Effects of bioelectricity on CO2 capture by the symbiosis between microalgae and soil bacteria
-
摘要:
农业CO2排放源具有分散性、移动性和低浓度等特点。为考察农业生产场景中广泛存在的微藻和细菌等对CO2的捕获效果,并据此发展就地取材的分布式原位生物固碳方法,该研究利用小球藻和土壤菌群构建了藻菌共生体系,分析了其对1% 体积浓度 CO2的捕获和转化能力。通过布置电极和外电路实现菌群生物电输出,对比了生物电刺激下,藻菌共生体系固碳能力和菌群结构差异。结果表明,藻菌共生体系在生物电作用下,CO2去除率由52%提升至81%。菌群消耗可溶性有机碳使小球藻将CO2更多转化为可溶性无机碳和生物质。生物电则通过加速菌群可溶性有机碳消耗,调节溶液pH值和溶解氧含量优化藻菌生长环境,进一步促进CO2转化。光照时,藻菌共生体系可在
1000 Ω外电阻两端产生200 mV稳态电压,并可实现仅依靠光能驱动。16s rRNA结果显示相比于纯土壤菌群,藻菌共生体系以固氮弧菌属Azoarcus取代明串珠菌属Leuconostoc为主要特征。生物电刺激会重塑该共生体系,拉乌尔菌属Raoultella将重现并取代理研菌属Rikenella和Tyzzerella。该研究表明,通过电化学构建的藻菌共生体系既可以捕获低浓度CO2,也可以通过调节pH值改良土壤结构、重塑土壤菌群组成。Abstract:Carbon sequestration can be expected to mitigate climate warming in sustainable agriculture. The soil structure can also be improved to reduce the application of fertilizers in the global carbon trading market. Agricultural CO2 emissions sources are characterized by high dispersion and mobility with low concentration. It is highly required to develop a series of suitable CO2 capture approaches in modern agriculture. Correspondingly, the coexistence of microalgae and flora has been commonly used in agricultural scenes, such as aquaculture farms, biogas digesters, composting, and irrigation systems. If this symbiotic system can be developed to consume CO2 emissions, great potential can be gained in agricultural carbon sequestration. This study aims to explore the effects of bioelectricity on CO2 capture by the symbiotic system between the chlorella and soil bacteria. A series of tests were then constructed to analyze the CO2 capture and conversion of the symbiotic system under 1% v/v CO2 emission. Furthermore, the bioelectricity generated by the bacteria was then collected by electrodes and external circuits. A comparison was finally made on the variations of CO2 sequestration and bacterial distributions after the stimulation of bioelectricity. The results indicate that the symbiotic system shared the highest efficiencies of CO2 removal, whether the external circuit was closed or not. By contrast, the CO2 removal efficiencies of the pure soil bacteria were the lowest, compared with the chlorella and symbiotic systems. The CO2 removal efficiency increased from 52% by chlorella to 81% by the symbiotic system with the bioelectricity. Dissolved organic carbon (DOC) that was consumed by the bacteria also enabled the chlorella to convert more CO2 into dissolved inorganic carbon (DIC) and biomass. Among them, the concentrations of DIC and the biomass of the bioelectric-treated symbiotic system reached 1.4 mmol/L and 2.7 g/L, respectively, after seven days. The CO2 was in situ reproduced in the symbiotic system via the metabolism of bacteria. The random collision between the microalgae and the bacteria cells effectively shortened the mass transfer distance from the CO2 molecules to the chlorella cells, thus improving the mass transfer efficiency of CO2. There was a great increase in the local CO2 concentration of the medium. Some chlorella cells were then avoided to suffer the CO2 concentrating mechanism (CCM). More energy was also saved for the rest enzymatic reactions to promote their growth. The bioelectric generation and transmission were accelerated to adjust the pH and dissolved oxygen content of the medium after the consumption of DOC. Therefore, the growth environment of the microbes was optimized to further promote the CO2 conversion. The output voltage of the symbiotic system fluctuated with the light-dark period. A 200mV output voltage was achieved over a 1000 Ω external resistance in the illumination period. The maximum power density also increased by 46% from the dark to the light. Moreover, the symbiotic system was powered only by light energy with a limited carbon source supply entirely from 1% v/v CO2 inlet. Compared with the pure soil microbial community, the chlorella was invaded to form a symbiotic system and then replaced the Leuconostoc with the Azoarcus. Simultaneously, the proportion of Citrobacter and Raoultella plummeted, whereas, the Rikenella, Tyzzerella, and Trichococcus increased significantly, leading to an overall enhanced diversity. The dominant bacterial composition remained almost unchanged in the microalgae-bacteria symbiotic system under the bioelectric stimulation. However, the Raoultella also re-emerged and then replaced Rikenella and Tyzzerella. In addition, the main components of Citrobacter, Alcaligenes, and Bacteroides reshaped the microbial community in the symbiotic system. KEGG metabolic pathway indicated that the microbial community was primarily formed the synergistic interactions with the microalgae. The soluble organic substances were also decomposed during microalgae photosynthesis and proteins in algae cell debris. This finding can provide a strong reference for in-situ CO2 capture in agriculture.
-
0. 引 言
农业作为人类赖以生存的基础产业,其CO2排放问题备受关注。在中国,农业CO2排放量占总排放量的17%~20%[1]。据估算,中国农业CO2排放量将在2050年达到239.99Mt[2]。发展适用于农业领域的固碳技术,不仅有助于缓解气候变暖、推动农业生产过程参与全球碳交易市场[3],还能够通过改善土壤结构[4]、减少化肥使用[5]等方式促进农业可持续发展。农业碳排放来源广泛,包括秸秆焚烧、化肥施用、农业机械尾气等。这些排放源CO2浓度范围多处于1%体积浓度以内[6]。由于排放源具有分散性、移动性和低浓度等特点,因此,发展分布式CO2捕获方法对于农业碳减排具有重要意义。
从原理上分类,CO2可通过物理、化学和生物方法捕获[7-8]。其中,以光合作用为基础的生物方法最适合农业领域。与绿色植物相比,微藻生长速率更快,光合效率更高,占地面积更小,已成为生物固碳的主要媒介之一[9]。除微藻外,土壤、河流、湖泊等生态系统中富含的细菌菌群也具有固碳能力[10-11]。农业领域中微藻和菌群共存场景丰富,如养殖场、沼气池、堆肥化、灌溉系统等。若能够就地取材,合理构筑藻菌共生体系,使二者协同作用,则其在农业领域CO2原位捕获方面将颇具潜力。
目前,关于藻菌共生体系在农业领域的研究以养殖业废水处理为主[12-16]。废水处理过程中的藻菌协同机制、菌群分布组成、关键基因表达是研究重点。然而,关于藻菌共生体系固碳则鲜有报道,此外,低浓度碳源供给条件下需要格外关注可溶性有机碳(dissolved organic carbon,DOC)对于藻菌共生体系的影响。微藻除将CO2转化为生物质外,还存在3种转化路径:可溶性无机碳(dissolved inorganic carbon,DIC)(碳酸盐和碳酸氢盐)、DOC(多糖等)以及挥发性有机物[17]。以小球藻为例,进气CO2浓度越低,DOC在捕获的总碳中占比越高[18]。HULATT等[19]研究表明,若以空气(CO2体积浓度约0.04%)为唯一无机碳源,12 h后小球藻能产生约12 mg/L的DOC。其在光合作用固定的总碳中占比可达80%。CHIU等[20]则发现2% CO2进气浓度下仅有3.9%的CO2被转化为小球藻生物质。DOC累积会抑制微藻生长。菌群则可以代谢有机物并产生CO2,若其能够分解微藻在低浓度CO2条件下产生的过剩DOC,并再次产生CO2供给微藻光合作用,则可将CO2更多转化为微藻生物质,提升固碳率。
菌群中部分菌种具有产电能力。已有研究证实细菌产生并输出生物电的过程能够促进其固碳,固碳率可由开路状态的2.0%提升至闭路时的10.7%,并从产电菌基因和代谢路径层面解释了固碳效果提升原因[21]。如果能在藻菌共生环境中借助天然氧化还原梯度构建并形成闭路生物电化学系统,例如微生物燃料电池[22-23],则一方面可通过生物电产生和传输过程调节pH值、溶解氧等藻菌生长环境因素;另一方面,可借助生物电的持续刺激筛选并改善菌群组成。然而,生物电对于藻菌共生体系的重塑及其固碳能力的影响尚不明晰。
本文利用小球藻和土壤菌群构建藻菌共生体系,考察其对于1% 低体积浓度CO2的捕获和转化能力,对比有无生物电输出和刺激下的固碳能力差异,并分析了低浓度CO2供给和生物电作用下藻菌共生体系中的菌群组成。
1. 试验方法
1.1 生物培养
微藻藻种选择蛋白核小球藻(盐城百诺生物科技有限公司)。将小球藻培养在10组含有150 mL BG11培养液的锥形瓶中。锥形瓶置于28 ℃的光照培养箱中。LED灯带波长400~700 nm,光照强度
6000 lx,光暗周期为12 h:12 h。从长安大学人工湖周边土壤中获得并培育细菌菌群。将5 g土壤投入500 mL LB培养基(10 g/L NaCl,5 g/L酵母提取物和10 g/L胰蛋白胨)中。在28 ℃的恒温箱中驯化。小球藻和菌群均需培养14 d以供后续试验使用。小球藻接种时,将离心藻液获得的小球藻细胞直接加入试验装置溶液基质中。菌群接种时,选择直径40 mm的碳布(HCP330N,上海河森电气)为载体,将其浸没于10 mL土培上清液中培养3 d,使菌群充分附着于碳布表面。1.2 试验装置与组别
装置采用有机玻璃材质保证透光性,如图1所示。
装置长100 mm,宽80 mm,高80 mm。内部为底面直径40 mm、高80 mm的圆柱形中空腔室,可形成100 mL的工作容积。圆柱腔室一端放置一片直径40 mm的碳布,并根据试验组别设置,可分为接种菌群碳布和未接种菌群的空白碳布两类。另一端放置一片直径40 mm、孔径为0.15 mm的304不锈钢网。碳布和不锈钢网均通过直径0.2 mm不锈钢丝引出。使用螺丝将两侧端板、腔室组件和橡胶垫片等进行固定以保证密封性。腔室上方开有4个直径为10 mm的圆孔,用于进气、出气和采样,工作时圆孔通过橡胶塞和热熔胶密封。
设置开路AO、BO、ABO和闭路A、B、AB组6个组别。溶液基质均为50 mmol/L KNO3。“A”表示仅接种小球藻,“B”表示仅接种土壤菌群,“AB”表示同时接种小球藻和土壤菌群,“O”表示开路状态。对于A、B和AB组,通过
1000 Ω外电阻连接碳布和不锈钢网形成闭路。各组别均设置3组重复试验,数据取平均值。1.3 CO2捕获和转化测试
CO2捕获测试时,对于涉及小球藻的组别,以
3500 r/min离心冲洗藻液得到100 mg小球藻,加入装置中与100 mL KNO3溶液基质形成初始生物质浓度为1 g/L的微藻悬浮液。进气为CO2和N2混合气,CO2体积浓度为1%,进气速率10 mL/min。温度设置为28 ℃。光源为LED白光,光照强度6000 lx,光暗比12 h:12 h。每24 h记录试验装置出口CO2浓度,共采集7 d。据此计算CO2去除率以表示CO2捕获能力[9]。CO2转化测试温度、进气、光照等条件保持不变,将微藻悬浮液初始浓度降为0.1 g/L。每12 h光暗周期交替时刻记录数据,共采集7 d。溶液pH值通过pH计测得(PHSF-4F, REX),溶解氧(dissolved oxygen, DO)通过溶解氧测试仪(OXI
3310 , WTW)测得。初始时刻,利用0.1 mol/L HCl和0.1 mol/L KOH调节pH值为8。利用N2曝气调节DO低于0.3 mg/L。溶液可溶性无机碳(DIC)含量通过pH值计算得到[24]。小球藻生物质含量根据光密度计算得到。光密度由紫外分光光度计(UV-1800 , SHIMADZU)在681 nm波长下测得[9]。1.4 产电测试和菌群分析
CO2转化测试后进行产电测试。产电测试仅选择闭路A、B和AB组。测试时,保持进气、温度、光照条件不变。每0.5 h记录外电阻两端电压,连续记录5 d。分别在光照和黑暗条件下测试并获得极化曲线和功率密度曲线。采用线性扫描伏安法(linear sweep voltammetry, LSV),起始电压为开路电压,终止电压为 0,扫描速率为0.2 mV/s[25]。
通过16s rRNA全长扩增子测序分析菌群组成[26, 27]。样本取自运行30 d后的装置内溶液,每组采样3次。在DNA提取和PCR扩增后制备SMRTbell文库。操作分类单元(operational taxonomic units,OTU)按照98.65%的相似性阈值聚类。分析内容包括菌群多样性、物种组成和基因功能预测等。
2. 结果与分析
2.1 CO2捕获
图2所示为在10 mL/min进气速率和1% 进气体积浓度条件下,6组生物体系出口CO2浓度随时间变化趋势。由于小球藻浓度处于1 g/L的稳定期,6组出口CO2在7 d内虽有波动,但整体保持平稳,这与文献一致[9, 25]。出口CO2浓度越低,说明更多的CO2被捕获,系统固碳率越高。由图2出口CO2浓度计算可得,AO、A、BO、B、ABO、AB组固碳率分别为52%、53%、10%、19%、69%和81%。总体上,土壤菌群固碳率(BO、B组)<小球藻固碳率(AO、A组)<藻菌共生体系固碳率(ABO、AB组)。对于仅接种土壤菌群的BO组和B组,当系统由开路切换至闭路时,固碳率可由10%提升至19%,说明若对土壤菌群进行电化学构建,其内部产电菌的胞外电子传递过程有助于提升固碳率[28]。纯小球藻AO组固碳率仅为52%,且不受是否闭路影响。这是由于在连续气流供给下,CO2溶解受试验装置结构和气流流速等因素影响显著,例如在入口处会出现局部CO2饱和现象,导致部分CO2在溶液中水力停留时间不足,难以被捕获并随出口气流流出。对于小球藻和土壤菌群共存的ABO组,系统固碳率可提升至69%。说明土壤菌群和小球藻形成的共生体系有助于提升固碳率。由于土壤菌群富含产电菌,若通过布置碳布阳极、不锈钢网阴极和外电路等结构收集并传输胞外电子,并利用生物电刺激重塑藻菌共生体系,则系统固碳率可进一步提升至AB组的81%。
图 2 6组生物体系出口CO2浓度注:AO、A分别为仅接种小球藻的开路组和闭路组;BO、B分别为仅接种土壤菌群的开路组和闭路组;ABO、AB分别为同时接种小球藻和土壤菌群的开路组和闭路组,下同。Figure 2. Outlet CO2 concentration of six biological systemsNote: AO, A are the open-circuit and closed-circuit groups inoculated with chlorella only; BO, B are the open-circuit and closed-circui groups inoculated with soil flora only; ABO, AB are the open-circuit and closed-circui groups inoculated with both chlorella and soil flora, same below.2.2 CO2转化
图3a所示为7 d内AO、ABO和AB组溶液DIC随时间变化。在本研究pH值条件下(7.0 ± 0.5),溶液中DIC主要由通入CO2后形成的H2CO3和HCO- 3组成[24, 29-30]。由图3a可知,随着CO2通入,0.5 d内3组DIC均迅速增加至峰值。小球藻光合作用对于DIC的消耗使得其含量在0.5 d后开始下降。2.5 d后,当通入CO2产生DIC速率和小球藻DIC消耗速率接近时,3组DIC逐渐进入稳态。稳态时DIC含量对于微藻生长至关重要。DIC含量过低不利于小球藻烟酰胺腺嘌呤二核苷磷酸(NADP+)合成。藻细胞中NADP+数量下降会形成活性氧自由基。这些活性氧自由基能够通过激活细胞信号级联反应破坏藻细胞大分子(如DNA和RNA等),进而破坏光系统II,最终导致藻细胞凋亡[31]。相比于AO组,ABO组具有更高的稳态DIC含量。说明菌群新陈代谢能够产生额外CO2。这些CO2可以再次溶解生成DIC。在生物电作用下,菌群代谢速率加快[21],因此AB组DIC含量最高。
图3b所示为小球藻生物质含量随时间变化关系。3组小球藻由初始浓度0.10 g/L开始迅速生长。3.5 d后,增长速率逐渐降低。7 d后,AO组、ABO组和AB组生物质含量分别为1.71、2.17 和2.65 g/L。其中,AB组效果最佳,相比于AO和ABO组,分别提升了1.55和1.22倍。
CO2进入装置后,CO2气体分子和溶解产生的HCO3-是小球藻生长的碳源。小球藻可将其转化为生物质和DOC。AB组的DIC和生物质含量均最高,说明在藻菌体系和生物电作用下,CO2被更多的转化成为了DIC和生物质,而这主要通过消耗DOC实现。藻菌共生体系通过菌群对小球藻光合作用产生的DOC再分解,在原有的CO2-DIC-生物质碳转化路径基础上,额外构筑了DOC-CO2-DIC转化路径。而生物电可以进一步加速该转化。CO2通过菌群的新陈代谢原位产生,一方面有效缩短了CO2到小球藻细胞的传质距离,微藻和细菌的随机碰撞提升了CO2传质效率;另一方面,增加了溶液中CO2的局部浓度。在低浓度进气条件下,由于碳源以HCO3-为主,微藻将会触发CO2浓缩机制(CO2 concentrating mechanism,CCM)。即先通过碳酸酐酶(carbonic anhydrase,CA)将HCO3-转化为CO2后再供叶绿体使用,该过程会消耗大量ATP。具体的说,当CO2浓度小于Km(CO2) 时(Km(CO2)表示达到最大光合作用速率50%所需的CO2浓度),CCM触发。例如,对于蛋白核小球藻,CO2的Km(CO2)为80~192 μmol/L,对应供气体积浓度在0.5%~1%,此时小球藻通过CCM固碳,其利用碳源主要为HCO3-。而适合小球藻生长的CO2进气浓度应在1%~30%[24]。菌群代谢DOC过程对于溶液中局部CO2浓度的提升,将使得部分小球藻细胞能够有机会避免CCM,并将更多能量用于其他酶促反应,促进自身生长。藻菌共生体系通过上述方式促使CO2更多转化为DIC和生物质,从而也提升了固碳率。综上,土壤菌群扮演了在溶液基质中富集CO2的角色,能够将DOC分解成为CO2供小球藻再次使用。这同样是应对低浓度CO2进气条件碳源不足的有效手段。
2.3 溶液环境变化
图4a所示为AO、ABO和AB组pH值随时间变化趋势。空白对照组表示未接种小球藻和土壤菌群,仅对50 mmol/L KNO3溶液基质通入CO2。初始时刻溶液pH值均为8.00。随着CO2的持续通入,3组生物体系溶液pH值均呈现下降趋势。这是由于CO2在溶解过程中持续形成碳酸所致。1.5 d内,AB组pH值下降最快,说明该组具有最快的CO2传质速率。2 d后,AB组pH值由最小值开始回升,并最终趋于稳定。ABO组pH值则在3 d后逐渐平稳,并略低于AB组。而仅依靠小球藻的AO组pH值则在2 d后持续下降,但下降速率减缓。通过对7 d后4组pH值(AO:6.57,ABO:6.85,AB:7.16,空白:5.97)进行统计分析可知,3组生物体系在7 d后pH值均显著高于空白组(P = 0.021)。这是由于随着时间推移小球藻生长,浓度提升,更多小球藻参与光合作用使得CO2和HCO3-消耗加快,能够缓解pH值进一步下降。而AB组中,菌群产生的电子会通过外电路在阴极参与氧还原反应持续消耗H+,这使得AB组pH值可以稳定在更高水平。3组pH稳定值均在微藻生长的理想范围(7.0 ± 0.5)内。在该范围内,pH值越高,微藻对CO2的吸收率越高,光合效率也越高,越有利于其生长[29]。这也说明构筑藻菌共生体系在酸性土壤改良方面具有应用前景。
图4b所示为3组生物体系DO随时间变化趋势。经过N2曝气,初始时刻3组DO均低于0.3 mg/L。试验开始后,在第1 天的前12 h,3组系统处于光周期,藻类光合作用释放O2,DO均逐渐上升,并在光周期结束后达到第一个峰值。随后进入暗周期,由于小球藻呼吸作用消耗O2,溶液DO下降。此后,由于小球藻生长繁殖,其生物质含量由初始值0.1 g/L逐渐上升(图3b)。溶液中小球藻浓度增加导致DO随天数呈现上升趋势,但在1 d内依然存在和试验条件光暗周期(12 h:12 h)一致的波动。第4天后,藻类生长进入平稳期,DO上升变缓。然而,无论是DO本身还是其随光暗周期波动幅度,AB组均明显更小。这是因为生物电产生过程会在阴极持续消耗O2进行氧还原反应,导致溶液DO下降,波动幅度减小。低DO溶液环境更有助于产电菌群[32]和藻类[33]生长。
2.4 生物电输出
对闭路A、B和AB组进行产电分析。3组生物体系碳源仅来自体积浓度为1% 的CO2进气。图5a所示为3组生物体系平稳运行时的电压输出。其中,A组和B组电压分别维持在8 和26 mV左右,且二者均不随光暗周期变化。说明小球藻几乎没有产电能力,而B组也由于土壤菌群缺乏有机碳源未能高效产电。对于AB组,电压输出则与试验条件光暗周期(12 h:12 h)存在显著依存关系。在光照条件下,电压输出约为200 mV;而黑暗条件下则逐渐降至120 mV左右,并在下一个光照周期开始后迅速回升,且均显著高于B组。说明藻菌共生体系在光照时更为高效。这也使得AB组能够在碳源完全来自体积浓度1%的CO2进气情况下,依靠光能驱动并产生周期性电压[28]。图5b所示为3组生物体系的极化曲线和功率密度曲线。对于AB组,开路电压、短路电流和最大功率密度等参数均在光照条件下更高。例如,光照时最大功率密度为
1342.45 mW/m2,较黑暗时提升了46%。光照时,小球藻通过光合作用将光能转化为有机物中化学能供给菌群生长。菌群分解有机物产生胞外电子并通过外电路输出电能。光合作用产生的O2也会参与阴极氧还原反应,增强产电效果。黑暗时,小球藻以呼吸作用为主,将消耗有机物并与菌群竞争碳源。环境中有机物含量降低是导致产电性能下降的主要原因。2.5 菌群组成分析
经过30 d平稳运行后,对BO、ABO和AB 3组菌群组成进行分析。表1所示为种水平α多样性。由表1可知,BO组各项α多样性指数均最小,说明纯土壤菌群经过30 d的1% 体积浓度CO2进气和30 d内每天12 h:12 h光暗周期驯化后,丰富度和多样性最低。当小球藻介入后,系统中菌群α多样性显著提升[28]。因为小球藻光合作用改变了溶液基质理化条件,特别是O2含量。若接通阴阳极,在生物电刺激下,菌群会被重塑,α多样性水平下降。这也说明藻菌共生体系中菌群多样性并非影响CO2捕获和转化性能的决定因素,生物电刺激的定向筛选也很重要。
表 1 BO、ABO和AB组菌群α多样性Table 1. α-diversity of flora from group BO, ABO, and AB组别
Group观测到菌种数
Observed species多样性指数Diversity index Chao1 ACE Shannon Simpson Pielou_J Pd_faith BO 2418 3026.36 3550.15 4.53 0.93 0.58 75.65 ABO 4140 7077.13 7870.49 5.92 0.97 0.71 114.28 AB 3564 5084.57 6183.08 5.43 0.95 0.66 101.44 注:Pielou_J指数为Pielou均匀度指数,Pd_faith指数为信仰多样性指数。 Note: Pielou_J index is Pielou evenness index, Pd_faith index is the faith diversity index. 图6所示为3组生物体系的主坐标分析(principal coordinates analysis,PCoA)。PCoA结果表明每组中的3个样本均具有良好的重复性,说明采样合理可信。3组间距较大,说明组间差异较大,小球藻引入和生物电输出对菌群组成影响显著。
图7a所示为门水平菌群组成。图7a表明,3组生物体系均以变形菌门Proteobacteria,厚壁菌门Firmicutes和拟杆菌门Bacteroidetes为主,三者占比之和在3组均超过99%。这3种细菌门在厌氧发酵过程中占主要地位[34]。对于纯菌BO组,变形菌门Proteobacteria,厚壁菌门Firmicutes和拟杆菌门Bacteroidetes占比分别为63%,24%,12%。当引入小球藻后,ABO组厚壁菌门Firmicutes占比24%几乎不变,拟杆菌门Bacteroidetes占比提升至26%,变形菌门Proteobacteria占比下降至49%。由表1可知,引入小球藻后,观察到的菌种数量由
2418 提升至4140 。这使得尽管变形菌门Proteobacteria占比由63%下降至49%,但其菌种总量提升,拟杆菌门Bacteroidetes总量更是大幅提升。这是由于小球藻光合作用和呼吸作用交替进行,菌群中兼性厌氧菌种类别和数量增加所致。接通外电路后,厚壁菌门Firmicutes占比迅速下降至8%,变形菌门Proteobacteria则升至75%。这是由于厚壁菌门Firmicutes普遍不具备产电能力。相反,目前已知的产电菌以变形菌门Proteobacteria为主。这也体现了生物电刺激对于藻菌共生体系中菌群的二次筛选作用。由图7b属水平物种组成可知,BO组中明串珠菌属Leuconostoc占比15%,而引入小球藻后,ABO和AB组中明串珠菌属Leuconostoc占比均小于0.2%,几乎消失。说明小球藻光合作用产氧抑制了明串珠菌属Leuconostoc生长[35]。另一方面,ABO和AB中固氮弧菌属Azoarcus占比提升,分别达到18%和5%。固氮弧菌属可以将进气中的N2转化为氮源供给小球藻生长,进而促进了这两组CO2捕获和转化能力提升。BO组中几乎未见束毛球菌属Trichococcus,小球藻的引入使其占比提升。总体上,相比于纯土壤菌群,小球藻引入后形成的藻菌共生体系以固氮弧菌属Azoarcus取代明串珠菌属Leuconostoc为主要特征,同时柠檬酸杆菌属Citrobacter、拉乌尔菌属Raoultella占比骤降,理研菌属Rikenella、Tyzzerella和束毛球菌属Trichococcus占比提升,整体多样性提升。在生物电刺激下,藻菌共生体系中优势菌种组成几乎不变,但Raoultella会重现并取代Rikenella和Tyzzerella。其中,Citrobacter,Alcaligenes,和Bacteroides成为AB组的主要组分,这3种均为产电菌且具有良好的胞外电子传输能力,藻菌共生体系中菌群组成被重塑。
通过对BO、ABO和AB组菌群KEGG代谢途径分析可知,3组生物体系中相对丰度最高的代谢途径均为碳水化合物代谢(Carbohydrate metabolism)和氨基酸代谢(Amino acid metabolism),如表2所示。KEGG以上述两种途径为主,说明菌群积极利用碳水化合物作为能量来源,细胞代谢较为活跃。引入小球藻和构建电化学体系后,二者丰度进一步提升,说明菌群主要通过分解小球藻光合作用产生的可溶性有机物和小球藻细胞残骸中的蛋白质等形成藻菌协同作用。具体来说,菌群对碳水化合物和氨基酸的代谢为藻类补充了额外碳源和氮源,促进了藻类生长。藻类反过来产生更多有机物供给菌群。菌群代谢并产生胞外电子,在生物电刺激下,菌群组成被重塑,使之更适合低浓度CO2碳源条件,系统固碳率和碳转化能力提升。
表 2 KEGG关键代谢途径Table 2. KEGG key metabolic pathway statistics代谢途径
Metabolic pathway相对丰度Relative abundance/ % BO ABO AB 碳水化合物代谢
Carbohydrate metabolism15.32 16.52 17.97 氨基酸代谢
Amino acid metabolism12.31 12.75 13.77 3. 结 论
本文利用小球藻和土壤菌群构建了藻菌共生体系,分析了其对1% 的低体积浓度CO2的捕获和转化能力,对比了生物电刺激前后,藻菌共生体系固碳能力和菌群组成差异。主要结论如下:
1)相比于纯小球藻,藻菌共生体系对于1% 低体积浓度CO2去除率由52%提升至69%。菌群通过代谢低浓度进气条件下溶液中过剩的可溶性有机碳,为小球藻补充了额外CO2,缓解了部分小球藻的局部碳匮乏,从而将有限的碳源更多转化为可溶性无机碳和小球藻生物质。此外,菌群分解氨基酸产生额外氮源供藻生长。
2)在生物电输出刺激下,藻菌共生体系固碳能力进一步提升至81%。光照时,共生体系可在
1000 Ω外电阻两端产生200 mV电压输出,并可实现自驱动。生物电输出既能加速菌群代谢,又能消耗溶液中H+和溶解氧,优化藻菌生长环境。3)相比于纯土壤菌群,藻菌共生体系以固氮弧菌属Azoarcus取代明串珠菌属Leuconostoc为主要特征,菌群多样性提升。在生物电输出刺激下,藻菌共生体系被重塑。菌群主要通过分解小球藻光合作用产生的可溶性有机物和小球藻细胞残骸中的蛋白质等与微藻协同作用。
在本文基础上,后续研究可通过扩展试验时长分析小球藻-土壤菌群共生体系的长期运行稳定性和可靠性;也可从湖泊、水库等水体中采集混合藻种作为藻类来源,分析其与土壤菌群的协同固碳效果,推动该研究在改良土壤结构、提升土壤养分含量和菌群活性、促进植物光合作用等农业生产场景中的实用化。
-
图 2 6组生物体系出口CO2浓度
注:AO、A分别为仅接种小球藻的开路组和闭路组;BO、B分别为仅接种土壤菌群的开路组和闭路组;ABO、AB分别为同时接种小球藻和土壤菌群的开路组和闭路组,下同。
Figure 2. Outlet CO2 concentration of six biological systems
Note: AO, A are the open-circuit and closed-circuit groups inoculated with chlorella only; BO, B are the open-circuit and closed-circui groups inoculated with soil flora only; ABO, AB are the open-circuit and closed-circui groups inoculated with both chlorella and soil flora, same below.
表 1 BO、ABO和AB组菌群α多样性
Table 1 α-diversity of flora from group BO, ABO, and AB
组别
Group观测到菌种数
Observed species多样性指数Diversity index Chao1 ACE Shannon Simpson Pielou_J Pd_faith BO 2418 3026.36 3550.15 4.53 0.93 0.58 75.65 ABO 4140 7077.13 7870.49 5.92 0.97 0.71 114.28 AB 3564 5084.57 6183.08 5.43 0.95 0.66 101.44 注:Pielou_J指数为Pielou均匀度指数,Pd_faith指数为信仰多样性指数。 Note: Pielou_J index is Pielou evenness index, Pd_faith index is the faith diversity index. 表 2 KEGG关键代谢途径
Table 2 KEGG key metabolic pathway statistics
代谢途径
Metabolic pathway相对丰度Relative abundance/ % BO ABO AB 碳水化合物代谢
Carbohydrate metabolism15.32 16.52 17.97 氨基酸代谢
Amino acid metabolism12.31 12.75 13.77 -
[1] JIANG J, ZHAO T, WANG J. Decoupling analysis and scenario prediction of agricultural CO2 emissions: An empirical analysis of 30 provinces in China[J]. Journal of Cleaner Production, 2021, 320: 128798. DOI: 10.1016/j.jclepro.2021.128798
[2] ZHANG X, WU L, MA X, et al. Dynamic computable general equilibrium simulation of agricultural greenhouse gas emissions in China[J]. Journal of Cleaner Production, 2022, 345: 131122. DOI: 10.1016/j.jclepro.2022.131122
[3] PRADHAN G, MEENA R S. Utilizing waste compost to improve the atmospheric CO2 capturing in the rice-wheat cropping system and energy-cum-carbon credit auditing for a circular economy[J]. Science of the Total Environment, 2023, 892: 164572. DOI: 10.1016/j.scitotenv.2023.164572
[4] KAUR R, KAUR N, KUMAR S, et al. Carbon capture and sequestration for sustainable land use: A review[J]. The Indian Journal of Agricultural Sciences, 2023, 93(1): 11-18.
[5] MAO C, BYUN J, MACLEOD H W, et al. Green urea production for sustainable agriculture[J]. Joule, 2024, 8(5): 1224-1238. DOI: 10.1016/j.joule.2024.02.021
[6] KIM N E, KIM D H, KIM Y J, et al. Comparison of carbon dioxide emission concentration according to the age of agricultural heating machine[J]. Journal of Bio-Environment Control, 2023, 32(3): 190-196. DOI: 10.12791/KSBEC.2023.32.3.190
[7] SOO X Y D, LEE J J C, WU W Y, et al. Advancements in CO2 capture by absorption and adsorption: A comprehensive review[J]. Journal of CO2 Utilization, 2024, 81: 102727. DOI: 10.1016/j.jcou.2024.102727
[8] MASHHADIMOSLEM H, ABDOL M A, KARIMI P, et al. computational and machine learning methods for CO2 capture using metal-organic frameworks[J]. ACS Nano, 2024, 18(35): 23842-23875. DOI: 10.1021/acsnano.3c13001
[9] HUANG D, LI M J, WANG R L, et al. Advanced carbon sequestration by the hybrid system of photobioreactor and microbial fuel cell with novel photocatalytic porous framework[J]. Bioresource Technology, 2021, 333: 125182. DOI: 10.1016/j.biortech.2021.125182
[10] KUMAR M, SUNDARAM S, GNANSOUNOU E, et al. Carbon dioxide capture, storage and production of biofuel and biomaterials by bacteria: A review[J]. Bioresource Technology, 2018, 247: 1059-1068. DOI: 10.1016/j.biortech.2017.09.050
[11] ONYEAKA H, EKWEBELEM O. A review of recent advances in engineering bacteria for enhanced CO2 capture and utilization[J]. International Journal of Environmental Science and Technology, 2023, 20(4): 4635-4648. DOI: 10.1007/s13762-022-04303-8
[12] 匡彬,周丽琳,蔡传林,等. 电活性菌藻膜耦合虹吸曝气技术处理海水养殖废水[J]. 农业工程学报,2023,39(21):205-212. DOI: 10.11975/j.issn.1002-6819.202305148 KUANG Bin, ZHOU Lilin, CAI Chuanlin, et al. Mariculture wastewater treatment using electroactive bacteria-algae biofilm coupled with siphon aeration[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2023, 39(21): 205-212. (in Chinese with English abstract) DOI: 10.11975/j.issn.1002-6819.202305148
[13] 朱林,车轩,刘兴国,等. 光照强度对菌藻共生生物膜细菌群落结构的影响[J]. 农业工程学报,2020,36(11):241-247. DOI: 10.11975/j.issn.1002-6819.2020.11.028 ZHU Lin, CHE Xuan, LIU Xingguo, et al. Effects of different light intensities on the community structure of symbiotic biofilm of bacterial-algae[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2020, 36(11): 241-247. (in Chinese with English abstract) DOI: 10.11975/j.issn.1002-6819.2020.11.028
[14] 倪智力,匡彬,林梓杨,等. 人工湿地-电活性菌藻膜技术处理养猪废水中试试验[J]. 农业工程学报,2023,39(4):180-187. DOI: 10.11975/j.issn.1002-6819.202209233 NI Zhili, KUANG Bin, LIN Ziyang, et al. Pilot-scale study on the treatment of swine wastewater by constructed wetland consisted with exoelectrogens-microalgae biofilm[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2023, 39(4): 180-187. (in Chinese with English abstract) DOI: 10.11975/j.issn.1002-6819.202209233
[15] 陈彪,朱勇,王锴瑜,等. 藻菌共生系统处理畜禽沼液的机制及影响因素研究进展[J]. 农业工程学报,2023,39(13):14-24. DOI: 10.11975/j.issn.1002-6819.202305029 CHEN Biao, ZHU Yong, WANG Kaiyu, et al. Research progress on the mechanisms and influencing factors for the microalgae-bacteria symbiosis system for treating biogas slurry from livestock and poultry industry[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2023, 39(13): 14-24. (in Chinese with English abstract) DOI: 10.11975/j.issn.1002-6819.202305029
[16] SUN X, LI X, TANG S, et al. A review on algal-bacterial symbiosis system for aquaculture tail water treatment[J]. Science of the Total Environment, 2022, 847: 157620. DOI: 10.1016/j.scitotenv.2022.157620
[17] JACOB-LOPES E, SCOPARO C H G, QUEIROZ M I, et al. Biotransformations of carbon dioxide in photobioreactors[J]. Energy Conversion and Management, 2010, 51(5): 894-900. DOI: 10.1016/j.enconman.2009.11.027
[18] HULATT C J, THOMAS D N. Productivity, carbon dioxide uptake and net energy return of microalgal bubble column photobioreactors[J]. Bioresource Technology, 2011, 102(10): 5775-5787. DOI: 10.1016/j.biortech.2011.02.025
[19] HULATT C J, THOMAS D N. Dissolved organic matter (DOM) in microalgal photobioreactors: a potential loss in solar energy conversion?[J]. Bioresource Technology, 2010, 101(22): 8690-8697. DOI: 10.1016/j.biortech.2010.06.086
[20] CHIU S Y, KAO C Y, CHEN C H, et al. Reduction of CO2 by a high-density culture of Chlorella sp. in a semicontinuous photobioreactor[J]. Bioresource Technology, 2008, 99(9): 3389-3396. DOI: 10.1016/j.biortech.2007.08.013
[21] LI X, JIA T, ZHU H, et al. Bioelectricity facilitates carbon dioxide fixation by Alcaligenes faecalis ZS-1 in a biocathodic microbial fuel cell (MFC)[J]. Bioresource Technology, 2024, 399: 130555. DOI: 10.1016/j.biortech.2024.130555
[22] SHARMA M, SALAMA E-S, ZHANG P, et al. Microalgae-assisted microbial fuel cells for electricity generation coupled with wastewater treatment: Biotechnological perspective[J]. Journal of Water Process Engineering, 2022, 49: 102966. DOI: 10.1016/j.jwpe.2022.102966
[23] TANG C C, HU Y R, HE Z W, et al. Promoting symbiotic relationship between microalgae and bacteria in wastewater treatment processes: Technic comparison, microbial analysis, and future perspectives [J]. Chemical Engineering Journal, 2024, 498: 155703
[24] HUANG Y, CHENG J, LU H, et al. Transcriptome and key genes expression related to carbon fixation pathways in Chlorella PY-ZU1 cells and their growth under high concentrations of CO2[J]. Biotechnology for biofuels, 2017, 10: 1-10. DOI: 10.1186/s13068-016-0693-9
[25] HUANG D, YANG Y W. Potassium alleviating power overshoot and promoting carbon capture of bufferless algae microbial fuel cells[J]. Fuel, 2023, 346: 128427. DOI: 10.1016/j.fuel.2023.128427
[26] RUMORA A, HOPKINS L, YIM K, et al. 16S rRNA analysis of electrogenic bacterial communities from soil microbial fuel cells[J]. Applied Microbiology, 2024, 4(2): 918-933. DOI: 10.3390/applmicrobiol4020062
[27] ZHOU Z, WU Y, XU Y, et al. Carbamazepine degradation and genome sequencing of a novel exoelectrogen isolated from microbial fuel cells[J]. Science of the Total Environment, 2022, 838: 156161. DOI: 10.1016/j.scitotenv.2022.156161
[28] WANG Q, ZHANG C, ZHAO X, et al. Algae-Bacteria cooperated microbial ecosystem: A self-circulating semiartificial photosynthetic purifying strategy[J]. Science of the Total Environment, 2023, 905: 167187. DOI: 10.1016/j.scitotenv.2023.167187
[29] YANG Y W, LI M J, HUNG T C. Enhancing CO2 dissolution and inorganic carbon conversion by metal–organic frameworks improves microalgal growth and carbon fixation efficiency[J]. Bioresource Technology, 2024, 407: 131113. DOI: 10.1016/j.biortech.2024.131113
[30] HAJINAJAF N, FALLAHI A, EUSTANCE E, et al. Managing carbon dioxide mass transfer in photobioreactors for enhancing microalgal biomass productivity [J]. Algal Research, 2024, 80: 103506.
[31] CHENG J, ZHU Y, XU X, et al. Enhanced biomass productivity of Arthrospira platensis using zeolitic imidazolate framework-8 as carbon dioxide adsorbents[J]. Bioresource Technology, 2019, 294: 122118. DOI: 10.1016/j.biortech.2019.122118
[32] YANG Y W, LI M J, HUNG T C. The study on coupled CO2 fixation and power generation in microalgae-microbial fuel cells embedded with oxygen-consuming biofilms[J]. Fuel, 2024, 367: 131410. DOI: 10.1016/j.fuel.2024.131410
[33] SONG B Y, LI M J, HE Y, et al. Electrochemical method for dissolved oxygen consumption on-line in tubular photobioreactor[J]. Energy, 2019, 177: 158-166. DOI: 10.1016/j.energy.2019.04.050
[34] DUAN X, CHEN Y, FENG L, et al. Metagenomic analysis reveals nonylphenol-shaped acidification and methanogenesis during sludge anaerobic digestion[J]. Water Research, 2021, 196: 117004. DOI: 10.1016/j.watres.2021.117004
[35] CANDELIERE F, SOLA L, BUSI E, et al. The metabolism of Leuconostoc genus decoded by comparative genomics[J]. Microorganisms, 2024, 12(7): 1487. DOI: 10.3390/microorganisms12071487