溶藻细菌Chryseobaterium sp.S7控藻实验
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摘要
为使溶藻细菌Chryseobaterium sp.S7在水体修复工程中得到科学应用,在单因素实验的基础上,确定细菌和Chl.a浓度的响应面中心点,以蓝绿藻Chl.a去除率为响应值,以细菌和Chl.a浓度为影响因素,采用Central-Composite响应曲面分析法,研究了影响Chryseobaterium sp.S7溶藻效应的2个重要因素的交互作用,得出Chl.a去除率与细菌初始浓度和水体Chl.a浓度的二次多项式模型。该模型具有显著性高(P<0. 01),模型拟合度好(R~2=0. 9071)等优点。当细菌初始浓度为9. 46×10~6cell/L、Chl.a初始浓度为175. 3 mg/m~3时,Chl.a理论去除率最大(83. 53%)。水槽实验也表明该模型具有一定实用性。针对不同水华水体,可以根据本文建立的模型确定细菌投放量,达到最优控藻效果。研究结果可为应用Chryseobaterium sp.S7控制蓝绿藻引起的水华污染提供参考。
In order to apply the algae-lysing bacterium Chryseobaterium sp.S7 in the water body repair project,based on the results of the single factor experiment,the response surface center points of the bacteria and Chl. a concentration were determined respectively,and taking removal rate of Chl.a in blue-green algae as the response value and the bacteria and Chl.a concentration as the influencing factors,this paper studied the interaction of the two important factors of the algae-lytic effect of Chryseobaterium sp.S7 by using the response surface methodology( RSM) of Central-Composite,and came to the quadratic polynomial model of Chl.a removal rate with the initial bacterial concentration and the water body Chl. a concentration. This model had the advantages of high significance( P<0. 01) and good model fitting result( R~2= 0. 9071),etc. When the initial concentration of bacteria was 9. 46×10~6 cell/L and the initial concentration of Chl.a was 175. 3 mg/m~3,the theoretical removal rate of Chl.a was the largest( 83. 53%). The sink experiment also proved that the model had certain practicability. Bacterial dosage for waters with different water bloom types could be determined by using the model established in this paper,thus realizing the optimal algae control effect. The conclusion in this paper provided a theoretical basis for applying Chryseobaterium sp.S7 to control the water bloom pollution caused by blue-green algae.
In order to apply the algae-lysing bacterium Chryseobaterium sp.S7 in the water body repair project,based on the results of the single factor experiment,the response surface center points of the bacteria and Chl. a concentration were determined respectively,and taking removal rate of Chl.a in blue-green algae as the response value and the bacteria and Chl.a concentration as the influencing factors,this paper studied the interaction of the two important factors of the algae-lytic effect of Chryseobaterium sp.S7 by using the response surface methodology( RSM) of Central-Composite,and came to the quadratic polynomial model of Chl.a removal rate with the initial bacterial concentration and the water body Chl. a concentration. This model had the advantages of high significance( P<0. 01) and good model fitting result( R~2= 0. 9071),etc. When the initial concentration of bacteria was 9. 46×10~6 cell/L and the initial concentration of Chl.a was 175. 3 mg/m~3,the theoretical removal rate of Chl.a was the largest( 83. 53%). The sink experiment also proved that the model had certain practicability. Bacterial dosage for waters with different water bloom types could be determined by using the model established in this paper,thus realizing the optimal algae control effect. The conclusion in this paper provided a theoretical basis for applying Chryseobaterium sp.S7 to control the water bloom pollution caused by blue-green algae.
引文
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[26] Liu C,Liu Y,Liao W,et al. Application of statistically-based experimental designs for the optimization of nisin production from whey[J]. Biotechnology Letters,2003,25(11):877-882.
[2] Davidson K,Anderson D M,Mateus M,et al. Forecasting the risk of harmful algal blooms[J]. Harmful Algae,2016,53:1-7.
[3] Kumari R,Barsainya M,Singh D P. Biogenic synthesis of silver nanoparticle by using secondary metabolites from Pseudomonas aeruginosa DM1 and its anti-algal effect on chlorella vulgaris and chlorella pyrenoidosa[J]. Environmental Science&Pollution Research,2016,24(5):1-10.
[4] Pouderoyen G V,Snijder H J,Benen J A E,et al. Structural insights into the processivity of endopolygalacturonase I from Aspergillus niger[J]. Febs Letters,2003,554(3):462-466.
[5]柴天,刘晓宇,许鹏成,等.治理富营养化水体藻华灾害的固定化菌藻技术研究[J].自然灾害学报,2015,(5):237-245.
[6]陈庆丽,张秀芳,付韵馨,等. 1株铜绿假单胞菌JM1的溶藻特征[J].吉林农业大学学报,2014,36(4):416-420.
[7]郭雪白,杜志敏.沼液中土著菌对斜生栅藻去除污染物效果的影响[J].环境工程学报,2016,10(7):3943-3948.
[8]彭超,吴刚,席宇,等. 3株溶藻细菌的分离鉴定及其溶藻效应[J].环境科学研究,2003,16(1):37-40.
[9]刘晶,潘伟斌,秦玉洁,等.两株溶藻细菌的分离鉴定及其溶藻特性[J].环境科学与技术,2007,30(2):17-19.
[10] Beutler M,Wiltshire K H,Arp M,et al. A reduced model of the fluorescence from the cyanobacterial photosynthetic apparatus designed for the in situ detection of cyanobacteria[J]. Biochimica Et Biophysica Acta,2003,1604(1):33-46.
[11]李燕,潘玮斌,杨丽丽.三株溶藻细菌胞外溶藻活性物质若干分离特性的研究[J].微生物学通报,2007,35(2):171-177.
[12]孔赟,陈剑,徐向阳,等. Streptomyces sp.HJC-D1溶藻过程产物光谱学特征及机理[J].光谱学与光谱分析,2013,33(1):167-171.
[13]方卫东,唐旭,刘源森,等.一株海洋生境芽孢杆菌FA08的筛选、鉴定及其酶学特性和抗菌性能分析[J].海洋与湖沼,2015,46(5):1228-1234.
[14]王佳,洪桂云,张瑾,等.一株铜绿微囊藻溶藻菌的溶藻机理研究[J].安徽建筑大学学报,2017,25(4):19-23.
[15]杨帆,石宝友,王东升,等.水质化学组分变化对管道铁释放及管垢特征的影响[J].中国给水排水,2012,28(23):59-64.
[16]李琳,马放,赵丹,等.微生物絮凝剂产生菌培养条件的响应面优化研究[J].中国给水排水,2015,15(1):82-84.
[17] Kumar R, Singh R, Kumar N, et al. Response surface methodology approach for optimization of biosorption process for removal of Cr(Ⅵ),Ni(Ⅱ)and Zn(Ⅱ)ions by immobilized bacterial biomass sp. Bacillus brevis[J]. Chemical Engineering Journal,2009,146(3):401-407.
[18] Wasser S P. Medicinal mushrooms as a source of antitumor and immunomdulating polysaccharides[J]. Applied Microbiology Biotechnology,2002,60(2):258-274.
[19]王金霞,罗固源,许晓毅,等.响应面法研究溶藻细菌溶藻效应的环境因子[J].中国给水排水,2012,28(9):82-85.
[20]国家环保总局水和废水监测分析方法编委会.水和废水监测分析方法[M]. 4版.北京:中国环境科学出版社,2002.
[21] Beutler M,Wiltshire K H,Arp M,et al. A reduced model of the fluorescence from the cyanobacterial photosynthetic apparatus designed for the in situ detection of cyanobacteria[J]. Biochimica Et Biophysica Acta,2003,1604(1):33-46.
[22]史顺玉.溶藻细菌对藻类的生理生态效应及作用机理研究[D].武汉:中国科学院研究生院(水生生物研究所),2006.
[23]王永斌,王允祥.雷蘑液态发酵工艺响应面法优化研究[J].中国农学通报,2006,22(6):76-82.
[24] Muralidhar R V,Chirumamila R R. A response surface approach for the comparison of lipase production by Canida cylindracea using two different carbon sources[J]. Biochemical Engineering Journal,2001,9(1):17-23.
[25] Abdel-Fattah Y R,Saeed M H,Gohar Y M,et al. Improved product ion of Beud omonas aeru ginosa uricase by optim ization of process paramet ers through st at is ti cal experim ental designs[J].Process Biochemistry,2005,40(11):1707-1714.
[26] Liu C,Liu Y,Liao W,et al. Application of statistically-based experimental designs for the optimization of nisin production from whey[J]. Biotechnology Letters,2003,25(11):877-882.
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