingly, by further diluting WVG to 1000 folds, the removal efficiency of pollutants in G and H increased to higher level than that being in I run. The removal efficiency of COD, TN, NH3-N and TP reached a maximum level in H, which was 7.11%, 7.54%, 10.36% and 9.34% higher than that of I run, respectively. The reason could be addressed to the fact that some organics with low concentration could promote the bacteria growth, enhance the metabolic activity of cells and promote their removal capacity of pollutants (Fan et al., 2014). For further optimization of sewage treatment efficiency, WVG with concentration of 0, 1, 2, 3, 4 and 5 μL/L was injected to the reactor. As shown in Fig. 2 (b), removal efficiency of pollutants increased with the WVG concentration from 0 to 4 μL/L, but was decreased rapidly at increased concentration of 5 μL/L. The maximum removal efficiency of COD, TN, NH3-N and TP was 8.74%, 7.25%, 9.57% and 10.74% higher than the comparative run (with the WVG concentration of 0 μL/L), respectively.

(2)

where dimension of SOUR was mgO2/(gMLVSS·h); OUR denoted the oxygen uptake rate, mgO2/(L·min). The SOURe was determined according to literature (Eckenfelder and Musterman, 1995) as follows: (1) 150 mL mixture (obtained from the cultivation aeration tank) was centrifuged for 10 min at 3000 r/min; (2) the sediment was washed three times with phosphate buffer (consists of Na3PO4 and Na2HPO4, pH = 7) and then transferred to a conical bottle filled with an oxygen-saturated phosphate buffer; (3) the endogenous oxygen uptake rate (OURe) and SOURe were measured according to the method similar to SOUR. 2.4.3. Determination of SOR (NH3-N) and SOR (HNO2-N) The SOR (NH3-N) and SOR (HNO2-N) were determined according to literature (Sumacz-Gorska et al., 1996) as follows: 1 mL thiourea acrylate solution (750 mg/L) with the function of inhibiting nitrite bacteria was injected to a conical bottle of 150 mL. Then, the bottle was filled with oxygen saturated mixture of SynDS and activated sludge with MLSS of ∼2800 mg/L. The SOUR was determined and recognized to SOUR2. Then the thiourea acrylate solution was substituted by 1 mL sodium chlorate solution (316.5 mg/L) with the function of inhibiting nitrate bacteria. Such experiment was repeated and the result was recorded as SOUR3. In the activated sludge process, oxidation of ammonia includes nitrosation and nitrification. The SOUR of the two processes could be described by SOUR (nitrosation) = SOUR-SOUR2, and SOUR (nitrification) = SOUR − SOUR3, respectively. Thus, SOR (NH3-N) and SOR (HNO2-N) could be calculated according to chemical equivalences as:

SOR (NH3 - N)(mgN/(gMLVSS h)) = 7 SOUR (nitrosation)/24

(3)

SOR(HNO2 - N)(mgN/(gMLVSS h)) = 7 SOUR(nitrification)/8

(4)

3.2. Effects of WVG on microbial activity It is believed that the dehydrogenase which catalyzes the oxidative dehydrogenation of substrates is the key enzyme for microbial degradation of organics to obtain energy. Such enzyme also indicates the activity of microbial cells. As shown in Fig. 3 (a), Dt and Ds of activated sludge increased once WVG concentration increased from 0 to 4 μL/L. But further increasing of WVG concentration to 5 μL/L decreased these values. According to the results, the maximum values of Dt and Ds respectively was 82.11 and 66.88 mg/(g.h), which was 20.48% and 33.52% higher than that of the comparative run (i.e. without WVG injection). The De increased slightly with the WVG concentration, and it increased by 5.32% when the WVG concentration increased from 0 to 5 μL/L. As also could be found, the increments (Δ) of Dt, Ds and De were highly proportional to the increments of WVG concentration (Δ c), characterized with high Pearson’s r (r) as illustrated in Fig. 3 (b). Once exogenous organics were added into activated sludge system, the microorganisms were induced to produce specific enzymes and the corresponding enzymatic activities of microbes were also enhanced (Itoh et al., 2000). The organics in WVG activated the dehydrogenase and improved the utilization of other organics by microbes in the SBR system. It could be seen from Fig. 2 (b) that the removal efficiency of COD reached the maximum of 87.86% at 4 μL/L WVG concentration, indicated most of the organics had been removed from water. However, the activated sludge still presented high value of Dt and Ds and revealed relatively high metabolic activity of microbes, as presented in the same WVG concentration of Fig. 3(a). These results might originate from the two stages included in pollutants removal by microbes. The adsorption of pollutants by microbial cells was the first stage, in which large quantities of pollutants were adsorbed on the surface of cells within 30 min (Liu et al., 2011), characterized by the relatively high pollutants removal efficiency from water (Fig. 2 (b)). Gradual degradation of the adsorbed pollutants was the second stage being in slower rate, characterized by the relatively high Dt and Ds in latter reaction stage (Fig. 3(a)).

2.4.4. DNA extraction and high-throughput sequencing analysis The samples were washed with sterile high purity water and centrifuged for 7 min at 14,000g (RCF) and 4 °C, and then the supernatant was removed and repeated for 3 times. Genomic DNA was extracted by MIO-BIO Power Soil DNA Isolation Kit, and the extracted DNA was stored at −20 °C. 3 μL of each sample was taken to carry out 1.2% agarose gel electrophoresis. PCR amplification was carried out with primers for V4-V5 region of 16S rRNA gene with 515F of front primer and 926 R reverse primer. The first PCR amplification was carried out in a 50 μL reaction system. Thermal cycling consisted of initial denaturation at 94 °C for 2 min, followed by 25 cycles of denaturation at 94 °C for 30 s, annealing 56 °C for 30 s, and elongation at 72 °C for 30 s. Finally 72 °C for 5 min and preserved at 10 °C. The second PCR amplification was carried out in a 40 μL reaction system. Thermal cycle process was the same with the previous one except the number of cycles was 8. The PCR amplification products were then recovered and quantified by FTC-3000 TM real-time PCR. MiSeq sequencing, sequence splicing and operational taxonomic units (OTUs) classification were performed by commercial sequencing companies (TinyGene Bio-Tech, Shanghai, China). 2.4.5. Analysis of effluent quality The DO, MLSS, MLVSS and COD, TN, NH3-N and TP of the effluents were analyzed according to standard methods (AWWA et al., 2012). 3. Results and discussion 3.1. Effects of WVG on removal efficiency of pollutants The effects of WVG with different dilution factors and volumes on the removal efficiency of pollutants were presented in Fig. 2 (a). The removal efficiency of pollutants increased gradually as the WVG 254

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75 0

COD

90

(a)

-75

75

TN-N 5emRYaleI¿FLeQF\()

5HPRYaOHI¿FLHQF\()

30

(b)

80

-150 -225

COD

85

15 0 -15 80 NH3-N 60 40 20 0 -20 80 TP 60 40 20 0 -20 A B

TN

35 30 25 80

NH3-N

75 70 65

TP

90 85

C

D

E

F

G

H

80

I

Sample

0

1

2

3

4

5

CRQFeQWUaWLRQ(ȝ//)

Fig. 2. Effects of WVG concentration on removal efficiency of pollutants.

usually associated with senescence and death of cells. The degradation and transformation of substrate was highly related to the growth and reproduction of cells. Relatively attenuation of endogenous respiration and obviously increased utilization of substrate were responsible for the increased net growth of microbes according to previous research (Zhang et al., 2019). As shown in Fig. 3(f), SOR (NH3-N) and SOR (HNO2-N) both increased firstly and then decreased with the increases of the WVG concentration. When the WVG concentration reached 4 μL/L, the maximum value of SOR (NH3-N) and SOR (HNO2-N) were 4.78 and 7.25 mg/(g.h), which was 52.72% and 33.76% higher than that of the comparative run, respectively. It demonstrated the degradation of nitrosation and nitrification were promoted by WVG. Moreover, ΔSOR (NH3-N) and ΔSOR (HNO2-N) were also highly proportional to Δ c (characterizing with r value as revealed in Fig. 3(g)). The promotion of nitrosation by the WVG was relatively stronger than nitrification, which was might attributed to the fact that the sensitivity of nitrate bacteria to exogenous substances was lower than that of nitrite bacteria (Fu et al., 2006). SOR (NH3-N) was always lower than SOR (HNO2-N), indicated the nitrite was not likely to accumulate in the system and nitrosation of ammonia was the controlling step of ammonia oxidation.

The influence of WVG on SOUR of the activated sludge was shown in Fig. 3(c). The SOURs included the oxygen consumed by microbes oxidizing organics, ammonia and other oxygen-consuming substances that characterized the degradation and transformation activities of microbes to substrate. SOUR and SOURs increased obviously firstly and then decreased evidently with the increase of WVG concentration, and reached a maximum value at 4 μL/L WVG concentration. The maximum of SOUR and SOURs was 92.37 and 81.08 mg/(g·h), which was 43.12% and 48.83% higher than that of the comparative run, respectively. It indicated that the WVG could significantly enhance the degradation and transformation of substrate by microorganisms. SOURe increased slightly with the WVG concentration increasing. It increased by 12.95% when the WVG concentration increased from 0 to 4 μL/L. It could also be found that Δ SOUR, Δ SOURs and Δ SOURe were highly proportional to Δ c with high r value (Fig. 3 (d)). Similar results also were found for Ds v.s. WVG concentration (Fig. 3(a)), SOURs v.s. WVG concentration (Fig. 3(c)), and Δ Ds v.s. increments of removal efficiency of COD (Δ R) and Δ SOURs v.s. Δ R (Fig. 3 (e)). These results indicated that the WVG could enhance the removal efficiency of organic pollutants in a SBR system, and the enhancement was the most significant with 4 μL/L WVG concentration. The increases of SOURs was apparently greater than that of Ds, because Ds mainly characterized the activity of microbial degradation and transformation of organics, but SOURs indicated the activity of microbial degradation and transformation of all oxygen-consuming substrates except intracellular substances. Variations of De and SOURe versus the WVG concentrations indicated the WVG also promoted the endogenous respiration of microorganisms slightly. The increasing of De and SOURe were significantly smaller than that of Ds and SOURs with the same WVG concentration, indicating that the WVG promoted the endogenous respiration more weakly than the degradation and transformation of substrate by microorganisms. On the one hand, the endogenous respiration mainly occurred while there is a lack of substrates in the environment (Li et al., 2014), but nutrients were abundant during the first couple of hours of each SBR reaction cycle. Thus, the endogenous respiration of microbes was rather weak. On the other hand, endogenous respiration occurs within the cells, which was quite hard to be impacted by external substances because of the cell membrane barrier. Actually, the endogenous respiration was

3.3. Effect of WVG on microbial communities 3.3.1. Effect of WVG on microbial species richness and diversity Species rarefaction curves were exhibited in Fig. 4. At 97% similarity level, when the sequencing quantity was less than 10000, the curve of OTUs was firstly steep with the number of sequences constantly growing, and then increased slowly. When the sequencing quantity was more than 30,000, the curve tended to be flat in the end. These results illustrated the existing sequencing data volume was reasonable to detect enough species. At 97% similarity level, the coverage, trimmed sequences, number of OTUs and diversity indexes were listed in Table1. The coverage of all the samples was greater than 0.9950, indicated the sequencing results could describe the samples veritably. 198391 trimmed sequences and 5043 OTUs were obtained from the samples. The Chao and ACE indexes in both 1# and 2# were greater than that of S0, and their value of S30(1#) and S60(1#) remained in 255

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Fig. 3. (a) Effects of WVG concentration on Dt, Ds and De; (b) Δ Dt, Δ Ds and Δ De v.s. Δ c dependencies; (c) Effects of WVG concentration on SOUR, SOURs and SOURe; (d) Δ SOUR, Δ SOURs and Δ SOURe v.s. Δ c dependencies; (e) Δ Ds and Δ SOURs v.s. Δ R dependencies; (f) Effects of WVG concentration on SOR of NH3-N and SOR of HNO2-N; (g) Δ SOR of NH3-N and Δ SOR of HNO2-N v.s. Δ c dependencies.

256

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1200 1000 800

Number of OTUs

abundance of all phyla, respectively, which was consistent with previous studies (Fan et al., 2018a). The abundances of Proteobacteria of S30 (1#) (51.01%) and S60 (1#) (52.28%) were 2.54% and 3.81% higher than that of S0, respectively; and that of S30 (2#) (53.18%) and S60 (2#) (55.38%) were 4.71% and 6.91% higher than that of S0, respectively. The abundances of Bacteroidetes of S30 (1#) (21.09%) and S60 (1#) (21.52%) were 0.19% and 0.62% higher than that of S0, respectively; and that of S30 (2#) (26.00%) and S60 (2#) (27.40%) were 5.10% and 6.50% higher than that of S0, respectively. Variation of the abundance of Nitrospirae was similar to that of Proteobacteria and Bacteroidetes. The abundance of Acidobacteria decreased gradually in 1# and 2# and remained in lower levels in 1# than 2#. As for Chloroflexi, its abundance increased slightly in 1# but decreased significantly in 2#. Similarly, the abundance of Actinobacteria gradually increased in 1# but decreased slightly in 2#. Regards to Firmicutes and Planctomycetes, their abundances decreased gradually during the operation and remained in lower levels in 2# than 1#. These results illustrated that the WVG promoted the growth of Proteobacteria, Bacteroidetes and Nitrospirae evidently, abated the decrease of abundance of Acidobacteria, and inhibited the growth of Chloroflexi, Actinobacteria, Firmicutes and Planctomycetes. Community structure and distribution at class level of the eight phyla was shown in Fig. 6. β-proteobacteria, γ-proteobacteria and α-proteobacteria ranked in the top three classes of Proteobacteria in abundance of S0, respectively, as shown in Fig. 6(a). The abundance of β-proteobacteria and γ-proteobacteria increased in both 1# and 2# and remained in significantly higher levels in 2# than 1#. The abundance of α-proteobacteria increased slightly in 1# and decreased obviously in 2#. As shown in Fig. 6(b), Sphingobacteriia, Flavobacteriia and Cytophagia ranked in the top three classes of Bacteroidetes in abundance, respectively. The abundance of Sphingobacteriia decreased in 1# and increased in 2#, and that of Flavobacteriia and Cytophagia increased in both 1# and 2#, and all the abundances of them remained in higher levels in 2# than 1#. Fig. 6(c) showed that Caldilineae, Chloroflexia and Thermomicrobia ranked in the top three classes of Chloroflexi in abundance, respectively. The abundance of Caldilineae increased evidently in 1#, but that of Chloroflexia and Thermomicrobia decreased in 1# during the operation. The abundance of all the three classes in 2# decreased sharply, in accordance with the significant decrease of the abundance of Chloroflexi (phylum) in 2# (Fig. 5). Blastocatellia, Holophagea and Solibacteres were the only three classified classes in Acidobacteria (Fig. 6(d)). Blastocatellia was the only class with abundance greater than 1% and its abundance decreased slightly in 1# but increased obviously in 2#. Nitrospira was the only class in the phylum of Nitrospirae, as exhibited in Fig. 6(e). Its abundance increased gradually with the operation time, and remained in higher levels in 2# than 1#. As shown in Fig. 6(f), Actinobacteria (class) and Acidimicrobiia ranked the first and the second classes of Actinobacteria (phyla) in abundance, respectively. The abundance of Actinobacteria (class) increased both in 1# and 2#, and remained in higher levels in 1# than 2#. Contrarily, the abundance of Acidimicrobiia remained in lower levels in 1# than 2#. As shown in Fig. 6(g), Bacilli and Clostridia ranked the first and the second classes of Firmicutes of S0 in abundance, respectively. During the operation, the abundance of the two classes remained in significantly lower levels than S0 (2.17% and 1.98%), and there was no obvious change of their abundance versus operational time in both 1# and 2#. Planctomycetacia was the main class in Planctomycetes (Fig. 6(h)). Its abundance decreased gradually in both 1# and 2#, and remained in evidently lower levels in 2# than 1#. The result revealed that the WVG affected the growth of microbes in the main classes of the eight phyla. The reason maybe that the main organic components like organic acids, phenol and ketone in the WVG could affect the bacterial community structure and characteristics of activated sludge (Cabrol et al., 2009; Gómez-Acata et al., 2017; Leong et al., 2011). The WVG promoted the growth of microbes in β-proteobacteria, γ-proteobacteria, Flavobacteriia, Cytophagia, Sphingobacteriia, Blastocatellia and Nitrospirae, abated the decrease of

S0 S30(1#) S60(1#) S30(2#) S60(2#)

(a)

600 400 200 0 0

10000

20000

30000

40000

Number of sequences

Fig. 4. Rarefaction curves. Table 1 Coverage, trimmed sequences, number of OTUs, and diversity index values. Sample

coverage

Trimmed sequences

Number of OTUs

Chao

Ace

Shannon

Simpson

S0 S30(1#) S60(1#) S30(2#) S60(2#)

0.9971 0.9960 0.9962 0.9957 0.9957

40,172 39,629 39,740 38,892 39,958

912 1091 1061 988 991

997 1197 1161 1097 1116

983 1183 1142 1100 1109

5.38 5.02 4.72 4.92 5.15

0.0105 0.0301 0.0477 0.0231 0.0145

higher levels than that of S30(2#) and S60(2#), respectively. It meant that the WVG hampered the increases of the microbial species richness of the activated sludge. Shannon indexes in 1# and 2# were lower than that of S0, indicated the species diversity decreased during the longterm operation. This might be because of the SynDS composition was relatively simple and the treatment capacity was rather small when compared with sewage treatment plants in site operation (N-Vargas et al., 2012). The species diversity in 1# decreased gradually, while in 2# it decreased more obviously first and then increased evidently, revealed that the WVG caused a more notable variety of microbial species diversity. Variation tendency of the Simpson index was contrary to that of the Shannon index, which also confirmed the above conclusion. 3.3.2. Effect of WVG on microbial community structures The bacterial relative abundances on phylum level (with abundance greater than 1% in any sample) were exhibited in Fig. 5. Eight phyla were detected and their sum accounted for more than 94% of the total reads. Proteobacteria and Bacteroidetes ranked the first and the second in

100%

80%

5.05% 4.74% 4.92% 4.58%

Abundance

6.21%

60%

20.90%

6.12% 5.12% 4.15% 6.31%

4.71%

3.68%

5.25%

4.89%

3.73% 6.52%

4.53%

21.09%

21.52%

51.01%

52.28%

26.00%

4.44% 4.50%

27.40%

Others Planctomycetes Firmicutes Actinobacteria Nitrospirae Acidobacteria &hloroÀe[i Bacteroidetes Proteobacteria

40%

20%

0%

48.47%

S0

53.18%

55.38%

S30(1#) S60(1#) S30(2#) S60(2#) Samples

Fig. 5. Community structure and distribution at phylum level. 257

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60%

(a)

50%

3.35%

2.32%

1.77%

4.31%

17.65%

15.02%

19.14%

21.23%

23.90%

30% 20%

16.20%

15.61%

17.16%

19.56%

13.30%

13.03%

S0

10.51%

1.05%

5%

1.64% 1.26% 1.59%

1.90% 0.69% 1.73%

15%

3.10%

3.69%

13.31%

13.08%

0%

0.94%

1.05%

5%

unclassi¿ed Ardenticatenia 7hermomicrobia &hloroÀe[ia &aldilineae

1.14%

0.88%

S0

1.03%

3.41%

0.64%

2.80%

0.53%

2.76%

3.55%

2.62%

1%

2.41% 0.46%

0.89%

0.60%

S0

0%

S30(1#) S60(1#) S30(2#) S60(2#)

6%

Nitrospira

(e)

4%

1.5% 2.55% 2.32%

2.17%

2.46%

2.63%

S0

S30(1#) S60(1#) S30(2#) S60(2#) Samples

(f)

0.67%

0.79%

0.66% 0.61%

1.11%

3%

0.98%

0.71%

1.55%

unclassi¿ed &oriobacteriia 7hermoleophilia Acidimicrobiia Actinobacteria

0.93%

1.75%

2% 2.87%

0.5%

1%

S0

0%

S30(1#) S60(1#) S30(2#) S60(2#)

5% 0.27%

(rysipelotrichia NeJatiYicutes &lostridia Bacilli

(g)

0.32%

4%

S0

3.0% 0.34%

2.5%

1.98%

0.80% 0.45%

0.54%

0.55%

0.54%

0.58%

0.52%

0.70%

0.74%

0.67%

0.75%

Abundance

0.62%

2.90%

S30(1#) S60(1#) S30(2#) S60(2#) Samples

unclassiIied Phycisphaerae Planctomycetacia

(h)

0.27%

0.78%

1.5% 2.34%

1.0%

0.63% 0.24% 0.82% 0.24%

0.5%

S0

2.68%

2.0%

2%

2.17%

3.23%

2.20%

Samples

Abundance

0.60%

2%

2.0%

0%

0.53%

unclassi¿ed 6olibacteres +olophaJae Blastocatellia

0.40%

1.03%

Abundance

Abundance

S30(1#) S60(1#) S30(2#) S60(2#)

0.77%

3%

5%

1%

15.29%

0.53%

2.5%

3%

15.20% 12.26%

1.02%

Samples

0.0%

5.28%

3.27% 3.19%

1.0%

4.52%

4.05%

0.63%

1.45%

2%

3.0%

2.50%

(d)

4%

Abundance

4%

3.90%

2.38%

Samples

1.14%

0%

3.55%

20%

unclassiIied Bacteroidia &ytophaJia FlaYobacteriia 6phinJobacteriia

8.30%

(c) 1.20%

Abundance

13.35%

S30(1#) S60(1#) S30(2#) S60(2#) Samples

6%

1%

2.28%

5%

0%

3%

2.71%

10%

22.09%

10%

7%

(b)

25%

Abundance

Ab u n d an c e

40%

30%

unclassi¿ed İproteobacteria įproteobacteria Ȗproteobacteria ȕproteobacteria Įproteobacteria

0.99% 0.68%

0.28% 0.40%

0.0%

S30(1#) S60(1#) S30(2#) S60(2#) Samples

S0

0.18%

S30(1#) S60(1#) S30(2#) S60(2#) Samples

Fig. 6. Community structure and distribution at class level of different phyla (a) Proteobacteria, (b) Bacteroidetes, (c) Chloroflexi, (d) Acidobacteria, (e) Nitrospirae, (f) Actinobacteria, (g) Firmicutes, (h) Planctomycetes.

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Table 2 Relative abundance of dominant genera of the samples (%). Phylum

Genus

S0

S30(1#)

S60(1#)

S30(2#)

S60(2#)

Bacteroidetes Proteobacteria Proteobacteria Proteobacteria Bacteroidetes Nitrospirae Proteobacteria Proteobacteria Proteobacteria Firmicutes Proteobacteria Proteobacteria Firmicutes Bacteroidetes Proteobacteria Proteobacteria Actinobacteria

Ferruginibacter Variovorax Thermomonas Arenimonas Terrimonas Nitrospira Dokdonella Novosphingobium Haliangium Trichococcus Dechloromonas Nannocystis Bacillus Flavobacterium Thauera Candidatus_Accumulibacter Micropruina

3.90 3.87 3.42 2.80 2.61 2.17 1.74 1.64 1.58 1.27 1.23 1.04 0.60 0.52 0.42 0.38 0.01

3.42 2.07 0.01 0.35 1.04 2.32 1.80 0.39 1.42 0.01 1.67 0.14 0.60 0.52 0.47 0.39 0.04

2.84 1.78 0.00 0.36 1.01 2.55 2.00 0.43 1.34 0.01 1.75 0.21 0.65 0.54 0.49 0.44 0.05

6.30 1.92 0.01 0.34 1.18 2.46 2.77 0.08 1.47 0.01 2.99 0.43 0.65 3.99 0.88 1.93 1.77

9.10 3.94 0.01 0.10 1.45 2.63 3.30 0.17 2.50 0.01 3.29 0.27 0.73 4.40 1.17 2.23 1.79

abundance of Acidimicrobiia, inhibited the growth of microbes in αproteobacteria, Caldilineae, Chloroflexia, Thermomicrobia, Actinobacteria and Planctomycetacia, but had no obvious effect on microbes in Bacilli and Clostridia.

sludge positively (Lee et al., 2013). 3.3.4. Effect of WVG on functional population Table 3 listed the functional population of the samples. Nitrifying bacteria are aerobic inorganic autotrophic bacteria and usually include ammonia-oxidizing bacteria (AOB), which convert ammonia nitrogen into nitrite, and nitrite-oxidizing bacteria (NOB), which convert nitrite into nitrate. In all the samples, Nitrosomonas and Nitrospira were the only AOB and the only NOB, respectively, indicating the community structure of nitrifying bacteria was relatively single. This might be due to the selectivity of microbes to SynDS and operating conditions which was not conducive to the growth of other nitrifying bacteria. The abundances of Nitrosomonas and Nitrospira increased in both 1# and 2# and remained in higher levels in 2# than in 1#, indicated the WVG had promoted the growth of microbes in Nitrosomonas and Nitrospira and promoted ammonia removal efficiency. The abundance of NOB was much higher than that of AOB, reveling again that nitrification rather than nitrosation was the main ammonia oxidation step, while the nitrosation of ammonia was the rate-determining step of ammonia oxidation process. Seventeen denitrifying bacteria were detected in the samples. Dechloromonas, Rhodobacter, Nitrosomonas, Bacillus, Hyphomicrobium and Flavobacterium were the most abundant denitrifying bacteria in S0. The abundance of Dechloromonas was obviously higher than the others during the operation with abundance of 1.23%, 1.67%, 1.75%, 2.99% and 3.29% in S0, S30 (1#), S60 (1#), S30 (2#) and S60 (2#), respectively. Dechloromonas is bacteria with the ability to remove nitrogen and phosphorus and degrade organics (Zhang et al., 2018c). Its obviously higher abundance might attribute to the SynDS which contained abundant nitrogen, phosphorus and sufficient high-quality carbon sources. Rhodobacter, Nitrosomonas, Hyphomicrobium and Flavobacterium were representatives of aerobic denitrifying bacteria, which played an important role in the degradation of organics and nitrogen removal. Their existence with relatively higher abundance was in accordance with their aerobic characteristics. The abundance of Dechloromonas, Bacillus and Flavobacterium increased in both 1# and 2#, and remained in obviously higher levels in 2# than 1#.Bacillus is bacteria with the function of heterotrophic nitrification, aerobic denitrification (Zhang et al., 2012), denitrifying phosphorus removal (Huang et al., 2018; Zhang et al., 2018c), and the degradation of complex organics (Kong et al., 2019). Flavobacterium was strictly aerobic denitrifying bacteria with the ability to degrade complex organics. It also has a positive effect on the flocculation of activated sludge and related to EPS formation (Tang et al., 2018). Their relatively higher abundance in 2# may promote the removal of organics, nitrogen and phosphorus. As for Rhodobacter and Hyphomicrobium, their abundance decreased in both 1# and 2#, probably because the experimental

3.3.3. Effect of WVG on dominant genera The classified OTUs and the relative abundance of each sample were analyzed at the genus level and the dominant genera (with relative abundance in any sample greater than 1%) were listed in Table 2. Major dominant genera in S0 were Ferruginibacter, Variovorax, Thermomonas, Arenimonas, Terrimonas and Nitrospira. Ferruginibacter is aerobic heterotrophic bacteria and dominant bacterial group for oxidizing organics in the five samples. Its abundance decreased in 1# gradually and increased from 3.90% to 9.10% after operation for 60 days in 2#, occupied the most prominent position in the increases of abundance among all the genera. The abundance of Variovorax, Terrimonas and Haliangium decreased gradually in 1#, but decreased first and then increased evidently in 2#. As to Nitrospira, Dechloromonas, Dokdonella, Bacillus, Thauera and Micropruina, their abundances increased in both 1# and 2#, and remained in higher levels in 2# than 1# during the operation. The abundance of Thermomonas, Arenimonas, Nannocystis, Novosphingobium and Trichococcus decreased in both 1# and 2#, and the abundances of Thermomonas and Trichococcus even decreased to ∼0.01% in the long-term operation. The reason may be that the operating conditions were not conducive to their growth. The abundance of Flavobacterium and Candidatus_Accumulibacter increased more significantly in 2# when compared with the slight increases in 1#. Among these genera, Variovorax, Dokdonella, Dechloromonas, Bacillus, Micropruina and Flavobacterium are related to the degradation of organics (Andrade et al., 2017; Chen et al., 2019; Kong et al., 2019; Wu et al., 2018; Wu et al., 2016), Nitrospira, Haliangium, Bacillus, Micropruina and Flavobacterium are bacteria with the ability to remove nitrogen (Fan et al., 2018a; Li et al., 2018b; Tang et al., 2018; Zhang et al., 2012), and Bacillus, Micropruina and Candidatus_Accumulibacter are related to the removal of phosphorus (Huang et al., 2018; Rout et al., 2017; Zhang et al., 2018c). With the effect of the WVG, the relatively higher abundance of Ferruginibacter, Variovorax, Nitrospira, Dokdonella, Haliangium, Dechloromonas, Bacillus, Flavobacterium, Micropruina and Candidatus_Accumulibacter in 2# may promote the degradation of the organics and removal of the nitrogen and phosphorus. Besides, the relatively higher abundance of Ferruginibacter, Flavobacterium, Thauera, Nitrospiralike and nitrite-oxidizing bacteria caused by the WVG in 2# may be the reason of EPS increases in SBR system (Fan et al., 2018b; Han et al., 2018; Sun et al., 2018; Zhang et al., 2019; Zhang et al., 2018d). The relatively higher abundance of Flavobacterium and Terrimonas caused by the WVG in 2# may affect the flocculation performance of the activated 259

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Table 3 Relative abundance of functional population (%). Type

Phylum

Genus

S0

S30(1#)

S60(1#)

S30(2#)

S60(2#)

AOB NOB DNB

Proteobacteria Nitrospirae Proteobacteria Proteobacteria Proteobacteria Firmicutes Proteobacteria Bacteroidetes Proteobacteria Proteobacteria Proteobacteria Proteobacteria Proteobacteria Proteobacteria Proteobacteria Proteobacteria Proteobacteria Actinobacteria Proteobacteria

Nitrosomonas Nitrospira Dechloromonas Rhodobacter Nitrosomonas Bacillus Hyphomicrobium Flavobacterium Thauera Zoogloea Pseudomonas Mesorhizobium Acinetobacter Achromobacter Enterobacter Alcaligenes Thiobacillus Propionibacterium Sphingomonas

0.61 2.17 1.23 0.80 0.61 0.60 0.57 0.52 0.42 0.41 0.26 0.22 0.13 0.12 0.03 0.02 0.02 0.01 0.01

0.63 2.32 1.67 0.24 0.63 0.60 0.48 0.52 0.47 0.43 0.26 0.23 0.07 0.01 0.51 0.00 0.00 0.00 0.02

0.63 2.55 1.75 0.15 0.63 0.65 0.34 0.54 0.49 0.53 0.35 0.12 0.06 0.00 0.64 0.00 0.00 0.01 0.03

0.63 2.46 2.99 0.32 0.63 0.65 0.05 3.99 0.88 0.51 0.28 0.14 0.01 0.01 0.02 0.00 0.00 0.00 0.00

0.64 2.63 3.29 0.18 0.64 0.73 0.04 4.40 1.17 0.62 0.35 0.05 0.00 0.01 0.03 0.00 0.00 0.00 0.01

PAOs

Proteobacteria Proteobacteria Proteobacteria

Dechloromonas Candidatus_Accumulibacter Acinetobacter

1.23 0.38 0.13

1.67 0.39 0.07

1.75 0.44 0.06

2.99 1.93 0.01

3.29 2.23 0.00

GAOs

Proteobacteria Proteobacteria

Candidatus_Competibacter Defluviicoccus

0.12 0.00

0.36 0.01

0.39 0.01

0.50 0.02

0.52 0.01

AOB: Ammonia-oxidizing bacteria; NOB: Nitrite-oxidizing bacteria; DNB: Denitrifying bacteria; PAOs: Phosphorus-accumulating organisms; GAOs: Glycogen-accumulating organisms.

conditions were unfavorable for their growth. The abundance of Hyphomicrobium remained in obvious lower levels in 2# than 1#, indicated the inhibition of the WVG on its growth. In all the samples, the main phosphorus-accumulating organisms (PAOs) were Dechloromonas, Acinetobacter and Candidatus_Accumulibacter and the main glycogenaccumulating organisms (GAOs) were Candidatus_Competibacter and Defluviicoccus (Tang et al., 2019). The abundance of all the PAOs and GAOs except Acinetobacter increased in both 1# and 2#, and remained in evidently higher levels in 2# than in 1#. These results revealed that the WVG could promote the growth of microbes in AOB, NOB, and most of the main genera of DNB, PAOs and GAOs, which were benefit to improve the nitrogen and phosphorus removal efficiency of the system.

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