不同磷浓度下光强、温度对水华鱼腥藻(Anabaena-flos-aquae)生长的动力学?
- 海之魂
-
0 次阅读
-
0 次下载
-
2020-03-09 10:59:53
文档简介:
Sci.(湖泊科学),2015,27(3):459-465http:#www.jlakes.org.E—mail:jlakes@niglas.ac.ca⑥2015byJournalofSciences不同磷浓度下光强、温度对水华鱼腥藻(Anabaenaflos—aquae)生长的动力学张萍。,李哲,,王胜,郭劲松,肖艳,刘静’(1:重庆大学城市建设与环境工程学院,重庆400045)(2:中国科学院重庆绿色智能技术研究院,重庆400714)(3:中国科学院水库水环境重点实验室,重庆400714)摘要:水华鱼腥藻是常见的有害水华藻种,但其在磷、光强、温度等生境要素协同作用下的生长动力学鲜见报道.本研究在Lehman模型、Steele模型基础上,设置8个PO一一P梯度:0、0,1、0.2、0.5、0.75、1.0、2.5、5.0brmol/L,4个温度水平:15、2O、25和3O℃和4个光强水平:1000、2000、3000和5000lx,采取单因素实验方法进行水华鱼腥藻室内纯培养.Monod模型表明水华鱼腥藻最适生长温度和光强条件分别是2O℃和3000lx,最大比生长速率(⋯)和半饱和常数(Ks)分别为0.447dI1和0.081ixmolP/L;分别用Lehman模型和Steele模型模拟水华鱼腥藻⋯在不同磷浓度下对连续变化的温度和光强的响应,Lehman动力学模拟结果表明水华鱼腥藻的最适生长温度为21.22±0.98℃,be⋯和s分别为0.421±0.O11d和0.055.4-0.009p~molP/L;Steele模型结果表明和分别为0.461±O.010d和0.051±O.009i~molP/L,水华鱼腥藻最适生长光强,k为2650.93±88.19Ix.关键词:水华鱼腥藻;动力学模型;光强;温度;磷ModelingofAnabaenaflos-aquaegrowthkineticsoflightintensityandtemperaturewithindiferentlevelsofphosphorusconcentrationsZHANGPingII2l,LIZhe,WANGSheng,GUOJinsong,XIAOYan’&LIUJing,’(1:FacultyofUrbanConstructionandEnvironmentalEngineering,ChongqingUniversity,Chongqing400045,P.R.China)(2:ChongqingInstituteofGreenandIntelligentTechnology,ChineseAcademyofSciences,Chongqing400714,P.R.China)(3:KeyLabofReservoirWaterEnvironment,ChineseAcademyofSciences,Chongqing400714,P.R.China)Abstract:Anabaenaflos—aquaeisoneofthecommonharmfulcyanobacteriaspecies.However,itsgrowthkineticsco-impactedbyphosphateconcentration,lightintensityandtemperaturehaverarelybeenreposed.Inthispaper,eightP0i一-Pconcentrationgra-dients(0,0.1,0.2,0.5,0.75,1.0,2.5and5.0i~mol/L),fourwatertemperatures(15,20,25and30qC)andfourlightintensities(1000,2000,3000and5000lx)wereselectedtoCalTyoutsinglefactorbatchexperiments.Amaximumspecificgrowthrate(be)wasusedtoindicatealgaespecificgrowthrateunderoptimalgrowthcondition.Half-saturationconstant(Ks)couldbeappliedtodescribeafinityofalgaetonutrients.RegressionresultsfromMonodmodelshowedthatAnabaena]los-aquaereachedat20oCand3000lx.Thebe“andKswere0.447d~and0.081tzmolP/L,respectively.RegressionresultsbasedonLehmanandSteeleModeltha
评论
发表评论