摘要
针对土壤中农药残留严重造成的污染问题,本研究旨在研究制备阿特拉津降解菌剂的最佳工艺条件,为采用微生物菌剂修复土壤的研究奠定基础。试验采用高效降解菌株HBT4,对其进行纯化与扩培,选择合适的保护剂用喷雾干燥法制成固体粉末菌剂,以含水量、菌体存活率及有效活菌数为指标,采用正交试验方法进行工艺条件的优化。探索结果的最佳工艺条件为:蠕动泵转速15%、热空气流速35 L/h、保护剂与菌泥的比例(V:V)为3:1、入口温度170℃。最佳工艺条件下得到的产品含水量为4.42%,有效活菌数为1.45×109cfu/mL,菌体存活率为82.6%。用喷雾干燥法在最佳工艺条件下所制得的HBT4的降解菌剂对阿特拉津有良好的降解效果,这将为未来除草剂的微生物降解菌剂的研制提供良好的菌种资源。
To solve the problem of serious pollution caused by pesticide residues in soil, this study aims at the optimal process condition of atrazine degradation bacteria. The highly efficient degradation strain HBT4 wasselected, purified and propagated. The appropriate protective agent was selected to prepare a solid powderedfungicide by spray drying. The orthogonal test method was used to optimize the process conditions. Theoptimum condition was: peristaltic pump speed of 15%, hot air flow rate of 35 L/h, ratio of protective agent tosludge(V:V) was 3:1 and inlet temperature of 170℃. The water content of the product obtained under theoptimum condition was 4.42%, the number of viable cells was 1.45×109 cfu/mL, and the viability of the cellswas 82.6%. The degradation agent of HBT4 obtained by the spray drying method under the optimum processcondition has good degradation effect on atrazine. It will provide a good bacterial species resource for thedevelopment of the microbial degradation agent of the herbicide in the future.
作者
孙丛
付瑶
王娅丽
杨峰山
付海燕
马玉堃
刘春光
Sun Cong;Fu Yao;Wang Yali;Yang Fengshan;Fu Haiyan;Ma Yukun;Liu Chunguang(Engineering Research Center of Agricultural Microbiology Technology,Ministry of Education, Heilongiang University,Harbin 150500;Heilongjiang Provincial Key Laboratory of Ecological Restoration and Resource Utilization for Cold Region,School of Life Sciences,Heilongjiang University,Harbin 150080;Key Laboratory of Microbiology,College of Heilongjiang Province,Harbin 150580)
出处
《中国农学通报》
2018年第35期86-92,共7页
Chinese Agricultural Science Bulletin
基金
黑龙江省普通本科高等学校青年创新人才培养计划“阿特拉津降解复合菌肥研发与农田土壤微生物修复”(UNPYSCT-2017119)
关键词
阿特拉津
降解菌剂
喷雾干燥法
工艺优化
atrazine
degrading bacteria
spray drying
process optimization
