Study On The Evolution Characteristics Of Pollutant Diffusion System In Rivers Based On The Information Entropy Theory

The characteristic of water pollution diffusion is an important fundamental research topic in environmental science and engineering.Analyzing the evolution characteristic of water quality components transport process in natural water system and extracting key spatial and temporal information to characterize the pollution process can guide the scientific management and control of water environment.Statistical information theory has been started to use in recent years to describe the diffusion process of groundwater solute reaction,providing an effective tool for the characterization and analysis of its thermodynamic behavior However,no one has analyzed the pollution diffusion process of river water environment from the perspective of information theory.The river water environment has its specific pollution source characteristics,water quality components and transport scales,and has a distinct water body function,which has important research value.Based on the definition of probability space,statistical sampling method and system boundary,this paper reveals the temporal and spatial evolution of single component and complex water quality process from the perspective of statistical information theory,and discusses its possible application in environmental modeling and monitoring.The evolution characteristic of information entropy in one-component solute diffusion system is studied by using the method of discrete information entropy analysis.The information entropy evolution of river pollutant diffusion process is analyzed in two dimensions of time and space,fixed and dynamic system boundary and four cases.An analytical solution of pollutant information entropy evolution with time in river pollutant diffusion system under one-dimensional solute transport generalization is deduced.The temporal and spatial variations of information entropy under different situations are found In the system boundary definition of fixed observation,the information entropy appears a peak in time and space dimension,and the peak value of information entropy appears in the first 20%-30%of the fixed observation interval,while in the system boundary definition of dynamic observation,information entropy decreases continuously with the increase of time and space distance.Through the local sensitivity analysis of the hydrodynamic parameters of the above analytical solutions,it is found that the sensitivity of information entropy H to diffusion coefficient Dx is relatively constant,and the greater the degradation coefficient k is,the more sensitive the monitoring time t is to k,the more sensitive the spatial change of information entropy is to the change of flow velocity ux with the increase of distance,while the change of time is insensitive to ux.The Coosa River tracer test in Alabama in 1984 and the River Goldersbach tracer test in Germany in 2013 were selected as a case study corresponding to the dynamic observation and the fixed observation respectively.The direct data-driven method is used to validate the analytical solution,which is coincide with the theoretical deduction.Furthermore,the evolution characteristic of information entropy in complex water quality process of rivers is studied.The Guangming section of Maozhou River in Shenzhen is taken as the research area.BOD-DO and nitrogen elements(NH3-N,NO3-N,Org-N)water quality process were selected,and one-dimensional S-P model and WASP EUTRO water quality model were constructed respectively.After model calibration and verification,the changing characteristics of information entropy,mutual information and information transfer index are analyzed under the system definition of fixed observation.It was found that the transformation reaction process gradually replaced the diffusion process in the complex water quality process as the main factor affecting the change of information entropy,and the information entropy change law in the single component diffusion process no longer exists in the complex water quality process.Furthermore,the significance of information entropy indecies in the process of pollutant diffusion is given,that is,the size of information entropy can reflect the intensity of diffusion reaction of water quality components in water system.The temporal and spatial variation of information entropy reveals the reaction process of water quality components,the degree of chaos and the intensity change of information interaction with other water quality components under different temporal and spatial conditions,and gives the information entropy application prospect of index in long-tailing and persistent pollutant monitoring.The significance of mutual information among different pollution factors in complex water quality process is given.That is,the mutual information among multi-water quality components represents the common changes of components,such as diffusion process and synchronous reaction process.Based on case analysis,a monitoring network optimization idea of information meaning range index of stations is summarized.The optimization idea can also be applied to the generalization of water quality model,which is convenient for us to select the most important parameters from a variety of river water quality parameters to obtain as much water quality information as possible.This study provides a new perspective for the analysis of water environment system and a new tool for watershed management.