Provenance Analysis And Simulation For Emerging Water Contaminants

As the urbanization process accelerates in most of countries,overcrowded megacities move into the dangerous condition of emerging pollutants among other prominent environmental issues.Water contaminants are supposed to be the critical source of emerging pollutants,such as environmental endocrine concentrated in fish.Provenance information identifying the source and the route of transmission of pollutants is paramount to the understanding of the danger and to the mitigation of the threat by the actors and agencies involved in the management of water resources.Contaminants can be traced by probing,for instance,their concentration levels in organisms such as fishes and crustaceans living in the bodies of fresh water of concern.However,existing provenance models and methods are not sufficiently accurate in their determination of the source and route of the contaminants because of their neglect of the impact of bio-magnification and the movement of the creatures.We devise and present a methodology to model and identify provenance of contaminants in water by mining the sampling results of fishes and crustaceans,which is also applicable to other available sensor readings along the food chain.In our methodology,the proposed provenance model is solved with Differential Evolution Markov Chain Monte Carlo(DE-MCMC)to locate the sources of contamination and Ant Colony Optimization to improve the accuracy.Firstly,a reservoir food chain model is proposed to analyze contaminant propagation through underwater creatures.The design of water pollution tracing and tracking method will start from the construction of food chain model,generating data similar to real food supply chain's one and provide the data structure for the latter steps.Then,we will discuss our effective and swift algorithm for tracing resource,solving the problems based on distance between sensors.