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Micro-graphite particles accelerate denitrification in biological treatment systems
Journal Pre-proofs Micro-graphite particles accelerate denitrification in biological treatment systems Junzhang Li, Zhaozhou Peng, Ruiyang Hu, Kaiyuan Gao, Chen Shen, Shouxin Liu, Runjing Liu PII: DOI: Reference:
S0960-8524(20)30204-2 https://doi.org/10.1016/j.biortech.2020.122935 BITE 122935
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Bioresource Technology
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5 December 2019 27 January 2020 29 January 2020
Please cite this article as: Li, J., Peng, Z., Hu, R., Gao, K., Shen, C., Liu, S., Liu, R., Micro-graphite particles accelerate denitrification in biological treatment systems, Bioresource Technology (2020), doi: https://doi.org/ 10.1016/j.biortech.2020.122935
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Micro-graphite particles accelerate denitrification in biological treatment systems
Junzhang Li, Zhaozhou Peng, Ruiyang Hu, Kaiyuan Gao, Chen Shen, Shouxin Liu*, Runjing Liu
College of Chemistry and Pharmaceutical Engineering, Hebei University of Science and Technology, Shijiazhuang 050018, P.R. China.
*Corresponding authors Shouxin Liu ([email protected]) Present address 26 Yuxiang Street, Shijiazhuang, 050018, P.R. China.
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Abstract Accelerated denitrification is an essential problem in the biological treatment of nitrogenous wastewater. In this study, we report that denitrification is accelerated by micro-graphite particles (MGPs). The denitrification rate was increased by 83.4% or 11.1% in synthetic (with 0.16 g/L MGPs) or industrial nitrogenous wastewater (with 0.12 g/L MGP), respectively. The mechanism was revealed via a quantitative polymerase chain reaction (q-PCR), high-throughput sequencing, and scanning electron microscopy (SEM). The abundance of denitrifying bacteria Paracoccus in the sludge was increased by micrographite particles. The number of denitrifying bacteria with the nirS gene was increased significantly (75.6%). To the best of our knowledge, this is the first report that MGP could enhance denitrification via the sludge. MGP can denitrify in industrial applications.
Keywords: Accelerated denitrification; industrial wastewater; micro-graphite particles; toxicity of graphite.
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1. Introduction Nitrogenous wastewater can lead to eutrophication and massive proliferation of algae (Bougarne et al., 2019). Nitrates in aqueous systems threaten human health by forming N-nitrosamines that damage DNA. Nitrates and nitrites are associated with cancer morbidity and mortality (Zhang et al., 2018). Membranes, adsorption, and ion exchange resins are the most popular methods for biocatalytic denitrification using sludge as catalysts. The denitrification of nitrogenous organic compounds in wastewater is very complex, but it includes ammonification of nitrogenous organic compounds, nitrification of ammonia nitrogen, and denitrification of nitrate to nitrogen (McCarty, 2018; Skiba and Hydrology, 2008; Yanai et al., 2007). The anammox process is a more energy-efficient and environmentally friendly technology to degrade the nitrogenous wastewater than denitrification and nitrification. NO can react with ammonium to form hydrazine and then to N2 by anammox bacteria without N2O being produced (Mandel et al., 2019). However, it requires strict control over the concentration of inlet water and needs shortcut nitrification or shortcut denitrification. Generally, the removal of inorganic ions in wastewater, such as nitrogen and phosphorus, requires the help of complex microbial degradation processes (dC Rubin et al., 2017; Mandel et al., 2019). The degradation removal of nitrate is a critical step in denitrification. Nitrates can be transformed to nitrogen via nitrite as the intermediate, but nitrite inhibits the expression of denitrification bacteria in the sludge. Thus, improved biological denitrification is urgently needed (McCarty, 2018). In order to achieve rapid removal of nitrates in wastewater, new reactors have been designed, such as Up-flow Anaerobic Sludge Bed (UASB), Moving Bed Biofilm Reactor (MBBR), Membrane Bio-Reactor (MBR), and Sequencing Batch Reactor (SBR). These have been gradually improved to enhance denitrification (Dai et al., 2019; Daija et al., 2016). In wastewater, organic matter is a crucial factor affecting denitrification or anammox performance as either an electron mediator or rhamnolipids (Lovley and Woodward, 1996; Peng et al., 2019). The application of redox mediators like anthraquinone, porphyrin, and riboflavin can enhance denitrification by accelerating 3 / 28
electron transfer (Aranda-Tamaura et al., 2007; Martins et al., 2015; Xi et al., 2013; Xie et al., 2018). For example, the metalhumic acid complexes in UASB reactors improved the biotransformation of iopromide (Cruz-Zavala et al., 2016). Anthraquinone-2,6-disulfonate (AQDS) increased the removal rate of total nitrogen in UASB reactors (Rikmann et al., 2014). The coupling of biodegradation and non-biodegradation for the degradation of nutrients in wastewater has been reported. Heterotrophic denitrification is one of the main reactions in BNR and can be used together in the A/O/A system with the Fenton reaction as the biological stage (Klein et al., 2017b, 2017a). Co-culturing bacteria is a new way to improve biological denitrification. Geobacter sulfurreducens is an electroactive bacterium that can accelerate the denitrification rate by 13%51% via enhanced expression of the nirS gene coding for a cytochrome cd1-nitrite reductase (Wan et al., 2018). However, practical applications of those technologies have not yet been reported. During the denitrification, the alkalinity of wastewater gradually increased. The alkalinity and the inorganic carbon source required for degradation of the nutrients in wastewater use higher pH that can affect processing (Tenno et al., 2018a, 2018b). Partial denitrification can be achieved by controlling pH (Jung et al., 2019; Qian et al., 2019). In general it is not suitable for accelerating denitrification. Many organic materials and inorganic materials have been tested to accelerate denitrification with sludge, and micro-graphite particles (MGPs) exhibited outstanding activity for accelerated denitrification. Hence, the behavior and mechanism of MGP to accelerate denitrification of nitrogenous wastewater with sludge were studied here. In industrial wastewater, the rate of denitrification in the experimental group was faster than that in the control group. To reveal the mechanism of acceleration, scanning electron microscopy (SEM), high-throughput sequencing, and quantitative polymerase chain reaction (q-PCR) were used to observe the sludge’s morphology, community distribution, and the proportion of the denitrifying bacteria. 2. Experimental 2.1. Chemicals and Organism 4 / 28
2.1.1. Chemicals MGPs were purchased from Hubei Xinrunde Chemical Co., Ltd. (Hubei, China). All of the other chemical reagents were of analytical grade and used without further purification from Aladdin Ltd. (Shanghai, China). 2.1.2. Organism and culture methods The sludge was inoculated with activated sludge collected from Shijiazhuang Qiaoxi Waste Water Treatment Plant. The sludge was cultivated in a conical flask, and the composition of the substances in the conical flask is shown in Supplementary material. The sludge grew at pH 6.87.5 and 35°C in 250-mL conical flasks rotated at 150 rpm for 10 hours. 2.2. Enhancement of denitrification activity experiments 2.2.1 Denitrification of synthetic nitrogenous wastewater The adsorption of nitrate on the MGP was studied first. MGP was added to synthetic wastewater with an initial concentration of 100 mg/L NO3--N. The concentration of nitrate in the synthetic wastewater was detected after 8 hours, and it did not decrease. This confirmed that MGP could not adsorb the nitrate. Wastewater with sludge, sodium succinate (600 mg/L CODcr), and nitrate (100 mg/L NO3--N) were mixed and then distributed immediately into 250-mL conical flasks with predetermined amounts of MGP in advance at 37°C. Wastewater with sludge but without MGP was a control. To evaluate the denitrification efficiency, all of the groups were sampled every two hours for the analysis of nitrate and nitrite and pH. All of the experiments were performed in triplicate. 2.2.2. Denitrification of industrial nitrogenous wastewater Here, 5 mL of industrial wastewater was added to a 250-mL conical flask with 245 mL sludge at 37°C. The finial concentrations of CODcr and NO3--N in the conical flask were 1198.4 mg/L and 264.6 mg/L, respectively. The denitrification of industrial nitrogenous wastewater was performed without the addition of an extra carbon source. During the denitrification process, all groups were sampled together for analysis of nitrate and nitrite.
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Details on the industrial wastewater are shown in supplementary material. All of the experiments were performed in triplicate. 2.3. Analysis 2.3.1. Wastewater quality analysis To detect pollutants, samples were removed from wastewater in the withdrawn flasks and centrifuged at 7000 rpm for 5 min to remove the insoluble particles. The concentration of nitrate, nitrite, and ammonia in the supernatant was measured calorimetrically by UV spectrophotometry (UV-1500, UV/VS spectrophotometer, Macy, Co., China) at 220 nm, 275 nm (for nitrate), 540 nm (for nitrite), and 410 nm (for ammonia). The nitrate results had an error of 5% because of variability in the system. The concentration of total inorganic nitrogen is composed of nitrate, nitrite and ammonia (Zekker et al., 2012). The changes of pH during the denitrification process were measured with a digital pH meter (PHS-2F, Shanghai INESA, China). 2.3.2. Microbial community analysis All of the samples were sequenced using the Illumina-MISeq platform (Sangon Biotech, Shanghai, China). The primer pair of 341F (5'-CCTACGGGNGGCWGCAG-3') and 804R (5'-GACTACHVGGGTATCTAATCC-3') were used (Tsao et al., 2019). The first PCR thermal program was performed at 94°C for 3 min; five cycles of 94°C for 30 s, 45°C for 20 s, 65°C for 30 s; 20 cycles of 94°C for 20 s, 55°C for 20 s, and 72°C for 30 s; 72°C for 5 min; and 10°C in the end. The second round of PCR thermal program was performed at 95°C for 3 min; five cycles of 94°C for 20 s, 55°C for 20 s, and 72°C for 30 s; 72°C for 5 min; and then 10°C at the end. The resulting sequence datasets were deposited in the NCBI Sequence Read Archive under study accession number PRJNA593635. The details of sampling time and conditions of each samples are given in supplementary material. All experiments were performed in triplicate. 2.3.3. 16S, NirS and nirK gene analysis Three target genes were quantified, including two denitrifying bacterial genes (nirS and nirK gene) and 16srDNA. All q-PCR assays were performed in triplicate with a LightCycler480II fluorescent q-PCR method (Roche, Rotkreuz, Switzerland). 6 / 28
The PCR procedure for 16srDNA included an initial denaturation step at 95°C for 3 min and 45 cycles of amplification (95°C for 5s, 60°C for 30s). The dissociation was according to the instrument guidelines. The primer pair 515F (5'GTGCCAGCMGCCGCGGTAA-3') and 806R (5'-GGACTACHVGGGTWTCTAAT-3') were utilized to amplify 16srDNA. The primer pair of 876C (ATYGGCGGVCAYGGCGA) and 1040 (GCCTCGATCAGRTTRTGGTT) were utilized to amplify nirK gene. The primer pair of cd3aF (GTSAACGTSAAGGARACSGG) and R3cdR (GASTTCGGRTGSGTCTTGA) were used to amplify the nirS gene(S. Liu et al., 2018). Standard curves were obtained by serial dilutions of linearized plasmids with cloned fragments for the specific genes. Standard curves were linear (R2 = 0.999) over the range used. The amplification efficiencies for 16S, NirS, and NirK were 90.7%,87%, and 105.4%, respectively. All of the experiments were performed in triplicate. 2.3.4. Characterization of MGPs A scanning electron microscope (SEM) (S-4800-I, Hitachi, Japan) was used to observe the morphology of sludge and MGP. A Mastersizer (LS-POP-6, OMEC, Zhuhai, China) was used to determine the size distribution of several MGP. An electrochemical workstation (CHI660E, ChenHua, Shanghai, China) collected cyclic voltammogram (CV) data. Attenuated total reflectance transform infrared spectroscopy (ATR-FT-IR) determined the functional group of MGP. 2.3.5. Statistical analysis The statistical significance of the changes was determined with Tukey's multiple range test (SPSS 26.0). The significant correlation between the dose and acceleration was determined with Pearson’s correlation coefficient. Here, p-values below 0.05 were considered to be statistically significant. 3. Results and Discussion 3.1. Effect of MGPs on denitrification in synthetic nitrogenous wastewater Denitrification is the most effective method for degrading nitrogenous wastewater. MGP can enhance the sludge to degrade nitrogenous wastewater more quickly, and the accelerating effect depends on the particle size and dose. 7 / 28
3.1.1 Effect of MGPs size on the denitrification To explore the effect of MGP size on denitrification, three sizes of MGP were used to the denitrify at 200 mg/L. Experimental groups A, B, and C used MGP with sizes were 2.58 μm, 4.514 μm, and 10160 μm, respectively. There was a significant decrease in the concentration of nitrates in the groups with MGP during the denitrification versus controls (Figure 1a). The concentrations of nitrate in groups AC were 10.0, 5.2, and 5.2 mg/L after 8 hours; the control group was 34.4 mg/L. The concentrations of nitrate gradually decreased with size of MGP (Ccontrol > C10160μm >
C4.5-14μm ≥ C2.5-8μm). A smaller MGP could enhance nitrate degradation more
efficiently (p < 0.05). Biocatalytic denitrification is a reducing process, and the accumulation of nitrite was noted during denitrification. Figure 1b shows that the highest accumulation of nitrite was in group C and group B at 13.8 and 9.4 mg/L; group A and the control group were 8.0 and 9.2 mg/L during denitrification, respectively. The highest accumulation of nitrite was in group C perhaps because the degradation of nitrite in group C accelerated better. However, concentrations of nitrite in group C and group B were barely detected after 8 hours, while these in group A and control group were 9.0 and 8.0 mg/L, respectively. There was a statistically significant difference between the highest accumulation of nitrite in groups AC (p < 0.05). The denitrification in groups B and C was nearly complete after 8 hours, but the denitrification in the control group and group A was still being performed. This implied that smaller MGP could enhance nitrite degradation more efficiently (p<0.05). Figure 1c shows that the average values of the removal rates of inorganic nitrogen are 57.5%, 81.0%, 94.7%, and 94.7% in the control group, groups AC, respectively, after 8 hours. The removal rates of inorganic nitrogen in groups B and C were almost the same after 8 hours, but it was obvious that the denitrification in group C was better than that in group B in terms of complete denitrification. The results showed that smaller MGP could significantly enhance denitrification (p < 0.05).
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Figure 1d shows pH data. After the fourth round of denitrification, pH in the control group and groups A, B, and C 8.08, 8.50, 8.67 and 8.81, respectively. Obviously, the pH values were higher in groups with MGP during the denitrification because there was more nitrate and nitrite degraded by sludge. This was consistent with the data above and indicated that smaller MGP could enhance denitrification more efficiently. In summary, the results indicated that sludge could be improved by MGP to degrade nitrate in wastewater, and the best size of MGP was 2.58 μm. Figure 1 3.1.2. Effects of MGPs dose on denitrification Different doses of MGP caused different degrees of denitrification acceleration. The experimental groups are named C0, C1, C2, C3, C4, and C5. These were related to 0, 0.04, 0.08, 0.12, 0.16, and 0.2 g/L MGP (2.58 μm) in the wastewater, respectively. Figure 2a shows that the concentrations of nitrate contained certain regularities with changes in MGP dose. The average nitrate concentrations were 36.5, 21.5, 16.3, 17.3, 16.6, and 18.1 mg/L in C0, C1, C2, C3, C4, and C5 after 8 hours, respectively. The concentrations of nitrate gradually decreased with increasing MGP dose. However, the concentrations of nitrate in C4 and C5 were nearly identical. There was an insignificant change of nitrate concentration from 0.16 to 0.2 g/L MGP. The concentrations of nitrate in C4 and C5 did not change after 7 hours, which might be due to the lack of a carbon source. The rate of the nitrate degradation in C5 was faster than that in C0 by 67.2% according to the average concentrations of nitrate in C0 and C5 during first seven hours of the denitrification. A higher dose of MGP could more efficiently enhance nitrate degradation. Figure 2b shows that the highest accumulations of nitrite are 8.1, 9.4, 6.9, 6.2, 5.2, and 5.7 mg/L in C0, C1, C2, C3, C4, and C5, respectively. The highest accumulation of nitrite gradually decreased along with the increasing MGP dose (00.16 g/L). The accumulation of nitrite was nearly the same as when the dose of MGP was more than 0.16 mg/L. This implied that smaller MGP could enhance nitrite degradation more efficiently. 9 / 28
There was no significant difference between the removal rates of inorganic nitrogen in C4 and C5 (p > 0.05); they were higher than these in the other samples (Figure 2c). The acceleration of denitrification in C4 group was 83.4%, and the value was not significantly different from that in C5 group (Figure 2d). Thus, there was a significant correlation between the acceleration of denitrification and the dose of MGP (p < 0.05). Thus, best condition for denitrification was C4. Figure 2 3.2. Effects of MGPs on denitrification in industrial nitrogenous wastewater. To explore the effect of MGP on the denitrification in industrial nitrogenous wastewater, we collected wastewater from a caprolactam hexanolactam factory. The experimental groups were named D1, D2, D3, D4, and D5 and related to 0.04, 0.08, 0.12, 0.16, and 0.2 g/L MGP (2.58 μm) added to the wastewater. The concentrations of nitrate and nitrite are shown in Figure 3a and Figure 3b. The inorganic nitrogen concentration in the wastewater was analyzed as in Figure 3c. The denitrification in the experimental groups was faster than the control groups as shown in Figure 3d. The denitrification in industrial nitrogenous wastewater gradually enhanced along with an increase in the dose of MGP (00.12 g/L). After 38 hours, the removal rate of inorganic nitrogen was 90.2% and 81.8% in D3 and control group, respectively. No significant increase in the final removal rate was seen over 0.12 g/L dose of MGP (p > 0.05). The degradation speed was 5.87, 6.09, 6.14, 6.39, 6.33, and 6.53 mg/(L*h) in the control group, D1, D2, D3, D4, and D5. In D5, the degradation speed of the inorganic nitrogen was the fastest (11.1% faster than the control group). The rate of denitrification significantly correlated with the dose of MGP (p < 0.05). Thus, MGP exhibited a potential industrialization on the denitrification. Figure 3 3.3. Effects of MGPs on the microbial community The variety of microbial community structure in the sludge was analyzed with 16SrDNA sequencing on an Illumina Miseq platform to understand how MGP enhanced the sludge for degrading nitrate and nitrite. The gram-negative strains Paracoccus and Acinetobacter were the dominant genus in the sludge: Their proportions in 0-C were 54.4% 10 / 28
and 28.8%, respectively (Figure 4). In 1-N, the proportion of Paracoccus increased to 61.2%, and the proportion of Acinetobacter decreased to 21.6% in the first hour of the denitrification. The proportions of Paracoccus and Acinetobacter in 1-C were 50.1% and 30.7%. Versus 1-C and 0-C, we note that the proportion of Acinetobacter increased by 1.9%, and Acinetobacter in the sludge multiplied faster than other genus with one hour of succinate treatment. The proportion of Paracoccus in 1-N was more than that in 1-C by 11.1%. This implied that Paracoccus in the sludge could multiply faster than the other genus species via nitrate for one hour. Versus succinate, the genus was more easily induced by nitrate at one hour of treatment. The proportions of the bacterial genus were almost the same in 1-N and 1-NG. The proportion of the bacterial genus was barely changed in the presence of nitrate for one hour. The proportion of Paracoccus in 1-G was more than that in 1-C by 8.1%, and the proportion of Acinetobacter in 1-G was less than that in 1-C by 8.6%. This implied that Paracoccus in the sludge was induced to multiply faster than other genera by MGP in one hour. Between 1-N and 2-N, the proportion of Paracoccus decreased by 8.5%, and the proportion of Acinetobacter increased by 5.2%. This might imply that Paracoccus was more difficult to be induced and multiply via nitrate when the concentration of nitrate reduced during the denitrification. The proportion of Paracoccus and Acinetobacter was almost the same in 1-NG and 2-NG. The proportion of Paracoccus in 2-NG was more than that in 2-N by 7.9% and the proportion of Acinetobacter in 2-NG was less than that in 2-N by 5.5%. The proportions of Paracoccus in 1-N and 1-NG were almost the same in the first hour, but Paracoccus multiplied faster by MGP at 8 hours. The proportion of Paracoccus in 2-G was 2.5% higher than in 2-C, but the proportion of Acinetobacter in 2-G was 10% less than in 2-C. The variation in the community structure at the first hour was more than that after 8 hours of denitrification in the presence of MGP. Acinetobacter was still inhibited more easily than the other genera by MGP at 8 hours.
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The proportion of Acinetobacter could be significantly reduced by MGP, while the proportion of Paracoccus had the opposite impact from MGP. Paracoccus includes many denitrifying species that could degrade nitrate and nitrite but the increase in the proportion of Paracoccus was not equal to the increase in denitrifying bacteria. Figure 4 3.4. Effects of MGPs on the abundance of 16s and denitrifying gene To explore the effect of MGP on biomass during denitrification (Segawa et al., 2005), we detected the abundance of 16S gene in the sludge via q-PCR analysis. The samples were sequenced on an Illumina Miseq platform. The abundance of the 16S gene in 1-N and 1-NG was significantly higher than that in 0-C by 10% and 6% at one hour (p > 0.05), respectively (Figure 5c). This increase might be due to the addition of succinate and nitrate that can be used as a carbon source and a nitrogen source, respectively. The abundance of 16S gene in 1-NG was not significantly lower than that in 1-N (p > 0.05), which might be due to the toxicity of MGP. The effect of MGP on biomass was less than the effect of nitrate at one hour. The abundance of the 16S gene in 2-N was not significantly lower than that in 0-C by 10.6% (p > 0.05), which might be due to the lack of nutrients; the abundance of the 16S gene in 2-NG was significantly higher than that in 0-C by 24.9% (p > 0.05), which might be due to the hormesis of MGP. This significant increase in the 16S gene abundance indicated that hormesis of MGP could reverse the decrease in sludge via nutrient scarcity. Although the proportion of Paracoccus and the abundance of the 16S gene increased in the group with MGP, not all Paracoccus were denitrifying bacteria. The bacteria with denitrifying functional genes such as nirK and nirS were the denitrifying bacteria that could degrade nitrite. The abundance of the denitrifying genes in the sludge was detected by q-PCR analysis to understand the possible mechanism underlying the MGP effect on the denitrification ability of the sludge. First, the abundance of the nirK gene from sludge was detected by q-PCR (Figure 5a). The abundance of the nirK gene in 1-N and 1-NG were 7.9% (p > 0.05) and 10.8% (p < 0.05) lower than that in 0-C, respectively. At one hour, the abundance of the nirK gene 12 / 28
was not influenced by MGP, but the reduction in the abundance might be due to nitrate, which can be considered xenobiotic. The abundance of the nirK gene in 2-N was less than that in 0-C by 1.9% (p > 0.05), and the abundance of the nirK gene in 2-NG was more than that in 0-C by 7.4% (p > 0.05). The abundance of the nirK gene was not significantly enhanced by MGP and reduced nitrate levels at 8 hours. The abundance of the nirS gene in all samples was far higher than the abundance of nirK gene in the samples; thus, the abundances of the nirS gene were more interesting. Figure 5b shows that the expression of the nirS gene in 1-N and 1-NG was higher than that in 0-C by 58.4% and 68.2%, respectively (p < 0.05). The expression of the nirS gene could be enhanced by nitrate and could be further promoted by MGP. The abundance of nirS gene in the 2-N was slightly lower than that in 0-C by 15.6% (p > 0.05), and the abundance of nirS gene abundance in 2-NG was significantly more than that in 0-C by 106.9% (p < 0.05). The abundance of nirS gene in the group with nitrate but without MGP was almost the same as 0-C after 8 hours (p > 0.05). The abundance of nirS gene in the sample with MGP could be enhanced after 8 hours. This implied that the amount of denitrifying bacteria was modulated by MGP during denitrification. The abundance of the nirS gene in 2-NG was not significantly higher than that in 1-NG (p > 0.05). This implied that bacteria harboring the nirS gene multiplied faster than other genera by MGP in a short time at the beginning of the denitrification. In other words, MGP significantly enhanced the number of denitrifying bacteria with nirS gene in the sludge, which accelerated denitrification. Figure 5 3.5. Toxicity of MGPs to bacteria via SEM The sludge samples in the control group as well as groups A, B, and C were imaged with SEM. The dominant genus in the samples were Paracoccus and Acinetobacter. The morphology of Paracoccus was a short rod-like or globular shape; acinetobacter was a long rod. To distinguish the two morphologies more easily, globular bacteria and short rod bacteria were colored blue, and long rod-like bacteria were colored yellow .
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In the absence of MGP, the cell walls were intact and surrounded by extracellular polymeric substances (EPS). In the presence of MGP, bacteria were not surrounded by EPS, and there were some holes in the cell walls. This agreed well with previous studies (Dong et al., 2017). Figure 6d shows a large hole in the cell wall in the presence of MGP (2.58 μm). This implied that smaller MGP were more toxic leading to better denitrification. The broken cell walls appeared in globular bacteria along with short and long rod-like bacteria suggesting that they were damaged by MGP non-selectively. 3.6. Possible mechanisms of the acceleration of denitrification. The CV of MGP showed in supplementary material, and there is no redox peak for the three kinds of MGP. This is different from Renduo Zhang’ biochars that are redox mediators. To the best of our knowledge, redox mediators can accelerate degradation of pollutant substance, including nitrate. Most redox mediators have a redox peak in the CV. The ATR-FT-IR substantiates MGP and has no peak in the carbonyl functional group, shown in supplementary material, which is considered necessary to achieve electron transfer as a redox mediator. The denitrification by the sludge in the presence of MGP was accelerated, and the mechanism of the acceleration is drawn schematically. Many carbon nanomaterials are toxic to bacteria such as graphene (Akhavan and Ghaderi, 2010; Sawangphruk et al., 2012; Sun and Wu, 2018). The origin of the toxicity of carbon nanomaterials is still debatable, and the physicochemical property of the carbon nanomaterials is complex and likely the reason for the toxicity (Kang et al., 2009). Some studies have compared other carbon nanomaterials, and the toxicity of MGP is the weakest (Liu et al., 2011). Nitrification was decelerated by graphite nanoparticles (Dong et al., 2017). Smaller MGP are more toxic so the toxicity of the MGP is lower than graphite nanoparticles. Denitrification was accelerated by MGP microparticles. The reason for the acceleration of denitrification might be that denitrifying bacteria were induced to multiply faster by the hypotoxicity (Ren et al., 2015). The toxicity of MGP microparticles was lower than the zero effect point (ZEP), which is the dose where the response crosses the control group 14 / 28
response and becomes toxic/inhibitory; the bacteria were induced to multiply at this point (Agathokleous et al., 2019; Calabrese, 2015). The toxicity of MGP was stronger than the toxicity of ZEP, and the bacteria multiplied. In this study, denitrification in the wastewater with 160 mg/L MGP was as fast as the denitrification in the wastewater with 200 mg/L MGP. This proved the hormesis of MGP during denitrification. The hypotoxicity and appropriate dose explained the MGP hormesis. The mechanism for hormesis remains inconclusive (Yao et al., 2019). In previous studies, a microbial community could be changed by charcoal and graphene oxide (Chen et al., 2019; Du et al., 2015; Wu et al., 2019). To further explore how the hormesis of MGP accelerates the denitrification, we analyzed the microbial community structure and the abundance of bacteria. The proportion of Paracoccus increased in the presence of MGP perhaps because Paracoccus is a gram-negative bacteria with less sensitivity to MGP (Pulingam et al., 2019). There is a correlation between the removal rate of nitrate and the abundance of nirS and nirK genes (Herbert et al., 2014); denitrifying genes could be influenced by xenobiotics such as Cu2+ and fertilizer (J. Liu et al., 2018; Yu et al., 2018). To further explore the mechanism, the abundances of nirS and nirK gene were detected in this study. The abundance of nirK gene changed only slightly, but the abundance of nirS gene increased markedly via addition of MGP. This implied that denitrifying bacteria was induced by MGP to multiply faster in the presence of nitrate. The mechanism was because the hormesis of MGP increased the number of denitrifying bacteria. 4. Conclusion In this study, the acceleration of denitrification is negatively correlated with the size of MGP and positively correlated with its dose. The best acceleration was 83.4% in synthetic wastewater with 0.16 g/L MGP or 11.1% in industrial nitrogenous wastewater with 0.12 g/L MGP. Paracoccus has a little increase (6.23%) and included several denitrifying bacteria. The significant increase (75.6%) of denitrifying bacteria with nirS gene might be due to the hormesis of MGP, which is the mechanism underlying
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denitrification acceleration. This is the first report that MGP could enhance the denitrification via the sludge. MGP can denitrify materials in industrial applications.
E-supplementary data for this work can be found in e-version of this paper online
Acknowledgments The authors are grateful for financial assistance received from the National Basic Research Program of China (No. 2012CB723501), the National Natural Science Foundation of China (Grant Nos. 21978067 and 21978068), and the Hebei Natural Science Foundation (No. 12966737D). We thank LetPub (www.letpub.com) for its linguistic assistance during the preparation of this manuscript.
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Figure 1. Effect of different sizes of IGM on the denitrification at the dose of 200 mg/L in terms of (a) NO3--N concentration, (b) accumulation of NO2--N during the denitrification, and (c) removal rate of inorganic nitrogen during the denitrification, (d) pH in each groups
during the denitrification. Initial conditions: 100 mg/L NO3--N; 37°C. The values for each sample are presented as averages with standard deviations (n = 3).
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Figure 2. Effect of the different doses of IGM (2.58 μm) on the denitrification in terms of (a) NO3--N concentration, (b) accumulation of NO2--N during the denitrification, (c) removal rate of inorganic nitrogen during the denitrification, and (d) acceleration of denitrification rate that was calculated at the first 7 hours. Initial conditions: 100 mg/L NO3--N; 37°C. The values for each sample are presented as averages with standard deviations (n = 3).
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Figure 3. Effect of the different doses of IGM (2.58 μm) on the denitrification in terms of (a) NO3--N concentration, (b) accumulation of NO2--N, (c) removal rate of inorganic nitrogen, and (d) degradable speed of inorganic nitrogen during the denitrification. Initial conditions: 264.6 mg/L NO3--N; 37°C. The values for each sample are presented as averages with standard deviations (n = 3).
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Figure 4. Taxonomic classification of the sequencing result from the bacterial communities in the samples at the genus levels (eliminating genera with less than 1%). Sample are named with X-Y. X means the sampling time, that 0, 1 and 2 are beginning, 1 hour and 8 hours, respectively. Y means the operating conditions, that C, G, N and NG are control group, group added IGM, group added nitrate and group added both of nitrate and IGM, respectively.
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Figure 5. The abundances of (a) nirK gene, (b) nirK gene, and (c) 16S in the flora after exposure to IGM. Sample 0-C, sample 1-N, and sample 2-N were not exposed to IGM, and sample 1-N and sample 2-N were exposed to IGM. The values for each sample are presented as the averages with standard deviations (n = 3).
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Highlight Low-dose MGP accelerates denitrification in synthetic and industrial wastewater. MGP can improve the proportion of Paracoccus in a sample. MGP can enhance the abundance of the nirS gene. MGP has hormesis effects on denitrifying bacteria.
Author contributions Junzhang Li: Conceptualization, Methodology, Resources, Writing - Review & Editing. Zhaozhou Peng: Validation, Formal analysis, Visualization, Software, Investigation, Writing - Original Draft. Ruiyang Hu: Validation, Formal analysis, Visualization. Kaiyuan Gao: Validation. Chen Shen: Resources, Writing - Review & Editing, Supervision, Data Curation. Runjing Liu: Writing: Review & Editing. Shouxin Liu: Resources, Writing - Review & Editing, Supervision.
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S0960-8524(20)30204-2 https://doi.org/10.1016/j.biortech.2020.122935 BITE 122935
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Bioresource Technology
Received Date: Revised Date: Accepted Date:
5 December 2019 27 January 2020 29 January 2020
Please cite this article as: Li, J., Peng, Z., Hu, R., Gao, K., Shen, C., Liu, S., Liu, R., Micro-graphite particles accelerate denitrification in biological treatment systems, Bioresource Technology (2020), doi: https://doi.org/ 10.1016/j.biortech.2020.122935
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© 2020 Published by Elsevier Ltd.
Micro-graphite particles accelerate denitrification in biological treatment systems
Junzhang Li, Zhaozhou Peng, Ruiyang Hu, Kaiyuan Gao, Chen Shen, Shouxin Liu*, Runjing Liu
College of Chemistry and Pharmaceutical Engineering, Hebei University of Science and Technology, Shijiazhuang 050018, P.R. China.
*Corresponding authors Shouxin Liu ([email protected]) Present address 26 Yuxiang Street, Shijiazhuang, 050018, P.R. China.
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Abstract Accelerated denitrification is an essential problem in the biological treatment of nitrogenous wastewater. In this study, we report that denitrification is accelerated by micro-graphite particles (MGPs). The denitrification rate was increased by 83.4% or 11.1% in synthetic (with 0.16 g/L MGPs) or industrial nitrogenous wastewater (with 0.12 g/L MGP), respectively. The mechanism was revealed via a quantitative polymerase chain reaction (q-PCR), high-throughput sequencing, and scanning electron microscopy (SEM). The abundance of denitrifying bacteria Paracoccus in the sludge was increased by micrographite particles. The number of denitrifying bacteria with the nirS gene was increased significantly (75.6%). To the best of our knowledge, this is the first report that MGP could enhance denitrification via the sludge. MGP can denitrify in industrial applications.
Keywords: Accelerated denitrification; industrial wastewater; micro-graphite particles; toxicity of graphite.
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1. Introduction Nitrogenous wastewater can lead to eutrophication and massive proliferation of algae (Bougarne et al., 2019). Nitrates in aqueous systems threaten human health by forming N-nitrosamines that damage DNA. Nitrates and nitrites are associated with cancer morbidity and mortality (Zhang et al., 2018). Membranes, adsorption, and ion exchange resins are the most popular methods for biocatalytic denitrification using sludge as catalysts. The denitrification of nitrogenous organic compounds in wastewater is very complex, but it includes ammonification of nitrogenous organic compounds, nitrification of ammonia nitrogen, and denitrification of nitrate to nitrogen (McCarty, 2018; Skiba and Hydrology, 2008; Yanai et al., 2007). The anammox process is a more energy-efficient and environmentally friendly technology to degrade the nitrogenous wastewater than denitrification and nitrification. NO can react with ammonium to form hydrazine and then to N2 by anammox bacteria without N2O being produced (Mandel et al., 2019). However, it requires strict control over the concentration of inlet water and needs shortcut nitrification or shortcut denitrification. Generally, the removal of inorganic ions in wastewater, such as nitrogen and phosphorus, requires the help of complex microbial degradation processes (dC Rubin et al., 2017; Mandel et al., 2019). The degradation removal of nitrate is a critical step in denitrification. Nitrates can be transformed to nitrogen via nitrite as the intermediate, but nitrite inhibits the expression of denitrification bacteria in the sludge. Thus, improved biological denitrification is urgently needed (McCarty, 2018). In order to achieve rapid removal of nitrates in wastewater, new reactors have been designed, such as Up-flow Anaerobic Sludge Bed (UASB), Moving Bed Biofilm Reactor (MBBR), Membrane Bio-Reactor (MBR), and Sequencing Batch Reactor (SBR). These have been gradually improved to enhance denitrification (Dai et al., 2019; Daija et al., 2016). In wastewater, organic matter is a crucial factor affecting denitrification or anammox performance as either an electron mediator or rhamnolipids (Lovley and Woodward, 1996; Peng et al., 2019). The application of redox mediators like anthraquinone, porphyrin, and riboflavin can enhance denitrification by accelerating 3 / 28
electron transfer (Aranda-Tamaura et al., 2007; Martins et al., 2015; Xi et al., 2013; Xie et al., 2018). For example, the metalhumic acid complexes in UASB reactors improved the biotransformation of iopromide (Cruz-Zavala et al., 2016). Anthraquinone-2,6-disulfonate (AQDS) increased the removal rate of total nitrogen in UASB reactors (Rikmann et al., 2014). The coupling of biodegradation and non-biodegradation for the degradation of nutrients in wastewater has been reported. Heterotrophic denitrification is one of the main reactions in BNR and can be used together in the A/O/A system with the Fenton reaction as the biological stage (Klein et al., 2017b, 2017a). Co-culturing bacteria is a new way to improve biological denitrification. Geobacter sulfurreducens is an electroactive bacterium that can accelerate the denitrification rate by 13%51% via enhanced expression of the nirS gene coding for a cytochrome cd1-nitrite reductase (Wan et al., 2018). However, practical applications of those technologies have not yet been reported. During the denitrification, the alkalinity of wastewater gradually increased. The alkalinity and the inorganic carbon source required for degradation of the nutrients in wastewater use higher pH that can affect processing (Tenno et al., 2018a, 2018b). Partial denitrification can be achieved by controlling pH (Jung et al., 2019; Qian et al., 2019). In general it is not suitable for accelerating denitrification. Many organic materials and inorganic materials have been tested to accelerate denitrification with sludge, and micro-graphite particles (MGPs) exhibited outstanding activity for accelerated denitrification. Hence, the behavior and mechanism of MGP to accelerate denitrification of nitrogenous wastewater with sludge were studied here. In industrial wastewater, the rate of denitrification in the experimental group was faster than that in the control group. To reveal the mechanism of acceleration, scanning electron microscopy (SEM), high-throughput sequencing, and quantitative polymerase chain reaction (q-PCR) were used to observe the sludge’s morphology, community distribution, and the proportion of the denitrifying bacteria. 2. Experimental 2.1. Chemicals and Organism 4 / 28
2.1.1. Chemicals MGPs were purchased from Hubei Xinrunde Chemical Co., Ltd. (Hubei, China). All of the other chemical reagents were of analytical grade and used without further purification from Aladdin Ltd. (Shanghai, China). 2.1.2. Organism and culture methods The sludge was inoculated with activated sludge collected from Shijiazhuang Qiaoxi Waste Water Treatment Plant. The sludge was cultivated in a conical flask, and the composition of the substances in the conical flask is shown in Supplementary material. The sludge grew at pH 6.87.5 and 35°C in 250-mL conical flasks rotated at 150 rpm for 10 hours. 2.2. Enhancement of denitrification activity experiments 2.2.1 Denitrification of synthetic nitrogenous wastewater The adsorption of nitrate on the MGP was studied first. MGP was added to synthetic wastewater with an initial concentration of 100 mg/L NO3--N. The concentration of nitrate in the synthetic wastewater was detected after 8 hours, and it did not decrease. This confirmed that MGP could not adsorb the nitrate. Wastewater with sludge, sodium succinate (600 mg/L CODcr), and nitrate (100 mg/L NO3--N) were mixed and then distributed immediately into 250-mL conical flasks with predetermined amounts of MGP in advance at 37°C. Wastewater with sludge but without MGP was a control. To evaluate the denitrification efficiency, all of the groups were sampled every two hours for the analysis of nitrate and nitrite and pH. All of the experiments were performed in triplicate. 2.2.2. Denitrification of industrial nitrogenous wastewater Here, 5 mL of industrial wastewater was added to a 250-mL conical flask with 245 mL sludge at 37°C. The finial concentrations of CODcr and NO3--N in the conical flask were 1198.4 mg/L and 264.6 mg/L, respectively. The denitrification of industrial nitrogenous wastewater was performed without the addition of an extra carbon source. During the denitrification process, all groups were sampled together for analysis of nitrate and nitrite.
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Details on the industrial wastewater are shown in supplementary material. All of the experiments were performed in triplicate. 2.3. Analysis 2.3.1. Wastewater quality analysis To detect pollutants, samples were removed from wastewater in the withdrawn flasks and centrifuged at 7000 rpm for 5 min to remove the insoluble particles. The concentration of nitrate, nitrite, and ammonia in the supernatant was measured calorimetrically by UV spectrophotometry (UV-1500, UV/VS spectrophotometer, Macy, Co., China) at 220 nm, 275 nm (for nitrate), 540 nm (for nitrite), and 410 nm (for ammonia). The nitrate results had an error of 5% because of variability in the system. The concentration of total inorganic nitrogen is composed of nitrate, nitrite and ammonia (Zekker et al., 2012). The changes of pH during the denitrification process were measured with a digital pH meter (PHS-2F, Shanghai INESA, China). 2.3.2. Microbial community analysis All of the samples were sequenced using the Illumina-MISeq platform (Sangon Biotech, Shanghai, China). The primer pair of 341F (5'-CCTACGGGNGGCWGCAG-3') and 804R (5'-GACTACHVGGGTATCTAATCC-3') were used (Tsao et al., 2019). The first PCR thermal program was performed at 94°C for 3 min; five cycles of 94°C for 30 s, 45°C for 20 s, 65°C for 30 s; 20 cycles of 94°C for 20 s, 55°C for 20 s, and 72°C for 30 s; 72°C for 5 min; and 10°C in the end. The second round of PCR thermal program was performed at 95°C for 3 min; five cycles of 94°C for 20 s, 55°C for 20 s, and 72°C for 30 s; 72°C for 5 min; and then 10°C at the end. The resulting sequence datasets were deposited in the NCBI Sequence Read Archive under study accession number PRJNA593635. The details of sampling time and conditions of each samples are given in supplementary material. All experiments were performed in triplicate. 2.3.3. 16S, NirS and nirK gene analysis Three target genes were quantified, including two denitrifying bacterial genes (nirS and nirK gene) and 16srDNA. All q-PCR assays were performed in triplicate with a LightCycler480II fluorescent q-PCR method (Roche, Rotkreuz, Switzerland). 6 / 28
The PCR procedure for 16srDNA included an initial denaturation step at 95°C for 3 min and 45 cycles of amplification (95°C for 5s, 60°C for 30s). The dissociation was according to the instrument guidelines. The primer pair 515F (5'GTGCCAGCMGCCGCGGTAA-3') and 806R (5'-GGACTACHVGGGTWTCTAAT-3') were utilized to amplify 16srDNA. The primer pair of 876C (ATYGGCGGVCAYGGCGA) and 1040 (GCCTCGATCAGRTTRTGGTT) were utilized to amplify nirK gene. The primer pair of cd3aF (GTSAACGTSAAGGARACSGG) and R3cdR (GASTTCGGRTGSGTCTTGA) were used to amplify the nirS gene(S. Liu et al., 2018). Standard curves were obtained by serial dilutions of linearized plasmids with cloned fragments for the specific genes. Standard curves were linear (R2 = 0.999) over the range used. The amplification efficiencies for 16S, NirS, and NirK were 90.7%,87%, and 105.4%, respectively. All of the experiments were performed in triplicate. 2.3.4. Characterization of MGPs A scanning electron microscope (SEM) (S-4800-I, Hitachi, Japan) was used to observe the morphology of sludge and MGP. A Mastersizer (LS-POP-6, OMEC, Zhuhai, China) was used to determine the size distribution of several MGP. An electrochemical workstation (CHI660E, ChenHua, Shanghai, China) collected cyclic voltammogram (CV) data. Attenuated total reflectance transform infrared spectroscopy (ATR-FT-IR) determined the functional group of MGP. 2.3.5. Statistical analysis The statistical significance of the changes was determined with Tukey's multiple range test (SPSS 26.0). The significant correlation between the dose and acceleration was determined with Pearson’s correlation coefficient. Here, p-values below 0.05 were considered to be statistically significant. 3. Results and Discussion 3.1. Effect of MGPs on denitrification in synthetic nitrogenous wastewater Denitrification is the most effective method for degrading nitrogenous wastewater. MGP can enhance the sludge to degrade nitrogenous wastewater more quickly, and the accelerating effect depends on the particle size and dose. 7 / 28
3.1.1 Effect of MGPs size on the denitrification To explore the effect of MGP size on denitrification, three sizes of MGP were used to the denitrify at 200 mg/L. Experimental groups A, B, and C used MGP with sizes were 2.58 μm, 4.514 μm, and 10160 μm, respectively. There was a significant decrease in the concentration of nitrates in the groups with MGP during the denitrification versus controls (Figure 1a). The concentrations of nitrate in groups AC were 10.0, 5.2, and 5.2 mg/L after 8 hours; the control group was 34.4 mg/L. The concentrations of nitrate gradually decreased with size of MGP (Ccontrol > C10160μm >
C4.5-14μm ≥ C2.5-8μm). A smaller MGP could enhance nitrate degradation more
efficiently (p < 0.05). Biocatalytic denitrification is a reducing process, and the accumulation of nitrite was noted during denitrification. Figure 1b shows that the highest accumulation of nitrite was in group C and group B at 13.8 and 9.4 mg/L; group A and the control group were 8.0 and 9.2 mg/L during denitrification, respectively. The highest accumulation of nitrite was in group C perhaps because the degradation of nitrite in group C accelerated better. However, concentrations of nitrite in group C and group B were barely detected after 8 hours, while these in group A and control group were 9.0 and 8.0 mg/L, respectively. There was a statistically significant difference between the highest accumulation of nitrite in groups AC (p < 0.05). The denitrification in groups B and C was nearly complete after 8 hours, but the denitrification in the control group and group A was still being performed. This implied that smaller MGP could enhance nitrite degradation more efficiently (p<0.05). Figure 1c shows that the average values of the removal rates of inorganic nitrogen are 57.5%, 81.0%, 94.7%, and 94.7% in the control group, groups AC, respectively, after 8 hours. The removal rates of inorganic nitrogen in groups B and C were almost the same after 8 hours, but it was obvious that the denitrification in group C was better than that in group B in terms of complete denitrification. The results showed that smaller MGP could significantly enhance denitrification (p < 0.05).
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Figure 1d shows pH data. After the fourth round of denitrification, pH in the control group and groups A, B, and C 8.08, 8.50, 8.67 and 8.81, respectively. Obviously, the pH values were higher in groups with MGP during the denitrification because there was more nitrate and nitrite degraded by sludge. This was consistent with the data above and indicated that smaller MGP could enhance denitrification more efficiently. In summary, the results indicated that sludge could be improved by MGP to degrade nitrate in wastewater, and the best size of MGP was 2.58 μm. Figure 1 3.1.2. Effects of MGPs dose on denitrification Different doses of MGP caused different degrees of denitrification acceleration. The experimental groups are named C0, C1, C2, C3, C4, and C5. These were related to 0, 0.04, 0.08, 0.12, 0.16, and 0.2 g/L MGP (2.58 μm) in the wastewater, respectively. Figure 2a shows that the concentrations of nitrate contained certain regularities with changes in MGP dose. The average nitrate concentrations were 36.5, 21.5, 16.3, 17.3, 16.6, and 18.1 mg/L in C0, C1, C2, C3, C4, and C5 after 8 hours, respectively. The concentrations of nitrate gradually decreased with increasing MGP dose. However, the concentrations of nitrate in C4 and C5 were nearly identical. There was an insignificant change of nitrate concentration from 0.16 to 0.2 g/L MGP. The concentrations of nitrate in C4 and C5 did not change after 7 hours, which might be due to the lack of a carbon source. The rate of the nitrate degradation in C5 was faster than that in C0 by 67.2% according to the average concentrations of nitrate in C0 and C5 during first seven hours of the denitrification. A higher dose of MGP could more efficiently enhance nitrate degradation. Figure 2b shows that the highest accumulations of nitrite are 8.1, 9.4, 6.9, 6.2, 5.2, and 5.7 mg/L in C0, C1, C2, C3, C4, and C5, respectively. The highest accumulation of nitrite gradually decreased along with the increasing MGP dose (00.16 g/L). The accumulation of nitrite was nearly the same as when the dose of MGP was more than 0.16 mg/L. This implied that smaller MGP could enhance nitrite degradation more efficiently. 9 / 28
There was no significant difference between the removal rates of inorganic nitrogen in C4 and C5 (p > 0.05); they were higher than these in the other samples (Figure 2c). The acceleration of denitrification in C4 group was 83.4%, and the value was not significantly different from that in C5 group (Figure 2d). Thus, there was a significant correlation between the acceleration of denitrification and the dose of MGP (p < 0.05). Thus, best condition for denitrification was C4. Figure 2 3.2. Effects of MGPs on denitrification in industrial nitrogenous wastewater. To explore the effect of MGP on the denitrification in industrial nitrogenous wastewater, we collected wastewater from a caprolactam hexanolactam factory. The experimental groups were named D1, D2, D3, D4, and D5 and related to 0.04, 0.08, 0.12, 0.16, and 0.2 g/L MGP (2.58 μm) added to the wastewater. The concentrations of nitrate and nitrite are shown in Figure 3a and Figure 3b. The inorganic nitrogen concentration in the wastewater was analyzed as in Figure 3c. The denitrification in the experimental groups was faster than the control groups as shown in Figure 3d. The denitrification in industrial nitrogenous wastewater gradually enhanced along with an increase in the dose of MGP (00.12 g/L). After 38 hours, the removal rate of inorganic nitrogen was 90.2% and 81.8% in D3 and control group, respectively. No significant increase in the final removal rate was seen over 0.12 g/L dose of MGP (p > 0.05). The degradation speed was 5.87, 6.09, 6.14, 6.39, 6.33, and 6.53 mg/(L*h) in the control group, D1, D2, D3, D4, and D5. In D5, the degradation speed of the inorganic nitrogen was the fastest (11.1% faster than the control group). The rate of denitrification significantly correlated with the dose of MGP (p < 0.05). Thus, MGP exhibited a potential industrialization on the denitrification. Figure 3 3.3. Effects of MGPs on the microbial community The variety of microbial community structure in the sludge was analyzed with 16SrDNA sequencing on an Illumina Miseq platform to understand how MGP enhanced the sludge for degrading nitrate and nitrite. The gram-negative strains Paracoccus and Acinetobacter were the dominant genus in the sludge: Their proportions in 0-C were 54.4% 10 / 28
and 28.8%, respectively (Figure 4). In 1-N, the proportion of Paracoccus increased to 61.2%, and the proportion of Acinetobacter decreased to 21.6% in the first hour of the denitrification. The proportions of Paracoccus and Acinetobacter in 1-C were 50.1% and 30.7%. Versus 1-C and 0-C, we note that the proportion of Acinetobacter increased by 1.9%, and Acinetobacter in the sludge multiplied faster than other genus with one hour of succinate treatment. The proportion of Paracoccus in 1-N was more than that in 1-C by 11.1%. This implied that Paracoccus in the sludge could multiply faster than the other genus species via nitrate for one hour. Versus succinate, the genus was more easily induced by nitrate at one hour of treatment. The proportions of the bacterial genus were almost the same in 1-N and 1-NG. The proportion of the bacterial genus was barely changed in the presence of nitrate for one hour. The proportion of Paracoccus in 1-G was more than that in 1-C by 8.1%, and the proportion of Acinetobacter in 1-G was less than that in 1-C by 8.6%. This implied that Paracoccus in the sludge was induced to multiply faster than other genera by MGP in one hour. Between 1-N and 2-N, the proportion of Paracoccus decreased by 8.5%, and the proportion of Acinetobacter increased by 5.2%. This might imply that Paracoccus was more difficult to be induced and multiply via nitrate when the concentration of nitrate reduced during the denitrification. The proportion of Paracoccus and Acinetobacter was almost the same in 1-NG and 2-NG. The proportion of Paracoccus in 2-NG was more than that in 2-N by 7.9% and the proportion of Acinetobacter in 2-NG was less than that in 2-N by 5.5%. The proportions of Paracoccus in 1-N and 1-NG were almost the same in the first hour, but Paracoccus multiplied faster by MGP at 8 hours. The proportion of Paracoccus in 2-G was 2.5% higher than in 2-C, but the proportion of Acinetobacter in 2-G was 10% less than in 2-C. The variation in the community structure at the first hour was more than that after 8 hours of denitrification in the presence of MGP. Acinetobacter was still inhibited more easily than the other genera by MGP at 8 hours.
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The proportion of Acinetobacter could be significantly reduced by MGP, while the proportion of Paracoccus had the opposite impact from MGP. Paracoccus includes many denitrifying species that could degrade nitrate and nitrite but the increase in the proportion of Paracoccus was not equal to the increase in denitrifying bacteria. Figure 4 3.4. Effects of MGPs on the abundance of 16s and denitrifying gene To explore the effect of MGP on biomass during denitrification (Segawa et al., 2005), we detected the abundance of 16S gene in the sludge via q-PCR analysis. The samples were sequenced on an Illumina Miseq platform. The abundance of the 16S gene in 1-N and 1-NG was significantly higher than that in 0-C by 10% and 6% at one hour (p > 0.05), respectively (Figure 5c). This increase might be due to the addition of succinate and nitrate that can be used as a carbon source and a nitrogen source, respectively. The abundance of 16S gene in 1-NG was not significantly lower than that in 1-N (p > 0.05), which might be due to the toxicity of MGP. The effect of MGP on biomass was less than the effect of nitrate at one hour. The abundance of the 16S gene in 2-N was not significantly lower than that in 0-C by 10.6% (p > 0.05), which might be due to the lack of nutrients; the abundance of the 16S gene in 2-NG was significantly higher than that in 0-C by 24.9% (p > 0.05), which might be due to the hormesis of MGP. This significant increase in the 16S gene abundance indicated that hormesis of MGP could reverse the decrease in sludge via nutrient scarcity. Although the proportion of Paracoccus and the abundance of the 16S gene increased in the group with MGP, not all Paracoccus were denitrifying bacteria. The bacteria with denitrifying functional genes such as nirK and nirS were the denitrifying bacteria that could degrade nitrite. The abundance of the denitrifying genes in the sludge was detected by q-PCR analysis to understand the possible mechanism underlying the MGP effect on the denitrification ability of the sludge. First, the abundance of the nirK gene from sludge was detected by q-PCR (Figure 5a). The abundance of the nirK gene in 1-N and 1-NG were 7.9% (p > 0.05) and 10.8% (p < 0.05) lower than that in 0-C, respectively. At one hour, the abundance of the nirK gene 12 / 28
was not influenced by MGP, but the reduction in the abundance might be due to nitrate, which can be considered xenobiotic. The abundance of the nirK gene in 2-N was less than that in 0-C by 1.9% (p > 0.05), and the abundance of the nirK gene in 2-NG was more than that in 0-C by 7.4% (p > 0.05). The abundance of the nirK gene was not significantly enhanced by MGP and reduced nitrate levels at 8 hours. The abundance of the nirS gene in all samples was far higher than the abundance of nirK gene in the samples; thus, the abundances of the nirS gene were more interesting. Figure 5b shows that the expression of the nirS gene in 1-N and 1-NG was higher than that in 0-C by 58.4% and 68.2%, respectively (p < 0.05). The expression of the nirS gene could be enhanced by nitrate and could be further promoted by MGP. The abundance of nirS gene in the 2-N was slightly lower than that in 0-C by 15.6% (p > 0.05), and the abundance of nirS gene abundance in 2-NG was significantly more than that in 0-C by 106.9% (p < 0.05). The abundance of nirS gene in the group with nitrate but without MGP was almost the same as 0-C after 8 hours (p > 0.05). The abundance of nirS gene in the sample with MGP could be enhanced after 8 hours. This implied that the amount of denitrifying bacteria was modulated by MGP during denitrification. The abundance of the nirS gene in 2-NG was not significantly higher than that in 1-NG (p > 0.05). This implied that bacteria harboring the nirS gene multiplied faster than other genera by MGP in a short time at the beginning of the denitrification. In other words, MGP significantly enhanced the number of denitrifying bacteria with nirS gene in the sludge, which accelerated denitrification. Figure 5 3.5. Toxicity of MGPs to bacteria via SEM The sludge samples in the control group as well as groups A, B, and C were imaged with SEM. The dominant genus in the samples were Paracoccus and Acinetobacter. The morphology of Paracoccus was a short rod-like or globular shape; acinetobacter was a long rod. To distinguish the two morphologies more easily, globular bacteria and short rod bacteria were colored blue, and long rod-like bacteria were colored yellow .
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In the absence of MGP, the cell walls were intact and surrounded by extracellular polymeric substances (EPS). In the presence of MGP, bacteria were not surrounded by EPS, and there were some holes in the cell walls. This agreed well with previous studies (Dong et al., 2017). Figure 6d shows a large hole in the cell wall in the presence of MGP (2.58 μm). This implied that smaller MGP were more toxic leading to better denitrification. The broken cell walls appeared in globular bacteria along with short and long rod-like bacteria suggesting that they were damaged by MGP non-selectively. 3.6. Possible mechanisms of the acceleration of denitrification. The CV of MGP showed in supplementary material, and there is no redox peak for the three kinds of MGP. This is different from Renduo Zhang’ biochars that are redox mediators. To the best of our knowledge, redox mediators can accelerate degradation of pollutant substance, including nitrate. Most redox mediators have a redox peak in the CV. The ATR-FT-IR substantiates MGP and has no peak in the carbonyl functional group, shown in supplementary material, which is considered necessary to achieve electron transfer as a redox mediator. The denitrification by the sludge in the presence of MGP was accelerated, and the mechanism of the acceleration is drawn schematically. Many carbon nanomaterials are toxic to bacteria such as graphene (Akhavan and Ghaderi, 2010; Sawangphruk et al., 2012; Sun and Wu, 2018). The origin of the toxicity of carbon nanomaterials is still debatable, and the physicochemical property of the carbon nanomaterials is complex and likely the reason for the toxicity (Kang et al., 2009). Some studies have compared other carbon nanomaterials, and the toxicity of MGP is the weakest (Liu et al., 2011). Nitrification was decelerated by graphite nanoparticles (Dong et al., 2017). Smaller MGP are more toxic so the toxicity of the MGP is lower than graphite nanoparticles. Denitrification was accelerated by MGP microparticles. The reason for the acceleration of denitrification might be that denitrifying bacteria were induced to multiply faster by the hypotoxicity (Ren et al., 2015). The toxicity of MGP microparticles was lower than the zero effect point (ZEP), which is the dose where the response crosses the control group 14 / 28
response and becomes toxic/inhibitory; the bacteria were induced to multiply at this point (Agathokleous et al., 2019; Calabrese, 2015). The toxicity of MGP was stronger than the toxicity of ZEP, and the bacteria multiplied. In this study, denitrification in the wastewater with 160 mg/L MGP was as fast as the denitrification in the wastewater with 200 mg/L MGP. This proved the hormesis of MGP during denitrification. The hypotoxicity and appropriate dose explained the MGP hormesis. The mechanism for hormesis remains inconclusive (Yao et al., 2019). In previous studies, a microbial community could be changed by charcoal and graphene oxide (Chen et al., 2019; Du et al., 2015; Wu et al., 2019). To further explore how the hormesis of MGP accelerates the denitrification, we analyzed the microbial community structure and the abundance of bacteria. The proportion of Paracoccus increased in the presence of MGP perhaps because Paracoccus is a gram-negative bacteria with less sensitivity to MGP (Pulingam et al., 2019). There is a correlation between the removal rate of nitrate and the abundance of nirS and nirK genes (Herbert et al., 2014); denitrifying genes could be influenced by xenobiotics such as Cu2+ and fertilizer (J. Liu et al., 2018; Yu et al., 2018). To further explore the mechanism, the abundances of nirS and nirK gene were detected in this study. The abundance of nirK gene changed only slightly, but the abundance of nirS gene increased markedly via addition of MGP. This implied that denitrifying bacteria was induced by MGP to multiply faster in the presence of nitrate. The mechanism was because the hormesis of MGP increased the number of denitrifying bacteria. 4. Conclusion In this study, the acceleration of denitrification is negatively correlated with the size of MGP and positively correlated with its dose. The best acceleration was 83.4% in synthetic wastewater with 0.16 g/L MGP or 11.1% in industrial nitrogenous wastewater with 0.12 g/L MGP. Paracoccus has a little increase (6.23%) and included several denitrifying bacteria. The significant increase (75.6%) of denitrifying bacteria with nirS gene might be due to the hormesis of MGP, which is the mechanism underlying
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denitrification acceleration. This is the first report that MGP could enhance the denitrification via the sludge. MGP can denitrify materials in industrial applications.
E-supplementary data for this work can be found in e-version of this paper online
Acknowledgments The authors are grateful for financial assistance received from the National Basic Research Program of China (No. 2012CB723501), the National Natural Science Foundation of China (Grant Nos. 21978067 and 21978068), and the Hebei Natural Science Foundation (No. 12966737D). We thank LetPub (www.letpub.com) for its linguistic assistance during the preparation of this manuscript.
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Figure 1. Effect of different sizes of IGM on the denitrification at the dose of 200 mg/L in terms of (a) NO3--N concentration, (b) accumulation of NO2--N during the denitrification, and (c) removal rate of inorganic nitrogen during the denitrification, (d) pH in each groups
during the denitrification. Initial conditions: 100 mg/L NO3--N; 37°C. The values for each sample are presented as averages with standard deviations (n = 3).
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Figure 2. Effect of the different doses of IGM (2.58 μm) on the denitrification in terms of (a) NO3--N concentration, (b) accumulation of NO2--N during the denitrification, (c) removal rate of inorganic nitrogen during the denitrification, and (d) acceleration of denitrification rate that was calculated at the first 7 hours. Initial conditions: 100 mg/L NO3--N; 37°C. The values for each sample are presented as averages with standard deviations (n = 3).
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Figure 3. Effect of the different doses of IGM (2.58 μm) on the denitrification in terms of (a) NO3--N concentration, (b) accumulation of NO2--N, (c) removal rate of inorganic nitrogen, and (d) degradable speed of inorganic nitrogen during the denitrification. Initial conditions: 264.6 mg/L NO3--N; 37°C. The values for each sample are presented as averages with standard deviations (n = 3).
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Figure 4. Taxonomic classification of the sequencing result from the bacterial communities in the samples at the genus levels (eliminating genera with less than 1%). Sample are named with X-Y. X means the sampling time, that 0, 1 and 2 are beginning, 1 hour and 8 hours, respectively. Y means the operating conditions, that C, G, N and NG are control group, group added IGM, group added nitrate and group added both of nitrate and IGM, respectively.
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Figure 5. The abundances of (a) nirK gene, (b) nirK gene, and (c) 16S in the flora after exposure to IGM. Sample 0-C, sample 1-N, and sample 2-N were not exposed to IGM, and sample 1-N and sample 2-N were exposed to IGM. The values for each sample are presented as the averages with standard deviations (n = 3).
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Highlight Low-dose MGP accelerates denitrification in synthetic and industrial wastewater. MGP can improve the proportion of Paracoccus in a sample. MGP can enhance the abundance of the nirS gene. MGP has hormesis effects on denitrifying bacteria.
Author contributions Junzhang Li: Conceptualization, Methodology, Resources, Writing - Review & Editing. Zhaozhou Peng: Validation, Formal analysis, Visualization, Software, Investigation, Writing - Original Draft. Ruiyang Hu: Validation, Formal analysis, Visualization. Kaiyuan Gao: Validation. Chen Shen: Resources, Writing - Review & Editing, Supervision, Data Curation. Runjing Liu: Writing: Review & Editing. Shouxin Liu: Resources, Writing - Review & Editing, Supervision.
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