The chemical terrorist attack is a type of unconventional terrorism that threatens the safety of cities. This kind of terrorist attack is highly concealed and difficult to be detected. Once the attack is successful, the consequences will be severe and the scope of impact will be enormous. Therefore, public security and emergency departments need to perform risk analysis and dynamic knowledge update to reduce risk or mitigate the effects of accidents. In order to quickly and effectively analyze the risk of chemical terrorist attacks, this article proposed a hybrid approach (B-R model) to analyze the risk of chemical terrorist attacks. First, a modular and customizable Bayesian network (BN) model library was built, which can satisfy users to select multi-dimensional risk factors. Based on the personalized BN, a risk knowledge graph (RKG) is constructed with multi-source data to realize the combination of risk analysis and knowledge acquisition. Then the threat degree of terrorist organizations, the strength of defensive forces, and the risk value of targets is calculated and displayed. The BN-RKG method provides data and theoretical support for defenders' resource allocation and emergency decision-making. Finally, a case study was conducted for a hypothetical scenario analysis. The result shows that the hybrid method can help with risk control and have the potential to support practical policymaking.

Objectives: Fine particulate matter (PM_2.5) is the principal air pollutant and poses a serious threat to public health. This study explored the effects of PM_2.5 on the action spectrum of ultraviolet radiation for vitamin D production (UV_vitD) received by manikin surfaces. Methods: Multi-inclination angle ultraviolet radiation monitoring was conducted with different concentrations of PM_2.5. Combining monitoring data with the PM_2.5 concentration, solar elevation angle (SEA), and inclination angle, a UV_vitD exposure model for human body multi-inclined surfaces was constructed through a multiple linear regression analysis. A 3D manikin model was used to examine the PM_2.5 effects on UV_vitD received by the manikin surface. Results: When PM_2.5 concentrations ranged from 35 μg/m³ to 100 μg/m³ (average concentration of PM_2.5 in this range: 62 μg/m³), the UV_vitD received by the whole body was reduced by approximately 8.45% to 19.82% compared with the UV_vitD received when PM_2.5 concentrations ranged from 6 μg/m³ to 35 μg/m³ (average concentration of PM_2.5 in this range: 17 μg/m³) with SEAs between 30° and 50°. Moreover, the UV_vitD dose was reduced by 11.82% in the above comparisons. When further comparing PM_2.5 concentrations from 100 μg/m³ to 161 μg/m³ (average concentration of PM_2.5 in this range: 132 μg/m³) with those from 6 μg/m³ to 35 μg/m³ (average concentration of PM_2.5 in this range: 17 μg/m³), the UV_vitD received by the whole body was reduced by approximately 21.6% to 50.64% at SEAs between 30° and 50°. The UVvitD dose was reduced by 30.2%. Conclusions: The occurrence of PM_2.5 obviously reduced the UV_vitD received by the manikin surface.