Real-time data driven simulation of air contaminant dispersion using particle filter and UAV sensory system

Abstract

Real-time prediction of the air contaminant dispersion is an important issue in hazard assessment and emergency management of air pollution. The conventional atmospheric simulation can seldom give the precise prediction results due to inaccurate input parameters. To improve the accuracy of the prediction of atmospheric model, a real-time data driven atmospheric dispersion simulation based on data assimilation is proposed by applying particle filer to the Gaussian puff based model. The coefficients of dispersion in this model are selected as the system state variables and updated by assimilating observed data into the model in real time. To obtain high-quality observed data, a UAV-based air contaminant sensory system is developed. Two experiments are designed and implemented to verify the performance of the method. The results show that the method proposed can update the model parameters and improve the accuracy of prediction results effectively.