Laboratory-acquired infections (LAIs) are defined as infections of laboratory staff by exposure to pathogenic microorganisms during an experimental procedure. For a biosafety level-3 (BSL-3) laboratory with a high potential of exposure, reducing risks and threats relevant to LAIs has become a critical concern, especially after the recent outbreak of Novel Coronavirus causing COVID-19 in Wuhan, China. This study aimed to investigate the spatial-temporal characteristics of bioaerosol dispersion and deposition of two kinds of bioaerosols (Serratia marcescens and phage ΦX174). A combination of laboratory experiment and numerical simulation was adopted to explore bioaerosol removal. Three-dimensional concentration iso-surface mapping in conjunction with flow field analysis was employed to elucidate bioaerosol migration and deposition behavior. The total deposition number and unit area deposition ratio were calculated for different surfaces. The results indicate that bioaerosol concentration remains stable for up to 400 s after release, and that almost 70% of all bioaerosol particles become deposited on the surfaces of walls and equipment. Vortex flow regions and high-concentration regions were determined, and the most severely contaminated surfaces and locations were identified. Our results could provide the scientific basis for controlling the time interval between different experiments and also provide guidelines for a laboratory disinfection routine. Furthermore, future work regarding laboratory layout optimization and high efficiency air distribution for bioaerosol removal in a BSL-3 laboratory should be emphasized.

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Reasonable equipment layout is essential for creating a healthy and safe environment, especially in a three-level biosafety laboratory with high potential risk factors of infection. Since 2019, COVID-19, an emerging infection has swept the world and caused severe losses. Biosafety laboratories are mandatory sites for detecting high-risk viruses, so related research is urgently needed to prevent further laboratory-acquired infections of operators. This study investigated the effects of obstacles on exposure infection of staff in a biosafety laboratory with related experimental equipment. The numerical simulation results are highly verified by the measured results. The results indicate that although the equipment layout does not affect the bioaerosol removal time, nearly 17% of the pollutant particles in the actual laboratory cannot be discharged effectively compared with the ideal situation. These particles lingered in the lower space under the influence of vortex, which would increase the respiratory risk of operators. In addition, after the experiment a large part of bioaerosol particles would be captured by equipment and floor, and the deposition rate per unit area is 0.45%/m 2 and 0.8%/m 2 , respectively. Although the results show that the equipment layout could reduce the pollution on the floor, the disinfection is still an important link, especially on the surfaces of equipment. Meanwhile, the result also indicates that the action should be light and slow when operating in BSL-3 laboratory, so as to avoid the secondary suspension pollution of bioaerosol particles on the equipment surface and floor.

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Allocation of reactive power equipment can relieve the transient overvoltage, which is a big threat to the sending-end electric power system of ultrahigh-voltage direct current (UHVDC). However, the dynamic reactive power allocation mostly depends on the trial-and-error method, lacking in an optimal allocation method based on the quantitative evaluation index. To deal with the abovementioned problem, in this study, a dynamic reactive power optimal allocation method is proposed based on the reactive power compensation sensitivity. In detail, first, based on the existing transient overvoltage assessment index, the general form of reactive power optimization problem is proposed. Then, taking the sending-end power system of UHVDC as an example, the reactive power allocation location is determined based on a reactive power compensation sensitivity. Furthermore, combined with the sensitivity, the compensation capacity of each place is determined by particle swarm optimization (PSO). The simulation results show that the proposed method can effectively allocate the dynamic reactive power and suppress the transient overvoltage after fault.