Data-Driven Hazardous Gas Dispersion Modeling Using the Integration of Particle Filtering and Error Propagation Detection.

Zhengqiu Zhu,Sihang Qiu,Bin Chen,Xiaogang Qiu,Rongxiao Wang

doi:10.3390/ijerph15081640

Abstract

The accurate prediction of hazardous gas dispersion process is essential to air quality monitoring and the emergency management of contaminant gas leakage incidents in a chemical cluster. Conventional Gaussian-based dispersion models can seldom give accurate predictions due to inaccurate input parameters and the computational errors. In order to improve the prediction accuracy of a dispersion model, a data-driven air dispersion modeling method based on data assimilation is proposed by applying particle filter to Gaussian-based dispersion model. The core of the method is continually updating dispersion coefficients by assimilating observed data into the model during the calculation process. Another contribution of this paper is that error propagation detection rules are proposed to evaluate their effects since the measured and computational errors are inevitable. So environmental protection authorities can be informed to what extent the model output is of high confidence. To test the feasibility of our method, a numerical experiment utilizing the SF6 concentration data sampled from an Indianapolis field study is conducted. Results of accuracy analysis and error inspection imply that Gaussian dispersion models based on particle filtering and error propagation detection have better performance than traditional dispersion models in practice though sacrificing some computational efficiency.

Highlights

Air contaminant emissions and contaminant gas leakage incidents in a chemical cluster pose a potential threat to public health and surrounding environment
A data-driven modeling method is proposed in this paper for updating model parameters to generate the simulation results being as close as possible to the real data
A data-driven method using a particle filter is developed to improve the accuracy of air contaminant dispersion predictions based on Gaussian-based models

Summary

Introduction

Air contaminant emissions and contaminant gas leakage incidents in a chemical cluster pose a potential threat to public health and surrounding environment. Modeling atmospheric dispersion is a popular issue these years since it plays an important role in evaluating the impact of hazardous gas leak accidents [1,2]. Due to the dynamic and stochastic nature of atmospheric dispersion, it is impractical to measure these model parameters precisely, especially the meteorological data (e.g., the wind field) [3]. The computational error of static model may be accumulated with time during the calculation process. To address this problem, a data-driven modeling method is proposed in this paper for updating model parameters to generate the simulation results being as close as possible to the real data. Some errors cannot be avoided, such as measurement errors and floating-point

Methods

Results

Conclusion