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Article

Functional and Microbiological Responses of Iron–Carbon Galvanic Cell-Supported Autotrophic Denitrification to Organic Carbon Variation and Dissolved Oxygen Shaking

by
Jinlong Li
1,
Xiaowei Wang
2,
Shi-Hai Deng
3,*,
Zhaoxu Li
3,
Bin Zhang
1 and
Desheng Li
4
1
Yatai Construction Science and Technology Consulting Institute, Beijing 100120, China
2
School of Ecology and Environment, Beijing Technology and Business University, Beijing 100048, China
3
School of Human Settlements and Civil Engineering, Xi’an Jiaotong University, Xi’an 710049, China
4
School of Civil Engineering, Beijing Jiaotong University, Beijing 100044, China
*
Author to whom correspondence should be addressed.
Water 2024, 16(17), 2455; https://doi.org/10.3390/w16172455
Submission received: 28 July 2024 / Revised: 21 August 2024 / Accepted: 23 August 2024 / Published: 29 August 2024
(This article belongs to the Special Issue Sustainable Water Treatment and Contaminants Control: Technologies and Strategies)
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Abstract

:
Iron–carbon galvanic-cell-supported autotrophic denitrification (IC-ADN) is a burgeoning efficient and cost-effective process for low-carbon wastewater treatment. This study revealed the influence of organic carbon (OC) and dissolved oxygen (DO) on IC-ADN in terms of functional and microbiological characteristics. The nitrogen removal efficiency increased to 91.6% and 94.7% with partial organic carbon source addition to COD/TN of 1 and 3, respectively. The results of 16S rRNA high-throughput sequencing with nirS and cbbL clone libraries showed that Thiobacillus was the predominant autotrophic denitrifying bacteria (ADB) in the micro-electrolysis-based autotrophic denitrification, which obtained nitrogen removal efficiency of 80.9% after 96 h. The ADBs shifted gradually to heterotrophic denitrifying bacteria Thauera with increasing COD/TN ratio. DO concentration of 0.8 rarely affected the denitrification efficiency and the denitrifying communities. When the DO concentration increased to 2.8 mg/L, the nitrogen removal efficiency decreased to 69.1%. These results demonstrated that autotrophic denitrification was notably affected by COD/TN and high DO concentration, which could be used to acquire optimum conditions for nitrogen removal. These results provided an in-depth understanding of the influential factors for galvanic-cell-based denitrification and helped us construct a stable and highly efficient treatment process for insufficient carbon source wastewater.

1. Introduction

Nitrogen pollution has increased the risk of eutrophication in water bodies and it is increasingly attracting worldwide attention [1,2]. Biological denitrification processes, including heterotrophic and autotrophic denitrification processes, are considered to be a promising approach for nitrogen removal due to the relatively low costs and environmentally friendly products [3]. However, it is a challenge for denitrifying bacteria to remove nitrogen in the treatment of wastewater with insufficient organic carbon. Heterotrophic denitrification with the addition of external organic carbon sources such as acetate, ethanol and methanol has been considered, but it requires high costs and has a high sludge production [4]. In contrast, autotrophic denitrification is a promising technique due to its advantages of low cost and low sludge production. To date, ferrous-, sulfur-, and hydrogen-based autotrophic denitrification processes have been developed and have attracted broad attention [5].
In the autotrophic denitrification processes, carbon dioxide or bicarbonate is used as a carbon source and inorganic reductants are utilized as electron donors by the autotrophic denitrifying bacteria (ADBs) for denitrification [6]. In recent years, many microorganisms, such as Thiobacillus [7,8], Thauera [9] and Flavobacterium, have been identified as ADBs [10]. Nitrate (NO3−-N) is reduced stepwise to nitrite (NO2−-N), nitric oxide, nitrous oxide, and finally N2 by the necessary enzymes, including, e.g., nitrate reductase, nitrite reductase, nitric oxide reductase, and nitrous oxide reductase, respectively [11]. Nitrite reductase is used to identify denitrifying community composition to construct a phylogenetic relationship, which contains cytochrome cd1-containing nitrite reductase (nirS) and copper-containing nitrite reductase (nirK) [12]. Previous studies indicated that the Calvin–Benson–Bassham cycle was the most abundant pathway for inorganic carbon fixation in chemoautotrophic bacteria [13]. The rate-limiting enzyme in the Calvin cycle is ribulose 1, 5-bisphosphate carboxylase/oxygenase (RubisCO), which contains four natural forms. The diversity of chemoautotrophic bacteria was analyzed by coding genes cbbL (form I) and cbbM (form II) [14]. Microorganisms representative of RubisCO form III were discovered in Archaea, and RubisCO form IV lacked catalytic activity [14].
As our previous studies described, novel iron–carbon micro-electrolysis carriers (MECs) were applied to support autotrophic denitrification (Fe(0)/C-ADN) for nitrogen removal, achieving a NO3−-N removal efficiency of 96.5% [6,15,16]. Numerous micro-scale galvanic cells were formed when wastewater flowed through the MECs. Fe2+ was generated from the anodes and the electron was transported to the cathodes to produce [H] or H2, both of which could be utilized as electron donors for ADBs [17,18]. MECs could provide appropriate and rapid electrons without an external additional power source using H2 and Fe2+, which indicated that only inoculated sludge and inorganic carbon were needed in the denitrification system.
This study aimed to evaluate the effect of partial organics addition and dissolved oxygen (DO) concentrations on nitrogen removal and microbial communities in autotrophic denitrification by the setting up and operation of Fe(0)/C-ADN. Further, the functional and microbiological response to organic carbon and dissolved oxygen were investigated by high-throughput sequencing of 16S rRNA and clone libraries of nirS and cbbL. This study will provide additional knowledge and an in-depth understanding of the optimal conditions and mechanisms for removing nitrogen from low chemical oxygen demand (COD) to total nitrogen (TN) ratio (COD/TN) wastewater through autotrophic denitrification.

2. Materials and Methods

2.1. Batch Experiments

Five batch experiments were established in 500 mL serum bottle bioreactors. A total of 450 g MECs with attached biofilm were collected from a continuous autotrophic denitrification reactor and 350 mL synthetic wastewater was moved into bioreactors in this study. The synthetic wastewater included identical NO3−-N (40 mg/L), inorganic carbon (40 mg/L), and trace element solution (1 mL/L). The trace element solution was described in a previous study [19]. COD/TN ratios were fixed as 0, 1, 3, 0, and 0, and DO concentrations in the five bioreactors were approximately 0, 0, 0, 0.8, and 2.8 mg/L, respectively (Figure S1). The bioreactors were placed in a shaking incubator at 30 °C and 90 r/min and operated for 25 d. Fresh influent was added instead of the substrate after 96 h. A total of 5 mL substrate solution was discharged and filtered through 0.45 μm filters to measure nitrogen concentration.
In this study, the MECs were applied as carriers for organic-insufficient wastewater treatment. Zero-valence iron, activated carbon, multiple catalysts, and adhesive and foaming agents were mixed, molded, and then roasted at ~1000 °C under oxygen-free conditions, as described in detail in our previous studies [20,21]. MECs were physically characterized by a specific surface area of 3.3 × 104–4.2 × 104 m2/kg and a porosity of 51–57%, which may support microbial enrichment. When MECs were applied, Fe2+ was generated in the anode according to Fe0 + 2e− → Fe2+; meanwhile, [H] or H2 were generated in the cathode according to H+ + e− → [H] or 0.5H2. NO3−-N was subsequently reduced by biological denitrification using Fe2+ and H2 as electron donors. Furthermore, NO3−-N could be reduced to ammonia (NH4+-N) and N2 by MEC-based chemical denitrification without microbial processes.

2.2. Water Quality Analysis

Nitrogen concentrations, including NO3−-N, NO2−-N, and NH4+-N, were measured when the liquid was extracted using a 2102C ultraviolet spectrophotometer (UNICO Co., Ltd., Shanghai, China), according to the APHA standard methods [22]. The sum of NO3−-N, NO2−-N, and NH4+-N was used to estimate TN concentration. Total organic carbon was measured using a TOC 5000A analyzer (SHIMADZU, Kyoto, Japan). DO was measured using a WTW 340i probe (WTW company, Weilheim, Germany).

2.3. DNA Extraction

To comprehensively reveal the microbial communities in the different operation conditions, all of the MECs were collected from each bioreactor and then brushed with sterilization deionized water during the final operation. Genomic DNA was extracted from 0.5 g to comprehensively reveal the microbial communities in the different operation conditions. Genomic DNA was extracted from 0.5 g sludge using the FastDNA SPIN Kit for Soil (Qbiogene, Irvine, CA, USA) according to the manufacturer’s protocol. Concentration and quality were measured using the NanoDrop spectrophotometer (NanoDrop Technologies, Wilmington, DE, USA).

2.4. High-Throughput Sequencing of 16S rRNA

The V4 hypervariable regions of 16S rRNA gene were amplified using barcode-modified primers 338F/806R in all five samples. PCR reactions were carried out in a total volume of 20 μL containing 10 ng of DNA template, 4 μM of each primer, and 10 μL of PrimerStar HS (Premix). The amplified processes are described as Table S1. Each sample was amplified in triplicate and then merged. The PCR products were purified and quantified and then sequenced on an Illumina MiSeq platform (Majorbio Bio-pharm Technology, Shanghai, China). Raw data of 16S rRNA was processed using the Quantitative Insights Into Microbial Ecology (QIIME) pipeline, and low-quality and short sequences were removed before further analysis. The sequences were divided and clustered to operational taxonomic units (OTUs) with similarity threshold of 97% [23]. Richness and diversity indices, including Ace, Chao1, Shannon, Simpson, and coverage, were conducted. Taxonomic classifications of the sequences were assigned using the SILVA 16S rRNA database. The raw sequencing data in this study have been deposited to the NCBI Sequence Read Archive with accession number SRP076530.

2.5. Construction of nirS and cbbL Libraries in DB0

The nirS and cbbL genes, which encode the limited enzymes nitrite reductase and RubisCO, were amplified to reveal ADBs in the denitrifying bioreactor numbered 0 (DB0). The primer sets nirS1F/6R and cbbL168F/786R were applied for nirS and cbbL, respectively (Table S1). PCR reactions were performed using Premix Ex Taq polymerase with a volume of 50 μL and amplified with 36 cycles. PCR amplification was carried out in duplicate and then mixed. The amplified fragments were re-extracted using Gel Extraction Kit (Omega, Norcross, GA, USA), and then recombined into a pMD18-T vector with a DNA Ligation Kit (TaKaRa, Kusatsu, Japan). Recombination vectors were transformed into DH5α competent cells and cultured in the LB valid medium overnight. Positive clones were transferred for cultivation and gene fragments were sequenced with universal primers M13F/M13R on the ABI 3730 analyzer (Applied Biosystems, Waltham, MA, USA). The sequence data were clustered to OTUs with 97% similarity by Mothur and representative OTUs were aligned with sequences from the GenBank database. Phylogenetic trees were constructed using MEGA 5.1.

2.6. Quantitative PCR of 16S rRNA, nirS, and cbbL

To reveal gene copies of bacteria, denitrifying bacteria, and autotrophs, qPCR was performed using primer sets 341F/515R, nirS1F/3R, and cbbL168F/786R, respectively (Table S1). The standard curves were established as follows: corresponding clones were ligated into pMD18-T vectors and subsequently transformed into DH5α competent cells. For double-helix DNA, DNA copies = (6.02 × 1023 × DNA amount)/(DNA length × 660). The standard plasmid was exacted and diluted with a serous concentration from 101 to 1010 copies/μL. The PCR reactions were performed on a CFX96 System (Bio-Rad Laboratories, Hercules, CA, USA) in a volume of 20 μL, which contained 10 μL of SYBR Premix Ex Taq (TaKaRa, Kusatsu, Japan), 0.4 μL of each primer (10 mM), and 1 μL of the DNA template. The qPCR reactions were performed in triplicate. Amplification efficiencies were in the range of 95–110% and R2 value was over 0.99. Melting curves were performed to check the specificity of primer sets.

3. Results

3.1. Influence of OC and DO on the Performance of Fe(0)/C-AND

Based on the reaction principle of MECs, NO3−-N could be reduced by chemical reduction, autotrophic denitrification, and heterotrophic denitrification in the bioreactors (Figure 1). NO3−-N concentrations in DB0, DB1, and DB3 decreased from 38.6 ± 0.7 mg/L, 40.3 ± 0.8 mg/L, and 38.9 ± 1.5 mg/L to 0.6 ± 0.1 mg/L, 0.6 ± 0.3 mg/L, and 0.5 ± 0.2 mg/L respectively, with a reaction time of 96 h (Figure 1a). Overall, NO2−-N accumulated maximally at 24 h, with concentrations of 12.7 ± 0.3 mg/L, 6.3 ± 0.1 mg/L, and 5.0 ± 0.2 mg/L in DB0, DB1, and DB3, respectively (Figure 1b). NH4+-N concentrations increased gradually in all bioreactors by chemical denitrification and were 4.5 ± 0.3 mg/L, 2.7 ± 0.9 mg/L, and 1.3 ± 0.2 mg/L in DB0, DB1, and DB3 at 96 h, respectively (Figure 1c). The effluent concentrations of organic carbon in DB1 and DB3 were both less than 1 mg/L. When organic carbon was partially added, NO3−-N removal rates were similar. NO2−-N and NH4+-N accumulation were declining with COD/TN ratio. TN removal efficiency increased from 80.9% in DB0 to 91.6% and 94.7% in DB1 and DB3, respectively (Figure 1d). It was found that TN consumption at 24 h in DB1 and DB3 was extremely significant compared with DB0 (p < 0.01).
NO3−-N concentrations in DB0.8 and DB2.8 decreased from 38.4 ± 1.5 mg/L and 38.6 ± 2.0 mg/L to 0.3 ± 0.2 mg/L and 11.1 ± 0.8 mg/L at 96 h, respectively (Figure 1a). Similarly, NO2−-N was accumulated maximally with concentrations of 11.1 ± 0.8 mg/L in DB0.8 at 24 h. However, the DB2.8 group had the lowest NO2−-N concentration variation and had an ultimate NO2−-N concentration of 0.3 ± 0.2 mg/L (Figure 1b). NH4+-N concentrations increased gradually to 2.4 ± 0.9 mg/L and 0.8 ± 0.2 mg/L in DB0.8 and DB2.8 at 96 h, respectively (Figure 1c). The nitrogen removal efficiency of 83.6% obtained in DB0.8 was similar to that obtained in DB0, which indicated that the anoxic condition had little influence on autotrophic denitrification in the MEC system. However, when the DO concentration increased to 2.8 mg/L, the TN removal efficiency was markedly reduced to 69.1% (Figure 1d). And TN consumption at 24 h in DB0.8 and DB2.8 was significant compared with DB0 (p < 0.05).

3.2. Community Structure of ADBs in Fe(0)/C-ADN

A total of 29,382 effective bacterial 16S rRNA sequences were obtained by high-throughput sequencing in DB0. Proteobacteria was the dominant phylum, which accounted for 89.2% of the total sequences. The relative abundances of dominant families, including Hydrogenophilaceae and Xanthomonadaceae, were 29.7% and 18.3%, respectively. A total of 340 genera were retrieved at the genus level. The most abundant genus was Thiobacillus, which contained 29.6% of the effective sequences. Other dominant genera, including Gallionella, Rhodobacter, Arenimonas, and Thermomonas, accounted for 8.4%, 7.6%, 7.6%, and 5.5%, respectively (Figure S2). The corresponding OTU1435, OTU608, OTU1282, OTU1658, and OTU735 were affiliated with Thiobacillus, Gallionella, Arenimonas, Thermomonas, and Rhodobacter (Figure 2).
Clone libraries of the nirS gene in the denitrification and CO2 fixation gene cbbL in the Calvin cycle were constructed to reveal chemoautotrophic denitrifying bacteria (ChemoADBs) in DB0. There were 30 clone sequences in each library. In the nirS library, nirS-OTU1 (four sequences), nirS-OTU5 (one sequence), nirS-OTU8 (two sequences), nirS-OTU9 (one sequence), nirS-OTU15 (one sequence) and nirS-OTU16 (one sequence) formed a clade. This clade was most closely related to Arenimonas sp. nirS-OTU2 and was most related to Thiobacillus denitrificans, which accounted for 23.3% of total nirS clone sequences. nirS-OTU3 and nirS-OTU10 were affiliated with Sulfuricella sp. and Sideroxydans lithorophicus, while other nirS-OTUs belonged to uncultured bacteria (Figure 3).
Most clone sequences in the cbbL library were closely related to Thiobacillus and consisted of cbbL-OTU1, cbbL-OTU5, cbbL-OTU6 and cbbL-OTU8. This clade contained 24 sequences. cbbL-OTU2 included two sequences and belonged to Rhodobacter sphaeroides. The remaining cbbL-OTUs contained unique sequences and were related to Nitrobacter winogradskyi, Comamonadaceae bacterium, and the uncultured bacterium, respectively (Figure 4).

3.3. Influencing Characteristics of OC and DO on ADBs in Fe(0)/C-ADN

A total of 21,143–34,526 high-quality reads in five samples were retrieved by 16S rRNA high-throughput sequencing. Diversity estimators including Ace, Chao1, Shannon, Simpson, and coverage were analyzed (Table S2). The coverage index showed that raw sequence data were adequate for the characterization of microbial community composition. Compared with the autotrophic denitrification in DB0, OTU richness in DB1, DB3, and DB2.8 became greater, unlike in DB0.8. Community diversity increased with the rise in COD/TN and decreased with the rise in DO based on Simpson indices. The results showed that the partial addition of organic carbon would lead to the coexistence of autotrophic and heterotrophic denitrifying bacteria and form a complex community structure.
At the phylum level, Proteobacteria was dominant in DB0, DB1, DB3, and DB0.8, accounting for 89.2%, 80.2%, 86.3%, and 95.8%, respectively. The major components were the β-, γ-, α-, and δ-subdivisions. The relative abundance of β-Proteobacteria in the four samples was 51.5%, 49.0%, 45.9%, and 57.4%, respectively. Hydrogenophilaceae, Xanthomonadaceae, Comamonadaceae, Rhodocyclaceae, and Nitrosomonadaceae were the dominant bacteria at the family level. However, there was a very different community in DB2.8. The phyla Proteobacteria and Nitrospirae accounted for 44.1% and 45.0% in DB2.8, respectively. A DO concentration of 2.8 mg/L provided a favorable condition for Nitrospirae bacteria.
Microbial communities were further investigated at the genus level to validate their function for nitrogen removal. A total of 472 genera were classified and analyzed in this study. The genera obviously changed with increased COD/TN and DO concentration. The autotrophic denitrifying bacterium Thiobacillus decreased clearly from 29.6% to 19.7% and 5.5% when COD/TN increased from 0 to 1 and 3 in anaerobic conditions. The same tendency was observed for Thermomonas, which decreased from 5.5% to 2.7% and 0.1%. On the contrary, the relative abundance of Thauera increased considerably, from 0.3% to 2.3% and 22.2%. The relative abundances of Arenimonas, Rhodobacter, and Gallionella were 15.2% and 4.6%, 0.6% and 5.9%, and 0.7% and 2.3% in DB1 and DB3, respectively. In DB0.8, Thiobacillus and Arenimonas were still the dominant bacteria, accounting for 22.9% and 15.7%, respectively. Moreover, the ammonia-oxidizing bacterium Nitrosomonas became dominant and contained 27.2% sequences. The dominant genera in DB2.8, including Nitrospira, Pseudomonas and Azospirillum, accounted for 45.0%, 10.9%, and 6.4%, respectively (Figure 5).

3.4. Quantification of 16S rRNA, nirS, and cbbL

Quantification analysis of 16S rRNA, nirS, and cbbL in all five samples was performed by qPCR. As shown in Figure 6, the copy numbers of bacterial 16S rRNA were 5.9 × 108, 5.4 × 1010, 3.8 × 1010, 2.9 × 108, and 3.2 × 108 per nanogram DNA in DB0, DB1, DB3, DB0.8, and DB2.8, respectively. The addition of a partial carbon source could markedly increase the abundance of bacteria. qPCR results showed that the copy numbers of nirS were 1.2 × 106, 2.3 × 108, 2.0 × 108, 5.0 × 105, and 1.4 × 105, while cbbL occupied 2.9 × 106, 2.0 × 106, 1.6 × 107, 1.4 × 106, and 4.0 × 105 in each nanogram DNA of five samples. According to the nirS and cbbL copy numbers of the overall bacteria, partial carbon sources caused a remarkable increase in nirS copies and a decrease in cbbL copies. A 0.8 mg/L oxygen concentration in the bioreactors did not seem to change the relative abundance of nirS and cbbL. However, nirS and cbbL gene copies were inhibited with a DO concentration of 2.8 mg/L, especially for the nirS gene.

4. Discussion

Nitrogen removal depended on the denitrification process. NH4+-N produced by chemical denitrification in the presence of NO3− and Fe(0) rapidly converted to NO2−-N or NO3−-N. Nitrogen removal efficiencies in micro-electrolysis denitrification were comparable to those obtained in other BNR processes, such as heterotrophic denitrification and hydrogen- and sulfur-based autotrophic denitrification [5]. A high pH was generated in the bioreactors by chemical denitrification. However, the nitrogen removal results performed better than those of ZVI- or nZVI-based denitrification [24,25]. COD/TN and DO were significant factors affecting the denitrification performance of micro-electrolysis denitrification. The addition of partial organic substrates could improve nitrogen removal efficiency instantly. For instance, Hao et al. demonstrated that a simultaneous heterotrophic and autotrophic denitrification process resulted in consistently higher and more stable NO3−-N removal efficiency, and heterotrophic denitrifiers shifted to autotrophic denitrifiers when the COD/TN ratio decreased from 1.07 to 0.36 [26]. Therefore, the cooperation of heterotrophic and autotrophic denitrifiers achieved highly efficient denitrification and inhibited chemical denitrification, in which NH4+-N was found to accumulate above 4 mg/L in the DB0. The denitrification rate changed little when the DO concentration was controlled at 0.8 mg/L, while denitrification was relatively weak when a higher DO concentration was aerated.
The phylum Proteobacteria was dominant in this study, and it is well known to play an important role in wastewater treatment and global nitrogen cycling, especially for β- and γ-Proteobacteria [27]. nirS and nirK, which encode nitrite reductase, are key functional genes for denitrifying bacteria [18]. nirS-based denitrifying bacteria played a greater role than nirK in the micro-electrolysis process, as found in a previous study [19]. Generally, most nirS sequences belong to β-Proteobacteria. The cbbL gene has been widely used as a functional marker to study autotrophs using the Calvin cycle for carbon fixation.
The results demonstrated that 29.6%, 23.3%, and 80.0% of all effective sequences were affiliated with Thiobacillus based on 16S rRNA, nirS, and cbbL gene analysis. It was suggested that Thiobacillus is a predominant chemoautotrophic denitrifying bacterium in micro-electrolysis denitrification. Thiobacillus was revealed as an anaerobic and chemoautotrophic bacterium involved in nitrate-dependent ferrous oxidation processes [28,29]. It was also identified as an autotrophic denitrifying bacterium by DNA-based stable isotope probing with a 13C-labeled inorganic carbon source [30]. Additionally, the genus Thiobacillus was characterized by the CO2 fixation function using the Calvin cycle [31]. The relative abundance of Thiobacillus was still dominant with the addition of partial organic and DO. Thiobacillus has been reported to dominate in autotrophic and mixotrophic denitrifying systems [32], as well as in anoxic batch bioreactor [33].
A similar tendency occurred for the genus Thermomonas, which accounted for 5.5%, 2.7%, 0.1%, 1.5%, and 0.1% of the total in DB0, DB1, DB3, DB0.8, and DB2.8, respectively. Thermomonas was found to be dominant in the micro-electrolysis autotrophic denitrifying reactor [16]. However, Thauera only existed in anaerobic conditions and increased dramatically with increased COD/TN, which indicated that it is a heterotrophic denitrifying bacterium. Thauera has been found to be the predominant genus in heterotrophic denitrifying sludges and reactors [34,35] and strictly anaerobic anode biofilms [36,37]. Thauera sp. was more significant at a COD/TN of 10 and decreased with a decline in COD/TN [38]. Nevertheless, it was also found in autotrophic denitrifying processes [9,39].
The relative abundance of Arenimonas was obviously higher in DB0, DB1, DB3, and DB0.8 than that in DB2.8. And representative sequences of Arenimonas, which is considered the main denitrifying bacterium, were present in the 16S rRNA and nirS data. Arenimonas has previously been reported in both heterotrophic and bioelectrochemical-based denitrifying systems [40,41]. The increase in the organic load resulted in the suppression of Arenimonas metallic-nirS-related genotypes [42]. A previous study showed the genus Arenimonas is widely found in aerobic and microaerobic tanks [43].
Sequences of Rhodobacter and Gallionella, which are considered chemoautotrophic bacteria, were the most abundant in DB0. Rhodobacter occurred in 16S and cbbL data in this study, and Rhodobacter sphaeroides contributes substantially to denitrification [44]. Gallionella is a typical nitrate-dependent iron oxidation species which dominates in ferrous-based autotrophic denitrifying reactors [45].
In addition, the relative abundances of Alishewanella and Pseudomonas were outstanding in high COD/TN and DO concentration conditions, respectively. Alishewanella and Pseudomonas were previously shown to be capable of NO3−-N removal [46,47,48]. Nitrosomonas and Nitrospira are important ammonia-oxidizing bacteria (AOB) and nitrite-oxidizing bacteria (NOB) in sewage treatment plants [49]. Furthermore, in its discovery and cultivation, Nitrospira was reported to be a completely nitrifying bacterium, encoding the nitrification pathways both for NH4+-N and NO2−-N oxidation in recent studies [50,51]. Thus, influent NH4+-N was converted to NO2−-N and NO3−-N with a DO concentration of 0.8 and 2.8 mg/L, and NO2−-N and NO3−-N were subsequently reduced to nitrogen gas by autotrophic and heterotrophic denitrifying bacteria.
The overall bacteria and nirS-type denitrifying bacteria increased with the partial addition of carbon source. nirS-possessing heterotrophic denitrifying bacteria and ADBs coexisted and performed highly efficient denitrification. A previous study suggested that nirS-possessing denitrifying bacteria decreased significantly at a lower COD/TN ratio [52]. It has been demonstrated that the nirS gene is more sensitive to oxygen [53], leading to its decline under higher DO concentration. The relative abundance of the cbbL gene was similar at different DO concentrations, as Nitrosomonas and Nitrospira contain the cbbL gene [13,54].
Therefore, partial carbon source addition instantly improved the denitrification rate and the abundance of heterotrophic denitrifying bacteria. Controlled low DO concentrations were beneficial to denitrification performance in micro-electrolysis-based autotrophic denitrification and were unfavorable under a DO concentration of 2.8 mg/L. Further studies are required to investigate the dissimilar reduction of NO3−-N to NH4+-N, nitrifier denitrification, and functional genes in more CO2 fixation and denitrification pathways.

5. Conclusions

Thiobacillus was the most abundant autotrophic denitrifying bacterium in the iron–carbon galvanic-cell-supported autotrophic denitrification process. Adding organic carbon sources could partially improve NO3−-N removal efficiency and the proportion of the heterotrophic denitrifying bacterium Thauera. Low DO concentrations had a slight effect on nitrogen removal and denitrifying communities. The denitrification rate was inhibited at a DO concentration of 2.8 mg/L, and the diversity and relative abundances of microbial communities obviously changed.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/w16172455/s1, Table S1: PCR primers and conditions used in this study; Table S2: Overall analysis of sequencing data for DB0, DB1, DB3, DB0.8 and DB2.8 in the 3% dissimilarity level; Figure S1: Schematic of the experimental procedures; Figure S2: Relative abundances of bacterial community in all five samples based on high-throughput sequencing.

Author Contributions

Conceptualization, J.L. and S.-H.D.; methodology, J.L.; software, Z.L.; validation, X.W. and Z.L.; formal analysis, B.Z.; investigation, J.L.; resources, J.L. and S.-H.D.; data curation, S.-H.D.; writing—original draft preparation, J.L.; writing—review and editing, S.-H.D. and D.L.; visualization, J.L.; supervision, B.Z.; project administration, X.W.; funding acquisition, S.-H.D. and D.L. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the Qinchuangyuan High-level Talents Recruiting Program of Shaanxi Province, China (No. QCYRCXM-2022-227), and the National Natural Science Foundation of China (No. 51278034).

Data Availability Statement

The data presented in this study are available on request from the corresponding author.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Denitrification performance under different organic loads and DO concentrations: variations in the concentrations of NO3−-N (a), NO2−-N (b), NH4+-N (c), and TN (d).
Figure 1. Denitrification performance under different organic loads and DO concentrations: variations in the concentrations of NO3−-N (a), NO2−-N (b), NH4+-N (c), and TN (d).
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Figure 2. Relative abundances of the predominant OTUs in the DB0 sample based on high-throughput sequencing of 16S rRNA (a), nirS clone library (b), and cbbL clone library (c).
Figure 2. Relative abundances of the predominant OTUs in the DB0 sample based on high-throughput sequencing of 16S rRNA (a), nirS clone library (b), and cbbL clone library (c).
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Figure 3. The phylogenetic tree of the nirS gene established by the neighbor-joining algorithm based on the clone library. Representative OTUs were clustered at a 95% similarity threshold. The bootstrap test was performed with 1000 replicates and values below 50 were omitted. Numbers in parentheses indicate sequence numbers in each OUT for all 30 clones.
Figure 3. The phylogenetic tree of the nirS gene established by the neighbor-joining algorithm based on the clone library. Representative OTUs were clustered at a 95% similarity threshold. The bootstrap test was performed with 1000 replicates and values below 50 were omitted. Numbers in parentheses indicate sequence numbers in each OUT for all 30 clones.
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Figure 4. The phylogenetic tree of the cbbL gene established by the neighbor-joining algorithm based on the clone library. Representative OTUs were clustered at a similarity threshold of 95%. The bootstrap test was performed with 1000 replicates and values below 50 were omitted. Numbers in parentheses indicate sequence numbers in each OUT for all 30 clones.
Figure 4. The phylogenetic tree of the cbbL gene established by the neighbor-joining algorithm based on the clone library. Representative OTUs were clustered at a similarity threshold of 95%. The bootstrap test was performed with 1000 replicates and values below 50 were omitted. Numbers in parentheses indicate sequence numbers in each OUT for all 30 clones.
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Figure 5. Hierarchical heat map analysis of DB0, DB1, DB3, DB0.8, and DB2.8 at the OTU level. OTUs with relative abundance over 1% are listed, and the relative abundance of OTUs is indicated by color intensity.
Figure 5. Hierarchical heat map analysis of DB0, DB1, DB3, DB0.8, and DB2.8 at the OTU level. OTUs with relative abundance over 1% are listed, and the relative abundance of OTUs is indicated by color intensity.
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Figure 6. 16S rRNA, nirS, and cbbL gene copy numbers in DB0, DB1, DB3, DB0.8, and DB2.8 assessed by qPCR analysis.
Figure 6. 16S rRNA, nirS, and cbbL gene copy numbers in DB0, DB1, DB3, DB0.8, and DB2.8 assessed by qPCR analysis.
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Li, J.; Wang, X.; Deng, S.-H.; Li, Z.; Zhang, B.; Li, D. Functional and Microbiological Responses of Iron–Carbon Galvanic Cell-Supported Autotrophic Denitrification to Organic Carbon Variation and Dissolved Oxygen Shaking. Water 2024, 16, 2455. https://doi.org/10.3390/w16172455

AMA Style

Li J, Wang X, Deng S-H, Li Z, Zhang B, Li D. Functional and Microbiological Responses of Iron–Carbon Galvanic Cell-Supported Autotrophic Denitrification to Organic Carbon Variation and Dissolved Oxygen Shaking. Water. 2024; 16(17):2455. https://doi.org/10.3390/w16172455

Chicago/Turabian Style

Li, Jinlong, Xiaowei Wang, Shi-Hai Deng, Zhaoxu Li, Bin Zhang, and Desheng Li. 2024. "Functional and Microbiological Responses of Iron–Carbon Galvanic Cell-Supported Autotrophic Denitrification to Organic Carbon Variation and Dissolved Oxygen Shaking" Water 16, no. 17: 2455. https://doi.org/10.3390/w16172455

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