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Electrons transfer determined greenhouse gas emissions in enhanced nitrogen-removal constructed wetlands with different carbon sources and carbon-to-nitrogen ratios
Bioresource Technology 285 (2019) 121313
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Electrons transfer determined greenhouse gas emissions in enhanced nitrogen-removal constructed wetlands with different carbon sources and carbon-to-nitrogen ratios
T
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Danyue Chena, Xushun Gua, Wenying Zhua,b, Shengbing Hea,c, , Jungchen Huanga, Weili Zhoua a
School of Environmental Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, PR China Department of Environmental Systems Science, Swiss Federal Institute of Technology (ETH) Zurich, 8092 Zurich, Switzerland c Shanghai Institute of Pollution Control and Ecological Security, Shanghai 200092, PR China b
A R T I C LE I N FO
A B S T R A C T
Keywords: Denitrification Methanogenesis Carbon addition Nitrogen removal Greenhouse gas emission Electrons transfer
A constructed wetland (CW) was established to explore the influence of carbon addition (glucose or sodium acetate) on nitrogen removal and greenhouse gas (GHG) emissions at chemical oxygen demand to nitrogen ratios (COD/Ns) of 0, 4, 7. Results showed that the type of carbon source and COD/N significantly influenced the CW performance, in which the electrons transfer determined the regulation of denitrification, methanogenesis and respiration. Higher N2O emissions were consistent with higher nitrite accumulation at low COD/N because of electrons competition. The residual carbon source after near-complete denitrification could be further utilized by methanogenesis. Sodium acetate was superior to glucose in promoting denitrification and reducing global warming potential (GWP). In addition, bacteria sequencing and functional genes confirmed the important role of the type of carbon source on controlling nitrogen removal, carbon consumption and GHG emissions in microbial communities.
1. Introduction Effluent from wastewater treatment plants (WWTPs) that is discharged into surface water is likely to cause eutrophication and water quality deterioration (Si et al., 2019), as the total nitrogen (TN) concentration still fails to meet the Surface Water Environmental Quality Standard (Gao et al., 2017; Wu et al., 2018). Constructed wetlands (CWs), artificially designed by simulating natural wetlands, have proved to be an effective approach in treating water pollution from point and nonpoint sources (Maltais-Landry et al., 2009; Vymazal, 2007). Horizontal subsurface flow (HSSF) CWs have been widely used for secondary treatment of domestic and municipal wastewater (Vymazal and Kröpfelova, 2008). Wastewater flows through porous beds of gravel or rock with a network of aerobic, anoxic and anaerobic zones in a horizontal path from the inlet to the outlet (Chen et al., 2019). Greenhouse gas (GHG) emissions during water purification have attracted increasing attention due to their contribution to global warming. Among these GHGs, methane (CH4), nitrous oxide (N2O) and carbon dioxide (CO2) are recognized as the most influential and destructive to the environment (Huang et al., 2013; IPCC, 2013). CH4,
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generated from the anaerobic metabolism of organic matters, has a global warming potential (GWP) of 25 relative to CO2. N2O is the byproduct of incomplete nitrification and an intermediate of denitrification with a GWP of 298 relative to CO2; it also has a destructive effect on the ozone sphere, decreasing its UV protective properties (Cole and Caraco, 2001). Although the GHG emissions from CWs are not high, their potential threat cannot be ignored considering that the GWPs of CH4 and N2O and the increasingly widespread CWs applications (Hernandez et al., 2018). According to engineering practice, the treatment performance of nutrient removal in CWs is unsatisfactory as WWTPs effluents are characterized by low concentrations of contaminants and inferior biodegradability of organic matters (Wu et al., 2017). Many studies suggested supplying additional carbon sources to improve nitrogen removal in CWs (Chen et al., 2017; Rustige and Nolde, 2007; Vidal et al., 2002). It has been reported that methanogenesis and denitrification processes might co-exist in bioreactors and sediment/soil systems that have nitrate, organics and anaerobic conditions (Hendriksen and Ahring, 1996; Kim et al., 2015; Lin and Chen, 1995). In enhanced nitrogen-removal CWs, methanogenesis may not only produce CH4, but also consume the organic matters for denitrification, which may result
Corresponding author at: School of Environmental Science and Engineering, Shanghai Jiao Tong University, Dong Chuan Road 800, Shanghai 200240, PR China. E-mail address: [email protected] (S. He).
https://doi.org/10.1016/j.biortech.2019.121313 Received 9 February 2019; Received in revised form 30 March 2019; Accepted 1 April 2019 Available online 01 April 2019 0960-8524/ © 2019 Elsevier Ltd. All rights reserved.
Bioresource Technology 285 (2019) 121313
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in more N2O emissions if the carbon source becomes deficient. However, researchers mainly focused on the effects of different factors (e.g. CW type and age, wastewater flow and characteristics, feeding strategy and hydroperiod) on the removal efficiencies of pollutants, as well as the composition and quantity of GHG emissions in CW studies (Jahangir et al., 2016; Mander et al., 2014; Maucieri et al., 2017). Few investigations reported the rules and mechanism of electrons transfer among methanogenesis, denitrification and microbial respiration, which is causative of GHG emissions. Given the urgent demands for secondary effluent treatment, it is imperative to simultaneously obtain a qualified water quality and minimize contributions to greenhouse effect (Maltais-Landry et al., 2009). Consequently, GHG emissions in enhanced nitrogen removal with carbon addition were studied using a lab-scale HSSF CW with the following objectives: (1) to investigate the nitrogen removal performance and GHGs (CH4, N2O and CO2) emission fluxes using different carbon source types and carbon-to-nitrogen ratios (defined as chemical oxygen demand to nitrogen ratios, COD/N); (2) to figure out the variations in microbial communities and functional genes; (3) to explore the relationship among denitrification, methane production and microbial respiration as well as the rules of electrons transfer among these.
Table 1 Operational scenarios with different carbon source, C/N and HRTs.
2. Materials and methods
2.3. Gas sampling and emission flux calculation
2.1. Experimental set up and operation
The static chamber technique was used to estimate gas emission fluxes (Wu et al., 2009). The chamber was made of polymethyl methacrylate without a bottom. The upside-down chamber was sealed with water to avoid gas exchange. Two pores on the chamber were connected to the pump inlet and outlet to ensure circular gas mixing (Fig. 1(b)). The inside air pressure and temperature were monitored (SPORTSTAR, China). To avoid external environmental disturbance (e.g. illumination intensity, air pressure, humidity) on fluxes as far as possible, gas samplings were always carried out on clear days between 9:00 and 12:00 a.m. Six gas samples were collected at 0, 40, 80, 120, 160 and 200 min after enclosure. The gas samples were drawn separately into 300 mL aluminum bags (HEDETECH, China) with a pump and intraday analysis of N2O was conducted after all samples were collected. N2O concentrations were measured by a gas chromatograph (6890N, Agilent Technologies, USA) with an electron capture detector and a Poropak Q packed column (2 m). Pure nitrogen (99.999%) at a flux of 15 mL/min was used as the carrier gas. The temperatures of the
Phase
Days (d)
Carbon source
C/N
HRT
S1 S2 S3 S4 S5 S6
16 38 19 27 23 25
/ Glucose Glucose Sodium acetate Sodium acetate Sodium acetate
0 4 7 4 7 7
2 2 2 2 2 1
2.2. Water sampling and chemical analysis Water samples were collected daily, filtered through a 0.45 μm-pore membrane and refrigerated at 4 °C until analysis. Nitrate, nitrite and ammonia were analyzed by ultraviolet spectrophotometry (UV-1800, SHIMADZU, Japan), and total nitrogen (TN) and total organic carbon (TOC) were measured by high-temperature catalytic oxidation method (Multi N/C 3100, Analyticjena, Germany). Dissolved oxygen (DO), temperature and pH were simultaneously detected by a multiparameter digital instrument (ProPlus, YSI, USA).
The HSSF CW was built up and put in a temperature-controlled greenhouse at Shanghai Jiao Tong University. The CW was operated with continuous flow by a peristaltic pump, flowing sequentially through the influent tank, CW bed and effluent tank. The CW bed (length × width × height = 50 cm × 20 cm × 50 cm) was filled by 20–30 mm diameter gravels (Fig. 1(a)). The synthetic wastewater was prepared with KNO3, CaNO3 and NH4Cl to simulate secondary effluent, in which the concentrations of ammonia and nitrate were set to 4 mg/L and 11 mg/L, respectively. Before adding carbon source, the CW was acclimated with the synthetic wastewater for six months. Glucose and sodium acetate, frequently utilized in wastewater treatment, were applied as two typical carbon sources. COD/Ns of 0, 4 and 7, representing non-carbon, carbon-deficient and carbon-sufficient, respectively, were adopted. Each phase (operational scenarios listed in Table 1) ran with stable operation for over 15 d.
(a)
(b)
Gas outlet
Chamber
CW bed
10cm
50cm
10cm
Fig. 1. Schematic representation of constructed wetlands at operating status (a) and gas sampling status (b). 2
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average effluent TN mainly comprised ammonia of 2.10 mg/L and nitrate of 1.21 mg/L, indicating incomplete nitrification or denitrification. In contrast to TN, the ammonia removal efficiency significantly decreased with COD/N (p < 0.05) as organic carbon consumed dissolved oxygen for nitrification. The average effluent ammonia increased from 0.19 to 1.69 mg/L after the carbon source was supplied. At COD of 4 and 7, the ammonia removal efficiency of sodium acetate was 39% and 37% higher than that of glucose, respectively. The effluent nitrate showed a similar trend as TN because denitrification was the predominant way for nitrogen removal in the gravel beds. Without carbon source (S1), the average effluent nitrate of 15.75 mg/L was close to the influent TN of 16.02 mg/L, which meant that ammonia was mostly oxidized to nitrate but little nitrate was reduced. The nitrite accumulation and nitrate residue confirmed that the carbon source was insufficient for denitrification at a COD/N of 4. Nitrite immediately accumulated to 6.09 mg/L and then gradually maintained at a certain concentration after glucose “shock”. The average effluent nitrite was 1.69 and 0.76 mg/L in S2 and S4, respectively. Her and Huang (1995) and Lyu et al. (2017) also obtained similar results, where glucose as the carbon source led to greater nitrite accumulation than did acetic acid. Nitrification and denitrification were both influenced by the quality of the carbon source. Carbon breakthrough, which meant the effluent still contained leftover carbon after denitrification (Carley and Mavinic, 1990), was not observed in the study. The average effluent TOC of 6.07 mg/L was close to that of tap water, so it would not cause secondary water pollution. From S2 to S6, the average TOC removal efficiencies were 73.46%, 83.29%, 79.63%, 82.63% and 90.53%, confirming that increasing carbon addition promoted nitrogen removal and carbon consumption; the ratios of TOC removal to TN removal (ΔTOC/Δ TN) were 2.19, 2.56, 2.02, 2.97 and 3.28, respectively, suggesting that the utilization of organics for denitrification would decline in the presence of excessive carbon source.
column, injector and detector were set at 70, 100 and 280 °C, respectively. The concentrations of CH4, CO2 and water vapor were measured simultaneously in real time by an Ultraportable Greenhouse Gas Analyzer (Los Gatos Research, Canada), recording data every second for 10 min. The gas emission flux was calculated according to Eq. (1) (Wang et al., 2006):
F = ρ0
V P T0 dCt A P0 T dt
(1) −2 −1
Here, F is the emission flux (mg m h ); V and A represent the chamber’s volume (m3) and surface area (m2), respectively; P and P0 are the air pressures (Pa) at sampling and standard state, respectively; T and T0 are the air temperatures (K) at sampling and standard state, respectively; and dCt is the slope of linear regression of concentration dt versus time. 2.4. Microbial community and qPCR assays Microorganism samples were collected from the gravel surface at the end of Phase S1, S2, S4 and S5 and then sent to BIOZERON Biotechnology Co. Ltd. (Shanghai, China) for microbial analysis. Bacterial community was extracted by QIAamp DNA stool Mini Kit (QIAGEN, Germany) following the manufacturer's protocol, and stored at −20 °C until the analysis was conducted. The extracted DNA was then detected by 1% agarose gel electrophoresis to testify its quantity and quality (Chen et al., 2019). High-quality sequencing was performed by Illumina PE250, and the sequences were clustered into operational taxonomic units (OTU) by setting a 97% similarity (Usearch, version 7.1). The bacterial diversity (Shannon and Simpson) and community richness (Chao1 and Ace) were analyzed (Mothur, version 1.30.1). The taxonomy was conducted at the phylum and genus level (RDP Classifier, version 2.2) on the Qiime platform at a 70% confidence threshold. The qPCR assays were performed to quantify the related functional genes (16S rRNA, amoA, nirS, nosZ, mcrA and pmoA) (Cao et al., 2018; Geets et al., 2007; Jin et al., 2010; Wen et al., 2016; Xu et al., 2013) by a Real-Time Quantitative PCR Detection System (BIOER, China). Each 20 μL reaction mixture contained 10 μL SYBR Green qPCR Mix (TaKaRa, China), 1 μL template DNA, 0.4 μL each of forward and reverse primers and sterile water. Each qPCR amplification was performed for 40 cycles, followed by a melting curve analysis. All the assays were conducted in triplicate.
3.2. Greenhouse gas emissions
3.1. Nitrogen removal and carbon consumption
The variation of N2O, CH4 and CO2 emissions was shown in Fig. 3. The GHG fluxes were largely associated with carbon addition, and the emission tendencies could be generally summarized as follows: (1) as an intermediate product, carbon deficiency might lead to serious N2O emissions especially with glucose as the carbon source; (2) CH4 emissions were observed only when sodium acetate was sufficient; (3) whether CW was a CO2 sink or source was determined by the type of carbon source, while the fluxes also depended on COD/N. The carbon dioxide equivalents (CO2e) of N2O, CH4 and CO2 and the sum GWP of the three GHGs were listed in Table 2. The positive or negative GWPs represented the intensification or mitigation of the greenhouse effect, respectively, ranging from –1412.87to 60299.12 mg/m2/d CO2e throughout the experiment. The GWP was mainly influenced by N2O and CO2, with negligible effect of CH4 in this study. In S2, N2O contributed to GWP to a very large extent, even though the N2O flux was similar to the CO2 flux. In other phases, the CO2e of N2O and CO2 were in the same order of magnitude, while that of CH4 was 1 or 2 orders lower by contrast.
Fig. 2 showed the concentrations of nitrogen compounds and organic matters, and the calculated nitrogen removal efficiency and carbon consumption. From S1 to S6, the average effluent TN was 15.99, 6.85, 2.02, 3.89, 1.99 and 3.34 mg/L, respectively; the TN removal efficiency increased linearly with increasing COD/N (p < 0.05). Insufficient glucose (S2) or sodium acetate (S4) both led to fluctuations of effluent TN, but TN removal was steadier in S4. The maximum TN removal efficiency reached 91.09% in S5 accompanied by an effluent ammonia of 1.24 mg/L. With a shorter hydraulic retention time (HRT) of 1 d (S6), the TN removal efficiency decreased to 77.16%. The
3.2.1. N2O emissions The applied carbon source and COD/N resulted in significant changes to the N2O emissions in the CW, which performed as a N2O source. Nearly no N2O emissions were detected in S1, which coincided with the insignificant nitrate reduction and nitrite accumulation due to limited denitrification. The N2O fluxes decreased from 201.82 mg/m2/d at a COD/N of 4 (S2) to 5.86 mg/m2/d at a COD/N of 7 (S3) using glucose as the carbon source. Only a slight difference in N2O fluxes using sodium acetate was observed, and the average flux was 4.88 ± 0.94 mg/m2/d. The highest proportion of N2O-N to TN removal
2.5. Data analysis All statistical analysis was conducted by SPSS (version 20.0) with results considered significant at the 0.05 level. The differences of TN, nitrate, nitrite, ammonia and TOC were analyzed by one-way ANOVA test. The ratios of different functional genes to 16S rRNA were calculated to analyze internal relationships among nitrification, denitrification, methanogenesis and methane oxidation. 3. Results and discussion
3
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S5
S6
Removal efficiency
100
40
80
30
60
20
40
10
20
0 Influent
30
60 Removal efficiency 90
Effluent
0 150 100
120
80
15
60 10
40
5
20
0
Influent
NH4+-N (mg/L)
0 (c)
30
Effluent
Removal efficiency 90 60
0 150 100
120
6
80 60
4
40 2
20
0
NO3--N (mg/L)
20
0 0 (d)
30 NO3 NO3--NInfluent Influent
60 NO3 NO3--NEffluent Effluent
- Influent NO2 NO90 Influent 2 -N
120 NO2--NEffluent Effluent NO2
150 6
15 4 10 2
5 0
NO2--N (mg/L)
TN (mg/L)
20
0 (b)
Removal efficiency (%)
Effluent
S4
Removal efficiency (%)
Influent
S3
Removal efficiency (%)
(a)
50
TOC (mg/L)
S2
S1
60
0 0
30
60
Time (d)
90
120
150
Fig. 2. Profiles of nitrogen and carbon concentrations and their removal efficiencies in different phases.
gained by different bacteria as metabolic substrates, so that fewer electron donors flowed to denitrifying bacteria (Elefsiniotis and Li, 2006). The methyl and carboxyl moieties made the structure of sodium acetate simpler and more available to be utilized (Elefsiniotis and Wareham, 2007), which might have contributed to the minimal differences among denitrification reductases and low N2O emissions when inadequate carbon source was supplied in S4. The higher and steadier TN removal efficiency with less accumulation of denitrification
was 13.43% in S1, followed by 10.00% in S2, and the proportions of other phases were all lower than 0.23%. Denitrification is a stepwise process accomplished by nitrate reductase (Nar), nitrite reductase (Nir), NO reductase (Nor) and N2O reductase (Nos), simultaneously (Vidal-Gavilan et al., 2014). This study confirmed that carbon deficiency would lead to nitrite accumulation and N2O emissions, as the reductases competed for electrons (Chen et al., 2017; Pan et al., 2013). Furthermore, glucose was prone to be
8
(a)
(c)
2000 CH4 flux (mg/m2/d)
200 N2O flux (mg/m2/d)
3000
(b)
150 100 50 0
6
CO2 flux (mg/m2/d)
250
4
2
1000 0 -1000 -2000 -3000
0 S1 S2 S3 S4 S5 S6
S1 S2 S3 S4 S5 S6
S1 S2 S3 S4 S5 S6
Phase
Phase
Phase
Fig. 3. Greenhouse gas N2O (a), CH4 (b) and CO2 (c) fluxes at different phases. 4
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occurred before methanogenesis when sufficient carbon source was present (Her and Huang, 1995). Rittmann (2002) proved that denitrification was more energetically favorable than methanogenesis according to thermodynamics. CH4 emissions were also affected by the quality of the organic matters (e.g. the amount of short chained fatty acids) (Garcia et al., 2007). Methane production can be divided into three processes including the hydrolysis and fermentation phase, the acetogenic phase and the methanogenesis phase. When sodium acetate was used, the process could commence directly with methanogenesis, so that CH4 emissions were measured only with sodium acetate as the carbon source.
Table 2 Carbon dioxide equivalents of greenhouse gas and global warming potentials at different phases. Phase
S1 S2 S3 S4 S5 S6
Carbon dioxide equivalents (mg/m2/d CO2e) CO2
CH4
N2O
global warming potentials(mg/m2/d CO2e)
−1007.93 155.57 2017.09 −1365.34 −2793.04 −1657.14
0.00 0.00 0.00 0.00 129.71 168.09
325.34 60143.55 1746.59 1772.85 1250.45 1336.29
−682.59 60299.12 3763.69 407.51 −1412.87 −152.75
3.2.3. CO2 emissions Despite being a major GHG, CO2 emissions have not been widely researched in CWs. In this study, CO2 emissions ranged from −2793.04 to 2017.09 mg/m2/d. In a GHG emissions review based on meta-analysis, a significant negative correlation was found between the inflow TOC loading and CO2 emissions (Mander et al., 2014). Yan et al. (2012) found that CO2 fluxes increased from 6805.68 to 10976.16 mg/m2/d when the C/N ratio was increased from 2.5 to 10 in a VSSF CW treating synthetic municipal wastewater. In this study, the findings were not uniform: CO2 fluxes increased with increasing COD/N when glucose was the carbon source; in contrast, CO2 fluxes decreased with increasing COD/N with the addition of sodium acetate.
intermediates when sodium acetate was added suggested that intensive denitrification favored volatile fatty acids rather than macromolecular organic matters (Lyu et al., 2017). 3.2.2. CH4 emissions In addition to the competition within denitrification, denitrification also contends with methanogenesis for electrons. CH4 emissions were 5.19 and 6.72 mg/m2/d for S5 and S6, respectively, and were not detected in other phases. The results showed that methanogenesis occurred with residual carbon after the nitrate was almost consumed and the redox potential was suitable, which indicated that denitrification
Fig. 4. Community structure analysis of the microbial samples at the phylum (a) and genus (b) level. Phylum making up less than 0.25% and genera making up less than 1% of the compositions were defined as others. 5
Bioresource Technology 285 (2019) 121313
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Functional gene (copies/g)
1.00E+12
S1
S2
S4
S5
(a)
1.00E+10 1.00E+08 1.00E+06 1.00E+04 1.00E+02 1.00E+00 16S rRNA
amoA
nirS
nosZ
mcrA
pmoA
1.00E+00
Functional gene to 16SrRNA ratio
S1
S2
S4
(b)
S5
1.00E-02
1.00E-04
1.00E-06
1.00E-08 amoA
nirS
nosZ
mcrA
pmoA
Fig. 5. Abundance of functional genes (a) and the ratios of functional genes to 16S rRNA (b) in nitrogen transformation and methane production.
3.3. Microbial analysis
The stoichiometric equations for anaerobic denitrification with glucose and acetate are summarized as follows (considering the microbial growth) (Matějů et al., 1992):
3.3.1. The richness, diversity and structure of the microbial community The presence of a carbon source introduced denitrification and metagenesis into microbial processes, which also included microbe assimilation and metabolism. Consequently, the abundance of the microbial community and functional genes would alter with changes in microbial processes and the physiochemical environment. The 796–1408 OUTs were clustered at 0.97 similarity, and the community structure analysis at the phylum and genus levels was shown in Fig. 4. Different carbon sources led to a diverse community structure, and the diversity and richness were lower with glucose than with sodium acetate as the carbon source. Proteobacteria was the most dominant phylum, followed by Bacteroidetes, Planctomycetes, Cyanobacteria and Chloroflexi. The organic matters promoted the relative abundance of Proteobacteria, which is often involved in the transformation of carbon and nitrogen compounds (Gao et al., 2017). In S2, the proportion of Proteobacteria and Bacteroidetes was 94.98%, the most abundant genera being Flavobacterium (27.66%), Tolumonas (22.78%), Aeromonas (10.84%), Hydrogenophaga (10.08%) and Thermomonas (6.52%), all of which all denitrification related (Chen et al., 2019; Gao et al., 2017). The microbial community has commonly been thought to be responsible for CW performances (Zhang et al., 2018), but the dominant genera in S2 (Flavobacterium, Tolumonas and Aeromonas etc.) has not been reported to facilitate N2O emissions. The
NO3– +
0.34C6H12O6 → 0.136C5H7NO2 + 1.35CO2 + 0.432 N2 + 1.06H2O + OH–
(2)
NO3– +
0.819CH3COOH → 0.068C5H7NO2 + HCO3–+0.301CO2 + 0·.902H2O + 0.466 N2
(3)
As seen from these equations, with identical COD for denitrification, the CO2 generate with acetate is less than one quarter of that generate with glucose. This was directly observed in this study, with the CW becoming a CO2 source when glucose was added (S2 and S3) and a CO2 sink when sodium acetate was added (S4, S5 and S6). CO2 emissions would be influenced by many microbial processes (e.g. denitrification, respiration, assimilation, catabolism) and physicochemical processes (e.g. hydrolysis, adsorption, algal photosynthesis). The reported combination of denitrification and anaerobic oxidation of methane to produce CO2 could also be a reason for the no CH4 emissions being observed, and the higher CO2 emissions. Furthermore, the enhanced denitrification at a COD/N of 7 with sodium acetate produced more alkalinity, which may result in the partial conversion of CO2 into HCO3– and CO32– and account for the lower CO2 fluxes (Lyu et al., 2017). The produced CO2 might also be absorbed and transformed by ammoniacontaining liquid (Zhang et al., 2019). 6
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Low COD/N
A appropriate range of COD/N
High N2O emissions Electrons
(1) Denitrification
CH4 emissions CO2 emissions
Low N2O emissions Electrons
NO2- accumulation NO3- remaining
(2) Complete Denitrification NH4+ remaining
High COD/N
Electrons
(3) Methanogenesis Or Respiration NH4+ remaining
Fig. 6. The mechanism of electrons transfer among denitrification, methanogenesis and microbial respiration controlling nitrogen removal and greenhouse gas emissions.
acids and gases for methanogenesis. Additionally, CH4 flux is the net effect of methane production and oxidation; the lower CH4 emissions in S2 could also be explained by the active oxidation of methane due to higher ratio of pmoA genes.
community structures of S4 and S5 were similar in dominant genera (Hydrogenophaga, Pseudomonas, Arenimonas, Denitratisoma, Photobacterium, Xanthomonadales_norank and Gemmobacter). The relative abundances of denitrification-related genera accounted for 83.78%, 33.66% and 27.32% in S2, S4 and S5, respectively. In particular, Anaerolineaceae is a specific denitrifying bacteria that produces formic acid (a substrate for methanogenesis), that was detected in S1 (0.38%), S4 (4.07%) and S5 (3.95%). The proportion of methanogenesis-related genera Methanobacterium was found only in S5 (0.30%) (Holt et al., 1989). Nitrification-related genera Nitrospira, Nitrosomonas and Nitrosomonadaceae_uncultured accounted for 1.31%, 0.06%, 0.71% and 0.58% in S1, S2, S4 and S5, respectively.
3.4. The rules of electrons transfer and carbon addition strategy To evaluate the CW performances in practice, nitrogen removal and GHG emissions should both be considered. The abundance of functional genera and genes is an important key to understanding the relationship between the apparent performance and internal microbial processes, as well as the electrons transfer among nitrification, denitrification and methanogenesis. The chemical structure and molecular weight of electron donors will significantly influence microbial processes and their efficiencies (Her and Huang, 1995). According to the COD/N, three conditions could be defined (Fig. 6): (1) with carbon deficiency, the reductases responsible for different denitrification steps competed for electrons, contributing to nitrite accumulation and N2O emission; the nitrogen removal rates would increase gradually with increasing COD/N; (2) when the COD/N was in the appropriate range for complete denitrification, the nitrogen removal efficiency reached nearly 100% without N2O emission; (3) with excessive carbon source, electrons finally flowed to methanogenesis resulting in CH4 emissions (e.g. with sodium acetate as the carbon source) or microbial growth and respiration resulting in CO2 emissions (e.g. with glucose as the carbon source). The regulation of denitrification, methane production, microbial growth and respiration is determined by the quantity and quality of the carbon source, which controls the electrons transfer and thereby the GHG emissions. Unfortunately, the focus in wastewater treatment engineering practice has been on the removal efficiency of contaminants without much attention paid to GHG emissions. Yan et al. (2012) and Zhao et al. (2014) reported that an optimal C/N of around 5 could simultaneously achieve the best nutrient removal and lowest CO2 and CH4 fluxes. Her and Huang (1995) investigated the appropriate range of COD/N using four different carbon sources and found that using glucose and acetic acid did not decrease the denitrification efficiency despite a COD/N beyond the appropriate range, suggesting that these two carbon sources were not harmful for denitrification. However, excessive glucose can result in high levels of CO2 release, which is not beneficial for carbon sequestration. Conversely, though excessive sodium acetate would release methane, its contribution to GWP would be insignificant considering the carbon dioxide equivalent of CH4. In summary, this study recommends a COD/N of 7 with sodium acetate as the carbon source to bring low effluent nitrite and N2O flux. Previous studies have reported the superiority of acetate to glucose as the carbon source in facilitating nitrogen removal efficiencies and reducing N2O emissions (Li et al., 2008; Lyu et al., 2017; Rungkitwatananukul et al., 2016). This study confirmed this conclusion and further found its advantages in CO2 emissions and GWP values, while only the relative expense may hinder its use in large-scale operations. Since the effect of the carbon source on
3.3.2. The abundance of functional genes The abundances of the functional genes involved in nitrification (amoA), denitrification (nirS and nosZ), methanogenesis (mcrA) and methane oxidation (pmoA) were shown in Fig. 5. The ratios of functional genes to 16S rRNA were then calculated. In general, the abundances and ratios of functional genes were consistent with the community structure. The 16S rRNA reflected the total number of bacteria in the samples, which was lowest without carbon addition (S1) and highest with glucose at COD/N of 4 (S2). Comparing S1, S4 and S5, the 16S rRNA, nirS genes and nosZ genes all increased with increasing COD/N because the carbon source supplied the microorganisms, especially denitrifying bacteria, with food and energy. Although the number of amoA genes was higher in S2 and S5 than in S1, the proportion of amoA genes decreased with increasing COD/N because the competition arising from ammonia and organics oxidation led to the suppression of nitrification. The abundance of nosZ genes was less than ten percent of that of nirS, indicating that nirS genes played the predominant role in denitrification. The proportion of nirS was higher in S5 and S4 with sodium acetate than in S2 with glucose, while the proportion of nosZ was the lowest in S5. The higher N2O emissions with glucose than with sodium acetate could be interpreted as follows: as a weakly basic compound, sodium acetate increased the CW pH during hydrolysis, whereas the lower pH with glucose decreased the activity of nosZ genes (Lyu et al., 2017). The shifts of microbial populations and consequent changes in metabolism were both likely to generate the different accumulations of denitrification intermediates with different carbon sources and COD/Ns (Ge et al., 2012; Martienssen and Schops, 1999). In S5, CH4 emissions were detected for the first time, as the abundance and ratio of mcrA genes were the highest and the ratio of pmoA was the lowest. The proportions of mcrA genes to 16S rRNA increased with increasing COD/N in S5 and S6, indicating that more electrons were flowing to methane production when sufficient sodium acetate was available. The fact that no CH4 emissions were detected even with sufficient glucose could be explained by the lower ratio of mcrA genes and the higher ratio of pmoA genes in S2. Based on Bergey’s Manual of Determinative Bacteriology (Holt et al., 1989), Flavobacterium, the most dominant genera when glucose was added (27.66%), cannot produce 7
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the GHG is less clear, the development of an efficient, environmentally friendly and economic electron donor, and the corresponding appropriate COD/N, should be further investigated.
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An improved gas chromatography for rapid measurement of CO2, CH4 and N2O. J. Environ. Sci. 18 (1), 162–169. Wen, L.-L., Lai, C.-Y., Yang, Q., Chen, J.-X., Zhang, Y., Ontiveros-Valencia, A., Zhao, H.-P., 2016. Quantitative detection of selenate-reducing bacteria by real-time PCR targeting the selenate reductase gene. Enzyme Microb. Technol. 85, 19–24. Wu, H., Fan, J., Zhang, J., Ngo, H.H., Guo, W., 2018. Large-scale multi-stage constructed wetlands for secondary effluents treatment in northern China: carbon dynamics. Environ. Pollut. 233, 933–942. Wu, H., Zhang, J., Ngo, H.H., Guo, W., Liang, S., 2017. Evaluating the sustainability of free water surface flow constructed wetlands: Methane and nitrous oxide emissions. J. Cleaner Prod. 147, 152–156. Wu, J., Zhang, J., Jia, W., Xie, H., Gu, R.R., Li, C., Gao, B., 2009. Impact of COD/N ratio on nitrous oxide emission from microcosm wetlands and their performance in removing nitrogen from wastewater. Bioresour. Technol. 100 (12), 2910–2917. 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4. Conclusions (1) With increasing COD/N, the TN removal efficiency increased, accompanied by decreased nitrite accumulation and ammonia oxidation. (2) The use of glucose resulted in greater nitrite accumulation and N2O emissions at a COD/N of 4 and greater CO2 emissions at a COD/N of 7 than sodium acetate. The GWP with glucose as the carbon source was significantly higher than with the sodium acetate. (3) Glucose and sodium acetate enriched particular microbial communities, and carbon addition significantly increased the proportion of denitrifying bacteria. The use of excess sodium acetate encouraged the growth of Methanobacterium with mcrA genes. Acknowledgements The National Natural Science Foundation of China Under Grant (No. 51678356) and the Program for Chinese National Key Projects of Water Pollution Control and Reclamation (2017ZX07205003) provided support for this study. Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.biortech.2019.121313. References Cao, J., Wang, C., Dou, Z., Liu, M., Ji, D., 2018. Hyphospheric impacts of earthworms and arbuscular mycorrhizal fungus on soil bacterial community to promote oxytetracycline degradation. J. Hazard. Mater. 341, 346–354. Carley, B.N., Mavinic, D.S., 1990. The effects of external carbon loading on nitrification and denitrification of a high-ammonia landfill leachate. Res. J. Water Pollut. Contr. Federat. 63 (1), 51–59. Chen, D.Y., Chen, X.C., Huang, X.B., He, S.B., Huang, J.C., Zhou, W.L., 2017. Controlling denitrification accompanied with nitrite accumulation at the sediment-water interface. Ecol. Eng. 100, 194–198. Chen, D.Y., Gu, X.S., Zhu, W.Y., He, S.B., Wu, F., Huang, J.C., Zhou, W.L., 2019. Denitrification- and anammox-dominant simultaneous nitrification, anammox and denitrification (SNAD) process in subsurface flow constructed wetlands. Bioresour. Technol. 271, 298–305. Cole, J.J., Caraco, N.F., 2001. Emissions of nitrous oxide (N2O) from a tidal, freshwater river, the Hudson River, New York. Environ. Sci. Technol. 35 (6), 991–996. Elefsiniotis, P., Li, D., 2006. The effect of temperature and carbon source on denitrification using volatile fatty acids. Biochem. Eng. J. 28 (2), 148–155. Elefsiniotis, P., Wareham, D.G., 2007. Utilization patterns of volatile fatty acids in the denitrification reaction. Enzyme Microb. Technol. 41 (1–2), 92–97. Gao, L., Zhou, W.L., Huang, J.C., He, S.B., Yan, Y.J., Zhu, W.Y., Wu, S.Q., Zhang, X., 2017. Nitrogen removal by the enhanced floating treatment wetlands from the secondary effluent. Bioresour. Technol. 234, 243–252. Garcia, J., Capel, V., Castro, A., Ruiz, I., Soto, M., 2007. Anaerobic biodegradation tests and gas emissions from subsurface flow constructed wetlands. Bioresour. Technol. 98 (16), 3044–3052. Ge, S., Peng, Y., Wang, S., Lu, C., Cao, X., Zhu, Y., 2012. Nitrite accumulation under constant temperature in anoxic denitrification process: The effects of carbon sources and COD/NO3-N. Bioresour. Technol. 114, 137–143. Geets, J., de Cooman, M., Wittebolle, L., Heylen, K., Vanparys, B., De Vos, P., Verstraete, W., Boon, N., 2007. Real-time PCR assay for the simultaneous quantification of nitrifying and denitrifying bacteria in activated sludge. Appl. Microbiol. Biotechnol. 75 (1), 211–221. Hendriksen, H.V., Ahring, B.K., 1996. Integrated removal of nitrate and carbon in an upflow anaerobic sludge blanket (UASB) reactor: Operating performance. Water Res. 30 (6), 1451–1458. Her, J., Huang, J., 1995. Influences of carbon source and C/N ratio on nitrate/nitrite denitrification and carbon breakthrough. Bioresour. Technol. 54 (1), 45–51. Hernandez, M.E., Galindo-Zetina, M., Carlos Hernandez-Hernandez, J., 2018. Greenhouse gas emissions and pollutant removal in treatment wetlands with ornamental plants under subtropical conditions. Ecol. Eng. 114, 88–95. Holt, J.G., Williams, S.T., Holt, 1989. Bergey's Manual of Systematic Bacteriology 4 Lippincott Williams & Wilkins. Huang, L., Gao, X., Guo, J.S., Ma, X.X., Liu, M., 2013. A review on the mechanism and affecting factors of nitrous oxide emission in constructed wetlands. Environ. Earth Sci. 68 (8), 2171–2180. IPCC, 2013. Climate Change 2013: The Physical Science Basis. 25. Cambridge University
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Bioresource Technology journal homepage: www.elsevier.com/locate/biortech
Electrons transfer determined greenhouse gas emissions in enhanced nitrogen-removal constructed wetlands with different carbon sources and carbon-to-nitrogen ratios
T
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Danyue Chena, Xushun Gua, Wenying Zhua,b, Shengbing Hea,c, , Jungchen Huanga, Weili Zhoua a
School of Environmental Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, PR China Department of Environmental Systems Science, Swiss Federal Institute of Technology (ETH) Zurich, 8092 Zurich, Switzerland c Shanghai Institute of Pollution Control and Ecological Security, Shanghai 200092, PR China b
A R T I C LE I N FO
A B S T R A C T
Keywords: Denitrification Methanogenesis Carbon addition Nitrogen removal Greenhouse gas emission Electrons transfer
A constructed wetland (CW) was established to explore the influence of carbon addition (glucose or sodium acetate) on nitrogen removal and greenhouse gas (GHG) emissions at chemical oxygen demand to nitrogen ratios (COD/Ns) of 0, 4, 7. Results showed that the type of carbon source and COD/N significantly influenced the CW performance, in which the electrons transfer determined the regulation of denitrification, methanogenesis and respiration. Higher N2O emissions were consistent with higher nitrite accumulation at low COD/N because of electrons competition. The residual carbon source after near-complete denitrification could be further utilized by methanogenesis. Sodium acetate was superior to glucose in promoting denitrification and reducing global warming potential (GWP). In addition, bacteria sequencing and functional genes confirmed the important role of the type of carbon source on controlling nitrogen removal, carbon consumption and GHG emissions in microbial communities.
1. Introduction Effluent from wastewater treatment plants (WWTPs) that is discharged into surface water is likely to cause eutrophication and water quality deterioration (Si et al., 2019), as the total nitrogen (TN) concentration still fails to meet the Surface Water Environmental Quality Standard (Gao et al., 2017; Wu et al., 2018). Constructed wetlands (CWs), artificially designed by simulating natural wetlands, have proved to be an effective approach in treating water pollution from point and nonpoint sources (Maltais-Landry et al., 2009; Vymazal, 2007). Horizontal subsurface flow (HSSF) CWs have been widely used for secondary treatment of domestic and municipal wastewater (Vymazal and Kröpfelova, 2008). Wastewater flows through porous beds of gravel or rock with a network of aerobic, anoxic and anaerobic zones in a horizontal path from the inlet to the outlet (Chen et al., 2019). Greenhouse gas (GHG) emissions during water purification have attracted increasing attention due to their contribution to global warming. Among these GHGs, methane (CH4), nitrous oxide (N2O) and carbon dioxide (CO2) are recognized as the most influential and destructive to the environment (Huang et al., 2013; IPCC, 2013). CH4,
⁎
generated from the anaerobic metabolism of organic matters, has a global warming potential (GWP) of 25 relative to CO2. N2O is the byproduct of incomplete nitrification and an intermediate of denitrification with a GWP of 298 relative to CO2; it also has a destructive effect on the ozone sphere, decreasing its UV protective properties (Cole and Caraco, 2001). Although the GHG emissions from CWs are not high, their potential threat cannot be ignored considering that the GWPs of CH4 and N2O and the increasingly widespread CWs applications (Hernandez et al., 2018). According to engineering practice, the treatment performance of nutrient removal in CWs is unsatisfactory as WWTPs effluents are characterized by low concentrations of contaminants and inferior biodegradability of organic matters (Wu et al., 2017). Many studies suggested supplying additional carbon sources to improve nitrogen removal in CWs (Chen et al., 2017; Rustige and Nolde, 2007; Vidal et al., 2002). It has been reported that methanogenesis and denitrification processes might co-exist in bioreactors and sediment/soil systems that have nitrate, organics and anaerobic conditions (Hendriksen and Ahring, 1996; Kim et al., 2015; Lin and Chen, 1995). In enhanced nitrogen-removal CWs, methanogenesis may not only produce CH4, but also consume the organic matters for denitrification, which may result
Corresponding author at: School of Environmental Science and Engineering, Shanghai Jiao Tong University, Dong Chuan Road 800, Shanghai 200240, PR China. E-mail address: [email protected] (S. He).
https://doi.org/10.1016/j.biortech.2019.121313 Received 9 February 2019; Received in revised form 30 March 2019; Accepted 1 April 2019 Available online 01 April 2019 0960-8524/ © 2019 Elsevier Ltd. All rights reserved.
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in more N2O emissions if the carbon source becomes deficient. However, researchers mainly focused on the effects of different factors (e.g. CW type and age, wastewater flow and characteristics, feeding strategy and hydroperiod) on the removal efficiencies of pollutants, as well as the composition and quantity of GHG emissions in CW studies (Jahangir et al., 2016; Mander et al., 2014; Maucieri et al., 2017). Few investigations reported the rules and mechanism of electrons transfer among methanogenesis, denitrification and microbial respiration, which is causative of GHG emissions. Given the urgent demands for secondary effluent treatment, it is imperative to simultaneously obtain a qualified water quality and minimize contributions to greenhouse effect (Maltais-Landry et al., 2009). Consequently, GHG emissions in enhanced nitrogen removal with carbon addition were studied using a lab-scale HSSF CW with the following objectives: (1) to investigate the nitrogen removal performance and GHGs (CH4, N2O and CO2) emission fluxes using different carbon source types and carbon-to-nitrogen ratios (defined as chemical oxygen demand to nitrogen ratios, COD/N); (2) to figure out the variations in microbial communities and functional genes; (3) to explore the relationship among denitrification, methane production and microbial respiration as well as the rules of electrons transfer among these.
Table 1 Operational scenarios with different carbon source, C/N and HRTs.
2. Materials and methods
2.3. Gas sampling and emission flux calculation
2.1. Experimental set up and operation
The static chamber technique was used to estimate gas emission fluxes (Wu et al., 2009). The chamber was made of polymethyl methacrylate without a bottom. The upside-down chamber was sealed with water to avoid gas exchange. Two pores on the chamber were connected to the pump inlet and outlet to ensure circular gas mixing (Fig. 1(b)). The inside air pressure and temperature were monitored (SPORTSTAR, China). To avoid external environmental disturbance (e.g. illumination intensity, air pressure, humidity) on fluxes as far as possible, gas samplings were always carried out on clear days between 9:00 and 12:00 a.m. Six gas samples were collected at 0, 40, 80, 120, 160 and 200 min after enclosure. The gas samples were drawn separately into 300 mL aluminum bags (HEDETECH, China) with a pump and intraday analysis of N2O was conducted after all samples were collected. N2O concentrations were measured by a gas chromatograph (6890N, Agilent Technologies, USA) with an electron capture detector and a Poropak Q packed column (2 m). Pure nitrogen (99.999%) at a flux of 15 mL/min was used as the carrier gas. The temperatures of the
Phase
Days (d)
Carbon source
C/N
HRT
S1 S2 S3 S4 S5 S6
16 38 19 27 23 25
/ Glucose Glucose Sodium acetate Sodium acetate Sodium acetate
0 4 7 4 7 7
2 2 2 2 2 1
2.2. Water sampling and chemical analysis Water samples were collected daily, filtered through a 0.45 μm-pore membrane and refrigerated at 4 °C until analysis. Nitrate, nitrite and ammonia were analyzed by ultraviolet spectrophotometry (UV-1800, SHIMADZU, Japan), and total nitrogen (TN) and total organic carbon (TOC) were measured by high-temperature catalytic oxidation method (Multi N/C 3100, Analyticjena, Germany). Dissolved oxygen (DO), temperature and pH were simultaneously detected by a multiparameter digital instrument (ProPlus, YSI, USA).
The HSSF CW was built up and put in a temperature-controlled greenhouse at Shanghai Jiao Tong University. The CW was operated with continuous flow by a peristaltic pump, flowing sequentially through the influent tank, CW bed and effluent tank. The CW bed (length × width × height = 50 cm × 20 cm × 50 cm) was filled by 20–30 mm diameter gravels (Fig. 1(a)). The synthetic wastewater was prepared with KNO3, CaNO3 and NH4Cl to simulate secondary effluent, in which the concentrations of ammonia and nitrate were set to 4 mg/L and 11 mg/L, respectively. Before adding carbon source, the CW was acclimated with the synthetic wastewater for six months. Glucose and sodium acetate, frequently utilized in wastewater treatment, were applied as two typical carbon sources. COD/Ns of 0, 4 and 7, representing non-carbon, carbon-deficient and carbon-sufficient, respectively, were adopted. Each phase (operational scenarios listed in Table 1) ran with stable operation for over 15 d.
(a)
(b)
Gas outlet
Chamber
CW bed
10cm
50cm
10cm
Fig. 1. Schematic representation of constructed wetlands at operating status (a) and gas sampling status (b). 2
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average effluent TN mainly comprised ammonia of 2.10 mg/L and nitrate of 1.21 mg/L, indicating incomplete nitrification or denitrification. In contrast to TN, the ammonia removal efficiency significantly decreased with COD/N (p < 0.05) as organic carbon consumed dissolved oxygen for nitrification. The average effluent ammonia increased from 0.19 to 1.69 mg/L after the carbon source was supplied. At COD of 4 and 7, the ammonia removal efficiency of sodium acetate was 39% and 37% higher than that of glucose, respectively. The effluent nitrate showed a similar trend as TN because denitrification was the predominant way for nitrogen removal in the gravel beds. Without carbon source (S1), the average effluent nitrate of 15.75 mg/L was close to the influent TN of 16.02 mg/L, which meant that ammonia was mostly oxidized to nitrate but little nitrate was reduced. The nitrite accumulation and nitrate residue confirmed that the carbon source was insufficient for denitrification at a COD/N of 4. Nitrite immediately accumulated to 6.09 mg/L and then gradually maintained at a certain concentration after glucose “shock”. The average effluent nitrite was 1.69 and 0.76 mg/L in S2 and S4, respectively. Her and Huang (1995) and Lyu et al. (2017) also obtained similar results, where glucose as the carbon source led to greater nitrite accumulation than did acetic acid. Nitrification and denitrification were both influenced by the quality of the carbon source. Carbon breakthrough, which meant the effluent still contained leftover carbon after denitrification (Carley and Mavinic, 1990), was not observed in the study. The average effluent TOC of 6.07 mg/L was close to that of tap water, so it would not cause secondary water pollution. From S2 to S6, the average TOC removal efficiencies were 73.46%, 83.29%, 79.63%, 82.63% and 90.53%, confirming that increasing carbon addition promoted nitrogen removal and carbon consumption; the ratios of TOC removal to TN removal (ΔTOC/Δ TN) were 2.19, 2.56, 2.02, 2.97 and 3.28, respectively, suggesting that the utilization of organics for denitrification would decline in the presence of excessive carbon source.
column, injector and detector were set at 70, 100 and 280 °C, respectively. The concentrations of CH4, CO2 and water vapor were measured simultaneously in real time by an Ultraportable Greenhouse Gas Analyzer (Los Gatos Research, Canada), recording data every second for 10 min. The gas emission flux was calculated according to Eq. (1) (Wang et al., 2006):
F = ρ0
V P T0 dCt A P0 T dt
(1) −2 −1
Here, F is the emission flux (mg m h ); V and A represent the chamber’s volume (m3) and surface area (m2), respectively; P and P0 are the air pressures (Pa) at sampling and standard state, respectively; T and T0 are the air temperatures (K) at sampling and standard state, respectively; and dCt is the slope of linear regression of concentration dt versus time. 2.4. Microbial community and qPCR assays Microorganism samples were collected from the gravel surface at the end of Phase S1, S2, S4 and S5 and then sent to BIOZERON Biotechnology Co. Ltd. (Shanghai, China) for microbial analysis. Bacterial community was extracted by QIAamp DNA stool Mini Kit (QIAGEN, Germany) following the manufacturer's protocol, and stored at −20 °C until the analysis was conducted. The extracted DNA was then detected by 1% agarose gel electrophoresis to testify its quantity and quality (Chen et al., 2019). High-quality sequencing was performed by Illumina PE250, and the sequences were clustered into operational taxonomic units (OTU) by setting a 97% similarity (Usearch, version 7.1). The bacterial diversity (Shannon and Simpson) and community richness (Chao1 and Ace) were analyzed (Mothur, version 1.30.1). The taxonomy was conducted at the phylum and genus level (RDP Classifier, version 2.2) on the Qiime platform at a 70% confidence threshold. The qPCR assays were performed to quantify the related functional genes (16S rRNA, amoA, nirS, nosZ, mcrA and pmoA) (Cao et al., 2018; Geets et al., 2007; Jin et al., 2010; Wen et al., 2016; Xu et al., 2013) by a Real-Time Quantitative PCR Detection System (BIOER, China). Each 20 μL reaction mixture contained 10 μL SYBR Green qPCR Mix (TaKaRa, China), 1 μL template DNA, 0.4 μL each of forward and reverse primers and sterile water. Each qPCR amplification was performed for 40 cycles, followed by a melting curve analysis. All the assays were conducted in triplicate.
3.2. Greenhouse gas emissions
3.1. Nitrogen removal and carbon consumption
The variation of N2O, CH4 and CO2 emissions was shown in Fig. 3. The GHG fluxes were largely associated with carbon addition, and the emission tendencies could be generally summarized as follows: (1) as an intermediate product, carbon deficiency might lead to serious N2O emissions especially with glucose as the carbon source; (2) CH4 emissions were observed only when sodium acetate was sufficient; (3) whether CW was a CO2 sink or source was determined by the type of carbon source, while the fluxes also depended on COD/N. The carbon dioxide equivalents (CO2e) of N2O, CH4 and CO2 and the sum GWP of the three GHGs were listed in Table 2. The positive or negative GWPs represented the intensification or mitigation of the greenhouse effect, respectively, ranging from –1412.87to 60299.12 mg/m2/d CO2e throughout the experiment. The GWP was mainly influenced by N2O and CO2, with negligible effect of CH4 in this study. In S2, N2O contributed to GWP to a very large extent, even though the N2O flux was similar to the CO2 flux. In other phases, the CO2e of N2O and CO2 were in the same order of magnitude, while that of CH4 was 1 or 2 orders lower by contrast.
Fig. 2 showed the concentrations of nitrogen compounds and organic matters, and the calculated nitrogen removal efficiency and carbon consumption. From S1 to S6, the average effluent TN was 15.99, 6.85, 2.02, 3.89, 1.99 and 3.34 mg/L, respectively; the TN removal efficiency increased linearly with increasing COD/N (p < 0.05). Insufficient glucose (S2) or sodium acetate (S4) both led to fluctuations of effluent TN, but TN removal was steadier in S4. The maximum TN removal efficiency reached 91.09% in S5 accompanied by an effluent ammonia of 1.24 mg/L. With a shorter hydraulic retention time (HRT) of 1 d (S6), the TN removal efficiency decreased to 77.16%. The
3.2.1. N2O emissions The applied carbon source and COD/N resulted in significant changes to the N2O emissions in the CW, which performed as a N2O source. Nearly no N2O emissions were detected in S1, which coincided with the insignificant nitrate reduction and nitrite accumulation due to limited denitrification. The N2O fluxes decreased from 201.82 mg/m2/d at a COD/N of 4 (S2) to 5.86 mg/m2/d at a COD/N of 7 (S3) using glucose as the carbon source. Only a slight difference in N2O fluxes using sodium acetate was observed, and the average flux was 4.88 ± 0.94 mg/m2/d. The highest proportion of N2O-N to TN removal
2.5. Data analysis All statistical analysis was conducted by SPSS (version 20.0) with results considered significant at the 0.05 level. The differences of TN, nitrate, nitrite, ammonia and TOC were analyzed by one-way ANOVA test. The ratios of different functional genes to 16S rRNA were calculated to analyze internal relationships among nitrification, denitrification, methanogenesis and methane oxidation. 3. Results and discussion
3
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Fig. 2. Profiles of nitrogen and carbon concentrations and their removal efficiencies in different phases.
gained by different bacteria as metabolic substrates, so that fewer electron donors flowed to denitrifying bacteria (Elefsiniotis and Li, 2006). The methyl and carboxyl moieties made the structure of sodium acetate simpler and more available to be utilized (Elefsiniotis and Wareham, 2007), which might have contributed to the minimal differences among denitrification reductases and low N2O emissions when inadequate carbon source was supplied in S4. The higher and steadier TN removal efficiency with less accumulation of denitrification
was 13.43% in S1, followed by 10.00% in S2, and the proportions of other phases were all lower than 0.23%. Denitrification is a stepwise process accomplished by nitrate reductase (Nar), nitrite reductase (Nir), NO reductase (Nor) and N2O reductase (Nos), simultaneously (Vidal-Gavilan et al., 2014). This study confirmed that carbon deficiency would lead to nitrite accumulation and N2O emissions, as the reductases competed for electrons (Chen et al., 2017; Pan et al., 2013). Furthermore, glucose was prone to be
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Fig. 3. Greenhouse gas N2O (a), CH4 (b) and CO2 (c) fluxes at different phases. 4
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occurred before methanogenesis when sufficient carbon source was present (Her and Huang, 1995). Rittmann (2002) proved that denitrification was more energetically favorable than methanogenesis according to thermodynamics. CH4 emissions were also affected by the quality of the organic matters (e.g. the amount of short chained fatty acids) (Garcia et al., 2007). Methane production can be divided into three processes including the hydrolysis and fermentation phase, the acetogenic phase and the methanogenesis phase. When sodium acetate was used, the process could commence directly with methanogenesis, so that CH4 emissions were measured only with sodium acetate as the carbon source.
Table 2 Carbon dioxide equivalents of greenhouse gas and global warming potentials at different phases. Phase
S1 S2 S3 S4 S5 S6
Carbon dioxide equivalents (mg/m2/d CO2e) CO2
CH4
N2O
global warming potentials(mg/m2/d CO2e)
−1007.93 155.57 2017.09 −1365.34 −2793.04 −1657.14
0.00 0.00 0.00 0.00 129.71 168.09
325.34 60143.55 1746.59 1772.85 1250.45 1336.29
−682.59 60299.12 3763.69 407.51 −1412.87 −152.75
3.2.3. CO2 emissions Despite being a major GHG, CO2 emissions have not been widely researched in CWs. In this study, CO2 emissions ranged from −2793.04 to 2017.09 mg/m2/d. In a GHG emissions review based on meta-analysis, a significant negative correlation was found between the inflow TOC loading and CO2 emissions (Mander et al., 2014). Yan et al. (2012) found that CO2 fluxes increased from 6805.68 to 10976.16 mg/m2/d when the C/N ratio was increased from 2.5 to 10 in a VSSF CW treating synthetic municipal wastewater. In this study, the findings were not uniform: CO2 fluxes increased with increasing COD/N when glucose was the carbon source; in contrast, CO2 fluxes decreased with increasing COD/N with the addition of sodium acetate.
intermediates when sodium acetate was added suggested that intensive denitrification favored volatile fatty acids rather than macromolecular organic matters (Lyu et al., 2017). 3.2.2. CH4 emissions In addition to the competition within denitrification, denitrification also contends with methanogenesis for electrons. CH4 emissions were 5.19 and 6.72 mg/m2/d for S5 and S6, respectively, and were not detected in other phases. The results showed that methanogenesis occurred with residual carbon after the nitrate was almost consumed and the redox potential was suitable, which indicated that denitrification
Fig. 4. Community structure analysis of the microbial samples at the phylum (a) and genus (b) level. Phylum making up less than 0.25% and genera making up less than 1% of the compositions were defined as others. 5
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Functional gene (copies/g)
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Fig. 5. Abundance of functional genes (a) and the ratios of functional genes to 16S rRNA (b) in nitrogen transformation and methane production.
3.3. Microbial analysis
The stoichiometric equations for anaerobic denitrification with glucose and acetate are summarized as follows (considering the microbial growth) (Matějů et al., 1992):
3.3.1. The richness, diversity and structure of the microbial community The presence of a carbon source introduced denitrification and metagenesis into microbial processes, which also included microbe assimilation and metabolism. Consequently, the abundance of the microbial community and functional genes would alter with changes in microbial processes and the physiochemical environment. The 796–1408 OUTs were clustered at 0.97 similarity, and the community structure analysis at the phylum and genus levels was shown in Fig. 4. Different carbon sources led to a diverse community structure, and the diversity and richness were lower with glucose than with sodium acetate as the carbon source. Proteobacteria was the most dominant phylum, followed by Bacteroidetes, Planctomycetes, Cyanobacteria and Chloroflexi. The organic matters promoted the relative abundance of Proteobacteria, which is often involved in the transformation of carbon and nitrogen compounds (Gao et al., 2017). In S2, the proportion of Proteobacteria and Bacteroidetes was 94.98%, the most abundant genera being Flavobacterium (27.66%), Tolumonas (22.78%), Aeromonas (10.84%), Hydrogenophaga (10.08%) and Thermomonas (6.52%), all of which all denitrification related (Chen et al., 2019; Gao et al., 2017). The microbial community has commonly been thought to be responsible for CW performances (Zhang et al., 2018), but the dominant genera in S2 (Flavobacterium, Tolumonas and Aeromonas etc.) has not been reported to facilitate N2O emissions. The
NO3– +
0.34C6H12O6 → 0.136C5H7NO2 + 1.35CO2 + 0.432 N2 + 1.06H2O + OH–
(2)
NO3– +
0.819CH3COOH → 0.068C5H7NO2 + HCO3–+0.301CO2 + 0·.902H2O + 0.466 N2
(3)
As seen from these equations, with identical COD for denitrification, the CO2 generate with acetate is less than one quarter of that generate with glucose. This was directly observed in this study, with the CW becoming a CO2 source when glucose was added (S2 and S3) and a CO2 sink when sodium acetate was added (S4, S5 and S6). CO2 emissions would be influenced by many microbial processes (e.g. denitrification, respiration, assimilation, catabolism) and physicochemical processes (e.g. hydrolysis, adsorption, algal photosynthesis). The reported combination of denitrification and anaerobic oxidation of methane to produce CO2 could also be a reason for the no CH4 emissions being observed, and the higher CO2 emissions. Furthermore, the enhanced denitrification at a COD/N of 7 with sodium acetate produced more alkalinity, which may result in the partial conversion of CO2 into HCO3– and CO32– and account for the lower CO2 fluxes (Lyu et al., 2017). The produced CO2 might also be absorbed and transformed by ammoniacontaining liquid (Zhang et al., 2019). 6
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Low COD/N
A appropriate range of COD/N
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CH4 emissions CO2 emissions
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Fig. 6. The mechanism of electrons transfer among denitrification, methanogenesis and microbial respiration controlling nitrogen removal and greenhouse gas emissions.
acids and gases for methanogenesis. Additionally, CH4 flux is the net effect of methane production and oxidation; the lower CH4 emissions in S2 could also be explained by the active oxidation of methane due to higher ratio of pmoA genes.
community structures of S4 and S5 were similar in dominant genera (Hydrogenophaga, Pseudomonas, Arenimonas, Denitratisoma, Photobacterium, Xanthomonadales_norank and Gemmobacter). The relative abundances of denitrification-related genera accounted for 83.78%, 33.66% and 27.32% in S2, S4 and S5, respectively. In particular, Anaerolineaceae is a specific denitrifying bacteria that produces formic acid (a substrate for methanogenesis), that was detected in S1 (0.38%), S4 (4.07%) and S5 (3.95%). The proportion of methanogenesis-related genera Methanobacterium was found only in S5 (0.30%) (Holt et al., 1989). Nitrification-related genera Nitrospira, Nitrosomonas and Nitrosomonadaceae_uncultured accounted for 1.31%, 0.06%, 0.71% and 0.58% in S1, S2, S4 and S5, respectively.
3.4. The rules of electrons transfer and carbon addition strategy To evaluate the CW performances in practice, nitrogen removal and GHG emissions should both be considered. The abundance of functional genera and genes is an important key to understanding the relationship between the apparent performance and internal microbial processes, as well as the electrons transfer among nitrification, denitrification and methanogenesis. The chemical structure and molecular weight of electron donors will significantly influence microbial processes and their efficiencies (Her and Huang, 1995). According to the COD/N, three conditions could be defined (Fig. 6): (1) with carbon deficiency, the reductases responsible for different denitrification steps competed for electrons, contributing to nitrite accumulation and N2O emission; the nitrogen removal rates would increase gradually with increasing COD/N; (2) when the COD/N was in the appropriate range for complete denitrification, the nitrogen removal efficiency reached nearly 100% without N2O emission; (3) with excessive carbon source, electrons finally flowed to methanogenesis resulting in CH4 emissions (e.g. with sodium acetate as the carbon source) or microbial growth and respiration resulting in CO2 emissions (e.g. with glucose as the carbon source). The regulation of denitrification, methane production, microbial growth and respiration is determined by the quantity and quality of the carbon source, which controls the electrons transfer and thereby the GHG emissions. Unfortunately, the focus in wastewater treatment engineering practice has been on the removal efficiency of contaminants without much attention paid to GHG emissions. Yan et al. (2012) and Zhao et al. (2014) reported that an optimal C/N of around 5 could simultaneously achieve the best nutrient removal and lowest CO2 and CH4 fluxes. Her and Huang (1995) investigated the appropriate range of COD/N using four different carbon sources and found that using glucose and acetic acid did not decrease the denitrification efficiency despite a COD/N beyond the appropriate range, suggesting that these two carbon sources were not harmful for denitrification. However, excessive glucose can result in high levels of CO2 release, which is not beneficial for carbon sequestration. Conversely, though excessive sodium acetate would release methane, its contribution to GWP would be insignificant considering the carbon dioxide equivalent of CH4. In summary, this study recommends a COD/N of 7 with sodium acetate as the carbon source to bring low effluent nitrite and N2O flux. Previous studies have reported the superiority of acetate to glucose as the carbon source in facilitating nitrogen removal efficiencies and reducing N2O emissions (Li et al., 2008; Lyu et al., 2017; Rungkitwatananukul et al., 2016). This study confirmed this conclusion and further found its advantages in CO2 emissions and GWP values, while only the relative expense may hinder its use in large-scale operations. Since the effect of the carbon source on
3.3.2. The abundance of functional genes The abundances of the functional genes involved in nitrification (amoA), denitrification (nirS and nosZ), methanogenesis (mcrA) and methane oxidation (pmoA) were shown in Fig. 5. The ratios of functional genes to 16S rRNA were then calculated. In general, the abundances and ratios of functional genes were consistent with the community structure. The 16S rRNA reflected the total number of bacteria in the samples, which was lowest without carbon addition (S1) and highest with glucose at COD/N of 4 (S2). Comparing S1, S4 and S5, the 16S rRNA, nirS genes and nosZ genes all increased with increasing COD/N because the carbon source supplied the microorganisms, especially denitrifying bacteria, with food and energy. Although the number of amoA genes was higher in S2 and S5 than in S1, the proportion of amoA genes decreased with increasing COD/N because the competition arising from ammonia and organics oxidation led to the suppression of nitrification. The abundance of nosZ genes was less than ten percent of that of nirS, indicating that nirS genes played the predominant role in denitrification. The proportion of nirS was higher in S5 and S4 with sodium acetate than in S2 with glucose, while the proportion of nosZ was the lowest in S5. The higher N2O emissions with glucose than with sodium acetate could be interpreted as follows: as a weakly basic compound, sodium acetate increased the CW pH during hydrolysis, whereas the lower pH with glucose decreased the activity of nosZ genes (Lyu et al., 2017). The shifts of microbial populations and consequent changes in metabolism were both likely to generate the different accumulations of denitrification intermediates with different carbon sources and COD/Ns (Ge et al., 2012; Martienssen and Schops, 1999). In S5, CH4 emissions were detected for the first time, as the abundance and ratio of mcrA genes were the highest and the ratio of pmoA was the lowest. The proportions of mcrA genes to 16S rRNA increased with increasing COD/N in S5 and S6, indicating that more electrons were flowing to methane production when sufficient sodium acetate was available. The fact that no CH4 emissions were detected even with sufficient glucose could be explained by the lower ratio of mcrA genes and the higher ratio of pmoA genes in S2. Based on Bergey’s Manual of Determinative Bacteriology (Holt et al., 1989), Flavobacterium, the most dominant genera when glucose was added (27.66%), cannot produce 7
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the GHG is less clear, the development of an efficient, environmentally friendly and economic electron donor, and the corresponding appropriate COD/N, should be further investigated.
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4. Conclusions (1) With increasing COD/N, the TN removal efficiency increased, accompanied by decreased nitrite accumulation and ammonia oxidation. (2) The use of glucose resulted in greater nitrite accumulation and N2O emissions at a COD/N of 4 and greater CO2 emissions at a COD/N of 7 than sodium acetate. The GWP with glucose as the carbon source was significantly higher than with the sodium acetate. (3) Glucose and sodium acetate enriched particular microbial communities, and carbon addition significantly increased the proportion of denitrifying bacteria. The use of excess sodium acetate encouraged the growth of Methanobacterium with mcrA genes. Acknowledgements The National Natural Science Foundation of China Under Grant (No. 51678356) and the Program for Chinese National Key Projects of Water Pollution Control and Reclamation (2017ZX07205003) provided support for this study. Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.biortech.2019.121313. References Cao, J., Wang, C., Dou, Z., Liu, M., Ji, D., 2018. Hyphospheric impacts of earthworms and arbuscular mycorrhizal fungus on soil bacterial community to promote oxytetracycline degradation. J. Hazard. Mater. 341, 346–354. Carley, B.N., Mavinic, D.S., 1990. The effects of external carbon loading on nitrification and denitrification of a high-ammonia landfill leachate. Res. J. Water Pollut. Contr. Federat. 63 (1), 51–59. Chen, D.Y., Chen, X.C., Huang, X.B., He, S.B., Huang, J.C., Zhou, W.L., 2017. Controlling denitrification accompanied with nitrite accumulation at the sediment-water interface. Ecol. Eng. 100, 194–198. Chen, D.Y., Gu, X.S., Zhu, W.Y., He, S.B., Wu, F., Huang, J.C., Zhou, W.L., 2019. Denitrification- and anammox-dominant simultaneous nitrification, anammox and denitrification (SNAD) process in subsurface flow constructed wetlands. Bioresour. Technol. 271, 298–305. Cole, J.J., Caraco, N.F., 2001. Emissions of nitrous oxide (N2O) from a tidal, freshwater river, the Hudson River, New York. Environ. Sci. Technol. 35 (6), 991–996. Elefsiniotis, P., Li, D., 2006. The effect of temperature and carbon source on denitrification using volatile fatty acids. Biochem. Eng. J. 28 (2), 148–155. Elefsiniotis, P., Wareham, D.G., 2007. Utilization patterns of volatile fatty acids in the denitrification reaction. Enzyme Microb. Technol. 41 (1–2), 92–97. Gao, L., Zhou, W.L., Huang, J.C., He, S.B., Yan, Y.J., Zhu, W.Y., Wu, S.Q., Zhang, X., 2017. Nitrogen removal by the enhanced floating treatment wetlands from the secondary effluent. Bioresour. Technol. 234, 243–252. Garcia, J., Capel, V., Castro, A., Ruiz, I., Soto, M., 2007. Anaerobic biodegradation tests and gas emissions from subsurface flow constructed wetlands. Bioresour. Technol. 98 (16), 3044–3052. Ge, S., Peng, Y., Wang, S., Lu, C., Cao, X., Zhu, Y., 2012. Nitrite accumulation under constant temperature in anoxic denitrification process: The effects of carbon sources and COD/NO3-N. Bioresour. Technol. 114, 137–143. Geets, J., de Cooman, M., Wittebolle, L., Heylen, K., Vanparys, B., De Vos, P., Verstraete, W., Boon, N., 2007. Real-time PCR assay for the simultaneous quantification of nitrifying and denitrifying bacteria in activated sludge. Appl. Microbiol. Biotechnol. 75 (1), 211–221. Hendriksen, H.V., Ahring, B.K., 1996. Integrated removal of nitrate and carbon in an upflow anaerobic sludge blanket (UASB) reactor: Operating performance. Water Res. 30 (6), 1451–1458. Her, J., Huang, J., 1995. Influences of carbon source and C/N ratio on nitrate/nitrite denitrification and carbon breakthrough. Bioresour. Technol. 54 (1), 45–51. Hernandez, M.E., Galindo-Zetina, M., Carlos Hernandez-Hernandez, J., 2018. Greenhouse gas emissions and pollutant removal in treatment wetlands with ornamental plants under subtropical conditions. Ecol. Eng. 114, 88–95. Holt, J.G., Williams, S.T., Holt, 1989. Bergey's Manual of Systematic Bacteriology 4 Lippincott Williams & Wilkins. Huang, L., Gao, X., Guo, J.S., Ma, X.X., Liu, M., 2013. A review on the mechanism and affecting factors of nitrous oxide emission in constructed wetlands. Environ. Earth Sci. 68 (8), 2171–2180. IPCC, 2013. Climate Change 2013: The Physical Science Basis. 25. Cambridge University
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