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Article

Nitrogen Removal from Polluted Water by an Integrated Constructed Wetland-Microbial Electrolysis Cell System

by
Ruina Zhang
1,2,†,
Kexin Li
1,3,†,
Longqiang Yi
1,4,
Xin Su
2,
Changyuan Liu
1,
Xinyu Rong
1,
Haoxin Ran
1,
Yingjie Wei
1,
Li Wan
1,
Rui Han
1,4,* and
Yinghai Wu
1,*
1
College of Ocean and Civil Engineering, Dalian Ocean University, Dalian 116023, China
2
Key Laboratory of Environment Controlled Aquaculture (KLECA), Ministry of Education, Dalian Ocean University, Dalian 116023, China
3
College of Life and Environmental Science, University of Birmingham, Birmingham B15 2TT, UK
4
College of Civil Engineering, Southwest Jiaotong University Hope College, Chengdu 610400, China
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Water 2024, 16(17), 2368; https://doi.org/10.3390/w16172368
Submission received: 2 August 2024 / Revised: 21 August 2024 / Accepted: 22 August 2024 / Published: 23 August 2024
(This article belongs to the Section Wastewater Treatment and Reuse)
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Abstract

:
An integrated constructed wetland-microbial electrolysis cell (ICW-MEC) system was investigated for nitrogen removal under different pollution loads, hydraulic loads (HLRs), and aeration conditions. The treatment performance of each unit and the microbial community characteristics for nitrogen removal were elucidated. The results showed that, on average, 80% of NH4+-N, around 70% of nitrate nitrogen (NO3−-N), and 70% of total nitrogen (TN) were removed by the system under three pollution loads, with less influence by pollution loads. The high removal efficiencies of NH4+-N (81.8%), NO3−-N (71.4%), and TN (72.8%) indicated tolerable to high HLRs. The intermittent aeration negatively affected NH4+-N removal, while increasing NO3−-N and TN removals by 3.2–13.0% and 3.7–16.7%. The contribution efficiencies of the secondary unit to the removal of NH4+-N, NO3−-N, TN, and total organic carbon (TOC) reached 47.4%, 55.0%, 45.9%, and 38.8%, respectively. The distinct microbial communities existed in various units of the ICW-MEC system, which were strongly affected by environmental factors and shaped by diverse fillers and structures of the system. The dominant bacteria contributed to the efficient nitrogen removal performance of the ICW-MEC system. The three units exerted their advantages to ensure efficient and stable system operation.

1. Introduction

Currently, many natural water environments worldwide are polluted by excessive chemical substances [1]. Large amounts of nitrogen were discharged into the water environment and caused eutrophication. The excessive algae proliferation and the high DO consumption in water bodies led to imbalances and function losses in river ecosystems [2]. Therefore, it is necessary to reduce nitrogen from wastewater discharge and polluted water bodies.
A constructed wetland (CW), as an ecological sewage treatment facility, has considerable advantages and potential in nitrogen removal [2]. It has evolved from the initial single type of surface flow constructed wetland (FWSCW) to the horizontal subsurface flow constructed wetland (HSFCW) and vertical subsurface flow constructed wetland (VSFCW), to an integrated constructed wetland (ICW) that utilizes the advantages of different types of CWs for improving nitrogen removal [1,2]. A study found that during six years of operation, an ICW system maintained stable removal efficiencies for most water quality parameters under high hydraulic load (HLR) conditions [3]. An ICW system that combined HSFCW and VSFCW had high removal efficiency for organic matter and pathogens, with over 90% total removal efficiency, regardless of the duration of contact or the type of vegetation [4]. Another study reported that a novel ICW system removed over 80% of chemical oxygen demand (COD) and ammonia nitrogen (NH4+-N) regardless of the size of the HLRs. This result proved its excellent water purification performance [5]. Different structures of CWs lead to a diverse microbial environment within CWs, which should facilitate nitrogen removal [6].
The filler in CWs is a very important factor that directly or indirectly affects pollutant removals, especially in providing the carrier and electron transfer donor or acceptor for microbial growth needs. A study reported that the growth of nitrogen-removal bacteria was promoted by the use of pyrite fillers in CWs, optimizing nitrogen-removal pathways, and increasing total nitrogen (TN) removal efficiency by 37.74%. SO42− and Fe2+, and increased plant chlorophyll content and growth rate by 34.2% and 50%, respectively [7]. A novel composite material Starch-FeS@PSB biochar, which was prepared with FeS, starch, and biochar, increased the abundance of microbes involved in microbial nitrogen-removal and acted as an electron donor to drive denitrification in an ICW system [8]. Another study found that the increase of limonite in the filler significantly improved the removal efficiencies of NH4+-N, nitrate nitrogen (NO3−-N), and TN in a CW system [9]. The relative abundance of nitrification, denitrification, and iron-related bacteria in the treated water samples was increased [9]. Limonite nourished iron-reducing bacteria and increased effective electrons through the cycle between Fe2+ and Fe3+ in the CW system [9].
In recent years, the coupling technology between CWs and microbial electrochemical processes has received a lot of attention. Microbial electrolysis cells (MEC) have been provided as a new strategy for wastewater treatment, using electrodes to facilitate electron transfer and enhance nitrogen compound removal. The coupling system of CW and MEC shows considerable potential in nitrogen-removal performance. A study investigated the possibility of the CW-MEC system to enhance denitrification under low-carbon conditions and found that the removal efficiency of NO3−-N by the CW-MEC system was 69.3% at C/N 2 and the current 0.583 mA [10]. While under the same conditions, the removal efficiency of NO3−-N by the conventional CW system was 66.2%, demonstrating that the CW-MEC system had strong denitrification ability under low-carbon conditions [10]. A study applied solar cells as an external power source to the CW-MEC system, which operated continuously at low temperatures (<10 °C) for 4 months, and the removal efficiency of NH4+-N in the system reached 88.2 ± 7.0%, an increase of 11.7 ± 6.5% compared to conventional CWs [11]. The study found that NH4+-N was mainly removed through the nitrification-autotrophic denitrification process, and electricity promoted both NH4+-N oxidation and denitrification when NH4+-N was used as the main electricity-producing substrate [11]. However, the factors that affect the operation of the coupling CW-MEC system are very complex, including both the influences of the system constructions/configurations (substrate material, electrode type, electrode position, combination form, etc.) and operating conditions. Therefore, it is particularly important to explore the treatment performance and operating parameters in different constructions of CW-MEC systems. ICW systems are being applied to practical engineering. The coupling of ICW and MEC may have better performance and application potential than CW-MEC. Due to the differences in structure, performance, and internal conditions between CW and ICW, the research experience obtained from CW-MEC may not be applicable to the coupled system of ICW and MEC [3,6]. So far, the performance, operating condition, and microbiological mechanisms of ICW systems coupled with MEC still need further revealing.
This study aims to investigate the nitrogen removal performance and microbiological mechanisms of a novel integrated constructed wetland-microbial electrolysis cell (ICW-MEC) system. The ICW-MEC system was constructed and investigated for efficient nitrogen removal under different pollution loads, hydraulic loads, and aeration conditions. The removal contribution of each treatment unit in the system was estimated by treating real polluted river water. Furthermore, the microbial community characteristics for nitrogen removal were elucidated using 16S rRNA amplicon sequencing technology.

2. Materials and Methods

2.1. Construction and Start-Up of Experimental Systems

The ICW-MEC system included a main reactor, an electrochemical workstation, an influent bucket, a peristaltic pump, an aeration pump, four air-stones, silica tubes, and titanium wires (Figure 1). The main body of this system was a square steplike reactor made of organic glass with a volume of 9.76 L, an overall length of 37 cm, the highest point at 31 cm, and the lowest point at 25 cm, which consists of three units of VSFCW connected in series. The primary unit was a down-flow VSFCW, with gravel (2 cm high) and zeolite (20 cm high), from bottom to top. The secondary unit was an up-flow VSFCW filled with gravel (2 cm high), iron-doped porous filler (IDPF) (12 cm high), sponge iron (1 cm), and volcanic rock (7 cm high), from bottom to top. The tertiary unit was a down-flow VSFCW with gravel (2 cm high) and activated carbon (20 cm high), from bottom to top. IDPF was synthesized by doping multiple minerals with reduced iron powder. The detailed ratios of the various substances can be found in the Supplementary Materials. Eleusine indica, Humulus scandens, Trigonotis peduncularis, and Pinelliaternata were domesticated for one month before being planted in the system. The secondary unit with IDPF served as the core treatment unit, which was equipped with a microbial electrochemical system. The iron-carbon cathode and graphite anode were installed 4 cm and 18 cm away from the bottom of the second unit, respectively. The electrodes were connected to the electrochemical workstation to obtain stable low voltage using titanium wires. The air-stone connected to the aeration pump was installed in the inlet zone. The synthetic wastewater was injected into the system using a peristaltic pump. Three same ICW-MEC systems, named H1, H2, and H3, were constructed in this study.
In order to minimise the variability, synthetic wastewater was pumped into the reactors during the start-up period. The wastewater was prepared using 0.076 g·L−1 NH4Cl, 0.432 g·L−1 KNO3, 0.1 g·L−1 MgSO4, 0.275 g·L−1 C8H5KO4, 0.045 g·L−1 KH2PO4, 0.014 g·L−1 NaCl, 0.01 g·L−1 CaCl2, 0.138 g·L−1 CH3COOH, and distilled water. Meanwhile, microbial suspension collected from the Xiajiahe Wastewater Treatment Plant and the Lingshui River in Dalian City, China, was inoculated into the reactors to accelerate the startup. The synthetic wastewater was recycled and treated in the systems. The nutrients used for preparing synthetic wastewater were replenished in the system every 3 days for 6 months of continuous operation. The inlet and outlet water quality of each reactor, DO, pH, temperature, total organic carbon (TOC), NH4+-N, NO3−-N, and TN were tested every 5 days. The stable removal efficiency indicated that the system had successfully started up.

2.2. Experiment Operation

These reactors were operated under different pollution loads, different HLRs, and with or without aeration to investigate the effect of these factors on nitrogen removal. All three systems, H1, H2, and H3, were operated continuously at a voltage of 1.8 V and a return ratio of 240%. The inlet water quality parameters were DO 7.5 ± 0.5 mg/L, temperature 28 ± 1 °C, and pH 5.6 ± 0.3. The synthetic wastewater was prepared using 0.038 g·L−1 NH4Cl, 0.144 g·L−1 KNO3, 0.1 g·L−1 MgSO4, 0.102 g·L−1 C8H5KO4, 0.045 g·L−1 KH2PO4, 0.014 g·L−1 NaCl, 0.01 g·L−1 CaCl2, 0.051 g·L−1 CH3COOH, and distilled water, which had lower concentration than that used in start-up period.
H1 system was used to investigate the effect of pollution loads and aeration conditions on treatment performance, in which the component of the synthetic wastewater was adjusted to high, medium, and low pollution loads. The experiment of the H1 system was conducted in six stages, with HLR 0.01 m3/(m2·h) in each phase. Specifically, Stage I: TOC 70 ± 4.3 mg/L, NH4+-N 10 ± 1.1 mg/L, NO3−-N 16 ± 9 mg/L, aeration; Stage II: stop aeration; Stage III: TOC 48.3 ± 9.1 mg/L, NH4+-N 5 ± 0.5 mg/L, NO3−-N 10 ± 1.8 mg/L, aeration; Stage IV: stop aeration; Stage V: TOC 20.4 ± 3.2 mg/L, NH4+-N 2.7 ± 0.2 mg/L, NO3−-N 4.8 ± 2.6 mg/L, aeration; and Stage VI: stop aeration.
The H2 system was used to investigate the effect of HLRs and aeration conditions on the treatment performance, in which HLR was adjusted to high, medium, and low HLRs. The experiment of the H2 system was conducted in six stages. Specifically, Stage I: HLR 0.015 m3/(m2·h), aeration; Stage II: stop aeration; Stage III: HLR 0.023 m3/(m2·h), aeration; Stage IV: stop aeration; Stage V: HLR 0.051 m3/(m2·h), aeration; and Stage VI: stop aeration.
The H3 system was used to investigate the treatment performance of the system on real polluted river water. Specifically, Stage I: HLR 0.01 m3/(m2·h), aeration; Stage II: stop aeration. The polluted river water collected from Lingshui River, Dalian, China was pumped into the system in place of the synthetic wastewater.

2.3. Sampling, Physical and Chemical Analysis

Water samples were collected from the inlet and outlet of each unit. Three parallel water samples were collected every 2 days. The biological samples, U1-b, U2-b, and U3-b, were collected from the zeolite intermediate layer, IDPF intermediate layer, and activated carbon intermediate layer of the first, second, and third units in the H3 system, respectively. The biological samples of the cathode electrode and U2-c were collected from the second units of the H3 system. The biological samples were crushed under sterile conditions and stored at −20 °C.
The surface characteristics of the biological samples were observed using scanning electron microscopy (SEM) (Nova NanoSEM 450, FEI, Hillsboro, OH, USA).
NH4+-N, NO3−-N, and TN were determined by Nessler’s reagent spectrophotometry, UV spectrophotometry, and alkaline potassium persulphate digestion UV spectrophotometry. TOC, DO, and pH were determined using a TOC analyser (TOC-L CPN, Shimadzu, Japan), a portable dissolved oxygen meter (600R-50-C-T-DO, Xylem, Washington, DC, USA), and a multiparameter tester (S220, Mettler-Toledo, Zurich, Switzerland). Total Fe (Fe2+ + Fe3+) was determined by phenanthroline spectrophotometric method using a UV visible spectrophotometer (G10S UV-Vis, Thermo Fisher Scientific, Shanghai, China). Ultrapure water was used in water quality tests. Analytical grade reagents were used in the measurement of the parameters. All chemicals were purchased from Shanghai Aladdin Biochemical Technology Co., Ltd. (Shanghai, China). Three replicates were tested for each water sample.

2.4. High-Throughput Sequencing and Microbial Informatics Analysis

The biological samples were crushed in an ultra-clean bench for DNA release. Specifically, 1 g of biological samples were placed in a mortar and immediately ground after adding an appropriate amount of liquid nitrogen. The step was repeated three times to grind the biological samples into powder. Each powder sample was mixed with 5 mL of buffer (0.5 M Na2HPO4, 1.5 M NaCl, CTAB 1.5% (w/v); 0.5 M Na EDTA, pH 8; 1 M Tris-HCl, pH 7.5) and 25 μL of proteinase. After incubated at 65 °C overnight, the supernatants of the samples were used for DNA extraction according to a previous method [12]. Purity and concentrations were tested using a Qubit 2.0 (Invitrogen, Carlsbad, CA, USA). The integrity was verified by 2% agarose gel electrophoresis using 4 μL of Gel-Red® dye. The amplification primers were 515F (5′-GTG CCA GCM GCC GCG GTA A-3′) and 806R (5′-GGA CTA CHV GGG TWT CTA AT-3′). The amplified product was purified and sequenced on the NovaSeq PE250 platform of Beijing Novogene Biotechnology Co., Ltd. (Beijing, China). The raw reads obtained in the present study were deposited in the NCBI’s Sequence Read Archive (SRA) under the accession number PRJNA1141724. After quality control, operational taxonomic units (OTUs) were clustered based on 97% similarity, and compared and annotated with the Mothur database to analyse microbial community compositions of the biological samples. Qiime software (Version 1.9.1) was used to calculate the alpha diversity index and Unifrac distance. Canonical correspondence analysis (CCA) was used to analyse the effects of environmental factors on the microbial community with a Vegan bag in R. The distance matrix heatmap based on Weighted Unifrac distance and Unweighted Unifrac distance and Principal Component Analysis (PCA) was used to analyse Beta diversities of microbial communities. The cluster heatmap of the top 35 functions of microbial communities was analysed using Functional Annotation of Prokaryotic Taxa (FAPROTAX).

2.5. Statistical Analysis

All water quality data in this study were shown by mean ± standard deviation, which was statistically analysed using SPSS software (version 23.0, IBM). One-way ANOVA was used to analyse the differences in removal efficiencies under different hydraulic loads, pollution loads, and aeration conditions. Mantel-test analysis was used to verify the significance of the correlation between microbial communities and environmental factors. The results of high-throughput sequencing and water quality tests were plotted using the software R (Version 2.15.3) and Origin 9.0, respectively.

3. Results and Discussion

3.1. Characterisation of Bio-Carriers and Electrode Surface Morphology

Zeolite carriers had uneven surfaces with irregularly shaped substances attached, indicating strong adsorption capacity in the primary unit (Figure 2a) [13]. IDPF carrier had a rough surface covered with dense honeycomb-like chambers, crystalline precipitates, diverse microbes, and microbial extracellular polymers, providing good adhesion conditions for microbes in the secondary unit (Figure 2b) [14]. The surface of the activated carbon filler was covered with irregular agglomerates, indicating excellent conditions for removing pollutants in the tertiary unit (Figure 2c) [15]. The cathode also was covered with dense honeycomb-like chambers. However, there were fewer microorganisms attached to the cathode than to the IDPF carrier, suggesting that the cathode was selective towards microbes growing on its surface (Figure 2d). The results indicate that the combination of these carriers and electrodes provides diverse conditions and has the potential to achieve rapid adsorption and sustainable biodegradation of pollutants in the ICW-MEC system [16].

3.2. Effect of Operating Conditions on the Pollutant Removal Performance in the System

3.2.1. Effect of Pollutant Loading on the Pollutant Removal Performance

Stage I, Stage III, and Stage V of the H1 system were operated at high, medium, and low influent pollution loads and under aeration conditions. The average removal efficiencies of NH4+-N were 83.0%, 87.7%, and 96.1% at Stage I, Stage III, and Stage V, respectively (Figure 3a). The removal efficiencies of NH4+-N increased as the pollution load decreased. In contrast, the average removal efficiencies of NO3−-N and TN decreased slightly with the reduction of pollution load but remained at around 70% (Figure 3b,c). The results suggested NO3−-N and TN removals were less affected by influent pollution load. The removal efficiencies of TOC showed a decreasing trend through the three stages, with average removal efficiencies of 64.2%, 58.7%, and 36.6%, respectively (Figure 3d). It can be seen that the TOC removal was greatly affected by the influent pollutant load. The removal efficiencies of TOC were higher at high influent pollution load than those at medium influent pollution load. However, there was no significant difference in the removal efficiency of organic matter between Stage I and Stage III (p > 0.05).
The influent pollution load is an important factor that affects the treatment efficiency of the reactor, as well as the size and form of the reactor. When the influent pollution load does not exceed the maximum removal rate in the reactor, the increase in the influent pollution load will not reduce the treatment efficiency. At this point, it manifests as an increase in influent pollution load and treatment efficiency. When the influent pollution loads exceed the maximum removal rate in the reactor, the treatment efficiencies decrease as the influent pollution loads increase. The removal efficiencies of NH4+-N in Stage I, Stage III, and Stage V were negatively correlated with the pollution load, suggesting the three NH4+-N influent loads exceeded the removal rate of the system, and the removal rate in Stage V was close to the maximum removal rate. The average removal of NH4+-N remained above 80% throughout the experiment period, which was higher than that of conventional ICWs in previous studies [3]. Under high pollution loads, the aerobic degradation of organic matter may inhibit the nitrification process by competing with DO, resulting in a decrease in the removal of NH4+-N. Overall, NO3−-N and TN maintained relatively good removal efficiencies under different pollution load conditions. This result may be attributed to the increase in the population of nitrifying and autotrophic denitrifying bacteria resulting from the addition of IDPF filler, which was consistent with the findings in a previous study [17]. A study found that high concentrations of organic matter affected the redox reactions in CW but had no significant effect on the removal efficiency of organic matter [18]. Under medium and high pollution loads, microorganisms with the ability to degrade organic matter had a higher growth rate [19]. Therefore, TOC removal efficiencies of the system under medium and high concentration pollution loads in this study were relatively high and had a small difference. The treatment system had a high load treatment capacity and exhibited high treatment efficiency at high concentrations in this study. The slightly lower treatment efficiency at low loads than at high loads was due to the technical limitations of the CW system itself. CW effluent concentrations include background concentrations, which account for a higher proportion of the total effluent concentration at low loads, leading to reduced treatment efficiency. The pollutants of rainfall that first flush into rivers or in polluted river water may vary over a large range, thus the influent pollution load is a variable for treatment systems. The results of this study may contribute to the design of practical water pollution control engineering.

3.2.2. Effect of HLR on the Pollutant Removal Performance

The removal efficiencies of pollutants by the treatment system under different HLR conditions are shown in Figure 4. The removal efficiencies of NH4+-N showed a decreasing trend as the HLR increased from 0.015 m3/(m2·h) to 0.051 m3/(m2·h), with average removal efficiencies of 94.1%, 87.6%, and 81.8% in Stage I, III, and V, respectively. The removal efficiency of NH4+-N can also be maintained at over 80% when the HLRs change (Figure 4a). In Stage I, the average removal efficiencies of NO3−-N and TN by the system were 67.9% and 65.6%, respectively (Figure 4b,c). The difference in the average removal efficiencies of NO3−-N and TN was not significant with the increase of HLRs (p > 0.05). The average removal efficiencies of NO3−-N and TN could be maintained above 70%, indicating which were less affected by HLRs. The impact of HLRs on TOC removal was similar to those on NO3−-N and TN. As HLRs increased, the average removal of TOC first increased and then decreased (Figure 4d). In Stage III, the average removal efficiency of TOC was 63.5%, an increase of 8.2% compared with that in Stage I. The average removal efficiency of TOC in Stage V was 60.9%, a decrease of 2.6% compared with that in Stage III.
HLR is an important factor affecting the removal of pollutants in CW systems [20]. A study found the microbial community had relatively low randomness under low HLR conditions, and the symbiotic network displayed a simpler microbial community in a CW system [21]. A short HLR was detrimental to the growth of microorganisms and would result in nitrite accumulation [22]. A long HLR ensured the reaction time between wastewater and microorganisms but would waste the wastewater treatment capacity of the system [23]. Therefore, HLR needs to be adjusted according to reactor characteristics, wastewater characteristics, and actual operating conditions. In this study, the microorganisms in the system had a long contact time with the substrate under low HLR conditions, resulting in a decrease in hydraulic shear force and a high removal efficiency of NH4+-N. Studies show that HLR directly affects the dominant microbes by altering the physicochemical properties in CWs, resulting in the increase of denitrifying bacteria and the removal of NO3−-N under higher HLRs [21]. The average removal efficiencies of TN and NO3−-N decreased slightly but remained stable as the HLR further increased to 0.051 m3/(m2·h). The average removal efficiency of TOC by the system was consistently above 55%, under different HLR conditions. When HLR increased to 0.051 m3/(m2·h), the average concentration of TOC in the effluent of the system increased slightly compared with that at an HLR of 0.023 m3/(m2·h), which can be attributed to the inadequate degradation of organic matter in a short period of time [24].

3.2.3. Effect of Aeration on the Removal Performance

Aeration has an obvious impact on biological nitrogen treatment in the H1 and H2 systems. At different pollution loads and HLR conditions, the removal of NH4+-N was reduced at the stop aeration stages (Figure 3 and Figure 4). In the H1 system, the average removal efficiencies of NH4+-N decreased by 8.95%, 12.94%, and 11.05% in the three-stop aeration stages compared with the aeration stages, respectively (Figure 3a). Similarly, the average removal efficiencies of NH4+-N in the H2 system decreased by 8.78%, 12.94%, and 10.4% in the three aeration stages compared to the aeration stage, respectively (Figure 4a). These results indicated that the nitrification process was inhibited in the H1 and H2 systems when aeration was turned off. On the contrary, the average removal efficiencies of NO3−-N in the two systems increased when the aeration was turned off (Figure 3b and Figure 4b). The removal efficiencies of NO3−-N in the H1 system increased by 3.2%, 3.39%, and 3.81% in three stop aeration stages compared to the aeration stage, respectively (Figure 3b). And those in the H2 system increased by 12.95%, 10.66%, and 7.5%, respectively (Figure 4b). The results indicated that the two systems were conducive to denitrification when the aeration was stopped. In addition, the average removal of TOC was obviously reduced in both systems after switching off aeration (Figure 3d and Figure 4d). In the H1 and H2 systems, 67.9%, 67.2%, 64.1% and 82.3%, 80.1%, and 76.5% of TN removal could be achieved for the three pollutant loads and HLR stages, respectively (Figure 3d and Figure 4d).
Aeration alters the DO concentration in the influent water, which influences the aerobic (DO > 2 mg/L) and anoxic/anaerobic (DO < 2 mg/L) conditions that the bacteria need for nitrification and denitrification reactions in the H1 and H2 systems [25]. Appropriate aeration can save electricity consumption while ensuring ICW-compliant effluent quality. A study found that more than 98% nitrification could be achieved in the CW system by means of intermittent aeration [26]. However, the DO concentration in subsurface flow CW systems without aeration was kept very low, which was not conducive to NH4+-N nitrification and thus hindered the overall nitrogen removal performance [6]. It is worth noting that the intermittent aeration method was used to regulate the DO concentration in the system, which not only could improve TN removal but also save 33.3% of electricity in this study. In addition, the drop structure of the H1 and H2 systems in this study could restore DO concentrations. Intermittent aeration created alternating aerobic/anaerobic environments in the system, which facilitated iron element release by the bio-carriers to be involved in the nitrogen cycle through chemical and biological processes. Thus, the efficiencies of nitrogen removal in the H1 and H2 systems were improved.

3.3. Contribution to the Removal Performance by Each Unit

The H3 system was used to study the contribution of each unit to pollutant removal in a combined system when treating actual polluted river water (Figure 5). The water quality of the polluted river showed a low degree of pollution (NH4+-N 11.3 mg/L, NO3−-N 6.7 mg/L, TN 18.6 mg/L, TOC 34.2 mg/L) in the influent (Figure 5a–d). The concentrations of the four parameters gradually decreased through three units. Finally, 84.1%, 84.5%, 84.4%, and 95.8% of the four pollutants were removed in the effluent, respectively. The contribution of each unit in the H3 system to the pollutant removal showed significant differences (p < 0.05) (Figure 5e). The secondary unit had the highest removal contributions, 47.4%, 55.0%, 45.9%, and 38.8% to NH4+-N, NO3−-N, TN, and TOC, respectively. The contributions of primary units to the removal of the four pollutants were lower than those of the secondary unit, reaching 26.3%, 24.9%, 31.9%, and 38.2%, respectively. The tertiary unit had the smallest contributions, accounting for 26.3%, 20.1%, 22.3%, and 22.9%, respectively.
Bio-carrier is an essential component of the ICW-MEC system, playing a comprehensive role in the physical, chemical, and biological transformation processes of pollutants. The selection and combination of fillers obviously affected the pollutant removal performance of the ICW-MEC system [27]. The large number of pores and skeletal structure of zeolite could efficiently enrich microbes and achieve excellent pollutant removal performance in the primary unit [28]. In addition, the crystal structure enabled zeolite to adsorb NH4+-N through ion exchange, thereby achieving effective removal of NH4+-N. IDPF not only provided attachment surfaces for microorganisms but also enhanced microbial metabolism and activity due to iron elements, which increased the abundance of nitrifying and denitrifying bacteria in the secondary unit [29,30]. Fe2+ and Fe3+ ions released by IDPF could serve as electron donors and acceptors in the biological transformation of nitrogen. Sponge iron and volcanic rocks also played an indispensable role in providing iron elements and serving as attachment carriers for microbial growth. In addition, the coupling between the CWs and MECs could also improve nitrogen removal through hydrogen autotrophic denitrification. Therefore, the secondary unit exhibited a high TN removal performance [31]. The oxygen produced by the anode could provide suitable growth conditions for nitrifying bacteria, thereby improving the removal of NH4+-N. Simultaneously, the hydrogen produced by the cathode could improve the removal of NO3−-N through hydrogen autotrophic denitrification [32]. In the tertiary unit, activated carbon had a large surface area and could efficiently remove nitrogen through adsorption and microbial metabolic activity [33]. Overall, the combined effect of the three units enabled the system to achieve efficient nitrogen removal performance.

3.4. Structure, Diversity, Impact Factors and Function of Microbial Communities

Proteobacteria had the highest relative abundances of more than 50% in all biosamples (Figure 6). Bacteroidota and Firmicutes were the second and third most abundant phyla in U1-b, whose abundances were 20.7% and 4.5%, respectively. Desulfobacterota exhibited a high relative abundance (2.7%) in U2-b. Bacteroidota was also another dominant phylum in group U3-b and had a relative abundance of 5.9%. Firmicutes became dominant and accounted for 8.5% of the entire community. Proteobacteria dominated all groups, with a significantly higher abundance in U2-b than in U1-b, U3-b, and U2-c (p < 0.05). This result is consistent with the maximum contribution of the secondary unit to NO3−-N removal. Proteobacteria is the most common phylum-level microorganism in various wastewater treatment processes, which contain many microbes capable of degrading organic matter and removing nitrogen [34].
The relative abundance of Vibrio, Flavobacterium, Rhodobacter, and Terrimonas was significantly higher in U1-b than in other samples (p < 0.05) (Figure 7). The dominant genera in U2-b were Ralstonia and Pseudomonas. The relative abundance of Leptothrix and Sporocytophaga had significantly higher relative abundance in U3-b than in other samples (p < 0.05). The dominant genera in U2-c, Ammoniphilus, Paenibacillus, Magnetospirillum, Ferrovibrio, Bacillus, and Thauera, had significantly higher relative abundance than those in the other samples (p < 0.05).
Vibrio can remove nitrogen by directly assimilating NH4+-N and converting NH4+-N to N2 through the HN-AD pathway [35]. The high abundance of Vibrio in the primary unit indicated that the HN-AD pathway probably made an important contribution to the microbial removal of NH4+-N. Flavobacterium, Rhodobacter, and Terrimonas in group U1-b were reported to be heterotrophic denitrifiers [36,37,38]. More carbon sources in the primary unit were in favour of the metabolism of dominant heterotrophic genera in the zeolite, which promoted the denitrification process. The secondary unit removed most of the nitrogen, which may be due to the advantage of IDPF carriers and the coupling of CWs and MECs. Pseudomonas, one of the high abundant genera in IDPF, was reported to be electrochemically active and could participate in NO3−-N reduction [39]. Electrochemical active bacteria can exchange electrons with insoluble conductive substances such as minerals and electrodes, which enrichment suggests an important role of the IDPF carrier and the coupling of CWs and MECs in the second unit [40]. Ralstonia also had a relatively high abundance in the IDPF and some bacteria which were found to promote the nitrification process [41]. Additionally, the high abundance of Bacillus on the cathode was an aerobic heterotrophic denitrifier [42]. Thauera was reported to remove NH4+-N and NO3−-N without NO2−-N accumulating [43]. Some bacteria in Ammonophilus were found to be iron-reducing bacteria [44]. It can be inferred that the electron transfer during the iron cycle was involved in the nitrogen conversion process, which improved the nitrogen removal efficiency of the H3 system. Previous studies showed that Azoarcus and Pseudorhodaferax can play an important role in NO3−-N reduction [45,46]. It is speculated that it may be the main microorganism responsible for NO3−-N removal in the tertiary unit.
Alpha diversities of microbial communities showed that samples U2-b and U2-c in the secondary unit had relatively lower alpha diversities indices of microbial communities than samples U1-b and U2-c (Figure 8a). As the number of samples increases, the boxplot shows a linear rise, indicating the continuous discovery of microbes in the community (Figure 8b). These results indicate that unique fillers and cathode conditions have selective effects on microorganisms in the secondary unit. However, the rate of new microorganisms continuously appearing in samples U2-b and U2-c did not slow down due to lower alpha diversity. The diverse fillers and structures of the system shaped the good microbial communities.
Beta diversities of microbial communities on all samples were analysed using the distance matrix heatmap and PCA (Figure 9). The higher dissimilarity coefficients between U2-b vs. samples U1-b, U3-b, and U2-c suggested there were more differences in microbial communities between them than those between other pairs (Figure 9a). The total contribution of the first principal component and the second principal component to the sample difference was 82.21% (Figure 9b). The four samples were distributed in three quadrants, indicating there were obvious differences in microbial communities between them. Samples U2-b and U2-c were in the same quadrant, possibly because they were in the same unit. The microbial communities of the four samples undoubtedly were greatly affected by the carrier types and direct current [11,27].
The impact of environmental factors (DO, pH, TOC, and Fe) on the microbial communities and the top 10 genera in the ICW-MEC system were evaluated using CCA (Figure 10). The pH and total Fe concentration were positively correlated with the microbial community of U2-b and the abundance of Pseudomonas and Ralstonia, suggesting these factors influenced the denitrification process [39,47]. DO and TOC were positively correlated with the microbial community of U1-b and the abundance of Vibrio, Azohydromonas, UKL13−1, and Flavobacterium. It is important to maintain DO concentration in the aerobic bacteria in the system [26]. The result was reasonable for that Vibrio was reported to be a heterotrophic aerobic denitrifier [35]. Thauera, Azoarcus, and Paenibacillus, as facultative anaerobes, had no obvious relationship with DO in this study [43,45,46,48]. The results also indicated that anoxic/anaerobic microbes had a considerable abundance in the secondary unit. The correlation between TOC and microbial communities explained the high removal efficiency of ICW-MEC systems under high pollution loads [49]. The total contribution of the first principal component and the second principal component to the sample difference was 85.85%. Total Fe had a high positive correlation with the first principal component, suggesting its important role in shaping microbial communities.
The cluster heatmap shows the top 35 predictive microbial functions ranking and the abundance of information in each sample (Figure 11). The predominant bacteria in sample U2-b included chemoautotrophic microbes, organic matter-degrading bacteria, and microbes with other functions. The highly abundant bacteria in sample U2-c had nitrogen reduction and respiration functions. For sample U1-b, the highly abundant microbes were aerobic chemoheterotrophic microbes, nitrogen reduction microbes, and microbes with other functions. For sample U3-b, the highly abundant microbes were methane-oxidizing microbes, photoautotrophic microbes, cyanobacteria, and microbes with other functions. It is necessary to point out that these functions are predicted using software based on discovered functions, and the bacteria may have more functions that need further investigation [50].

4. Conclusions

A novel ICW coupled with a MEC system was constructed to improve nitrogen removal from wastewater in this study. The results showed that the system had an excellent performance, and removed 96.1% of NH4+-N, 70.4% of NO3−-N, and 73.6% of TN at low, high, and high pollution load conditions, respectively. The system also achieved the highest nitrogen removal, namely, 94.1% of NH4+-N, 71.4% of NO3−-N, and 76.7% of TN under low, high, and medium hydraulic load conditions, respectively. The results indicated that the system had good tolerance to changes in pollution load and hydraulic load. Stopping aeration had an obvious effect on the removal of NH4+-N in the system, but intermittent aeration improved the reduction of NO3−-N and TN removal and had the potential to save electricity. The secondary unit had the highest contribution to the removal of NH4+-N, NO3−-N, TN, and TOC from the real polluted river water, reaching 47.4%, 55.0%, 45.9%, and 38.8%, respectively. Distinct microbial communities existed in various units of the ICW-MEC system. The environmental factors (DO, pH, TOC, and Fe) had important impacts on microbial communities and the top 10 genera and were shaped by diverse fillers and structures of the ICW-MEC system. The top 35 functions of microbial communities included heterotrophic, autotrophic nitrogen conversion, and other metabolic functions. The results indicate that the excellent performance attributes to the combination of three units which exert their advantages in the ICW-MEC system. This study will provide a potential technology for non-point source pollution control/polluted water treatment.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/w16172368/s1.

Author Contributions

Conceptualization, R.H. and Y.W. (Yinghai Wu); methodology, R.Z. and K.L.; formal analysis, L.Y. and X.S.; investigation, K.L.; data curation, C.L., X.R., L.W. and Y.W. (Yingjie Wei); writing—original draft preparation, R.Z., K.L. and H.R.; writing—review and editing, R.H. and Y.W. (Yinghai Wu); supervision, R.H.; project administration, Y.W. (Yinghai Wu); and funding acquisition, R.H. and Y.W. (Yinghai Wu). All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the National Natural Science Fund of China (No. 32273186), the Science and Technology Program of Liaoning Province (2021JH2/10200012), the Basic Scientific Research Project of Education Department of Liaoning Province (LJKMZ20221099; LJKMZ20221103), the National Key R and D Program of China (2019YFD0900501), the Modern Agro-industry Technology Research System (CARS-49) and the Innovation Support Program for High-Level Talents of Dalian City (2019RD12).

Data Availability Statement

Data sharing is available for this article on request.

Acknowledgments

We gratefully acknowledge the support from the key laboratory of environment-controlled aquaculture (KLECA), Ministry of Education.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. The integrated constructed wetland-microbial electrolysis cell system used in this study.
Figure 1. The integrated constructed wetland-microbial electrolysis cell system used in this study.
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Figure 2. The SEM images of zeolite (a) IDPF, (b) activated carbon, (c) cathode, and (d) samples collected from the ICW-MEC system after experimental operation.
Figure 2. The SEM images of zeolite (a) IDPF, (b) activated carbon, (c) cathode, and (d) samples collected from the ICW-MEC system after experimental operation.
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Figure 3. The treatment performance of the ICW-MEC system under different pollution loads: NH4+-N (a); NO3−-N (b); TN (c); and TOC (d). Stage I: TOC 70 ± 4.3 mg/L, NH4+-N 10 ± 1.1 mg/L, NO3−-N 16 ± 9 mg/L, aeration; Stage II: stop aeration; Stage III: TOC 48.3 ± 9.1 mg/L, NH4+-N 5 ± 0.5 mg/L, NO3−-N 10 ± 1.8 mg/L, aeration; Stage IV: stop aeration; Stage V: TOC 20.4 ± 3.2 mg/L, NH4+-N 2.7 ± 0.2 mg/L, NO3−-N 4.8 ± 2.6 mg/L, aeration; and Stage VI: stop aeration.
Figure 3. The treatment performance of the ICW-MEC system under different pollution loads: NH4+-N (a); NO3−-N (b); TN (c); and TOC (d). Stage I: TOC 70 ± 4.3 mg/L, NH4+-N 10 ± 1.1 mg/L, NO3−-N 16 ± 9 mg/L, aeration; Stage II: stop aeration; Stage III: TOC 48.3 ± 9.1 mg/L, NH4+-N 5 ± 0.5 mg/L, NO3−-N 10 ± 1.8 mg/L, aeration; Stage IV: stop aeration; Stage V: TOC 20.4 ± 3.2 mg/L, NH4+-N 2.7 ± 0.2 mg/L, NO3−-N 4.8 ± 2.6 mg/L, aeration; and Stage VI: stop aeration.
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Figure 4. The treatment performance of the ICW-MEC system under different hydraulic loads: NH4+-N (a); NO3−-N (b); TN (c); and TOC (d). Stage I: HLR 0.015 m3/(m2·h), aeration; Stage II: stop aeration; Stage III: HLR 0.023 m3/(m2·h), aeration; Stage IV: stop aeration; Stage V: HLR 0.051 m3/(m2·h), aeration; and Stage VI: stop aeration.
Figure 4. The treatment performance of the ICW-MEC system under different hydraulic loads: NH4+-N (a); NO3−-N (b); TN (c); and TOC (d). Stage I: HLR 0.015 m3/(m2·h), aeration; Stage II: stop aeration; Stage III: HLR 0.023 m3/(m2·h), aeration; Stage IV: stop aeration; Stage V: HLR 0.051 m3/(m2·h), aeration; and Stage VI: stop aeration.
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Figure 5. The treatment performance of the ICW-MEC system treating real polluted river water. NH4+-N (a); NO3−-N (b); TN (c); TOC (d); and the contribution of each unit to the removal of pollutants from the system (e).
Figure 5. The treatment performance of the ICW-MEC system treating real polluted river water. NH4+-N (a); NO3−-N (b); TN (c); TOC (d); and the contribution of each unit to the removal of pollutants from the system (e).
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Figure 6. The relative abundance of the microbial community at phylum levels in the biofilm samples from the ICW-MEC system. U1-b: bio-carrier sample on the primary unit; U2-b: bio-carrier sample on the secondary unit; U3-b: bio-carrier sample on the tertiary unit; U2-c: bio-carrier sample on the cathode.
Figure 6. The relative abundance of the microbial community at phylum levels in the biofilm samples from the ICW-MEC system. U1-b: bio-carrier sample on the primary unit; U2-b: bio-carrier sample on the secondary unit; U3-b: bio-carrier sample on the tertiary unit; U2-c: bio-carrier sample on the cathode.
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Figure 7. Microbial community structure of the biofilm samples in the ICW-MEC system at genus levels. U1-b: bio-carrier sample on the primary unit; U2-b: bio-carrier sample on the secondary unit; U3-b: bio-carrier sample on the Tertiary unit; U2-c: bio-carrier sample on the cathode.
Figure 7. Microbial community structure of the biofilm samples in the ICW-MEC system at genus levels. U1-b: bio-carrier sample on the primary unit; U2-b: bio-carrier sample on the secondary unit; U3-b: bio-carrier sample on the Tertiary unit; U2-c: bio-carrier sample on the cathode.
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Figure 8. Alpha diversities of microbial communities (a) and microorganisms’ accumulation boxplot (b). U1-b: bio-carrier sample on the primary unit; U2-b: bio-carrier sample on the secondary unit; U3-b: bio-carrier sample on the Tertiary unit; U2-c: bio-carrier sample on the cathode.
Figure 8. Alpha diversities of microbial communities (a) and microorganisms’ accumulation boxplot (b). U1-b: bio-carrier sample on the primary unit; U2-b: bio-carrier sample on the secondary unit; U3-b: bio-carrier sample on the Tertiary unit; U2-c: bio-carrier sample on the cathode.
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Figure 9. Beta diversities of microbial communities using the distance matrix heatmap based Weighted Unifrac distance and Unweighted Unifrac distance (a) and Principal Component Analysis (PCA) (b). In the same square, the upper and lower values represent the distance between Weighted Unifrac distance and Unweighted Unifrac distance. U1-b: Bio-carrier sample on the primary unit; U2-b: Bio-carrier sample on the secondary unit; U3-b: Bio-carrier sample on the Tertiary unit; U2-c: Bio-carrier sample on the cathode.
Figure 9. Beta diversities of microbial communities using the distance matrix heatmap based Weighted Unifrac distance and Unweighted Unifrac distance (a) and Principal Component Analysis (PCA) (b). In the same square, the upper and lower values represent the distance between Weighted Unifrac distance and Unweighted Unifrac distance. U1-b: Bio-carrier sample on the primary unit; U2-b: Bio-carrier sample on the secondary unit; U3-b: Bio-carrier sample on the Tertiary unit; U2-c: Bio-carrier sample on the cathode.
Water 16 02368 g009
Water 16 02368 g010
Figure 10. The correlation between environmental factors and microbial communities using Canonical Correspondence Analysis (CCA). U1-b: bio-carrier sample on the primary unit; U2-b: bio-carrier sample on the secondary unit; U3-b: bio-carrier sample on the Tertiary unit; U2-c: bio-carrier sample on the cathode.
Figure 10. The correlation between environmental factors and microbial communities using Canonical Correspondence Analysis (CCA). U1-b: bio-carrier sample on the primary unit; U2-b: bio-carrier sample on the secondary unit; U3-b: bio-carrier sample on the Tertiary unit; U2-c: bio-carrier sample on the cathode.
Water 16 02368 g010
Water 16 02368 g011
Figure 11. The cluster heatmap for the top 35 functions of microbial communities using Functional Annotation of Prokaryotic Taxa (FAPROTAX). U1-b: bio-carrier sample on the primary unit; U2-b: bio-carrier sample on the secondary unit; U3-b: bio-carrier sample on the tertiary unit; U2-c: bio-carrier sample on the cathode.
Figure 11. The cluster heatmap for the top 35 functions of microbial communities using Functional Annotation of Prokaryotic Taxa (FAPROTAX). U1-b: bio-carrier sample on the primary unit; U2-b: bio-carrier sample on the secondary unit; U3-b: bio-carrier sample on the tertiary unit; U2-c: bio-carrier sample on the cathode.
Water 16 02368 g011
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MDPI and ACS Style

Zhang, R.; Li, K.; Yi, L.; Su, X.; Liu, C.; Rong, X.; Ran, H.; Wei, Y.; Wan, L.; Han, R.; et al. Nitrogen Removal from Polluted Water by an Integrated Constructed Wetland-Microbial Electrolysis Cell System. Water 2024, 16, 2368. https://doi.org/10.3390/w16172368

AMA Style

Zhang R, Li K, Yi L, Su X, Liu C, Rong X, Ran H, Wei Y, Wan L, Han R, et al. Nitrogen Removal from Polluted Water by an Integrated Constructed Wetland-Microbial Electrolysis Cell System. Water. 2024; 16(17):2368. https://doi.org/10.3390/w16172368

Chicago/Turabian Style

Zhang, Ruina, Kexin Li, Longqiang Yi, Xin Su, Changyuan Liu, Xinyu Rong, Haoxin Ran, Yingjie Wei, Li Wan, Rui Han, and et al. 2024. "Nitrogen Removal from Polluted Water by an Integrated Constructed Wetland-Microbial Electrolysis Cell System" Water 16, no. 17: 2368. https://doi.org/10.3390/w16172368

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