The Research On Limiting Actors And The Prediction Model Of The Algal Bloom In The Urban Slow-flowing Water

In this article, it selected the characteristic urban slow-flowing water as the researchobject and it used the field observation, monitoring analysis and mathematical modelanalysis to find limiting factors of the algal bloom in the urban slow-flowing water and toconstruct the algal bloom model. The research was divided into2stages including the2012-5-15to2012-9-30and the2013-7to2013-8. The monitoring data during2012-5-15to2012-9-30was used to find the limiting factors of the algal bloom, and to construct theprediction model. The monitoring data during2013-7to2013-8was used to verify themodel. According to the character of the urban water and related literatures, it used themechanism prediction model and the risk prediction model to predict the algal bloom. Themechanism prediction model was constructed by the dynamic growth model and theinterior-outer-set model. The dynamic model was used to predict the biomass of algae inthe water and the interior-outer-set model was used to determine the threshold of the algalbiomass. The risk prediction model was constructed by risk assignment model and theprobability prediction model. The risk assignment could change the correlated factors ofalgal bloom into the risk of the algal bloom(the degree of algal bloom risk). Theprobability prediction model could change the risk into probability. Through theconstruction and analysis of2models, it got some conclusions.(1)As the surrounding non-point sources and the endogenesis of the urban water, thenutrient salt was not the limiting factor of the algal bloom. Due to the slow-flowing waterbeing suitable to the growth and the aggregation of the algae, the hydrodynamic conditiondid not limit the algal bloom. The temperature and the photosynthetic activity werelimiting factors of the algal bloom. The temperature, hydrodynamic activity and the algalbiomass were variables of the model.(2)Through the analysis, it constructed the dynamic growth model and divided themodel into2situations namely the existence water exchange or none. In the situation ofexistence water exchange, results of model parameters-Gmax, Dmaxand θ were3.31d-1,0.61d-1and0.29. In the situation of none existence water exchange, results ofmodel parameters-Gmax, Dmaxand θ were7d-1,1.16d-1and0.29. The threshold of algalbiomass of the mechanism prediction model was40μg/L.(3)To the risk prediction model, weights of the temperature, the hydrodynamic activity and the algal biomass were0.14,0.13and0.73. In the risk prediction model, when the riskof the algal bloom was no less than0.52, the algal bloom was the great possibility eventand the possibility was0.65.(4)2models were well to predict the urban slow-flowing water. Mechanism predictionmodel was suitable to timely prediction of the algal bloom. The risk prediction model wassuitable to the risk management in the algal bloom.