The Research On Algal Bloom Rules And Optimized Prediction Model In The Urban Slow-flowing Landscape Water

Algal bloom is one of the most commonly occurred apparent pollution in urban landscape water.It is an effective method for preventing and treating blooms to forecase the possible outbreak by building the prediction model.Present study is based on the research of existing research results and takes the landscape waterbody in Suzhou University as examples to investigate the formation mechanisms and the possible alert model in depth.According to the datas from 2014-11 to 2015-11,the field observation,sampling and laboratory monitoring were conducted to get the values of the indicators and the visual determination results of the blooms outbreak.Through the theoretica analysis and data processing,it has improved causes of blooms and recognition of omen.Therefore,it restructed the upper algal bloom prediction model,came up with new conditons for algal blooms.The major study results include:（1）The algal vertical distribution properties was changed at the photic,it means the algal in the lower layer water was suffer from the process of ‘light inhibit’ and taken place upward migration,which lead to the differential concentration in water.This change of algal was vital precursor information which represented the vertical distribution characteristic and integrated with the crucial fator of blooms early warning.（2）The concept of vertical separation degree was put forward,which equals to the non-negative value of “the difference between the Ch-a concentration of upper layer and lower layer” numerically.when Vs greater than 20μg/L,it means the worse growth conditions occurred which normally leads to the outbreaks of the algal blooms.（3）With the combination of theoretical analysis and the F significance test in statistical calculations,it showed that water temperature,F_V/F_M,V_S and TP came to the correlation factors in the urban landscape water.The unified model was obtained by using the above mentioned factors as the variables in the upper layer algal biomass prediction model,and is presented in the following formula:（4）The finally calculated parameters in the model were: 3.237/d^-1 of the maximum algal growth rate Gmax;1.055d^-1 of the maximum death rate Dmax;0.252/d^-1 of the optical correction coefficient θ;0.34 of the buoyancy correction coefficient η;0.002 of the half saturation coefficient KTP.（5）The fitting process was conducted on the upper layer algal biomass prediction model based on the data of the modeling sites,the results showed a superb fitting between the prediction value and the observation value,and the correlation coefficients were 0.94,0.93,0.91 and 0.93 with little mean differences and no significant difference test,and the success prediction rate on the blooms is 85.8% for in-situ water by using the double threshold value.By using the data of the none modeled site to predict the outbreak of the algal blooms,the highest two-day predicting results is observed,and the success ratio were 93.3%,92.3%,the results showed low prediction accuracy for long-term period.（6）The research of algal blooms in urban landscape water summarized the application condition was double threshold values: when the daily Vs value is greater than 20μg/L,the outbreak conditions of the algal blooms were confirmed,the prediction value is greater than 45μg/L,the outbreak was confirmed as would occurred after n days.