# Causality Analysis and Risk Assessment of Haze Disaster in Beijing
###### Abstract  
Due to the lack of training data and effective haze disaster prediction model, the research on causality analysis and the risk prediction of haze disaster is mainly qualitative. In order to solve this problem, a nonlinear dynamic prediction model of Beijing haze disaster was built in this study. Based on the macroscopic evaluation of multiple influencing factors of haze disaster in Beijing, a causality model and flow diagrams of the Beijing crude oil consumption system, Beijing coal consumption system, Beijing urban greening system and sulfur dioxide emission system in Hebei and Tianjin were established. The risk prediction of Beijing haze disaster was simulated at different conditions of air pollutant discharge level for the Beijing-Tianjin-Hebei region. Compared with the governance strategies of vehicle emission reduction, petrochemical production emission reduction, coal combustion emission reduction, greening and reducing dust and collaborative governance policy, the Beijing-Tianjin-Hebei cross-regional collaborative governance policy was more effective in controlling the haze disaster of Beijing. In the prediction, from 2011 to 2017, the air quality of Beijing changed from light pollution to good. By 2017, the PM2.5 of Beijing reduced to 75 \(\mathrm{\SIUnitSymbolMicro m}\)/\(\mathrm{m}^{3}\). From 2017 to 2035, the control effect of urban haze disaster for Beijing further strengthened. By 2035, the PM2.5 of Beijing reduced to 35 \(\mathrm{\SIUnitSymbolMicro m}\)/\(\mathrm{m}^{3}\). Finally, the PM2.5 of Beijing continued to reduce from 2035 to 2050. The speed of reduction for PM2.5 in Beijing slowed down. Meanwhile, the achievements of haze control in Beijing were consolidated. By 2050, the risk of haze disaster for Beijing was basically solved. The nonlinear dynamic prediction model in this study provides better promise toward the future control and prediction of global haze disaster under the condition of limited data. risk prediction; PM2.5; haze disaster; stochastic nonlinear dynamics; controlling strategies +
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evaluate the effect of various governance measures to control urban haze, especially when the complex cross-regional effects are considered [11; 12; 13; 14; 15]. Most haze prediction models mainly focus on one administrative region, but rarely consider the interaction and game relationship of haze in multiple regions [16]. Clarifying the causal relationship between the influencing factors of haze disaster is the premise for the establishment of a haze prediction model. Researchers have studied the atmospheric haze causality of the haze system using many methods, including Granger causality analysis [17; 18], convergent cross mapping [19; 20] and machine learning [21; 22]. The influencing factors of haze disaster include marine transport at the marine level [23; 24], local and global pollution emissions [25; 26] and the interaction between industrial emissions and atmospheric diffusion [27; 28]. Unfortunately, these studies cannot describe the dynamic formation and evolution mechanism of cross-regional haze disaster. In order to realize the prediction of haze disaster, incorporating the causal relationship between the influencing factors should be applied to the data analysis. A large number of researchers have studied the mathematical models of haze prediction, including nonparametric regression models [29; 30], deep recurrent neural networks [31; 32], inverse-matrix-free machine learning models [33], nonlinear grey models [34; 35] and graphic networks [36; 37; 38]. These methods avoid the analysis of complex details and mechanisms for haze disaster. [PERSON] et al. [39] developed a rapid, reliable and computationally easy method of vulnerability assessment, known as rapid visual screening (RVS). [PERSON] et al. [40] proposed the haziness degree evaluator model to predict haze density from a single image without reference to a corresponding haze-free image. [PERSON] et al.